<figure><img src="image_52.png" /></figure>

(projects), each with specific temporalities within the programme life cycle, coordinated as an integral part for the successful delivery of the entire programme (Lycett, Rassau, and Danson 2004; Pellegrinelli 2011; Pollack and Anichenko 2022). In summary, a programme cannot be delivered without the completion of all its interdependent projects, and programme management unlocks outcomes that would be impossible if projects are managed in isolation. The coordination of resources at programme level includes strategic topics that often go beyond the technical dimension, moving into the interfaces with organisational, institutional, and political spheres (Maylor et al. 2006; Rijke et al. 2014). Therefore, programme management should not be conceptualised simply as an expansion and amplification of project management. Successful project managers are often challenged when promoted to lead programmes, where they face strategic topics, high ambiguity, amplification of political dynamics, personal interfaces with internal and external stakeholders, and decisions with less information. Programme managers need to have constant external awareness and have the skills to navigate topics in close negotiations with the strategic and political levels.

## Dynamics of programme management

The decomposition of a programme in multiple projects, each project in sub-projects and systems, provides the technical and hierarchical structure of reporting and integration (Davies, Gann, and Douglas 2009; Whyte and Davies 2021). Although essential to the programme, the technical integration (inward-looking) is only one part of programme management and needs to be complemented by the organisational and institutional engagement and integration (outward-looking). Programme management needs to account for multiple relationships within and outside the focal/client organisation (contracting authority), creating alignments that will enable and shield the technical delivery of projects from external disturbances (Denicol, Davies, and Krystallis 2020). The establishment and development of key relationships with internal and external stakeholders will bring a degree of stability to an environment that is intrinsically changing at fast pace (Muruganandan et al. 2022). The clarity of purpose and objectives tend to be an enabler if the relationships and alignments are in place, contributing to collectively achieve the outcomes of the programme.

The coordination at programme level is of utmost importance, as only the focal entity (e.g. client) or appointed delivery partner (e.g. programme management organisation) would have the overall visibility of multiple projects (Castañer and Oliveira 2020; Denicol,

Davies, and Pryke 2021; Oliveira and Lumineau 2017; Zani, Denicol, and Broyd 2024b). The teams and organisations (often Joint Ventures) delivering the individual projects (e.g. part of a new product development, one section of a railway line, a stadium within the Olympic Park) will be entirely focussed and driven to achieve their mission, yet with little or no detailed visibility of other projects happening in parallel or sequentially as part of the programme. Considering the fragmentation of the supply network and potential low profit margins, organisations tend to allocate resources to maximise the delivery of their own specific parts of the contract (Denicol 2020). This follows a commercial logic often connected with the cost of managing transactions rather than overall value, which brings a clear tension between best for individual projects (and organisations delivering them), and best for the overall programme.

Programme management will have an interface with the strategy of the focal organisation (Denicol, Davies, and Pryke 2021; Gulati, Puranam, and Tushman 2012), therefore it is essential to analyse the strategy of organisations (and their maturity levels) to assess their suitability and capability gap to embark in the journey of delivering a major programme. The organisational strategy is often implemented through portfolios, programmes, and projects – building upon teams, and individuals. Programmes might be organised as a one-off major project or ongoing programmes of work. Programmes of work are often smaller and by inherent characteristics (e.g. maintenance) might offer more opportunities for repetition and standardisation. Major projects are on the other end of the spectrum, often bringing dimensions of complexity, novelty and uncertainty, yet organised and managed as a programme, with clients decomposing the scope in sub-projects and engaging several tier one contractors (Denicol, Davies, and Krystallis 2020). Given the evolution of scope to build the asset, tier one contractors will be appointed according to their expertise throughout the project's lifecycle (e.g. from professional service firms in planning, to integrators of systems and sub-systems in execution). Structurally, they might be embedded in a permanent organisation as part of their capital projects directorate or delivered outside through a new entity created with the purpose of being responsible for the programme and act independent from the parent/permanent organisation (Stefano et al. 2023). The dynamics of multiple projects within a programme deserves further attention and scientific exploration, as they might happen concurrently or sequentially embedded in multiple networks.

Programme structures have more permanency than project-related boundaries, which are organised within
<figure><img src="image_53.png" /></figure>

the programme. Different projects will have multiple temporalities (i.e. projects within phase 1 of a railway line), moving to sequential phases (Phase 1 and Phase 2), then a meta level of corporate entity coordinating the entire programme (i.e. the client organisation; Davies and Mackenzie 2014). This entity can be a temporary (Bakker 2010; Burke and Morley 2016), semi-permanent, quasi firm, or virtual firm, which is responsible for setting the scene for multiple levels of programme management. There is significant interest in how different organisations (e.g. clients, delivery partners, tier 1s, tier 2s) are creating programme management strategies (Denicol and Davies 2022) and the connections and implications to the organisational system across the multi-level structure.

Programmes are delivered by a nested network of suppliers, ultimately procured by a client and resource orchestrator (Roehrich et al. 2023; Stefano et al. 2023). The focal organisation acts as the coordinator and integrator of a large network of organisations involved in multiple projects that form the programme (Denicol, Davies, and Pryke 2021). The organisational/enterprise level is essential to unlock the potential of programme management. Choices related to organisational design is one of the most powerful strategic levers available to the leadership of contemporary organisations (Browning 2009; George et al. 2023; Gulati, Puranam, and Tushman 2012; Zani, Denicol, and Broyd 2024a). The capabilities of the enterprise in charge of programme management are underexplored and present an opportunity for further scientific investigation.

A well designed and resourced focal entity would provide the conditions to unlock the potential of programme management, maximising the performance within and across different projects of the programme (Denicol 2020). A capable focal entity would be able to identify pockets of performance across the supply chain, find synergies between project boundaries, and add value from interactions with the institutional level. Such dynamics create a tension of intra- and inter-organisational levels. First, there is an emphasis on the capabilities of the client/contracting authority (intra-organisational), which creates the conditions for the focal entity to establish the rules and design the structure and relationships with the programme network (inter-organisational); see Provan, Fish, and Sydow (2007), Roehrich et al. (2024), Sydow and Braun (2018), and Zhang et al. (2024). Second, different organisations of the programme supply network will have to implement their own programme management strategies, as tier one contractors will subcontract parts of their scope (products and services) to specialist suppliers in lower tiers (Denicol, Davies, and Pryke 2021; Stefano et al. 2023).

## Future research avenues

Future research is needed to investigate how the different programme management at multiple levels might be coordinated and integrated, vertically as part of individual projects and horizontally as part of the programme (Denicol and Davies 2022). The capabilities of the focal entity to effectively coordinate and integrate a large network of suppliers, the information and knowledge flows across multiple actors, are the foundation to enable systemic and programmatic actions. Avenues that can support future investigation relate to the design of the focal organisation, the necessary capabilities, and models of engagement with delivery partners that might provide complementary capacity and capability at client side (Zani, Denicol, and Broyd 2024a). Researchers might be interested in exploring the role of integrated teams within the programme organisation, trajectories that may lead to high-performing teams, and the organisational impact in designing and managing the programme supply network (Roehrich et al. 2024; Zhang et al. 2024).

Researchers could expand the boundaries of public and private enterprises, by exploring the role of public procurement in the creation of the inter-organisational network (Stefano et al. 2023). Procurement models and strategies could be examined to push the boundaries of our understanding regarding standardisation and novelty across different parts of the programme. This line of investigation would unlock synergies and potential strategies to integrate suppliers involved in delivering the programme, with the network of partners hired by the focal entity in their ongoing operational activities.

## 2.18. New product/service development¹⁹

The Product Development and Management Association defines a new product as ‘a product (either a good or service) new to the firm marketing it’ and new product development (NPD) as ‘the overall process of strategy, organisation, concept generation, product and marketing plan creation and evaluation, and commercialisation of a new product’ (Kahn et al. 2012, 458). While it should be noted that there are parts of the NPD literature that focus on physical products or services only, this section applies a more general perspective. NPD research has resulted in many papers and books across various fields, such as marketing, innovation, operations management, and supply chain management. This section discusses some of the major topics across NPD research: (1) NPD and firm strategy, (2) portfolio management for NPD, (3) the NPD process, (4) product architecture, (5) customer involvement, (6) supplier involvement, (7) cross-functional collaboration, (8) the use of technology, and (9) servitisation.
<figure><img src="image_54.png" /></figure>

Research on NPD and firm strategy has explored how firm characteristics and strategies affect NPD. A prominent example is the use of the concept of ‘dynamic capabilities’, which describe a ‘firm’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments’ (Teece, Pisano, and Shuen 1997, 516). Specifically, by reconfiguring NPD capabilities according to market and technology needs, firms may increase their ability to deal with turbulent environments and rapidly changing technologies (Pavlou and El Sawy 2011), as well as improve NPD performance (Bruni and Verona 2009). NPD performance has also been linked to different strategic orientations. For instance, Narver, Slater, and MacLachlan (2004) found that firms need both responsive and proactive market orientation to create and sustain new product success. Similarly, Calantone, Garcia, and Dröge (2003) found that firm innovativeness, market orientation, and top management risk-taking relate positively to NPD speed, which is particularly important in highly turbulent environments. In recent decades, research has increasingly focussed on green (or sustainable) NPD. Such research focuses on topics such as the conditions for green NPD (Yalabik and Fairchild 2011), the role of (green) dynamic capabilities (Chen and Chang 2013), and the link to green process innovation (Xie, Huo, and Zou 2019).

Portfolio management for NPD is the manifestation of the firm’s strategy and dictates NPD investments (Cooper and Sommer 2023). It involves the evaluation, selection, and prioritisation of new projects, as well as the acceleration, termination, or downgrading of existing ones. Research on this topic has explored how to increase the NPD project portfolio value, identify a profitable mix of projects, and strategically allocate NPD resources (Cooper, Edgett, and Kleinschmidt 2001). In this context, several tools have been introduced for portfolio reviews, including the value-based scorecard, the expected commercial value method, the productivity index, and bubble diagrams (Cooper, Edgett, and Kleinschmidt 1997; Cooper and Sommer 2023; Si, Kavadias, and Loch 2022). For a review of research on portfolio management for NPD, see Si, Kavadias, and Loch (2022).

Research on NPD processes has investigated the content and sequence of NPD activities, such as planning, product design, testing and validation, and commercialisation. This has led to the introduction of the stage-gate model, which is a popular approach for managing NPD processes from idea to launch (Cooper 2019). It provides an overview of NPD activities, where ‘stages’ reflect the sequence of these activities, and ‘gates’ act as quality control checkpoints. In this context, NPD lead

time can be reduced by making NPD activities overlap (Cooper 2014). This, however, requires a detailed representation of the information exchanges necessary between individual activities, which can be created with a design structure matrix (Browning 2001; Eppinger et al. 1994). It should also be noted that the stage-gate model can be combined with strategies such as agile, lean, design thinking, and open innovation (Cocchi, Dosi, and Vignoli 2024; Cooper 2019). For a comprehensive description of NPD processes, see the textbook by Ulrich, Eppinger, and Yang (2019).

Another area of NPD research focuses on the choices pertaining to product architecture. A central conceptualisation in this research is the five categories of architectural modularity by Ulrich and Tung (1991): (1) component swapping, (2) component sharing, (3) fabricate-to-fit, (4) bus, and (5) sectional modularity, which Pine (1993) extended with a sixth type named ‘mix modularity’. Later, Ulrich (1995) provided another oft-cited categorisation of product architectures by conceptualising three distinct types of product modularity – slot, bus, and sectional – which are contrasted to a non-modular (integral) architecture. The use of modular product architectures has often been associated with increased operational performance (Salvador, Forza, and Rungtusanatham 2002). Furthermore, modularisation allows firms to pursue a ‘mass customisation’ strategy, which aims to target individual customer requirements while having near-mass production efficiency Pine (1993). For a review of the literature on modular product design, see Bonvoisin et al. (2016).

Since customers may contribute to the generation of new ideas and knowledge (Chang and Taylor 2016), research has also focussed on customer involvement (CI) in NPD. CI is often associated with the concept of ‘open innovation’, which describes a strategy of integrating ideas and knowledge from external sources to improve innovation performance (Chesbrough 2003). Research on CI has shown that customers can take different roles in NPD, including that of information providers and codevelopers (Wang et al. 2020). In this context, it should be noted that excessive or poorly managed CI can negatively impact NPD performance. For instance, customers may be unable to articulate needs for advanced, technology-based products or be unable to conceptualise ideas beyond their own experience (Knudsen 2007). For a meta-analysis of the contextual factors moderating the relationship between CI and NPD performance, see Chang and Taylor (2016).

Research on supplier involvement (SI) in NPD gained traction with the observation that Japanese automakers outperformed their Western counterparts in time-to-market and NPD cost through SI (Clark 1989). Research
<figure><img src="image_55.png" /></figure>

has shown that SI can take different forms, depending on
the level of supplier design responsibility and the moment
of SI. By accessing – rather than acquiring – supplier
knowledge, firms can expect higher NPD efficiency and
effectiveness (Suurmond, Wynstra, and Dul 2020). For
instance, SI can have a positive effect on product design
quality and time-to-customer targets (Ragatz, Handfield,
and Petersen 2002; Takeishi 2001). However, research
has also shown that SI may involve risks such as the
loss of proprietary information (Wagner and Hoegl 2006;
Yan et al. 2020) and suppliers being uncooperative or
unmotivated (Primo and Amundson 2002). For a meta-
analysis of the relationship between SI and performance
outcomes, see Suurmond, Wynstra, and Dul (2020).

NPD research has also explored the role of cross-
functional collaboration. Such studies have, for instance,
revealed the potential benefits of increased collabora-
tion between the research and development function
and other internal functions such as marketing, purchasing,
logistics and manufacturing. While marketing and
manufacturing involvement tends to slow down NPD,
it increases a product's competitive advantage and a
project's return on investment (Swink and Song 2007).
Furthermore, since the purchasing function has become
the common interface with suppliers, it can contribute
to NPD by identifying innovation potentials from the
supply market and facilitating the absorption of supplier
knowledge (Picaud-Bello, Johnsen, and Calvi 2022). In
this context, Reitsma, Hilletofth, and Johansson (2023)
provided examples of cross-functional NPD activities,
while Yao and Askin (2019) discussed decision-support
tools that can support such activities. It should be noted
that functional diversity in NPD may decrease NPD
speed; therefore, it may be desirable to keep the core
NPD team small and integrated (Chen, Damanpour,
and Reilly 2010). Furthermore, it is highly important
that project managers can detect and resolve knowledge-
sharing-related problems in NPD teams (Haug 2023).
For a meta-analysis of how cross-functional collabora-
tion may have an impact on NPD success, see Troy,
Hirunyawipada, and Paswan (2008).

Another area of NPD research focuses on the role
of technology. This research has shown that informa-
tion technology (IT) can have a positive effect on NPD
performance (Mauerhoefer, Strese, and Brettel 2017),
for instance, through facilitating collaboration with cus-
tomers and suppliers in NPD (Zheng et al. 2019). In
this context, the use of social media (particularly blogs
and forums) can facilitate knowledge sharing with cus-
tomers and suppliers and enhance NPD performance
(Cheng and Krumwiede 2018). More recently, the con-
cept of 'Industry 4.0' (I4.0) (Lasi et al. 2014) has been
studied extensively in relation to NPD. One of the most

discussed I4.0 technologies is ‘additive manufacturing’ (or 3D printing), which allows fast production of prototypes and manufacturing of highly complex components (Huang et al. 2013). Another often discussed I4.0 technology is ‘digital twins’, which allow the creation of a digital model of possible or actual real-world objects to be used for simulation, testing, monitoring, and maintenance (Lo, Chen, and Zhong 2021). For an overview of the potential applications of artificial intelligence across NPD activities, see Cooper and Sommer (2023).

Finally, in recent decades, the concept of ‘servitisation’
has drawn increasing interest, which has been defined
as ‘a process of building revenue streams for manufac-
turers from services’ (Baines et al. 2017, 257). In this
context, the review by Raddats et al. (2019) identified
five main themes in the servitisation literature: (1) ser-
vice offerings, (2) strategy and structure, (3) motivations
and performance, (4) resources and capabilities, and (5)
service development, sales, and delivery. The literature
has also identified several aims of pursuing a servitisation
strategy, including increased revenue or profit, improved
response to customer needs, improved product inno-
vation, new revenue streams, higher customer loyalty,
and increased barriers to competition (see the review by
Baines et al. 2017).

As demonstrated by this section, NPD research covers
many topics across different research fields. Thus, when
searching for NPD literature, we advise researchers to
craft their search strategy carefully to avoid excluding
potentially relevant research.

2.19. *Pricing*²⁰

Pricing is a crucial element in operations and supply
chain management, influencing firms' strategic and oper-
ational decisions. It extends beyond the act of setting
prices, requiring firms to navigate a complex landscape
of considerations that affect overall supply chain effi-
ciency and market positioning. This section explores
various pricing issues relevant to supply chain manage-
ment, reflecting critical considerations in the literature.
By examining these topics, we provide insights into how
firms can address complex pricing challenges to optimise
performance, enhance market positioning, and improve
coordination across supply chains, offering a compre-
hensive understanding of the factors that shape pricing
strategies.

Price competition (Bertrand competition) and quantity competition (Cournot competition) are two models for competitive market. In price competition, firms set prices based on competitors, often leading to price wars that drive prices to marginal costs. Quantity competition involves setting production levels based on competitors'
<figure><img src="image_56.png" /></figure>

output, generally resulting in higher prices and profits. However, price competition can also lead to higher prices and lower consumer welfare when essential inputs are controlled by a vertically integrated competitor with upstream market power (Arya, Mittendorf, and Sappington 2008). Hirose and Matsumura (2019) find that price competition enhances profits and welfare under public leadership, while private leadership sees higher profits with price competition but greater welfare with quantity competition. Singh and Vives (1984) suggest firms choose price contracts when products are complements and quantity contracts when they are substitutes. When the firms choose either a price to charge or a quantity to produce, Klemperer and Meyer (1986) show that both firms would make the same choice in the presence of demand uncertainty. Miller and Pazgal (2001) argue that price and quantity competition are equivalent when managers, incentivised by owners, decide on the competition type.

Beyond the impact of competition on pricing, double marginalisation occurs when both the manufacturer and retailer add markups to the product price, leading to a higher final consumer price. This happens because each firm maximises its own profit without considering the overall efficiency of the supply chain. As a result, the combined markup reduces total demand and overall supply chain profitability. Heese (2007) explores double marginalisation in the context of inventory record inaccuracy, showing that such inaccuracies worsen inefficiencies in decentralised supply chains. Dellarocas (2012) highlights that pay-per-action advertising, as used by companies like Google and Microsoft, can lead to double marginalisation, distorting product prices and reducing consumer welfare. Li, Li, and Cai (2013) examine supply chains with uncertainty in input levels, finding that double marginalisation occurs under wholesale price contracts, and propose a coordination mechanism for random demand and yield. Chen, Lee, and Moinzadeh (2016) analyse supply chains with long lead times and short selling seasons, where poor inventory cost distribution between supplier and retailer leads to incentive misalignment, suggesting a coordination contract with inventory subsidising to address these issues.

Pricing strategies are heavily influenced by power dynamics among stakeholders significantly impacting final pricing and supply chain profitability, often favouring stronger firms while disadvantaging weaker ones (Davis and Leider 2018; Luo et al. 2017). Models like Nash bargaining and Stackelberg illustrate how power dynamics shape pricing decisions. In Nash bargaining, both parties negotiate simultaneously, with outcomes reflecting their bargaining strengths. In Stackelberg model, featuring a leader-follower dynamic, the leader sets the price

first, followed by the followers pricing decision. This sequential decision-making creates different equilibrium prices compared to simultaneous negotiations, as the leader's actions influence the follower's pricing decisions (Wang, Niu, and Guo 2013).

Leider and Lovejoy (2016) show that when multiple retailers compete for a single supplier, their bargaining power shapes pricing dynamics. Retailers with greater power can secure better wholesale prices, influencing their pricing strategies and competitiveness. Conversely, suppliers may use strategies to mitigate pricing risks, such as contracts that share inventory risks or incentivise retailers to maintain certain pricing levels (Cachon and Lariviere 2005).

Price discrimination is a multifaceted strategy that encompasses various forms—first, second, and third degree. Effective implementation of these strategies helps firms improve their competitive position by navigating consumer behaviour and market dynamics. First-degree price discrimination, or perfect price discrimination, charges each consumer the maximum they are willing to pay, often enabled by detailed consumer data that allows personalised pricing. Choudhary et al. (2005) demonstrate how firms can use customer insights to enhance competitive strategies and maximise profits through personalised pricing.

Second-degree price discrimination involves varying prices based on quantity consumed or product version, allowing firms to capture consumer surplus through different pricing tiers. This approach is effective in markets with diverse consumer preferences, enabling segmentation and pricing optimisation based on demand elasticity. Besanko, Dubé, and Gupta (2003) highlight how competitive price discrimination influences pricing strategies, market dynamics, and profitability in oligopolistic settings.

Third-degree price discrimination, which charges different prices to distinct consumer groups based on observable characteristics, is common in supply chains. This strategy maximises revenue across various market segments. Luo et al. (2017) demonstrate how optimal pricing policies can be applied under different supply chain power structures, emphasising the importance of understanding consumer demand intensity for effective implementation.

Price discrimination impacts not only revenue but also market competition. Candogan, Bimpikis, and Ozdaglar (2012) show that optimal pricing in networks can affect consumer surplus and market equilibrium, demonstrating the link between pricing strategies and network externalities. Montes, Sand-Zantman, and Valletti (2019) emphasise the role of consumer privacy and information asymmetry in competitive pricing,
<figure><img src="image_57.png" /></figure>

highlighting the need for firms to balance the use of consumer data with privacy considerations.

Firms often adjust prices to reflect changing market dynamics, making a multi-period model effective for optimising long-term outcomes. In a multi-period model, firms can choose committed pricing, setting a fixed price throughout all periods to signal a stable market strategy, or dynamic pricing, where prices are adjusted based on changes in demand, competition, or costs. The choice between these strategies depends on the market environment, competitive dynamics, and the firm's long-term goals. For example, Monahan, Petruzzi, and Zhao (2004) explore dynamic pricing for products with random demand and fixed supply, framing it as a price-setting newsvendor problem and offering insights into optimal pricing, procurement, and profits. Levin, McGill, and Nediak (2009) develop a model for oligopolistic firms selling differentiated perishable goods, showing that consumer strategic behaviour can impact revenues and that firms might benefit more from limiting consumer information than fully responding to it. Wang and Hu (2014) study a duopoly with demand uncertainty, finding that firms with greater capacity tend to commit to fixed pricing, while those with less capacity prefer dynamic pricing. Cohen, Lobel, and Paes Leme (2020) examine pricing for differentiated products, where firms adjust prices dynamically based on past sales data.

Pricing strategies are influenced by information asymmetry between firms, leading to inefficiencies and suboptimal pricing. Corbett, Zhou, and Tang (2004) show that specific contract types, like two-part tariff, can mitigate inefficiencies by acting as signalling mechanisms, allowing suppliers to convey their private information to buyers and thus influence pricing decisions. Mukhopadhyay, Zhu, and Yue (2008) emphasise the importance of optimal contract design in the presence of information asymmetry, proposing models that account for the incomplete information. Li, Gilbert, and Lai (2015) extend this discussion by investigating supplier encroachment and its implications for nonlinear pricing strategies under information asymmetry. Zhang et al. (2015) introduce a product life cycle perspective on pricing strategies under information asymmetry, suggesting that manufacturers must adapt their pricing approaches in response to consumer expectations and purchase timing. Furthermore, Narayanan and Raman (2004) study the impact of strategic information sharing on pricing, highlighting how deliberately shared information can influence competitive dynamics and lead to more efficient pricing.

Integration of technology into pricing has garnered significant scholarly attention, particularly with the

advent of data-driven pricing models and artificial intelligence (AI). The emergence of e-business technologies has transformed traditional pricing mechanisms, enabling firms to adopt dynamic pricing strategies that respond in real-time to market conditions and consumer behaviour. However, while e-business technologies have been widely adopted for coordination, the adoption of price determination technologies remains relatively low (Johnson et al. 2007). Brynjolfsson and Smith's (2000) seminal work illustrates how the Internet has facilitated a competitive pricing environment, leading to lower prices for consumers compared to conventional retail settings. Firms use data analytics to analyse large datasets, predict consumer behaviour, and dynamically optimise pricing. This approach enables firms to implement price discrimination strategies effectively. Li and Wang (2017) highlight AI's role in pricing, showing how sensor data in perishable food supply chains can enhance pricing accuracy.

When setting prices, businesses must also consider psychological factors such as fairness concerns, which influence consumer perceptions of price justification and transparency, affecting satisfaction and loyalty. Ho, Su, and Wu (2014) demonstrate that in a one-supplier, two-retailer supply chain, fairness concerns can lower wholesale prices but also lead to complex outcomes, such as higher prices set by retailers. Nie and Du (2017) show that such supply chain requires fixed fees rather than quantity discount contracts for coordination. Risk and loss aversion are also crucial psychological factors, with players favouring predictable pricing. Firms must carefully manage and communicate price changes, particularly when consumers are sensitive to increases. Pun and Heese (2015) show that economic incentives or enforcement can motivate suppliers' subcontracting decisions under risk aversion. Choi (2018) examines high-risk fashion supply chains, finding that make-to-order quick response systems can achieve Pareto improvements for risk-averse retailers. Zhang et al. (2022) further explore the coordination of Pareto optimality for risk-averse supply chain members using mean-variance and mean-downside-risk objectives.

For a comprehensive understanding of pricing strategies in supply chain management, readers are directed to foundational texts like 'Pricing and Revenue Optimization' by Phillips (2021) and 'The Theory of Industrial Organisation' by Tirole (1988) that offer valuable insights into this complex field.

## 2.20. Maintenance²¹

In this section we discuss maintenance and its role in operations management. Maintenance can be defined as all actions taken to keep a system into a state in which
<figure><img src="image_58.png" /></figure>

it can function as prescribed. We identify different types and indicate when these are appropriate. Next we present theory from different angles, both from a management and from an optimisation point of view. In the sequel we use the following terminology: system, unit and component. A system consists of units and units consist of components. Maintenance actions can be executed at each of these levels. A failure indicates the event by which a system, unit, or component is no longer able to function.

## Typology of maintenance

Several types of maintenance can be distinguished. Corrective maintenance (CM) is initiated by failures and intends to bring a system back into a working condition. Preventive Maintenance (PM) refers to actions which improves a system's state and is carried out to prevent failures, reduce energy use or improve the quality of the output. PM is generally done based on calendar time or usage of a system, unit or component (e.g. kilometres in the case of a car). Inspection-based maintenance (IBM) refers to inspecting a system, unit or component and replacing/repairing it in case it is in a bad state. Condition-based maintenance (CBM) can be applied when a system, unit or component has a clear degradation process that describes the health status. The state of this degradation process is monitored continuously or at least with a high frequency (e.g. every day or week). When a certain state has been reached or passed, then a repair or replacement is executed. It is also possible that there is no clear degradation process, but that all kinds of data is collected and that failures/faults can be predicted via a certain data analysis/AI method. If maintenance is based on such predictions, we refer to it as predictive maintenance (PDM).

Maintenance varies depending on the environment it is applied to. In case of manufacturing, systems have a clear output which facilitates assessing the value of maintenance. In transport systems, companies operate fleets (e.g. trucks and planes) which allows to standardise maintenance. In case of civil infrastructure, lifetimes are long and deterioration rates are low. These systems often have indirect use, which complicates the justification of maintenance. On one hand there are many identical systems like roads and railway tracks, but also many unique systems like bridges and buildings. For consumer products maintenance has become less important as replacing by a new product is often cheaper. From a sustainability viewpoint there is a renewed emphasis on repairing products, yet labour costs are quite high.

## Role of Maintenance in Operations Management.

Maintenance can be seen as a necessary evil with yearly costs of 2-10% of the total capital investment. It requires specific manpower and may tie to specific companies.

Maintenance is difficult to manage as the effect is only seen on a longer term, but then deterioration may be irreversible. Companies may have an own maintenance department or may (partially) outsource maintenance. Original Equipment Manufacturers (OEMs) typically provide services, like advice and spare parts for maintenance. In several industries, it is common that OEMs offer full-service maintenance contracts to customers. Under these contracts, the OEM is responsible for all maintenance actions and for realising the required system uptime percentages. Customers may also lease systems and pay for the use of the system and the uptime percentage (see also Section 2.21). For many OEMs, the revenue generated by after sales service is more than 25% of their total revenues and often the profit percentage on after sales activities is higher than on the manufacturing activities (Deloitte 2006). For very advanced systems, advanced knowledge and specialised service engineers are needed, and then it is attractive for OEMs to offer full-service maintenance contracts. There is limited competition in that case and it leads to close relationships with customers.

## Optimisation models

Optimisation models balance maintenance work in terms of manhours and materials against reduced downtime and increased efficiency. In particular the aim is to avoid failures by doing maintenance preventively. The ideal is to do the maintenance just before the failure, but it is a challenge to predict failures correctly.

**Single component.** Originally one let systems (and underlying units and components) run until they broke down and CM has to be done. This is called run-to-failure. About half a century ago one introduced PM. Extensive maintenance schemes were developed to make sure that no critical failures could happen. This is still the case in airplane maintenance. At the same time the first optimisation models were developed by Barlow and Proschan (1996) to determine the best time for PM. The models use statistical distributions of the time to failure. Especially the Weibull distribution was and still is popular. In the simplest case one does PM at fixed intervals, regardless of failures in between. This is a so-called block replacement policy (BRP) and it requires the evaluation of the so-called renewal function. In age replacement policy (ARP) one does maintenance when the component reaches a critical age, while the age is also reset in case of failure replacement. Evaluation of an ARP policy is much easier and it can be shown to outperform BRP.

Many variants of these models were developed and analysed. A recent review is given by De Jonge and Scarf (2020). We like to mention the Modified
<figure><img src="image_59.png" /></figure>

Block Replacement model, where one does a preventive replacement at fixed time-intervals, but skips one if CM has recently been done (Berg and Epstein 1976). While in the basic ARP and BRP models maintenance costs are the same at all times, a stream of models appeared which considers occasions at which PM can be done at much lower costs (so-called opportunity maintenance, see e.g. Ab-Samat and Kamaruddin (2014) for a review).

A major bottleneck in using these models is the difficulty to obtain appropriate lifetime distributions (Dekker 1996). On one hand because there are not always indications of wear out, justifying PM, and on the other hand not many failures occur. Moreover, these models do not use indications that failures are upcoming.

In the early nineties, ideas on CBM were coming up. In his last lecture, Geraerds (1991) gave a list of trends for the field of maintenance. He stated that: ‘Periodic, usage based maintenance will considerably decrease, while condition-based maintenance will start to dominate’. And: ‘Together with the automation of condition-based maintenance, one will see a centralisation of inspections and analysis of the measurements (remote monitoring and remote analysis)’. He was right with seeing the trend that IBM, CBM, and PDM would become more important. IBM came up first (see Jardine and Tsang 2005). For IBM, the basis is already to know the degradation modes, which may be identified by executing a Failure Mode and Effect Analysis (FMEA). Next, you try to describe the degradation processes, e.g. by stochastic processes. Many different processes are being applied (Si et al. 2011). A model/process that requires limited data and has been applied a lot is the Delay Time concept introduced by Christer (1982). For CBM and PDM, it is required to collect many data, and they came up together with the increased use of sensor technologies and the Internet of Things (IoT); see Tortorella et al. (2021). These forms of maintenance are studied extensively in the current literature; see De Jonge and Scarf (2020).

**Multiple components.** Systems often consist of many identical or nonidentical components. Several types of relations may exist between components effecting maintenance (see Dekker, Wildeman, and Van der Duyn Schouten, 1997, and Nicolai and Dekker, 2008, for reviews). In *structural dependence* maintenance of one component has to be accompanied by maintenance of other components. In *stochastic dependence* the condition of a component influences the deterioration of other components. Next in *economic dependence* maintaining multiple components may be cheaper or more expensive than individual ones. The first is the case when there are large logistical costs, e.g. road maintenance (Golabi, Kulkarni, and Way 1982). The latter may be the case for redundant systems, like a 1-out-of-2 system,

where one component can be maintained without shutting down the system, but two would require system shutdown.

In economic dependence one may do a direct grouping of components into a fixed maintenance package, or an indirect grouping. The block replacement model is very suited for the first as it allows easy coordination. In case of age replacement one can only anticipate on opportunities for combination and that is indirect grouping.

In case CBM is applied to multiple components of a system, while a fixed maintenance schedule is followed for other components, coordinating maintenance is much more difficult as maintenance can be initiated at many different moments. Such systems are studied as well and in these studies also the trade-off between total maintenance costs and system availability is studied; see, e.g. Kivanc et al. (2024).

**Maintenance and production.** There are several relations between maintenance and production (see Budai, Dekker, and Nicolai 2008, for an overview). If maintenance can be done during production or if production is regularly stopped for other reasons, then maintenance work can be fit within the production schedule. A nice example is the night maintenance which is done on the Dutch railway track, when no trains are running. Yet often maintenance needs a shutdown of production to carry out the work in a safe environment. Especially if production increases and PM is delayed one sees a growing conflict. This is for example visible in the maintenance of civil infrastructure where bridges need to be replaced, but closing roads gives very much inconvenience to road users.

A quite popular recent topic in maintenance optimisation is the maintenance of offshore wind turbines. On one hand logistic costs are very high as maintenance personnel has to be shipped to offshore locations, while shutting down a wind turbines for PM or CM induces high costs. A recent review is given by Ren et al. (2021).

## 2.21. Servitisation²²

Servitisation describes a strategic transformation where manufacturers expand from a product-focussed business to a service-focussed business. It is a long-term transformation process that involves significant changes to the manufacturer but also its customers and the wider delivery network. The majority of servitisation research is focussed on the transformation of manufacturers in a business-to-business (B2B) industrial context. Prominent examples include manufacturers of high capital value assets such as aerospace engines, trains, and trucks (Baines 2013), but the approach is increasingly
<figure><img src="image_60.png" /></figure>

also applied to lower unit-cost items such as commercial vehicle tyres, and to some consumer offerings.

The approach draws in part on the tradition of industrial ecology, where the motivation for moving to a service-based model – whereby users pay for access to assets rather than buying them outright – was to reduce material use. This environmentally motivated form of servitisation became known as Product-Service Systems (Mont, 2002). A second motivation (Neely 2008) came from a strategic agenda for manufacturers, especially of capital equipment, to ‘move downstream’ (Wise and Baumgartner 1999) in order to capture profits from the assets in use, as well as or instead of the sale of the asset, and to lock in customers with an offering differentiated by the service element. This shift along the supply chain helps customers to concentrate on serving their own customers, rather than expending effort on managing maintenance and management of their process equipment or other capital assets. The manufacturer’s technological knowledge of their product allows them effectively to provide services closely related to the product, and their intimate knowledge of contexts of use can allow them to improve product designs (Davies 2004).

## Servitisation as process and outcome

The term servitisation refers to the process of strategic transformation away from a purely product-based offering. A related question is the extent of the process, more specifically how and to what degree services form part of the overall offering. A popular framework (Baines 2013) for describing this differentiates between base, intermediate, and advanced services:

* Base services focus on spare part provision and repair.

* Intermediate services focus on condition maintenance, such as scheduled overhauls and upgrades, condition monitoring and operator training.

* Advanced services see manufacturers committing to providing the outcome of the use of the product, for example when a production machine manufacturer enters into an outcome-based contract with its customer and is paid per pack produced.

Advanced services create critical operational implications, as asset ownership often remains with the manufacturer, and the manufacturer takes on more risk. For the manufacturer, the advantages are that they can overcome commoditisation of the product, create customer stickiness, capitalise on product quality, and smooth the revenue streams by shifting from occasional capital sales to continuous sales for services provided. The advantages and disadvantages for the customer mirror those of the manufacturer: they can focus on their own activities,

reduce capital employed and reduce risk, but can become locked in to one supplier and lose important technical capabilities.

For both sides, manufacturer and customer, servitisation and particularly advanced services offerings forge a higher mutual dependence than would emerge in conventional product-based commercial relationships. For the customer to become so dependent on the manufacturer's capabilities and the manufacturer taking on so much of its customer's risk requires high levels of trust and transparency, but also extensive relationship management. Yet, although much existing research examines these phenomena in particular cases, we still lack econometric large-scale studies that show a complete picture of the amount and circumstances of the benefits.

## Digital servitisation

Research in servitisation began to gain momentum in the early 2000s: for the first 10–15 years, the focus was mostly on defining servitisation strategies and understanding the transition challenges for manufacturers. More recently, servitisation research has been dominated by work on the intersection of digital technologies and servitisation (Gebauer et al. 2020). One perspective is that digital connectivity in products enables manufacturers to deliver outcome-based advanced services (Schroeder et al. 2020). Alternatively, it can be argued that servitisation provides a pathway by which to profit from digitisation. This intersection has seen servitisation research combine streams from operations management and information systems research, with prominent areas including the alignment between these domains (Kohtamäki et al. 2022), the affordances that are required to leverage the opportunities digitisation offers (Naik et al. 2020), and the strategic implications the digital twin and the industrial metaverse will create for the future of servitisation (Bertoni and Bertoni 2022).

## Critical servitisation research areas

Because servitisation is an empirical phenomenon rather than a sub-discipline of operations management, it has practical and research implications that cut across various areas of operations, as well as across industrial marketing, information systems and accounting & finance. We identify some existing and promising research areas, which connect to some of the other operations themes in the present paper.

Servitisation is essentially an innovation of the manufacturer's value proposition and business model. This presents challenges for manufacturers who are accustomed to product innovation, but not to innovation in service offerings and processes, and the organisational changes that accompany the shift to service. Early
<figure><img src="image_61.png" /></figure>

research asked whether manufacturing and service pro-
vision should be separated (Oliva and Kallenberg 2003);
more recently, researchers have attempted to understand
whether manufacturers should simultaneously innovate
product and business model, or concentrate on one
(Visnjic, Wiengarten, and Neely 2016). Innovation in
the product itself is transformed by servitisation, as
the product must be designed for service, and increas-
ingly must incorporate digital functionality; this radi-
cally transforms the capabilities required and the finan-
cial basis on which product innovations are evaluated
(Solem et al. 2022) and, because the manufacturer often
retains ownership, can create opportunities for more dar-
ing product innovations. Designing and resourcing ser-
vice delivery is often a major challenge for manufacturers,
especially when it comes to delivering service at scale
(Sklyar et al. 2019) Most research on the innovation asso-
ciated with servitisation has been based on larger firms,
but recent research (Kolagar et al. 2022) demonstrates
that SMEs can be especially adept at shifting to service,
due to their flexibility and close customer relationships.

Supply chains and networks are becoming a more
important area of concern in servitisation research
and practice. Much early research concentrated on the
changes required within the manufacturing firm and, to a
lesser extent, the customer (Shen, Sun, and Parida 2023).
Some earlier studies have addressed the role of third par-
ties such as dealers (Karatzas, Johnson, and Bastl 2017)
and suppliers (Johnson and Mena 2008) but the wider
network is now becoming increasingly important as more
divergent technologies, especially digital technologies,
play a greater part (Marcon et al. 2022), and the impor-
tance of actors such as asset finance providers becomes
clearer. Important current research issues in this area
include the alignment in the network and the balancing
of value capture.

Sustainability was one of the early motivations for a
shift to service (Mont 2002; Stahel 1981) but, as indus-
trial adoption took hold, sustainability concerns became
less prominent. Recently, however, as the climate emer-
gency makes sustainability an almost universal main-
stream business objective, sustainability has become a
central rationale for servitisation (Rabetino et al. 2024).
Manufacturers who retain ownership of products under
outcome-based contracts can be incentivised, and have
the capabilities, to monitor the product in use and pro-
vide advisory services for the efficient and sustainable
consumption of the service. Research is concerned with
evidencing the environmental benefits and exploring
how they can be further enhanced. The notion of ‘Scope
3’ of the Greenhouse Gas Protocol provides additional
impetus and structure to such initiatives. Shifts in own-
ership and responsibility within servitisation can also

promote the adoption of circular economy approaches
(Spring and Araujo 2017).

Conclusion

Although the areas discussed have been the most active in
servitisation research, as a novel empirical setting, servi-
tisation builds on many core OM topic areas, such as:
managing capacity in supporting a distributed fleet of
capital assets; quality management and assurance; job
design for service-delivery staff. Yet, in a lot of servitisa-
tion research these core OM topics are not yet sufficiently
integrated despite their practical importance and theo-
retical relevance for understanding success and failure
of servitisation. Thus, as well as developing further the
distinctive themes examined earlier, researchers can use
servitisation as an empirical context for research into
classic operations themes using a variety of methods.
Baines, Bigdeli, and Kapoor (2024) provides an accessi-
ble overview of the field, and recent review papers present
the state-of-the art in key areas of servitisation research
related to sustainability (Rabetino et al. 2024) and digi-
talisation (Shen, Sun, and Parida 2023).

3. Supply chain management

3.1. Supply chain strategy²³

The origins and purpose of supply chain strategy are best
understood through early research on strategy formu-
lation and implementation, as detailed in the dominant
process model linking corporate and functional strate-
gies. Hill (1985) and Leong, Snyder, and Ward (1990)
observed that a firm starts with an overarching corpo-
rate strategy and objectives. Strategic business units then
develop marketing strategies to meet these objectives.
Functions like new product development assess product
competitiveness, while manufacturing sets up processes
for production. During implementation, manufacturing,
logistics, procurement, and other functions build the
necessary physical and digital infrastructure. Informa-
tion continuously flows between corporate, business unit,
and functional leadership, making strategy formulation
and implementation a dynamic, ongoing process aimed
at aligning objectives and actions across different levels.

Supply chain strategy emerged from the challenge of
aligning the diverse strategies of functions, particularly
as manufacturing, logistics, and procurement. Adopting
an end-to-end perspective that encompasses the firm's
entire network – including suppliers and customers –
enhances alignment between corporate strategy and inte-
grated functions and prevents functions working at cross-
purposes. This approach enables comprehensive strat-
egy formulation and implementation through the diverse
<figure><img src="image_62.png" /></figure>

Design Considerations

<figure><img src="image_63.png" /><figcaption>**Figure 5.** Supply chain strategy diagram.</figcaption></figure>

Underpinning Concepts

capabilities and connections of the different functions closely involved with demand satisfaction. Although academic definitions of supply chains and supply chain management are not universally agreed upon (Burgess, Singh, and Koroglu 2006), the Supply Chain Operations Reference (SCOR) model effectively illustrates the scope of supply chain strategy. The SCOR model outlines key activities – plan, source, make, deliver, return, and enable – that address customer demand. Consequently, supply chain strategy focuses on satisfying this demand by coordinating functions such as manufacturing, logistics, and procurement to execute SCOR model activities effectively.

Taking a holistic view, supply chain management is one of the three core business processes identified by Srivastava, Shervani, and Fahey (1999), alongside new product development and customer relationship management. For effective supply chain strategy, alignment with product and marketing strategies is essential, ensuring coherence among all three (Godsell, Birtwistle, and van Hoek 2010), as shown in the supply chain strategy diagram (see Figure 5). Traditionally, supply chains have been tasked with supporting corporate growth objectives by cutting costs through productivity improvements. However, this focus can undermine other goals; overly lean systems narrow flows of products and reduced inventory and capacity buffers may struggle with resilience and absorbing variability in supply and demand. Additionally, cost-cutting measures can lead to short-term decision-making, such as reliance on carbon-intensive energy whose externality costs are increasingly redirected at supply chains by regulation.

An example is the European construction industries' reliance on imports from countries reliant on coal for energy, the externality costs of which are redirected to importers through the European Union's carbon border adjustment mechanism (CBAM). Supply chain strategy thus must go beyond productivity to also balance resilience and sustainability while aligning with overall corporate, product, and marketing objectives to satisfy diverse customer expectations (Ketchen and Hult 2007). One of the best examples of a firm consistently aligning product, marketing, and supply chain strategy with this goal would be Apple and particularly its iPhone product line.

Once such alignment has been achieved, the supply chain strategy is configured through two primary components: (a) the infrastructure and (b) the operating model. While the infrastructure is developed through network design (see Sections 3.3 and 3.4) and reflects the physical supply chain and its assets, the operating model is the outcome of the sum of various supply chain management design decisions. When correctly configured and aligned with the overall supply chain strategy, supply chains can achieve more balanced outcomes and avoid productivity initiatives coming at the expense of resilience and sustainability.

The concepts of dynamic and structural flexibility (Christopher and Holweg 2011, see also Figure 5) are key to understanding how such a balance can be struck. Firms are able to build a certain degree of flexibility into their supply chains via specifically configured operating models that, through segmentation, account for the particular features of customers, products, and the
<figure><img src="image_64.png" /></figure>

underlying supply chains. Again the origins of supply chain strategy in manufacturing strategy shine through here as such operating models and their underlying segmentation concepts are informed by the product-process matrix (Hayes and Wheelwright 1979) and the idea that a supply chain can be optimised around a product (Fisher 1997). Coupled with a nuanced understanding of customer demand acquired through demand profiling (Godsell et al. 2011), firms can then consider what proportion of demand can be met through a lean, cost-efficient solution and how much short-term agility is necessary to cope with ongoing demand variations (Naylor, Naim, and Berry 1999, see also Section 4.2). Zara serves as an illustrative example here; while basic products with a predictable demand profile (e.g. white t-shirts) are provided through a lean supply chain, fashion products with significant demand uncertainty are supplied through an agile solution closer to the end-consumer. Firms like Zara thus continuously run parallel supply chain configurations depending on products' demand profiles. Other firms have also found ways to frequently assemble and disassemble supply chains for planned but non-continuous surge demand periods. For example, grocery retailers such as Aldi and Lidl temporarily offer products and create large supply chains for the occasion that are scheduled to be scaled down or wound down entirely when new products are cycled in. In practice, lean is the preferred SC strategy, when demand is stable and predictable. However, when demand is more unpredictable, then a more agile strategy is preferred. Both strategies require buffers (inventory, manufacturing capacity or time) to be held to buffer against the uncertainty, with larger buffers required as unpredictability increases. Dynamic flexibility is a reflection of the agility of the supply chain and particularly its ability to respond rapidly to variations in volume and mix is thus an important but often neglected strategic objective of the operating model.

There are also limits to what can done by tweaking the operating model; as Christopher and Holweg (2011, 64) state, dynamic flexibility achieved through clever management only allows firms to respond and

> cope with certain shifts in demand and technology, but only within the set structure of their existing supply chain design [...] to meet the challenges of a turbulent business environment, we need structural flexibility that builds flexible options into the design of supply chains.

Such structural flexibility, achieved through network design, can be created through the strategic configuration of infrastructure. Strategic inventory and capacity buffers, dual sourcing, asset sharing, postponement, rapid manufacture, outsourcing, and other approaches

can build a supply chain's ability to adapt to fundamental change. Such fundamental change may be caused by various human-made and natural factors; its primary effect is the shifting of the 'centre of gravity' between supply and demand locations. Greater consolidation in the infrastructure with fewer, more centralised assets likely far removed from either suppliers and/or customers may improve efficiency but also lowers structural flexibility, which would benefit from a more distributed infrastructure that can adjust to a changing 'centre of gravity'. Without structural flexibility built into the supply chain through purposeful network design, change cannot be adjusted to as the existing infrastructure lacks the levers that would allow for a reconfiguration of flows.

While supply chain strategy aims for an end-to-end approach, practical execution is challenging due to limited visibility and growing complexity in coordinating activities across numerous firms. Consequently, many firms adopt a more focussed approach. Frohlich and Westbrook's (2001) 'arcs of integration' concept is valuable here; it reflects how firms integrate activities with suppliers and customers. Firms implementing a new supply chain strategy might begin with a narrow, inward-facing arc with minimal integration or a peripheral arc that integrates selectively. Over time, they may expand integration by enhancing access to planning systems, sharing inventory information, and using common logistics resources. Frohlich and Westbrook (2001) and subsequent research indicate that while many firms focus primarily on either supplier or customer integration, a more comprehensive outward-facing arc, i.e. integrating with both, tends to yield the highest performance improvements. However, as integration extends to more tiers of suppliers and customers, firms must evaluate when the costs of further integration exceed the anticipated benefits.

Addressing evolving challenges, there is an urgent need to balance social, environmental, and economic outcomes (Sections 4.4 and 4.5). Long-term, we must establish a solid social foundation that meets basic human needs while maintaining an ecological ceiling to mitigate the adverse effects of economic activity, as outlined in Raworth's (2017) doughnut economics. To achieve this, promising macro-level ideas are being translated into actionable strategies at the meso-level of supply chains. Concepts such as the circular economy (Section 4.3) and its meso-level components of closed-loop supply chain management and cradle-to-cradle design offer valuable approaches. Additionally, many supply chains still have untapped opportunities for improvement through simpler, underutilised practices. Crucially, firms should better integrate consumers
<figure><img src="image_65.png" /></figure>

into the supply chain, as they influence demand patterns and contribute to preventable social, environmental, and economic costs. Reducing inventory and capacity buffers through a closer connection with consumers can thus help decrease the overall supply chain footprint. Digitalisation and emerging technologies (see Sections 3.2 and 4.8.2) will be key in facilitating this transition toward more balanced supply chains and will play a greater role at the formulation stage of supply chain strategy.

Beyond the literature referred to above, we direct the reader to several reviews that offer more insight into particular topics within the supply chain strategy literature, such as flexibility and resilience (Stevenson and Spring 2007; Tukamuhabwa et al. 2015) and integration (Ataseven and Nair 2017). Formal theories relevant to supply chain strategy are scarce, but Schmenner (2001) develops a seminal perspective with the Theory of Swift, Even Flow. Given the cross-functional scope of supply chain strategy, contributions from other literatures, particularly strategic management (Hitt 2011; Ketchen and Craighead 2020) and marketing (e.g. Jüttner, Christopher, and Baker 2007; Kozlenkova et al. 2015), are also relevant. Textbooks by Chopra and Meindl (2021) and Cohen and Roussel (2013) provide structured, accessible, and illustrated insight. Practitioner books, such as Sarkar (2017) and Handfield and Linton (2022), offer perspectives on emerging developments.

## 3.2. Digitalisation²⁴

Digitalisation has fundamentally reshaped how individuals interact, how societies function, and how organisations operate. In a business context, digitalisation is understood as using digital technologies to enhance and enable value creation. Digital technologies are electronic tools and systems that generate, store, process, or exchange data. Traditional examples of digital technologies include computers, smartphones, and the Internet. Emerging digital technologies (Section 4.8.2) refer to, for instance, the Internet of Things (IoT), Artificial Intelligence (AI), blockchain, drones, robotics, and cloud computing (e.g. Choi et al. 2022; Klöckner, Schmidt, and Wagner 2022; Maghazei, Lewis, and Netland 2022).

Firms' digitalisation efforts increasingly include their supply chain partners. Modern supply networks (Section 3.3) are characterised by complex global structures involving diverse actors worldwide. As emerging digital technologies (Section 4.8.2) enable seamless coordination and real-time information sharing, they exhibit a 'natural fit' with the challenges associated with complex supply networks. The firm's digitalisation efforts

can enhance communication, collaboration, and visibility across these interconnected relationships (e.g. Choi et al. 2022; Hastig and Sodhi 2020; Olsen and Tomlin 2020).

Digitalisation impacts firms and supply chains on three levels (see Baiyere et al. 2023). The data level (i.e. digitisation) involves converting analog data into digital formats for easier management, access, and use. At the process level, digitalisation optimises processes within and across firm boundaries, enhancing efficiency and effectiveness. At the business level, new technologies can also potentially shift business models and practices at the business level (i.e. digital transformation). Overall, digitalisation transforms how firms and their supply chains operate, collaborate, and compete in a global digital economy.

### The role of data

The role of data in supply chain management has radically changed over time. Digitisation vastly increases the amount of data generated by the firm and its supply chain partners. This data includes a wide variety of types, such as real-time sensor data, transactional data from supply chain exchanges, customer-related data, as well as information on production (Section 2.11), inventory levels (Section 2.3), and maintenance requirements (Section 2.20). Additionally, data from external sources like market trends, weather conditions, and geopolitical events can also be integrated to enhance decision-making across the supply chain (e.g. Colombari et al. 2023; Xu et al. 2023).

Research explores how firms can use the new data and the associated financial and operational performance implications. Advances in (big) data analytics, utilising machine learning algorithms and artificial intelligence approaches, allow firms to leverage these data (e.g. Choi, Wallace, and Wang 2018; Sanders and Ganeshan 2018). Data-driven operations and supply chain management offers numerous benefits, such as enhanced visibility, real-time decision-making, predictive analytics, and improved customer satisfaction (e.g. Li and Li 2023; Olsen and Tomlin 2020). Current findings generally suggest that utilising big data analytics can translate into increased firm performance (e.g. Cohen 2018; Li and Li 2023).

Various research streams examine how the availability of new data types and higher volumes influences decision-making in operations and supply chain management. Specifically, related studies cover pricing, inventory, scheduling, or maintenance decisions (e.g. Li and Li 2023; van der Laan et al. 2022; Xu et al. 2023). In addition, research also focuses specifically on the opportunities and challenges of using specific types and
<figure><img src="image_66.png" /></figure>

data sources. For example, weather forecasts are used to optimise logistics and inventory, and social media data provides valuable insights into consumer sentiment and trends (e.g. Agnihotri et al. 2022; Roth Tran 2023; Schmidt et al. 2023). Huang, Potter, and Eyers (2020) provide an overview of the social media application areas. Overall, data has become a strategic asset in supply chain management, essential for decision-making and optimisation.

One of the major insights in research and practice is that the increasing reliance on data in operations and supply chain management presents clear downsides. The threats to a firm's digital infrastructure and data integrity are operational risks, frequently referred to as cyber risk (Kumar, Mookerjee, and Shubham 2018). On the one hand, globally integrated IT infrastructures exhibit new vulnerabilities, and cybersecurity and information systems security threats pose significant risks. Materialised risks can result in data breaches or supply chain disruptions with substantial negative performance implications for firms and their supply chain partners (e.g. Durowoju et al. 2021; Foerderer and Schuetz 2022). Investing in operational and supply chain resilience related to information security, for example, through secure data sharing or advanced encryption, can partially protect against these threats (e.g. Ampel et al. 2024). Kumar and Mallipeddi (2022) elaborate on the growing importance of cybersecurity in operations and supply chain management.

On the other hand, there are increasing concerns regarding the handling and exposure of data, highlighting the importance of data privacy and confidentiality (e.g. Fainmesser, Galeotti, and Momot 2022; Massimino, Gray, and Lan 2018). Customers are more sensitive to data breaches, understood as the unintended exposure of confidential information, and these incidents have received increasing attention from customers and policymakers. Massimino, Gray, and Lan (2018) outline data privacy and confidentiality issues and discuss their importance for operations and supply chain management research and practice.

Researchers continue to use established methods like case studies, surveys, regression analyses, event studies, and various analytical approaches to explore how firms utilise new digital technologies. However, new data sources also allow for innovative research approaches. For instance, textual data is now analysed using machine learning techniques to extract topics, identify similarities, or measure sentiment, providing fresh insights into supply chain dynamics and decision-making processes (e.g. Bao and Datta 2014; Frankel, Jennings, and Lee 2022).

## Digital technologies in supply chain management

Supply chain management is a promising and prominent area for digitalisation due to its complex and interconnected nature. As global supply chains face an increasing need for efficiency, transparency, sustainability, and resilience, digitisation becomes a key driver for innovation and competitive advantage (Choi et al. 2022; Kumar and Mallipeddi 2022).

Digitalisation efforts increase supply chain efficiency by automating or supporting production, logistics, or retailing processes across the supply chain (e.g. Pournader et al. 2020). Enhanced data sharing, for example, fosters better coordination among partners, optimises inventory management, and reduces lead times, resulting in more streamlined supply chains (e.g. Choi et al. 2022). Digital technologies also increase supply chain transparency, which is understood as the ability to access and share accurate information across the supply chain, improving collaboration and coordination between supply chain partners (e.g. Dolgui and Ivanov 2022; Kalaiarasan et al. 2022). A major development is the global importance of transparent supply chains, as they are a prerequisite to clear customer and investor communication, for example, regarding product sourcing and quality and increasing regulatory requirements (e.g. Hastig and Sodhi 2020). Digital technologies, such as IoT, RFID, and blockchain, have been shown to improve visibility and transparency across supply chains (e.g. Hastig and Sodhi 2020; Kalaiarasan et al. 2022).

Digital technologies, such as AI and predictive analytics, can strengthen risk management and enhance resilience in supply chains (e.g. Huang et al. 2023; Pettit, Croxton, and Fiksel 2019). New tools allow firms to identify potential risks early, assess their impact, and implement proactive mitigation strategies while also increasing firm operational and financial performance (e.g. Bao and Datta 2014; Zhao, Hong, and Lau 2023). By leveraging new technologies, firms can also better withstand and adapt to both global and local disruptions, ensuring stability and continuity (e.g. Ivanov 2024c). Digitalisation can also significantly enhance sustainability efforts in supply chains by, for example, optimising transportation routes, reducing energy consumption, and minimising emissions (e.g. Pournader et al. 2020; Tsolakis et al. 2022). It also supports better resource use and waste management through continuous production monitoring and improved supply tracking (e.g. Olsen and Tomlin 2020; Schilling and Seuring 2024). Schilling and Seuring (2024) provide an overview of the challenges and opportunities of digitalisation efforts for sustainable supply chain management.
<figure><img src="image_67.png" /></figure>

Firms are increasingly pushing *digital transformation* initiatives involving their supply chain partners. Supply chains move beyond traditional systems, and firms drive the development of new business models, enabled by integrating advanced digital technologies such as AI, IoT, blockchain, and big data analytics (e.g. Choi et al. 2022; Gökalp and Martinez 2022). New business models relate to platform-based ecosystems, and on-demand manufacturing (e.g. Lerch, Horvat, and Jasny 2024; Stark et al. 2023), all of which offer greater flexibility and scalability. Digital transformation creates significant changes in organisational structures, roles, and capabilities and requires that firms build digital competencies across their workforce to stay competitive in a global market (Gökalp and Martinez 2022; Schilling and Seuring 2024). In conclusion, digitalisation is transforming operations and supply chain management in various ways, bringing both opportunities and challenges.

### 3.3. Network design²⁵

A supply chain network is a complex system interconnecting multiple individual supply chains. This includes both supplier and consumer supply chains, as well as a company's internal supply chain. The design of this network determines how resources, information, and goods flow from raw material suppliers to end consumers. In today's globalised economy, supply chain networks are essential for the efficient production and distribution of goods and services. The design of a supply chain network involves strategic planning to ensure that the entire system operates efficiently, minimises costs, and mitigates risks.

In the current landscape, several pressing issues impact supply chain network design. Some challenges are supply chain disruptions, which have been exacerbated by events such as the COVID-19 pandemic and ongoing geopolitical tensions. These disruptions have underscored the vulnerability of global supply chains, leading to delays, shortages, and increased costs. As a result, many companies are reassessing their supply chain networks to enhance resilience by diversifying their supplier base, increasing local sourcing, and establishing strategic reserves. The design of resilient supply chains should support disruptions, whether from natural disasters, economic shifts, or pandemics. For example, one study proposes a bio-inspired framework for designing resilient and sustainable resource and infrastructure networks, drawing on ecological principles to balance redundancy and efficiency (Chatterjee and Layton 2020). Another research explores the use of probabilistic-stochastic programming to create a robust humanitarian logistics network, emphasising the importance of scenario-based

planning in the face of unpredictable disruptions like earthquakes or pandemics (Nezhadroshan, Fathollahi-Fard, and Hajiaghaei-Keshteli 2021). Similarly, a study on earthquake relief logistics during COVID-19 highlights the challenges of simultaneous disasters, proposing a mathematical model that considers facility failures and multi-trip vehicle routing to enhance reliability and timely delivery (Abbasi et al. 2023).

The stream of research focussed on risk management highlights that strategies such as diversifying supply sources, adopting multi-sourcing approaches, and maintaining safety stocks are effective strategies in mitigating risks (Esmizadeh and Mellat Parast 2021; Schätter, Hansen, and Haas 2024; Vieira, Figueira, and Fragoso 2023). However, these approaches often lead to increased operational costs, necessitating a balance between risk mitigation and cost efficiency based on a company's specific risk acceptance and business objectives.

Another significant issue is the growing emphasis on sustainability and environmental concerns. There is an increasing pressure on companies to minimise their carbon footprints, which has driven a shift towards greener logistics, sustainable sourcing practices, and the adoption of circular supply chains. While integrating sustainability into supply chain design can introduce additional complexity and costs, it is crucial for long-term viability and compliance with evolving environmental regulations. Some studies have investigated the multi-period, multi-echelon, multi-product, and multi-modal sustainable supply chain network design problem developing a Mixed-Integer Linear Programming model that explicitly considers the environmental footprint and social responsibilities (Guo et al. 2021). Several studies address the integration of environmental, economic, and social objectives into supply chain design. Jiang et al. (2018) introduced a multi-objective mixed-integer linear programming model to design a sustainable supply chain network that minimises costs while reducing carbon emissions and improving social outcomes, such as local employment. Another research discusses the use of graph theory in social network analysis to explore the relationships within local food systems, revealing how diversified marketing practices contribute to system resilience and sustainability (Brinkley 2018). Additionally, a study of the bioenergy supply chain emphasises the importance of designing networks that balance economic viability with environmental impacts, particularly in the context of transitioning to renewable energy sources (Rafique, Jat, and Hakeem-Ur-Rehman 2021).

The literature underscores the transformative impact of digital technologies on supply chains. The digital transformation and integration of advanced technologies
<figure><img src="image_68.png" /></figure>

such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain are reshaping supply chain management (Ghomi-Avili, Niaki, and Tavakkoli-Moghaddam 2023). Modares, Kazemi, and Emroozi (2023) assessed the use of IoT and blockchain to optimise supply chain management by improving information exchange between suppliers and manufacturers, reducing waste and returns. A multi-criteria decision-making model, coupled with genetic algorithms and particle swarm optimisation, is used to select optimal suppliers, with the approach validated through a food supply chain case study. These technologies enhance visibility, improve decision-making processes, and enable real-time tracking of goods and information. However, incorporating these technologies into existing supply chain networks presents challenges, including a significant investment and the complexity associated with the system integration. Also, other challenges emerge, such as data security concerns, the skills gap in the workforce that must be addressed to fully realise the benefits of digitalised supply chains. Schulz and Freund (2019) investigated the role of blockchain technology in enhancing data security within Industry 4.0's interconnected supply chains. By integrating blockchain with the industrial Internet of Things, the study addresses critical concerns such as data integrity, access control, and transaction security, proposing a decentralised information processing system to support the secure and efficient management of self-organised production networks. On the other hand, Ronchini, Moretto, and Caniato (2023) examined the impact of additive manufacturing adoption on upstream supply chain design, revealing that decisions to make, buy, or vertically integrate depend on factors like additive manufacturing (AM) experience, application type, and the need for production control. High initial investments and a lack of skills emerged as significant barriers, influencing supply chain restructuring and the make-or-buy decision.

Additionally, the debate between globalisation and rationalisation is influencing supply chain network design. While globalisation has been a dominant trend, recent shifts towards rationalisation are driven by factors such as trade wars, rising tariffs, and the need for greater supply chain resilience. Companies are increasingly focussing on regional hubs rather than extensive global networks, which can reduce risks associated with long-distance logistics but may also limit economies of scale. One paper proposed and examined the impact of the Regional Comprehensive Economic Partnership agreement on supply chain resilience, using a fuzzy optimisation model to address demand uncertainty and evaluate the effectiveness of resilience strategies like multiple sourcing and capacity redundancy (Cheng, Wang, and

Wang 2024). Another study analyses the challenges faced by multinational companies in managing complex production networks, proposing a stochastic programming model to optimise the allocation of production stages to suppliers under uncertain demand conditions (Zhen, Zhuge, and Lei 2016).

Although the literature identifies certain common themes and shared issues, there are undoubtedly sector-specific traits that necessitate special attention. For instance, the challenge of managing perishable goods presents unique logistical and supply chain difficulties that are not as prevalent in other industries (Biuki, Kazemi, and Alinezhad 2020; Gong et al. 2007). For example, one study on the perishable goods supply chain integrates considerations of product spoilage and stochastic demand, proposing a network model that aims to optimise availability and net profit for all members of the supply chain (Dagne, Jayaprakash, and Gebeyehu 2020). Another paper explores the design of closed-loop supply chains for durable products, highlighting the complexities of managing return flows and the need for compliance with legislative recovery targets (Jeihoonian, Kazemi Zanjani, and Gendreau 2016).

Similarly, the rapid growth of e-commerce has introduced distinct operational complexities, such as the need for efficient last-mile delivery and inventory management in a digital marketplace (Janjevic and Winkenbach 2020). In the context of e-commerce, Calzavara et al. (2023) investigated different strategies for e-grocery logistics, comparing the costs and performances of various network designs to determine the most efficient approach in response to fluctuating online demand. With reference to the e-commerce sector, Liu et al. (2022) further emphasised that an optimised network configuration may, in certain scenarios, influence the demand pattern itself, potentially leading to a situation where the initial optimisation no longer yields the best performance, thus highlighting the dynamic interplay between logistics design and consumer behaviour and demand. Another paper (Agac et al. 2023) introduced a novel mathematical model using Mixed-Integer Nonlinear Programming for optimising blood supply chain networks, focussing on location, allocation, and routing. A variant of the Genetic Algorithm is applied, and the model's effectiveness is demonstrated through a case study in the Eastern Black Sea Region of the Turkish Red Crescent.

Several case studies illustrate these concepts in practice. Apple, for example, is known for its highly efficient global supply chain, which relies on a vast network of suppliers while maintaining strict control over key components. Zara, the fashion retailer, exemplifies agility and speed in supply chain management. Their approach involves nearshoring and vertical integration,
<figure><img src="image_69.png" /></figure>

enabling rapid response to changing fashion trends and quick turnaround from design to store shelves. Toyota's supply chain, particularly its Just-In-Time (JIT) methodology, has long been a model of efficiency. However, some disrupting events revealed the vulnerabilities of this approach, prompting the company to re-evaluate its strategy, demonstrating the necessity of balancing efficiency and resilience.

In conclusion, supply chain network design is a critical aspect of supply chain management that must navigate a range of complexities and challenges. Current issues such as disruptions, sustainability, digital transformation, and the tension between globalisation and rationalisation are driving significant changes in how supply chains are structured and managed. Insights from recent literature and practical case studies highlight the importance of adaptability, technological integration, and a balanced approach to designing supply chain networks. As the global business environment continues to evolve, supply chain managers must remain proactive in adapting their supply networks to meet both current challenges and future demands.

### 3.4. Outsourcing²⁶

Outsourcing can be broadly defined as the practice of contracting out specific operations or services to external providers instead of performing them internally (Simchi-Levi, Kaminsky, and Simchi-Levi 2021). Outsourcing has become a critical element in the modern businesses, especially in the fields of operations and supply chain management. Firms constantly need to make a boundary decision regarding how to carry out activities (Fine and Whitney 2002). One way of doing this is internalisation that is the firm dedicates its own resources to carry out activities in-house for example, for the need have a better control of the processes (Holcomb and Hitt 2007). Sometimes, many firms choose to organise their activities by outsourcing, meaning they rely on third-party providers to access the capabilities and resources they need (Holcomb and Hitt 2007).

Historically, outsourcing was primarily driven by the desire to reduce costs. A notable example is the manufacturing outsourcing boom in the electronics industry during the 1990s, where companies outsourced to cut expenses and improve operational efficiency (Harland et al. 2005). However, in recent years, outsourcing has evolved into a more sophisticated strategy. Modern companies now use it to enhance their competitiveness by acquiring specialised capabilities and cutting-edge technologies, helping them stay ahead in rapidly changing markets (Holcomb and Hitt 2007; Kremic, Icmeli Tukel, and Rom 2006).

There are many reasons why firms consider outsourcing. Firms' decisions to outsource are often closely aligned with their broader operational and corporate strategies, including goals related to growth, innovation, and maintaining a competitive edge (Simchi-Levi, Kaminsky, and Simchi-Levi 2021). Outsourcing enables companies to focus on their strategic objectives while leveraging the capabilities of external partners to improve efficiency and flexibility.

One of the primary reasons firms outsource is to achieve cost efficiency. By outsourcing in-house activities, firms can significantly reduce their capital expenditure and operational costs, freeing up their limited and valuable resources for other priorities (Harland et al. 2005; Hendry 1995). This is particularly advantageous when outsourcing partners possess specialised capabilities or are located in regions with lower operational costs (Simchi-Levi, Kaminsky, and Simchi-Levi 2021).

Another key motivation for outsourcing is the ability to focus on core competencies. It is often impractical for companies to carry out all necessary activities internally. As a result, many firms choose to outsource non-core activities, allowing them to concentrate their limited resources on activities that are more strategically important (Arnold 2000; Harland et al. 2005; Prahalad and Hamel 2009; Simchi-Levi, Kaminsky, and Simchi-Levi 2021).

Furthermore, outsourcing also grants firms access to specialised knowledge and advanced technologies that may not be available internally (Harland, Brenchley, and Walker 2003; Venkatesan 1992). In industries where technology evolves rapidly and customer preferences change frequently, outsourcing offers a valuable means to stay updated without the heavy investment or risks of internal development (Venkatesan 1992). As a result, outsourcing knowledge creation to external partners has become increasingly common, particularly in high-tech sectors (Ahuja 2000; Son et al. 2024).

Lastly, outsourcing provides companies with scalability and flexibility, allowing them to adjust their operations in response to fluctuating demand (Harland et al. 2005; Holcomb and Hitt 2007). This is especially valuable in industries where demand is difficult to predict and products may become obsolete quickly (Simchi-Levi, Kaminsky, and Simchi-Levi 2021). The ability to scale up or down through outsourcing helps firms remain agile and responsive to market changes.

Firms often have more than one motivation for outsourcing. A prime example is Apple, which strategically retains core activities such as research and development and marketing in-house-areas critical to its brand identity and product innovation, while outsourcing
<figure><img src="image_70.png" /></figure>

production to external specialist manufacturers like Hon Hai Precision and Pegatronics (Mudambi and Venzin 2010). This approach enables Apple to achieve flexibility and cost efficiency without sacrificing the quality or premium image of its products. By heavily relying on outsourcing for manufacturing, Apple can concentrate on its core strengths: fostering innovation, enhancing its brand, and maintaining its leadership in the market (Mudambi and Venzin 2010).

Despite the advantages discussed above, however, outsourcing presents significant challenges and risks. Many companies find their outsourcing experiences disappointing (Harland et al. 2005), and a substantial number eventually choose to bring previously outsourced processes back in-house (Handley and Benton 2009). This means, firms must carefully weigh these risks against the benefits to ensure their outsourcing strategies align with long-term objectives.

One of the primary risks of outsourcing is the potential loss of control, as companies hand over responsibility for certain functions to external providers (Kremic, Icmeli Tukel, and Rom 2006; Simchi-Levi, Kaminsky, and Simchi-Levi 2021). This can lead to various issues, particularly concerning quality, which becomes especially problematic when the outsourced function is customer-facing. In the early 2000s, many firms outsourced their customer service operations to low-cost locations for cost saving. However, this often resulted in significant challenges related to service quality, leading to widespread customer dissatisfaction (Tate and van der Valk 2008). As a result, some companies were forced to bring these functions back in-house to regain control and improve service standards (Treanor 2007).

Another critical risk is the issue of hidden costs. While firms often view outsourcing as a means of achieving cost savings, there are frequently overlooked cost that arise throughout the process (Kremic, Icmeli Tukel, and Rom 2006). These hidden costs can include contracting, coordination, and ongoing monitoring, which many firms fail to fully anticipate (Kremic, Icmeli Tukel, and Rom 2006; Simchi-Levi, Kaminsky, and Simchi-Levi 2021). For instance, managing relationships with external providers often requires additional resources, and discrepancies between expectations and actual service delivery can lead to further financial burdens eroding potential savings.

Another significant risk tied to outsourcing is the potential loss of competitive knowledge. When firms outsource essential functions, especially those related to core products or services, they run the risk of loss of competitive knowledge to competitors. This can happen when outsourcing providers work with multiple clients, inadvertently sharing insights across different companies

(Simchi-Levi, Kaminsky, and Simchi-Levi 2021). Additionally, over-reliance on external partners for innovation can stifle a company's own capacity to innovate. As firms become dependent on the knowledge and capabilities of their outsourcing partners, they may lose their ability to generate unique ideas and solutions independently (Kremic, Icmeli Tukel, and Rom 2006; Simchi-Levi, Kaminsky, and Simchi-Levi 2021).

In recent years, there has been a notable shift in outsourcing trends across industries, driven by the need for greater resilience in supply chains. This is because recent global events such as the COVID-19 pandemic, trade wars, and semiconductor shortages have exposed vulnerabilities in these extended supply chains, prompting firms to rethink their outsourcing strategies. The automotive industry, for instance, has begun moving away from outsourcing to regain control over critical components, such as electronic vehicle (EV) batteries and semiconductors (The Economist 2022). Tesla's vertically integrated model, which includes in-house production of key materials and components, has inspired traditional automakers to shift towards a more integrated approach. Companies like Ford, BMW and VW are planning to make more EV motors in their factories (The Economist 2022).

For more information on outsourcing, please refer to the book by Simchi-Levi, Kaminsky, and Simchi-Levi (2021), Chapter 9.

## 3.5. Purchasing and procurement²⁷

### Definition and organisational contribution

Purchasing and supply management can contribute to organisational efficiency, risk management and innovation (Axelsson and Håkansson 1984). In financial terms, this translates into cost reduction, assets optimisation and revenue growth. Growing emphasis is placed on the contribution to non-financial performance dimensions such as environmental and social sustainability, and governance in terms of the accountability, legitimacy and ethics of the organisation's activities (Quarshie, Salmi, and Leuschner 2016). The exact definition of purchasing and supply management – or, in short: procurement – has seen a shift in emphasis since the 1960s, when the field became subject of academic education and research (Wynstra, Suurmond, and Nullmeier 2019). The first textbooks focussed on differences with consumer buying behaviour and on the tactical and operational purchasing process (e.g. Webster and Wind 1972). By the 1990s, definitions started to explicate the variety of buying needs, such as the one by Van Weele (1994) that distinguished between direct (Bill of Material) and indirect spend. In
<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /><figcaption>**Figure 6.** Purchasing and supply management process model.</figcaption></figure>

Source: Van Raaij (2016), 14; published with the permission of the author.

the 2000s, definitions put the spotlight on the identification, development and maintenance of relations with suppliers (e.g. Axelsson, Wynstra, and Rozemeijer 2005; Leenders et al. 2006). Consequently, purchasing and supply management is now seen to govern both tactical and operational, more short-term and internally oriented decisions and actions (purchasing) and strategic, more long term and externally oriented decisions and actions (supply), as depicted in Figure 6.

The upper, strategic/tactical part of this process model is commonly referred to as ‘sourcing, and is the focus of this section. The bottom part of the model is referred to as ‘purchase to pay’. This particular process model thus captures parts of the procurement process that are often treated separately, dividing between tactical and operational processes (e.g. Monczka, Trent, and Handfield 2002, 29) versus strategic processes (e.g. Monczka 1999).

## Sourcing analysis

Sourcing or spend analysis examines historic sourcing and business process data to improve or cut inefficient spend. It creates the foundation for spend visibility, compliance and control (Pandit and Marmanis 2008). Spend analysis typically starts from four key elements in accounts payable data: invoice amount, supplier, spend category (see below), and cost centre (internal customer); see also Telgen (2004). After this data extraction, subsequent validation, classification, and analysis are required to derive actionable insights.

## Specification of need

Specification of need pertains to both quantities and qualities. Especially for direct purchase items, need quantities are primarily derived from final demand forecasts

(see Section 4.6). Specification of needs in terms of qualities is typically a cross-functional process, where the procurement department gathers information and preferences from internal customers and technical specialists. This specification process is one of the typical areas where the mandate of the procurement function is being contested. In those organisations where there is no substantial mandate, procurement's role in the specification-setting process is usually highly limited, and they are relegated to processing orders based on the specifications provided by internal customers. This is very different in organisations where procurement does have a substantial mandate, for instance because of proven capabilities, achieved successes and general management support. In those situations, procurement is involved early on in the process, and has an explicit role in challenging possibly 'gold-plated' specifications. Also, current and potential suppliers can provide valuable inputs. Giving suppliers influence over the specifications of their offerings positively affects both the efficiency and effectiveness of new product development projects (Suurmond, Wynstra, and Dul 2020).

## Sourcing strategy development

Every organisation buys different products and services, with varying technical, financial and market characteristics, and therefore would benefit from segmenting its spend into categories. A category strategy defines sourcing objectives, and how to select and interact with the supply base. One of the most common models to segment spend into categories is the portfolio approach of Kraljic (1983). This model distinguishes four groups of sourcing categories (strategic, bottleneck, leverage and non-critical), based on profit impact (defined in terms of the volume purchased, impact on product quality or business growth, etc.) and supply risk (number of suppliers, storage risks and substitution possibilities, etc.). Each of the groups has a distinct recommended sourcing strategy: performance-based partnership for strategic items, secure and diversify for bottleneck items, competitive bidding for leverage items and systems contracting for non-critical or routine items (van Weele and Rozemeijer 2022). While this and other so-called portfolio models have been widely adopted, criticism has been raised that such models promote a static view of the product or service being bought and disregard the interdependencies between categories and between supplier relationships (Dubois and Pedersen 2002).

## Supplier selection

Once a category strategy has been defined, is it possible to set up and execute an effective tendering and supplier selection process. This involves three choices: which
<figure><img src="image_73.png" /></figure>

tendering process to follow, which criteria to apply, and
which selection method to use.

**Tendering process.** To reduce the workload for suppliers and themselves, buying organisations can make a distinction between qualification and selection decisions in the tendering process. For the selection stage, four main process varieties exist. In a non-competitive purchase there are no competitive procedures or formal evaluations of supplier bids, while informal negotiations are somewhat more competitive. Closed tenders are on invitation only, whereas open tenders are open to everyone. The choice between these process varieties depends on the purchase situation. For instance, an open tender is typically chosen for large volume purchases, and where a buying firm is not fully knowledgeable about suitable suppliers (de Boer, Labro, and Morlacchi 2001). In the closed and open tender procedures, various requests may be sent out to suppliers: request for information (market sensing), request for proposal (when specifications are not fully set) and request for quotation (specifications are set). Together, these requests can be seen as a funnel process, although not all steps have to be executed each time (Beil 2010).

**Selection criteria.** One can distinguish two main levels of selection criteria: supplier (e.g. financial stability) and product/service offer (e.g. price). In the qualification stage, or when there is no actual product/service yet, buyers emphasise supplier features and only in the final selection consider product/service criteria. Regarding the latter, price, quality and delivery (reliability and flexibility) have traditionally been the most prevalent but other criteria such as sustainability (e.g. CO2 emissions) and diversity (e.g. type of workers employed) are gaining importance. Another important choice is the relative importance of the different criteria, which is partly determined by the selection method.

**Selection methods.** For supplier selection, various methods exist (de Boer, Labro, and Morlacchi 2001). Categorical methods are qualitative models mainly used for supplier qualification. Most common in final selection are linear weighting models, in which weights are allocated to criteria based on their importance – typically, either compensatory (no minimum score required for any of the criteria) or non-compensatory. A special group of ‘monetary’ models uses the concept of Total Cost of Ownership, by which all indirect costs before, during and after the transaction are calculated and added to the acquisition price to arrive at a total cost (Ellram 1993). Recent ‘Total Value Contribution’ approaches additionally include the revenue generating potential differences between sourcing alternatives (Gray, Helper, and Osborn 2020; Wouters, Anderson, and Wynstra 2005). AI and Machine Learning offer potential benefits for

supplier selection, through processing larger amounts of data, pattern recognition and dynamic scoring – creating additional opportunities for techniques such as Data Envelopment Analysis (DEA) and mathematical programming. When using (most of) these selection methods, buying organisations also need to choose a rule to assign scores to supplier bids for each criterion. Scores can be qualitative or quantitative, linear or non-linear, and absolute or relative (Schotanus and De Boer 2025).

**Contracting.** Buying organisations can split their purchasing contract with a supplier into different levels of agreements: a purchase order (including quantities and delivery dates), which is covered by a general framework agreement (terms and conditions, assortment, pricing), which is in turn sometimes preceded by a letter of intent (strategic intentions, etc.). Obviously, just one single contract document could be applied too, as in the case of one-off buys.

With respect to contracts, there are two main groups of spend: physical products versus services and investment goods (capital expenditure). For physical products, contracts are traditionally based on fixed unit prices, possibly with volume discounts. Such contracts, however, are often subject to the risk of ‘double marginalisation’, which reduces supply chain profits because each actor makes its decisions considering only its own margin. Alternative contract types – such as quantity flexibility, buy back and revenue-sharing contracts – increase overall supply chain profits by making the supplier share some of the buyer’s demand uncertainty (Chopra and Meindl 2021).

For services and project-based capital expenditure, one can find a different range of contracts based on three main provisions or mechanisms: pricing, payment and activity allocation (Van der Puil and Van Weele 2014). Pricing has three main alternatives: fixed price, cost reimbursable and unit rates. Under fixed price pricing, the supplier performs an activity for a predetermined price. Under cost-reimbursable pricing (or: time and materials), supplier compensation is based on the actual resources used. Various intermediate forms have been developed, such as fixed price contracts with a price adjustment clause. Under unit rate pricing, buyers pay suppliers a predetermined rate per given amount of (highly standardised) output – the total volume of which is determined only afterwards. Payment mechanisms exist in two main alternatives: either the full amount is paid at once (lump-sum), or the payments are split up into instalments (milestone payments). The latter is used in case of a long project duration and when the supplier is incurring substantial expenses upfront.

Activity allocation provisions exist in five main varieties. In a construct contract, a supplier only needs to produce or construct the object, while in a design and
<figure><img src="image_74.png" /></figure>

construct contract, the supplier also designs the object. In an Engineering, Procurement and Construction (EPC) contract, the supplier additionally procures the materials and manage all subcontractors. The final two varieties allocate responsibilities to the supplier even after the delivery of the object. In a Design, Build, Finance and Maintain (DBFM) contract, a supplier arranges for the financing of the investments and the maintenance of the object and the buying firm pays a periodic service fee. In the case of a DBFMO contract, the supplier also operates the object. The choice for a specific activity allocation depends on how much risk a buying firm wants to allocate to suppliers, and how much expertise and capital a buyer has compared to suppliers.

## Outlook

In a growing number of organisations, procurement will have the possibility if not the imperative to step up from just purchasing goods and services to becoming a function of real end-to-end value entrepreneurs. Whether the procurement function will be able to fulfill such an increasingly strategic role in a given organisation, depends on multiple factors including the capabilities and attitudes of procurement personnel and managers, its working relationships with other functions, but surely also its effective application of digital technologies. While successful blockchain applications have become quite sparse by now, the uptake of Artificial Intelligence (AI) applications in procurement is rapidly growing, particularly in areas such as negotiations with suppliers of non-critical items (Van Hoek et al. 2022) and other routine tasks such as tender preparations and (initial) bid evaluations (Guida et al. 2023). As with the implementation of digital tools in the early 2000's, the rationale for these applications is largely that it will enable the procurement function to increase its efficiency in the operational and tactical processes, so that its critical resources can be dedicated to the more strategic processes. It is likely however, that AI will also become instrumental in these processes in the near future. To study these developments and associated challenges, procurement researchers can draw from a diverse and multidisciplinary set of both established and relatively new theories (Tate, Ellram, and Bals 2022).

## 3.6. Relationship management²⁸

The origins of academic study on the management of buyer-supplier relationships can be briefly traced to the history of economics and specifically contracting through the theories of agency, finance, property rights and stakeholder theory Jensen and Meckling (1976). Following the industrial revolution as firms moved towards

'new' mass production techniques in the late 1920s and early 1930s scholars began to study the idea of 'what is a firm' and think about where a firm begins and end.

The seminal work of Coase which was first published in the journal *Economica* in 1937 offered an economic explanation of why individuals should form businesses and the benefits and risks associated with these interactions. This formed the basis for what became known at the 'theory of the firm' and 'transaction cost economics' (TCE). Other authors such as Cyert and March (1964) added behavioural aspects and the concept of 'stakeholders' identifying interested internal and external influencers on the behaviour of the organisation and its markets. This was then formalised by Williamson (1976, 1985) in a series of seminal contributions in the 1970s and 80s which formalised the core tenets of TCE as we know them today. Subsequently, scholars of corporate strategy such as Dyer (1996) and Barney (1991) moved away from analysis of the risk associated with individual transactions and towards the idea that competitive advantage and value creation can be obtained through inter-firm relationships processes and networks (Porter and Kramer 2011).

This work was synthesised by the Industrial Marketing and Purchasing (IMP) Group in the early 1980s and 1990s under the leadership of Håkansson (1982) to develop an empirical analysis of firm networks focussing on the relationship interdependencies and typologies of interactions ranging from short-term episodic to long-term 'deep' and embedded interactions (Ellram and Ueltschy Murfield 2019). Collaborative business relationships are viewed as value adding process within the network of organisations or actors. Building on this foundational work we suggest a working definition of buyer-supplier relationships as follows:

> [Buyer-Supplier Relationships] ...refer to the interactions and collaborations between (and that are supported within) organisations that purchase goods or services and the various actors in the network that supply them. It involves managing these relationships effectively and efficiently [over time] to ensure a constant supply of resources that delivers 'value' for all actors involved in the transaction(s).

## Background

The strategic importance of buyer-supplier relationships has been increasingly studied by scholars since the late 1970s and 1980s with the development of Kraljic's (1983) seminal portfolio analysis tool, positioning spending based on its relative market availability (essentially a measure of risk exposure) and cost or value to suggest management strategies based on a four-quadrant model. Moreover, development in buyer-supplier relationship
<figure><img src="image_75.png" /></figure>

thinking closely correlates to changes in the economic business environment and particularly in the paradigm changes in manufacturing which can be described as moving from craft, mass, lean and agile (Lamming 1993) and laterally towards mass-customisation. Combining the portfolio analysis of Kraljic and his analysis of sourcing risk with the literature on contracts, firm boundaries and competitive advantage, led scholars to observe and develop a variety of buyer-supplier positioning and relationship measure concepts, tools and techniques (Ben-saou 1999; Cousins and Spekman 2003). The practical implications of these portfolios were recognised by consulting firms and industry alike who used them to optimise spend, analyse risk, and manage their supplier relationships to better effect (Cousins and Spekman 2003).

During this time the business environment moved from local/regional sourcing to international and then global sourcing. The success of Japan following World War II as a major manufacturer employing innovative and novel manufacturing techniques began to change the way Western manufacturers built, designed and sourced their products – moving from one-stop manufacturers to systems integrators, particularly true of the automotive and aerospace industries (Hines et al. 1999). Techniques such as Just-In-Time (JIT) and Total Quality Management (TQM) involved working and coordinating closely with suppliers (Flynn, Sakakibara, and Schroeder 1995). Practically, this meant manufacturers across many industries had to think much more strategically about how they would work with suppliers to compete. They restructured their supply bases to create tiered levels of suppliers, creating fewer but higher value suppliers, and started to work with their suppliers on new product development projects.

The next trend coincided with the rise of China (and since other parts of East Asia, India and currently Africa) as a low-cost manufacturing environment which encouraged large manufacturing organisations to outsource manufacturing and source products globally. This shift enabled low-cost production but also came with significant risks. More recently, organisations have slowed, or in some cases even reversed, these decisions as they seek to reshore production and diversify the locations of their supply bases in an effort to reduce a broad range of risks, including geographic concentration, geo-political challenges, and sustainability concerns.

## Theory lenses (firm, transaction, social interactions)

Over the last fifty or so years there has been a substantial discourse on all aspects of buyer-supplier relationships creating a rich and varied body of knowledge. It reasons that most problems and questions focussed on buyer-supplier relationships are multi-faceted. For scholars in

this area, before embarking on research it is important to consider the unit of analysis and then select an appropriate theoretical lens(es). Essentially the research in this area can be classified into three broad but inter-related theoretical areas, economic, strategic and behavioural. Firstly, the economic lens uses the theory of Coase (1937) and specifically Williamson (1985) which is Transaction Cost Economics (TCE). The unit of analysis here is the transaction at product or service level and focuses primarily on the risks of manufacturing within the boundaries of the firm or outside of it – known as market vs hierarchy. The risks are known as transaction costs and contain a variety of characteristics that can potentially be modelled. That said, transaction costs are notoriously difficult to calculate. The second major theoretical lens developed by Prahalad and Hamel (2009) and extended by Barney (1991) is the Resource Based View of the Firm (RBV) – the focus here is on competitive advantage at the level of the firm, creating and managing value added relationships becomes a strategic competitive advantage to the focal firm (Barney 2012). The seminal work of Dyer and Singh (1998) in this area builds on the previous ideas of the IMP of networks and dyads and theorises the concept of the ‘relational’ view. This paper has been extended (Dyer, Singh, and Hesterly 2018) to consider the value creation and capture benefits of building alliances and collaborations, viewing this as a dynamic capability. The third and final theoretical lens is ‘social capital’. Social capital can broadly be defined as the network of relationships among people who live and work in a particular society or organisation. Tools such as Social Network Theory (SNA) facilitate the mapping of these network relationships and to understand the strength of these ties and how information and knowledge flows between them (Granovetter 1983; Y. Kim et al. 2011; Wu and Choi 2005). Leveraging these networks within buyer-supplier relationships can add significant value to the buyer and supplier organisations by facilitating innovations, learning, new product development and cost reduction and process improvement programmes (Lawson et al. 2009). These three theoretical lenses are not mutually exclusive however scholars tend to select one meta theory, this can be further supported by a secondary theory approach aimed at explaining some of the more nuanced issues within the research question (McIvor 2009).

## Future impact: technology, sustainability, risk, supply network design

The current political, economic, social and technological business environment is extremely difficult to navigate, fraught with supply chain disruptions, aggressive competition, sustainability pressures and move towards
<figure><img src="image_76.png" /></figure>

de-globalisation and protectionism. It is further com-
pounded with profound changes in the way new tech-
nologies such as Artificial Intelligence (AI), Blockchain
and the analysis of Big Data are impacting business and
supply chain operations. These technologies are creating
greater visibility and traceability allowing organisations
to analyse and adjust the way they do business offering
positive as well as negative consequences (Gligor, Pil-
lai, and Golgeci 2021). Facilitating relationships through
the analysis of big data sets, help to analyse and resolve
quality problems and maintain standards and improve
innovation and problem solving (Rahman et al. 2023).
However, it can also drastically reduce personal interac-
tions leading to a lack of personal trust, socialisation and
knowledge exchange (Cousins and Menguc 2006; Law-
son et al. 2009). These relational bonds are essential for
efficient and effective problem solving and innovation
development and knowledge transfer (Lawson, Tyler, and
Cousins 2008). The impact of new technology while
offering many advantages it can also negatively impact
interfirm relationships.

Inter- and intra-firm relationships are an essential
process in how firms interact and manage their sup-
ply networks. AI will inevitably impact this process as
researchers and practitioners we must find ways to use
it to enhance and deliver high value returns to mutual
benefit (Borah et al. 2022).

**3.7. Stakeholder management<sup>29</sup>**

Stakeholder management is an increasingly critical
aspect of operations and supply chain management
(OCSM). Contemporary economic activity is increas-
ingly complex, entailing coordination and collabora-
tion within and between large networks of globally dis-
tributed actors that partner in activities through which
a given product or service is brought into the market
(Co and Barro 2009; Silvestre et al. 2023). The effi-
ciency and efficacy of the entire supply chain depend on
successful coordination among diverse actors (including
customers, retailers, financial institutions, governments,
logistics providers, distributors, manufacturers, and pri-
mary resource extractors and processors) within com-
plex practices and processes (including forecasting, plan-
ning, inventory management, scheduling, delivery, just-
in-time sourcing, co-location, collaboration, and infor-
mation sharing; see also Najjar and Yasin 2023; Silvestre
et al. 2023). In this sense, the global economy is increas-
ingly relational and the success of organisations and
economies depends on the development and active man-
agement of relationships. These relational interdepen-
dencies have been shown to be especially significant in
the recent geopolitical and pandemic threats to the global

economy and society (Devi et al. 2023; Montoya-Torres,
Muñoz-Villamizar, and Mejia-Argueta 2023).

Stakeholder management research proposes that organ-
isational success depends on considering the interests of
multiple stakeholders in strategic decision-making (Don-
aldson and Preston 1995; Freeman 1984; Jones 1995;
Laplume, Sonpar, and Litz 2008; Parmar et al. 2010). Fun-
damental to stakeholder thinking is the suggestion that ‘if
we adopt as a unit of analysis the relationships between a
business and the groups and individuals who can affect or
are affected by it, then we have a better chance to’ create
and distribute value to multiple actors globally (Parmar
et al. 2010). Various stakeholder definitions have been
proposed in previous research. Freeman (1984) defines
stakeholders as individuals or groups that could influ-
ence or be influenced by the activities of the firm while
Donaldson and Preston (1995, 68) define stakeholders as
‘persons or groups with legitimate interests in procedu-
ral and/or substantive aspects of corporate activity’. In
light of the breadth of actors encompassed by these defi-
nitions, considerable effort in stakeholder management
research has sought to classify stakeholders to under-
stand how they influence a focal firm (Mitchell, Agle, and
Wood 1997). Mitchell, Agle, and Wood (1997) identi-
fied three attributes of firm-stakeholder dyads that raises
their salience in management consideration: (1) Power
refers to the ability of a stakeholder to influence, pro-
duce or effect behaviour, outcomes, processes, objectives,
or direction; (2) Legitimacy relates to a claim’s consis-
tency with expected behaviour, structures, values, beliefs,
norms, and rules; (3) Urgency refers to the stakeholder
and claims that it is critical to the stakeholder and time-
sensitive. Combinations of these relational attributes
drive the level of strategic attention warranted within a
firm’s consideration at a given point in time.

Building on stakeholder classifications, research sug-
gests several broad approaches to stakeholder manage-
ment that an organisation can adopt, including proac-
tion, accommodation, defence, and reaction (Clark-
son 1995; Wartick and Cochran 1985). Research has also
classified the influence strategies of stakeholders, with
Frooman (1999); Frooman and Murrell (2005) propos-
ing two basic influence strategies – coercion and com-
promise – adopted by stakeholders to influence a focal
firm. Most research using stakeholder management dif-
ferentiates between two fundamental motives for firms,
including stakeholders, in their decisions, while recog-
nising that both are always at play to some degree. The
first body of stakeholder thinking, what might be termed
the instrumental approach, sees stakeholder manage-
ment as important because managing stakeholder rela-
tionships generates significant expected returns for the
organisation. A second body of research – the normative
<figure><img src="image_77.png" /></figure>

approach - emphasises the intrinsic value of taking stakeholders into account and sees stakeholder management as a moral responsibility of contemporary organisations. Studies on instrumental traditions have provided significant evidence that stakeholder management generates organisational benefits mainly through improved reputation, increased trust from stakeholders, an improved 'license to operate', and stakeholders acting reciprocally (Berman et al. 1999; Choi and Wang 2009; Clarkson 1995; Hillman and Keim 2001; Jones 1995). Normative research in the stakeholder tradition has argued that moral imperatives should consider stakeholders, grounding arguments in social contract theory (Donaldson and Dunfee 1999), property rights (Donaldson and Preston 1995), and Kantianism (Evan and Freeman 1988). To some extent, research drawing on signalling theory has sought to bridge the instrumental and normative perspectives by exploring what attributions, inferences, and conclusions stakeholders attach to firms' breadth and depth of stakeholder management activities (Aronson, Hanson, and LaFont 2025; Fu, Boehe, and Orlitzky 2022).

To a significant extent, the major strands of stakeholder research in OSCM reflect the schism between normative and instrumental stakeholder thinking. To date, the most prominent strand of stakeholder OSCM research focuses on the roles played by stakeholders in supporting or inhibiting sustainable outcomes throughout firms' operations and supply chains. Stakeholders are understood to exert pressure on firms for sustainable business conduct, thereby suggesting that firms pay attention to improving their environmental and social conditions (Carter and Liane Easton 2011; Meixell and Luoma 2015). This attention extends beyond individual firms' operations, in that it also includes their direct (Foerstl et al. 2010) and indirect (Hartmann and Moeller 2014) suppliers. Stakeholder pressures increase awareness of sustainability in the supply chain, push buying firms to adopt sustainability-related goals, and influence them in implementing sustainability in the supply chain (Meixell and Luoma 2015). Within this research firms' CSR activities and reporting are understood to signal their commitments to stakeholders regarding social and environmental issues (Friske, Hoelscher, and Nikolov 2023). When stakeholders' sustainability-related expectations are unfulfilled, irresponsible supplier behaviour may be projected onto buying firms, leading to adverse publicity, reputational loss, and costly legal obligations (Bregman, Peng, and Chin 2015). Thus, non-compliance with stakeholders' requests for sustainability poses a risk to buying firms (Hajmohammad and Vachon 2016), which this study refers to as supply chain sustainability risk (SCSR). Accordingly, SCSR is defined

as 'a condition or a potentially occurring event' residing 'within a focal firm's supply chain' which can 'provoke harmful stakeholder reactions' (Hofmann et al. 2014, 168). During times of global sourcing and ubiquitous information availability, SCSR poses a major challenge to buying firms (Busse et al. 2017).

Previous research suggests that, in general, a success factor for sustainable supply chain management (SSCM) is that buying firms should 'reconceptualise who is in the supply chain. Rather than viewing NGOs and the like as adversaries, sustainable supply chains leverage the skills and abilities of these nontraditional chain members' (Pagell and Wu 2009, 52). Hence, an attentive and cooperative stance toward stakeholders is often advisable for firms (Meixell and Luoma 2015; Wong, Wong, and Boon-itt 2015). To identify, assess, and manage SCSR, firms must understand the differing perspectives, expectations, and values of stakeholders (Wu, Ellram, and Schuchard 2014). Thus, when faced with the lack of visibility in the upstream supply chain, attention to stakeholders may be the strategic direction that firms should also pursue to identify SCSR, seeking to incorporate stakeholders' SCSR knowledge. Stakeholders vary in numerous ways, including their interests and roles (Wu, Ellram, and Schuchard 2014). Different groups of stakeholders can be interested in the economic, environmental, and social dimensions to different degrees (Meixell and Luoma 2015). Some stakeholders hope for the firm's success (e.g. employees and customers), while others may not mind failure (e.g. competitors and media; Hofmann et al. 2014). Therefore, firms should refrain from treating their stakeholders as homogenous aggregates; rather, they should differentiate between them and dedicate specific attention to stakeholder groups (Gualandris et al. 2015).

Regarding stakeholder-OSCM research in the instrumental tradition, considerable attention has been given in OCSM research to the circumstances in which trust, collaboration, mutual investment etc. exist in firm-stakeholder relations, and how these support greater innovation, operational, and financial performance. Empirical studies have often linked supply chain collaboration to performance improvement (Co and Barro 2009). Managing the relationships among trading partners along the supply chain is an important question that must be addressed in supply chain collaboration (Johnston et al. 2004). A long-term perspective between the buyer and supplier increases the intensity of buyer-supplier coordination (De Toni 1999), and supplier integration through strategic partnerships 'will have a lasting effect on the competitiveness of the entire supply chain' (Choi and Hartley 1996). Carr and Pearson (1999) also found that strategically managed long-term relationships
<figure><img src="image_78.png" /></figure>

with key suppliers have a positive impact on the firm's financial performance.

Supply chain visibility entails trust and collaboration between trading partners. Higher levels of interorganisational cooperative behaviours, such as shared planning and flexibility in coordinating activities, were found to be strongly linked to the supplier's trust in the buyer firm (Johnston et al. 2004). Issues of trust and risk can be significantly more important in supply chain relationships because they often involve a higher degree of interdependency between competitors (La Londe 2002). The premise of trust and collaboration is that trading partners have comparable desires and commitments to collaborate. Morgan and Hunt (1994) argued that commitment and trust produce outcomes that promote efficiency, productivity and effectiveness. Otherwise, it would require an advocate with power and influence over its trading partners to mandate cooperation.

Moving beyond the reasons and stakeholder-OSCM research, considerable research has begun to examine the structural, attitudinal, and informational pre-cursors of successful stakeholder management. For example, research on supply chain visibility emphasises that relationships can be effectively managed only when information is visible across a supply chain and between supply chain partners. Supply chain visibility can be broadly defined as 'traceability and transparency of supply chain process' (Tse and Tan 2012, 51). Buying firms often have low supply chain visibility because they possess little knowledge about indirect suppliers or cannot independently verify information about their components or practices (Pagell and Wu 2009). In particular, fast-moving industries such as the retail and fashion sectors often lack supply chain visibility beyond second-tier suppliers (Roth et al. 2008). Carter, Rogers, and Choi (2015) argue that actors in supply chains often lack sufficient knowledge and visibility of their supply chain beyond first-tier suppliers (upstream) and direct customers (downstream), meaning that 'what lies beyond the realm of [the] visible range simply emerges' (Carter, Rogers, and Choi 2015, 90). This visibility boundary poses a severe management problem, since 'beyond the visible range, the agent has no choice but to accept what happens there' (Carter, Rogers, and Choi 2015, 90). Prior research has acknowledged that missing visibility in supply chains is a critical factor in effective supply chain risk management (Durach, Wieland, and Machuca 2015). Lack of visibility may cause knowledge deficits, loss of control, and distrust, thereby enhancing sustainability and corporate social risks (Busse et al. 2017). The practical challenges of engaging inclusively with a wide range of geographically dispersed stakeholders have been emphasised in research (Siems and Seuring 2021), with

research suggesting that collective stakeholder orientations can help overcome barriers to effective stakeholder involvement (Soundararajan, Brown, and Wicks 2019).

Research suggests that firms can adopt various stances toward their stakeholders, ranging from adversarial to welcoming (Pagell and Wu 2009). Pagell and Wu (2009) observed that leading firms reconceptualise who is in the supply chain, such that they regard not only their direct buyers and suppliers as part of the supply chain, but also other stakeholders. Essentially, they suggest the opening up of firms to these stakeholders. Further studies have also found that some firms leverage the expertise and skills of stakeholders, resulting in better-informed managerial decision-making (Wong, Wong, and Boonitt 2015). Wong, Wong, and Boon-itt (2015, 56) argued that 'feedback from [...] stakeholders represents key resources because (they) sometimes know more about the environmental problems facing part of the supply chains than the focal firm'. Stakeholders can provide assistance, develop policies, engage in evaluation and monitoring, and identify improvement potential in a firm's upstream supply chain with regard to sustainability (Gualandris et al. 2015; Wong, Wong, and Boon-itt 2015). In the context of SCSR, leading firms have begun to proactively search for valuable information to help them identify their SCSR (or other objectives) by constantly scanning the external environment or conducting regular stakeholder consultations and round tables (Foerstl et al. 2010).

Recent macro-environmental uncertainty and turbulence has encourage OSCM scholarship to explore the role of stakeholders in supporting organisational success in light of these conditions. Indeed, Freeman (1984) himself drew attention to 'the quantity and kinds of change that occur in the business environment' (Freeman 1984, chapter 5), necessitating a new paradigm of strategic management.

The rapid growth and diversity of stakeholder-related research, both within and beyond OSCM, provide scholars with numerous valuable sources for further reading and inspiration. We have drawn the reader's attention to some important review articles. For example, Aaltonen and Kujala (2016) review literature on stakeholder management in the project management field; Bhattacharya and Fayezi (2021) review research on stakeholder theory in supply chain management; de Gooyert et al. (2017) review stakeholder management research in the operations research field; Govindan and Bouzon (2018) examine stakeholder-related research in reverse logistics. Sustainable supply chain management has been reviewed extensively by Carter and Liane Easton (2011) and Sarkis, Zhu, and Lai (2011). Additionally, there are a number
<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /><figcaption>**Figure 7.** Examples of real-world bullwhip. Panel (a) A UK grocery product, bullwhip ratio = 7.68. Panel (b) A US IT consumable product, bullwhip ratio = 3.58.</figcaption></figure>

of excellent reviews on the development of stakeholder management outside OSCM, including Laplume, Sonpar, and Litz (2008). Technology, especially AI and big data analytics, and government policy play a significant role in shaping how firm-stakeholder relationships evolve and are managed, and rapid changes in these in recent years will continue to show that stakeholder relationships play an increasingly salient role in OSCM.

### 3.8. Bullwhip effect³⁰

The bullwhip effect refers to a supply chain phenomenon where the variance of customer demand is amplified in the production (or supplier) orders. Figure 7, panel (a) highlights the weekly demand and production for a grocery product. The production order variance is 7.68 times that of demand. This amplification is not uncommon in real supply chains. Panel (b) of Figure 7 illustrates the weekly demand and raw material order of a consumable IT product. The variance of the shipments to the assembly factory is 3.58 times that of the demand at the factory. Highly variable demand placed on the upstream eche-lons of a supply chain creates excess costs. For example, highly variable demand requires extra capacity to meet peak demands; extra inventory is needed to protect service levels; reduced transport efficiency; hiring, firing, and onboarding costs increase.

The bullwhip effect has been discussed in the economics literature for over 100 years Mitchell (1924). The dynamics of supply chains were well understood by Forrester (1958). Solutions to the bullwhip problem were proposed by Deziel and Eilon (1967), Towill (1982) and John, Naim, and Towill (1994). Academic interest in the bullwhip really gained momentum when Lee, Padmanabhan, and Whang (1997) coined the *bullwhip effect* moniker and explored four causes of the bullwhip effect via the ratio in Equation (4). This paper has become one of the most highly cited papers in the operations and

supply chain management field.

$$ \text{Bullwhip ratio} = \frac{\text{Variance of the orders}}{\text{Variance of the demand}} \quad (4) $$

### Methodological approaches to studying bullwhip

Control theory has long been one of the most powerful ways of solving *linear* bullwhip problems. Simon (1952) applied the Laplace transform to a production control problem described by differential equations. Towill (1982) studied an inventory replenishment policy denoted IOBPCS (inventory and order-based production control system) in continuous time using the Laplace transform. John, Naim, and Towill (1994) added a work-in-progress feedback loop to the IOBPCS to create the APIOBPCS model (automatic pipeline, inventory, and order-based production control system). Continuous-time bullwhip measures are commonly determined by the system's *noise bandwidth*.

Most supply chains operate on discrete time, where one ordering decision per planning period is made. Vassian (1955) studied a discrete-time replenishment system with *z*-transforms, finding the inventory optimal *order-up-to* (OUT) policy. Disney (2001) used the *z*-transform to study the dynamics of a vendor-managed inventory supply chain. The Fourier transform can be used to understand how supply chains create bullwhip under arbitrary (stationary and non-stationary) demands and (constant) lead times by investigating the system's frequency response. Dejonckheere et al. (2003) revealed the OUT policy with exponential smoothing and moving average forecasts always creates bullwhip, for all demand patterns and lead times. Li, Disney, and Gaalman (2014) extended these results, showing the OUT policy with Holt's forecasts always creates bullwhip, but the OUT policy with damped trend forecasts does not always create bullwhip for certain demand processes. Linear systems can also be studied directly in the time domain and using difference equations and expectation (Hosoda and
<figure><img src="image_81.png" /></figure>

Disney 2012). Larger, more complex linear supply chain models can also be analysed using state space techniques and matrix methods, (Gaalman and Disney 2006; Wang and Disney 2017).

For non-linear problems, simulation is a practical approach (Ponte et al. 2022). However, sometimes analytical progress can be made by studying time domain difference equations and probability density functions (Disney, Ponte, and Wang 2021). More qualitative approaches like system dynamics, Sterman (2000), influence diagrams (Coyle 1977), and causal loop modelling (Morecroft 2020) can provide insight. The *beer game* is a popular tabletop game for teaching the dynamics of supply chains (Sterman 1989).

Surveys and secondary data can also be used to investigate bullwhip. At an individual product level, bullwhip appears almost inevitable. However, when measured at the company level, perhaps aggregated over a financial reporting period of many months, bullwhip may not exist (Cachon, Randall, and Schmidt 2007). Bullwhip has also been studied empirically: Fransoo and Wouters (2000) consider how to measure bullwhip in real supply chains (which may have convergent and/or divergent material flows). Disney et al. (2013) report on a project removing bullwhip from a global printer toner supply chain. Meng, Purvis, and Disney (2018) improve the forecasting and production planning activities in a UK dairy product manufacturer to reduce bullwhip.

## The replenishment policy

Most bullwhip studies consider high-volume demand where the items are ordered and replenished every planning period. Planning periods range from several hours to a day in grocery supply chains, or weekly (most likely) to monthly in manufacturing environments. Hedenstierna and Disney (2018) investigate the consequences of different planning cycle lengths; longer cycles have a natural pooling effect that reduces demand variance. With no ordering (capacity-related) costs, the OUT policy is the optimal replenishment policy for minimising inventory holding and backlog costs (Karlin and Scarf 1958). When the objective function includes bullwhip-related (capacity-related) costs, the OUT policy is no longer optimal, Boute et al. (2022); the dual base stock (DBS) policy is known to be the optimal policy (Boute and Van Mieghem 2015; Gijsbrechts et al. 2024). However, the DBS policy is not a demand replacement policy. The DBS policy has a region of inaction where demand is not fully replaced and only *k* units (where *k* is the nominal capacity without overtime) are ordered. Generally, only numerical solutions for the two base stock levels in the DBS policy can be obtained (Gijsbrechts et al. 2024).

The high-performing linear proportional (POUT) policy, grounded in control theory, is amenable to analytical study. The POUT policy generalises the OUT policy by incorporating two proportional feedback controllers into the policy, an established technique in the design of hardware systems. The feedback controllers in the POUT policy regulate the speed at which discrepancies in inventory and WIP levels are corrected (Disney and Towill 2003). While the POUT policy requires careful set-up to avoid instability (Disney 2008), it is the optimal linear quadratic regulator for minimising costs proportional to the variance of order and inventory (Wang and Disney 2016). When piece-wise linear convex inventory costs and capacity costs associated with an installed capacity and overtime are present Boute et al. (2022) show the POUT performs well compared to the optimal non-linear DBS policy.

## Lead times and the bullwhip effect

Increasing lead times will always increase the inventory variance, no matter the demand process. However, increasing the lead times does not always increase the OUT policy's order variance (Gaalman, Disney, and Wang 2022). Depending on the demand process, the bullwhip may increase or decrease in the lead time. For negatively correlated demand patterns, bullwhip exhibits an odd-even lead time effect, increasing or decreasing based on the parity of the lead time. It can even have more complex behaviour; a multi-period oscillating effect, or be increasing (decreasing) in the lead time when the lead time is short (long) and vice versa.

Under stochastic lead times, the impact of the lead times is very sensitive to modelling assumptions. If we assume past realisations of the lead time are representative of future lead times, the POUT policy bests the OUT policy when minimising inventory costs alone (Disney et al. 2016). Furthermore, setting the POUT policy to minimise inventory cost alone under stochastic lead times also removes bullwhip for free. However, dynamically forecasting lead times can create large amounts of bullwhip (Michna, Disney, and Nielsen 2020).

## Demand processes

Most bullwhip studies assume demand is random. The simplest random demand is the independent and identically distributed (i.i.d.) demand. Forecasted with minimum mean squared error (MMSE) forecasts, i.i.d. demand produces a bullwhip ratio of unity, as the OUT policy acts as a *pass-on-orders* policy.

More realistic demand patterns can be modelled using the auto-regressive, integrated, moving average (ARIMA) modelling framework of Box and Jenkins (1976). Ali, Boylan, and Syntetos (2012) studied a
<figure><img src="image_82.png" /></figure>

data set of weekly demand for 1798 products from a European retailer. They identified the first order auto-regressive, AR(1), demand processes in 30.3% of the time series, integrated moving average, IMA(0,1,1), processes accounted for 23.7%, and i.i.d. demand accounted for 16.3%. The data set also contained higher-order ARIMA demands. ARIMA models represent many real demand patterns and have been studied extensively. Graves (1999) considered IMA(0,1,1) demand, Lee, Padmanabhan, and Whang (1997) considered AR(1), Gaalman and Disney (2006) considered ARMA(1,1) demand, Luong and Phien (2007) studied AR(p) demand, and Gaalman, Disney, and Wang (2022) investigated ARMA(p,q) demand. Zhang (2004) and Gilbert (2005) have investigated how ARIMA demand is transformed into other ARIMA demand processes as it passes up the supply chain.

## Supply chain structure

It is natural to consider how the bullwhip effect propagates in different supply chain structures. Chen et al. (2000) consider a multi-echelon supply chain. The sharing of demand information throughout the supply chain leads to a bullwhip effect that increases linearly, rather than geometrically, in a traditional supply chain (Dejonckheere et al. 2004; Lee, So, and Tang 2000). Whereas, a vendor managed inventory supply chain allows multiple supply chain echelons to act as one, effectively eliminating bullwhip transmission (Disney 2001).

Closed loop supply chains are popular areas for bullwhip studies (Ponte et al. 2022). Marketplace returns can have a positive effect on the production of new items. However, care must be taken as it is not always economical to remanufacture everything returned, even when the remanufacturing cost is less than the cost to produce new items (Hosoda, Disney, and Zhou 2021).

Issues with global dual sourcing have also been studied from a bullwhip perspective. The tailored-base-surge allocation scheme (Allon and Van Mieghem 2010), places large constant orders to the offshore supplier, whereas the nearshore supplier satisfies peak demands with a shorter lead time. Boute et al. (2022) showed that local Speed-Factories could maintain tighter inventory control than a single offshore supplier, facilitating the relocation of production back home before reaching price parity.

## Emerging bullwhip research topics

There are a number of critical areas related to the bullwhip effect that are relatively underexplored. For example, understanding the role of artificial intelligence/machine learning (AI/ML) in forecasting demand and regulating production and distribution. There is a lot of industrial interest in using AI/ML to forecast demand. Presumably, the large and diverse datasets that can be

used as input data will lead to more accurate forecasts. However, in cases where we know the optimal replenishment policy for a given cost function (for example, the OUT policy is the optimal linear inventory cost policy for a given set of forecasts), ML/AI may not be able to improve upon current best practice.

Geopolitical issues such as tariffs, trade barriers, conflicts, and sanctions are altering supply chains and trading relationships. As companies restructure their supply chains, there is an opportunity to design out bullwhip problems by shortening lead times, removing supply chain echelons, re-shoring, and dual sourcing. The impact of bullwhip on the carbon emissions and other climate consequences generated by supply chains is also an important area for future bullwhip research.

## 3.9. Information sharing³¹

Supply chain (SC) information has become an integral part of global businesses. In the past few decades global SCs of commodities, including basic food, textile and medical supplies, have grown enormously. The critical importance of information and technology in supporting modern global SCs has been well recognised in the literature. A number of digital tools and techniques are enhancing operational efficiency of global SCs alongside of the SC information, including a transparent information exchange among SC players.

SC information facilitates several operations including warehouse management, inventory management, capacity planning, quality management, production control and very importantly accurate forecasting for planning production and demand management. Three decades ago, Aviv (2007), Lee, So, and Tang (2000) and Gavirneni, Kapuscinski, and Tayur (1999) insisted on the importance of information sharing for forecasting and warehouse management. Later, Ramanathan and Muyldermans (2010, 2011) demonstrated a strong link between the transparent information exchange and the forecast accuracy through empirical evidence from a case study of a soft drink manufacturing firm.

In the presence of collaborative networks, the global SCs have started sharing high volume of data, including traditional production data, warehouse and inventory data, alongside of transportation and customer data in real-time using Big Data (BD) platform (Balakrishnan and Ramanathan 2021; Ramanathan, Subramanian, and Parrott 2017; Singh and Singh 2019). Thanks to the advancement in technology, people and SCs remained connected through information without having physical contact or human intervention at the time of COVID. The latest connected technologies such as Internet of Things (IoT) technology, BD Analysis and Blockchain
<figure><img src="image_83.png" /></figure>

have brought several SC players together to exercise the power of information and automation, without needing to have human-intervention.

## Evolution of SC management with integration of information sharing

Several prominent SC management practices have evolved in the past decades to nurture the global operations of SCs. Various strategic alliances of SCs decided the level of information sharing among partners (Mentzer et al. 2001; Ramanathan 2012). For example, vendor managed inventory (VMI) was introduced to support the business relationship between suppliers (or vendors) and retail stores in the 1970s. In the VMI, the information on inventory status, both in-store and warehouse, was shared by the retailers to the vendors. Based on the information shared, the vendor maintained the optimal level of inventory in line with the demand fluctuations (Barratt and Oliveira 2001). Speed and accuracy of the information supported the SC decision making and also helped avoiding bull-whip effect (Ramanathan and Muyldermans 2010).

In late 80s continuous replenishment (CR) was adapted by the SC players who expected somewhat stable and consistent demand. Especially the functional products such as soap and soup followed this trend. However, in these SCs, information was not considered as the main element to create a perfect balance between the supply and the actual demand. On the other hand, the CR was not considered an effective approach for innovative or fast-moving products as it could not avoid the excess inventory due to lack of communication or lack of information sharing among the SCs (Cachon and Fisher 2000; Fisher 1997; Ramanathan 2012).

In 1990s, quick response (QR) and accurate response (AR) approaches were introduced in the SC management which needed a huge amount of real-time demand data to plan timely production, timely delivery, and timely replenishment to meet the customers' ever increasing demand expectations (Fernie 1994; Fisher 1997). Both QR and AR have necessitated the importance of information exchange among these SC partners and developed electronic data interchange (EDI).

In early 2000s, another real-time information-based system called Collaborative Planning Forecasting and Replenishment (CPFR) framework was developed by Walmart and Warner-Lambert to enhance the response of businesses to the demand from the customers (Seifert 2003). This CPFR connected all partners through formal collaboration using information and smart technology (Ramanathan and Muyldermans 2010; Seifert 2003). Collaboration is considered a critical tool to

achieve success in CPFR and emerged as a winning strategy for all SC partners to help avoid bullwhip effect with the help of transparent information (Lee, So, and Tang 2000). SC collaboration predominantly relies on network agreements and information sharing.

Transparent SC information on materials including raw materials, works-in-progress and finished products, order data, delivery information and local events impacting sales, can be shared seamlessly. This open information sharing approach connects all the SC partners from sourcing, production planning, inventory, and warehousing, until the final delivery which will help reducing the risk of stockout or excess inventory and hence it completely avoids bullwhip effect from SCs. The companies using the CPFR framework managed to achieve accurate forecasting of demand and hence the actual sales were meeting the forecasted demand. After the introduction of the CPFR, importance of data in SCs became very much prevalent. Following this, after 2010, a digital technology era has embraced the global SCs to gain resilience against SC risks.

## Role of information in modern SCs and reverse logistics

In traditional SCs, materials and finished products moved from upstream to downstream SCs. On the other hand, the movement of goods from downstream to upstream was not usual, except for some special cases such as defective product-returns or excess stocks returns; the journey from downstream to upstream also carried a minimal level of information such as orders and payment details. In early 1990's, information technology evolution has hit the forward SCs with the real-time information sharing among all collaborating partners around the globe to control SC operations such as production, store and deliver in line with the customers demand and preferences (Kembro and Näslund 2014; Wiengarten et al. 2010). At this point, forward SC was challenged by various newly introduced product-return policies and hence the introduction of reverse logistics (RL) was dominant feature of all retail SCs during this period (Chen 2011; Ramanathan et al. 2023).

Carter and Ellram (1998) listed socio-economic and environmental benefits of RL through a detail literature review. Although recycling or remanufacturing are decided by the businesses, internal pressure from SCs and external pressure from various stakeholders such as government, policies, market, and competitors are considered as drivers of RL (Govindan and Bouzon 2018; Ramanathan et al. 2023). Here, SC information plays a crucial role to decide whether to use the RL or not. Ramanathan et al. (2023) expressed various causes of the reverse logistics, varies from simple product returns to
<figure><img src="image_84.png" /></figure>

end of product life cycle. Accordingly, every manufac-
turer and raw material supplier will need to know the
information on reverse SCs and logistics to plan their
remanufacturing or recycling activities as this will help
them to decide the production level of new or existing
products. Customer preference data including favourite
products, changing trend, brand loyalty, location of pur-
chase and interest in online or offline or omni chan-
nel purchase, is helping the SCs to react quickly to the
demand of the market (Jain et al. 2022; Zhang et al. 2023).

Since the outbreak of COVID, majority of the global
businesses have opted to have omni-channel strategy
which required an increased level of SC services as the
local delivery and pick-up points are ever increasing. This
has made the SC information sharing mandatory rather
than optional (Ramanathan, Mathirajan, and Balakrish-
nan 2024).

Information sharing in the digital SC era

In 21st century of digital era, global SCs use informa-
tion from within the SCs and also use the market infor-
mation effectively to integrate production, delivery and
sales. Some of the SC information will not have any con-
trol by the SC players but this will be used for planning
and combating the external risks. SC resilience is gained
with the power of information and BD Analysis. For
example, simple GPS sensors attached in the transporta-
tion vehicles keep sending the data related to location of
the products in the SC to alert suppliers while IoT sen-
sors send data on quality of the perishable food items
(Ramanathan et al. 2023). Some of the frequently used
technologies in global SCs are radio frequency identifica-
tion (RFID), blockchain technology, IoT, cloud comput-
ing, business data analytics, virtual and augmented reality
(Misra et al. 2022; Sharma et al. 2021; Singh et al. 2018).
Ikea, the leading furniture manufacturer, is using RFID
for several decades to monitor inventories in-store. Bar-
code sales information from Ikea combines the inventory
data and trigger the automated ordering system of sup-
pliers to replenish on time. This helps avoiding any com-
plexity in the SCs alongside of enhancing the traceability.
Role of sensors and connected technologies were highly
appreciated at the time of COVID when human interven-
tion was at its record low (Inman and Green 2024).

After COVID, several global SCs started using the
IoT technology comfortably in almost all SC operations
staring from sourcing, production, delivery and recy-
cling or remanufacturing, having real-time information
and validation of the data through BD analysis. Food
SCs increasingly use IoT such as smart farming, sensors
and tracking system to assure quality. The information
from the sensors is analysed and integrated with the SC
decision making (Ramanathan et al. 2023).

Other SCs namely fashion SCs and technology SCs are increasingly converging their business fully digitalised. Modern world after the introduction of fast fashion is struggling with over production and less recycling. The SC information can help them to avoid risk and build resilience. Guo, Sun, and Lam (2023) explained the use of Blockchain in fashion SCs for information sharing to maintain sustainability. Similarly, retail omni-channel SCs need consumers preference data to utilise the warehouse space effectively and need consumers' pickup location to plan logistics in a real-time basis. In a nutshell, SC information is indispensable for successful operation of all global businesses.

3.10. Transportation³²

Transportation is the backbone of operations and supply
chain management, facilitating the movement of goods
from raw material sources to end consumers. This pro-
cess involves many segments, from global shipping lines
that connect continents to the last-mile deliveries that
complete the journey to the final destination. Each seg-
ment plays a distinct role, yet they work together to form
a cohesive system that supports the seamless flow of prod-
ucts. Understanding these layers, their challenges, and
the evolving trends within each is essential for achiev-
ing both efficiency and sustainability in supply chain
operations.

Global production networks rely on complex, inter-
related structures comprising multiple transportation
modes – maritime, air, pipeline, rail, and road – con-
connected by ports and terminals. Maritime transportation
accounts for the majority of global freight volumes. Inno-
vations in transport technology, particularly the intro-
duction of the standardised container, have changed
transportation by significantly increasing the efficiency
and capacity of ports and terminals to handle large vol-
umes of freight (Fransoo and Lee 2013; Notteboom and
Rodrigue 2008). As critical components of the mod-
ern transportation, containers have been instrumen-
tal in driving substantial growth in world trade since
their introduction in 1956 (Bernhofen, El-Sahli, and
Kneller 2016). Although responsible for a much smaller
volume of freight, air transportation carries a significant
share of world trade value due to its speed and suitability
for high-value, time-sensitive goods. Pipeline transporta-
tion, though less commonly discussed, is the main mode
for transporting energy resources like oil and gas over
long distances. Rail and road freight play a minimal role
in intercontinental transportation.

Research on global transportation networks is broad,
covering topics such as network design (Section 3.3),
routing, and scheduling (Agarwal and Ergun 2008;
<figure><img src="image_85.png" /></figure>

Christiansen, Fagerholt, and Ronen 2004), empty container movements (Song and Carter 2009), and port and terminal operations (Stahlbock and Voß 2007). A significant portion of the academic literature addresses these topics through optimisation methods. Another line of research aims to describe current and predict future freight flows based on empirical data, often referred to as freight transportation modelling (Holguín-Veras et al. 2013). Freight transportation modelling can help identify the different roles and levels of criticality of ports and terminals in the global transportation network (Bombelli, Santos, and Tavasszy 2020; Verschuur, Koks, and Hall 2022) and can be used to design and evaluate transport policies.

Recent transportation research focuses on the environmental sustainability (Section 4.4) and resilience (Section 4.10) of global transportation networks. Freight transportation is widely recognised as a major contributor to global carbon emissions (International Transport Forum 2023), driving the current research focus on carbonising the sector. At the same time, freight transportation is slow to adapt and highly vulnerable to the impacts of climate change, while it will be crucial in enabling mitigation and adaptation efforts across other sectors (McKinnon 2024). The considerable economic consequences of port shutdowns (Rose and Wei 2013) and recent blockages of maritime shipping routes have sparked increased scholarly attention on the robustness and (cyber)security of global transportation networks (De Liso and Zamparini 2022).

As goods transition from global routes to regional markets, the focus shifts to inland freight transportation, which plays an important role in connecting major ports to distribution centres and warehouses. Inland waterways, rail, and especially road are the main modes in this part of the transportation system. Road freight, often considered the most flexible mode, handles the majority of inland freight – accounting for over 75% of inland transportation in the European Union in 2022 (Eurostat 2024). Efforts to encourage a shift from road to rail and inland waterways were initially aimed at improving the efficiency of the transportation system. More recently, these policies have expanded to also address sustainability concerns. However, despite significant investments and initiatives, particularly in Europe, the anticipated shift in transportation modes has largely failed to materialise (Takman and Gonzalez-Aregall 2023).

A significant portion of inland transportation research focuses on the development and application of optimisation models to support decision-making in areas such as fleet management (Crainic and Laporte 2012) and the planning and scheduling of transportation services

(Crainic, Gendreau, and Potvin 2009). Empirical and conceptual research in this area addresses topics such as mode choice and carrier selection (Meixell and Norbis 2008), examining the criteria shippers use to choose the most suitable mode of transportation by balancing cost, reliability, and service quality. Another critical area of research explores the decision-making process regarding whether to manage transportation in-house or outsource (Section 3.4) to specialised carriers, as well as how relationships between shippers and carriers evolve when outsourcing is chosen (Wallenburg 2009).

Digitisation (Section 3.2) and information sharing (Section 3.9) has been a major focus of recent transportation research, with studies examining how digital platforms, real-time data, and market intelligence can enhance decision-making, improve efficiency, and foster more dynamic planning of transportation services (Cichosz, Wallenburg, and Knemeyer 2020). Another strand of recent research focuses on optimising transportation services across multiple supply chains. When applied to a single mode of transportation, such as road freight, this is often referred to as horizontal collaboration (Buijs and Wortmann 2014; Pan et al. 2019). Synchromodality extends this concept by striving for dynamic transportation scheduling across multiple supply chains and multiple modes (Acero, Saenz, and Luzzini 2022). Some researchers even envision a 'Physical Internet', using the digital internet as a metaphor to achieve fully open and seamlessly interconnected private transportation networks (Montreuil 2011). A key challenge with the ongoing transitions in road freight transportation – such as those related to sustainability and digital transformation – is the sector's high level of fragmentation. There are over 500,000 European road carriers, and 99% of these companies have fewer than 50 employees (Toelke and McKinnon 2021).

As goods progress towards their final destinations, the focus shifts to last-mile logistics, where products are ultimately handed over to end consumers – whether through retail stores or e-commerce. The closer goods get to the final customer, the more challenging it becomes to achieve the economies of scale common upstream in the transportation system. Stringent service level targets, small order sizes, high delivery frequencies, and dispersed pickup and delivery locations hinder the efficient use of larger trucks, leading this part of the transportation system to rely on smaller trucks, vans, and even smaller vehicles to supply retail stores, restaurants, offices, and deliver parcels to consumer homes or pickup points. In the last mile, the negative externalities of transportation become more evident, as this stage occurs near where people live, work, and spend their leisure time (Cárdenas, Beckers, and Vanelslander 2017).
<figure><img src="image_86.png" /></figure>

Much of the research attention in this area focuses on innovative solutions to mitigate these externalities, with particular emphasis on business-to-consumer e-commerce logistics (Mangiaracina et al. 2019). For last-mile logistics broadly, these include the adoption of low-carbon vehicle technology, e-commerce delivery through pickup points, off-hour delivery programmes, dynamic loading and unloading bays, urban vehicle access restrictions, consolidation, and demand management (Holguín-Veras et al. 2020). Vehicle routing optimisation has long been a central focus in the academic literature on last-mile logistics (Savelsbergh and Van Woensel 2016), but recently, scholars have begun to recognise the significant impact of parking, loading, and unloading on last-mile efficiency (Fransoo, Cedillo-Campos, and Gámez-Pérez 2022; Ghizzawi, Galal, and Roorda 2024). In e-commerce, there is ongoing debate regarding the sustainability of home delivery versus retail shopping (Buldeo Rai 2021). Consensus remains elusive, with some solutions – such as pickup points – often marketed as more sustainable but potentially increasing carbon emissions when consumer travel is considered (Niemeijer and Buijs 2023), highlighting the complexity of this area of study.

For those seeking a deeper understanding of the complexities across global, inland, and last-mile transportation, the textbooks by Rodrigue (2024), Tavasszy and De Jong (2013), and Monios, Budd, and Ison (2023) provide a good starting point. Rodrigue (2024) offers an accessible introduction to the geography of transportation. Tavasszy and De Jong (2013) present a comprehensive overview of freight transport modelling, systematically introducing the approaches used to support public transport policy analysis. Monios, Budd, and Ison (2023) deliver a state-of-the-art overview of urban logistics, addressing the unique challenges posed by modern demands such as e-commerce and sustainability.

### 3.11. Emerging economies³³

The section briefly introduces institutional voids, forming a key challenge for managing supply chains in emerging economies. The next step argues for a more differentiated approach to SCM, considering differences among regions and countries. Looking at possible solutions, communication and coordination of suppliers and further supplier development related aspects are considered suitable approaches for addressing voids and strengthening the supply chain in emerging economies.

Emerging and developing economies span a wide range of countries. One persistent challenge for managing supply chains in such environments is typically that

(governmental) institutions are less developed, leading to so-called institutional voids, which are typically seen as a core reason challenging the management of supply chain (e.g. Zomorrodi, Fayezi, and Bals 2024).

The related body of literature (Brenes, Ciravegna, and Pichardo 2019; Parmigiani and Rivera-Santos 2015) lists particularly product market voids and contracting voids on the dyadic, i.e. supplier-buyer level. Operational issues driving product market voids are, for example, lack of intermediaries or standards, in some cases also lack of communication infrastructure. Contracting voids lead to issues in enforcing written agreements, so even if they exist, there might not be a governmental body to turn to if the contract is not fulfilled. There are labour market voids on the supply network side due to a lack of education and, therefore, unavailability of a skilled workforce. Capital market voids are a particular issue as funding is unavailable or might be comparatively more expensive. Last, regulatory voids derive from corruption, unexpected regulatory conditions, or limited access to formal justice.

These voids offer challenges for managing supply chains, where standardised (Western) approaches might come to limits. Recent contributions call for a more differentiated approach, as one-size-fits-all approaches might overlook the challenges and opportunities. In their Delphi Study covering six regions, Seuring et al. (2022) point to different needs for respective regions. Europe and North America serve as the developed world reference points against which other countries and regions are evaluated. For (selected countries in) Africa, the availability of SC finance options is a core challenge, particularly on the sourcing side, so materials and preproducts are made available (El Baz, Laguir, and Stekelorum 2019). Digital technologies might enable some supply chain solutions not available so far, linking farmers and producers directly to markets and customers (e.g. Schilling and Seuring 2023). Brazil shows some similarities but also has challenges in having a qualified workforce available, causing additional challenges and risks in the supply chain (e.g. Fritz and Silva 2018). China seems comparatively closer to developed economies, so aspects of SC volatility and agility move more into the foreground. India and Pakistan show some similarities to the observations from Africa, while more emphasis is placed on having financial resources available for the manufacturing part of the SC and aiming for resilient supply chains. Last, Iran seems to be a very particular situation, which might be a consequence of its geopolitical isolation. This seems to lead to a high emphasis on circular SC solutions (MahmoumGonbadi, Genovese, and Sgalambro 2021), as primary resources are in short supply.
<figure><img src="image_87.png" /></figure>

<figure><img src="image_88.png" /><figcaption>**Figure 8.** Core elements of managing supply chains in emerging economies.</figcaption></figure>

These examples serve as a background for identifying suitable and adjusted solutions in related supply chains. It is evident that many of the mentioned aspects have their original cause in the mentioned institutional voids. In this respect, solutions could be derived by overcoming the related institutional voids, which are typically beyond the comprehension and achievability of supply chain management in a narrower sense, but link into the debate on, for example, Global Production Networks (Yawar and Seuring 2024). Within this body of literature, governance, power dynamics and stakeholder approaches are discussed, all find their equivalent in the (sustainable) supply chain management debate.

On the level of the supply chain itself, there would be a wide range of suitable measures. Based on data from Kenya and Uganda, one kind of analysis has been provided by Seuring, Brix-Asala, and Khalid (2019), which builds on the conceptualisation of sustainable SCM as offered by Seuring and Müller (2008). There are arguments that both environmental (e.g. Tate, Bals, and Marshall 2019) as well as social aspects are particularly relevant in emerging economies (e.g. de Lima, Neutzling, and Gomes 2021), there are several strategies to manage related issues.

The centrality of communication and coordination is much discussed and holds even more for emerging economy environments, where suppliers might not be aware of developed market demands (Brix-Asala and Seuring 2020). A central line of reasoning is that this should create win-win situations where environmental (and/or social) goals are achieved in line with economic ones. This is typically complemented by monitoring suppliers towards fulfilling minimum standards, recently advocated in respective supply chain due diligence legislation (Buttke et al. 2024). In many cases, this might require that stakeholders play a crucial role, moving beyond being divers of related developments (Seuring and Müller 2008), thereby also acting as inspectors, such as in monitoring processes or even facilitators helping in implementing related process and indicators

(e.g. Liu et al. 2018). Social norms are particularly relevant in emerging economy situations (Silvestre 2015) and are often challenging to monitor. However auditing and monitoring suppliers and, therefore avoiding trade-off situations are key demands for the long-term success of such ventures (Seuring, Brix-Asala, and Khalid 2019). Following such a logic, often demand deep and continuous engagement with supplier and supply chain members along multiple tiers of the supply chain. In such settings, institutional voids challenge the inclusion of emerging economies (Emerging economies in international SCs; Silvestre 2015). To overcome this challenge, entrepreneurs – in this case, emerging economy actors operating in weak institutional environments – can acquire information, knowledge, and external resources via their relational networks.

More particular solutions seem to be offered by supplier development measures, which can enable suppliers to take part in local and international supply chains (Brix-Asala and Seuring 2020). Case based research is a typical approach to getting access to such empirical fields, which might be required due to illiteracy or cultural differences, where researchers are seen as sceptical, so appropriate rigour-within-context has been argued for (e.g. Halme et al. 2024). In such settings, the applicability of indirect supplier development (e.g. evaluation and feedback, supplier rewards) and direct supplier development (e.g. training and evaluation, on-site consultation) have been assessed (Brix-Asala and Seuring 2020). Figure 8 sums up the line of reasoning.

Looking at future developments, there are a couple of issues which require further assessment. Among them is the role of social enterprises in supply chains (Longoni et al. 2024), which play an important role in taking up and fulfilling the needs of large parts of the population in emerging economies. This links back into the already mentioned application of digital technologies (first of all smartphones) for managing businesses and the respective supply chains (Schilling and Seuring 2023) and thereby contributing to the digital and sustainable transformation
<figure><img src="image_89.png" /></figure>

of supply chains (Schilling and Seuring 2024). It will be interesting to see how e.g. blockchain technology might contribute to such supply chains, for example by enabling trust (Yavaprabhas, Pournader, and Seuring 2024) or what role artificial intelligence might have to play (Fosso Wamba et al. 2024). Against such observations, research on supply chain management in emerging economies still seems to be in an early stage, opening up multiple opportunities for future research. It should be kept in mind to integrate researchers from such countries, thereby creating inclusive research.

# 4. OM-SCM intersection

## 4.1. *Behavioural operations*³⁴

Behavioural Operations Management (BOM) focuses on understanding and improving decision-making processes within supply chains and other operational settings by considering human behaviours and the individual, group, and organisational processes that lead to the behaviours. BOM emerged as a discipline after observing that human decision making often deviates from traditional theoretical models developed in neoclassical economics, ‘hard’ operations research, and operations management. (Bendoly, Van Wezel, and Bachrach 2015; Croson et al. 2013; Donohue, Katok, and Leider 2018).

The foundations of BOM lie in disciplines such as behavioural economics and cognitive and social psychology (Fahimnia et al. 2019). BOM researchers employ various methodologies, including laboratory experiments, field studies, mathematical modelling, and computer simulation. Some of the broadly studied empirical phenomena include the bullwhip effect, coordination failures, the impact of incentives on supply chain performance (Perera, Fahimnia, and Tokar 2020), and the pull-to-centre effect in placing inventory orders under uncertain demand (Schweitzer and Cachon 2000). Key theoretical concepts include bounded rationality, heuristics and biases, and fast-and-frugal heuristics (Katsikopoulos and Gigerenzer 2013).

### Theories

Neoclassical economics and standard approaches to operations research and operations management have been assuming that individuals, groups, and organisations attempt and are able to maximise their subjective expected utility. However, since the 1950s, Herbert Simon (1955; 1956) – Nobel prize winner in economics, and winner of the Turing award in computer science, occasionally wearing the hats of an operations researcher and organisation scientist – and the many researchers

influenced by him (Gigerenzer, Mousavi, and Viale 2024; Viale 2021) have been protesting that this assumption is a fantasy. Rather, the idea of bounded rationality suggests that agents must and do make decisions in the real world with limited information, time, and other resources such as computation. While Simon's position is commonly framed as being that such practical decision making is always sub-optimal or clearly inferior to theory, it does not reflect Simon's research programme which sought to understand the conditions under which practice falls short of theory and under which it does not (Gigerenzer, Hertwig, and Pachur 2011; Katsikopoulos and Gigerenzer 2013).

Human bounded rationality has been captured by optimisation as well as heuristic mathematical models (Katsikopoulos 2023). BOM applies both to tasks such as decisions under risk (i.e. choices between gambles), strategic interaction (i.e. games), operational decisions (e.g. inventory management), and more challenging decisions where uncertainty cannot be reduced to probabilistic risk (King and Kay 2020). Examples of models include prospect theory (Kahneman and Tversky 1979) and the priority heuristic (Brandstätter, Gigerenzer, and Hertwig 2006) for decisions under risk; inequity aversion theory (Fehr and Schmidt 1999) and the mirror tree (Fischbacher, Hertwig, and Bruhin 2013) for strategic interaction; and prospect theory and the anchoring and adjustment heuristic (Tversky and Kahneman 1974) for inventory management. Optimisation models set up a utility or value function that reflects monetary and other considerations, such as perceptual distortion of probabilities (prospect theory) or aversion to monetary inequalities (inequity-aversion theory). To do so, free parameters might be needed, which enter relatively flexible mathematical forms. On the other hand, heuristic models may postulate the sequential processing of information without distorting values and probabilities (priority heuristic and mirror tree), or a simple additive form (anchoring and adjustment heuristic), typically employing just one or even zero free parameters. Empirical evidence suggests that both types of models have regions of superior performance on criteria such as predicting accurately out-of-sample or out-of-population, modelling cognitive processes, and being transparent to users (Katsikopoulos 2023).

Can such models generate insightful explanations about supply chain and operational behaviours? Yes. For example, in the context of supply chains, prospect theory suggests that decision makers might overreact to demand fluctuations, amplifying the bullwhip effect. Behavioural agency theory, which examines how incentives influence decision making (Pepper and Gore 2015),
<figure><img src="image_90.png" /></figure>

speaks to how aligning incentives among different stakeholders within a supply chain is crucial for coordination and performance. Misaligned incentives can optimise individual performance at the expense of overall efficiency. For example, if salespeople are rewarded based on short-term sales, they may push for larger orders, causing overstocking and increased holding costs. Behavioural game theories, such as inequity aversion theory, can help address how fairness, reciprocity, and altruism influence decisions (Camerer 2003; Fehr and Schmidt 1999). For instance, suppliers and retailers may engage in cooperative behaviour even when it is not the most profitable choice, driven by long-term relationship considerations. This theory helps explain why some supply chain partners maintain collaborative relationships despite short-term incentives to act opportunistically. Heuristics such as the mirror tree (Fischbacher, Hertwig, and Bruhin 2013) can help understand why some decisions, such as a retailer rejecting a supplier's offer, may take longer. Finally, the priority heuristic logically implies major empirical violations of the normative standard of expected utility theory, including the four-fold pattern of risk-attitude reversals, violations that cannot be all predicted at the same time by other theories (Katsikopoulos and Gigerenzer 2008; Wakker and Tversky 1993).

Finally, specific concepts can also be useful in BOM. One example is mental accounting which examines how people categorise and evaluate economic outcomes (Thaler 1999). In supply chains, this can influence how costs and revenues are perceived and managed. For instance, managers might treat budget allocations for different departments as separate accounts, leading to suboptimal investment decisions that do not maximise overall supply chain performance.

## Applications

A notable BOM application is in inventory management (Perera, Fahimnia, and Tokar 2020). Research has shown that decision makers can improve performance by incorporating behavioural insights into demand forecasting and inventory policies. For example, retailers can use decision support systems that adjust for common biases such as the tendency to overreact to recent sales trends.

Another area is supply chain coordination (Croson et al. 2014). Behavioural studies have demonstrated that misalignment in goals and incentives between suppliers and buyers often creates issues. For instance, the beer game illustrates how lack of coordination and information sharing can exacerbate the bullwhip effect (Sterman 1989). BOM interventions, such as aligning incentives and improving communication channels, have

been effective in mitigating such issues. Additionally, collaborative planning, forecasting, and replenishment practices can help synchronise activities and reduce inefficiencies.

Behavioural approaches have also been applied to contract design (Becker-Peth, Katok, and Thonemann 2013; Chen and Wu 2019). BOM highlights the importance of trust and fairness in negotiations. Studies have shown that contracts incorporating relational norms and fair profit-sharing clauses can enhance cooperation and performance (Cao and Lumineau 2015). Firms can design contracts that include mechanisms for dispute resolution and performance monitoring, fostering long-term partnerships.

Case studies illustrate the impact of BOM interventions. In the retail sector, companies such as Zara have leveraged behavioural insights to optimise inventory and supply chain responsiveness. By understanding decision-making patterns, Zara has implemented agile supply chain practices that reduce lead times and align production with demand (Sampath M and Cholli 2024). This approach not only improves inventory turnover but also enhances customer satisfaction by ensuring product availability.

In the healthcare sector, BOM has been instrumental in improving supply chain efficiency for medical supplies and pharmaceuticals. Behavioural interventions, such as enhancing communication and trust between suppliers and healthcare providers, have led to better coordination and reduced stockouts, improving patient care. For example, hospitals can implement inventory management systems that account for biases in ordering and usage, ensuring that critical supplies are always available.

The automotive industry also provides compelling examples of BOM ways of thinking. Toyota's production system incorporates principles of human behaviour, such as respect for people and continuous improvement (Kaizen). These principles have led to practices like just-in-time inventory management and lean manufacturing, which reduce waste and improve efficiency. The Toyota system practices have been interpreted as fast-and-frugal heuristics (de Treville et al. 2023).

The food and beverage industry has seen the implementation of BOM principles in managing perishable goods. Companies like Coca-Cola have optimised their supply chains by considering the urgency of replenishing stock and consumer purchasing patterns. By using analytics and behavioural insights, Coca-Cola can predict demand more accurately and adjust production schedules accordingly, minimising waste and ensuring product freshness.
<figure><img src="image_91.png" /></figure>

## Looking ahead

A criticism of the existing experiments is that participants are usually Western, educated, intellectual, rich, and democratic, commonly referred to as WEIRD (Henrich, Heine, and Norenzayan 2010; Özer, Zheng, and Ren 2014; Perera and Fahimnia 2024). This emphasises the need for more research incorporating diverse cultural contexts to ensure a holistic approach to BOM.

Researchers typically need ethics approval for conducting behavioural experiments. It is, however, argued that experiments should not mislead participants (Katok 2019), although psychologists and economists sometimes disagree on this view (Hertwig and Ortmann 2001). Emphasis should be placed on ethically acceptable supply chain contexts and avoid scenarios that might promote unethical behaviour. Crafting clear and unbiased cover stories for participants is essential to prevent misconceptions and ensure that data collection does not inadvertently bias participants' actions outside the experiment.

The behaviour of diverse actors within the supply chain should be understood, regardless of gender, culture, and background. For examples, studies can explore the behaviour of individuals and teams at various levels and echelons of the supply chain (Donohue, Katok, and Leider 2018; Özer, Zheng, and Ren 2014).

As it should, sustainable decision-making is a key area which requires further investigation. Other emerging trends in BOM research include product development, retail supply chains, sourcing and procurement, healthcare supply chains, warehousing, and logistics.

BOM is an established, evolving field that addresses limitations of traditional models by incorporating human behaviour and asking its own research questions (Donohue, Özer, and Zheng 2020; Fahimnia et al. 2019; Kat sikopoulos 2023). Research has generated invaluable insights on improving decision-making in supply chains and other operational settings, and there is every reason to expect that the field will help create more resilient, efficient, and ethical supply chains.

## 4.2. Lean and agile³⁵

Supply chain management ensures the efficient flow of products, materials, services, and information from suppliers to customers, often through a complex network, directly impacting cost, quality, and customer satisfaction (Le Caous and Huarng 2021). Organisations must balance efficiency with adaptability to stay competitive. Lean and Agile frameworks are widely used to address these challenges within supply chain management. Lean emphasises minimising waste and inefficiencies throughout the supply chain by focussing on continuous improvement efforts (Cudney, Furterer, and

Dietrich 2021; Holweg 2007). Agile emphasises flexibility and responsiveness to change (Amajuoyi, Benjamin, and Adeusi 2024). Organisations use these frameworks together to provide a comprehensive approach to improving performance, customer satisfaction, and overall operational resilience (Dahinine et al. 2024), particularly in supply chain management.

Lean, rooted in the Toyota Production System (TPS), emphasises eliminating non-value-added activities or waste (muda) to deliver greater customer value. Womack and Jones (1996) proposed a five-step thought process to lean transformation: identify value, map the value stream, create flow, establish pull, and seek perfection. These principles guide organisations to streamline processes, reduce costs, and improve lead times across production and supply chains (Melton 2005).

Value stream mapping (VSM) is a key Lean tool that enables organisations to visualise the flow of materials and information through a process. Using a team-based approach, organisations use VSM to identify bottlenecks and inefficiencies, which are then systematically eliminated to improve operational performance (Bhamu and Singh Sangwan 2014; Shah and Ward 2007), which fosters a culture of continuous improvement (Cudney et al. 2023; Liker 2004). This focus on operational excellence has proven effective in manufacturing and service industries, enabling organisations to reduce lead times, lower costs, and improve resource utilisation (Cudney and Elrod 2011; Núñez-Merino et al. 2020).

Lean principles are increasingly being applied beyond traditional manufacturing environments to service industries and healthcare, where waste reduction can significantly enhance service delivery and patient outcomes (Santos et al. 2022). A growing body of research has demonstrated Lean's impact on improving efficiency and customer satisfaction across sectors (Mamoojee-Khatib et al. 2025).

Initially developed in the software development industry, Agile has been adapted for broader use in operations and supply chain management (Rigby, Sutherland, and Takeuchi 2016). Agile frameworks, such as Scrum and Kanban, prioritise flexibility, iterative development, and rapid response to changing customer needs and market conditions (Amajuoyi, Benjamin, and Adeusi 2024). Agile's core values emphasise customer collaboration, individuals and interactions, and responding to change rather than following rigid processes or documentation (Behrens et al. 2021).

In supply chain management, Agile provides a structure for organisations to respond quickly to demand variability, supply disruptions, or market shifts (Pal-sodkar, Yadav, and Nagare 2023). For example, Kanban originated in Lean and is often used within Agile
<figure><img src="image_92.png" /></figure>

environments to manage workflow, track progress, and adapt tasks dynamically (Zayat and Senvar 2020). Agile's iterative cycles, or sprints, facilitate continuous feedback, allowing teams to make quick adjustments and deliver incremental improvements (Francisco et al. 2024). This capability is particularly valuable in environments with high levels of uncertainty or fluctuating demand, such as during global supply chain disruptions like the COVID-19 pandemic (Ivanov and Dolgui 2021a; Kazancoglu et al. 2022; van Hoek 2021).

Recent research highlights the growing use of Agile practices in non-software industries, including manufacturing, healthcare, and retail, to improve operational flexibility (Cano et al. 2021; Žužek et al. 2020). Agile methodologies are proving particularly effective in industries where rapid innovation and responsiveness are critical, such as technology and consumer goods (Josyula, Suresh, and Raghu Raman 2023). As global supply chains become increasingly complex, Agile offers a valuable framework for managing complexity and ensuring resilience (Kazancoglu et al. 2022).

While Lean and Agile have distinct origins and focuses, their integration can significantly enhance efficiency and adaptability in operations and supply chain management. The Lean-Agile hybrid approach, commonly referred to as leagile, combines Lean's waste-reduction and process optimisation with Agile's adaptability and iterative development, enabling organisations to respond to changing market conditions while maintaining operational efficiency (Piotrowicz, Ryciuk, and Szymczak 2023; Silva, Veronese, and Matos 2024). By integrating Lean tools such as VSM with Agile's iterative cycles, organisations can identify bottlenecks, eliminate inefficiencies, and make continuous adjustments to meet customer needs (Tripathi et al. 2021).

For example, Lean's Just-in-Time (JIT) production system, which ensures that materials and products are delivered exactly when needed, aligns well with Agile's sprint-based development cycles. This synchronisation allows organisations to deliver high-quality products quickly without overproduction or excessive inventory (Gunasekaran, Lai, and Edwincheng 2008; Liker 2004; Poppendieck and Poppendieck 2003). Using cross-functional teams in both Lean and Agile further enhances collaboration and decision-making, enabling organisations to respond more effectively to changes in demand, production capacity, or supplier conditions (Naylor, Naim, and Berry 1999; Rigby, Sutherland, and Takeuchi 2016).

Research also indicates that integrating Lean and Agile can significantly improve efficiency and customer satisfaction, as both frameworks emphasise customer value and continuous feedback (Narasimhan, Swink, and

Kim 2006). This integrated approach is crucial in industries where speed, flexibility, and efficiency are critical to competitive success (Agarwal, Shankar, and Tiwari 2006; Power, Sohal, and Rahman 2001).

Despite the benefits of integrating Lean and Agile, organisations may encounter challenges balancing Lean's focus on efficiency with Agile's emphasis on flexibility. Cultural differences between the structured, process-driven approach of Lean and the dynamic, adaptive nature of Agile can create friction within teams. Critical success factors for implementing Lean and Agile include collaborative relationships, trust, motivation, leadership capabilities, strategic management, human resource management, and organisational capabilities (Saini, Arif, and Kulonda 2019; Virmani, Saha, and Sahai 2018). Additionally, some organisations may struggle to implement both methodologies simultaneously due to differing operational requirements or resource constraints (Krishnamurthy and Yauch 2007; Power, Sohal, and Rahman 2001).

Organisations should adopt a phased approach to address these challenges, starting small and expanding as the integration matures. This approach involves initially implementing Lean and Agile in a few select areas or teams, allowing them to experiment, learn, and gradually align their practices. As the integration matures, more teams and areas can be brought into the Lean-Agile fold. Open communication and collaboration across cross-functional teams are essential to bridge gaps and ensure smooth integration (Ding, Ferràs Hernández, and Agell Jané 2021). Tailoring the implementation to fit the organisation's and its customers' unique needs can also help mitigate potential issues (Almeida and Bálint 2024).

Lean and Agile frameworks are essential for modern operations and supply chain management. While Lean focuses on waste reduction and efficiency, Agile emphasises adaptability and responsiveness to change. Integrating these two methodologies enables organisations to streamline operations, reduce costs, and respond rapidly to market and customer needs. By combining the strengths of Lean's operational excellence with Agile's flexibility, organisations can achieve greater resilience, customer satisfaction, and overall performance.

### 4.3. Circular economy³⁶

A circular economy (CE) is a production and consumption model that aims to reduce waste and extend the lifetime of products and materials. The current 'take, make and dispose' economic model has serious negative impacts on the environment, human health, and social well-being (Grossi, Lange, and Amaral 2024; Pera and Ferrulli 2024). These effects result in an unstable