<figure><img src="image_131.png" /></figure>

**Operational Enablers:** This stage focuses on backend operational elements, facilitating the entire consumer-shopping journey. Among these, inventory management has been a fruitful area. High inventory levels stimulate demand, which should be considered in inventory replenishment policies (Balakrishnan, Pangburn, and Stavrulaki 2004). Inventory models mostly assume that inventory data is accurate. Yet, substantial inventory records are inaccurate (DeHoratius and Raman 2008). 'Phantom stockout' and 'phantom inventory' are examples of inventory record inaccuracies (Chen 2021).

As indicated earlier, assortment is important for consumers, making its optimisation a key operational focus. Dynamic assortment models help retailers learn consumer preferences and optimise inventory replenishment (Ulu, Honhon, and Alptekinoğlu 2012). Such models provide important managerial insights; for example, an optimal assortment is not always composed of the most dominated or profitable products (Honhon, Jonnalagedda, and Pan 2012).

Fulfilment is another central topic, especially with the rise of online channels. Key areas include deciding the locations to fulfil orders (Acimovic and Graves 2015), the sequence in which orders are processed (Figueira et al. 2023), and whether or how to split a multiple-item order into separate shipments (Zhang et al. 2018).

**Social Aspects and Emerging Issues:** Retailing and socio-environmental factors are mutually influential. First, consumers are more aware of their purchases' impact on the planet; hence, retailers must satisfy conscious shoppers. Sustainability initiatives, like providing fair-trade products, affect customers' store choices (Hampl and Loock 2013). Second, retailers in developing economies experience unique challenges, like poor infrastructure or the lack of distribution channels, causing high inventory replenishment costs (Gui, Tang, and Yin 2019). Nanostores, a common retail model in such markets, operate under strict cash constraints (Boulaksil and Van Wijk 2018). Third, equity concerns relate to job design and workload. Responsible scheduling (Kesavan et al. 2022), spreading promotions to make store traffic manageable (Kalkanci, Rahmani, and Toktay 2019), or removing gender bias in job assignments (Corbett 2024) show how retailers can incorporate equity. Research on responsible retailing is growing, as evidenced by dedicated special issues (e.g. Fransoo and Honhon 2024).

## Methods

Retail research employs a wide variety of methods, which we categorise as analytical, empirical, experimental, and numerical. Among analytical models, the

traditional optimisation approach is common to provide a decision model to mathematically optimise retail variables, including assortment (Ulu, Honhon, and Alptekinoğlu 2012), order replenishment (Balakrishnan, Pangburn, and Stavrulaki 2004), and phantom stock-outs (Chen 2021). Theoretical models are also common. These stylised models are used to model pricing and consumer welfare (Gui, Tang, and Yin 2019), assortment (Heese and Martínez-de Albéniz 2018), showroom network design (Lim, Gao, and Tan 2024), and fulfilment (Jin, Li, and Cheng 2018).

Empirical methods for causal inference are increasingly being used, in part due to the availability of real-world data. These methods use reduced-form regressions and structural models. Reduced-form models are used to generate insights related to showrooms (Bell, Gallino, and Moreno 2018), subscription models (Lim et al. 2021), return policies (Ertekin and Agrawal 2021), omnichannel modes (Gallino and Moreno 2014), and pricing (Brynjolfsson and Smith 2000). Structural demand models are popular for understanding consumer preferences and behaviours. These models have been used in research on pricing (Wang 2018), assortment (Honhon, Jonnalagedda, and Pan 2012), inventory replenishments (Lim et al. 2022), and fulfilment (Ertekin, Gümüş, and Nikoofal 2022).

Lab and online experiments are widespread given their ease of implementation and low costs. They are used in research on consumer preferences related to sustainability (Hampl and Loock 2013), information precision (Lim, Gao, and Tan 2024), and store environment (Zhang et al. 2023). This experiment type is common in behavioural research, employing decision-making theories (Liang, Wang, and Li 2024). Field studies are also conducted to analyse retail concepts such as store environment (Zhang et al. 2023) and fitting rooms (Lee, Kesavan, and Deshpande 2021).

Among numerical methods, researchers utilise simulation to demonstrate performance of their optimisation or heuristic models (Acimovic and Graves 2015). This allows them to compare different policies and to test how the prescribed policy performs under various scenarios. Machine learning (ML) is another numerical method that is commonly used for prediction problems. Researchers utilise ML to examine online retailing design (Shi, Wedel, and Pieters 2013) and pricing (Ferreira, Simchi-Levi, and Wang 2018). A prediction method can be followed by an optimisation algorithm to provide prescriptions on variables like optimal location (Glaeser, Fisher, and Su 2019) or network expansion (Huang, Bergman, and Gopal 2019).
<figure><img src="image_132.png" /></figure>

Further reading and future research

Rooderkerk, de Leeuw, and Hübner (2023) discuss key decisions, risks, and opportunities in omnichannel retail fulfilment models. Caro, Kök, and Martínez-de Albéniz (2020) provide a review of retail research, categorising topics and methodologies and assessing their impact on both academia and industry. Meanwhile, Mou, Robb, and DeHoratius (2018) offer a review focussed on retail store operations decisions.

Future research can explore last-mile delivery innovations, which are essential to meet growing consumer demand in the retail sector (Lim 2023). This segment of the supply chain will be stress-tested in coming years. The integration of artificial intelligence into various retailing decisions, such as inventory or pricing, can also be investigated. Furthermore, the extant literature typically employs discrete choice models, such as the multinomial logit, where consumers are assumed to select a single item, or the multivariate logit, where consumers choose multiple items, each in a unit quantity. A more realistic approach is to consider basket-level choices, including varying quantities of multiple items (see Lim et al. 2025, for an application). We hope these discussions inspire further studies and method contributions to the retail operations literature.

5.9. Small and medium-sized enterprises⁵⁹

Small and medium-sized enterprises (SMEs) matter – they make a sizeable contribution to gross value added and total employment as they represent the bulk of all firms internationally. In the United Kingdom, for instance, there were 5.6 million SMEs at the beginning of 2023, which corresponded to over 99% of all companies registered in the country. These SMEs accounted for about half of total turnover in the UK private sector and three-fifths of employment (Federation of Small Businesses 2024). Beyond their macroeconomic significance, SMEs play a key role in supply chains which, in many sectors, have become increasingly long and complex. In the context of global supply chains, SMEs supply raw materials, manufacture and export critical components, parts and finished goods, and provide core business services such as transport and logistics (Kull, Kotlar, and Spring 2018). They also possess specialised sets of knowledge and can be a source of technological innovations that help to address contemporary societal challenges including climate change and affordable and accessible healthcare (Selviaridis, Luzzini, and Mena 2023).

SMEs are defined mainly by the number of their employees, although there is no uniform definition internationally. In the European Union, SMEs are defined as

companies with up to 250 employees and a maximum annual turnover of €50 million, or a balance sheet total of up to €43 million (European Commission 2020). The UK is using the same defining criteria. In the United States, the SME definition is also based on workforce size and annual turnover, but it varies by sector – overall, an SME is a company who employs between 250 and 1,500 people and has a maximum turnover of $47 million (U.S. Small Business Administration 2023). Regardless of the definition used, the term ‘SMEs’ incorporates a broad church of firms: from micro (less than 10 employees, according to the European definition) and small (between 10 and 50 employees) companies to medium-sized enterprises. These categories of firms can differ significantly with respect to the challenges they face and their ability to function as knowledgeable suppliers, service providers, or customers in the supply chain. SME firms can also be distinguished based on their technological knowledge intensity: high-technology SMEs have considerable growth potential and drive innovation and productivity improvements more than low-technology SMEs do (Sato and Goenka 2023).

The operations and supply chain management (OSCM) literature has identified some distinctive characteristics of SMEs vis-à-vis large firms, and discussed the advantages and limitations of small businesses that shape their contributions in supply chains. By virtue of their less formal, flatter governance structures and agile decision-making processes, SMEs are often responsive to changing circumstances and flexible in handling crises (Ketchen and Craighead 2021). They are also fast in exploiting market opportunities and introducing new products and services at pace, relying on founding entrepreneurs as their driving force (Kull, Kotlar, and Spring 2018). On the other hand, SMEs face finance and human resource restrictions; have limited production and distribution capabilities; and lack connections and market reputation (Selviaridis 2021; Zaremba, Bode, and Wagner 2016). These limitations are particularly germane to micro and small businesses, compared to medium-sized enterprises which tend to have more resources available.

OSCM scholars have examined why and how these SME peculiarities influence the ability of small businesses to collaborate and integrate with their larger supply chain counterparts to achieve a variety of performance outcomes including, among others, quality improvement and innovation (e.g. Rezaei, Ortt, and Trott 2015). For instance, research has shown that innovative small firms have difficulty in collaborating with large buying organisations for innovation purposes. This difficulty stems from SME limitations in terms of resources, capacity and social capital. It also relates to institutional failures and weak abilities of large buying firms to engage
<figure><img src="image_133.png" /></figure>

with small supplier firms with specific needs including fast-paced decisions and cash flow (Knight et al. 2022). Public policies targeting technology-intensive SMEs seek to address these issues and foster collaborative innovation in supply chains by, for example, incentivising SMEs and buying organisations to invest in collaborative R&D projects, connecting SMEs to buying organisations and their incumbent suppliers, and supporting the development of SMEs as (possible) suppliers (Selvaridis and Spring 2022, 2025).

Supplier development is an important element of a firm's sourcing strategy (Section 3.4) and while such support programmes are not restricted to SMEs, they are especially relevant in the context of efforts to improve the capabilities and productivity of small suppliers. Supplier development programmes originally focussed on quality improvement (Wagner, 2010), but their scope has been extended to digitalisation and sustainability goals, among others (e.g. Jia, Stevenson, and Hendry 2021). In the public sector context (Section 5.3), many governments seek to increase the involvement of SMEs in public contracting, either as direct suppliers or subcontractors in the case of high-value contracts (Harland et al. 2019). SMEs are also important partners in efforts to promote sustainability and resilience goals in supply chains: increasing spend with SME suppliers helps to create jobs and wealth in local communities, reduce carbon emissions, and diversify the supply network of a buying organisation (Mills, Reynolds, and Herculano 2022; Selvaridis, Luzzini, and Mena 2023). Supplier diversity programmes seeking to increase engagement with 'minority' suppliers (e.g. based on gender, ethnicity or disabilities) are directly relevant to the SME population, as most of these minority suppliers are simultaneously micro or small businesses (Bateman, Barrington, and Date 2020).

In addition to SME support initiatives, OSCM research has examined supply chain finance solutions (Caniato, Henke, and Zsidisin 2019). In brief, these seek to ensure the financial stability of suppliers – especially SME suppliers – against a backdrop of major crises such as the crunch of 2008. Effective cash-flow management is imperative for SMEs given their finance limitations – to this end, supply chain finance aims to optimise financial flows within the supply chain (Hofmann and Belin 2011). The solutions, typically led by financial institutions or technology providers, involve collaboration among supply chain counterparts to ensure effective and fair outcomes such as paying small suppliers in a timely fashion.

Research has also emphasised SME capabilities in procurement, supply chain management (SCM) and logistics (Arend and Wisner 2005; Gelinas and Bigras 2004; Maloni, Hiatt, and Astrachan 2017), among other areas. Regarding procurement, for instance, SMEs often have

limited resources to pursue structured supplier selection, contracting, and supplier relationship management practices (Ellegaard 2006). On the other hand, they can leverage the boundary-spanning capacity of their founders to effectively manage their supplier relationships (Son, Ha, and Lee 2019). Overall, however, there has been considerably less attention to the roles and activities of SMEs as customer firms who need to manager their supply chains, relative to studies focussing on SMEs as supplier firms.

Researching the contributions of SMEs in supply chains presents methodological and theoretical issues. Methodologically, the preference for multiple-respondent survey designs has meant that the perspective of SMEs is largely neglected because micro and small firms often lack multiple informants. Kull, Kotlar, and Spring (2018) have highlighted this challenge and argued for inclusive research designs using single-respondent approaches and expanded units of analysis. Furthermore, data triangulation strategies typically used in qualitative research are more challenging to pursue in that SMEs are not obliged to disclose their performance and business conduct to the extent that large firms do, meaning that valuable sources of secondary data may be in short supply. Compensating for such limitations might involve using participant observation techniques, diaries, and data collection through SME representative organisations such as national associations.

Theoretically, studying SMEs can help us understand better supply chain phenomena which are inherently inter-organisational. Consider incentive alignment for example, a key tenet of SCM (Lee 2002; Selvaridis and Spring 2018) – it has been studied mainly from the perspective of large firms, with the unit of analysis being a focal buying firm or the relationship between a buyer and its first-tier supplier. However, incentive alignment along multi-tier supply chains requires understanding the motivations, goals and constraints facing small firms upstream the supply network. In the context of supply chain cyber security, for instance, SME suppliers of products, services and software are often regarded as 'weak links'. And yet, these firms are reluctant or unable to invest in cyber defences (Melnyk et al. 2022). Unless we examine the perspective of these SME suppliers farther upstream, we cannot fully grasp why that is the case, and how incentives can be aligned across the supply chain to ensure cyber security.

In conclusion, OSCM research has only relatively recently began to pay attention to SMEs. For a thoughtful review of SME research in SCM, readers are directed to Kull, Kotlar, and Spring (2018). Selvaridis and colleagues offer insights regarding how innovative SMEs can be more effectively integrated in supply chains,
<figure><img src="image_134.png" /></figure>

and the role of targeted public policies in this respect (Selviaridis 2021; Selviaridis and Spring 2022). Moving forward, as a discipline we need to understand better how SMEs can function as knowledgeable buyers and customers in supply chains, and the capabilities they need to effectively manage their own supply chains. We must also unpack what SCM entails for micro firms, including young, technology-based businesses who are good at developing novel products but may not necessarily have an established supply chain to source materials, and manufacture and distribute their innovations. More generally, pushing the frontiers of SME-oriented OSCM research requires synthesising operations and supply chain knowledge with insights from the longstanding research on small business, family business, and entrepreneurship (e.g. Arend and Wisner 2005; De Massis et al. 2018).

**5.10. Utilities<sup>60</sup>**

Utilities are a backbone of modern society, providing essential services such as electricity, water, gas, and telecommunications. As such, the management of utility assets, i.e. the often large-scale, expensive, and technically complex physical assets underpinning these services, has a direct impact on societal well-being, economic growth, and environmental sustainability. Operations and supply chain management principles play a critical role in ensuring these assets are effectively managed across their entire life cycle, from planning (Section 2.14) and acquisition, to operations (Section 2) and maintenance (Section 2.20), and eventual disposal or renewal (Sections 4.3 and 2.5).

Utility assets, such as power plants, water treatment facilities, and energy grids, provide the infrastructure from which essential services can be rendered, and are hence of critical importance to society at large. They are also often long-lived, with life spans stretching decades, making decisions regarding these assets highly important, as their effects may be substantial and last for a long time. As such, effective management of utilities is, either directly or indirectly, imperative for meeting the United Nations' Sustainable Development Goals (SDGs), such as access to clean water (SDG 6) and energy (SDG 7), or sustainable cities and communities (SDG 11). Finally, they are quite often state-owned or heavily regulated, partly due to their natural monopoly characteristics. The large capital requirements, network-based operations, and economies of scale make parallel systems inefficient, further emphasising the need for careful operations and supply chain management strategies to optimise performance while adhering to regulatory, safety, and sustainability requirements. Without such strategies, the societal and economic consequences of infrastructure failure or

underperformance can be catastrophic (see e.g. Frangopol and Liu 2007), as evidenced by events such as the collapses of Morandi bridge in Italy in 2018, and the one in Baltimore in 2024. The risks have been signalled by frequent reports of aging bridges, outdated power grids, and vulnerable water systems. Nevertheless, research on operational excellence for utilities is limited. For overviews of utilities-focussed empirical research studies and of industry-specific studies see Akkermans et al. (2024) and Joglekar, Davies, and Anderson (2016).

For these reasons, the management of utility assets must adopt a long-term, systemic view that takes into account the various and deeply interconnected phases of their existence, known as the asset life cycle: planning, acquisition, operations, maintenance, and end-of-life (EOL); see Browning and Honour (2008). With decisions made in one phase affecting performance in subsequent phases, effective utility asset life cycle management is essential for ensuring their longevity, efficiency, and sustainability.

The *planning phase* of the asset life cycle is critical because it sets the stage for all future decisions. In this phase, forecasting (Section 4.6) plays a central role in determining demand, budgeting, and setting performance expectations for the assets to be acquired or constructed. For instance, demand forecasting in the energy sector can guide decisions about how many new power plants to build or how to expand grid capacity to meet future energy demands. Once planning is complete, the *acquisition phase* involves sourcing and procurement (Section 3.5) strategies to secure the necessary assets. Here, managers of utilities (often public) engage with a limited number of (often private) contractors through tendering processes that are subject to European and national procurement laws. This results in a lock-in for managers, that need to activate relationships for specific acquisition projects with a long yet temporary nature, while these relationships also need to be reproduced and sustained beyond individual projects (Manning 2017). Hence, management of long-term relations between partnering organisations needs to be balanced alongside contracting for specific projects (Chakkol, Selviaridis, and Finne 2018; Roehrich et al. 2024). An interesting development in response is that some organisations are moving away from a project-by-project approach and towards contracting strategies that address the need for efficiency, speed, and standardisation when facing large-scale challenges (Frederiksen, Gottlieb, and Leiringer 2021).

Research furthermore highlights the challenges associated with risk allocation between asset owners and contractors: shifting too much risk to contractors can discourage participation, leading to fewer bids and higher costs, as seen in cases like bridge renovation projects in
<figure><img src="image_135.png" /></figure>

the Netherlands (Vestergaard 2024). Performance-based contracting, which aligns contractor payments with the achievement of predefined goals, has emerged as a potential solution. However, such contracts require collaboration and deep understanding between service providers and utilities to ensure smooth operations throughout the asset's life cycle (Akkermans et al. 2019). See Van der Valk, Lumineau, and Wang (2021) and Selviaridis and Wynstra (2015) for reviews on (performance-based) contracting; and Fang et al. (2024) for a recent empirical example.

Once the assets are *operational*, the focus shifts to maximising their performance, reliability, and efficiency. This phase of the asset life cycle involves routine and preventive maintenance to ensure the asset functions as expected, while minimising downtimes and extending the useful life of the asset. Maintenance strategies have evolved significantly in recent years, particularly with the rise of condition-based maintenance, which relies on real-time monitoring and data analytics to predict when maintenance is needed based on the asset's condition (Akkermans et al. 2024; uit het Broek et al. 2020). This approach, enabled by digital technologies such as IoT, big data, and AI, improves asset reliability while reducing unnecessary maintenance costs. Also promising is the use of digital twins, which allow compiling a digital shadow of past behaviour (Ladj et al. 2021) based on historical data of operations, conditions, and maintenance activities. However, there are challenges associated with the digitalisation of asset management. Many asset owners struggle when adopting digital solutions for managing utilities due to organisational silos, data-sharing barriers, and misaligned incentives between manufacturers, asset owners, and service providers (Holmström et al. 2019). Overcoming these challenges requires enhanced supply chain coordination and collaboration. The concept of supply chain control towers – centralised platforms for real-time data integration and decision-making – has been proposed as a way to align operations and maintenance activities across stakeholders (Gerrits, Topan, and van der Heijden 2022; Topan et al. 2020).

The final phase of the asset life cycle – *disposal* or *end-of-life* (EOL) – is increasingly being viewed through the lens of sustainability and circularity. Traditionally, utilities have not focussed much on asset disposal, often resulting in significant environmental impacts. For example, old oil wells in Pennsylvania, many of which were abandoned without proper decommissioning, continue to leak methane into the atmosphere, contributing to climate change (Aflaki, Atasu, and Van Wassenhove 2024). The same risks apply to newer energy technologies such as wind turbines and solar panels, where

proper disposal and recycling strategies are not always considered at the design stage (Atasu, Duran, and Van Wassenhove 2021). As a consequence, sustainable business models for utility asset management that incorporate circular economy principles need to be developed, such as designing assets for easier disassembly and recycling, as well as developing closed-loop supply chains for recovering valuable materials from decommissioned assets, thereby reducing reliance on scarce natural resources. For instance, in road and waterways infrastructure, identification and separation of waste and recyclable material streams pose serious challenges because of the extended lifetime these materials have been in service. At the same time, the benefits of improved reuse and recycling would be substantial: Concrete and its main ingredient cement not only constitute an important resource for public infrastructures, but also contribute substantially to CO₂ emissions.

As utilities face increasing pressure to modernise aging infrastructure, improve performance, and reduce environmental impacts, the application of these operations and supply chain management principles will become even more critical. One key area for further investigation is the further integration of end-of-life considerations into the planning and acquisition phases. Given the long life cycles of utility assets, the environmental and social impacts of disposal are often underestimated or ignored. Incorporating life cycle assessment tools into early-stage decision-making can help address these issues and promote more sustainable asset management practices (Akkermans et al. 2024). Another promising area for research is the use of advanced analytics and AI to improve forecasting, operations, and maintenance decisions. AI can analyse vast amounts of data from sensors and digital twins to predict asset failures and optimise maintenance schedules. However, these technologies also raise new challenges related to data governance, privacy, and security, which need to be carefully managed to avoid unintended consequences (Angelopoulos et al. 2023). Finally, the development of innovative contracting models that foster greater collaboration and alignment between utilities and their contractors is an area in need of increased attention. Programmatic tendering, for example, offers a way to bundle multiple projects into a single contract, creating economies of scale and encouraging innovation in project delivery (Frederiksen, Gottlieb, and Leiringer 2021). Empirical research is needed to understand the operational conditions that lead to successful outcomes under such models.

From planning and acquisition to operations, maintenance, and EOL, each phase requires careful consideration of technical, financial, and sustainability
<figure><img src="image_136.png" /></figure>

factors. By embracing digitalisation, fostering collaboration across the supply chain, and integrating sustainability into decision-making processes, utilities can ensure that their assets continue to provide reliable and efficient services for generations to come.

## 6. Conclusions⁶¹

This paper aims to provide an extensive review of the core principles and practices of operations and supply chain management (OSCM), tracing the field's evolution from its historical roots to its current challenges and innovations. It showcased how OSCM enables organisations to build sustainable competitive advantages. The paper also emphasised the growing importance of integrating broader societal, environmental, and technological trends into OSCM.

The structure of the paper was organised into four major sections. First, it explored key topics in operations management (OM), including diverse important topics that span from more strategic ones, such as operations strategy and capacity management to more operational ones, such as scheduling and maintenance. Next, it transitioned into supply chain management (SCM), addressing topics like supply chain strategy, stakeholder management, and information management, among others, as well it looks into the impacts of recent trends such as digitalisation and emerging economies. The paper also discussed the growing intersection between OM and SCM, particularly in areas such as sustainability, ethics, risk management, and innovation, which are becoming increasingly relevant in today's global economy. Finally, real-world applications of OSCM principles were examined across various industries, namely agriculture, construction, public and nonprofit sectors, healthcare, humanitarian operations, manufacturing, professional services, retail, small and medium-sized enterprises, showcasing the practical impact of OSCM methodologies and solutions.

### Future directions and research opportunities

While the paper has covered a wide range of topics, it has also revealed several areas where future research and practice could further enrich the field of OSCM. These areas of research represent both challenges and opportunities for scholars and practitioners:

**Enhanced Focus on Sustainability:** As environmental and social sustainability become critical imperatives for organisations, future research must deepen its exploration into how sustainability can be embedded into every aspect of OSCM. The paper highlighted discussions on the circular economy, environmental sustainability, and ethical supply chains. Future studies could

investigate how organisations can integrate sustainability goals, such as decarbonisation, into their supply chain strategies without compromising operational efficiency. Collaboration among supply chains towards the common share of sustainable goals is another area of great potential where symbiotic supply chains are an example that should be pursued. Further, exploring the role of sustainable innovations – such as renewable energy sources, sustainable materials, and waste reduction technologies – within supply chain frameworks could provide more actionable insights. Another key area could be to examine the impact of governmental regulations and how policy changes drive shifts towards greener supply chains.

**Technological Advancements and Digital Transformation:** Digitalisation, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are already revolutionising operations and supply chain management (OSCM). However, the paper highlights that many organisations remain in the early stages of adopting these transformative technologies. Future research could focus on how AI and ML can improve predictive analytics for demand forecasting, automate supply chain decisions, and create smarter, more resilient networks. There is also potential in advancing decision-support tools that combine these technologies with optimisation techniques, providing more strategic insights for OSCM decision-making. Additionally, blockchain technology offers promising opportunities for enhancing transparency and traceability within supply chains, particularly in industries like pharmaceuticals and food, where ethical sourcing and safety are paramount. Further studies could investigate how real-time data sharing through IoT can improve coordination across supply chain networks, increasing visibility and mitigating risks related to demand fluctuations and supply disruptions.

**Behavioural Operations and the Human Factor:** Human decision-making plays a crucial role in OSCM, and behavioural biases can significantly impact operational outcomes. The paper introduced the concept of behavioural operations, which highlights how psychological factors influence managerial decisions. Future research could focus on the development of decision-support systems that account for human biases, such as overconfidence or the tendency to anchor decisions on past experiences. Understanding how to better manage the interplay between human judgment and technological systems (like AI or automated tools) will be key as more companies adopt hybrid decision-making models. Furthermore, exploring the dynamics of workforce management in an increasingly automated world, including how human-robot collaborations can be optimised, could also provide valuable insights.
<figure><img src="image_137.png" /></figure>

**Risk Management and Supply Chain Resilience:** The disruptions caused by global events such as the COVID-19 pandemic have underscored the need for resilient supply chains. The paper addressed risk management and the ripple effect, but more work is needed to develop frameworks that help companies build resilience. Future studies could examine how supply chain managers can proactively design systems that anticipate and adapt to sudden disruptions, whether from pandemics, geopolitical events, or climate-related risks. Research could also explore how companies can balance the need for lean, cost-efficient operations with the need for redundancy and buffer capacity in critical areas of their supply chains. There is also a need for new models that combine traditional risk management practices with innovative concepts like supply chain decentralisation, onshoring, and the use of local suppliers to mitigate global risks.

**Collaboration and Ethical Supply Chains:** As global supply chains become more complex, the need for collaboration among diverse stakeholders is increasingly important. The paper's exploration of ethical supply chains suggested that there is growing pressure on organisations to ensure their operations adhere to ethical standards, such as fair labour practices and responsible sourcing. Future research could investigate how companies can develop stronger relationships with their suppliers, customers, and communities to foster collaborative networks that prioritise ethical outcomes. This could include exploring how digital platforms facilitate transparency and accountability across the supply chain. Additionally, studying the role of cross-sector partnerships (e.g. between NGOs and corporations) in promoting ethical standards could offer new insights into managing supply chain challenges in a socially responsible manner.

**Broader Industry Applications and Contextual Adaptation:** While the paper provided applications of OSCM principles in industries such as agriculture, construction, and healthcare, there is significant room for research on how these principles can be adapted to other underexplored sectors. For instance, humanitarian logistics, which deals with the challenges of delivering aid in crisis situations, presents unique supply chain issues such as resource scarcity, high uncertainty, and the need for rapid response. Future work could investigate how supply chain strategies used in commercial sectors can be tailored to fit the unique constraints of humanitarian operations. Similarly, studying OSCM in emerging economies, where infrastructure and technological adoption levels vary widely, could reveal innovative approaches to overcoming challenges in resource-limited environments.

In conclusion, this paper has provided a thorough examination of the current state of OSCM, outlining

the challenges and opportunities that lie ahead for both researchers and practitioners. By focussing on key areas such as sustainability, technological advancements, behavioural operations, risk management, and ethical supply chains, future work can push the boundaries of OSCM, helping organisations navigate the complexities of global supply chains while making meaningful contributions to societal goals. The ongoing evolution of OSCM will require adaptability, innovation, and a continued emphasis on collaboration across industries and academia where multidisciplinary approaches should be explored. Through such efforts, OSCM will continue to be a powerful driver of organisational success and societal well-being.

We hope you have found this collection on the OSCM field insightful. While we believe it serves as a valuable reference, we acknowledge it captures only a moment in time—reflecting our past, present, and the path forward.

## Notes

1. This subsection was written by Joseph Sarkis.

2. This subsection was written by Michael A. Lewis and Malek El-Qallali.

3. This subsection was written by Joern Meissner and Fateh-meh Fakhredin.

4. This subsection was written by Mohamed Zied Babai and Dennis Prak.

5. This subsection was written by Nils Boysen and René de Koster.

6. This subsection was written by Veronique Limère and Nico André Schmid.

7. This subsection was written by Chuan Shi and Stanley B. Gershwin.

8. This subsection was written by Dirk Pieter van Donk.

9. This subsection was written by Tomislav Hernaus and Matija Marić.

10. This subsection was written by Maneesh Kumar and Andrea Chiarini.

11. This subsection was written by Alistair Brandon-Jones.

12. This subsection was written by Mélanie Despeisse and Björn Johansson.

13. This subsection was written by Andrew Kusiak and Yong-Hong Kuo.

14. This subsection was written by Olga Battaia and Alexandre Dolgui.

15. This subsection was written by Christoph H. Glock and Ting Zheng.

16. This subsection was written by Rainer Kolisch and Dirk Briskorn.

17. This subsection was written by Jens K. Roehrich and Tyson R. Browning.

18. This subsection was written by Andrew Davies and Juliano Denicol.

19. This subsection was written by Anders Haug and Ewout Reitsma.
20. This subsection was written by Hubert Pun and Salar Ghamat.

21. This subsection was written by Rommert Dekker and Geert-Jan van Houtum.

22. This subsection was written by Martin Spring and Andreas Schroeder.

23. This subsection was written by Steven J. Day and Janet Godsell. The author names are listed alphabetically as both contributed equally in the writing of this subsection.

24. This subsection was written by Christoph G. Schmidt.

25. This subsection was written by Piera Centobelli.

26. This subsection was written by Byung-Gak Son.

27. This subsection was written by Finn Wynstra. Parts of this subsection build on some public video lectures developed by the author; see http://www.youtube.com/@finnwynstra psmrsm5732

28. This subsection was written by Paul Cousins and Brian Squire.

29. This subsection was written by Stephen Brammer and Stephen Pavelin.

30. This subsection was written by Stephen M. Disney.

31. This subsection was written by Usha Ramanathan.

32. This subsection was written by Paul Buijs.

33. This subsection was written by Stefan Seuring and Sharfah Ahmad Qazi.

34. This subsection was written by Konstantinos V. Katsikopoulos and Behnam Fahimnia.

35. This subsection was written by Elizabeth A. Cudney.

36. This subsection was written by Kannan Govindan.

37. This subsection was written by Miriam Wilhelm and Gerald Reiner.

38. It should be noted that the concept of ‘cost of quality’ is now considered outdated in the quality management literature.

39. This subsection was written by Devika Kannan and Madan Shankar Kalidoss.

40. This subsection was written by Fotios Petropoulos and Nada R. Sanders.

41. This subsection was written by Juliana Hsuan.

42. This subsection was written by Vaggelis Giannikas and Ayse Begüm Kilic-Ararat.

43. This subsection was written by Omid Maghazei.

44. For instance, we refer to recent (2024-2025) calls for papers in leading OSCM and management journals on the impact of emerging technologies in sustainable humanitarian logistics (see *International Journal of Production Research*) and healthcare (see *Journal of Operations Management*). Special issues have also been published in the *International Journal of Operations and Production Management* on emerging technologies and emergency situations (Fosso Wamba et al. 2021) and in *Organization Science* on emerging technologies in organising (Bailey et al. 2019).

45. This concept is also reflected in the distinction between ERP ‘systems’ and the ERP ‘concept’, as discussed by Bendoly and Schoenherr (2005) and further detailed in Lewis (2020).

46. Moore's Law is the observation that the number of transistors on a microchip doubles approximately every two years, which enhances computing power and makes devices smaller and more affordable.

47. This subsection was written by Thanos Papadopoulos and Imran Ali.

48. This subsection was written by Pietro Micheli.

49. This subsection was written by Miranda Meuwissen and Maximilian Koppenberg.

50. This subsection was written by Dmitry Ivanov and Alexandre Dolgui.

51. This subsection was written by Argyris Kanellopoulos.

52. This subsection was written by Samudaya Nanayakkara and Srinath Perera.

53. This subsection was written by Claire Hannibal.

54. This subsection was written by Wendy Phillips and Linh Duong.

55. This subsection was written by Marianne Jahre and Maria Besiou.

56. This subsection was written by Jiyin Liu.

57. This subsection was written by O. Zeynep Aksin and Elif Karul.

58. This subsection was written by Stanley Frederick W. T. Lim and Bengü Nur Özdemir.

59. This subsection was written by Kostas Selviaridis.

60. This subsection was written by Henk Akkermans and Wendy van der Valk.

61. This subsection was written by Ana Barbosa-Povoa.

## Disclaimer

The views expressed in this paper are those of the authors and do not necessarily reflect the views of their affiliated institutions and organisations.

## Acknowledgments

Fotios Petropoulos would like to thank Alexandre Dolgui and Mohamed Zied Babai for inviting this article for publication to the *International Journal of Production Research*, as well as all the other co-authors who accepted his invitation to be part of this encyclopedic article.

Henk Akkermans and Wendy van der Valk would like to thank Finn Wynstra (RSM Erasmus University) and Luk van Wassenhove (INSEAD) who co-authored the *Journal of Operations Management's* Special Issue editorial that their subsection builds on.

Alistair Brandon-Jones thanks Maneesh Kumar (University of Cardiff) and Andrea Chiarini (University of Verona) for their support in developing this content.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Funding

Ana Barbosa-Povoa thanks FCT – Foundation for Science and Technology, I.P., under the project UIDB/00097/2020. Salar Ghamat's work is supported by Canada Research Chairs Program (CRC, Tier 2, in Business Analytics in Supply Chain, CRC-2022-00130). Maximilian Koppenberg's work was supported by 4TU.Federation under the project 4TU.Redesign.

## ORCID

Fotios Petropoulos https://orcid.org/0000-0003-3039-4955

Andrea Chiarini http://orcid.org/0000-0003-4915-5145
<figure><img src="image_138.png" /></figure>

Elizabeth A. Cudney http://orcid.org/0000-0002-3990-0474

Mélanie Despeisse http://orcid.org/0000-0002-1772-3884

Stephen M. Disney http://orcid.org/0000-0003-2505-9271

Alexandre Dolgui http://orcid.org/0000-0003-0527-4716

Linh Duong http://orcid.org/0000-0002-9415-1082

Vaggelis Giannikas http://orcid.org/0000-0002-5762-5488

Christoph H. Glock http://orcid.org/0000-0001-6006-0070

Dmitry Ivanov http://orcid.org/0000-0003-4932-9627

Maneesh Kumar http://orcid.org/0000-0002-2469-1382

Yong-Hong Kuo http://orcid.org/0000-0002-6170-324X

Gerald Reiner http://orcid.org/0000-0001-7560-3410

Stefan Seuring http://orcid.org/0000-0003-4204-9948

Ting Zheng http://orcid.org/0000-0003-4757-9075

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