<figure><img src="image_93.png" /></figure>

economy and the collapse of natural ecosystems, which
calls into question human survival. As an alternative
to this conventional economic model, circular economy
has become a central focus for policy makers to pro-
mote cyclical thinking by closing the loop to minimise
materials and energy consumption. With the closed loop
concept, the value of the resources (including prod-
ucts, materials, and energy) can be preserved in the
economy advantageously. The history and conceptual
framework of CE was first introduced by Pearce and
Turner (1989) to study existing links between economic
and environmental activities (Prieto-Sandoval, Jaca, and
Ormazabal 2018). Boulding (1966) discussed the closed
loop concept by acknowledging the problems of lim-
ited availability of natural resources (Merli, Preziosi,
and Acampora 2018). Several other contributors were
involved in creating the CE concept prior to Pearce and
Turner (1989), as presented by Winans, Kendall, and
Deng (2017). With numerous benefits of implementing
CE, scholars and practitioners became more interested
in exploring its context, resulting in endless conceptu-
alised interpretations. Such interpretations resulted in a
wide range of definitions for CE; Kirchherr et al. (2023)
revisited 221 definitions of CE, an expansion from their
114 definitions (Kirchherr, Reike, and Hekkert 2017).
Although several definitions exist, a boilerplate definition
was given by the Ellen MacArthur Foundation as ‘an
industrial economy that is restorative or regenerative
by intention and design’ (Ellen MacArthur Founda-
tion 2013). Since there is no clear single definition of
CE, concepts and theories of CE have been devised by
various principles, including 3Rs (reduce, reuse, recy-
cle), 6Rs (reuse, recycle, redesign, remanufacture, reduce,
recover), industrial ecology, laws of ecology, regenerative
design, cradle to cradle, blue economy, and biomimicry.
In addition, the concept of CE has been developed
with the political and social outlook of various global
nations. China started adopting CE through a top-down
approach, while other nations generated bottom-up poli-
cies. The difference between the approaches of different
nations can be apparent even within their definitions.
China defined CE as ‘a general term for the activity
of reducing, reusing and recycling in production, cir-
culation and consumption’ (Chinese National People’s
Congress 2008), whereas the EU chose a definition of
‘where the value of products, materials and resources is
retained in the economy for as long as possible and the
production of waste is minimised’ (European Commis-
sion 2014, 2015).

Despite these conceptual overlaps and wide range
of interpretations, implementing CE in today's busi-
ness environment is essential to balance the three pillars
of sustainability (economy, environment, and society).

Contrary to necessity, worldwide CE is still in the early
stages of implementation, mainly focussing on ‘recy-
cling’ rather than other R’s (such as reuse, reduction,
and so on). To facilitate CE, researchers and practitioners
are exploring various circular business models (CBMs)
that decouple value creation from resource consump-
tion. According to Linder and Williander (2017), CBM
is defined as ‘a business model in which the concep-
tual logic of value creation is based on exploiting the
economic value retained in products after use in the pro-
duction of new offers’ (Lewandowski 2016). Better clari-
ity about the design of such CBMs may result in better
implementation of CE at both the macro and micro lev-
els. The proposed CBMs should focus on three key areas:
value creation, value transfer, and value capture (Cen-
tobelli et al. 2020). Although several CBM frameworks
have been proposed in the literature (Abbate, Cento-
belli, and Cerchione 2023; Madanaguli et al. 2024; Susur
and Engwall 2023), a popular business model framework
for CE was proposed by the Ellen MacArthur Founda-
tion (2015a) as the ‘Regenerate, Share, Optimise, Loop,
Virtualise, and Exchange’ framework map (ReSOLVE).
The CBM framework in the existing literature offers spe-
cific guidelines for different industry sectors, including
supply chain, automotive, textile, mining, and medicine.
Expansion of current CBMs is more focussed on innova-
tions among business models, whereas recent research is
shifting from 3R and 6R strategies to focus on improved
versions of CE, including 9R (Refuse, Rethink, Reduce,
Reuse, Repair, Refurbish, Remanufacture, Repurpose,
Recycle, and Recover) and 12R (above 9R’s and Research,
Re-skill, Re-design, and Re-vision). Such improved CE
concepts require innovation in business models, as dis-
cussed by Susur and Engwall (2023). Business model
innovations include themes of changing the existing lin-
ear business model with circular strategies, optimising
existing CBMs through adding more CE strategies, and
helping companies to produce the finest version of CBMs
themselves from scratch.

To create functioning CBMs and business model inno-
vations, it is necessary to understand the basic metrics
of CE as drivers/enablers/pressures, barriers/challenges,
and practices. These metrics support practitioners to
design appropriate CBM frameworks. Several studies
focussed on CE drivers (Abdulai et al. 2024; Truant
et al. 2024), barriers (Gonella et al. 2024; Nyffeneg-
ger et al. 2024) and practices (Patra, Wankhede, and
Agrawal 2024; Schöggl, Stumpf, and Baumgartner 2024)
with different application areas. Researchers group these
metrics based on the context of the study. For exam-
ple, Aloini et al. (2020) categorise the drivers of CE as
institutional, economic, environmental, organisational,
social, supply chain, and technological. Govindan and
<figure><img src="image_94.png" /></figure>

Hasanagic (2018) categorise the drivers as politics and economy, health, environmental protection, society and product development, whereas the same study categories barriers as governmental issues, economic issues, technological issues, knowledge and skills, management issues, CE framework issues, cultural and social questions, and market challenges. In the CE literature, both the drivers and barriers are broadly categorised based on the sustainability pillars (economy, environment, and society) along with some management (stakeholders) and business elements (risk, supply chain, technologies). Based on the existing studies, it is difficult to conclude one potential driving force and barrier for CE implementation, because several connections exist among these drivers and barriers. Therefore, the potential drivers and barriers to CE implementation depend on the scope and nature of the case considered. Unlike CE drivers and barriers, practices are often associated with organisational performance (Magnano et al. 2024; Yin et al. 2023). Several CE practices have been reported in the literature, and these practices are different for each industrial application. However, the overarching concepts of these practices follow the principles of design for 12R/9R, extended producer responsibility, industrial symbiosis, eco-industrial development, life cycle analysis, and nutrient recovery.

The performance related to CE practice is measured with indicators and different assessment models. Indicators have been considered one of the most important initiatives to communicate the effectiveness of CE implementation through the measurement and quantification of the process (Saidani et al. 2019). The existing literature proposes several circular indicators for measuring progress. For example, Saidani et al. (2019) proposed 55 circular indicators and De Pascale et al. (2021) listed 61 circular indicators with three different levels of application (meso, micro, and macro). All indicators were set up based on 'R' principles and dimensions of sustainability. Besides CE indicators, CE implementation performance is also assessed through various performance assessment methods. Mostly, these involve Life Cycle Analysis (LCA), Material Flow Analysis (MFA), Material Flow Cost Accounting (MFCA), Design for X (DfX), Design for Disassembly (DfD), Data Envelopment Analysis (DEA), Discrete Event Simulation (DES), Multi-Criteria Decision Making Methods (MCDM), and fuzzy approaches. Recently, these indicators and assessment methods were used to correlate CE practices with various organisational performances, including environmental performance, operational performance, and sustainability performance.

To improve performance through CE, recent studies (Liu, Song, and Liu 2023; Lu, Zhao, and Liu 2024; Sahoo

and Jakhar 2024) began to integrate technologies within CE principles. Technologies identify useful methods to improve product lifecycles through real-life tracking and effective predictive analytics aligned with CE principles. Therefore, several studies argued that the effectiveness of CE implementation to improve organisational performance goes hand in hand with technology integration. Among various technologies, big data, Internet of Things (IoT), blockchain, and cloud computing are often used in the CE studies. Researchers began to explore various elements for the successful integration of Industry 4.0 technologies with CE, including studies of the challenges of digitisation-led CE (Costa et al. 2023; Kannan et al. 2024) and digitisation-led business model innovations for CE (Ranta, Aarikka-Stenroos, and Väisänen 2021). With the abundant literature that has been published in the digital CE context, several researchers (Chauhan, Parida, and Dhir 2022; Sarc et al. 2019; Seyyedi et al. 2024) conducted a literature review on this digitisation-led CE, in which in-depth discussions have been made along with opportunities for future extensions.

Current research trends show that several studies are focussed on business model innovation and technology integration with CE. However, the social dimension of sustainability is underexplored compared to economic and environmental aspects, which also receive limited attention (Mies and Gold 2021; Pitkänen et al. 2023). The importance of consumer behaviour, a key CE stakeholder, is similarly undervalued. To achieve global sustainability goals like the SDGs, decolonisation, and net zero targets, integrating social, economic, and environmental dimensions, while considering stakeholder behaviour including leaders, regulators, customers, and media, is crucial. Digitisation is essential to enhance CE practices such as industrial symbiosis and accelerate progress towards these goals. To further strengthen the understanding of CE, it is worth reading the reports of Ellen MacArthur Foundation (2013, 2015a, 2015b, 2016). Other studies provide detailed discussions on definitions, taxonomy, business models, metrics, and digital revolutions happening with CE (e.g. Geissdoerfer et al. 2017; Ghisellini, Cialani, and Ulgiati 2016; Kirchherr, Reike, and Hekkert 2017; Korhonen, Honkasalo, and Seppälä 2018; Lieder and Rashid 2016; Murray, Skene, and Haynes 2017).

### 4.4. Environmental sustainability³⁷

Environmental sustainability in an operations and supply chain management context relates to the direct and indirect impact productive activities of a firm and its suppliers have on the natural environment. The World Commission on Environment and Development
<figure><img src="image_95.png" /></figure>

(WCED 1987) defined sustainable development as ‘devel-
opment that meets the needs of the present without
compromising the ability of future generations to meet
their own needs’. This definition has been criticised for
its all-encompassing scope, but it points to the evident
inefficiency of our current products and production pro-
cesses in their use of the planet’s resources. In fact,
the framework of planetary boundaries by the Stock-
holm Resilience Centre shows quite drastically that seven
out of nine planetary boundaries are currently trans-
gressed, making our planet increasingly uninhabitable
for humans (Richardson et al. 2023). Because of these
growing concerns, businesses are under strong pressure
to minimise their adverse impacts on the environment
by reducing the resources they use and the resulting foot-
print they leave behind. Primary activities contributing to
their footprint are producing and transporting products
(Kleindorfer, Singhal, and Van Wassenhove 2005).

The origins of environmental sustainability man-
agement in operations can be traced back to TQM
(Section 2.10) and lean production, as many of the tools
and principles that apply to quality management are
equally relevant for environmental improvements (Cor-
bett and Van Wassenhove 1993). The perceived synergy
between quality management systems and environmen-
tal management was fuelled by the development of the
international ISO 14000 standards that started in 1991
after the successful deployment of ISO 9000 standards
(Corbett and Kirsch 2001; Pil and Rothenberg 2003). In
their seminal discussion of the parallels between TQM
and environmental management, Klassen and McLaugh-
lin (1993) point out that the ‘cost of quality’³⁸ includes
both cost of defects and cost of prevention. Environmen-
tal costs similarly include costs related to pollution and
pollution prevention.

Lean production (Section 4.2), and its core philos-
ophy of eliminating waste in the process, has further
contributed to the boundary expansion of TQM through
the development of environmental management sys-
tems (Corbett and Klassen 2006). While waste origi-
nally focussed on time, quality defects, and excess inven-
tory, it is now being used effectively to reduce the
use of natural resources and/or eliminate environmen-
tal waste (King and Lenox 2001; Rothenberg, Pil, and
Maxwell 2001). Souza (2012) lists seven types of waste
that can be reduced through lean production, namely,
overproduction, transportation, inventory, waiting time,
motion waste, processing waste, defects.

The potentially synergistic perspective, as captured in
the mantra ‘lean is green’ (Florida 1996), has formed

the basis for almost all past research on lean production
and sustainability. In one of the first studies on this rela-
tionship, King and Lenox (2001) find that lean produc-
tion, as measured by ISO 9000 adoption and low chemical
inventories, correlates with greater waste prevention
and lower emissions. The other way around, improved
environmental, health, and safety performance can aid
plant-level productivity efforts (Klassen 2001). However,
subsequent research on the association between lean and
green has produced inconsistent results (e.g. Hajmoham-
mad et al. 2013; Rothenberg, Pil, and Maxwell 2001).
For a review of the literature, see Garza-Reyes (2015)
and Dhingra, Kress, and Upreti (2014). Piercy and
Rich (2015) see the reasons behind the difficulties of
exploiting the synergies between lean and green in
a reductionist definition of lean as a mere JIT tool
(Section 4.2). They argue that a full lean implementation,
which includes changes in both internal operations and
external supply chains, is more likely to reduce environmental
impact. Thus, lean should be seen as a philosophy
rather than a toolkit. Moreover, lean is not only limited
to environmental benefits but can help to meet a wide
range of sustainable outcomes, including supply monitoring,
transparency, workforce treatment, and community
engagement (Piercy and Rich 2015). A later study
by Distelhorst, Hainmueller, and Locke (2017) confirmed
that adopting lean production practices was associated
with improved labour standard compliance in supplier
factories, thereby contributing to ethical sustainability
(Section 4.5) as well.

Beyond waste reduction, environmental sustainabil-
ity also requires that companies track and measure their
carbon footprint. The concept of corporate carbon foot-
print describes the total amount of direct and indir-
ect Greenhouse gas (GHG) emissions that come from
all company's activities (Lee 2012). The Kyoto Protocol
addresses six greenhouse gases: carbon dioxide, methane,
nitrous oxide, sulfur hexafluoride, perfluorocarbons, and
hydrofluorocarbons, all of which are measured as met-
ric tons of their carbon dioxide-equivalent (CO2) emis-
sions. GHG emissions are further categorised into three
groups or 'scopes' by the most widely used international
accounting tool, the Greenhouse Gas Protocol. It distin-
guishes between emissions from one's operations (scope
1), emissions from purchased energy, heating, and cool-
ing (scope 2), and emissions from any other supply chain
process (scope 3).

Scope 3 emissions include indirect emissions from
purchased products (upstream) and indirect emissions
from sold products (downstream); see also Lee (2012).
<figure><img src="image_96.png" /></figure>

The latter includes the use and disposal of sold products at the end of life. According to the Carbon Disclosure Project, scope 3 emissions account for an average of three-quarters of a company's emissions (Carbon Disclosure Project 2022). The importance of scope 3 emissions varies considerably by sector, and scope 3 emissions from energy-intensive industries are increasing faster than their scope 1 and 2 emissions (World Economic Forum 2021). Thus, it is not sufficient to consider direct emissions of a (manufacturing) company only, but it is important to consider the total emissions of the supply chain as well (Jammernegg, Reiner, and Wakolbinger 2018).

However, only a few firms are currently accounting for scope 3 emissions. This will have to change as investors and regulators demand more disclosure. Starting in 2025, companies with European operations, including those headquartered elsewhere, will be required to report emissions across their supply chain, including scope 3 (European Commission 2023). One of the challenges is that there is currently no globally accepted standard for calculating carbon emissions, but different tools, frameworks, and methods co-exist in practice (McKinnon and Piecyk 2009). For example, Wild (2021) lists 18 different calculators commonly used in practice.

A growing criticism regarding environmental sustainability in operations and supply chain management is, however, that companies typically focus on those areas of sustainability that are easier to measure and where metrics are quantifiable. This is best exemplified by Life Cycle Analysis (LCA), which is a prominent methodology to quantitatively assess the environmental impacts of goods and processes from 'cradle to grave', that is, across the entire lifespan of a product and throughout the entire value chain (supply chain plus use and disposal phases); see Hellweg and Milà i Canals (2014). LCA has traditionally focussed on carbon emissions as a quantifiable metric but has often ignored impacts that are harder to measure or poorly understood, such as land use, impact on biodiversity, and human and ecotoxicological impact categories (Finnveden 2000). A related criticism is that more emphasis has been on areas directly benefiting corporations, reflected in larger progress made in measuring and accounting for energy, water, waste, and packaging. Sustainability initiatives without direct cost savings have not advanced as far or as fast (see also Villena, Wilhelm, and Xiao 2021). Also, voices are getting louder that operations and supply chain management must go beyond a minimal harm approach. In other words, accounting for carbon along the supply chain, and reducing and preventing pollution through lean operations might not be sufficient to stop the transgression of planetary boundaries. Instead, operations and

supply chain management must become regenerative by positively contributing to the social-ecological systems surrounding it Gualandris et al. (2024).

### 4.5. Ethical sustainability³⁹

Ethical sustainability is a concept to identify an organisation's ability to understand and act on sustainable values and principles within the legal and moral norms of the given context. In contrast to other dimensions (economy, environment, and society) of sustainability, the ethical dimension has often been left out of the debate. Compliance with business ethics within the sustainability context improves brand value and reputation, resulting in good organisational performance and long-term financial gains (Flores-Hernández, Cambra-Fierro, and Vázquez-Carrasco 2020). If the taxonomy of ethical sustainability digs further, then 'ethics' can be defined as a practical philosophy that teaches us to practice certain habits and customs that lead to a better life. Within the context of sustainability, however, several researchers (de Bakker, Rasche, and Ponte 2019; Hockerts and Searcy 2023) define business ethics as the concept of evaluating moral 'right' and 'wrong'. By understanding this morality, organisational leaders can make ethical decisions, and such ethical behaviour can improve customer satisfaction, shareholder loyalty, and employee satisfaction. Compared to sustainability, ethics in business is an older context. The earliest publication of business ethics can be seen from the early 1960s by (Baumhart 1961), when the relationship between ethics and the values of business leaders was investigated. Several studies (Donaldson and Dunfee 1994; Kolk 2016; Reidenbach and Robin 1990; Rydenfelt 2025; Schneider and Murray 2025) examine different scenarios of business ethics, and the role of sustainability began to gain momentum about thirty years after the Brundtland report was published in 1987 (Khalili et al. 2015; World Commission on Environment and Development 1987).

Following this report, global nations began to explore different strategies to implement sustainability in their operations regardless of application area. Implementing sustainability or sustainable development involves multiple decision-making processes mostly involving human connection. To deal with such human connections and networks in the implementation process, issues of rights and interests must be addressed. The interpretation of ethical sustainability differs with contexts, applications, and cultures, which explains why no single definition is available in the literature. Some studies (Vaupel et al. 2023) classify ethical sustainability as a subset of social sustainability. This classification clearly
<figure><img src="image_97.png" /></figure>

demonstrates a need for a conceptual exploration of eth-
ical sustainability with better definitions and different,
more comprehensive thinking.

Despite the lack of clarity about definitions, organ-
isations know that considering ethical sustainability in
their operations can improve their reputation, extend
their market reach, and make them more competitive
(Nicholson and Kurucz 2019). Therefore, practitioners
and researchers have explored the ethical aspects of sus-
tainability through empirical (Baah et al. 2024), norma-
tive (Becker 2023), and conceptual (Laasch, Moosmayer,
and Antonacopoulou 2023) methods. In the row, the
business models (Anshari et al. 2022; Schaltegger and
Burritt 2018) and metrics for ethical sustainability have
begun to be examined (Raoult-Wack and Bricas 2002;
Schaltegger and Burritt 2018; Sharpe and Barling 2019).
A few studies have reported on drivers/metrics of ethical
sustainability, but these works are compromised by lim-
iting their focus to the single driver of ‘ethical climate’.
According to Lee and Ha-Brookshire (2017), an ethical
climate provides a collective ethical perception among
employees, which influences each employee’s attitudes
and outcomes, along with the influence of internal eth-
ical decision-making processes. Due to that fact, studies
(Altahat and Atan 2018; Lee and Ha-Brookshire 2018)
began to correlate the relationship between the impact
of an organisation’s ethical climate and its sustainable
performance.

Besides understanding the success factors/drivers/
metrics, certain strategies were highlighted in the lit-
erature to implement ethical sustainability; corporate
social responsibility (CSR) is one of the key strate-
gies often associated with successful implementation
of ethical sustainability. The definition of CSR can be
found in McWilliams and Siegel’s (2001, 117) work. Belas
et al. (2022) explore the relationship between CSR theo-
retical frameworks and the ethical factors on engineers’
attitudes towards achieving sustainability. In business
ethics, CSR offers more to ethical sustainability than
other similar sustainable strategies, including circular
economy (CE).

Under ethical business development, several prac-
tices can be seen across the research, but for ethi-
cal sustainability, ‘ethical investment’ is sought to be
the most popular practice among researchers. Simply
put, ‘ethical investment/responsible investment/socially
responsible investment’ is the investment made in eth-
ical practices, such as the development of human wel-
fare, regenerative capacities, community education, and
investment in pro-environmental technologies and pro-
cesses. Such investments are not constrained by profit
motives. Several studies can be seen in the literature on
different aspects of ethical investments; alternate terms

such as responsible investments or socially responsi-
ble investments are also employed. Although several
studies on ethical investments in general have been
reported, only a few studies specific to ethical sustainabil-
ity are available (Becker 2023; Richardson 2009; Uddin
et al. 2024). As discussed in the earlier parts of this
chapter, ethical sustainability improves the organisation's
reputation across value chain stakeholders. However,
making these ethical sustainability practices visible to
such potential stakeholders requires effective commu-
nication. There are several studies (Boiral et al. 2019;
Hamrouni et al. 2023; Parguel, Benoît-Moreau, and
Larceneux 2011) which provided specific guidelines
for the preparation and publication of such an ethical
communication report. In the literature, however, this
reporting is often referred to as CSR reporting or sus-
tainability reporting. Despite the reputation improve-
ment from ethical sustainability reporting, organisa-
tions can occasionally experience a negative impact from
activities commonly called ‘greenwashing’. According
to Bradford (2007) and Parguel, Benoît-Moreau, and
Larceneux (2011), greenwashing is defined as ‘tactics
that mislead consumers about a company’s environmen-
tal practices or the environmental benefits of a prod-
uct or service’. Such greenwashing by companies makes
the ethical reporting less significant and/or debatable.
To understand more about the concept of greenwashing
in ethical sustainability reporting, researchers (Horobet
et al. 2024) began to look at various associated prob-
lems.

In addition to the discussions on ethical sustainabil-
ity, one of the hottest topics that comes under ethical
responsibility in recent years is ‘digital responsibility’.
After the intervention of Industry 4.0 technologies (such
as blockchain, big data, IoT, AI) in a company’s daily
operations, there is an increased risk of misusing the
collected data. Throughout the process of Industry 4.0
implementation in a company’s activities, numerous data
were collected from both people and materials involved.
Sometimes, such data is too confidential to show publicly;
therefore, the organisation or person responsible for han-
dling such data should adhere to a strict ethical policy
to protect this data from the public. Several studies (Cri-
celli et al. 2024; de Mendonça Santos and Sant’Anna 2024;
Matarneh et al. 2024) admit that Industry 4.0 technolo-
gies are the main driver for the implementation of sus-
tainability in any organisation, regardless of its applica-
tion. This also includes the ethical dimension of sus-
tainability, Hermann, Hermann, and Tremblay (2021)
listed several benefits of having AI-based equitable R&D
where injustice, inequalities, and discrimination were
monitored and reported. However, such studies are in
the initial stages. A strong business model is needed to
<figure><img src="image_98.png" /></figure>

manage and optimise the integration of technologies in sustainability implementation by addressing all pillars of sustainability, including financial, environmental, legal, social, and ethical. Apart from the above-discussed topics, few attempts were made to explore the impact of ethical sustainability with the application context, including design (Chan 2018; van Gorp 2007), supply chain (Agyabeng-Mensah et al. 2023; Blowfield 2000), and education (Christensen et al. 2007).

As ethical sustainability is a relatively new and less explored topic of sustainability, there are several opportunities for future research. Among these, ethical leadership for sustainability is a topic worth exploring. Future studies can establish the relationship between the moral theory of business ethics with stakeholder theory and relational management theories. In addition to top leadership, it is necessary to understand employees' perceptions on implementing and promoting ethical sustainability in companies. Legislation and policies remain the major obstacles for implementing ethical sustainability, so initiating new policies under circular economy and other sustainable supply chain approaches specific to ethical sustainability will help practitioners with their transition. Such studies will help researchers identify the impact of 'ethics of care' in sustainability implementation. In addition to leadership, there is room to analyse the behaviour of employees in an ethical climate or in a multidimensional organisational commitment. Further, this analysis can be expanded with an understanding of the connections between organisational sustainability performances. In terms of sustainability performance linked to ethical sustainability, no sustainability indicator currently focuses on ethical investments. There is a need for ethical sustainability indicators that can measure ethical investment footprints such as carbon footprints to measure environmental performance. Therefore, there is a need for regulatory and legal reforms to track and to improve ethical investment footprints. Communication of indicator outputs to different stakeholders through ethical sustainability reporting must be less biased. More studies are needed to check and identify 'greenwashing' by the company during reporting. It is also important to study the relationship between sustainability ratings offered by external sources with 'greenwash' activities. To identify such greenwash activities globally, Industry 4.0 technologies were integrated into the operations to increase transparency. To facilitate this process, a more open science approach is needed that will be limited to essential data but, at the same time, should make some potential data accessible to different value chain actors to improve collaboration opportunities and to move science forward. To balance these things, researchers need to develop more standards for digital responsibility such

as certification as discussed in previous studies (Anshari et al. 2022; Lobschat et al. 2021). By addressing these existing gaps in ethical sustainability, it is possible to accelerate the process of achieving several global goals for sustainability, including SDGs, ESGs, net zero, and decarbonisation. To understand more about ethical sustainability, some key studies (Consolandi et al. 2009; Guerci et al. 2015; Parguel, Benoît-Moreau, and Larceneux 2011; Richardson 2009; Schaltegger and Burritt 2018) highlight primary topologies such as ethics, sustainability, social sustainability, digital responsibility, and so on.

### 4.6. Forecasting<sup>40</sup>

Forecasting is a critical aspect of operations and supply chain management, as forecasts are the foundation for all other business decisions. This includes decisions such as which markets to pursue, which products to produce, how much inventory to carry, and which sources of supply to use. Forecasts are the input to decisions in inventory management (Section 2.3), planning (Section 2.14), new product and service development (Section 2.18), sourcing and procurement (Section 3.5), and project management (Section 2.16), among others. Whenever a decision is made it is always based on a forecast – whether a formal (systematic/statistical) or informal (judgmental) forecast. Forecasts aid operational, tactical, and strategic decisions within organisations and enable consideration of associated uncertainties. Poor forecasting results in incorrect business decisions and leaves the organisation unprepared to meet future demands. The consequences can be very costly in terms of lost sales and may even force a company out of business.

It is important to differentiate between an *actual forecast* – an objective evaluation of how the future may unfold – and a *target* or *goal* which is the desirable future position. In practice, it is advisable to start with an objective formal forecast and then plan towards achieving set objectives given that forecast. One of the most common approaches to forecasting involves the extrapolation of established patterns in sets of time-ordered observations that are recorded in fixed frequencies forming *time series*.

A popular time series forecasting family of models is exponential smoothing (usually acronymised as ETS or ES). While originally developed to predict stationary time series (i.e. where there is no trend or seasonality), later developments allowed its extension to include additive or multiplicative forms of trend and seasonality (Gardner 2006; Hyndman et al. 2008). Autoregressive Integrated Moving Average (ARIMA) is also popular for time series forecasting (Box and Jenkins 1976). It involves regressing current values against past values and past errors once the time series has been rendered
<figure><img src="image_99.png" /></figure>

stationary through transformations and differencing. To
select between the many available exponential smooth-
ing models or ARIMA models, automatic selection cri-
teria (such as information criteria that balance perfor-
mance and complexity) have been proposed (Hyndman
and Khandakar 2008; Petropoulos and Siemsen 2023).
One challenge related to time series forecasting is the
lack of observations needed to make such extrapolations.
This problem is exacerbated by established Enterprise
Resource Planning (ERP) systems which often limit the
available history to only 3–4 years of data.

Instead of simply using past values of the variable of
interest to produce forecasts, regression-type approaches
to forecasting are based on relationships across many
variables. As an example, the forecast for the demand
of a particular product (the variable of interest) could
be a function of the types of promotion that the com-
pany is running in the next periods and the weather
(the predictors or external regressors). Utilising vari-
ables that are ‘internal’ to the company (such as promo-
tions, marketing, price, availability, supply chain condi-
tions) is straightfoward as accurate predictions for these
predictors can be obtained; and such information can
enhance the accuracy of the forecast of the variable of
interest. Other ‘external’ variables (such as macroeco-
nomic indicators, competitors conditions) may be also
used, but such predictors will be by definition harder
to forecast. Popular regression approaches include ordi-
nary least squares regression models, ordinal, logistic,
Poisson, negative binomial regression models and gen-
eralised linear models. Extensions for ETS and ARIMA
(namely ETSx and ARIMAx respectively) also allow for
the consideration of such predictors.

Apart from formal/systematic approaches to forecasting, judgment can also be used in many facets of forecasting for operations and supply chain management. Reasons for using judgment include lack of data, expert information/knowledge not captured in hard data, and lack of expertise or systems to produce formal forecasts. However, when judgmental forecasting is practised, users need to be aware of associated biases, that include anchoring, availability, recency, hindsight, and confusion of noise with patterns (Goodwin 2017). It is common that a hybrid approach is usually implemented: a formal forecast is initially produced (usually based on time-series forecasting approaches) and then judgmental adjustments are made on that formal forecast. Research has shown that such adjustments are common, usually upwards, and often have negative forecast-value added (Fildes et al. 2009; Franses and Legerstee 2009). Finally, judgment may be also applied in other stages of the forecasting process, such as model selection (Petropoulos et al. 2018) or model parameterisation. For overviews

on the use of judgment in forecasting, see Lawrence
et al. (2006) and Fahimnia, Sanders, and Siemsen
(2020).

Forecasting is also needed when deciding what new
products/services to introduce, the need to build a new
facility, expanding in new markets, and other long-
term/strategic plans or technological aspects of forecast-
ing. To this direction, suitable statistical tools include
Bass and other diffusion models (Meade and Islam 2006).
Structured judgmental approaches that are based on
(small) groups of experts are also popular and have
shown to produce good results to this direction. Popular
expert knowledge elicitation methods include the Del-
phi method (Rowe and Wright 1999), structured analo-
gies (Green and Armstrong 2007), prediction markets
(Wolfers and Zitzewitz 2004), and interaction groups
(Van de Ven and Delbeco 1971).

One major lesson-learned through the research and
practice of forecasting is that future patterns will never
perfectly follow what happened in the past. In other
words, identifying and applying a single ‘optimal’ model
may not be possible (or relevant) when dealing with
the future. Instead, we can combine the forecasts from
multiple models/experts. Forecasts combinations (even
in their simplest forms) have shown to significantly
improve forecasting performance and minimise uncer-
tainty. Claeskens et al. (2016) offer a possible explanation
on why the performance of forecast combinations is bet-
ter than that of the individual forecasts. Forecast combi-
nations can be applied across multiple formal forecasts or
even to aggregate multiple judgments through concepts
such as the ‘wisdom of the crowds’ (Surowiecki 2005). For
an excellent review of forecast combinations, we direct
the reader to the article by Wang et al. (2023).

Time series data are often organised in hierarchical
structures that follow the operations and data structure
of a particular organisation. For instance, in a retail set-
ting, the bottom-level nodes of such an hierarchy could
be the sales for each stock keeping unit (SKU) at each
store; whereas aggregate levels could include total sales
of each SKU across multiple stores, total sales of a single
store, total/SKU sales within a region, sales for particular
product categories, and others. Forecasts can be pro-
duced with the methods described for any/all levels of
such a hierarchy. Implementing decision making using
the forecasts of each level independently would lead to
misalignments of decisions across the hierarchy as fore-
casts produced at different levels generally do not sum up.
One way around this challenge would be for forecasts of
a particular, single level to be propagated to other (upper
or lower) levels via suitable summation or disaggrega-
tion. Alternatively (ideally), forecasts across many levels
may be combined towards producing reconciled forecasts
<figure><img src="image_100.png" /></figure>

across the hierarchy. Such reconciled forecasts are usu-
ally more accurate and allow for aligned decision making.
For an overview of hierarchical forecasting and forecast
reconciliation, see Athanasopoulos et al. (2020, 2024).

The products of the forecasting process could be point
(mean) forecasts, prediction intervals (given a confidence
level), probabilistic distributions, and/or scenarios. In
any case, the forecasting process majorly benefits from
ongoing monitoring of the forecasting performance on
out-of-sample sets of observations. Towards this, per-
formance measures can be used. Popular point-forecast
measures include the mean absolute error (MAE), the
root mean squared error (RMSE), the mean absolute
percentage error (MAPE) which is very popular in prac-
tice, the mean absolute scaled error (MASE) which
has better theoretical underpinnings (Hyndman and
Koehler 2006; Koutsandreas et al. 2022). Popular mea-
sures of the estimation of uncertainty include the interval
score (IS), the pinball score, the continuous ranked prob-
ability score (CRPS), and the energy score (Gneiting and
Raftery 2007). Beyond performance measures, we also
recommend the evaluation of forecasts on their utility in
practice (Yardley and Petropoulos 2021).

We direct the reader to the encyclopedic arti-
cle by Petropoulos et al. (2022) for a comprehensive
review of the forecasting field. The textbooks by Hyn-
dman and Athanasopoulos (2021) and Ord, Fildes,
and Kourentzes (2017) offer detailed presentations of
state-of-the-art methods and approaches to forecast-
ing, while Vandeput (2021) presents a data-science per-
spective to supply chain forecasting. It is also worth-
mentioning that the forecasting community is very active
in the open-source software space; *forecast* (Hynd-
man et al. 2024), *smooth* (Svetunkov 2022), and *hts*
(Hyndman et al. 2021) are three notable packages for
R statistical software. Finally, we cannot overlook the
impact AI will have on revolutionising forecasting. AI
will enable more accurate predictions through the analy-
sis of vast datasets and real-time information, identifying
patterns and trends that were previously undetectable.
The predictive capabilities of AI will allow businesses to
make more informed decisions, reduce risks, and opti-
mise their strategies for future growth.

**4.7. Innovation**<sup>41</sup>

Innovation at the OM-SCM intersection entails manage-
ment of technology and development of new products,
services, and processes that link operational processes
within the firm (e.g. R&D, marketing, manufacturing,
etc.) with the supply chain (e.g. suppliers and customers).
Advancement of technologies, such as information and
transportation technologies, has improved the speed,

transparency and efficiency of transportation of goods
globally. Emerging technologies, such as artificial intel-
ligence, will inevitably prompt firms to innovate within
and across the supply chain. Technological innovation
within the firm can be push- or pull-, often realised from
new product or service development (Section 4.8).

New product development (NPD) aims at the design
of new products and/or refinement of existing products
with respect to technological development of compo-
nents. NPD decisions impact how suppliers are man-
aged, be through strategic partnerships (e.g. early sup-
plier involvement in design), or minimum involve-
ment (e.g. outsourcing). Outsourcing decisions in NPD
lead to a certain degree of supplier-buyer interdepen-
dence, that can vary between arm's-length relationship
(e.g. off-the-shelf components) and strategic partner-
ship (e.g. co-development of new components); see also
Section 3.4. Process innovation typically relates to tech-
nology changes and adoption in production processes
(Carrillo and Gaimon 2004; Lee, Swink, and Pande-
jpong 2011), administrative processes (Kim, Kumar, and
Kumar 2012), and business processes (Girotra and Nets-
sine 2014).

Anchored in the marketing literature, new service
development (NSD) refers to the development of offer-
ing(s) not previously available to a firm's customers, with
focus on customer and market orientation, internal pro-
cess organisation, and external networks (Ordanini and
Maglio 2009). NSD includes topics, such as organising for
NSD, NSD processes and stages, critical success factors
and performance measurement, customer involvement,
new service strategy, and new service design (Papas-
tathopoulou and Hultink 2012). As new services are
developed, the firm must continuously sustain its NSD
competence (Menor and Roth 2008).

An innovation pursued by many firms is on prod-
uct platforms, where a stream of derivative products can
be efficiently developed, hence increasing commonality
sharing among product variants (Kim and Moon 2017;
Muffatto and Roveda 2000). A notable example is Volk-
swagen Group that applies platform strategy to sup-
port its brand strategy for cost leadership (e.g. Skoda
Oktavia, Seat Leon, and VW Golf) and differentiation
(e.g. Audi TT and A3). The platform thinking is extended
to VW Group's manufacturing plants as well as ser-
vice dealerships around the world. Adopting a platform
strategy enables firms to develop customised products
and services to gain from economies of scale and scope
(Magnusson and Pasche 2014), often engaging in joint
development of product and services (Jagstedt and Pers-
son 2019); see also Section 2.18. Popular streams of lit-
erature on mass customisation (e.g. Fogliatto, da Silveira,
and Borenstein 2012; Salvador, de Holan, and Piller 2009)
<figure><img src="image_101.png" /></figure>

and postponement (Choi, Narasimhan, and Kim 2012) pose rich debate on the implications of NPD and NSD that are tailored to customer choices vis-à-vis optimal differentiation points in the supply chain (Olhager and Van Donk 2024).

Firms have to constantly innovate to stay competitive, and as such must consider how to revitalise their business models, by delineating how firms do business (Zott, Amit, and Massa 2011) and how new ideas and technologies are commercialised. Business model innovation paves the way for firms to acquire corporate transformation and renewal (Demil and Lecocq 2010). Increasingly, many manufacturers are embarking on digital servitisation (DS) as a strategy to compete through value propositions that integrate products with development of services and software systems into the offerings (Hsuan, Jovanovic, and Clemente 2021; Kohtamäki et al. 2019; Vendrell-Herrero et al. 2017). DS bridges a firm's internal processes (e.g. NPD, NSD, manufacturing, IT) with the supply chain (e.g. value propositions and activities that go into the customer's processes). Depending on the level of innovativeness, DS can be quite complex in terms of configuring products that are coupled with a wide range of services and software offerings. The question pounded by many manufacturers is on how to manage such complexity and how to strike the balance between innovation and standardisation, which is not trivial, as there are endless strategic options. While innovation prevents imitation from competitors, standardisation enables flexibility and gains from economies of scale and scope. As every manufacturer has its own strategic goals to stay competitive, there is no one-size-fits all strategy and there is no guarantee for success because balancing standardisation and innovation considers a wide range of tradeoffs that are specific to each manufacturer (Section 2.21).

One approach to manage complexity is through modularisation strategies, where complex systems are decomposed into smaller pieces with well-defined interfaces so that each piece can be dealt with independently and yet operate together as a whole (Baldwin and Clark 2000). Modularity enables customisation and increased variety of offerings (Mikkola 2003) through mixing-and-matching (Sanchez and Mahoney 1996), commonality sharing (Bask et al. 2011), substitutability (Mikkola 2006), upgrades (Kamrad, Schmidt, and Ulku 2013), and more. Innovation can be achieved in various ways, such as in the form of modular innovation where it can be scaled up and substituted through upgrades of system families in platform-based designs. For example, by decomposing DS business model into product, service, and software architectures and respective resources, it is possible to determine the standard and specific offerings for creating the desired integrated

configurations (Hsuan, Jovanovic, and Clemente 2021). Such process identifies the unique and generic resources required for innovation and standardisation respectively.

Modularity can provide means for innovation and conceptualised as to how components can be put together for the optimal configurations. Through configuration, firms are able to get an overview of their capabilities and resources required (e.g. physical assets, operational, and human resources) vis-à-vis the competitive landscape and viability of supply chain resilience. Modular configurations typically are comprised of standard elements, hence low level of innovativeness. Conversely, integral configurations have high levels of innovativeness. As such, spectrums of modularity strategies exist along product (modular and integral), service (basic and advanced), and software (open and proprietary) dimensions, ranging from simplest (modular product, basic service, open software) to the most complex configuration (integral product, advanced service, proprietary software).

Modularisation of physical products is conceptualised in terms of product architectures, ranging between modular and integral. Modular product architectures enable flexibility in platforms to create product variety, rapid introduction of technologically improved products as well as outsourcing decisions. Integral product architectures, on the other hand, aim at maximum performance and craftsmanship where knowledge sharing and interactive learning are enhanced (Mikkola 2006). In modularisation of services, service architectures act as enablers of service agility, that is, to respond rapidly and effectively to changing market demands (Voss and Hsuan 2009). Basic service systems typically offer generic services that can be offered to all customers cheaply (e.g. help desks, call centres, and service kiosks). Advanced service systems, by contrast, can be expensive as they are developed and tailored to specific customers' needs (e.g. R&D-oriented services, professional consulting, and high-tech remote monitoring services). Application of service modularity is increasing across industries, including, e.g. healthcare (de Blok et al. 2014; Peters et al. 2020; Vähätalo and Kallio 2015), tourism (Avlonitis and Hsuan 2017), logistics services (Bask, Merisalo-Rantanen, and Tuunanen 2014; Yurt, Sorkun, and Hsuan 2023), and education (Sorkun, Yurt, and Hsuan 2022). In software systems, there are modular open digital architectures containing high data homogeneity and accessibility (e.g. blockchain platforms) as well as integral proprietary architectures characterised with heterogenous data and closed access (e.g. management of sensors systems).

Innovation within a firm and across the supply chain creates research opportunities, especially those that are
<figure><img src="image_102.png" /></figure>

inter-disciplinary, both in methodologies and theoretical development (Carrillo, Druehl, and Hsuan 2015). With increasing focus on sustainability, an increased effort is put on NPD and NSD on design-for-sustainability that has great focus on environmental and social responsibility, that inevitably fosters the emergence of sustainable supply chains. Such transitions entail the renewal or development of new business models, where innovation at OM-SCM interface provides the foundation for competitiveness. In NPD, design for sustainability has great focus on environmental and social responsibility, which links to sustainable supply chains (be linear or circular), where end-of-life products are recycled, remade, and reused (ElMenshawy, Ülkü, and Hsuan 2024; Ülkü, Skinner, and Yıldırım 2022). It incorporates concepts such as design for environment, life cycle analysis, lean product designs, and design for disassembly (Section 4.3).

Innovation will continue to advance and shape our world beyond our imagination, and it is at the heart of all organisations. Its success (and failure) is dependent on how the supply chain is structured and how actors' relationships are managed (Section 3). Applying inter-organisational theories to NPD (transaction cost analysis, principal-agent theory, network theory, and resource-based view) adds rich insights into how to structure a supply chain collaborating organisations and how to manage a particular structure (Halldórsson, Hsuan, and Kotzab 2015; Halldorsson et al. 2007, Section 3.6). The book by Moreira, Ferreira, and Zimmermann (2018) offers a collection of key topics on innovation and supply chain management. Refer to Baldwin and Clark (2000) and Mertens et al. (2023) for the foundations of product modularity and a comprehensive review of this field, respectively. Finally, as interdisciplinarity research continues to evolve in the field of servitisation, Kim, Lim, and Hsuan (2023) provide an overview of servitisation and product-service systems literature in Industry 4.0 and outlines a research framework for research on servitisation for innovation and circular economy.

## 4.8. Technology management

### 4.8.1. Adoption⁴²

Technology adoption studies in the OM/SCM literature examine why and how technology is first adopted and – often – how it is subsequently diffused in an organisation (with the term ‘assimilation’ used to describe both phases together). A simple, yet very useful way to categorise technology adoption research studies is by distinguishing whether they examine how an organisation (primarily companies but also non-business organisations) or an

individual (employees in said organisations) adopt technologies. We will use this categorisation to structure the main part of this section.

At the organisation level, many OM/SCM researchers have used the innovation diffusion theory (IDT; Rogers 1962), a widely recognised theory which originates from sociology and seeks to explain how new advancements spread throughout a specific group. IDT has been applied in various operational contexts, such as in the adoption of supply chain service platforms (Hong et al. 2021), and mobile supply chain management systems (Chan and Chong 2013), and the implementation of drone technologies (Ali et al. 2024). IDT is very often used in conjunction with the TOE (technology-organisation-environment) framework, including in the examples listed above. TOE – even though not technically a theory – provides a lens to examine the different types of factors influencing an organisation's views towards adoption. In manufacturing, for example, it has been used to examine whether different determinants act as enablers or inhibitors to RFID adoption (Wang, Wang, and Yang 2010) or the adoption of AI-empowered industrial robots (Pillai et al. 2022).

Another popular theory in relevant literature has been institutional theory; this is driven by the fact that organisations adopting technology operate in institutional environments that might influence their behaviour towards adoption (Lai, Wong, and Cheng 2006). Saldanha et al. (2015) used institutional theory when studying the implementation of supply chain technologies to show that there is a need to reassess mental models developed in the West as they might not consider the institutional environments of emerging markets. Like IDT and TOE, institutional theory has also been used in conjunction with other theories, such as resource-based view, to demonstrate how firms can benefit from both external and internal technology adoption (and their interaction) to improve operational performance (Zhang and Dhaliwal 2009) or with contingency theory to study how black swan external contingencies (like pandemics) impact technology adoption (Tiwari, Sharma, and Jha 2024). Contingency theory has also been used to investigate the moderating role of Industry 4.0 (I4.0) technologies on the relationship between lean production and operational performance improvement (Tortorella, Giglio, and van Dun 2019). Finally, researchers have examined how learning and knowledge might influence adoption and how firms adapt and succeed in rapidly changing technological environments. Mishra, Singh, and Papadopoulos (2024) conducted an in-depth case study, integrating dynamic capability and organisational learning theories, to explore how digital orientation supports the adoption of digital technologies for enhancing innovation.
<figure><img src="image_103.png" /></figure>

Even though it is organisations that adopt the tech-
nologies, one must look into the behaviour of individual
employees to understand better the technology adoption
phenomenon (Morris and Venkatesh 2010). The unified
theory of acceptance and use of technology (UTAUT)
proposed by Venkatesh et al. (2003) is one of the most
frequently used theories and researchers have used it –
among others – to examine RFID adoption in the health-
care supply chain (Chong et al. 2015) and AI acceptance
in SCM (Hasija and Esper 2022). Recently, van Dun and
Kumar (2023) combined UTAUT and social exchange
theory to explore the social enablers of employee accep-
tance of I4.0 technologies. Another established informa-
tion systems theory, the technology acceptance model
(TAM; Davis 1989) has also been used in operations and
supply chain management; for example, Brandon-Jones
and Kauppi (2018) used it to examine technology adop-
tion in e-procurement. These popular theories have been
used together with others to study technology adoption
such as by Kamble, Gunasekaran, and Arha (2019) who
explored blockchain adoption in supply chains combin-
ing TAM and the theory of planned behaviour. Interest-
ingly, there are cases where theories initially developed to
study technology adoption at the individual level, are also
used at the organisational one, such as Khan, Mehmood,
and Soomro (2024).

The studies listed so far in this section make use
of established theories, that often come from elsewhere
rather than the OM/SCM discipline. Due to the nature
of the phenomenon under consideration and the strong
focus on technology itself, many of the theories pre-
sented earlier were initially proposed or are extensively
used by sociology and information systems scholars.
However, OM/SCM scholars have occasionally exam-
ined technology adoption without the explicit use of
a theory. In a recent example, Margherita and Brac-
cini (2024) studied the tensions that arise in Industry 4.0
adoption in lean production systems. Maghazei, Lewis,
and Netland (2022) investigated drone applications in
warehousing, manufacturing and inventory management
by looking at the relationship between adoption inten-
tion and the actual adoption. Leoni et al. (2022) looked
at how manufacturing firm performance is affected by
AI, knowledge management processes and supply chain
resilience as well as their mutual relationships. Another
empirical example is on adopting data provision tech-
nologies to explore taxi drivers' routing decisions (Lu
et al. 2023) showing that both operational efficiency and
customer satisfaction can be improved using technol-
ogy. Lastly, the impact of after-sales services on one's
intentions to adopt a new technology has been explored
empirically using models developed by Kundu and Ram-
das (2022). On the other side of the value chain, the

OM literature has examined cases where technology is
adopted for the design of new products rather than the
management of relevant operations. For example, Singh,
Kemerer, and Ramasubbu (2021) studied the component
selection and commonality problems to understand their
impact on manufacturing and supply.

At the same time, one can find recent studies which
have chosen to navigate away from well-established the-
ories, and apply less frequently used ones, to explore
the role of technology adoption in managing operations
and supply chains. For example, componential theory of
individual creativity and valence theory – relatively new
theories to the OM/SCM area – are used by Verma and
Singh (2022) to explore employees’ intentions to work in
AI-based hybrid environments, concluding that human
factors are as important as the benefits provided by the
technologies for successful human-robot collaboration.
In supply chains, where multiple actors are involved
by nature, Sternberg, Hofmann, and Roeck (2021) used
midrange theorising to understand why blockchain tech-
nology has seen very few successful implementations in
interorganisational settings. Similarly, new supply chain
specific theories, like the supply chain practice view, has
been argued to be more suitable for the study of the
adoption of digital procurement (Kosmol, Reimann, and
Kaufmann 2019). Johansson et al. (2024) address contra-
dictions between lean production systems and advanced
digital technology adoption by using paradox theory.
Finally, Liu, Fang, et al. (2024) developed a framework
to investigate how supply chain resilience is affected by
blockchain adoption, building on organisational infor-
mation processing theory. They also highlight the role of
transformational supply chain leadership in blockchain
adoption.

Even though we focussed on the ‘how’ and ‘why’ ques-
tions of technology adoption in our review, a noteworthy
stream of literature is often concerned with the *impact*
of adoption, i.e. how adopting a technology will affect an
organisation’s operations or supply chains. This can be
done via ex-post impact studies (measuring the impact
after solution deployment thus involving assessment of
actual gains) or ex-ante impact studies (estimating the
impact of technology adoption before the actual adop-
tion thus providing insights into the expected gains). An
example of an ex-post study is the work by Mcafee (2002)
that aims to establish a link between technology adop-
tion and subsequent operational performance improve-
ment using a longitudinal case study. Similarly, an ex-ante
approach is taken in Lee and Özer (2007) where the value
of RFID is measured via its impact on supply chain vis-
ibility. Since impact studies are –to their vast majority–
context and technology dependent, the reader is referred
to Section 5 for some examples.
<figure><img src="image_104.png" /></figure>

We conclude by suggesting four research avenues technology adoption scholars might want to explore further in the OM/SCM discipline. Firstly, even though there exist theories that have proven to be suitable to study technology adoption phenomena over the years, the characteristics of some emerging technologies (see also Section 4.8.2) might make these theories less appropriate. As a result, new approaches and models to theorising might be required for a more detailed analysis of the subject matter. Similarly, researchers can explore the applicability of contemporary theories developed by academics in other social sciences and disciplines studying the impact of technology on organisations, especially those developed in sociology, psychology, economics and, of course, information systems. As technology advances, of particular importance is expected to be the role of people in the adoption and assimilation of different technologies, especially in those cases where human-technology collaboration is essential for the successful completion of a task. In OM settings in particular, where many activities and tasks take place in the physical world, the way people interact with technologies can be key to a successful adoption. Lastly, we have observed a lack of in-depth, longitudinal studies that cover both the pre-, during and post-adoption phases that would enhance our knowledge of the technology adoption phenomenon and provide a more holistic view of it.

### 4.8.2. Emerging technologies⁴³

Emerging technologies are often characterised as (radically) novel, fast-growing, and marked by high uncertainty and ambiguity (Bailey et al. 2019; Kapoor and Teece 2021; Rotolo, Hicks, and Martin 2015). Emerging technologies and their capabilities have long been at the forefront of attention for OSCM scholars⁴⁴ (e.g. Amoako-Gyampah and Meredith 1989), as well as practitioners and consultants (the ‘hype cycle’ framework developed by Gartner is a noteworthy example). Their strategic advantages, as well as their operational and economic impacts, have been extensively examined whenever a technology or a group of technologies emerges. For example, the advent of advanced manufacturing technologies (AMTs) in the early 1980s not only triggered a revolution in production but also offered OSCM scholars a strong basis for studying these then-emerging technologies in depth. Reviewing the existing literature on AMTs provides a reliable foundation for understanding their emergence and impact on industries. However, the unique characteristics of contemporary emerging technologies – many of which are associated with ‘Industry 4.0’ – necessitate a reassessment of established concepts and theories.

Industry 4.0 technologies, such as additive manufacturing, advanced robotics, artificial intelligence, autonomous vehicles, blockchain, cloud computing, drones, and the Internet of Things enable companies to sense their environment, analyse information, collaborate within and across firms, and enhance task execution (Mithas et al. 2022). This section highlights some of the key differences between AMTs and Industry 4.0 technologies, focussing on strategic considerations, adoption barriers, and buyer-supplier relationships. It then examines the unique challenges of Industry 4.0 technologies and suggests reassessing methodological approaches to their study. Lastly, it briefly touches on what it means to classify a technology as ‘emerging’, inviting reflection on the ontological and epistemological assumptions underlying these phenomena.

A review of publications on AMTs from 1986 to 1995 shows that strategic decision-making, planning, and economic evaluation were the most recurring themes in the early stages of research on AMTs, which were arguably emerging at the time (Amoako-Gyampah and Meredith 1989; Maghazei and Netland 2017). One of the concepts that stands out in the early literature on AMTs emphasises adopting a ‘strategic’ and ‘systemic’ view⁴⁵ of planning and implementation (Voss 1986). Similar strategic considerations are advocated for contemporary emerging technologies that can facilitate their integration and enhance their likely performance improvements (Dohale et al. 2022; Venkatesh, Raman, and Cruz-Jesus 2024). Given the technological interdependencies and ‘convergence’ characteristic of the digital era (Venkatesh, Raman, and Cruz-Jesus 2024), such systematic approaches may be even more crucial for implementing Industry 4.0-type technologies in practice. Despite the importance of maintaining a clear strategic perspective on new technologies, observations from a specific category of emerging technologies, as documented by Maghazei, Lewis, and Netland (2022), reveal a ‘shift’ from formal strategic and economic considerations to operational and supply chain concerns during early-stage assessments, with a focus on use case identification.

A key challenge with emerging technologies, which have limited real-life implementations, is identifying or predicting adoption barriers before it becomes ‘too late’ for practitioners. This challenge also applies to OSCM scholars, who, due to their ‘process logic’ (see Lewis 2020), may overlook how an emerging technology unfolds beyond its initial phase. Although primarily ex-post analyses, past studies have well-documented barriers to adopting AMTs, such as short-term business strategies, poor strategic orientation, and incomplete risk assessment (Kotha and Swamidass 2000;
<figure><img src="image_105.png" /></figure>

Lefley 1994). Additional challenges include complex cost-benefit analyses, liquidity constraints, and the need to justify costs and identify economic rationales for the new technology (Mithas et al. 2012a, 2012b; Mithas, Tafti, and Mitchell 2013; Sambasivarao and Deshmukh 1995; Spanos and Voudouris 2009). Barriers also arise when the selected technology misaligns with a firm's assets, including resources, capabilities, and competencies (Boyer et al. 1997; Meredith 1987; Sohal et al. 2006). Other obstacles include inexperienced project managers or teams, insufficient support from top management, and a lack of technical expertise (Bessant 1985; Co, Eddy Patuwo, and Hu 1998; Dimnik and Johnston 1993; Meredith 1987; Sambasivarao and Deshmukh 1995; Small and Chen 1997; Small and Yasin 1997).

While it is likely that many of these identified barriers remain applicable to contemporary emerging technologies, one challenge expected to intensify barriers to adopting emerging technologies and fully realising their benefits involves uncertainty and the rapid pace at which these technologies are changing (cf. Moore's law⁴⁶). Bessant (1985, 98) argues that in situations where 'the rate of change in technology is increasingly fast', the learning curve may lag behind, which can also exacerbate the 'problem of adaptation'. More broadly with a systemic view, 'the more profound and far-reaching the potential restructuring, the longer the time lag may be between the initial invention of the technology and its full impact on the economy and society' (Lewis 2020, 91). One way to overcome obstacles stemming from the fast-evolving nature of emerging technologies is exemplified by IKEA's initiative to pilot and scale drone usage in its warehouses. They developed a new governance structure for experimentation, adopted a more dynamic approach to managing buyer-supplier relationships, and transitioned toward a use case-driven adoption model (Netland, Maghazei, and Lewis 2023). This initiative has thus far resulted in 250 fully autonomous drones flying and counting inventories (Kell 2024).

One of the areas that is influencing the analysis of emerging innovations concerns with changes in buyer-supplier relationships. Traditional buyer-supplier relationship has been often characterised into two extreme of 'adversarial' and 'collaborative' modes (Imrie and Morris 1992). The adversarial relationship is known as an exit, antagonistic, or arm's-length contractual relationship, whereas the collaborative relationship is referred to as a voice, cooperative, or obligatory contractual relationship (Gules and Burgess 1996). Improving buyer-supplier relationships is identified as a success factor for implementing new technologies, as well as overall competitiveness of the company (Gules and Burgess 1996;

Lamming 1986). Evidence shows that new forms of buyer-supplier relationships are being exercised by companies to better accommodate and respond to emerging technologies' characteristics and (re)structure new type of commitment, adaptability, flexibility, responsiveness, conflict management, appraisal mechanisms, and hardware-software integration. When the technological complexity and uncertainty increase, companies are more likely to increase collaboration with technology suppliers (Abd. Rahman, Bennett, and Sohal 2009). More recently, Kurpjuweit and Wagner (2020) advocate the implementation of new 'outside-in' programmes between startup suppliers and established firms and introduce a subset of such programmes and name them 'startup supplier programs'. Startup supplier programmes represent a new paradigm shift to see startups as potential suppliers as opposed to the traditional views that look at such collaborations through the lens of corporate venturing.

In addition to the identified differences in established OSCM concepts, future research could further explore the 'dark side' of emerging technologies, including their implications for behavioural operations, cybersecurity risks, sustainability impacts, user acceptance, and their effects on individuals and broader society (see an exhaustive list of research questions for future studies in Mithas et al. 2022). For example, Choi et al. (2022, 24) focus on human-machine conflicts in terms of worker welfare, privacy, security, health problems, and legal protection and (re)introduce sustainable social welfare 'to highlight that only by considering human welfare would the achieved social welfare be long-lasting'.

OSCM scholars may need to reassess their methodological approaches when grappling with uncertain, rapidly changing, and interrelated technologies (Bailey et al. 2022). This need aligns with the growing interest among OSCM scholars in employing field experiments to analyse emerging technologies, such as Kwasnitschka, Franke, and Netland's (2024) investigation of the effects of feedback using smartwatches, as well as field-testing-sometimes combined with prototyping-of technologies like live-streaming virtual reality (Netland et al. 2023) and process mining (Lorenz et al. 2021). Furthermore, OSCM scholars could embrace multidisciplinary and multi-methodological approaches (e.g. see Bai, Li, and Xiao 2022, who combine empirical data with analytical modelling) and reconsider new units of analysis (Choi et al. 2022; Gaimon, Hora, and Ramachandran 2017; Lewis 2020; Venkatesh, Raman, and Cruz-Jesus 2024).

After all, defining 'emergence' presents ontological challenges due to the evolving nature, boundaries, and
<figure><img src="image_106.png" /></figure>

diverse applications of these technologies (e.g. drones in humanitarian logistics versus photography), their contextual variability (e.g. specific sectors or regions), and socio-technical aspects (e.g. technology in isolation or broader contexts). These characteristics may also create epistemological challenges in knowledge acquisition and validation, managing bias and uncertainty (often amplified by 'hype'), and balancing prescriptive versus descriptive knowledge. Furthermore, scholars' timing in engaging with such technologies and their perceptions of their emergence (e.g. perceived newness) can influence epistemological assumptions and inform different research designs.

### 4.8.3. Systems⁴⁷

Information Systems (IS) refer to a set of technologies that span organisational boundaries and connect customers and supply chains (Núñez-Merino et al. 2020). They play a vital role in operations and supply chain management (OM/SCM), serving as the foundation for information, cash and physical flows to move within the supply chain processes (Núñez-Merino et al. 2020), as well as data-informed decision-making. In disruptions, IS could facilitate the continuity of these flows and create supply chain resilience. Therefore, the benefits of IS to OM/SCM could be summarised in coordination, enhancing production efficiency, responding to market fluctuations, adopting sustainable practices (Kouhizadeh, Saberi, and Sarkis 2021), agility, resilience, and competitive advantage. IS applications used in OM/SCM include for instance, the early Electronic Data Interchange (EDI), Computer Aided Manufacturing (CAM), Enterprise Resource Planning (ERP), and more recently Internet of Things (IoT), Blockchain, Cloud Computing/ERP, and big-data analytics and artificial intelligence (AI); see also Gilchrist (2016), Toorajipour et al. (2021), and Sharma et al. (2022).

IS equip organisations with capabilities to manage diverse data types, ranging from structured information like inventory levels to unstructured data such as customer insights and real-time sensor inputs from IoT. The incorporation of advanced technologies – including AI, Machine Learning (ML), Blockchain, and IoT – into organisations and supply chains significantly improves decision-making, transparency, and operational efficiency (Ivanov and Dolgui 2021a). Such data influences critical decisions in areas such as production planning, inventory oversight, logistics, and customer service. To maximise the potential of data, organisations must utilise specific analytical techniques tailored to each dataset's characteristics. This might involve employing AI and ML for predictive analytics, using Blockchain for secure transactions, or leveraging IoT for immediate

monitoring (Ali and Govindan 2023; Papadopoulos, Baltas, and Balta 2020). Effective systems management is essential for both operational and strategic choices; inadequate data analysis or mis-implementation of systems can result in inefficiencies, higher costs, and lost innovation opportunities. Therefore, organisations need to adopt appropriate advanced systems and associated data analysis methodologies to gain useful insights from the data and maintain competitiveness in an ever-changing business landscape.

Blockchain enhances security and transparency within supply chain management by facilitating decentralised systems crucial for tracking product authenticity. Analytical techniques such as hashing algorithms and consensus methods – like Proof of Work (PoW) and Proof of Stake (PoS) – are employed to uphold data integrity across blockchain networks (Fahim, Rahman, and Mahmood 2023). This approach proves advantageous particularly in industries that require transparency and traceability since blockchain aids compliance efforts while verifying product authenticity.

The Internet of Things (IoT) generates massive amounts of real-time sensor data across various areas such as manufacturing and logistics. Connected devices facilitate the monitoring of machinery, shipments, and environmental factors. Stream processing solutions such as Apache Kafka and Flink allow for real-time analysis of this data while predictive analytics models help anticipate machine failures and optimise maintenance schedules – thereby minimising operational disruptions (Cakir, Guvenc, and Mistikoglu 2021; Hernandez and Roberts 2020). In predictive maintenance scenarios, Markov models along with Hidden Markov Models (HMMs) analyse both historical trends and current sensor readings to enable proactive responses to equipment issues.

Time series data are essential for activities including demand forecasting, production scheduling, and inventory control. These datasets consist of sequential observations recorded at regular intervals typically managed through cloud-based Enterprise Resource Planning (ERP) systems where analytical models like Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) are commonly utilised (Box and Jenkins 1976; Shmueli and Lichtendahl 2016). For more complex forecasting requirements, hierarchical forecasting models can synthesise information from different levels, such as product categories or geographical areas, to maintain consistency throughout the supply chain (Athanasopoulos et al. 2024). Additionally, Vector Autoregression (VAR) models can analyse interdependent time series to enhance decision-making within interconnected systems (Kaytez 2020).
<figure><img src="image_107.png" /></figure>

Unstructured data, such as customer feedback or social media interactions necessitates, sophisticated analysis techniques like Natural Language Processing (NLP), which is often incorporated into Customer Relationship Management (CRM) systems. NLP methodologies such as Latent Dirichlet Allocation (LDA) alongside sentiment analysis reveal underlying patterns within extensive text datasets while evaluating customer sentiment; this information is valuable in refining product offerings and elevating service quality standards (Goodwin 2017; Zhou et al. 2023). By capturing consumer preferences effectively through these models businesses can inform their marketing strategies accordingly.

In complex environments where multivariate data involving multiple simultaneous variables are prevalent – a common occurrence in Product Lifecycle Management (PLM) and Supply Chain Management Systems (SCMS) – analytical techniques such as multivariate regression or logistic regression are frequently used to predict outcomes based on relationships between independent variables. For example, multivariate regression could forecast product quality influenced by factors like temperature or material type. Additionally, Principal Component Analysis (PCA) – a technique designed for dimensionality reduction – simplifies large datasets by isolating key influencing factors which aids process optimisation (Jolliffe 2002). Linear programming mixed-integer programming models also play a crucial role when optimising resource allocation transportation planning within supply chains ensuring effective management constraints surrounding capacity cost (Bertsimas and Tsitsiklis 1997).

Probabilistic modelling serves an important function when addressing inherent risks and uncertainties present in operation supply chains. Monte Carlo simulations are commonly employed to simulate risks associated with variable conditions, for instance in fluctuating demand supplier disruptions (Rubinstein and Kroese 2016). Bayesian Networks provide another probabilistic framework mapping risk factor relationships thereby assisting organisations develop robust contingency plans mitigating possible impacts on supply chains. These approaches prove invaluable in dynamic risk assessment-informed decisions reducing potential disruptions.

Each type of dataset – whether it be time series, unstructured, sensor-generated, multivariate, or probabilistic – demands specific analytical methods reinforced by suitable technologies and relevant modelling approaches. Time series datasets find efficient management through ERP systems utilising ARIMA hierarchical forecasting; unstructured datasets undergo processing via NLP sentiment analysis integrated CRM frameworks;

IoT-generated sensor outputs benefit from real-time analyses employing predictive modelling stream-processing infrastructures; multivariate sets leverage regression PCA optimisation strategies; finally probabilistic risk assessments utilise Monte Carlo Bayesian Networks. By fusing these methodologies IS enterprises and SCs can enhance their decision-making capabilities, mitigate risks, and bolster their overall operational efficacy/efficiency, and performance.

We would refer the reader to the seminal articles by Gunasekaran and Ngai (2004) for an early but comprehensive review of the IS role in OM/SCM, and the later articles by Daneshvar Kakhki and Gargeya (2019), Dutta et al. (2020), and Sharma et al. (2022) for an overview of systems on OM/SCM.

## 4.9. Performance measurement and benchmarking⁴⁸

Performance measurement and benchmarking are critical management processes, as they help organisations evaluate their efficiency, productivity, effectiveness, and overall success in achieving their strategic goals. In this chapter, we first review the main principles and tools, and then conclude by outlining the main challenges in their application.

### Performance measurement

Performance measurement involves the systematic collection, analysis, and reporting of information related to attributes of individuals, teams, processes, and organisations (Micheli and Mari 2014). For example, performance indicators can be deployed to assess the motivation of individual employees, the productivity of a team or function, the efficiency of a process, the financial results of an organisation, and the environmental impact of a network of firms. Measurement instruments range from highly standardised, quantitative performance indicators and targets to context-specific, qualitative case studies (Beer, Micheli, and Besharov 2022). Often, these tools are part of multi-dimensional performance measurement systems, such as the Balanced Scorecard (Kaplan and Norton 1996) and the Performance Prism (Neely, Adams, and Kennerley 2002).

The first measurement instruments focussed on financial and operational aspects of performance such as profitability, cost, efficiency and quality. Subsequently, performance measurement systems began to include also aspects related to resources (e.g. intellectual and social capital), and customers and stakeholders more widely (Neely, Adams, and Kennerley 2002), sometimes leading to the creation of measurement systems across firms. More recently, organisations have started to capture
<figure><img src="image_108.png" /></figure>

also aspects related to social and environmental out-
comes (Searcy 2016). This increase in scope has made
performance measurement more prominent within and
between organisations; this has also been facilitated by
the much increased availability of data thanks to tech-
nological advances and the ability for organisations to
collect data using a variety of sources. However, it has
also led to challenges, as further discussed in the latter
sections of this chapter.

Research has shown that the implementation of per-
formance measurement systems can lead to several pos-
itive consequences. These include monitoring core pro-
cesses, correcting deviations from intended trajectories
and results, identifying strategic opportunities and new
ways of operating (de Leeuw 2011; Koufteros, Vergh-
ese, and Lucianetti 2014), as well as improving alignment
and coordination (Mura et al. 2018) and linking strategy
with value creation (Melnyk et al. 2014). In the con-
text of inter-organisational networks, the introduction of
a supply chain measurement system can lead to oper-
ational performance improvement, lower information
overload, and higher supply chain integration (Maestrini
et al. 2018), and using collaborative performance indica-
tors to align partners in presence of performance-based
contracts has a positive effect on performance (Akker-
mans et al. 2019). At the same time, measurement instru-
ments have been found to negatively impact employee
motivation (Malina and Selto 2001) and lead to gam-
ing and an obsession with hitting targets (Franco-Santos
and Otley 2018; Melnyk, Hanson, and Calantone 2010).
This is particularly pronounced when considering short-
term targets such as quarterly or yearly ones, for exam-
ple, in relation to sales (as discounts may be applied to
hit quantity targets, although negatively impacting prof-
itability). Similarly, gaming can be used to explain the
lack of investment in longer-term projects, which would
not provide positive results in a short time frame, such as
the one to which financial rewards are tied (Gray, Micheli,
and Pavlov 2014).

These conflicting results have led researchers to
explore the dynamics triggered by the design, imple-
mentation and use of performance measurement systems
(Pavlov and Bourne 2011). For example, at the micro
level, recent studies show how these systems can both
reinforce and challenge perceptions of meaningful work
by influencing whether individuals can successfully carry
out their work tasks, identify the impact of their work on
service users, and have a valued voice in their interac-
tions with colleagues (Beer, Micheli, and Besharov 2022).
At the macro level, authors have challenged the tra-
ditional performance measurement paradigm founded
on control, objectivity and predictability (Bititci 2015;
Pavlov and Micheli 2023), and proposed alternative ones

rooted in complexity theory and that explicitly take
into account the increasing dynamism that characterises
organisational environments. In relation to management
practice, this means considering performance measure-
ment more as a way to influence, support and pro-
vide direction, rather than to control, determine, and
predict.

Benchmarking

Benchmarking is the process of comparing an organ-
isation's performance, processes, products or services
against those of competitors or industry leaders, as well as
comparing the performance of units, teams and individ-
uals within a single organisational context (Dattakumar
and Jagadeesh 2003). As such, benchmarking is often
supported by performance measurement tools, such as
key performance indicators (KPIs), and results in the
definition of performance targets and, more broadly, in
the identification of particular areas of strength (that
may act as the source of an organisation's competitive
advantage) or weakness.

Benchmarking can be classified into several types,
each serving different purposes (Anand and Kodali
2008):

(1) Internal benchmarking: Comparing performance across different departments or units within the same organisation. This type helps in identifying internal better / best practices that can be replicated in other areas.

(2) Competitive benchmarking: Comparing performance with direct competitors. It focuses on evaluating how well an organisation is performing against its rivals in the market.

(3) Functional benchmarking: Comparing similar func-
tions or processes with those of companies outside
the direct competitive landscape, often in different
industries. This type aims to find approaches and
practices that are new to a specific industry context.

(4) Generic benchmarking: Comparing operations and processes that are common across industries. It helps in identifying universal good/best practices that can be applied irrespective of the industry. Some are typically related to typical performance attributes (e.g. operational efficiency), whereas others are not (e.g. the design characteristics of supply chains affecting intrinsic resilience of systems).

Whatever the type, the deployment of benchmarking
processes typically has two main consequences:

* Target setting: comparing with other organisations and identifying best performers (often indicated as
<figure><img src="image_109.png" /></figure>

‘the benchmark’) enables the definition of perfor-
mance targets, intended as goals that are in line with
market conditions and expectations.

* Performance improvement: by comparing with competitors and industry leaders as well as with other functions, organisations can learn what works well and adopt new practices that can lead to significant improvements in performance.

The main intent of benchmarking is therefore to learn
from others (internal and external to the organisation),
improve and innovate processes, and enhance products
and services, which often leads to improved customer
loyalty and satisfaction, and profitability.

Challenges in performance measurement and
benchmarking

Despite their benefits, performance measurement and
benchmarking come with several challenges:

(1) Identification of what to measure (so called mea-
surand) and/or benchmark on: while some aspects
may be widely established and standardised across
organisations (e.g. how to calculate operational costs
or greenhouse gas emissions), many measurands
are defined differently depending on context or
approach adopted (e.g.organisational culture, inno-
vativeness). Importantly, measurands do not have to
be related to tangible results, but also to structural
(design of supply chains, as mentioned above) and
perceptual aspects (e.g. organisational reputation).
When measuring and comparing performance, it
is essential that the definition of measurands is as
unambiguous and consistently adopted as possible,
although this may not be always possible or appro-
priate (Gray, Micheli, and Pavlov 2014).

(2) Data quality and availability: Obtaining accurate, relevant, and timely data can be difficult. Poor data quality can lead to misleading conclusions and inef- fective decision-making.

(3) Cost and resource intensity: Implementing a com-
prehensive performance measurement and bench-
marking system can be resource intensive, requiring
significant time, effort, and financial investment.

(4) Maintaining relevance: performance measurement tools and benchmarks must evolve with the organisation and industry. Indicators, targets and benchmarks that were relevant in the past may no longer be applicable and should therefore be regularly reassessed and modified.

(5) Unintended behavioural consequences: perhaps the
main challenge in measuring and comparing per-
formance is related to the negative effects that these

processes may have on individual and collective
behaviours. The literature on this subject is vast
(see, e.g. Bevan and Hood 2006; Franco-Santos and
Otley 2018; Gray, Micheli, and Pavlov 2014), but the
risks of gaming, tunnel vision, and measure fixation
are still very high especially when there is pressure
on the achievement and demonstration of results (vs.
the adoption of a specific practice).

Conclusions

Performance measurement and benchmarking are essen-
tial tools for organisations striving to understand and
improve their processes, products and services. While
challenges exist, the benefits outweigh the difficulties,
making performance measurement and benchmarking
indispensable components of modern management prac-
tices. In this chapter, we have focussed on extant research
and practices: the sources cited are a good starting
point to appreciate the richness of the work under-
taken so far. In the future, these processes are likely to
become even more relevant, as the success of new tech-
nologies – including business analytics and AI-based
algorithms – will depend on the quality of informa-
tion generated by measurement systems and on organ-
isations’ capacity to effectively compare with others
and adopt new practices. Moreover, behavioural effects,
both positive and negative, are likely to be more pro-
nounced as measurement becomes more widespread and
includes aspects increasingly related to human motiva-
tion, perceptions, and performance (see, e.g. so called
‘Industry 5.0’).

4.10. Risk management and resilience⁴⁹

Risk can be defined as the probability of an event multi-
plied with the impact of that event, where risks can have
positive and negative implications (Aven 2016). We dis-
tinguish here between risks within an organisation, i.e.
internal risks, and risks emerging in the supply chain and
the wider environment of the organisation, i.e. external
risks. Literature mostly assumes that risks are generally
calculable. For instance, Chopra and Meindl (2021) illus-
trate implications of supply risks for inventory planning
(see also Sections 2.3, 2.4, and 2.14) based on data from
the past. Recent advances are the combination of his-
torical and real-time data to further optimise stochastic
supply chain processes (Wu et al. 2023). Traditional risk
management strategies often aim at preventing negative
implications of adverse high-impact events, i.e. when
negative risks materialise. An example are insurances
which only pay out in case of an event, or contracts
with suppliers that define repercussions in times of sup-
plier failure. In the past decades, companies have built
<figure><img src="image_110.png" /></figure>

complex global supply chains to benefit from comparative advantages, e.g. due to lower labour costs in developing countries, which has, however, introduced new risks because the geographical scope of supply chains has tremendously widened and companies have outsourced substantial parts of their production processes (Thun and Hoenig 2011). With increasing levels of uncertainty due to new types of risk and accumulating challenges, the nature and severity of disruptions are becoming more difficult to predict – especially for external risks – and standard risk management practices no longer suffice (Finger et al. 2022). This leads to a shift from risk management to resilience thinking. A resilient system is able to ensure the provision of its functions, even in the face of increasingly complex risks and uncertainties (Meuwissen et al. 2019).

In a ‘makeable’ world, which is harmonious and calculable, resilience is generally interpreted as robustness – aiming to keep systems in their status quo situation despite the occurrence of shocks. It is thus assumed that resilience can be ‘engineered’ (Ge et al. 2016). However, in the current risk environment, the interpretation of resilience moves beyond this narrow view of robustness and includes capacities to adapt and transform operations and supply chains as a means to deal with the increasingly complex shocks and stresses (Meuwissen et al. 2019). Adaptation refers to relatively small changes, e.g. in inputs, production and marketing. In contrast, transformation involves fundamental shifts in structures, processes, relationships, behaviours and values (Termeer and Metze 2019).

Which adaptations and transformations should businesses and supply chains aim for in the current risk environment? Here, the resilience attributes give guidance (Meuwissen et al. 2022; Reidsma et al. 2023). They are rooted in the generic principles of resilience as proposed by the Resilience Alliance (2010). The premise is that systems which are grounded in resilience attributes, such as redundancy and diversity of resources, have the individual and collective competences to respond, adapt and transform to deal with a multitude of economic, social and environmental disruptions (Meuwissen et al. 2019). De Steenhuijsen Piters et al. (2022) summarised the resilience attributes as ‘ABCD’ – representing agency, buffers, connectivity, and diversity. Developing resilience attributes in businesses and supply chains proves difficult due to various factors, such as human mental models which tend to focus on maintaining status quo, experts being educated mostly in improving efficiency, and a series of vested interests creating lock-in situations (Meuwissen et al. 2020). Besides, there is additional complexity as incorporating resilience attributes also requires innovations in institutions, governance mechanisms and

other systems of accountability, as well as changes in culture, individual behaviour and technology (Ingram et al. 2023).

Resilience at the operational (internal) and supply chain (external) level shows important interactions (Dubey et al. 2021; Kamalahmadi and Parast 2016) where a company can increase its overall resilience either through making its operations and/or its supply chain more resilient. At the same time, resilience of operations can also be enhanced through improvements in the resilience of the remaining supply chain, thereby creating synergies between operational and supply chain resilience. Take the example of sourcing (see also Section 3.5) and the resilience capacity of robustness. On the operational level, companies keep certain levels of inventories of raw materials as a buffer to ensure that operations will not be interrupted in case of a supplier failure (Christopher and Holweg 2011; Ivanov, Tsipoulanidis, and Schönberger 2021). On the supply chain level, a high diversity of suppliers can have a similar effect where other suppliers of the same raw material may compensate the failure of one supplier (Yang et al. 2012; Yu, Zeng, and Zhao 2009). Higher diversity on the supply chain level will thus positively affect the robustness of operations as well as the resilience of the company overall.

In addition to these synergies however, two important considerations need to be taken into account. First, improvements in resilience attributes (agency, buffers, connectivity and diversity) often involve trade-offs that must be considered by a company. These include costs and changes in risks. Coming back to the procurement example, higher inventories of raw materials imply higher costs (Carvalho, Azevedo, and Cruz-Machado 2012). Diversifying the supply base will, however, also entail changes in the average price paid in procurement which impacts costs (Burke, Carrillo, and Vakharia 2007; Heese 2015; Yu, Zeng, and Zhao 2009). Further, there is often a trade-off in the diversification of the supply base (Yang et al. 2012; Yu, Zeng, and Zhao 2009). In single sourcing, companies usually rely on the preferred supplier with the best prices, highest quality or highest reliability. When adding new suppliers to the supply base, the new supplier(s) will often perform worse in at least one of the three dimensions (price, quality and reliability) than the preferred supplier (Yang et al. 2012; Yu, Zeng, and Zhao 2009). Quantitatively evaluating the trade-offs regarding costs and changes in risks between different options to enhance resilience on the operational and the supply chain level is thus important in the optimisation of a company’s overall resilience (see also Section 4.9). Second, for very high impact events at the supply chain level, operational resilience may not
<figure><img src="image_111.png" /></figure>

necessarily be able to compensate a lack of supply chain
resilience (Ivanov, Tsipoulanidis, and Schönberger 2021).
For instance, automotive companies suffered a tremen-
dous shortage in semiconductors in 2020 and beyond
as the production for semiconductors was concentrated
geographically to a few suppliers, which were affected by
lockdowns (Ramani, Ghosh, and Sodhi 2022), and auto-
motive companies had designed their supply chains in
a lean and transactional way (see also Section 4.2) with
usually only one or few suppliers of a given part (Car-
valho et al. 2022). Even though certain levels of inventory
generally need to be kept either at the automotive com-
pany or the input supplier (Carvalho et al. 2022), these
could not compensate the disruptions which lasted for
several months in the semiconductor industry (Ramani,
Ghosh, and Sodhi 2022). The example illustrates that it
is important to think beyond robustness and into adapt-
ability and transformability of a business. As this requires
commitments for the mid- to long-term future, sophis-
ticated methods to identify the core areas for devel-
oping adaptability and transformability capacities are
necessary to guarantee an efficient use of resources (see
also Section 4.6).

With regard to the measurement of resilience, two
approaches can be distinguished. The first consid-
ers the resilience of system outcomes, i.e. whether
system outcomes recover from shocks. Most of this
work is quantitative. For instance, Lesk, Rowhani, and
Ramankutty (2016) quantify recovery likelihoods of
global food supplies after extreme weather events. Sli-
jper et al. (2022) estimate recovery rates of income after
income shocks. Next to such empirical analyses, there
are normative studies, such as the paper by Van Voorn,
Hengeveld, and Verhagen (2020). These authors develop
an agent-based model to assess how shocks influence
performance of a food supply chain. A shortcoming of
the ‘outcome-based approach’ is that shock-induced sys-
tem adaptations and transformations, resulting in, e.g.
the production of other outputs, remain unnoticed. Even
more, it would be concluded that a system is not resilient
as it does not recover to its prior outputs and output lev-
els. However, the system might well be more resilient for
future shocks.

The second approach to measure resilience inves-
tigates the systems themselves, i.e. how do systems
respond to shocks and challenges. This strand of lit-
erature is mostly based on participatory approaches.
For instance, resilience capacities (robustness, adaptabil-
ity, transformability) are studied through the scoring of
statements (Slijper et al. 2020), biographical narratives
(Nicholas-Davies et al. 2021), focus groups with multi-
ple supply chain actors (Soriano et al. 2023), bottom-up
assessment of policy documents (Buitenhuis et al. 2020),

and analysis of supply chain actors' actions during lock-
downs (Meuwissen et al. 2021). Also assessments on the
role of resilience attributes are part of this literature.
Paas, Coopmans, et al. (2021) measure the importance
of resilience attributes in relation to systems' current
capacities to deal with shocks. In a subsequent paper,
Paas, Accatino, et al. (2021) link resilience attributes,
and the absence thereof, to critical system thresholds.
Reidsma et al. (2023) identify resilience attributes for
future systems. As all of these resilience attributes are
context-specific, further research needs to address how
resilience attributes materialise in different operations
and systems.

For further reading in the domain of risk manage-
ment, Fan and Stevenson (2018) and Pournader, Kach,
and Talluri (2020) review the literature on supply chain
risk management. Munir et al. (2020) analyse the impor-
tance of supply chain risk management for operational
performance. Baryannis et al. (2019) further discuss
the potential of artificial intelligence as an emerging
tool in supply chain risk management. With regards
to the intersection of risk management and resilience,
we recommend the book by Ivanov, Tsipoulanidis, and
Schönberger (2021), as they explicitly consider risk and
uncertainty as part of operational and supply chain
decision making. In addition, the accompanying book
(Ivanov 2024e) presents the concept of stress tests. These
tests are very useful in understanding possible con-
sequences of disruptions – although one should pre-
vent to restrict them only to risks for which data are
available. Further reading is also recommended in the
domain of standard risk management instruments, such
as insurance. It is especially interesting to consider how
these instruments can contribute to resilience. Vyas
et al. (2021) postulate that agricultural insurance can be
designed such that it stimulates adoption of more resilient
practices. For further reading in resilience, the paper by
Meuwissen et al. (2019) is recommended; it elaborates
a resilience framework which can be applied to multi-
ple types of systems, and lists a wide set of methods to
measure various facets of resilience. For a reflexive per-
spective on the topic of resilience, we recommend reading
Feindt et al. (2022) who conclude that systems face a
resilience crisis.

4.11. Ripple effect⁵⁰

The ripple effect is one of the major phenomena stud-
ied in the field of supply chain resilience. Supply chain
resilience is a firm's capability to withstand, adapt to, and
recover from disruptions to meet customer demand and
ensure target performance (Hosseini, Ivanov, and Dol-
gui 2019). In short, supply chain resilience deals with
<figure><img src="image_112.png" /></figure>

disruptions in supply chain networks and develops capa-
bilities to protect against and recover from these disrup-
tions. One particular context in this field is disruption
propagation, i.e. the effect when a disruption, rather than
remaining localised or being contained within one part
of the supply chain, cascades through the network and
impacts the performance of the entire supply chain. And
this is exactly what the ripple effect is about. Managing
the ripple effect is crucial for decisions in risk manage-
ment and resilience (Section 4.10), inventory manage-
ment (Section 2.3), network design (Section 3.3), global
production networks, and bullwhip effect (Section 3.8),
among others.

The ripple effect in supply chains was first defined
in academic literature by Ivanov, Sokolov, and Dol-
gui (2014), building on disruption propagation studies by
Liberatore, Scaparra, and Daskin (2012), Mizgier, Jüttner,
and Wagner (2013), and Ghadge et al. (2013). Ivanov,
Sokolov, and Dolgui (2014) stated that the ‘ripple effect
describes the impact of a disruption on supply chain per-
formance and disruption-based scope of changes in the
supply chain structures and parameters’. The ripple effect
refers to multi-stage networks and the triggering of fail-
ures in the network elements similar to a domino or cas-
cading effect. Its impact may include lost sales and deliv-
ery delays leading to adverse effects on the profitability of
the supply chain (Li et al. 2021).

Consider some practical examples the common fea-
tures of which are summarised in Figure 9 The earth-
quake and tsunami in Japan in 2011 disrupted multiple
suppliers in the automotive industry and led to produc-
tion breaks and material shortages worldwide. In May
2017, production at BMW was disrupted as a conse-
quence of a supply shortage of steering gears. BMW’s
1st tier supplier, Bosch, was unable to deliver the steer-
ing gears since an Italian second tier supplier experienced
production delays for certain steering parts due to inter-
nal machine breakdowns. In 2020, the COVID-19 out-
break caused production disruptions at many locations
in China. Missing deliveries from/to China impacted the
global supply chains and even the stock values of many
multi-national companies.

The ripple effect is distinctively characterised by four
major dimensions, i.e.:

* Structural dynamics of the supply chain (Pavlov et al. 2022).

* Low-frequency-high-impact effect (Goldbeck, Angeloudis, and Ochieng 2020; Levner and Ptuskin 2018).

* Disruption propagation through the network (Brusset et al. 2023).

* Adverse impacts on resilience and performance (Dolgui, Ivanov, and Rozhkov 2020).

Research on the ripple effect has essentially analysed
how one or more disruptive events propagate through
the supply chain, impacting material flows and perfor-
mance. Management, modelling and assessment of the
ripple effect became visible research avenues with a grow-
ing number and scope of contributions (Dolgui, Ivanov,
and Sokolov 2018). The methodologies used include but
are not limited to mathematical optimisation, simula-
tion, game theory, control theory, data-driven analyt-
ics, network science, and reliability theory (Dolgui and
Ivanov 2021). Being different in scale and scope, the
ripple effect studies share a common set of research ques-
tions, i.e.:

* What are the reasons for the ripple effect? (Chauhan, Perera, and Brintrup 2021; Ivanov, Dolgui, and Sokolov 2019)

* What mitigation actions and recovery policies can be used to cope with the ripple effect? (Dolgui et al. 2023)

* How can supply chain stress tests be conducted to identify which network structures are especially prone to the ripple effect? (Ivanov 2017)

* How can the severity of the ripple effect be measured? (Li et al. 2020; S. Singh et al. 2021)

* How can digital technologies assist in managing the ripple effect? (Ivanov 2023)

The key findings to these questions are as follows. The
common reasons for the ripple effect are lean operations
reducing structural and process variety, the complexity of
supply chains with low visibility, and insufficient coordi-
nation and collaboration between supply chain partners
in managing disruptions (Deng et al. 2019; Ivanov 2024d;
Scheibe and Blackhurst 2018; Shi, Yuan, and Deng
2022).

The scope and scale of the *mitigation actions against the ripple effect* and recovery policies depend on redundancy (e.g. inventory pooling and multiple sourcing), flexibility (e.g. flexible manufacturing lines), agility (e.g. omnichannel distribution system), and visibility (e.g. digital supply chain twins); see also Ivanov (2024b) and Namdar et al. (2024).

For running supply chain stress tests against the rip-
ple effect, different methodologies can be used. Simu-
lation, stochastic and robust optimisation, network sci-
ence, machine learning and AI, and optimal control
dominate the ripple effect stress-test domain (Dolgui
and Ivanov 2021). Simulation methodologies include
discrete-event simulation (DES; Ivanov 2020), system
dynamics (Ghadge et al. 2022), agent-based simulation
(Proselkov et al. 2024), and Monte Carlo simulation
(Mizgier, Jüttner, and Wagner 2013).
<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /><figcaption>**Figure 9.** The ripple effect in the supply chain: changes in the network structure and associated performance degradation.</figcaption></figure>

DES has been the most popular method for the ripple effect analysis. DES is capable for a dynamic examination of disruption propagations and associated changes in supply chain structures, processes, and performance (Llaguno, Mula, and Campuzano-Bolarin 2022). Network theory studies are centred around stress-testing of supply chain network structural connectivity and robustness (Li et al. 2020; Sokolov et al. 2016). In contrast to the simulation and optimisation methods, network-theoretical studies do not consider processes and material flow dynamics mostly restricting the scope of the analysis to the network (i.e. structure) level (Li et al. 2021). Looking beyond of merely structural assessments, Bayesian Networks became a popular method, particularly among researchers who are studying disruption propagation in complex networks due to its ability to connect disruption events, triggers, and consequences (Garvey, Carnovale, and Yeniyurt 2015).

The main *application areas and insights* which can be obtained with the use of differ methodologies are summarised as follows. In the area of simulation and AI, the scope of analysis is comprised of the following areas:

* Dynamic stress-testing of supply chain networks to the ripple effect (Ivanov 2023).

* Identification of critical suppliers and facilities triggering disruption propagation (Mizgier, Jüttner, and Wagner 2013).

* Analysis of contingency/preparedness plans (Libertore, Scaparra, and Daskin 2012).

* Recovery plan selection (Pavlov et al. 2022).

* Prediction of performance impacts in supply chains for different disruption scenarios (Ivanov 2020).

* Dynamic analysis of disruption impacts on service level, inventory levels, delivery delays, and costs (Ivanov 2017).

The network science studies have the following focus:

* Identification of disruption propagation scenarios of different severity (Pavlov et al. 2022).

* Stress-test of supply chain networks against node/link failures (Li et al. 2020).

* Analysis of structural robustness to disruptions (Sokolov et al. 2016).

* Identification of hidden suppliers and links (Namdar et al. 2024).

*Measurement of the ripple effect* can be done in different ways. Kinra et al. (2020) proposed a method to
<figure><img src="image_115.png" /></figure>

measure the ripple effect based on maximum possible financial loss triggered by missing parts and components in the supply chain leading to lost sales at the customers. In other studies, service level indicators such as on-time delivery, fill rate, and demand fulfilment have been used (Sawik 2023). The gaps between the planned and actual values of these indicators show the ripple effect severity. In addition, financial indicators such as annual revenues, costs, sales, and cash conversion cycle have been used in extant literature (Badakhshan and Ball 2023; Ivanov 2017, 2024a). In network theory, such metrics like spatial complexity, network density, degree and betweenness centrality, connectivity, reachability, and structural robustness to analyse structural integrity in the presence of the ripple effect (Habibi, Chakrabortty, and Abbasi 2023; Li et al. 2020; Pavlov et al. 2022). Time-related metrics like time-to-recover and time-to-survive allow to quantify how long can the supply chain withstand the ripple effect and when it will recover (Simchi-Levi et al. 2015). Finally, specific resilience and vulnerability indexes bounded between 0 and 1 can be considered to analyse the ripple effect impacts on the supply chain (Ghadge et al. 2022; Hosseini and Ivanov 2022; Ojha et al. 2018; Zobel et al. 2021).

We refer to the Handbook of the Ripple Effects in Supply Chains (Ivanov, Dolgui, and Sokolov 2019), Special Issue on the ripple effect (Dolgui and Ivanov 2021), as well as literature review papers (Dolgui and Ivanov 2021; Ivanov and Dolgui 2021b; Llaguno, Mula, and Campuzano-Bolarin 2022) for a comprehensive state-of-the-art analysis. *Future trends* in the research on supply chain ripple effect can be seen in the following areas:

* Digital twins, Metaverse and supply chain visibility (Ivanov 2023).

* Supply chain viability and Industry 5.0 (Ivanov 2022).

* Intertwined supply networks and cross-industry ripple effect (Dolgui et al. 2023).

* Financial ripple effect (Proselkov et al. 2024).

* Ripple effect in the presence of hyper-disruptions like COVID-19 pandemic and global trade system disruptions (Brusset et al. 2023; Ivanov 2020).

* Ripple effects in closed loop supply chains and circular economy (Park et al. 2022).

The simultaneous existence of different disruptive stressors (e.g.natural catastrophes, epidemics and pandemics, global geopolitical vulnerabilities, critical material shortages, climate change, and financial crises) along with a rapid development of digital technologies, industrial Metaverse and Industry 5.0 raises a number of novel

research questions where novel and substantial contributions can be done.

# 5. Applications

## 5.1. Agriculture⁵¹

Global problems related to climate change, depletion of natural resources, biodiversity loss, population's growth, urbanisation and dietary shifts, challenge current practices in agricultural and food production systems (FAO 2022). Moreover, consumers have become more aware about the impacts and the quality of the products they consume. As a result, businesses in agricultural supply chains (ASCs) try to improve their efficiency by redesigning their own operations but also the operations within the supply chains they control (Ahumada and Villalobos 2009).

To (re)design efficient ASCs, novel technologies and alternative logistical concepts should be explored. The potential of sensor-based technologies, blockchains and decision support systems, to facilitate information sharing, transparency and decision-making, should be evaluated. In addition, alternative business models and supply chain structures that offer social benefits to farmers (e.g. short food supply chains) may also be considered (Kamble, Gunasekaran, and Sharma 2020; Lezoche et al. 2020).

In operations and supply chain management field, quantitative methods and models have been used to support managerial decision making and to quantify the impacts of such alternative technologies and concepts (Zhu et al. 2018). These models provide guidelines to eliminate inefficiencies, and they unravel the relationships and trade-offs between different performance indicators. Applications of such models result in valuable insights for designing more efficient ASCs.

The objective of this section is to provide examples and to present key results of model-based applications in ASCs and operations management literature. To structure the section, we use the theoretical framework of Tsolakis et al. (2014) and we focus on strategic and operational decisions related to: establishing performance measuring systems; designing supply chain networks; selecting farming technologies; planning harvesting operations; planning transportation and inventory management operations; and ensuring food quality, food safety and traceability.

### Establishing performance measuring systems

An important first step towards redesigning ASCs is addressing the question of how to measure performance
<figure><img src="image_116.png" /></figure>

of alternative technologies and logistical concepts. A literature review study conducted by Banasik et al. (2018) showed that different sets of indicators have been used in planing, distribution and inventory management applications. Ivo de Carvalho, Relvas, and Barbosa-Póvoa (2022) proposed a universal framework to evaluate sustainability performance of companies operating in ASCs using qualitative comparative analysis and a content-based literature review. It was concluded that a large set of indicators is required to capture the complexity of measuring performance in ASCs. Literature reviews on sustainability assessment applications in agricultural and food supply chains revealed that life cycle assessment protocols have been accepted by researchers and the industry as the performance standard in ASCs (Arcese, Fortuna, and Pasca 2023; Vidergar, Perc, and Lukman 2021).

## Designing supply chain networks

Designing supply chain networks involves long term and investment-intensive decisions related to the optimal location, the number, and the capacity of facilities. Moreover, tactical and operational decisions related to supply and demand allocation, and material flows between network's facilities are involved. In the literature, a wide variety of applications aim to improve efficiency and performance of supply chain networks (Eskandar-pour et al. 2015; Joshi 2022; Yu and Solvang 2020). Specific features of agricultural and food supply chains can affect the network design process in ASCs. Such features include the seasonality of production, the uncertainty of yield and product quality, the product quality decay, the product-specific requirements for conditioned transportation and the specific traceability challenges and regulations of agricultural products (Bloemhof and Soysal 2017; Bourlakis and Weightman 2008). A variety of methods, e.g. Life Cycle Assessments, mathematical programming, multi-objective decision making and Stochastic programming have been used in applications that aimed to redesign networks in ASCs (Yadav et al. 2022). An informative literature review of model applications of network design studies in perishable food supply chains was conducted by Jouzdani and Govindan (2021). According to the authors, in current applications for perishable products, the environmental impact of supply chain networks is measured mainly through CO2 emissions while other important environmental indicators that can affect network designing decisions are often ignored. Moreover, the social impacts are included only as salient aspects of the design process. The authors point to specific gaps in the literature of perishable network design studies and propose future research on modelling issues like traffic congestion, conditioned

transportation, interest rates and uncertainty related to product perishability.

## Selecting farming technologies

Given the contribution of agriculture to environmental problems, selecting appropriate farming technologies has a substantial impact on supply chain efficiency and performance. In the agricultural economics literature, bio-economic models have been widely used for this purpose. These models optimise farmer's resource management decisions taking into account available production options and technologies (Britz et al. 2021). Biophysical simulation models are used to quantify input-output parameters of environmental and ecological performance of the production options. Crop rotation, available resources and policy related constraints are used to determine feasible combinations of technologies. According to Reidsma et al. (2018), bio-economic farm models have been used to select farming technologies based on different indicators related to the use of agricultural inputs (like artificial fertilisers, agrochemicals, energy), groundwater quality, nutrient use efficiency, eutrophication, terrestrial and aquatic ecotoxicity, acidification, greenhouse gas emissions, crop diversification, biodiversity value, soil erosion, labour, working conditions, social benefits, eligibility for subsidies, food safety, animal welfare, animal health, landscape quality/values. In cases where the adoption of farming technologies require collaboration between farmers, or coordination between supply chain actors, agent based and multiactor models have been used (Jonkman, Kanellopoulos, and Bloemhof 2019; Utomo, Onggo, and Eldridge 2018). Applications of such models contribute in understanding the adoption mechanisms of sustainable technologies and provide managerial insights about the impact of a new technology to the decision making of individual actors in the chain.

## Planning harvesting operations

Harvesting is the final step and a crucial operation of agricultural production because it affects substantially the initial product quality, the supply of raw material and the economic performance of farming. Good yield predictions provide useful information for optimising planning and resource allocation during the harvest and post-harvest activities. In the literature, applications of machine learning and data mining techniques have been used to provide accurate yield predictions (Sharma et al. 2020). Such predictions are inputs to harvesting planning models which optimise decisions related to harvesting rates, routing/sequencing/scheduling of harvesters and resource allocation. Some applications also combine cultivation planning decisions e.g. determining
<figure><img src="image_117.png" /></figure>

optimal levels of cropping activities (Kusumastuti, van Donk, and Teunter 2016). Mathematical programming and simulations are commonly used methods in such applications. Features like harvesting time windows, labour and machinery capacity, perishability, storage, waste and uncertainty of yield and product quality are often included (Jarumaneeroj et al. 2021; Kusumastuti, van Donk, and Teunter 2016).

## Planning transportation and inventory management operations

Road transportation and logistics are directly related to the circulation of goods along the entire ASC and they play an important role in maintaining quality and compliance with performance criteria and regulations. Examples of transportation planning applications in ASCs are presented in Tsolakis et al. (2014). These applications provide case studies that focus on optimising transportation operations and the scheduling of road transportation in food and biomass supply chains. Stellingwerf et al. (2018) provided an overview of vehicle routing planning models that account for environmental impacts of transportation, while Lin et al. (2023) optimised post-harvest operations with novel mobile pre-cooling technologies for fruits and vegetables in short food supply chains. In ASCs, transportation planning problems occur also in field logistics, and agricultural routing planning. Representative applications of agricultural routing planning studies have been reviewed by Utamima and Djunaidy (2022). Applications sectors include orchards, vineyards, arable farming and biomass production. While objectives related to total finish time of machinery, task time, costs, and capacity utilisation are optimised. Applications of inventory management models in ASCs focus on optimising inventory, storage and material handling decisions (Becerra, Mula, and Sanchis 2021). An application article by Paam et al. (2019) focussed on optimising inventory management decisions related to fruit in apple supply chains. A mathematical programming model was used to calculate trade-off between fruit loss and inventory costs. Stellingwerf et al. (2018) integrated inventory routing planning models in a case study of fresh food logistics between supermarket chains in the Netherlands. Economic and environmental benefits of collaboration were quantified.

## Ensuring food quality, food safety and traceability

The relationship between food quality, food safety and food waste has been thoroughly discussed in Akkerman, Farahani, and Grunow (2010). Often, in model-based applications in ASCs, food quality is treated as a continuous process of product degradation while food safety is treated as a binary state. It was demonstrated

that preventing quality loss and ensuring food safety requires an increase in energy use due to temperature-controlled storage and distribution, which results in substantial environmental costs. Failing to preserve quality standards throughout the supply chain leads to wasted products which had environmental impacts without adding value (Akkerman, Farahani, and Grunow 2010). Bosona and Gebresenbet (2013) reviewed traceability systems in different agricultural sectors and identified benefits related to customer's satisfaction, food crises management, competence development, sustainability, and promotion of technological and scientific innovation. Moreover, Zhu et al. (2018) provided examples of applications in sustainable ASCs. RFID based traceability systems were evaluated and contributed in improving social dimension of sustainability either by mitigating food safety risks and allocating liability among supply chain actors or by ensuring animal welfare by incorporating restriction on livestock transport.

## 5.2. Construction<sup>52</sup>

The construction industry makes a significant impact on the global economy. The estimated value of the global construction industry output in 2022 is over 15 trillion USD and approximately 13% of the global gross domestic productions (Wood 2022). The construction industry produces highly complex structures, necessitating collaboration across multiple supply chains (Ashworth and Perera 2018). Modern Supply Chain Management Systems (SCMS) incorporate the latest Information and Communication Technologies (Nanayakkara, Perera, et al. 2021).

This subsection offers a detailed exploration of key stages in the construction lifecycle phases and the uniqueness of construction supply chains and focuses on procurement aspects. Other parts address resource procurement in construction, challenges in supply chain management and examine advancements in e-procurement through its historical eras. By integrating these elements, the subsection provides a comprehensive overview of the complexities and advancements in operations and supply chain management in construction.

### Key stages in the construction lifecycle

The construction lifecycle is not the only phase of construction activities that are carried out. The most common classification of lifecycle stages includes Pre-design, Design, Construction, Handover, Use/Operation, and End of Life (Çimen 2023).

**Pre-design stage:** This is the initial stage of a construction project and has a broader meaning than the planning stage. It focuses on defining project requirements,
<figure><img src="image_118.png" /></figure>

goals, and feasibility. The key activities are identifying key stakeholders, conducting feasibility studies, defining project scope and objectives, site selection and environmental assessments, budget estimation, and scheduling (Çimen 2023).

**Design stage:** creates detailed plans and specifications for the project. The key activities in design stage are creating architectural and engineering design, developing construction drawings and specifications, obtaining permits and regulatory approvals, cost estimation, and value engineering (Çimen 2023). There could be the development of sustainability designs, risk assessments and others (Ceschin and Gaziulusoy 2016).

**Construction stage:** The construction phase is crucial, where the building is physically constructed according to design and plan. In this stage, construction supply chains support project objectives through activities like mobilisation, site preparation, and various works (fondation, structural, mechanical, electrical, etc.). This stage faces challenges such as weather, site conditions, supply chain issues, regulatory compliance, safety, quality assurance, and others (Nafe Assafi, Hoque, and Hossain 2024).

**Handover stage:** The completed project is transferred to the project owner in the handover stage. There are a couple of key activities, including final inspections and system testing, addressing punch/ snag list items, delivery of operation manuals and warranties, certification, and official handover to the owner (Ceschin and Gaziulusoy 2016).

**Operation stage:** This stage is sometimes defined as the operations and maintenance stage. During this stage, the completed construction object or built asset will be utilised by occupants or end users based on project objectives. The key activities in this stage are facility management and operations, regular maintenance and repairs, monitoring building performance and energy usage, and renovations and upgrades if needed (Nafe Assafi, Hoque, and Hossain 2024).

**End of life:** stage refers to where the asset reaches the end of its functional life. The key activities in this stage include decommissioning or demolition of the building, recycling or disposal of materials, site restoration or redevelopment, and possible repurposing (Ceschin and Gaziulusoy 2016).

## The uniqueness of supply chains in construction

A SC involves firms linked upstream (suppliers) and downstream (customers) to deliver products or services to consumers (Mentzer et al. 2001). Construction Supply Chains (CSC) differ from general SCs due to their unique characteristics, such as project-specific factors and construction processes. CSCs are often geographically dispersed with new suppliers and subcontractors. Therefore,

each project represents a new supply chain (Butkovic, Kauric, and Mikulic 2016). These factors create significant challenges in achieving supply chain maturity and performance, distinguishing CSCs from those in other industries.

The second distinctive feature of CSC compared to a common SC arises from the inherited construction procurements process flows. The highest decision-making power in CSC is held by the client, and main contractor, who are responsible for financing, designing, planning, and key activities. This zone can be identified as a construction procurement. The upstream of the CSC is lengthier and consists of specialist contractors, subcontractors, sub-subcontractors, suppliers, and manufacturers who bear some costs and heavily spend resources such as material, labour, and machinery.

These unique characteristics make construction supply chains a distinct domain of knowledge and practices compared to supply chains in other sectors.

## Construction procurement

Construction procurement can be recognised as one of the most complex procurement operations. During the pre-design stage, the client typically works with consultants to identify the most suitable procurement routes for achieving project goals and objectives in an optimum manner, ensuring value for money, and assessing the associated risks (Aziz 2007). The construction procurement route can be divided into two main categories: basic and advanced procurement routes. Figure 10 illustrates the most common routes within each category. The procurement route is the primary agreement type, especially between the client and the main contractor. Each construction procurement route has its own advantages and weaknesses. Therefore, it is essential to carefully evaluate the procurement route and the project's nature before moving forward with the construction procurement (Oyegoke et al. 2009).

## Resources procurement in construction projects

Muya, Price, and Thorpe (1999) has classified the previous research identifications into three types of construction supply chains: primary supply chain, support supply chain and human resource supply chain.

The primary supply chain in construction focuses on the procurement of materials (e.g. cement, sand, steel, etc.) essential for the project's execution commonly called as purchasing process. This process involves sourcing, purchasing, and delivering construction materials from suppliers or directly from the manufacturer (Caldas et al. 2015).

In construction, supply chains for plants and equipment (support supply chains) are vital for providing
<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /><figcaption>**Figure 10.** Construction procurement common routes.</figcaption></figure>

the necessary machinery and tools. This includes sourcing, acquiring, and maintaining equipment for tasks like excavation and lifting (Perera, Nanayakkara, and Perera 2014). Key activities involve selecting suppliers or rental companies, negotiating contracts, ensuring safety and performance standards, and coordinating logistics (Edwards and Holt 2009).

In construction, human resource supply chains for procuring labour are essential for securing the skilled workforce needed to complete various project tasks. This process involves identifying, hiring, and managing both permanent employees and specialised subcontractors who perform specific functions such as electrical work, plumbing, or masonry (Karim, Marosszeky, and Davis 2006).

## Challenges and issues in construction supply chain management

The construction industry faces numerous challenges across project phases, including design, construction, operation, maintenance, and decommissioning. Key issues identified include trust, knowledge management, integration, collaboration, and quality-related problems.

SCM and SCMS often fail to adequately address these time, cost, quality, and trust issues within construction supply chains.

Low productivity, driven by a lack of skilled workers and outdated technology, leads to significant time-related issues and schedule overruns. Material delays, a frequent cause of project delays, impact multiple sequential activities (Ala-Risku and Kärkkäinen 2006). Quality and compliance issues are common in construction projects (Zhang et al. 2011). Financial challenges are notable due to the long payment cycles in construction. Subcontractors and suppliers often face delays and partial payments, exacerbated by inefficient supply chain processes (Nanayakkara, Perera, et al. 2021).

Trust is a critical factor in supply chains, yet it is often neglected in many CSC models. Short-term project nature negatively impacts essential success factors like long-term trust, collaboration, and risk-sharing. Researchers propose that improving supply chain maturity and transparency can enhance trust, though significant issues remain (Koskela, Vrijhoef, and Dana Broft 2019).
<figure><img src="image_121.png" /></figure>

## Advancements in e-procurement in the construction industry

e-Procurement or electronic procurement is a vital function of any supply chain management system, and it was the primary function of forming the computer-based supplier interaction (Puschmann and Alt 2005).

The first e-procurement era began in the 1980s, initiated with centralised systems like Enterprise Resource Planning systems addressing internal information silos (Nanayakkara, Kusumsiri, and Perera 2016; Nanayakkara, Perera, and Perera 2013). Business-to-business communication happened through electronic media such as a floppy disk, compact discs (CDs) and emails (Hewavitharana et al. 2019; Nanayakkara, Perera, and Perera 2015). The second era of e-procurement was initiated in the 1990s, the enterprise computing era began with organisations adopting enterprise-wide infrastructures and the development of the Internet and suppliers could directly interact with e-procurement portals (Afolabi et al. 2006). The third era of e-procurement initiated in the 2000s, cloud computing revolutionised procurement and supply chains. System-to-system communication became common, enabling direct interaction between supplier and client systems directly (Nanayakkara, Perera, and Shantha 2014).

Blockchain technology was introduced in 2008 (Nanayakkara et al. 2019) and it could revolutionise procurement and supply chains by smart contracts and decentralised applications (DApps; Perera et al. 2020). Smart contracts automate and secure transactions with unprecedented accuracy and transparency, while DApps enable decentralised, peer-to-peer operations (Nanayakkara, Rodrigo, et al. 2021; Perera et al. 2021). Blockchain's advantages include immutable transaction records and enhanced trust in a trustless environment, reducing the need for intermediaries and lowering costs (Perera et al. 2021). Therefore, many researchers predict that blockchain and smart contracts will usher in a new era by addressing issues related to time, cost, quality, and trust in construction supply chains (Nanayakkara, Perera, and Senaratne 2019).

## Conclusion

Effective supply chain management is essential for construction projects due to the industry's complexity and need for stakeholder coordination. Each project represents a new, dispersed supply chain, facing challenges like delays and financial inefficiencies. Advancements in e-procurement, especially blockchain and smart contracts, offer solutions by enhancing transparency, streamlining payments, and building trust. As the industry evolves, digital tools and innovative procurement will improve efficiency and sustainability.

## 5.3. Public and nonprofit sectors⁵³

In what has been described as a 'results-oriented management movement' (Poister and Streib 1999, 325), the application of operations and supply chain management (SCM) principles to the public and nonprofit sectors has received increasing research attention over the last 30 years (Ghobadian and Ashworth 1994; Radnor and McGuire 2004; Shevchenko, Hajmohammad, and Pagell 2024; Taylor and Taylor 2014). Funded by the state, for most public services (e.g. registering to vote or applying for state benefit) there is no alternative choice of provider, therefore public services must be appropriate for all citizens. Frequently in the spotlight in relation to the measurement and management of performance (Garengo and Sardi 2021; Zheng et al. 2019), a key driver for the application of operations and SCM principles to the public sector has been governmental pressure to demonstrate improvement in the productivity and performance of public service provision (Radnor and Noke 2013). The nonprofit sector is funded by grants, donations and service delivery contracts. The application of operations and SCM to the nonprofit sector has been identified as a mechanism to demonstrate the provision of continuously improving services that offer value for money to both funders and beneficiaries; accountability criteria that are essential to securing continued resources (Moxham and Boaden 2007).

Against this backdrop of service improvement, the application of operations and SCM principles to the public and nonprofit sectors can be problematic, and the assumption that private sector operations improvement approaches can be directly applied to these sectors has been criticised (Bush 1992; Hazlett, McAdam, and Walker 2013; Radnor and O'Mahoney 2013; Walley 2013). For example, public sector organisations have to meet multiple, often conflicting, goals and are characterised by different ownership, funding, goal setting and control norms and expectations when compared with the private sector (Goldstein and Naor 2005). These characteristics lead to complexities when attempting to 'implant' improvement approaches taken directly from the private sector into the public sector context (Hazlett, McAdam, and Walker 2013, 988). Similarly, the context in which nonprofits operate differs from that of the private sector. For example, nonprofits are required to be both cost-effective and have a positive impact on society, giving rise to vulnerabilities and tensions that may not be present in the private sector (Fagbemi et al. 2024; Shevchenko, Hajmohammad, and Pagell 2024). The competitive landscape also differs, with humanitarian aid organisations, for example, having to rapidly collaborate with sectoral competitors to form consortia to deliver
<figure><img src="image_122.png" /></figure>

life-saving services (Schiffling et al. 2020); a requirement that would be unusual for most private sector companies. In response to these differing contexts, the application of operations and SCM principles to the public and nonprofit sectors often requires situational adjustment and operations management frameworks, including balanced scorecards for public and nonprofit sectors, have been developed (Kaplan and Norton 1999; Ronchetti 2006).

As noted, the continuous improvement of services, specifically in terms of quality, cost and experience, is often the focus of the application of operations and SCM principles to the public sector (Dobrzykowski 2019). Comparison between different types of public sector organisations are common, with research focussing on emergency services, local and central government (Radnor and Johnston 2013; Walley 2013). Studies situated in the public sector are wide ranging in terms of geography and context, examining for example, e-government in Slovenia (Groznik and Trkman 2009), procurement reform in South Africa (Ambe and Badenhorst-Weiss 2012), public sector performance measurement in Germany (Greiling 2005) and supply chain innovation in public procurement in Finland (Karttunen et al. 2022). Much of the research on the application of operations and SCM principles to the public sector is situated in Europe and North America. For applications in developing countries the reader is directed to Mimba, van Helden, and Tillema (2013), in South Africa to Ambe and Badenhorst-Weiss (2012) and in the United Arab Emirates to Al-Dhaafri and Alosani (2020).

The application of operations and SCM principles to the nonprofit sector emerged slightly later than that of the public sector. As with the public sector, performance management and measurement were initial themes, with published works emerging from 2007 (Moxham and Boaden 2007). The success of the application of operations and SCM principles to the nonprofit sector appears to be mixed, with earlier studies highlighting implementation challenges. For example, research highlights a disconnect between measurement criteria and performance goals (Moxham 2009; Moxham and Boaden 2007), a performance measurement focus on the use of funds at the expense of continuous improvement (Moxham 2010) and variable responses to performance measurement from nonprofit stakeholders (Beer and Micheli 2017). More recently studies have showcased good practice as regards to operations and SCM in the nonprofit sector. Examples include collaborative service delivery (Best et al. 2018) and cost effective operations (Shevchenko, Hajmohammad, and Pagell 2024). Operations and SCM principles appear to be more embedded

in the practices of nonprofits when compared with studies of 10 years ago and the benefits are being realised. For example, recent research demonstrates how an awareness of SCM principles has a positive impact on organisational financial independence, thus allowing nonprofits to fulfil their mission (García-Dastugue and Rousseau 2024), and how the application of supply chain management to volunteer groups results in much needed rapid emergency response (Shaw, Zanjirani Farahani, and Scully 2025).

As with the public sector, research situated in the nonprofit sector is wide ranging. Studies focus on, for example, performance management in a UK housing association (Manville and Broad 2013), supply chain integration in food banks in the United States (Ataseven, Nair, and Ferguson 2020), reverse logistics in textile supply chains in Finland (Zhuravleva and Aminoff 2021) and the distribution of surplus food to individual beneficiaries in the United Arab Emirates (Grassa and Bocanet 2023). The size and shape of nonprofits is also a fertile line of enquiry as the nonprofit sector is so diverse. Studies examine large nonprofits characterised by numerous paid staff, formal governance structures and an international reach (Boateng, Akamavi, and Ndoro 2016; Moxham 2009), medium sized nonprofits with paid staff and a single country focus (Best et al. 2018), small nonprofits with a minimal number of paid staff and a large volunteer base (Moxham and Boaden 2007), short-term temporary collaborations between nonprofits (Schiffling et al. 2020) and informal structures that rely solely on volunteers (Shaw, Zanjirani Farahani, and Scully 2025). These studies show how the diversity of the nonprofit sector offers challenges and opportunities for the application of operations and SCM principles.

For a comprehensive discussion of the application of operations and SCM approaches to both the public and nonprofit sectors, the reader is directed to Poister (2003). For a focussed discussion on the application of operations and SCM to public services, the research handbook edited by Radnor et al. (2016) provides an excellent compilation of agenda setting articles. For nonprofits, Gazzola and Amelio (2022) provide a contemporary application of operations improvement tools and techniques that are relevant to the sector. Finally, whilst research on the application of operations and SCM to the public and nonprofit sectors is receiving increased research attention, we cannot overlook the continuing challenges facing both sectors. In a global context where the mantra for public and nonprofit organisations continues to be 'how do to more with less?' the future of services provided by the public and nonprofit sectors is far from certain and applications of operations and SCM principles continue to be crucial.
<figure><img src="image_123.png" /></figure>

5.4. Healthcare⁵⁴

In healthcare, the adoption of operations and supply chain thinking has played a pivotal role in enabling healthcare organisations engage with key partners to deliver quality healthcare products and services to patients through the efficient allocation of resources, effective delivery systems, and deployment of technology to support innovative healthcare provision (Kc, Scholtes, and Terwiesch 2020). Healthcare supply chain management (HSCM) is defined as ‘the management of people, processes, information, and finances to deliver medical products and services to consumers, in the pursuit of enhancing clinical outcomes and user experience, while controlling costs’ (Betcheva, Erhun, and Jiang 2021, 1334). HSCM involves three core entities: healthcare product manufacturers, distributors or wholesalers, and service providers or procurement agencies, all of which directly contribute to the flow of products, services, finances, and information within the supply chain. These entities can span diverse organisational types, including private providers, public agencies, and non-profits (Zhao et al. 2012). Recent healthcare business models – such as specialist clinics, telehealth, and urgent care centres – particularly during the COVID-19 pandemic, have underscored the importance of operations and supply in improving quality of care, end-user experience, and cost-effectiveness (e.g. Sampson 2023; Thirumalai and Devaraj 2024).

Recent works (e.g. Phillips et al. 2022; Roscoe et al. 2022; Srai et al. 2020) have examined the role of global supply chains (GSCs) in healthcare, where globally connected organisations work together to bring products to market (Kano, Tsang, and Yeung 2020). GSCs allow healthcare organisations to access affordable products by leveraging economies of scale and scope, fostering economic value and technological advancements (De Marchi et al. 2020; Perez-Aleman and Sandilands 2008). However, healthcare organisations within GSCs often operate across multiple regions or countries, each with different regulations and policies (Handfield, Graham, and Burns 2020), and these organisations often have conflicting objectives (Phillips et al. 2022). For instance, the Turkish government has mandated that foreign suppliers of certain healthcare products produce locally or through a local Turkish company (European Commission 2024). Such challenges can cause inefficiencies, such as healthcare product shortages (Müller, Hoberg, and Fransoo 2023), as seen during the COVID-19 pandemic when nations struggled to procure essential supplies such as personal protective equipment (PPE), diagnostic tests, and oxygen.

The growing frequency and severity of geopolitical disruptions have heightened interest in supply chain resilience (SCR) to secure the continuous availability of critical healthcare products and services (Bednarski et al. 2025; Finkenstadt and Handfield 2021; Holgado and Niess 2023; Moradlou et al. 2021). Reviews of the literature on SCR (Sawyerr and Harrison 2019; Tukamuhabwa et al. 2015) have uncovered four key SCR strategies employed by healthcare organisations to mitigate disruptions: flexibility; redundancy; collaboration; and agility (Scala and Lindsay 2021). Key themes emerging from the literature emphasise the need for faster delivery times, the movement of production closer to the point-of-care (Betcheva, Erhun, and Jiang 2021; Peters et al. 2023; Zavattaro 2023), and localised sourcing of components and production (Gereffi, Pananond, and Pedersen 2022; Kapletia et al. 2019).

Studies on the transition from global to local healthcare supply chains (HSCs) suggest that local procurement is vital in preparing for future disruptions (Hartland et al. 2021). For example, in response to the United Kingdom's (UK) exit from the European Union (BREXIT), the majority of pharmaceutical companies planned to relocate production sites from the UK to EU states and distribution centres from the EU to the UK (Moradlou et al. 2021). Post-pandemic, similar strategies are likely to be adopted more frequently to shorten HSCs and enhance responsiveness and responsiveness (Duong et al. 2025; Finkenstadt and Handfield 2021; Xu et al. 2020).

However, a shift away from global HSCs presents two significant challenges that may render localisation impractical (Panwar, Pinkse, and De Marchi 2022). First, large-scale localisation requires substantial investments in facilities, labour and knowledge and can be particularly testing for developing countries which often have poor physical and logistical infrastructures as well as a lack of skilled labour (Besiou, Pedraza-Martinez, and Van Wassenhove 2021). Second, dismantling GSCs could result in social and economic hardships for countries relying on international trade. Additionally, healthcare products and services are subject to strict regulations, requiring significant alignment between the regulatory frameworks of different countries within an HSC. These regulatory challenges may further complicate localisation decisions (Moradlou et al. 2021). Consequently, many organisations, especially large ones, tend to adopt a cautious, ‘wait-and-see’ approach, gathering information from partners to minimise uncertainty before making relocation decisions (Roscoe et al. 2020).

To achieve high-quality patient care while reducing costs and waste, HSCM faces significant obstacles
<figure><img src="image_124.png" /></figure>

(Schneller et al. 2023), underscoring the need for innovation and new technologies. With advancements in digital technology and the rise of the sharing economy, Wang et al. (2024) review new service-sharing models in healthcare, offering fresh avenues for research. For example, many hospitals have implemented smart healthcare systems, enabling healthcare professionals to provide both online and offline diagnoses and treatment (Lee and Lee 2021), while wearable devices have been adopted for healthcare monitoring (Huang, Yu, and Lee 2022).

It is worth noting a recent surge in the use of Artificial Intelligence (AI) applications within healthcare operations, driven by the increasing availability of data and advances in machine learning and data analytics (Guha and Kumar 2018). AI systems offer several potential benefits for HSCM, such as improving integration among supply chain partners, addressing demand forecasting challenges, enhancing risk and performance management, aiding inventory control, supporting greater resilience, and mitigating the ripple effects of disruptions (Dubey et al. 2021; Ivanov and Dolgui 2020).

Despite clear evidence of the advantages of AI-enabled systems, their adoption is hindered by high implementation costs and a lack of confidence in their ability to deliver immediate results (Adhikari, Joshi, and Basu 2025). Furthermore, the use of AI in healthcare raises issues related to data protection, patient safety, legal accountability, and ethical considerations (Ghadge et al. 2023). Lack of awareness and understanding of AI systems amongst healthcare professionals and patients further complicates their adoption (Arfi et al. 2021). Kumar and Mallipeddi (2022) have suggested several research directions to address these challenges, such as exploring how healthcare organisations can use information and communication technology (ICT) to securely share data with supply chain partners, developing blockchain solutions to reduce cybersecurity risks, and implementing regulatory policies to enhance supply chain cybersecurity.

In addition to SCR, localisation and AI, existing studies on healthcare applications over the last decade have covered several themes from the operations and supply chain management perspective. For example, business analytics (Terekhov, Demirezen, and Aytug 2024), capacity strain (Kim, Pinker, and Rimar 2025), decision making (van Rijn, de Vries, and Van Wassenhove 2024), inventory management (Yang, Mishra, and Sinha 2025), quality of care (Xu et al. 2025) and sustainability (Berenguer, Costas Lorenzo, and Sáez de Tejada Cuenca 2024). Furthermore, studies on this topic are mostly empirical (Kc, Scholtes, and Terwiesch 2020) and modelling works (Ali and Kannan 2022; Keskinocak and Savva 2020). For a comprehensive and up-to-date review

of the role of operations and supply chains in healthcare, readers are encouraged to consult recent literature. Liu, Brandon-Jones, and Vasilakis (2024) present a framework for patient engagement in remote consultations; Moons, Waeyenbergh, and Pintelon (2018) analyse key performance indicators in hospital logistics; and for a broader strategic perspective, Schneller et al. (2023) provide a comprehensive overview of the issues facing HSCM and its role in healthcare strategy. These works are essential for understanding the modern requirements of HSCM, offering valuable case studies and insights into the complexities of contemporary healthcare supply chains. Moreover, they offer some suggestions for future studies. For example, future studies could adopt different methods or mixed methods to explore mechanisms that affect the effectiveness of healthcare operations management (Kc, Scholtes, and Terwiesch 2020). Also, to increase the research impact in practice, closer collaboration between researchers and practitioners is vital for accessing actual and timely data (Keskinocak and Savva 2020). Moreover, we call for more interdisciplinary studies, combining healthcare management with fields such as public sector, engineering and pharmaceutical manufacturing.

## 5.5. *Humanitarian operations*<sup>55</sup>

Three big disasters changed the way that we are thinking of humanitarian operations: the Hurricane Katrina in the USA in 2004, the Asian-Pacific Tsunami in 2004, and the Pakistan Earthquake in 2005. The disasters were followed closely by the media, making the general population aware of the big logistics challenges occurring in disaster response and the need for improvement. That was the trigger for the humanitarian operations research field to take off.

### *Key concepts in humanitarian operations*

Humanitarian operations constitute a relatively new field of research in Operations Management (OM). With one exception (Long and Wood 1995), the first OM journal papers date back to 2006 (Altay and Green, III 2006; Van Wassenhove 2006). A year later the first review was published (Kovács and Spens 2007). This has since been followed by more than 150 literature reviews, one of the latest being the one of Kembro et al. (2024). The last two decades have not been busy only for the researchers, but even more for humanitarian organisations and other actors responding to mega-disasters like the 2010 Haiti earthquake, the Syrian refugee crisis, and the Russian-Ukrainian war.

Similar with disaster management in general, humanitarian operations are not only about the response. The
<figure><img src="image_125.png" /></figure>

logistics involved in mitigation (attempting to reduce future disasters occurring), preparedness (minimising the consequences for affected populations by being prepared before disasters occur) and recovery (getting back to normal) are equally important parts of what is termed as the disaster management cycle and are interconnected (Tomasini and van Wassenhove 2009). International humanitarian organisations (IHOs) including non-governmental organisations (NGOs), the UN and the International Red Cross & Red Crescent Movement, play a central role in all four phases. For example, they operate programmes like building wells or restoring medical centres to help the local population 'building back better'. They also help local governments prepare better for future disasters, for example by prepositioning relief items or training local organisations. Traditionally, however, focus was on response. IHOs mostly operate in contexts where the local response teams (typically the fire brigade, police forces, medical emergency teams, local NGOs and military) cannot address the needs on their own. When a disaster strikes, the first to respond is always the local community and the beneficiaries themselves. International actors come in if asked by the government.

A typical supply chain of activities starts with assessing and publishing needs and budgeting (Keshvari Fard, Ljubić, and Papier 2022), followed by sourcing and procurement, transport, warehousing and distribution. Nowadays, humanitarian supply chains also include waste management. Some of these activities take place locally aiming to increase localisation (Frennesson et al. 2022), while others internationally. Which activities should take place locally and which internationally depends significantly on the available local resources: very often humanitarian organisations operate in countries where key infrastructures such as water, transport, and power are already struggling to meet in-country needs and placing further demand on these systems could give rise to tensions between humanitarian organisations and local communities (Duong et al. 2025). Humanitarian organisations have an increasing understanding of such conditions, shown for example by their decision to implement cash-based assistance or not. The supply chain often uses a push system immediately after the disaster has occurred and changes to pull with more time and better information on the needs.

Items and services related to health, water and sanitation, food and shelter are provided by the HOs supported by (non-)earmarked funding from private and public donors and resources from private sector companies like storage, transport and relief items. Distributing cash to the beneficiaries to complement (in-kind) donated relief items has become a norm (Juntunen et al. 2023) in an effort to also reduce failures of in-kind donations like

equipment that quickly becomes redundant due to poor adaptation to local environmental conditions, and limited local resourcing to use, effectively staff or maintain it Tomasini and van Wassenhove (2009). Key resources also include information and communication systems, standards, supplier agreements (Balcik and Ak 2014) and last, but certainly not least, human resources. Experienced and skilled personnel is critical for humanitarian operations as is leadership (Salem, Van Quaquebeke, and Besiou 2022). However, due to the limited funding available and the tough operating conditions, training and maintaining staff can be hard. The media's main role is to increase awareness and hold the actors accountable for their actions, so that funding is used wisely and fair in accordance with humanitarian principles.

## Key issues – literature reviews and empirical research

Humanitarian operations often take place in resource austere settings with limited access to power, clean water, poor transport links, disrupted supply chains, limited cold chain as well as political volatility. Country access, the import and resupply of key equipment and medical consumables, security and communication issues and local environmental conditions all have an impact. The experiences in 2004/2005 clearly demonstrated the need for logistics preparedness (Jahre, Pazirandeh, and Van Wassenhove 2016; Stumpf, Besiou, and Wakolbinger 2023) and coordination, collaboration and co-operation (Balcik et al. 2010; Jahre and Jensen 2010; Kovács and Falagara Sigala 2021) to increase operational efficiency and effectiveness by closing gaps, reducing duplications, and managing resources better. Substantial efforts by the organisations followed. Until 2015 the most common strategy was strategic stock of relief items, but also other 'stock' including vehicle fleets (Pedraza Martinez, Stapleton, and Van Wassenhove 2011), emergency funds and rosters of trained human resources (Jahre 2017). The use of postponement through non-earmarking of items, prepositioning of semi-finished goods, centralisation and outsourcing, collaboration (Altay and Pal 2014; Ergun et al. 2014) as well as use of flexible transportation and supply base have also been common. Providers must navigate a range of extremes, e.g. temperature, humidity, rainfall, etc., all of which can have a profound effect on the ability to effectively deliver humanitarian operations. Humanitarian organisations should always consider whether collaboration is appropriate given the need to maintain integrity and independence, and depending on aspects like interoperability of technologies and systems, the use and implementation of different regulations and standards, and information asymmetry (Phillips, Roehrich, and Kapletia 2023). They should also consider more
<figure><img src="image_126.png" /><figcaption>**Table 2.** Recent literature reviews and their respective themes.</figcaption></figure>

<table><thead><tr><td>Theme</td><td>Example of literature review</td></tr></thead><tbody><tr><td>Cash distribution</td><td>Juntunen et al. (2023)</td></tr><tr><td>Coordination</td><td>Jahre and Jensen (2021)</td></tr><tr><td>Drones</td><td>Rejeb et al. (2021)</td></tr><tr><td>Forecasting</td><td>Altay and Narayanan (2022)</td></tr><tr><td>Health in disasters</td><td>Dixit and Dutta (2024)</td></tr><tr><td>Human resources</td><td>de Camargo Fiorini et al. (2022)</td></tr><tr><td>Information sharing</td><td>Negi (2025)</td></tr><tr><td>Localisation</td><td>Frennesson et al. (2021)</td></tr><tr><td>Modularity &amp; standards</td><td>Paciarotti, Piotrowicz, and Fenton (2021)</td></tr><tr><td>Optimisation models</td><td>Kamyabniya et al. (2024)</td></tr><tr><td>Outsourcing</td><td>Gossler, Wakolbinger, and Burkart (2020)</td></tr><tr><td>Procurement</td><td>Moshtari et al. (2021)</td></tr><tr><td>Refugees</td><td>Dantas and Amado (2023)</td></tr><tr><td>Role of media</td><td>Abdulhamid et al. (2021)</td></tr><tr><td>Sustainability</td><td>Anjomshoe et al. (2025)</td></tr></tbody></table>

collaboration with the commercial sector: temporary supply chains dismantled once the disruption has been addressed are particularly important during humanitarian events, ensuring the delivery of critical supplies over a limited timeframe (Duong et al. 2025; Müller, Hoberg, and Fransoo 2023).

During the past decade research and practice have expanded a lot and cover a range of themes. Table 2 gives an overview of recent literature reviews within many of these themes, mainly from OSCM journals.

The OSCM research community has come a long way since the mid-2000 with regards to the number of researchers involved, papers published and cooperation with practice. The focus has changed from logistics to supply chain/operations management (Thomas and Kopzack 2005), securing a more holistic approach by increasingly taking the system into account (Besiou, Stapleton, and Van Wassenhove 2011). However, there is an ongoing discussion of whether the published research match the needs in practice. Besiou and Van Wassenhove (2020) conclude that many papers tend to deal with traditional OM problems without much consideration for the specific disaster context. They provide advice on how to conduct relevant research. De Vries and Van Wassenhove (2020) find that optimisation models for humanitarian operations need a paradigm shift towards cost-effectiveness maximisation and to provide more empirical evidence. Similarly, Kovacs and Moshtari (2019) conclude that research must be contextualised and present a roadmap for higher research quality.

## Closing remarks – the past, the present, and future

Besiou and Van Wassenhove (2015) discuss the roles of different methodologies in ‘solving’ problems and ‘developing’ theory. For example, literature reviews, case studies and surveys help more in understanding how reality

works, ‘soft’ OR to study the behaviour of complex systems and ‘hard’ OR when solutions need to be provided in specific subsystems. We agree and suggest our community to reflect on when to use which hammer, depending on the nail we want to hit. To conclude we briefly summarise the past and the present with suggestions for the future.

**Different methods:** In the beginning, research was characterised by conceptualisation based on literature reviews, case studies and OR in separate papers. During the past decade there has been more use of mixed methods, combining cases or surveys with OR and simulation. Mixed methods are becoming the norm in many high ranked journals, even if there are still many pure OR-papers, conceptual papers and case studies. What about the future? We expect to see more experiments, more use of big data and machine learning.

**Different foci:** Research on humanitarian operations has traditionally focussed much on response and preparedness. In the future we should see more on mitigation and recovery. Related to this we will see more focus on sustainability, i.e. going beyond efficiency and effectiveness in the traditional sense. Further, research has focussed a lot on cost and response time, while recently more metrics like equity (Breugem et al. 2025) and pain (Holguín-Veras et al. 2013; Wang et al. 2019) are considered important.

**Different disciplines:** Applications from OSCM have traditionally included facility location, supplier selection, network design (Balcik and Beamon 2008; Dufour et al. 2018), inventory location-allocation, forecasting, transportation planning/routing supply chain risk management (Gralla and Besiou 2024). During the past few years, we have seen OSCM combined with other management disciplines like organisation theory, leadership, marketing, strategy, finance (Özer, Urrea, and Villa 2024). For the future, we expect to see more interdisciplinary research beyond management, combining OSCM for example with civil engineering, political science, and medicine.

The field started off relatively narrow, focussing on logistics in the response phase, demonstrating the importance of logistics to humanitarian practice and the humanitarian context to logistics research. During the past two decades the field has developed in depth and breadth, encompassing a multitude of themes. However, there is a danger of driving research too much toward areas and problems where data is readily available (Gralla and Besiou 2024), allowing use of fancy methods which are ‘in’, and readily publishable rather than practically implementable. For example, we have seen a lot of focus on funding/financial flows lately. While getting funding is essential, we should not forget our field is operations, i.e.
spending the available funding wisely and with the goal of saving lives.

## 5.6. Manufacturing⁵⁶

Manufacturing is a central link in the supply chain, responsible for transforming raw materials into finished products to meet customer demands and create value. The efficiency and effectiveness of manufacturing operations directly impact lead times, cost structures, product quality, and customer satisfaction, all of which contribute to the overall performance of the supply chain. Many foundational concepts in operations management originated from manufacturing practices. Frederick Taylor's Scientific Management principles (Taylor 1911), representing the studies on improving productivity in the late 19th and early 20th centuries as mass production arose, laid the groundwork for modern operations management. The need to make production systems more efficient also led to studies on plant layout (see Stephens and Meyers 2013) and material handling (see Section 2.5).

With mass production and standardisation, quality control became vitally important in manufacturing, leading to the development of quality management practices focussed on inspection, testing and process control (Wetherill and Brown 1991). Over time, quality management evolved to emphasise broader concepts such as customer satisfaction, total quality management (TQM), and continuous improvement. See Section 2.10 for more on quality management. While quality management principles are now widely applicable across all business sectors, they continue to play a crucial role in manufacturing.

Locating manufacturing facilities has been a critical decision in supply chain management, traditionally considering supply proximity, market access, and transportation costs. Globalisation and offshoring of production have profoundly reshaped the manufacturing landscape. In global supply chain networks, manufacturing facility location decisions must account for a broader set of factors (MacCarthy and Atthirawong 2003). These include the availability of skilled labour, the quality of infrastructure, political stability, the legal and regulatory environment, and the ease of doing business. Additionally, considerations such as cultural compatibility, environmental regulations, and proximity to innovation hubs have become increasingly important. These complexities make global location decisions critical to the success of the overall supply chain strategy.

A critical area of operations management for manufacturing is production planning and control (PPC). There used to be textbooks focussing specifically on PPC (e.g. Bolton 1994), but it is now usually included as an important part of Operations Management books (e.g.

Slack and Brandon-Jones 2019). Within the PPC framework, the production planning level makes production and inventory plans for different products over multiple periods based on customer orders or demand forecasting to optimise the overall objective. The production plan for final products is translated to production plans of sub-assemblies and components at different levels according to the product structures. This is performed through a material requirement planning (MRP) system. The MRP evolved to manufacturing resources planning (MRP-II) considering processing capacities as well (Sheikh 2002). Further expansion led to a company-wide system covering all business functions – Enterprise resources planning (ERP) that is widely used in different sectors not limited to manufacturing.

In manufacturing, except for raw materials, inventory is usually replenished gradually through production. Order quantity is thus optimised with an economic production quantity (EPQ) formula (Taft 1918) instead of economic order quantity (EOQ). While inventory acts as a safety buffer, it can also hide many problems and increase costs. The just-in-time (JIT) production system originated in Toyota (Monden 1998) aims to eliminate inventory and produce items only when they are needed. This has been extended to minimising all wastes in general, leading to lean manufacturing and lean operations (see also Section 4.2).

At a lower level of PPC, scheduling makes detailed plans to allocate specific resources over time to processing tasks. Since Johnson's pioneering work (Johnson 1954), scheduling has quickly developed into a mature research field with books published (e.g. French 1982). As the production environment can be very complex and involve more practical constraints, the gap between the scheduling theory and practice was noted and the need for more practical studies was pointed out (MacCarthy and Liu 1993a). In recent decades increasingly more production scheduling studies have been addressing scheduling problems in practical manufacturing environments. This benefits manufacturing operations and enriches production planning and scheduling research. Meanwhile, scheduling problems in other sectors are widely studied as well. See Section 2.15 for more.

Different manufacturing industries have their specific characteristics which are reflected in their operations and supply chain management problems. We briefly discuss several manufacturing industries below. The automotive industry has highly complex and tiered supply chains involving thousands of suppliers globally. Safety and regulatory standards require stringent quality management. Just-in-time principle is commonly applied to minimise inventory costs and enhance efficiency. Besides the routine operations and supply chain management
<figure><img src="image_127.png" /></figure>

decisions, automotive manufacturers also make long-term plans for the allocation of products to production sites and investments in additional capacity in the global production network (Fleischmann, Ferber, and Henrich 2006). Increasingly consumers can choose the configurations for the vehicle they purchase. This makes it challenging to quickly fulfil the customised orders while keeping a low inventory of finished products. Reassignment of products in the production and distribution pipeline and inter-dealer trading have been demonstrated to be effective in reducing lead time and achieving quicker fulfilment (Brabazon and MacCarthy 2006; Brabazon et al. 2010).

The process industries include a wide range of businesses, such as the chemical and pharmaceutical industry, food industry, and steel industry. They often involve continuous or batch processes, different from discrete manufacturing as indicated by Van Donk and Francoo (2006). For continuous chemical processing, the cost of switching from one product to another is typically sequence dependent. Considering this, Cooke and Rohleder (2006) took a continuous economic lot scheduling problem (ELSP) approach to make production schedules. Pharmaceutical and food products are subject to high health and safety standards as well as variations in supply and demand. Therefore, quality and inspection must be considered carefully in designing a supply chain and selecting suppliers (Pariazar, Root, and Sir 2017). Supply chain resilience is equally important. Lücker and Seifert (2017) studied the problem of enhancing resilience against disruption risks by considering mitigating inventory, double sourcing and agile capacity. In food supply chains, decision making needs to consider the fact that food products are perishable (Huang, He, and Li 2018, also see Section 5.1). While the food and pharmaceutical industries are directly related to human life, the iron and steel industry supports the economy by supplying materials to other industries such as automotive, construction and shipbuilding. There are various operations planning decisions in steel production. Tang et al. (2014) developed decision support systems for several such problems including batching problems, as well as a problem of allocating/reallocating slabs to customer orders which shares some similarities with the order fulfilment problem in the automotive supply pipeline described earlier. Product sequencing is also very important to minimise switching costs in finishing processes such as electro-galvanising lines (Tang, Yang, and Liu 2010).

The electronics and semiconductor manufacturing industry has been growing more and more important. Electronics assembly operations have been studied with various objectives. Throughput maximisation is a typical

objective and is used in a telecommunication equipment assembly (Kobza, Ellis, and Vittes 2002). For a light-emitting diode (LED) array assembly, the objective was set to reduce the inventory level of the LED parts used as they may become obsolete with frequent change of product models (Chang and Chung 2013). Semiconductor chips are key components for electronics products. Semiconductor manufacturing includes wafer fabrication and chip assembly. Wafer fabrication involves hundreds of process steps with recirculating process flows. In such an extremely complex system, detailed operations scheduling usually adopts dispatching rules considering waiting time or job urgency. The mid-term production planning optimises product lot release times to the system considering process capacities, cycle times, lot priorities and work-in-process (Kriett, Eirich, and Grunow 2017), while the master production planning sets targets considering demands. The system of chip assembly and testing is also complex but less so than fabrication. While a mid-term planning model schedules product groups to the assembly and testing processes (Zhang, Bard, and Chacon 2017), a more detailed scheduling problem can be solved to decide on machine setups (Hur, Bard, and Chacon 2019), to maximise fulfilment of customer demands and maximise throughput. Allocation of wafer lots and chip lots to fulfil specific customer orders can be decided as lot-to-order matching problems in semiconductor supply chain settings to minimise unfulfillment and over-fulfilment (Ng, Sun, and Fowler 2010).

Manufacturing processes and systems have been advanced with computer and digital technology, which in turn affect their operations and supply chain management problems. This applies to most industries while the machining industry is a typical example. Combining computer numerical control machines and automated material handling under central computer control resulted in the development of flexible manufacturing systems (FMS) capable of quickly responding to dynamic market demands of products in high variety and low volume, supporting mass customisation. See MacCarthy and Liu (1993b) for a classification of FMS. The flexibility also introduces additional complexity to the planning and control of the operation, e.g. extra loading decisions on setting up the machines and routing the parts, as first studies in Stecke and Solberg (1981). Computer-integrated manufacturing (CIM) automates all processes and integrates data and information across the entire production system to achieve more efficient operations, improved accuracy, and faster time-to-market. See Hanam (1997) for more details.

Technology continues to advance rapidly in the 21st century. At the process level, additive manufacturing
<figure><img src="image_128.png" /></figure>

(Abdulhameed et al. 2019) has evolved from a prototyping tool into a potential method for efficiently producing individualised products. On a global scale, under frameworks such as Industry 4.0, new information and communication technologies, including the internet of things, cloud computing, big data analytics, and artificial intelligence, are being integrated into manufacturing systems to enable smart or intelligent manufacturing. This shift presents both challenges and opportunities for manufacturing operations management to leverage these technologies to fully realise their benefits. Zhou et al. (2022) conducted a systematic review of research on intelligent manufacturing and proposed future research directions under several themes.

### 5.7. Professional services⁵⁷

Professional services make up a large and growing portion of the service sector. Management consulting, information technology (IT) services, architecture and engineering, legal services, and investment banking are prominent examples. Besides being an important sector in the economy, professional services act as a value-generation catalyst for many other private and public organisations.

The service management literature has attempted to characterise distinguishing features of professional services and proposed various schemes for their classification (Lewis and Brown 2012; Schmenner 2004; Von Nordenflycht 2010). Broadly speaking, the product of a professional service is a process. These service processes typically require knowledge work performed by professionals. So new product and service design (see Section 2.18) implies process design (see Section 2.7) and job design (see Section 2.8). Professional services are tailored to customer needs. Their processes display high levels of customisation. Work in most professional service firms is project-based. Thus, project management (see Section 2.16) is an essential pillar of professional service management. Customer interaction is high and professional service firms rely on their customers as one of the key inputs in their production processes. Performance is driven by co-production between service professionals and clients. Many professional services are B2B and are active in service supply chains (see Section 3). In summary, the operational performance of a professional service hinges on process design and management, the management of knowledge work, and systems that enable customer efficiency and effectiveness.

The process structure of a professional service drives operational performance outcomes including capacity, cost, and quality. Service processes can be characterised by their complexity, the number and elaborateness of the

steps involved, and by their divergence, namely including a high level of executional latitude (Shostack 1987). Professional services have a large share of high complexity-high divergence processes. A highly complex process requires longer throughput time and additional skills to perform the work. Taking a process view implies managing the entire customer experience in an integrated manner, however, complexity makes it challenging for one professional to handle the entire customer encounter. Expertise and task specialisation are inevitable but require handoffs that can create interruptions in a process. On the other hand, multi-skilled professionals will require higher compensation. Proper structuring of complex processes with these tradeoffs in mind leads to superior performance. Processes that combine multi-tasking by flexible cross-trained servers (Aksin et al. 2015; Iravani, Van Oyen, and Sims 2005) are designed to balance capacity, quality, and labour costs. Process designs with multiple hierarchical stages are a common approach to managing these tradeoffs in settings with heterogeneous customer needs. In technical support, generalist gatekeepers refer certain cases to expert specialists (Shumsky and Pinker 2003). In health settings, triage plays a similar role (Sun, Argon, and Ziya 2022). Collaboration and coordination among professionals (Gurvich et al. 2020; Gurvich and Van Mieghem 2018; Rahmani, Roels, and Karmarkar 2017; Roels and Corbett 2024; Siemsen, Balasubramanian, and Roth 2007) is critical to ensure seamless integration in a complex workflow. A process having high divergence, on the other hand, implies service workers who need to make judgments affecting the process flow. A standard operations procedure cannot be established, the process suffers from high variability, and quality is difficult to assess or manage. Processes involve discretionary tasks (Debo and Li 2021; Hopp, Iravani, and Yuen 2007). Customer-intensive professional services have an inherent tradeoff between quality and capacity. Higher quality in outcomes requires spending more time with customers thus consuming capacity (Anand, Pac, and VeeraragHAVAN 2011). Diagnostic services require tradeoffs between diagnostic accuracy and time taken for additional tests at the discretion of the professional (Alizamir, De Véricourt, and Sun 2013). In expert service settings, like medical, legal, and consultancy services, clients may not be able to assess the appropriateness of the provided service giving these services a credence characteristic (Debo, Toktay, and Van Wassenhove 2008).

The main product of a professional service is knowledge work performed by employees with varying talent and experience levels. Professional service work is organised around projects completed by teams. Demand for these services strongly depends on quality and
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reputation, which is partially driven by the employees who perform the work. Team composition can act as a signal of quality for clients (Talluri and Tsoukalas 2023). It is thus essential to manage the employee talent pool and understand the implications of team dynamics on performance. Managing this critical resource involves hiring and retaining the right employees and ensuring they continue to learn (Arlotto, Chick, and Gans 2014; Gans and Zhou 2002; Musalem, Olivares, and Yung 2023). Designing operations and forming teams that enable the best learning outcomes is important (Aksin et al. 2021; Huckman, Staats, and Upton 2009; Ramdas et al. 2018). Frequently teams in professional service work are fluid teams that are formed temporarily. Evidence from the team learning literature suggests that team familiarity leads to improved performance. However, diversity in experiences, tasks, and current and former team members are all shown to affect performance outcomes. Team formation that considers these tradeoffs is essential for a professional service. By scheduling or controlling the mix of tasks performed by professionals, operational outcomes like throughput, process time, and quality can also be optimised (Bray et al. 2016; Legros et al. 2020; Pinker and Shumsky 2000).

Customers not only drive the demand for professional services but also play a role as an input in defining their outcome. Customer satisfaction and retention are critical for a sustainable professional service business. Past experiences determine contract renewals. Thus, effort drives retention in a service relationship (Aflaki and Popescu 2014). For a professional service, it is not just the effort of the service firm but also their clients' effort that matters. Co-production refers to the joint production effort by service providers and customers. Xue and Harker (2002) propose the concept of customer efficiency, arguing that customer work also needs to be managed just like employee productivity. The design of co-produced services requires determining how work is allocated between the parties, for instance, consultants and clients, and contracts need to be specified that manage the effort of both sides (Roels 2014; Xue and Field 2008). Karmarkar and Roels (2015) provide a more general analytical framework for value co-production in services.

Technology is a major driver in the market for professional services and an enabler in their production and delivery. Developments in cloud computing have created a huge market for IT as a service. Different organisational structures that can exploit cost efficiencies, simultaneously creating value, have emerged in this domain. Given their technical nature, professional services in the

technology arena are particularly affected by their customer capabilities leaving a space for intermediaries in their use (Wu, Jin, and Veeraraghavan 2023). Differences in capabilities drive firms' purchasing choices regarding outsourcing these services (Chang and Gurbaxani 2012) or keeping them in-house (see Section 3.4). For in-house professional services, the possibility of remote delivery has made the adoption of shared service structures that combine finance, HR, IT, and other shared services in one organisation more prevalent (Aksin and Masini 2008). Digital technologies have further enabled online outsourcing platforms for IT services (Hong and Pavlou 2017). Online platforms have also led to changes in the labour market for professional services through the possibility of gig work. Web-based platforms that match service demands to service supply have enabled professionals to provide their expertise at their convenience while allowing firms to access capacity flexibly and timely (Taylor 2018). Traditional professional service firms decide on which professionals to hire and retain, however in online service marketplaces this dynamic is two-sided and applies to workers' decisions regarding the platforms in which they participate (Allon, Cohen, and Sinchaisri 2023; Liu, Lou, et al. 2024). More recently, artificial intelligence (AI) technologies present the potential to transform professional service work. As with most new technologies, despite their enormous prospects for value creation, they present new hurdles to overcome in business contexts (Davenport and Ronanki 2018). AI is shifting how professionals work, possibly redefining the scope of their work (Huang and Rust 2018). Decision support systems for knowledge work are incorporating AI features (Gupta et al. 2022). Integration of AI tools in decision-making by professionals requires a better understanding of when they are superior to human experts' judgment. Even when superiority is established, acceptance by stakeholders in processes, including the experts themselves, is not always ensured (Dai and Tayur 2022; De Véricourt and Gurkan 2023). Designing new processes that embrace the value-creation potential of these shifts in the technology landscape and that overcome major adoption barriers constitutes the future of professional services.

We direct the reader to the book by Maister (1993) for more on managing professional service firms. The special issue on professional services (Harvey, Heineke, and Lewis 2016) contains work focussing on operations management issues in these firms. Field et al. (2018) present exciting research directions under key themes in service operations, including a theme on the management of knowledge-based service contexts.
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## 5.8. Retail⁵⁸

Retailing has been a fruitful application area for many operations and supply chain management topics, including inventory management (Section 2.3), pricing (Section 2.19), and purchasing (Section 3.5), owing to its critical role in driving global economic growth. The retail sector generates millions of jobs and contributes 20% to the United States' annual gross domestic product (NRF 2024). Caro, Kök, and Martínez-de Albéniz (2020) define retailing as 'all the activities associated with the selling of goods to the final consumer'. To systematically examine these activities, we adopt a consumershopping journey framework, comprising *before, during, and after purchase*. We then discuss key operational enablers underlying the efficiency of these consumerfacing activities, emerging topics of interest, methodologies employed, and further research opportunities.

### Consumer-shopping journey framework

**Before Purchase:** This stage focuses on how consumers acquire product information, become aware of a brand, and decide to purchase. Traditionally, brick-and-mortar stores were the primary points of purchase. With omnichannel retail, these stores now also serve as catalogues where consumers can acquire information by seeing, touching, and comparing products. To capitalise on this, some retailers are opening showrooms that do not carry inventory. Showrooms are associated with higher demand (Bell, Gallino, and Moreno 2018), fewer returns (Bell, Gallino, and Moreno 2020), improved operational efficiency (Bell, Gallino, and Moreno 2018), and lower sensitivity to fulfilment lead times (Lim, Gao, and Tan 2024). In addition to brick-and-mortar stores and showrooms, consumers use online brands as an information acquisition channel (Gallino and Moreno 2014), leading to research on online retail design (Shi, Wedel, and Pieters 2013).

Regardless of the channel, price is essential information to consumers, which is affected by retailers' distribution channel (Brynjolfsson and Smith 2000), replenishment contracts (Lim et al. 2022), assortment strategy (Heese and Martínez-de Albéniz 2018), and inventory constraints (Ferreira, Simchi-Levi, and Wang 2018). In addition to impacting prices, assortment (product variety) is central to the customer experience. Wang (2018) indicates that a wide assortment signals expertise, increasing purchase likelihood. Product availability is another key component; for example, in online retailing, low product availability generates pressure to buy quickly, leading to increased sales (Calvo, Cui, and Wagner 2023). Having products available only online, or both online and

in-store, is a strategic decision for omnichannel retailers (Ertekin, Gümüş, and Nikoofal 2022).

**During Purchase:** This stage consists of the actual buying and interactions with the retailer. In a physical store, interactions with sales associates shape the customer experience. Ertekin, Ketzenberg, and Heim (2020) demonstrate that salesperson competence and friendliness are associated with return prevention. To improve sales associates' competence, retailers can invest in training. Fisher, Gallino, and Netessine (2021) find that online training increases sales. The customer experience is also affected by the store environment, including ambience (Ertekin, Ketzenberg, and Heim 2020), fixture types (Zhang et al. 2023), or fitting room designs (Lee, Kesavan, and Deshpande 2021).

The shift from the brick-and-mortar model to omnichannel retailing changes the way consumers and retailers handle fulfilment and transportation. In traditional in-store settings, consumers incur the effort to fulfil their orders and transport goods home. In omnichannel settings, retailers share part of the fulfilment and transportation effort. Various channel strategies offer customers different convenience levels. These include buy-online, pick-up-in-store (Gallino and Moreno 2014); reserve-online, pick-up-in-store (Jin, Li, and Cheng 2018); pick-up-from-locker/delivery trucks (Glaeser, Fisher, and Su 2019); and buy-online, ship-from-store (Das, Ravi, and Sridhar 2023).

**After Purchase:** This stage focuses on the post-purchase customer experience, emphasising returns and customer feedback. Product returns are costly and harmful to the environment, but they are linked to increased sales and customer satisfaction. Consumers value return policies because of the potential mismatch between the product and their expectations. Also, some customers may be opportunistic by buying products with the intention of returning them after a brief use (Shang, Ghosh, and Galbreth 2017). Therefore, retailers test different return policies involving return window (Ertekin and Agrawal 2021), reimbursement method (e.g. store credit versus refund Abdulla, Abbey, and Ketzenberg 2022), or modes of return (e.g. mail versus in-store Nageswaran, Cho, and Scheller-Wolf 2020).

After making a purchase, customers often provide feedback. Star ratings and review sentiment influence future demand (Cho, Sosa, and Hasija 2022). Therefore, some retailers may create fake reviews for themselves and competitors (Luca and Zervas 2016), leading to customers questioning e-commerce platforms' credibility. To address these concerns, e-commerce platforms use strategies like purchase verification or utilising moderators to manually approve reviews (Kokkodis, Lappas, and Kane 2022).