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This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore adaptation-based and stability-based views of supply chain resilience to analyse what insights these different perspectives, individually and collectively, offer for theory and practice. In the stability-based view, resilience is triggered by disruptions and performance deviationsto return to some 'normal' states. This view accounts for known-known uncertainty. The adaptation-based view shifts the focus from avoiding oscillations and recovering some stable states toward proactive adaptation and performance persistence. The adaptation-based view aims at designing structurally adaptable networks with process flexibility and actively used redundancy. It considers resilience from the value-creation perspective accounting for unknown-unknown uncertainties. Stability-based approach views resilience as an outcome or quantity. Adaptation-based approach considers resilience as a property or quality. A combination of stability- and adaptationbased approaches is imperative for building a strong supply chain immunity through an integration of general protection and adaptability. These approaches complement each other depending on the knowledge of and attitude to uncertainty by decision-makers. A combination of the two views helps consider resilience both as a quantity to measure how sick the supply chain is and to understand how the resilience comes about to ensure the quality of the network health and viability. [ABSTRACT FROM AUTHOR]

Digital twins became of greater interest to researchers and practitioners in supply chain and operations management (SCOM). Literature has addressed the need to understand digital twins in SCOM, mostly focusing on fragmented technological solutions and use cases. We start with an integrative literature review to determine which elements belong to research on digital twins in SCOM. We define the seven major elements of a digital twin in SCOM: technology, people, management, organisation, scope, task, and modelling. We also distinguish five major types of digital twins in SCOM: product, process, organisation, supply chain and network-of-networks. Illustration of a SCOM digital twin is provided using an anyLogistix example. We conclude that digital twins in SCOM are not merely a simulation-based replica of a real object but a complex socio-technical phenomenon involved in continuous human-artificial intelligence interactions. This leads to an understanding of the role of digital twins through the lens of Industry 5.0, reconfigurable and viable supply chains. Researchers and practitioners alike can use our framework to structure the knowledge on SCOM digital twins and consider all seven elements when designing and using digital twins. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic's impacts have been devastating for the global economy, and particularly for manufacturing companies. Many firms were unprepared for a crisis of this magnitude and adapted their operations in an ad-hoc manner. Some of these adaptations were facilitated by digital technologies (DTs). This article examines extant literature using a systematic review methodology in order to extend our understanding of what DTs have been used in manufacturing companies during the pandemic, how DTs have helped manufacturing companies cope with the pandemic, and how these innovations can be used in the post-pandemic world. The major outcome of our study is a novel classification of DT applications in the pandemic setting in the manufacturing industry. In addition, we propose an agenda for future research. [ABSTRACT FROM AUTHOR]

Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models. [ABSTRACT FROM AUTHOR]

Disruptions can adversely affect profitability, service level, and even the viability of supply networks (SNs). Natural disasters, the COVID-19 pandemic, and geopolitical tensions heightened disruption risks for SNs. We propose a methodological framework to guide the design of resilient SNs, with a focus on efficient resilience. In fact, despite an increasing economic and social interest in SN resilience and a large variety of qualitative resilience frameworks, there is a lack of model-based frameworks to guide decision makers in the complex task of designing resilient SNs. Differently to the existing literature, that mostly focuses on industry- or disruption-specific approaches, the framework of this paper is general, independent from sectors and causes. It consists of three steps to guide practitioners through model-based stress testing of their SNs: it integrates and organises methods from the literature, from the selection of data to be collected to the validation of the resilient and efficient SN. We illustrate the application of descriptive, predictive, and prescriptive analytics at each phase using real-life case studies data supplemented by literature references. Finally, we present an industrial case study with the step-by-step application of the framework highlighting its strengths and objectives. [ABSTRACT FROM AUTHOR]

The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability). [ABSTRACT FROM AUTHOR]

Decision-making in supply chains is challenged by high complexity, a combination of continuous and discrete processes, integrated and interdependent operations, dynamics, and adaptability. The rapidly increasing data availability, computing power and intelligent algorithms unveil new potentials in adaptive data-driven decision-making. Reinforcement Learning, a class of machine learning algorithms, is one of the data-driven methods. This semi-systematic literature review explores the current state of the art of reinforcement learning in supply chain management (SCM) and proposes a classification framework. The framework classifies academic papers based on supply chain drivers, algorithms, data sources, and industrial sectors. The conducted review revealed a few critical insights. First, the classic Q-learning algorithm is still the most popular one. Second, inventory management is the most common application of reinforcement learning in supply chains, as it is a pivotal element of supply chain synchronisation. Last, most reviewed papers address toy-like SCM problems driven by artificial data. Therefore, shifting to industry-scale problems will be a crucial challenge in the next years. If this shift is successful, the vision of data-driven decision-making in real-time could become a reality. [ABSTRACT FROM AUTHOR]

In anticipation of price hikes and shortages caused by supplier disruptions and manufacturer production stops, customers might stockpile extra products. In the case of a supplier disruption, a manufacturer may decide to continue producing using a contingent source. Capturing the price dynamics in four disruption-related periods (i.e., responding, rising, recovering, and recovered), we derive optimal hoarding policies for customers. The results indicate that customer hoarding decisions fall into multiple patterns depending on the interactions between disruption events, market responses (quick and slow), and market recovery (instant, quick, slow, and never). We next present contingent sourcing tactics for manufacturers to mitigate disruptions with and without customer hoarding. We find that future price increases could induce contingent sourcing even if it is unprofitable to resume production during the price-responding phase. Our results offer recommendations regarding when and how to use hoarding and contingent sourcing accounting for uncertain disruption duration and asymmetric information along with disruption- and recovery-driven price dynamics. These recommendations can be of particular value for supply chain decision-making at times of growing inflation. We also demonstrate the impacts of customer hoarding and disruption information on the value of contingent sourcing. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered new research areas in supply chain resilience. One of these new areas is viability. Viability extends the resilience understanding from performance-based assessment of firm’s responses to disruptions towards survivability of both supply chains and associated ecosystems not only during some short-term disruptions but also under conditions of long-term crises. To explore the state-of-the-art knowledge on methods, models, capabilities, and technologies of supply chain viability, we edited this important IJPR special issue. To introduce the special issue, we review the existing literature on supply chain viability, conceptualise seven major pillars of supply chain viability theory (i.e. viable supply chain design, viability in process planning and control, ripple effect, intertwined and reconfigurable supply networks, ecosystems, digital supply chain, and Industry 5.0), and establish some associated future research directions. The findings of this editorial paper, as well as the articles in the special issue, can be used by researchers and practitioners alike to consolidate recent advances and practices of viability in supply chain networks and lay the solid foundation for further developments in this area. [ABSTRACT FROM AUTHOR]

Industry 5.0 is a combination of organisational principles and technologies to design and manage operations and supply chains as resilient, sustainable, and human-centric systems. While the general notion of Industry 5.0 has been elaborated, its implications for future operations and supply chains remain underexplored. This paper contributes to the conceptualisation of Industry 5.0 from the perspective of viability. We contextualise a framework of Industry 5.0 through the lens of the viable supply chain model, the reconfigurable supply chain, and human-centric ecosystems. Our study uncovers the major dimensions that characterise Industry 5.0 as a technological-organisational framework. First, the major technological principles of Industry 5.0 are collaboration, coordination, communication, automation, data analytics processing, and identification. Second, Industry 5.0 covers four areas: organisation, management, technology, and performance assessment. Third, Industry 5.0 spans three levels: society level, network level, and plant level. Last but not least, Industry 5.0 frames a new triple bottom line: resilient value creation, human well-being, and sustainable society. We provide a definition of Industry 5.0 and discuss its implications by elaborating on the understanding of value in Industry 5.0, which spans the dimensions of profit, people, and society. We also discuss open research areas. [ABSTRACT FROM AUTHOR]

During a crisis, manufacturing processes in supply chains of different industries may network with each other as an adaptation response. We propose and examine a "network-of-networks" mechanism of such a cross-industry adaptation to learn about the value of reducing uncertainty through collaborative crisis preparedness and response during the COVID-19 pandemic. Our study allows revelation of the underlying trade-offs between the manufacturing capacity conversion time and effort required to adapt and the gains from collaborative preparedness to uncertainty. Through a real-life data-based analysis with the help of mathematical optimization, we connect the networks-of-networks coordination design and the outcomes of scenario modeling demonstrating a superiority of the coordinated capacity repurposing when compared to ad-hoc adaptation. We conclude that an appropriate collaboration of governmental agencies, healthcare, and industry is crucial for a prompt capacity conversion to healthcare production in a pandemic. Concrete implementation ways are visibility, healthcare inventory monitoring, technology backup plans, and repurposing contingency plans at the preparedness stage. At the response stage, a correct adaptation start time determines success. The results obtained can be instructive to develop technological and managerial plans for a cross-industry adaptation. The proposed "network-of-networks" perspective contributes to theory of supply chain viability and adaptation under disruptions using intertwined supply networks. [ABSTRACT FROM AUTHOR]

Supply chain (SC) and operations management has been developed for decades under the fundamental assumption of continuous availability (with some temporary fluctuations and disruptions) of resources to satisfy demand. Under extreme shocks and long-lasting disruptions, SCs and operations face long-term shortages of components, energy, capital, and labour, as well as rapidly rising prices. These long-term resource shortages and risks of hyper-inflation pose novel and unexpected challenges potentially leading to global ripple effects. In this conceptual paper, we first analyse and categorise the available literature on SC and operations management with resource shortages. We then systemise some managerial implications of resource shortages and rising inflation, conceptualising some potential research directions which appeared or may appear in the settings of shortage economy. We conclude that the shortage economy provides a distinct and specific context which goes beyond the conventional problems under constrained or disrupted resources – i.e. the long-term resource shortages with simultaneous disruptions in labour, material, energy, and capital availability. This paper could be useful for researchers and practitioners alike to systemise potential impacts of a shortage economy on SCs and operations, and to navigate adaptation and recovery processes in a structured way to help society cope with deep uncertainties. [ABSTRACT FROM AUTHOR]

Supply chains (SCs) are exposed to multiple risks and vulnerable to disruption propagation (i.e. the ripple effect). Despite established literature, quantitative analysis of the ripple effect in SCs considering simultaneous, long-term disruptions (i.e. induced by the COVID-19 pandemic) remains limited. This study defines, applies and demonstrates the capability of system dynamics modelling to recognise and visualise the ripple effect subject to supply, demand, and logistics disruptions as well as a combined, simultaneous disruption of supply, demand and logistics. Simulation results for these four risk scenarios indicate that disruption propagation and its impacts vary based on risk type, combination of risks and the impacting node. The bi-directional, increasing effect is significant for disruptions of longer duration. Retailers and manufacturers are most fragile to multiple disruptions due to broader risk exposure points. In generalised terms, systems theory-based study provides insights into the complex behaviour of simultaneous risks and associated disruptions occurring at a node and across the SC. The outcomes derived can help practitioners visualise and recognise the dynamic nature of the ripple effect cascading across the SC network. In addition, some novel insights on the systemic nature and delayed impact of disruption propagations are uncovered and discussed. [ABSTRACT FROM AUTHOR]

While the majority of companies anticipated the negative and severe impacts of the COVID-19 pandemic on the supply chains (SC), most of them lacked guidance on how to model disruptions and their performance impacts under pandemic conditions. Lack of such guidance resulted in delayed reactions, incomplete understanding of pandemic impacts, and late deployment of recovery actions. In this study, we offer a method of modelling and quantifying the SC disruption impacts in the wake of a pandemic. We develop a multi-layer Bayesian network (BN) model that can be used to identify SC disruption triggers and risk events amid the COVID-19 pandemic and quantify the consequences of pandemic disruptions. The unique features of BN, such as forward and backward propagation analysis, are utilised to simulate and measure the impact of different triggers on SC financial performance and business continuity. In this way, we combine resilience and viability SC perspectives and explicitly account for the pandemic settings. The outcomes of this research open a novel theoretical lens on application of BNs to SC disruption modelling in the pandemic setting. Our results can be used as a decision-support tool to predict and better understand the pandemic impacts on SC performance. [ABSTRACT FROM AUTHOR]

5G technology enables end-to-end connectivity in real time at a highly granular level along with the associated end-to-end visibility through the Internet-of-Everything. While some potential benefits of 5G for digital supply chain and operations management have been declared, literature is still silent about theoretical underpinning and structured conceptualisation of application areas, underlying implementation challenges, and the role of 5G in future transformations of value creation. This paper aims to offer some directions of how to close this research gap. We organise the discussion around five major capabilities of the digital supply chain and smart operations which can be enhanced by 5G, i.e. intelligence, visibility, transparency, dynamic networking, and connectivity. We delineate possible future research topics related to 5G in different areas of Industry 4.0-driven, digital supply chain and operations management which can be useful for researchers and practitioners alike when seeking to understand the impact of 5G on both short-term and long-term time scales. Our analysis encompasses both operational processes (e.g. transformations of manufacturing and warehouse operations by end-to-end connectivity of devices) and strategic perspectives (e.g. transformations of business models and supply network structures through end-to-end real-time visibility and connectivity of industry, public infrastructure, and consumers). Finally, cost-benefits trade-offs are discussed. [ABSTRACT FROM AUTHOR]

The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has challenged supply chains (SC) on an unprecedented scale testing viability and adaptation under severe uncertainty. However, the literature on the adaptation strategies and quantification of their impacts is still scarce. Mixing literature analysis, case study approach, and quantitative techniques for performance assessment under disruptions, our study generalises four adaptations strategies – intertwining, scalability, substitution, and repurposing – to maintain SC viability when facing a pandemic, and offers a model to analyse and quantify deployment and impact of adaptation. First, we analyse the recent literature and identify some of the general characteristics of adaptation strategies during the COVID-19 pandemic. We then describe case studies to illustrate the practical context and supplement the literature analysis in order to derive relevant determinants for building of a conceptual framework and construction of a formal model. In the conceptual framework, we show how the adaptation strategies can be aligned with the SC viability, encompassing the levels of the ecosystem, network, and resources. In the generalised model, we formalise the impacts and efforts in deploying and assessing the adaptation strategies as both a process and an outcome. We close by proposing some open research questions and outline several future research directions. [ABSTRACT FROM AUTHOR]

While Industry 4.0 has been trending in practice and research, operations management studies in this area remain nascent. Our intent is to understand the current state of research in Industry 4.0 in different disciplines and deduce insights and opportunities for future research in operations management. In this paper, we provide a focused analysis to examine the state-of-the-art research in Industry 4.0. To learn about researchers' perspectives about Industry 4.0, we conducted a large-scale, cross-disciplinary and global survey on Industry 4.0 topics among researchers in industrial engineering, operations management, operations research, control and data science at the 9th IFAC MIM 2019 Conference in Berlin in August 2019. By using our survey findings and literature analysis, we build structural and conceptual frameworks to understand the current state of knowledge and to propose future research opportunities for operations management scholars. Glossary of Abbreviations AGV: Automated guided vehicle; AI: Artificial intelligence; APS: Advanced planning system: a wide variety of software tools and techniques, with many applications in manufacturing and logistics (including the service sector); BDA: Big data analytics; CAS: Complex adaptive system: a system composed of many interacting parts that evolve and adapt over time; CIM: Computer integrated manufacturing; CPFR: Collaborative planning, forecasting and replenishment; CPS: Cyber-physical system: a seamless integration of computation and physical components; DAMCLS: Decision analysis, modelling, control and learning systems; ERP: Enterprise resource planning; FMS: Flexible manufacturing system; I4.0: Industry 4.0; IFAC: International Federation of Automatic Control: a federation is concerned with the impact of control technology on society; IME: Industrial and mechanical engineering; IoT: Internet-of-Things; IT: Information technology; M2M: Machine-to-machine; MAS: Multi-agent system: a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver; OR: Operations research; RFID: Radio frequency identification: a technology that uses electromagnetic fields to automatically identify and track tags attached to objects; RMS: Reconfigurable manufacturing system: a manufacturing system that can change and evolve rapidly in order to adjust its productivity capacity and functionality; OM: Operations management; T&T: Track and trace system; VCA: VOS viewer co-occurrence analysis: a software tool for visualising bibliometric networks; VMI: Vendor-managed inventory. [ABSTRACT FROM AUTHOR]

Ripple effect is a specific area of SC disruptions and a strong stressor to SC resilience. Research on the ripple effect analyses how one or more disruptive events propagate through the SC and impact its resilience and performance. The phenomenon of the ripple effect, immensely existing in practice, has received great research interest in recent years. Ripple effect management, modelling and assessment became visible research avenues with a growing number and scope of contributions. This Special Issue presents recent developments on the ripple effect in SCs. The Special Issue focuses on studies that address the ripple effect and provide a comprehensive picture of the state of the art and future perspectives. The methodologies comprise of mathematical optimisation, simulation, game theory, control theoretic, data-driven analytics, network complexity, reliability theory research, and empirical research. Even though a variety of valuable insights have been developed in this area in recent years, new research avenues and ripple effect taxonomies are identified for further exploring the ripple effect in the settings of the COVID-19 pandemic, SC viability, viable SC model, and reconfigurable SCs. [ABSTRACT FROM AUTHOR]

This article, published in IISE Transactions, features a special section on modeling and optimization of supply chain resilience to pandemics and long-term crises. The papers in this section address decision-making settings in supply chains and manufacturing systems during the COVID-19 pandemic, semiconductor crises, global container shortages, and geopolitical tensions. The papers cover topics such as collaborative inventory transshipment, hoarding and contingent sourcing strategies, vaccine center location and allocation, dynamic vaccine ordering policies, and incentivizing compliance behaviors. These papers provide practical insights and recommendations for supply chain decision-making during crises. [Extracted from the article]

Supply chain (SC) disruptions are considered events that temporarily change the structural design and operational policies of SCs with significant resilience implications. The SC dynamics and complexity drive such disruptions beyond local event node boundaries to affect large parts of the SC. The propagation of a disruption through a SC and its associated impact is called the ripple effect. Previous approaches to ripple effect modelling have mainly focused on estimating the likelihood of a disruption; our study looks at the disruption consequences. We develop a new model to assess the ripple effect of a supplier disruption, based on possible maximum loss. Our risk exposure model quantifies the ripple effect, comprehensively combining features such as financial, customer, and operational performance impacts, consideration of multi-echelon inventory, disruption duration, and supplier importance. The ripple effect quantification is validated with simulations using actual company data. The findings suggest that the model can be of value in revealing latent high-risk supplier relations, and in prioritising risk mitigation efforts when probability estimations are difficult. The performance indicators proposed can be used by managers to analyse disruption propagation impact and to identify the set of most critical suppliers to be included in the disruption risk analysis. [ABSTRACT FROM AUTHOR]

Research on supply chain (SC) digitalization, resilience, sustainability and leagility has remarkably progressed, most of it focused on the individual contributions of these four major frameworks. However, a lack of integration spanning these individual frameworks can be observed. In this conceptual paper, we hypothesize that reconfigurability can be considered such an integral perspective that consolidates the research in SC adaptation to ever changing environments. We theorize a new notion – a Reconfigurable SC or the X-network – that exhibits some crucial design and control characteristics for complex value-adding systems in highly vulnerable environments. We support our argumentation and conceptual viewpoints by a literature analysis along with tertiary studies to review and structure contextual factors of designing the X-networks. We propose respective frameworks and discuss the implementation principles and technologies at the macro and micro levels. Two novel concepts – dynamic SC meta-structures and dynamic autonomous services – are introduced. Distinctively, we go beyond the existing knowledge to predict proactively the future directions in the reconfigurable SCs. Our results can be of value for decision-makers to decipher chances and barriers in contemporary SC transformations. [ABSTRACT FROM AUTHOR]

The ripple effect can occur when a supplier base disruption cannot be localised and consequently propagates downstream the supply chain (SC), adversely affecting performance. While stress-testing of SC designs and assessment of their vulnerability to disruptions in a single-echelon-single-event setting is desirable and indeed critical for some firms, modelling the ripple effect impact in multi-echelon-correlated-events systems is becoming increasingly important. Notably, ripple effect assessment in multi-stage SCs is particularly challenged by the need to consider both vulnerability and recoverability capabilities at individual firms in the network. We construct a new model based on integration of Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) to quantify the ripple effect. We use the DTMC to model the recovery and vulnerability of suppliers. The proposed DTMC model is then equalised with a DBN model in order to simulate the propagation behaviour of supplier disruption in the SC. Finally, we propose a metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level. This ripple effect metric is applied to two case studies and analysed. The findings suggest that our model can be of value in uncovering latent high-risk paths in the SC, analysing the performance impact of both a disruption and its propagation, and prioritising contingency and recovery policies. [ABSTRACT FROM AUTHOR]

We investigate the interrelations of structural and operational vulnerabilities in the supply chain (SC) using discrete-event simulation for a real life case study. We theorise a notion of SC overlays and explore conditions surrounding their appearance. Such overlays occur if the negative consequences of changes in a SC structure as a result of a disruption are either amplified or mitigated by changes in the operational environment. We hypothesise that these overlays can be both reciprocal (i.e. complementary or mitigating) and aggravate (i.e. concurrent or enhancing). Our approach can be used for an efficient management of SC resilience capabilities by varying their levels over time. We show different ripple and bullwhip effect profiles, which lead to either reciprocal or aggravate overlays, and then we develop recommendations on the overlay-driven dynamic variation of resilience capability levels in order to enhance both SC resilience and efficiency through dynamic redundancy allocation. The results can be of value in selecting and deploying operational policies at the right time and scale during and after the recovery periods. Restricting analysis to the disruption period only and ignoring operational dynamics after capacity recovery can result in misleading or inefficient SC resilience and recovery policies. [ABSTRACT FROM AUTHOR]

An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviours by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g. food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualisation of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas. [ABSTRACT FROM AUTHOR]

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept 'virtual operation'. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC. [ABSTRACT FROM AUTHOR]

The ripple effect refers to structural dynamics and describes a downstream propagation of the downscaling in demand fulfilment in the supply chain (SC) as a result of a severe disruption. The bullwhip effect refers to operational dynamics and amplifies in the upstream direction as ordering oscillations. Being interested in uncovering if the ripple effect can be a driver of the bullwhip effect, we performed a simulation-based study to investigate the interrelations of the structural and operational dynamics in the SC. The results advance our knowledge about both ripple and bullwhip effects and reveal, for the first time, that the ripple effect can be a bullwhip-effect driver, while the latter can be launched by a severe disruption even in the downstream direction. The findings show that the ripple effect influences the bullwhip effect through backlog accumulation over the disruption time as a consequence of non-coordinated ordering and production planning policies. To cope with this effect, a contingent production-inventory control policy is proposed that provides results in favour of information coordination in SC disruption management to mitigate both ripple and bullwhip effects. The SC managers need to take into account the risk of bullwhip effect during the capacity disruption and recovery periods. [ABSTRACT FROM AUTHOR]

Giant retailers sell their store brands and (re)sell substitute national brands for manufacturers (suppliers) simultaneously, which becomes an important sourcing and selling supply chain strategy in the sharing economy era. In this paper, we develop a game-theoretic model to examine the impact of risk-averse suppliers on dual sourcing decisions under supply disruption when two substitute products are sold in a common market. We examine the impact of risk-aversion, demand volatility, and supply disruption on the supply chain profitability and pricing policies. We hypothesise and then analytically derive several propositions on the dependencies of the profitability and risk-aversion, demand volatility and pricing, and risk-aversion and pricing. We also conduct numerical analysis to explore the impact of disruption. We offer insights for managers and researchers alike by uncovering the role of supplier risk-aversion for decision-making settings with substitute products and dual sourcing when product supply may be disrupted. [ABSTRACT FROM AUTHOR]

This study suggests a new approach to supply chain (SC) disruption risk management where SC behaviour is less dependent on the certainty of our knowledge about the environment and its changes. The unpredictability of the occurrence of disruption and its magnitude suggests that designing SCs with a low need for 'certainty' may be as important, if not more so, than predetermined disruption control strategies. In this setting, this study calls for the development of a new perspective in SC disruption management, i.e. low-certainty-need (LCN) SCs. A number of principles and concepts is derived in recent, relevant literature to structure the characteristics of the LCN framework and its management. Structural variety, process flexibility, and parametrical redundancy are identified as key LCN SC characteristics that ensure efficient disruption resistance as well as recovery resource allocation. Two efficiency capabilities of the LCN SC are shown, i.e. low need for uncertainty consideration in planning decisions and low need for recovery coordination efforts based on a combination of lean and resilient elements. The results allow the identification of an LCN SC framework, concepts and technologies for its implementation as well as missing themes and new research questions which contribute to a better understanding of SC disruption risks. Special focus is directed on the digital technology usage in the LCN framework implementation. [ABSTRACT FROM AUTHOR]

The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two isolated areas, i.e. the impact of digitalisation on SC management (SCM) and the impact of SCM on the ripple effect control. To the best of our knowledge, this is the first study that connects business, information, engineering and analytics perspectives on digitalisation and SC risks. This paper does not pretend to be encyclopedic, but rather analyses recent literature and case-studies seeking to bring the discussion further with the help of a conceptual framework for researching the relationships between digitalisation and SC disruptions risks. In addition, it emerges with an SC risk analytics framework. It analyses perspectives and future transformations that can be expected in transition towards cyber-physical SCs. With these two frameworks, this study contributes to the literature by answering the questions of (1) what relations exist between big data analytics, Industry 4.0, additive manufacturing, advanced trace & tracking systems and SC disruption risks; (2) how digitalisation can contribute to enhancing ripple effect control; and (3) what digital technology-based extensions can trigger the developments towards SC risk analytics. [ABSTRACT FROM AUTHOR]

This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal-logical constraints with applications to short term job and flow shop scheduling, and hybrid structural-terminal-logical constraints with applications to customised assembly systems such as Industry 4.0. Computational algorithms in state, control and adjoint variable spaces are discussed. [ABSTRACT FROM AUTHOR]

Supply chain engineering models with resilience considerations have been mostly focused on disruption impact quantification within one analysis layer, such as supply chain design or planning. Performance impact of disruptions has been typically analysed without scheduling of recovery actions. Taking into account schedule recovery actions and their duration times, this study extends the existing literature to supply chain scheduling and resilience analysis by an explicit integration of the optimal schedule recovery policy and supply chain resilience. In particular, we compute a schedule optimal control policy and analyse the performance of this policy by varying the perturbation vector and representing the outcomes of variations in the form of an attainable set. We propose a scheduling model that considers the coordination of recovery actions in the supply chain. Further, we suggest a resilience index by using the notion of attainable sets. The attainable sets are known in control theory; their calculation is based on the schedule control model results and the minimax regret approach for continuous time parameters given by intervals. We show that the proposed indicator can be used to estimate the impact of disruption and recovery dynamics on the achievement of planned performance in the supply chain. [ABSTRACT FROM AUTHOR]

Increasing product proliferation, customisation, competition and customer expectations, as well as supply side disruptions, pose significant challenges to firm operations. Such challenges require improved efficiency and resilience in manufacturing, service and supply chain systems. New and innovative flexibility concepts and models offer a prospective route to such operational improvements. Several emerging issues in flexibility, such as risk and uncertainty management, environmental sustainability, optimal strategies under competition, optimal operations with strategic consumer behaviours are being examined in this regard. This overview provides a concise review of these critical research issues, and discusses related papers featured in this special issue. Four major flexibility drivers are classified: disruption risks, resilience and the ripple effect in the supply chain; digitalisation, smart operations and e-supply chains; sustainability and closed-loop supply chains; and supplier integration and behavioural flexibility. [ABSTRACT FROM AUTHOR]

Dynamics of structures and processes is one of the underlying challenges in supply chain management, where multiple dimensions of economic efficiency, risk management and sustainability are interconnected. One of the substantiated issues in supply chain dynamics is resilience. Resilience has a number of intersections with supply chain sustainability. This paper aims at analysing disruption propagation in the supply chain with consideration of sustainability factors in order to design resilient supply chain structure in regard to ripple effect mitigation and sustainability increase. Ripple effect in the supply chain occurs if a disruption at a supplier cannot be localised and cascades downstream impacting supply chain performance. This simulation-based study helps to identify what sustainability factors mitigate the ripple effect in the supply chain and what sustainability factors enhance this effect. The results indicate that (i) sustainable single sourcing enhances the ripple effect; (ii) facility fortification at major employers in regions mitigates the ripple effect and enhances sustainability; and (iii) a reduction in storage facilities in the supply chain downstream of a disruption-risky facility increases sustainability but causes the ripple effect. [ABSTRACT FROM AUTHOR]

In this study, the ripple effect in the supply chain is analysed. Ripple effect describes the impact of a disruption propagation on supply chain performance and disruption-based scope of changes in supply chain structural design and planning parameters. We delineate major features of the ripple effect as compared to the bullwhip effect. Subsequently, we review recent quantitative literature that tackled the ripple effect explicitly or implicitly and give our vision of the state of the art and perspectives. The literature is classified into mathematical optimisation, simulation, control theoretic and complexity and reliability research. We observe the reasons and mitigation strategies for the ripple effect in the supply chain and present the ripple effect control framework that includes redundancy, flexibility and resilience analysis. Even though a variety of valuable insights has been developed in the said area in recent years, some crucial research avenues have been identified for the near future. [ABSTRACT FROM AUTHOR]

Recent research underlines the crucial role of disruption events and recovery policies in supply chains. Despite a wealth of literature on supply chain design with disruption considerations, to the best of our knowledge there is no survey on supply chain with disruptions and recovery considerations. We analyse state-of-the-art research streams on supply chain design and planning with both disruptions and recovery considerations with the aim of relating the existing quantitative methods to empirical research. The paper structures and classifies existing research streams and application areas of different quantitative methods subject to different disruption risks and recovery measures. We identify gaps in current research and delineate future research avenues. The results of this study are twofold: operations and supply chain managers can observe which quantitative tools are available for different application areas; on the other hand, limitations and future research needs for decision-support methods in supply chain risk management domains can be identified. [ABSTRACT FROM AUTHOR]

In light of low-frequency/high-impact disruptions, the ripple effect has recently been introduced into academic literature on supply chain management. The ripple effect in the supply chain results from disruption propagation from the initial disruption point to the supply, production and distribution networks. While optimisation modelling dominates this research field, the potential of simulation modelling still remains under-explored. The objective of this study is to reveal research gaps that can be closed with the help of simulation modelling. First, recent literature on both optimisation and simulation modelling is analysed. Second, a simulation model for multi-stage supply chain design with consideration of capacity disruptions and experimental results is presented in order to depict major areas of simulation application to the ripple effect modelling. Based on both literature analysis and the modelling example, managerial insights and future research areas are identified in regard to simulation modelling application to the ripple effect analysis in the supply chain. The paper concludes by summarising the most important insights and outlining a future research agenda. [ABSTRACT FROM AUTHOR]

We consider time-critical supply chains (SCs) in the Australia dairy industry and recovery policies in the presence of the ripple effect. Ripple effect is the impact of a disruption on SC economic performance and disruption-based scope of changes needed in the supply structures and parameters to preserve the resilience. First, we describe the ripple effect in general and one example of the ripple effect in the dairy SC in Australia. Second, we present a model for reactive recovery policies in the dairy SC under conditions of the ripple effect and exemplify them on a simulation example. The results of this study can be used in future for comparing proactive and reactive approaches in tackling the ripple effect from resilience and flexibility views. [ABSTRACT FROM PUBLISHER]

In the context of heterogeneous catalysis, oxygen on metals and oxygen on metal oxides are often considered as two different phenomena that are little related to each other. The former is mainly investigated in the field of Surface Science and the calculation chemistry, while the latter in the field of conventional catalysis. In this review we performed a joint analysis of the literature data obtained in both fields, which showed that in many aspects these two types of oxygen are chemically similar. It is usually assumed that, as in the case of oxides, the reactivity of oxygen on metals is determined by its binding energy to the surface. Trying to confirm this idea by comparing the catalytic activity of metals with the heat of oxygen adsorption (QO2), we came across a paradox of excessive metal activity, which was associated with the presence of "hot" or super active (SA) oxygen. This oxygen is capable of oxidizing methane and other substances at cryogenic temperatures but does not make a significant contribution to QO2. A comparison of SA oxygen on metals with the O•– radicals on oxides revealed a clear similarity of these species. This allows one to conclude that the oxygen on metals is a radical, which well explains its super high reactivity without using the idea of an energetically excited state. A hypothesis is proposed that not only O•–, but also the surface O2- species have a certain degree of the radical nature. Results of low-temperature reactions of SA oxygen offer a more precise interpretation of the selectivity rule. Not the high or low reactivity of surface oxygen in itself is essential for the selective catalyst. A consent between the rates of the product formation and its desorption from the surface is of vital importance. This concept opens the possibility for new approaches in the development of selective catalysts, including those based on metals. [ABSTRACT FROM AUTHOR]

Continuous flow scheduling problems have their place in many industries such as gas, oil, chemicals, glass and fluids production as well as production of granular goods and steel details. The disruptions in processing capacities may result in schedule performance decrease. In this paper, we develop a new method for robustness analysis of those schedules that are formulated in continuous time in the state-space domain. The developed method is based on attainable sets (ASs) that allow computing a form to represent the states and performance of schedules in regard to different capacity degradation levels. Having such a form, it becomes possible to estimate the schedule robustness. The technical development and approximation of ASs are presented. A robustness index is developed on the basis of the minimax regret approach, and it can be used for decision-makers regarding the trade-off ‘performance vs. robustness’. As such, it becomes possible to compare maximal possible profits in situations without disruptions and realistic profits subject to some robustness investments and costs of protection against disruptions. With the presented results, it becomes possible to obtain ASs for interval data with no a priori information about perturbation impacts, i.e. for non-stationary perturbations. ASs permit to consider perturbations and schedule performances astime functions. Perturbation functions may be set up for different uncertainty scenarios, including interval perturbations. [ABSTRACT FROM AUTHOR]

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed. [ABSTRACT FROM AUTHOR]

An introduction to this issue of the "International Journal of Production Research," focusing on supply network dynamics, control and disruption management, is presented.

In recent years, remarkable advancements have been achieved in quantitative analysis methods for supply chain design (SCD). Typically, cost or service level optimisation has been included in the objective functions. At the same time, supply chain managers face the ripple effect that arises from vulnerability, instability and disruptions in supply chains. This research aimed to quantify the ripple effect in the supply chain from the structural perspective. The research agenda of this study includes issues of integrating operability objectives as new key performance indicators, e.g. resilience, stability, robustness into SCD decisions. The research is based on a simultaneous consideration of both static structural properties of SCD and execution dynamics subject to uncertainty and disruptions. Due to high dimensionality of real SCD problems, such integration can hardly be implemented in only one model. In this study, an original two-model multi-criteria approach is proposed in order to assess the potential ability of an SCD to remain stable and resilient. This modelling approach is based on a combined application of a static and a dynamic model. A multi-criteria approach relies on the analytic hierarchy process method. The results of this research can be used as an additional quantitative analysis tool in order to select an SCD. An additional application of the developed method is that it can be used at the control stage in order to adapt supply chain execution subject to the achievement of desired economic performance. [ABSTRACT FROM AUTHOR]

We study the dynamic planning decisions of a logistics service provider that is in charge of the integrated supply chain (SC) planning. We examine an SC with multiple products, suppliers, transit nodes and customers in a multi-period mode. The logistics service provider is responsible for aggregate distribution planning and operative dynamic transportation planning. It is to decide on aggregate distribution flows as well as on the time-dependent intensities (i.e. transportation batches). These decisions are tightly incorporated but previous research considered them mostly isolated. This is quite natural since these problems contain data of different detail degree, which can be hardly incorporated in only one model. To resolve this problem, we present a hybrid multi-period, multi-commodity distribution–transportation model as an optimal control problem blended with mathematical programming. This contribution has some particular features. First, it extends previous research by elaborating on the dynamic optimal control model. Second, it considers multi-period problem statement with multiple products and alternative transportation channels. Third, we represent the integrated dynamic SC planning in order to take into account the ripple effect. With the results of this study, the dynamic issues in integrated logistics planning in the SC can be addressed, and an intelligent solution to an important problem of SC management has been proposed. [ABSTRACT FROM PUBLISHER]

We theorize a notion of a digital supply chain (SC) twin – a computerized model that represents network states for any given moment in real time. We explore the conditions surrounding the design and implementation of the digital twins when managing disruption risks in SCs. The combination of model-based and data-driven approaches allows uncovering the interrelations of risk data, disruption modeling, and performance assessment. The SC shocks and adaptations amid the COVID-19 pandemic along with post-pandemic recoveries provide indisputable evidences for the urgent needs of digital twins for mapping supply networks and ensuring visibility. The results of this study contribute to the research and practice of SC risk management by enhancing predictive and reactive decisions to utilize the advantages of SC visualization, historical disruption data analysis, and real-time disruption data and ensure end-to-end visibility and business continuity in global companies. [ABSTRACT FROM AUTHOR]

We study the problem of scheduling in manufacturing environments which are dynamically reconfigurable for supporting highly flexible individual operation compositions of the jobs. We show that such production environments yield the simultaneous process design and operation sequencing with dynamically changing hybrid structural-logical constraints. We conceptualize a model to schedule jobs in such environments when the structural-logical constraints are changing dynamically and offer a design framework of algorithmic development to obtain a tractable solution analytically within the proven axiomatic of the optimal control and mathematical optimization. We further develop an algorithm to simultaneously determine the process design and operation sequencing. The algorithm is decomposition-based and leads to an approximate solution of the underlying optimization problem that is modeled by optimal control. We theoretically analyze the algorithmic complexity and apply this approach on an illustrative example. The findings suggest that our approach can be of value for modeling problems with a simultaneous process design and operation sequencing when the structural and logical constraints are dynamic and interconnected. Utilizing the outcomes of this research could also support the analysis of processing dynamics during the operations execution. [ABSTRACT FROM AUTHOR]

An integrated multi-stage scheduling and routing problem with alternative machines at each supply chain (SC) stage and non-preemptive operations is studied. The peculiarity of the considered problem is the consideration of both continuous flows in operations and discrete assignments. Such problems are typical in the process industry. Taking into account the basic aspects that pertain to this case, a dynamic decomposition of the problem on the basis of optimal programme control is proposed. A particular feature and novelty of the presented study is the consideration of non-preemptive operations and their execution dynamics. The solution is based on a combined application of discrete and continuous optimization. With the special properties of the proposed scheduling model, it becomes possible to develop an optimal solution to the integrated scheduling and routing of jobs. Since the schedule generation is formulated in control terms, the models of scheduling and adaptation can be integrated. From the practical point of view, such integration can be advantageous subject to further dynamic analysis of the robustness, stability, and adaptability of SC schedules. [ABSTRACT FROM AUTHOR]

This study aims at presenting the Ripple effect in supply chains. It develops different dimensions of the Ripple effect and summarises recent developments in the field of supply chain (SC) disruption management from a multi-disciplinary perspective. It structures and classifies existing research streams and applications areas of different quantitative methods to the Ripple effect analysis as well as identifying gaps in current research and delineating future research avenues. The analysis shows that different frameworks already exist implicitly for tackling the Ripple effect in the SC dynamics, control and disruption management domain. However, quantitative analysis tools are still rarely applied in praxis. We conclude that the Ripple effect can be the phenomenon that is able to consolidate research in SC disruption management and recovery similar to the bullwhip effect regarding demand and lead time fluctuations. This may build the agenda for future research on SC dynamics, control, continuity and disruption management, making supply chains more robust, adaptable and profitable. [ABSTRACT FROM PUBLISHER]

In this paper, a dual problem formulation is developed in order to solve a multi-objective, multi-period and multi-commodity production–distribution (re)planning problem for a multi-stage centralised upstream network with structure dynamics considerations. The original idea of this study is to use the real logic of decision-making in companies and exclude demand constraint from the linear programming (LP) model. This allows transit from the classical transportation problem, formulated as an LP model in order to improve service level at the strategic inventory holding point. The formulation as an LP model makes it possible to formulate the dual problem. The performed structural analysis revealed some interesting managerial insights into building robust distribution plans and interconnecting decisions on distribution network design, planning, and sourcing. [ABSTRACT FROM AUTHOR]