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This study addresses a practical problem within a multi-level supply chain where a wide range of customers can be served through different strategies such as make-to-stock, make-to-order, or vendor-managed inventory. The customer demand is stochastic, and sensitive to pricing associated with different production-inventory strategies. We propose a two-stage stochastic mixed-integer non-linear programming model. In the first stage, decisions are made regarding the selection of production-inventory strategies and pricing to maximise the expected profit. The second stage involves decisions related to production, inventory, and distribution, which are used to evaluate the first-stage decisions under various scenarios with different levels of accuracy. To solve the model, a metaheuristic approach based on the Simulated Annealing algorithm is developed. To showcase the practical applicability of our model and solution approach, we use a real case study in a Canadian pulp and paper supply chain. The results revealed that both the production-inventory strategy assigned to customers and the sales price underwent changes across scenarios. Furthermore, we demonstrated that by implementing the SA algorithm, we could improve the initial profit by up to 1.43% through slight adjustments in the sales price and assigned strategies for customers. [ABSTRACT FROM AUTHOR]

The pulp and paper industry has significant sustainability implications and necessarily requires a careful consideration of direct and lower-tier suppliers for effective sustainable supply chain management. This paper utilized an extensive literature review and semi-structured interviews of experts in the Chinese pulp and paper industry to unearth the factors that highly influence multi-tier supply chain sustainability. A Technological-Organizational-Environmental (TOE) and Human-Organizational-Technological (HOT-fit) based theoretical framework was employed to classify the identified factors. Then, an Analytical Hierarchy Process (AHP) based methodology was applied to determine the relative importance of the factors. A comparison analysis of the relative importance of the factors as determined by the experts in the focal companies, Tier-1 suppliers and Tier-2 suppliers of the Chinese pulp and paper industry is presented. The results show that institutional and technological factors are most critical to actualizing multi-tier supply chain sustainability. Thus, the study outcomes present relevant theoretical and practical implications for the managers and practitioners in the pulp and paper industry on how to facilitate multi-tier supply chain sustainability for increased competitiveness. Furthermore, this study provides guidelines for other industries as well and sets the stage for subsequent theorization and exploration of multi-tier supply chain sustainability for expected performance gains. [ABSTRACT FROM AUTHOR]

Transforming Pulp and Paper (P&P) mills into Integrated Forest Biorefineries (IFBR) is a prominent solution to save Canadian P&P industry that has been facing decline of conventional paper demand. We propose a comprehensive decision model for the design of IFBR value chains by taking the uncertain demand of bioproducts into consideration. In particular, we propose a multi-stage stochastic programming model to obtain the optimal investment plan over a long-term planning horizon in the presence of various market trends. We also develop a Monte-Carlo simulation platform to validate the proposed model and to compare its performance with alternative decision models. The model is applied to a realistic case study inspired from P&P companies in Canada, where the value of incorporating the dynamic nature of uncertain demand has been estimated. Further, we elaborate on the value of considering flexibility in terms of adjusting the investment plan in response to changes in the demand. Our results indicate that the demand for bioproducts has a substantial impact on the profitability of the IFBR. We also demonstrated the significant value of explicitly incorporating the uncertainty in IFBR network design as well as adapting the investment plan to the changes in the demand. [ABSTRACT FROM AUTHOR]

This editorial introduces the special issue "The Interplay Between Artificial Intelligence, Production Systems, and Operations Management Resilience.' We selected twelve papers, encompassing many angles that illuminate the advances and challenges dealing with artificial intelligence tools and approaches in the production systems and operations management resilience domains. This editorial presents the papers with a smart view, highlighting the essentials of each article, such as full paper title, background, theory/literature scope, methodology design/analysis approach, and the main findings/contributions. Finally, the conclusions, future pathways, and research directions are presented. [ABSTRACT FROM AUTHOR]

This special issue of the International Journal of Production Research, focuses on the 'Carbon Neutrality' and 'SCM'. Nine articles have been selected for this special issue, and they cover various aspects to understand the manifestation of 'Carbon Neutrality' in supply chains, and to further illuminate how to achieve 'Carbon Neutrality' with supply chain innovation. This editorial provides a brief overview of the research domain, then introduces each article in the context of the state-of-the-art and highlights the contributions of selected papers to the field. Finally, the research perspectives are discussed. [ABSTRACT FROM AUTHOR]

The paper describes a smart model for palletising as main intra-logistics task for product-on-pallet distribution, and its development in Logistics 4.0 framework aligned to Industry 4.0. The model is developed in holonic paradigm as a 2-layer Holonic Logistics Execution System (HLES) in semiheterarchical topology. Scheduling of logistics activities and allocation to resources are optimised for global efficiency on compact time periods. The logistics model virtualises physical entities: resources, pallets and orders as holons implemented with digital twin software, and categories of task workloads: resource health monitoring, dispatching and tracking pallet orders, re-assigning jobs at resource failure, which grants reality-awareness and robustness. Global workload optimisation uses Constraint Programming as decision making technology with ILOG optimiser engine as situation-specific solver tool in SaaS cloud model, and delegate multi-agent system (MAS) technology for intelligence distribution. The optimised objective function is a combination of palletising cost weighted by robot speed limits and pallet storage cost in payable stocks. The main constructs are exemplified and validated on a real-life structure with multiple palletising resources in which programmable logic controllers (PLC) coordinate locally the parallel execution of order holons according to their globally optimised sequence. [ABSTRACT FROM AUTHOR]

The integrated planning of several activities implicated in paper production can lead to remarkable gains in terms of raw materials and resource usage. However, activities such as order assignment, production sequencing and cutting planning are usually addressed separately while ignoring the interactions among these processes. But the quality of the solution resulting from the juxtaposition of the partial solutions is not guaranteed, and may have a significant impact in terms of inefficiency of global performance. This article considers production planning in a pulp and paper industry in order to meet a set of orders of diverse types of products, admitting the possibility of producing simultaneously in two or more paper machines with their own features. The developed approach, ASC-3Steps, considers not only cutting optimisation but also extends itself to the processes of assigning orders to paper machines and production sequencing at each machine. Minimisation of wasted paper is assumed to be the overall goal. A set of computational results based on real data is presented. [ABSTRACT FROM AUTHOR]

For manufacturing companies, quality management is a key feature for increasing the competitiveness, productivity, profitability, and sustainability of their systems. Quality improvement (QI) methods aim to achieve high-quality parts without reducing performance. The Industry 4.0 framework brought technological developments that cannot be used by traditional QI methods, such as Six Sigma, Lean, Lean Six, the Theory of Constraints, and Total Quality Management, which are widely used in manufacturing companies. The need for higher manufacturing sustainability and market requirements has led to the search for alternative QI methods with superior performance to traditional QI methods such as Zero-Defect Manufacturing (ZDM). The current paper is a position paper with a goal to present the ZDM approach and providing a clear definition about ZDM to align everyone in one common understanding of ZDM. Many researchers and manufactures are skeptical about ZDM, therefore, numerous argumentative questions have been created and answered, to convince them why they should migrate from traditional QI methods to ZDM. The migration to ZDM has already started, to support this statement numerous facts from the literature have been presented. Finally, several directions were identified, demonstrating that there is still plenty of room for research in several domains. [ABSTRACT FROM AUTHOR]

This study examines a two-stage two-dimensional cutting stock problem encountered by a paper mill company. The problem includes various machine-related and operational constraints based on real-world situations. Paper products are manufactured using two major cutting processes. Each cutting machine has a specific minimum and maximum width for input and output rolls and is limited by the maximum number of rolls it can cut at the same time. A mathematical model is presented to formally address the problem and an efficient multiple-choice knapsack-based heuristic algorithm is proposed to solve the problem. To demonstrate the efficiency of the proposed heuristic algorithm, computational experiments are conducted on test data-set generated from real-world data provided by a large paper mill company in the Republic of Korea. [ABSTRACT FROM PUBLISHER]

This paper presents a green manufacturing-recycling network design approach for multi-echelon electronic assembly process under a complex product mix scenario which incorporates trade-off between economic cost and heavy metal pollutants. The approach is developed by formulating the green manufacturing-recycling network into a closed-loop logistics bi-objective optimisation model, in order to alleviate negative environmental influence by applying proper cleaner production technologies. The environmental influence is quantified by pollution equivalent numbers which are integrated in the model. Moreover, the volume of the heavy metal pollutants is optimised by selecting the cleaner production level and thus is jointly coordinated with the operation cost. To solve the bi-objective optimisation model, an enhanced global criterion approach is presented to improve the effectiveness on dealing with non-convexity of the Pareto-optimal frontier with computational efficient solutions. The proposed model is implemented on a case study to verify its flexibility to handle the closed-loop manufacturing-recycling network design of different sizes, as well as to obtain trade-off between operation cost and environmental influence by the heavy metal pollutants under various scenarios. [ABSTRACT FROM AUTHOR]

This paper introduces the background, concept and definition of the Industry Commons. It initiates a discussion on the positioning of the Industry Commons Ecosystem (ICE) with respect to current research directions in advanced manufacturing and production systems that shape advances in engineering and technology, novel business models and innovation breakthroughs. The potential value of data sharing across industrial domains is estimated at over $100 billion, particularly in view of optimising manufacturing processes. Data sharing across domains however faces a series of well-documented challenges associated with the lack of semantic interoperability and related standards, management of trust and sustainability. Solving bottlenecks in data sharing requires a systemic approach to data management, which can account for all aspects of data use, levels of application, attribution and dynamic exchanges. In this paper we propose a high-level ecosystem approach that integrates societal values with digital affordances of industry's cognitive-assisted processes, remote interfacing, hybrid applications and large-scale value networks. Early development of an Ontology Commons EcoSystem (OCES) is presented as the key enabling framework for Industry Commons interoperability and a series of enabling frameworks form the basis of future research directions in Trusted Data Sharing and Closed-Loop Lifecycle Management for greater sustainability. Abbreviations: AI – Artificial Intelligence; AIOTI – Alliance of Internet-of-Things Innovation; ALM – Asset Lifecycle Management; ALO – Application-Level Ontology; AP – Application Protocol; API – Application Programming Interface; B2B – Business-to-Business; B2C – Business-to-Customer; CDE – Cross-Domain Ecosystem; CDEI – Cross-Domain Ecosystem Interoperability; CL2M – Closed-Loop Lifecycle Management; CNO – Collaborative Networked Organisations; CPS – Cyber-Physical Systems; CSR – Corporate Social Responsibility; DLO – Domain-Level Ontology; DLT – Distributed Ledger Technology; EM – Enterprise Modelling; FAIR – Findable, Accessible, Interoperable and Reusable; GUI – Graphical User Interface; ICE, Industry Commons Ecosystem; IOF – Industrial Ontology Foundry; IP – Intellectual Property; IPR – Intellectual Property Rights; ISN – Intertwined Supply Network; MIR – Music Information Retrieval; MLO – Middle-Level Ontology; MO – Meta-Ontology; OCES – Ontology Commons EcoSystem; PI – Physical Internet; PLM – Product Lifecycle Management; ROI – Return-on-Investment; SC – Supply Chain; SCM – Supply Chain Management; SOS – System-of-Systems; TDS – Trusted Data Sharing; TLO – Top-Level Ontology; TRO – Top Reference Ontology; TUI – Tangible User Interface. [ABSTRACT FROM AUTHOR]

Maintenance activities are crucial for all manufacturing industries. To ensure availability and lifetime of production equipment, operations management and logistics support for maintenance need to evolve year after year. Besides, Centralised Maintenance Workshops are one of the most interesting approaches to reduce the cost and time required to repair faulty equipment. Generally known in the research community as 'repair shops', they aim to pool all the resources needed to repair defective equipment provided by different production sites. This paper aims to provide a comprehensive overview of repair shops and to present opportunities for future research with a focus on the circular economy context. The most relevant papers have been rigorously selected and analyzed, providing interesting reference materials on the subject. Repair shops are a set of workstations, operators, and spare parts inventories required to restore a group of failed production equipment. After detecting the origin of the failures, there are two options: either repair the equipment by restoring its defective components or replace the defective components with others in good working order. In the case of non-repairable components/equipment, circular strategies allow identification of components/equipment that could be restored and used to supply the spare parts warehouse. [ABSTRACT FROM AUTHOR]

The privatisation of the Iran newsprint industry has caused an increase in production and decrease in importation. Recognition of effective criteria in newsprint privatisation will accelerate privatisation of the industry. The goal of the research is expediting privatisation in the Iran newsprint industry through benefits, costs, opportunities and risk merits. We applied analytical hierarchy process (AHP) to solve the problem plus questionnaires and pair wise comparisons of the matrices by the experts have also been done in the research. The results show a decrease of budget deficiency, national debts (economic and financial), limiting rules, heavy taxes (economic and executive), encouragement possibility, absorption of foreign, internal investment (economic and financial) and limiting regulative costs (economic and financial) which are the most important sub criteria regarding benefits, costs, opportunities and risks merits, respectively. [ABSTRACT FROM PUBLISHER]

At present, the evaluation index for low carbon manufacturing was mainly focused on the relationship between carbon emission and economic benefits or removal volume. But there was rarely comprehensive evaluation index to evaluate the carbon utilisation level of processing. Based on this, this paper presented the carbon utilisation efficiency as the comprehensive evaluation index in low carbon manufacturing. The carbon utilisation efficiency was defined as the ratio of the carbon emission of materials removal to the whole carbon emission in manufacturing process. A carbon utilisation efficiency model was established in milling process, based on flow characteristics and removal mechanism of carbon emission during milling. Then a multi-objective optimisation model was established based on Particle Swarm Optimisation, and the minimum processing time and high carbon utilisation efficiency were set as the optimisation objectives. And the experiment was performed to confirm the undetermined constant in the optimisation model and verify the effectiveness of the optimisation model. The optimised milling parameters were verified to reduce the processing time and improve the carbon utilisation efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the literature concerning the implementation of digital twins (DTs) for zero-defect manufacturing (ZDM) following a systematic method and, guided by a preliminary finding that a structured and standardised approach to the development of the DT applications is lacking, we provide a standardised design methodology to guide researchers and practitioners in their efforts to develop DTs regardless of the domain. After examination and interpretation of the literature, we also present the results of our state-of-the-art analysis, discuss the current state and limitations of research and practice, and provide useful insights on this important and complex topic. The design methodology proposed in our study will benefit both practitioners and academicians by covering the essential elements to be considered when developing DTs for ZDM for any applications in this domain. The study also contributes to knowledge by presenting a structured overview of the specific research area with a comprehensive, systematic, and critical analysis of the literature and by providing answers to some fundamental questions in the context of DTs for ZDM. Finally, we provide suggestions for further developments in research and practice. [ABSTRACT FROM AUTHOR]

This paper provides a systematic review and analysis of the game-theroy-based research in Green Supply Chains Management (GSCM). Based on a rigorous content analysis of 108 papers carefully selected from the literature, we present a detailed picture of the current state of this research and point to the promising future directions. Our analysis shows a rapid increase in the number of publications during the last three years, with an imbalance in the contribution of the publishing outlets, as nearly two third of the reviewed papers are published by only four journals. Results also show the dominance of deterministic static models and simple two-level SC structures. Developing stochastic models, considering the risk and the evolutionary nature that inherently characterise the business environment and investigating more complex multi-level SC structures are among the promising future research opportunities. [ABSTRACT FROM AUTHOR]

The orders makespan and resources utilisation are considered as the objective function of the model, and the heterogeneous production resources and logistics resources are integrated to autonomously communicate and interact with each other to bidding for the dynamic production-logistics-integrated operation tasks. The aim of this special issue is to encourage original and latest contributions, and to review and survey research and development on industrial intelligence-driven production and operations management, focusing on state-of-the-art and potential future approaches and technologies and providing a good starting point for researchers entering these research areas. Then, to evaluate the tolerance and persistence capabilities of MSC under supply and demand uncertainties, a graph-based operational robustness analysis method of the IIoT platform for MSC is proposed. The 8th paper entitled 'An integrative decision-making model for the Internet of Things-enabled supply chains of fresh agri-product', by Han et al. proposed a mixed-integer programming model to generate integrative decision-making in the Internet of Things (IoT)-enabled fresh agri-products supply chains. [Extracted from the article]

This paper discusses mechanisms for the allocation of emission permits (AEP) among a group of manufacturing companies aiming at controlling the total emission level of the group. Data envelopment analysis is adopted and extended to construct the AEP models. Proposed methods allocate the permits on the basis of the current input and output levels of individual firms, rather than historical records. Two variations of AEP mechanisms are considered. One situation is that one of the member firms dominates the responsibility of allocating emission permits on behalf of the group (individual AEP scenario). This dominating firm will maximise its own emission permit before allocating permits among other members in the group. The other situation is that a third-party central governing body is centrally responsible for coordinating the AEP among all group members (Central AEP scenario). Proposed mechanisms are applied to analyse efficiencies of a group of paper mills. The result shows that the mode of central AEP mechanism is a better choice than that of the individual AEP mechanism. Central AEP not only maximises the whole efficiency of the group but also improves efficiencies of individual firms. In contrast, with individual AEP, one firm gains efficiency at the loss of others. [ABSTRACT FROM PUBLISHER]

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

Over the last few years, the fourth industrial revolution has attracted more and more attentions all around the world. In the current literature, there is still a lack of efforts to systematically review the state of the art of this new industrial revolution wave. The aim of this study is to address this gap by investigating the academic progresses in Industry 4.0. A systematic literature review was carried out to analyse the academic articles within the Industry 4.0 topic that were published online until the end of June 2016. In this paper, the obtained results from both the general data analysis of included papers (e.g. relevant journals, their subject areas and categories, conferences, keywords) and the specific data analysis corresponding to four research sub-questions are illustrated and discussed. These results not only summarise the current research activities (e.g. main research directions, applied standards, employed software and hardware), but also indicate existing deficiencies and potential research directions through proposing a research agenda. Findings of this review can be used as the basis for future research in Industry 4.0 and related topics. [ABSTRACT FROM AUTHOR]

Due to the complexity and the magnitude of optimisation models that appeared in sustainable supply chain management (SSCM), the use of meta-heuristic algorithms as competent solution approaches is being increased in recent years. Although a massive number of publications exist around SSCM, no extant paper explicitly investigates the role of meta-heuristics in the sustainable (forward) supply chain. To fill this gap, a literature review is provided on meta-heuristic algorithms applied in SSCM by analyzing 160 rigorously selected papers published by the end of 2020. Our statistical analysis ascertains a considerable growth in the number of papers in recent years and reveals the contribution of 50 journals in forming the extant literature. The results also show that in the current literature the use of hybrid meta-heuristics is overtaking pure meta-heuristics, the genetic algorithm (GA) and the non-dominated sorting GA (NSGA-II) are the most-used single- and multi-objective algorithms, the aspects of sustainability are mostly addressed in connection with product distribution and routing of vehicles as pivotal operations in supply chain management, and last but not least, the economic-environmental category of sustainability has been further noticed by the scholars. Finally, a detailed discussion of findings and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

Revenue sharing contracts (RSCs) have gained considerable popularity over the past two decades—particularly among prominent platform distributors such as the Apple App Store, Google Play, and Amazon— and have therefore attracted a great deal of research attention in the domain of supply-chain management. This study is the first to review the literature dealing with RSCs, a literature in which the number of articles has been growing consistently over the years. Specifically, we aim to provide a comprehensive overview of the state of the art of the research field, and to identify potentially fruitful areas for further exploration. To this end, we have carried out a content analysis of 148 carefully selected papers that address various aspects of RSCs in the domains of operations research and operations management. Our survey classifies and discusses the reviewed papers along numerous dimensions of interest, including the types of contract they incorporate and the research questions they address. We provide mathematical formulations of the two RSC structures that are most prevalent in research and in practice: (i) a wholesale-price contract that incorporates an added revenue-sharing mechanism; and (ii) a consignment contract with revenue sharing. [ABSTRACT FROM AUTHOR]

This special issue discusses the present and future of production in the Asia Pacific region, which drives the current world economy. Based on the region's massive working population, production in the Asia Pacific region has led to economic growth, and the GDP share of this region has increased from 20% to 30% in the past 40 years, almost equalling that of the other regions. Considering the above background, this special issue was planned for selecting and presenting interesting and meaningful research papers that deal with current and future production, not only just productions but also services, in the Asia Pacific region. [Extracted from the article]

Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

With significant advancement in information technologies, Digital Twin has gained increasing attention as it offers an enabling tool to realise digitally-driven, cloud-enabled manufacturing. Given the nonlinear dynamics and uncertainty involved during the process of machinery degradation, proper design and adaptability of a Digital Twin model remain a challenge. This paper presents a Digital Twin reference model for rotating machinery fault diagnosis. The requirements for constructing the Digital Twin model are discussed, and a model updating scheme based on parameter sensitivity analysis is proposed to enhance the model adaptability. Experimental data are collected from a rotor system that emulates an unbalance fault and its progression. The data are then input to a Digital Twin model of the rotor system to investigate its ability of unbalance quantification and localisation for fault diagnosis. The results show that the constructed Digital Twin rotor model enables accurate diagnosis and adaptive degradation analysis. [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]

The COVID-19 pandemic severely tested the resilience and robustness of supply chains for medically critical items and various common household goods. Severe and prolonged shortages of personal protective equipment (PPE) and ventilators in the United States have revealed vulnerabilities in the supply chains of such essential products in a time of need. Consequently, corporations have felt public pressure to rethink their supply chains. We begin this paper by examining the underlying causes of the prolonged shortages of critical products in the US as well as government’s and some companies’ initial response. Drawing from the lessons learned from the COVID pandemic, we propose a research agenda and opportunities to develop responsive supply chains to fight future pandemics. These opportunities revolve around measures that are intended to improve the supply chain responsiveness of essential products to combat future pandemics and other major public health emergencies. [ABSTRACT FROM AUTHOR]

Industry 4.0 aims to establish highly flexible production, enabling effective and efficient mass customisation of products. Modelling techniques and simulation of production processes are among the core techniques of the manufacturing industry that facilitate flexibility and automation of a shop floor in the era of Industry 4.0. In this paper, we present an approach to support production process modelling and process model management. The approach is based on Model-Driven (MD) principles and comprises a Domain-Specific Modelling Language (DSML) named Multi-Level Production Process Modelling Language (MultiProLan). MultiProLan uses a set of concepts to specify production process models suitable for automatic instruction generation and execution of the instructions in a simulation or on a shop floor. By using MultiProLan, process designers may create process models independent of the specific production system. Such process models can either be automatically enriched by matching and scheduling algorithms or manually enriched by a process designer via MultiProLan's modelling tool. In this paper, we also present an application of our approach in the assembly industry to showcase its dynamic resource management, generation of production documentation, error handling and process monitoring. [ABSTRACT FROM AUTHOR]

This paper evaluates how technical assistive devices for manual materials handling were analysed in the literature in a warehousing context. Works that discuss the economic and/or human factors impact of assistive devices on the warehousing system or the people employed therein were identified in a systematic literature review. Building on a conceptual framework proposed in this paper, our evaluation of the literature shows which types of assistive devices were analysed in the past, and from which perspective these devices were examined. Some works studied the devices exclusively from an operator well-being or an efficiency perspective, while several works analysed the devices' performance in terms of both dimensions. Several works contained in our literature sample highlighted trade-offs between both ergonomic and economic measures and, within the first category, between alternative ergonomic indicators, which shows that assistive devices have to be evaluated carefully in light of their intended application. The paper further identifies research gaps and emphasises the need to understand the interactions between human- and system-related variables that can be supported by assistive devices in designing effective manual materials handling systems. [ABSTRACT FROM AUTHOR]

This paper introduces novel mathematical optimisation models for the 2-Dimensional guillotine Cutting Stock Problem with Variable-Sized Stock that appears in a Spanish company in the honeycomb cardboard industry. This problem mainly differs from the classical cutting stock problems in the stock, which is considered variable-sized, i.e. we have to decide the panel dimensions, width, and length. This approach is helpful in industries where the stock is produced simultaneously with the cutting process. The stock is then cut into smaller rectangular pieces that must meet the customers' requirements, such as the type of item, dimensions, demands, and technical specifications. Furthermore, in the problem tackled in this paper, the cuts are guillotine, performed side to side. The proposed mathematical models are validated using real data from the company, obtaining results that drastically reduce the produced material and leftovers, reducing operation times and economic costs. [ABSTRACT FROM AUTHOR]

Co-opetition is gaining increasing attention as a potentially useful form of inter-organisational collaboration model to improve firms' sustainable performance. However, limited previous studies have provided a clear substantive theory or offered empirical evidence for the process of sustainability-driven co-opetition. This paper explores how competing companies can collaborate in their supply chains (SCs) to achieve a higher level of sustainability performance by identifying drivers, facilitators and barriers of co-opetition. Based on two explorative case studies of co-opetition in the UK, the findings of this paper lead to a number of propositions and a theoretical framework for sustainability-driven co-opetition in SCs. This study contributes to the literature by providing a more in-depth understanding of co-opetition as a strategic capability for firms. This paper also proves the feasibility of a combined use of Resource-Based View and Network Theory perspectives in explaining a paradoxical inter-organisational relationship like co-opetition. A road map for sustainability-driven co-opetition in SCs is also provided as a heuristic decision model for practitioners. [ABSTRACT FROM AUTHOR]

With the development of ecommerce and digital technologies, we have witnessed a boom of platform operations. Amid their strong growth prospects, it is observed that platforms have created controversial issues and challenges, which could be potentially solved by the combination of blockchain features. More and more studies exploring the implementation of blockchain in platform operations have appeared in recent years. In this paper, motivated by the industrial practices of different types of product selling and service platforms, we first conduct a holistic literature review of the most representative papers relevant to platform operations. Examining the value of different blockchain features in operations management field, we further review the related works on platform operations with blockchain technologies. Afterwards, we analyse the innovative applications and challenges in platforms such as physical and virtual product selling platforms, rental/sharing and on-demand service platforms. A future research agenda is established which lays the solid foundation for further studies on platform operations in the blockchain era. [ABSTRACT FROM AUTHOR]

In the past decade, many organisations have become much more conscious of sustainability with innovation in their manufacturing and supply chain management (Kusi-Sarpong, Gupta, and Sarkis [3]). In this paper, a fuzzy analytic hierarchy process evaluation approach is developed and simulation results the feasibility of the proposed supply chain configuration method. [Extracted from the article]

The article discusses the enabling of technologies and frameworks for new product development. This issue of "International Journal of Production Research" contains various papers from the "3rd International Conference on Manufacturing Research." Several of these papers discussed provisions and sharing of knowledge through digital frameworks and ontologies, supporting tools and evaluation systems, as well as design management and practice models for new product development.

The paper aims to describe a structured framework to implement, sustain and manage a quality-costing program in a process industry. After briefing about the advantages and disadvantages of various cost accounting approaches practiced by the industries, the authors described the need for attaching fuzziness to the notion of 'quality'. The proposed approach is discussed with the help of a case from paper industry. After obtaining expert elicitation, the imprecise, vague, and complex information related to quality cost items (under four key cost segments, i.e. prevention [P], appraisal [A], Internal failure [IF], and external failure [EF]) is synthesized using well-established principles of fuzzy set theory. To help the management in successful implementation of quality cost accounting system (QCAS) five alternatives for each cost category were considered. By obtaining the priority values with respect to various alternatives the implementation program was revived. The comparative analysis carried out after collecting the information under PAF cost segments showed a progressive and significant change in quality costs. [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]

Reverse Logistics and Closed Loop Supply Chain Management (RL&CLSCM) are universally recognised as two environmentally friendly practices that could help in greening conventional supply chains. The popularity of these fields of research has led to the evolution of a vast number of publications in the literature. Various journals have so far contributed to the advancement of the field, with the International Journal of Production of Research (IJPR) being one of the most prominent contributed journals. To celebrate the 55th volume anniversary of IJPR, this review study aims to investigate the studies in the area of RL&CLSCM published in IJPR. The paper presents a thorough bibliometric and content analysis of 94 studies that were published in IJPR from 2000 until July 2017. The studies initially underwent a rigorous bibliometric and mapping analysis that helped to identify their characteristics. Subsequently, the contents of the sample papers were evaluated thoroughly. The content analysis led to classification of the papers into two main categories and sub-categories, and then to rigorous identification of the papers' attributes. The bibliometric and content analysis disclosed several research gaps that remain to be addressed in IJPR. [ABSTRACT FROM AUTHOR]

With new market developments and e-commerce, there is an increased use of and interest in automation for order picking. This paper presents a systematic review and content analysis of the literature. It has the purpose of understanding the relevant performance aspects for automated, or partly automated, OPSs and identifying the studied links between design and performance, i.e. identifying which combinations of design aspects and performance aspects have been studied in previous research. For this purpose, 74 papers were selected and reviewed. From the review, it is clear that there has been an increased number of papers dealing with the performance of automated, or partly automated, OPSs in recent years. Moreover, there are differences between the different OPS types, but, overall, the performance categories of throughput, lead time, and operational efficiency have received the most attention in the literature. The paper identifies links between design and performance that have been studied, as well as links that appear to be under-researched. For academics, this paper synthesises the current knowledge on the performance of automation in OPSs and identifies opportunities for future research. For practitioners, the paper provides knowledge that can support the decision-making process of automation in OPSs. [ABSTRACT FROM AUTHOR]

Production planning and control (PPC) systems that employ aspects from both make-to-order (MTO) and make-to-stock (MTS) production control are known as hybrid MTS/MTO systems. While both MTO and MTS separately have been studied extensively, their combined use has received less attention. However, the literature on this topic is growing and this paper shows that the review performed in this paper is an important addition to the field. We categorise relevant literature according to a novel taxonomy and show that hybrid MTS/MTO production control can be used in different contexts. In addition, an overview of the modelling techniques and methods used in these papers is provided. Based on the reviewed literature, relevant research questions and directions for future research are identified. Finally, it is shown that hybrid MTS/MTO production control is prevalent in practice by discussing research with industrial applications. The paper contains an overview of research on hybrid MTS/MTO production control to be used as reference for researchers active in the field, and provides managerial insights and directions for future research on this topic. [ABSTRACT FROM AUTHOR]

The paper at hand addresses the Economic Lot Scheduling Problem (ELSP), which is concerned with finding a feasible and cost-minimal production schedule for multiple items produced in lots on a single machine. The ELSP started to attract the attention of researchers in the 1950s, where the focus was primarily on the development of simple heuristics for solving the problem. Over the subsequent decades, this topic has frequently been addressed in the literature, with the subject of research being the development of new scheduling policies or solution procedures or extensions of the scope of the original model. To date, a large number of journal articles has been published on the ELSP and its model variants. To identify key research themes, publication patterns and opportunities for future research, the paper at hand applies a content analysis to a sample of 242 papers published on the Economic Lot Scheduling Problem. The results of the content analysis indicate that prior research on this topic had a strong focus on the development of solution methodologies, and that several aspects that are directly connected to lot sizing and scheduling have not attracted much attention in research on the ELSP yet, such as, for example, energy cost and sustainability. [ABSTRACT FROM AUTHOR]

Changing customer requirements and turbulent markets require suitably adapted capabilities to proactively cope with rapid changes. Agile manufacturing emerged in the late 1990s as a competitive concept to respond to these business challenges. While several seminal research works address agility in terms of concept definition, drivers and strategic capabilities, there is a lack of implementation frameworks of agile manufacturing. The current paper is concerned with the question of how to improve the agility of manufacturing companies. To address this question, a framework is iteratively developed with the aim of supporting decision makers in planning and implementing agility improvement projects. To this end, Design Science Research (DSR) methodology is used in conjunction with CIMO-logic (Context, Intervention, Mechanism, Outcome). The iterative development of the framework relies heavily on insights from case studies and from practitioners in the broad manufacturing sector. The paper provides evidence of the validity of the framework as well as outlining further research perspectives. [ABSTRACT FROM AUTHOR]

This paper considers the optimisation of Mixed-model assembly lines with continuous paced line control. The two minimisation goals have a mixed-integer linear multi-objective dispute. The paper proposes a criterion-space method to define the Pareto front for this class of problems. The method combines Pareto fronts obtained from integer solutions and gradually refines them until the instance's global front is determined. Comparing paced to unpaced line controls can be challenging, since they can produce the same cycle time given sufficiently long line lengths or buffers. Hence, determining Pareto fronts between cycle time and line length for paced lines allows meaningful comparisons between line controls. An industrial case study shows that line length acts as continuously distributable buffers for paced lines, leading to weaker diminishing returns. This result suggests that paced lines are more efficient than unpaced ones for lower cycle time ranges. [ABSTRACT FROM AUTHOR]

In paper 'Energy cost optimization in two machine Bernoulli serial lines under time-of-use pricing', Cheng, Yan and Gao analyse the structural characteristics of an energy cost optimisation problem, and transform it into optimal allocation of production rate among time periods of different electricity rates. Performance analysis and optimisation play a key role to improve productivity in manufacturing systems. The central theme of this special issue is on Analysis, Design, and Optimisation in Smart and Connected Production and Service Systems. [Extracted from the article]

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

Production and distribution are two key constituents of a supply chain. In view of the growing availability of data and advances in big data analytics techniques, there have been more and more applications of data analytics to deal with the problems in production and distribution management. With this in mind, we proposed a special issue on 'Big Data Analytics in Production and Distribution Management' to report the latest development in this field. In this editorial, we first introduce the background and examine the existing review works on the applications of data analytics to operations management. We then introduce the papers accepted in the issue, and discuss how different types of big data analytics techniques are applied to production and distribution management, including demand forecasting, production scheduling, distribution management, manufacturing management, and supply chain management. Finally, we conclude the paper with a discussion of future research. [ABSTRACT FROM AUTHOR]

In disrupted environments, the concept of digitalisation, viability, and sustainability have attracted considerable attention for the last decade. Digital, lean, and green capabilities have stimulated enormous advances in the development of industries. This paper discusses the current state of the art literature regarding sustainable and digital supply chain management performance. It reviews the relationship between digital technologies, lean, green, sustainability, and supply chain performance. A literature review approach, combined with a bibliometric analysis was adopted for robust field analysis. A set of 86 papers has been analysed and synthesised. The analysis of these papers has enabled the identification of the impact of Industry 4.0 technologies on the supply chain performance; the relationship of lean, green, and sustainability capabilities and digital supply chain; and the implications of the digital transformation of those capabilities. It presents a roadmap model integrating lean and green for viable, sustainable, and digital supply chain performance. The study is useful for academics and practitioners as it provides a roadmap framework and a solid background for integrating lean and green in the digital supply chain to achieve sustainable and viable performance as it outlines the necessity for incorporating the sustainability and viability performances in the digital supply chains. [ABSTRACT FROM AUTHOR]

Lean Thinking has successfully challenged mass production practices, by providing 'leaner' processes and supply chains, i.e. with less waste. Industry 4.0 has become an important strategic approach in the technological change of manufacturing and others. It aims to connect the physical and virtual worlds in industrial production. With such automation associated with Industry 4.0, questions arise about the synergy between this approach and the role of Lean in this ongoing industrial revolution. Therefore, a systematic literature review was carried out in order to identify the role of Lean in this scenario. The review was conducted from 2011 to 2019 timeframe and resulted in a total of 33 papers. This review demonstrated this as an emerging research area with most of the studies published in recent years (2017–2019). A deep analysis was undertaken to understand the Lean effect as a trigger for Industry 4.0. Main findings from a 15 out of the 33 papers revealed elements and facts found in sentences of such papers that act as this trigger. Additionally, a words count indicated that management, processes and people were the most cited words by the 15 papers, reinforcing the role of these key players in the companies' transformation. [ABSTRACT FROM AUTHOR]

In total, more than 200 papers were reviewed and classified into 4 categories which are: machine learning assisted VRP modelling, machine learning guided VRP decomposition strategies, machine learning guided perturbative VRP algorithms, and finally learning to construct VRP solutions. It provides an extensive review of vehicle routing (VRP) researches that use both analytical optimisation approaches and machine learning (ML) modules and mechanisms. This special issue largely originated from various discussions during several cross-domain, multi-disciplinary conferences and workshops, especially the 9th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA2019), which attracted scientists, researchers and practitioners from Computer Science, Operations Research as well as Business and Management. " A Two-Stage Stochastic Programming Model for Collaborative Asset Protection Routing Problem Enhanced with Machine Learning; A Learning Based Matheuristic Algorithm.". [Extracted from the article]

A supply and demand mismatch, or imbalance of the amount of supplies in the market, is always an issue and can happen all the time. Capacity sharing is an effective way to address this problem, and the capacity sharing platform facilitates the optimal matching between multiple capacity buyers and sellers. In the context of Industry 4.0, many industries are adopting intelligent algorithms to assist in decision-making. This paper presents an optimal or near-optimal matching algorithm to cope with a large volume of capacity-sharing problems. The fairness of the matching solution is captured by including three objectives from platform, sellers and buyers. In this paper, a 2-dimensional crossover and an order-first mutation are developed and employed with genetic algorithms (GA), including GA and NSGA-II. Additionally, a novel repair mechanism is proposed by considering various constraints to transform infeasible solutions into feasible ones. Two matching schemes are studied based on whether orders from buyers can be split or not. The results show that both algorithms based on traditional GA and NSGA-II are effective for different schemes. In addition, it is found that GA has better performance in the case of 'more sellers' and NSGA-II shows better performance in the 'more buyers' case. [ABSTRACT FROM AUTHOR]

Ship maintenance service optimisation is of great significance for improving the competitiveness of shipbuilding enterprises. In this paper, we investigate a ship maintenance service scheduling problem considering the deteriorating maintenance time, distributed maintenance tasks, and limited maintenance teams. The objective is to minimise the service span. First, we construct an initial mixed-integer programming model for the studied problem. Then, through the property analysis of the problem with a single maintenance team, an exact scheduling algorithm is proposed. In addition, the lower bound of the problem with multiple maintenance teams is derived. A scheduled rule is developed to obtain the lower bound for the problem. Based on the property analysis, the original mixed-integer programming model is simplified to an improved mathematical programming model. Since the studied problem is NP-hard, this paper proposes two heuristic algorithms and an integrated metaheuristic algorithm based on the variable neighbourhood search to obtain approximate optimal solutions in a reasonable time. In computational experiments, the two models can solve problems on small scale, while metaheuristics can find approximately optimal solutions in each problem category. Moreover, the computational results validate the performance of the proposed integrated metaheuristic in terms of convergence and stability. [ABSTRACT FROM AUTHOR]