An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers

The aim of this study is to identify supply chain knowledge flow enablers (SCKFEs) to inspect interrelationships among these enablers and classify these enablers into driving power and dependence power using an integrated interpretive structural modelling (ISM) and fuzzy Matriced Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) methodology. While the ISM methodology analyses the interactions among the SCKFEs, fuzzy MICMAC analysis is employed to obtain insights into the dependencies among the SCKFEs. A total of 34 SCKFEs were identified through the literature review and expert opinion. As an example, an Indian manufacturing organisation is selected that is willing to adopt the successful knowledge flow for improving supply chain (SC) performance to overcome the intense competition among the SC versus SC. The research shows SCKFEs having high driving power and low dependence have strategic importance because of their driving nature, while the SCKFEs having high dependence and low driving power are more performance orientated. Therefore, it is the responsibility of SC executives to address the high driving power SCKFEs for the enhancement of SC performance. This categorisation provides a useful tool to top management to differentiate between independent and dependent SCKFEs and their mutual relationships, helping them focus on those key SCKFEs that are most significant. This gives a clear picture to SC practitioners and decision-makers about number of SCKFEs, interrelationship and dependencies existing among them.     

A graph-based model to measure structural redundancy for supply chain resilience

Globalisation and lean initiatives increase the vulnerabilities of the supply chains (SC), where disruptions in any plant in a supply chain network (SCN) can propagate throughout the whole SCN. Redundancy is part of the SC re-engineering to improve supply chain resilience (SCRES). This paper presents a conceptual model of an SCN using graph theory, considering the relationships between plants and materials. Based on the model, the structural redundancy of the SCN is measured, which is used to assess SCRES. This assessment approach focuses on the resilience of the SCN against disruptions. Case studies are discussed to illustrate the applicability of this model and show that increasing structural redundancy of the SCN improves SCRES against disruptions.     

Structural-aware simulation analysis of supply chain resilience

Supply chain resilience (SCRES) refers to the ability of a supply chain (SC) to both resist disruptions and recover its operational capability after disruptions. This paper presents a simulation model that includes network structural properties in the analysis of SCRES. This simulation model extends an existing graph model to consider operational behaviours in order to capture disruption-recovery dynamics. Through structural analysis of a supply chain network (SCN), mitigation strategies are designed to build redundancy, while contingency strategies are developed to prioritise recovery of the affected SCN. SCRES indexes are proposed by sampling SC performance measures of disruption for each plant and aggregating the measures based on the criticality of the plants in the SCN. The applicability of this simulation model is demonstrated in a real-world case study of different disruption scenarios. The application of mitigation and contingency strategies is shown to both improve recovery and reduce the total costs associated with disruptions. Through such simulation-based analysis, firms can gain insight into the SCRES of their existing SCNs and identify suitable strategies to improve SCRES by considering recovery time and costs.     

Supply chain resilience: definition,review and theoretical foundations for further study

There has been considerable academic interest in recent years in supply chain resilience (SCRES). This paper presents a timely review of the available literature on SCRES based on a three-stage systematic search that identified 91 articles/sources. We provide a comprehensive definition of SCRES before strategies proposed for improving resilience are identified and the contributions to the literature are critiqued, e.g. in terms of research method and use of theory. We take stock of the field and identify the most important future research directions. A wide range of strategies for improving resilience are identified, but most attention has been on increasing flexibility, creating redundancy, forming collaborative supply chain relationships and improving supply chain agility. We also find that only limited research has been conducted into choosing and implementing an appropriate set of strategies for improving SCRES. Much of the literature is conceptual, theoretical and normative; the few available empirical studies are mainly cross-sectional and confined to a large firm, developed country context; and, there has been limited use of theory frames to improve understanding. We propose Complex Adaptive Systems (CAS) theory as an appropriate lens for studying SCRES. We demonstrate that SCRES mirrors many characteristics of a CAS – including adaptation and coevolution, non-linearity, self-organisation and emergence – with implications for the direction of both future research and practice.     

Analysing the interaction of factors for resilient humanitarian supply chain

Emergencies and disasters place an exceptional demand on the managerial skills of the humanitarian aid community. Most of the developing countries lack in such kind of resilience and effective humanitarian supply chain. Therefore, the purpose of this paper is to identify and analyse the factors to develop the resilience in the humanitarian supply chain. Based on the literature review, total 12 factors related to resilient humanitarian supply chain have been identified. Some of these factors are process oriented and some are result oriented. Interpretive structural modelling with Fuzzy MICMAC analysis is used to develop structural relationships among these factors and to find the driving and the dependence power of these factors. Government support, strategy and capacity planning; and continuous assessment of project progress have emerged as the major drivers for the development of resilient humanitarian supply chain. By managing these driving factors, humanitarian aid programme can be made resilient and agile. The findings will be useful for the humanitarian aid agencies to develop effective and sustainable aid relief programme.     

Analysis of SCF under sales promotional schemes: an application of interpretive structural modelling approach

In the competitive market of today, supply chain flexibility (SCF) plays crucial way to address various supply chain uncertainties. But, in diverse situations, the supply chain needs different types of flexibilities. This paper analyses the flexibility of an automobile supply chain under sales promotional schemes (SPS) to meet the demand uncertainty. Taking the opinion of supply chain experts from automobile original equipment manufacturers (OEM’s), 14 SCF strategies have been identified and then using interpretive structural modelling (ISM) on the conducted survey, their interrelationships are established and a hierarchical model is developed. MICMAC analysis is done to classify the strategies into four categories viz. autonomous, dependent, linkage and independent strategies, which are based on their driving and dependence powers. Sensitivity analysis has been conducted to check the stability of the model. The thorough exposition of the model presents a handful of insights to supply chain managers to know the most important strategies affecting each other. The study helps in bridging the SCF with SPS, a huge gap existing in the literature.     

Application of interpretive structural modelling and structural equation modelling for analysis of sustainable manufacturing factors in Indian automotive component sector

Sustainable manufacturing practices are essential for automotive component manufacturing organisations to ensure competitive advantage. The interrelation between sustainability enablers' namely economic prosperity, environment well-being, social well-being, performance management and research and development need to be analysed for deriving practical insights. Theoretical model development of enablers using interpretive structural modelling (ISM) has been statistically verified using measurement models and structural model of partial least square, structural equation modelling (SEM). In this context, this article presents a combined ISM and SEM approach for building the models with the help of experts for automotive component manufacturing organisation. The measurement and structural model are constructed based on survey among 70 practitioners from automotive component manufacturing organisations in Southern India. The research hypotheses have been formulated to verify the relationship and are analysed. The results of the study indicated that the usage of ISM is very powerful to establish structural relationship between sustainable manufacturing enablers and there exist structural relationship between the enablers.     

Scheduling of recovery actions in the supply chain with resilience analysis considerations

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.     

Digitalization for supply chain resilience and robustness: The roles of collaboration and formal contracts

Black swan events such as the coronavirus (COVID-19) outbreak cause substantial supply chain disruption risks to modern companies. In today’s turbulent and complex business environment, supply chain resilience and robustness as two critical capabilities for firms to cope with disruptions have won substantial attention from both the academia and industry. Accordingly, this study intends to explore how digitalization helps build supply chain resilience and robustness. Adopting organizational information processing theory, it proposes the mediating effect of supply chain collaboration and the moderating effect of formal contracts. Using survey data of Chinese manufacturing firms, the study applied structural equation modelling to test the research model. Results show that digitalization has a direct effect on supply chain resilience, and supply chain collaboration can directly facilitate both resilience and robustness. Our study also indicates a complementary mediating effect of supply chain collaboration on the relationship between digitalization and supply chain resilience and an indirect-only mediation effect on the relationship between digitalization and supply chain robustness. Findings reveal the differential roles of digitalization as a technical factor and supply chain collaboration as an organizational factor in managing supply chain disruptions. Paradoxically, formal contracts enhance the relationship between digitalization and supply chain resilience but weaken the relationship between supply chain collaboration and supply chain resilience. The validation of moderating effects determines the boundary conditions of digitalization and supply chain collaboration and provides insights into governing supply chain partners’ behavior. Overall, this study enhances the understanding on how to build a resilient and robust supply chain.     

供应链资源依赖、信任及关系承诺对信息协同的影响

构建了供应链伙伴间资源依赖、信任、关系承诺与跨组织信息系统协同之间关系的理论模型。以广东省珠三角地区141家制造企业为调查对象,利用结构方程模型对供应链伙伴间资源依赖、信任、关系承诺与信息系统协同之间的关系进行实证研究。研究结果发现供应链伙伴间资源依赖程度对信任与关系承诺均有显著的正向影响,但对跨组织信息系统协同的正向影响并不显著。另外,研究还发现供应链伙伴间信任水平对关系承诺与跨组织信息系统协同均有显著的正向影响,然而关系承诺对跨组织信息系统协同则没有显著的正向影响。研究告诉企业管理者,在选择合作伙伴时应该着重考虑彼此之间的资源互补性与相互依赖性,并高度重视人际关系网络的构建,提高供应链伙伴间相互信任水平,从而可以更好地进行跨组织的信息系统协同。     

Supply chain resilience for single and multiple sourcing in the presence of disruption risks

This paper investigates the use of sourcing strategies to achieve supply chain resilience under disruptions. The coping strategies considered are single and multiple sourcing, backup supplier contracts, spot purchasing, and collaboration and visibility. Collaboration and visibility, which affect suppliers’ recovery capabilities and a buyer’s warning capability, have not been similarly modelled in the past. A scenario-based mathematical model is developed such that it considers objectives under uncertainties including disruption risks and operational risks. A broad numerical study examines its output for various risk attitudes in a decision-maker, ranging from risk neutral to risk averse. The sensitivity of procurement strategies to other key parameters such as recovery and warning capabilities is examined. One of the major findings is that buyer’s warning capability plays a vital role in enhancing supply chain resilience. We seek to build on these efforts to further support disruption planning and mitigation and to obtain a deeper understanding of the relationship between supply chain characteristics and resilience.     

Exploring the importance of collaborative assets to hospital-supplier integration in healthcare supply chains

Research has established the dominant influence of collaboration and integration in manufacturing supply chains. However, the role of these characteristics in healthcare supply chain operations and performance has not yet been investigated. The current research proposes three assets under collaboration as dominant enablers of hospital-supplier integration: collaborative planning, collaborative execution and collaborative decision-making. Considering the advances in technology in healthcare, we propose that technology orientation moderates the above linkages. Survey-based responses were collected from hospitals, chemistry and pharmaceuticals, and medical equipment and surgical suppliers that are key entities in healthcare supply chains. In total, 239 completed responses were collected and analysed using structural equation modelling. The findings suggest all three as dominant enablers of hospital-supplier integration. Furthermore, technology orientation was found to positively moderate the impact of planning and execution on hospital-supplier integration. This orientation also suggested that hospital-supplier integration results in positive operational performance.     

Supply chain risk management and its mitigation in a food industry

We create a model which analyses the various risks involved in a food supply chain with the help of interpretive structural modelling (ISM). The various types of risks were identified based on a review of the literature and in consultation with experts in the food industry. The types of risks are clustered into five categories and risk mitigation is discussed. The model developed is validated with the help of a case study involving a food products manufacturing firm.     

Analysis of flexible decision strategies for sustainability-focused green product recovery system

Identifying and analysing key decision variables that an organisation can opt for in order to initiate resource recovery from return activities and improve overall performance is a challenge. Therefore, to assist organisations which are involved in or about to initiate an effective recovery process, this paper provides guidelines to establish flexible decision strategies using the interpretive structural modelling (ISM) approach. This study has identified interrelationships between green variables such as supplier commitment, cost, regulations, etc. and has categorised relevant enablers. Further, variables such as capacity utilisation, customer satisfaction, energy consumption reduction, etc. are recognised as outcomes or results. After statistically verifying these variables, it has been inferred that driving enablers aid in promoting performance variables, while the result variables represent outcomes achieved via the driving variables. Further, using MICMAC analysis, a graphical categorisation of the variables is done on the basis of the impact on performance. Later prioritisation of variables having high driving power and low dependence is conducted. Inputs in the proposed model are taken through a case study conducted in the paper industry. The proposed model can be quite helpful and can act as a generic tool to achieve the desired performance improvement of green product recovery systems.     

Hybrid Petri-nets for modelling and performance evaluation of supply chains

Modelling and analysis of complex and co-ordinated supply chains is a crucial task due to its inherent complexity and uncertainty. Therefore, the current research direction is to devise an efficient modelling technique that maps the dynamics of a real life supply chain and assists industrial practitioners in evaluating and comparing their network with other competing networks. Here an effective modelling technique, the hybrid Petri-net, is proposed to efficiently handle the dynamic behaviour of the supply chain. This modelling methodology embeds two enticing features, i.e. cost and batch sizes, in deterministic and stochastic Petri-net for the modelling and performance evaluation of supply chain networks. The model is subsequently used for risk management to investigate the issues of supply chain vulnerability and risk that has become a major research subject in recent years. In the test bed, a simple productive supply chain and an industrial supply chain are modelled with fundamental inventory replenishment policy. Subsequently, its performance is evaluated along with the identification and assessment of risk factors using analytical and simulation techniques respectively. Thus, this paper presents a complete package for industrial practitioners to model, evaluate performance and manage risky events in a supply chain.     

Supply chain relationship quality and performance in technological turbulence: an artificial neural network approach

A well-functioning supply chain management relationship cannot only develop seamless coordination with valuable members, but also improve operational efficiency to secure greater market share, increased profits and reduced costs. An accurate decision-making system considering multifactor relationship quality is highly desired. This study offers an alternative perspective and characterisation of the supply chain relationship quality and performance. A decision-making model is proposed with an artificial neural network approach for supply chain continuous performance improvement. Supply chain performance is analysed via a supervised learning back-propagation neural network. An ‘inverse’ neural network model is proposed to predict the supply chain relationship quality conditions. Optimal performance parameters can be obtained using the proposed neural network scheme, providing significant advantages in terms of improved relationship quality. This study demonstrates a new solution with the combination of qualitative and quantitative methods for performance improvement. The overall accuracy rate of the decision-making model is 88.703%. The results indicated that trust has the greatest influence on the supply chain performance. Relationship quality among supply chain partners impacts performance positively as the pace of technological turbulence increases.     

Modeling of the resilient supply chain system from a perspective of production design changes

Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.     

An ISM approach for modelling the enablers of flexible manufacturing system: the case for India

The volatile condition of today's market is forcing the manufacturing managers to adapt the flexible manufacturing systems (FMS) to meet the challenges imposed by international competition, ever changing customer demands, rapid delivery to market and advancement in technology. There are certain enablers, which help in the implementation of FMS or in the transition process from traditional manufacturing systems to FMS. The utmost need is to analyse the behaviour of these enablers for their effective utilization in the implementation of FMS. The main objective of this paper is to understand the mutual interaction of these enablers and identify the ‘driving enablers’ (i.e. which influence the other enablers) and the ‘dependent enablers’ (i.e. which are influenced by others). In the present work, these enablers have been identified through the literature, their ranking is done by a questionnaire-based survey and interpretive structural modelling (ISM) approach has been utilized in analysing their mutual interaction. An ISM model has been prepared to identify some key enablers and their managerial implications in the implementation of FMS.     

Supply chain risk and resilience: theory building through structured experiments and simulation

The research literature of supply chain risk and resilience is at a critical developmental stage. Studies have established the importance of these topics both to researchers and practitioners. They also have identified factors contributing to risk, the impact of risk and disruptions on performance, and the strategies and tactics used to build the capacity for supply chain resilience. Although these efforts can provide support for constructing a theory of risk and resilience, researchers are currently restricted in their ability to build such a theory by the difficulty of collecting the necessary data. This paper contributes to this literature development effort by summarising prior research reviews and developing a three-component framework aimed at helping researchers to build better theories. This is accomplished through combining structured experimental design with discrete-event simulations of supply chains. The result is a methodology that allows researchers to develop better understanding of the factors that link a disruption to its impact on supply chain performance through both direct and interaction effects. Following the methodology development, the paper concludes with an example using the factors of shock interarrival time, supply chain connectivity and buffer stocks to illustrate the potential for contributing to the theory-building process.     

Distributed supply chain network modelling and simulation: integration of agent-based distributed simulation and improved SCOR model

Agent-based distributed simulation is an efficient methodology for modelling and analysing such complex adaptive systems as dynamic supply chain networks. However, it lacks an acceptable generic standard. Supply chain operations reference (SCOR) model is a cross-functional framework widely accepted as an industry standard. It provides the standard processes, performance metrics, best practices and associated software functionalities for modelling, evaluating and improving supply chain networks. However, it is a static tool. Integration of agent-based distributed simulation and SCOR model can exploit their advantages to form a generic methodology for modelling and simulation of a wide range of supply chain networks. Therefore, this paper proposes a methodology for distributed supply chain network modelling and simulation by means of integration of agent-based distributed simulation and an improved SCOR model. The methodology contains two components: a hierarchical framework for modelling supply chain network based on the improved SCOR model and agent building blocks integrating the standard processes from the SCOR model. The hierarchical framework provides an approach for structure modelling in any level with different granularities based on the improved SCOR model, and allows rapidly mapping a supply chain network into the structure model of a multi-agent system; while agent building blocks are quite useful and convenient to fill the structure model to fulfil its function modelling. With the approach of structure modelling and function filling, not only can the process of agent-based supply chain network modelling be accelerated, but also the built models can be reused and expanded. Because the hierarchical framework is based on the conceptual framework of SCOR model and agent building blocks integrate the standard processes from SCOR model, the proposed methodology is more generic. In addition, the issues of sub-model synchronisation and data distribution management in the agent-based distributed simulation implementation are taken into consideration and the corresponding solutions for these issues are proposed. Finally, an example of a supply chain network is modelled and implemented to illustrate the proposed methodology and related solutions.