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: model development and empirical analysis

The purpose of this study is to develop a hierarchy-based model for supply chain resilience (SCRES), explaining the dynamics between various enablers and validating the model empirically. Literature review and a survey identified the enablers. Interpretive structural modelling (ISM) is used to analyse the levels of relationships among enablers. Based on their driving power and dependence, these enablers are also classified into different categories. Structural equation modelling is used to validate the hierarchical SCRES model and test the path analytical model. The study provides empirical justification for a framework that identifies 13 key enablers of resilient supply chain practices and describes the relationship among them using ISM. It also classifies them using Matrix of Cross Impact Multiplications Applied to Classification analysis on the basis of their driver power and dependence. The key finding is that using the proposed model, organisations can enhance their resilience potential by modifying their strategic assets. The model was tested using rigorous statistical tests including convergent validity, discriminant validity and reliability. The holistic view offered by the proposed model depicts the relationship among enablers to achieve SCRES.     

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.     

‘A blessing in disguise’ or ‘as if it wasn’t hard enough already’: reciprocal and aggravate vulnerabilities in the supply chain

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.     

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.     

Ripple effect quantification by supplier risk exposure assessment

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.     

How do digital technologies improve supply chain resilience in the COVID-19 pandemic? Evidence from Chinese manufacturing firms

Digital technologies (DTs) can assist businesses in coping with supply chain (SC) disruptions caused by unpredictability, such as pandemics. However, the current knowledge of the relationship between DTs and supply chain resilience (SCR) is insufficient. This study draws on information processing theory to develop a serial mediation model to address this deficiency. We analyze a sample set consisting of 264 Chinese manufacturers. The empirical results reveal that digital supply chain platforms (DSCPs), as well as supply chain traceability (SCT) and supply chain agility (SCA), fully mediate the favorable association between DTs and SCR. Specifically, the four significant indirect paths indicated that firms can improve SCR only if they use DTs to directly or indirectly improve SCT and SCA (through DSCPs). Our study contributes to the literature on resilience by examining the possible mechanism of mediation through which DTs influence SCR. The findings also offer essential insights for firms to modify their digital strategies and thrive in a turbulent environment.     

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.     

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

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.     

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.     

A hybrid optimisation approach to configure a supply chain for new product diffusion: a case study of multiple-sourcing strategy

Configuring a supply chain for a new product is a challenging task due to the lack of historical demand data and the dynamic/uncertain nature of the new product diffusion process. An integrated supply chain configuration (SCC) and new product diffusion (NPD) model is developed to explicitly account for the impact of demand dynamics during a new product's diffusion on an optimal supply chain configuration. Our hybrid NPD-SCC model allows a manufacturer to source from multiple suppliers, vendors or modes for its supply chain entities. Such a multiple-sourcing approach not only helps the manufacturer to diversify its pool of suppliers and maintain bargaining power, but also builds redundancy into the supply chain to hedge against potential demand surge and supply disruption during the new product life cycle. Through a case study and a comprehensive computational study, we find that although the single-sourcing solution is able to achieve lower unit-manufacturing cost (UMC), the multiple-sourcing approach is superior to single-sourcing on the overall supply chain performance in the environment with random supply disruptions. By building-in redundancy as multiple suppliers and modes, the resultant supply chain has less chance of being disrupted and achieves higher overall profit on average. We also draw several other managerial insights closing the gap between some supply chain operations and marketing strategies.     

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.     

The impact of supply base complexity on disruptions and performance: the moderating effects of slack and visibility

In the face of increasing supply base complexity, organisations have to develop new ways to manage or mitigate risk. This paper investigates the impact of four dimensions of complexity on the frequency of disruptions and plant performance. We apply insights from organisational information processing theory to understand how organisations can mitigate against the impact of more frequent disruptions. We test the moderating effects of slack resources as a means to absorb the effects of disruptions and supply visibility as a means to improve the ability to handle disruptions. The model is tested with data from 264 supply chain management professionals. Our findings broadly support the original hypotheses and suggest that supply base complexity can increase the frequency of disruptions and reduce plant performance but that slack resources and visibility can help to mitigate the effects. The study offers valuable insights into the management of supply base complexity.     

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.     

Structural quantification of the ripple effect in the supply chain

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.     

Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach

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.     

New flexibility drivers for manufacturing,supply chain and service operations

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.     

Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet

This paper investigates the resilience of inventory models using interconnected logistics services in the Physical Internet (PI). With traditional supply chain network design, companies define and optimise their own logistics networks, resulting in current logistics systems being a set of independent heterogeneous logistics networks. The concept of PI aims to integrate independent logistics networks into a global, open, interconnected system. Prior research has shown that new inventory models enabled by and applied to PI could help reduce inventory levels thanks to its high flexibility. Continuing along these lines, this paper examines how inventory models applying PI deal with disruptions at hubs and plants. To attain this, a single product inventory problem with uncertain demands and stochastic supply disruptions is studied. A simulation-based optimisation model is proposed to determine inventory control decisions. The results suggest that the PI inventory model, with greater agility and flexibility, outperforms the current classic inventory models in terms of resilience. Moreover, the difference in performance increases when the product value, penalty costs and disruption frequency increases. This paper indicates a novel approach to build a resilient supply network.     

A hybrid multi-stage predictive model for supply chain network collapse recovery analysis: a practical framework for effective supply chain network continuity management

In this paper, we present a multi-stage hybrid model for analysing a supply chain network (SCN) collapse recovery possibility. In the first stage of the model, we analyse the ability of an SCN to fulfil its customers required due date (RDD). As soon as the SCN defaults to timely fulfil their customers RDD, the second stage of the model is triggered to measure the collapse recovery possibility (CRP) of the SCN. Then, we calculate the final collapse recovery possibility (FCRP) of the SCN. Since the operation times, customer demand and external supply of raw material are uncertain, we use fuzzy triangular numbers to estimate the value of foregoing parameters. Consequently, we employ fuzzy program evaluation and review technique (FPERT) to calculate the completion time of SCN operations. In the third stage, for the critical elements of the SCN obtained from FPERT, the SC simulator is developed to provide a dynamic view of the SC and assesses the impact of decisions recommended by the SC fuzzy models on SC performance. Moreover, an empirical example is presented to validate the effectiveness of the proposed model. Finally, we conduct sensitivity analysis on the parameters employed in the model to analyse the behaviour of each parameter.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.