Simulation-based ripple effect modelling in the supply chain

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

The Ripple effect in supply chains: trade-off ‘efficiency-flexibility-resilience’ in disruption management

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

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.     

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 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.     

Product complexity and supply chain design

To assemble a product, each and every part is required. Hence, the more parts in the product, the greater the risk of disruption. Compared with retailers or assemblers of simple products, manufacturers of complex products are much more sensitive to supply chain delays. This heightened vulnerability to supply chain disruptions should lead complex product assemblers to design less risky supply chains. Supply chain design should depend on the complexity of the product assembled or manufactured. This paper models how product complexity drives the likelihood of disruption for given component supply chain reliabilities. The paper provides insights for supply chain design that comprehends the impact of product complexity.     

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.     

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.     

Supply chain disruption risk management through strategic information acquisition and sharing and risk-sharing contracts

In this paper, we develop a framework that captures the effects of information management and risk-sharing contracts in supply chain networks. In particular, we analyse the impact of strategic information acquisition and sharing on supply chain disruption risks and costs and we evaluate the supply chain performance of risk-sharing contracts. Risk-sharing contracts specify who needs to incur the costs when supply chain disruptions occur. We develop a model that consists of three tiers of multi-criteria decision-makers, manufacturers, retailers, and demand markets. We describe the behaviour or each decision-maker, derive the finite-dimensional variational inequality formulation of the equilibrium conditions of the supply chain and present numerical examples. The numerical examples highlight that it is not a priori clear which participant in the supply chain network will benefit from increased information-sharing activities. Our models indicate that the beneficiary of reduced information-sharing costs is in some cases dependent on the negotiation power of participants and that it is also dependent on the type of risk-sharing contract used. Furthermore, the numerical examples show that, in some cases, information-sharing and risk-sharing contracts are complements while in other cases they are substitutes.     

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.     

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 digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

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

Methods for mitigating disruptions in complex supply chain structures: a systematic literature review

Supply chain risk management is extremely important for the success of a company. Due to the increasing complexity of supply chains, avoiding and mitigating the effects of disruptions is very challenging. This article presents the results of a systematic literature review and content analysis in order to provide a comprehensive overview of the methods that are currently used for mitigating supply chain disruptions. The results of the review indicate that research in this field is interdisciplinary and that no common modelling language has emerged thus far. Prior research mostly redraws to graph theory and/or social network analysis, although a few methods have been developed recently specifically for supply chain risk management. We observe that prior contributions addressed risk and structure mostly separately and that only a few works focused on their intersection. The results of this review are consolidated in a research agenda that calls for research on the risk-structure-interface and the development of proxy methods.     

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.     

Coordinating a supply chain under demand and cost disruptions

In this paper, we investigate the coordination of a supply chain consisting of one manufacturer and n Bertrand competing retailers under disruptions of market demand and production cost. We present a coordination model of a supply chain under normal scenarios. Our findings demonstrate that the coordination scheme designed for the initial production plan should be revised when disruptions of market demand and production cost occur. To resolve this issue, we consider the possible deviation costs caused by disruptions and propose optimal decision models for different disruptions under centralised decision-making. We present an improved revenue-sharing contract model to coordinate the decentralised supply chain under disruptions. The proposed models are then further analysed through numerical examples.     

Coordination of a dual-channel supply chain after demand or production cost disruptions

Coordinating a dual-channel supply chain could not only achieve the integrated profit of the supply chain but also alleviate the channel conflict. Although some researches addressed this area, there is scant literature to discuss the coordination issue in the situations of disruption. To fill this void, we utilise a contract with a wholesale price, a direct channel’s price and a lump sum fee to coordinate a dual-channel supply chain under the cases of demand disruptions and production cost disruptions. After deriving the optimal contract for each case, we find that the manufacturer can achieve coordination of the disrupted supply chain by adjusting the parameters of the coordination contract used in a normal environment. We also show that after disruptions, there exists a contract adjustment benefit zone, in which both the manufacturer and the retailer can benefit from the adjustment of coordination contract when demand increases or production cost decreases. A further analysis of the production and distribution strategies in the coordinated dual-channel supply chain after disruptions suggests that the adjustment of the total production and sales of each channel depends heavily on the level of disruptions and the degree of consumers’ loyalty to both channels.     

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.     

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.