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.     

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.     

Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain

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

Impact of modelling approximations in supply chain analysis – an experimental study

This paper presents a study of the comparison of the quality of results obtained at different levels of detail using a supply chain simulation. Analysis of supply chain is typically carried out using aggregated information to maintain the level of complexity of the simulation model at a manageable level. Advances in simulation have provided the ability to build comprehensive (detailed), modular models. The quantitative effect of detailed modelling on the corresponding analysis is investigated in this paper. A three-echelon supply chain is analysed using simulation models of varying levels of detail. Using each of these models, four sets of intensive experiments are performed. The first experiment intends to test whether the supply chain dynamics themselves depend on the modelling accuracy that represents the supply chain. The second and third experiments are conducted to test whether the effectiveness of the strategies employed to reduce the supply chain dynamics vary depending on the type (different detail) of model representing the supply chain. In the fourth experiment, statistical techniques are employed to identify which modelling aspect has the most influence on the supply chain dynamics. It is found that the approximations used in modelling, such as delays and capacity, have more impact on the outcome of supply chain analysis than end customer demand. Evidence that both the basic problem (supply chain dynamics) and the solution (strategy to reduce the dynamics) are greatly influenced by the modelling accuracy are presented.     

A decision support framework for global supply chain modelling: an assessment of the impact of demand,supply and lead-time uncertainties on performance

We developed a decision support framework for a global manufacturer of specialty chemicals to study the relative impact of demand, supply and lead-time uncertainties on cost and customer service performance. Our approach combines optimisation and simulation methodologies as follows: mathematical models provide optimal plans via a novel approach to the supply chain planning mechanism of the Company. Simulation models execute the supply chain plans so as to allow the examination of the outcomes under the various sources of uncertainty. The iterative use of optimisation and simulation methodologies allows the user the benefit of obtaining optimal solutions while revealing the impact of uncertainties on system performance. Our results indicate that demand uncertainty has the greatest negative impact on performance for the supply chain that we modelled in this study, emphasising the importance of effective forecasting. The relative importance of supply and lead-time uncertainties varies according to the performance measures. While our results are valid for the specific supply chain and the operating environment we modelled, our study emphasises the importance of the ability to model supply chains realistically to obtain valid and useful results.     

Decarbonising product supply chains: design and development of an integrated evidence-based decision support system – the supply chain environmental analysis tool (SCEnAT)

Based upon an increasing academic and business interest in greening the industrial supply chains, this paper establishes the need for a state-of-the-art decision support system (DSS) for carbon emissions accounting and management, mainly across the product supply chains by identifying methodological shortcomings in existing tools, and proposing a supply chain (SC) framework which provide businesses with a holistic understanding of their supply chains and ensuring partners within supply chain collaborative networks have a shared understanding of their emissions. It describes the design and development of a DSS now known as supply chain environmental analysis tool (SCEnAT) in detail, putting its unique and innovative features into a comparative perspective vis-à-vis existing tools and software of different types. The methodological framework used to design and develop SCEnAT integrates different individual techniques/methods of supply chain (SC) mapping, SC carbon accounting, SC interventions and SC interventions evaluation on a range of key performance indicators (KPIs). These individual methods have been used and applied innovatively to the challenge of designing SCEnAT within the desired framework. Finally, we demonstrate the application of SCEnAT, especially the advantage of using a robust carbon accounting methodology, to a SC case study. The SCEnAT framework pushes the theoretical boundary by addressing the problems of intra-organisational approach in decision making for lowering carbon along the supply chain; with an open innovation, cutting edge, hybridised framework that considers the supply chain as a whole in co-decision making for lowering carbon along the supply chain with the most robust methodology of hybrid life cycle analysis (LCA) that considers direct and indirect emissions and interventional performance evaluation for low carbon technology investment and business case building in order to adapt and mitigate climate change problems. This research has implications for future sustainability research in SC, decisions science, management theory, practice and policy.     

Inventory policies analysis under demand patterns and lead times constraints in a real supply chain

This paper reports a study on a real three-echelon supply chain operating in the beverage sector. The authors, starting from the actual supply chain configuration, propose a detailed study of the inventory systems. The test of a comprehensive set of different operative scenarios, in terms of customers’ demand intensity, customers’ demand variability and lead times, becomes a powerful tool for inventory systems analysis along the supply chain. The main objective is the comparison of the actual supply chain configuration with optimistic and pessimistic scenarios for studying the behaviour of different inventory control policies and optimizing the inventory system of each supply chain node. Due to the dynamically changing and stochastic behaviour of the supply chain variables as well as the complex interactions among its actors, the authors have been pushed to implement an advanced simulation model supported by a ‘well-planned’ experimental design. In addition the paper aims to underline the research effort for conceptualizing, modelling, validating and simulating a real stochastic supply chain.     

Modelling of cluster supply network with cascading failure spread and its vulnerability analysis

In this paper, a new entropy approach is developed to study the vulnerability of cluster supply chain network during the cascading failure spread from a holistic point of view. We use the tools of complex network theory and social network analysis to obtain the network representation of cluster supply chain system and explain its cascading phenomenon. Then we build a new cascading model for cluster supply chain network, and further introduce the concept of network load entropy, to analyse and predict the dynamic behaviours of the vulnerability during the process of failure spreading. Using a cluster supply chain network in an industrial district as a case study, we employ the modelling approach to explore its vulnerability. The simulation results demonstrate that the vulnerability of cluster supply chain network with cascading failures can be identified and predicted efficiently by using the modelling approach. In addition, this work may have practical implications for reducing the cluster supply chain network vulnerability in the cascade control and defence, then obtaining its healthy evolution.     

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB reduces the efficiency of the supply chain (SC) through heterogeneous distribution of cash among supply chain members. This paper aims to decrease both the BWE and the CFB across a SC through applying a simulation-based optimisation approach, which integrates system dynamics (SD) simulation and genetic algorithms. For this purpose, cash flow modelling is incorporated into the SD structure of the beer distribution game (BG) to develop the CFB function. A multi objective optimisation model is then integrated with the SD-BG simulation model. Finally, a genetic algorithm (GA) is applied to determine the optimal values for the inventory, supply line, and financial decision parameters. Results show that the proposed integrated framework leads to efficient liquidity management in the SC in addition to cost management.     

Information sharing in capacity constrained supply chains under lost sales

We present a modelling approach for quantifying the value of information in supply chains using Markov decision processes (MDP). The case where information sharing occurs is modelled using a completely observable MDP. A restricted observation MDP is used to model the case where no information sharing occurs. We illustrate the use of this framework on a two-stage capacity-constrained supply chain consisting of a supplier and a retailer. We quantify the value of information sharing in this setting and construct several performance measures to identify the benefits to both the retailer and supplier as a result of the information sharing partnership.     

Modelling engineering change management in a new product development supply chain

The changes within a new product development (NPD) process are handled differently depending on the stage of the project. The changes during the initial stages of the project are addressed by design iterations, while the changes after the product design is complete are addressed using a formal engineering change management (ECM) process. The ECM process is a complex process, especially under a collaborative environment, where various independent entities work together for a common cause of product development. The interactions between the NPD and ECM processes have rarely been investigated in the research community. In this paper, we attempt to study the interactions between the various NPD and ECM process parameters by modelling the processes and simulated the model to understand the parameter interactions. The organisations in a supply chain have been characterised based on their interactions with the original equipment manufacturer (OEM) during the NPD process. The organisation process templates representing the NPD and ECM processes of each type of organisation in the supply chain have been modelled. The templates have been used to develop a simulation model representing the NPD and ECM processes for a supply chain. The process variables, such as processing rates, resources, resource composition, resource allocation priority, processing quality and phase overlap, have been included in the model. The results indicate that most of the variables and interactions among the variables have a significant influence on the NPD lead time. By identifying the status of?the NPD process, the decision-makers can use these results to develop appropriate management policies to govern their product development projects.     

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.     

Decentralised capacitated planning with minimal-information sharing in a 2-echelon supply chain

This paper aims at modelling decentralised planning at the tactical level, with minimal-information sharing coordination, in a 2-echelon supply chain with multiple actors at each echelon. Suppliers manage production and storage at the upstream echelon, while retailers manage transportation and storage at the downstream echelon. The main features of the planning process are (1) decentralisation and coordination using contracts and sharing of only order/supply proposals, and (2) iteration on a rolling horizon. Actor planning is modelled as a capacitated lot-sizing problem on a finite horizon, with the focus on quality of service. The objective is to minimise costs, with a high lost sales penalty if demand is not met. Two other decision problems are pointed out and modelled with Mixed Integer Programming: (1) lost sales allocation between the retailers when their demands cannot be satisfied; and (2) allocation of orders between the suppliers. A multi-agent system combines simulation of the planning process and optimisation of the local decision processes. Several strategies, including retailers’ beliefs about suppliers’ production capacity are proposed and experimentally tested, with two patterns of production capacities. The results compare the proposed allocation strategies and highlight the relevance of the proposed framework for the studied planning problem.     

Making sense of transient responses in simulation studies

Traditional simulation modelling focuses upon the analysis of steady-state data. This focus may not be appropriate, however, for the study of transient responses – data reflecting some form of disruption or change in the system norms. Transient responses are often encountered when dealing with new product introductions, changes in production systems, or supply chain disruptions. In these situations, it is the transient response, how the system responds to these changes as well as the tactics and strategies used to deal with these changes, that tend to be of the greatest interest. Unfortunately, current approaches that focus on analysing such responses are limited. This paper introduces a new approach for analysing transient responses – one that merges outlier detection, a time series analysis tool, with simulation modelling. This combined approach allows the researcher to identify those factors that have the greatest impact upon operations during these transient conditions. Using a simulated supply chain disruption to illustrate the potential of the approach, it is shown that the new approach expands the applicability of simulation and enables certain types of problems to be investigated with confidence not previously provided.     

An Extended Kalman filter for collaborative supply chains

Supply chains can often be complex due to the large mesh of interconnected suppliers, manufacturers, distributors and customers. Recent advances in communication technologies can help participants collaborate across a supply chain. However, the huge amount of data generated can impede effective decision-making, particularly since some data may be incomplete or have errors. Inaccurate estimates of the state of the supply chain system can lead to incorrect decisions, with consequent adverse effects on product availability, lead times and inventory levels. What would be beneficial in overcoming this problem is an approach to obtain a better state estimation of the supply chain system. The paper aims to address this issue by proposing an approach that combines an extended Kalman filter with a network approach that models the supply chain as an abstraction. This approach is termed Augmented Trans-Nets and has several potential advantages: multiple participants in a supply chain can be modelled without undue complexity; and different considerations can be examined, such as cost and lead time. Furthermore, by using this approach, it is relatively straightforward to achieve an improved system estimation, which can help in managing the supply chain effectively.     

A multi-agent system for chemical supply chain simulation and management support

Modern chemical production is customer-driven and the desired delivery time for the products is often shorter than their campaign length. In addition, the raw materials supplying time is often long. These features make it desirable to provide tools to support collaborative supply chain decision making, preferably over the Internet, and where there are conflicts, compromise decisions can be quickly reached and the effects of the decisions can be quantitatively simulated. This paper des cribes such a multi-agent system (MAS) that can be used to simulate the dynamic behaviour and support the management of chemical supply chains over the Internet. Geographically distributed retailers, logistics, warehouses, plants and raw material suppliers are modelled as an open and re-configurable network of co-operative agents, each performing one or more supply chain functions. Communication between agents is made through the common agent communication language KQML (knowledge query message language). A t the simulation layer, the MAS allows distributed simulation of the chain behaviour dynamically, so that compromise decisions can be rapidly and quantitatively evaluated. Because in a chemical supply chain the scheduling of the plant often dominates the chain performance, an optimum scheduling system for batch plants is integrated into the MAS. The functions of the system are illustrated by reference to a case study for the supply and manufacture using a multi-purpose batch plant of paints and coatings.     

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.     

A GIS-based green supply chain model for assessing the effects of carbon price uncertainty on plastic recycling

Recycling plastic can abate the environmental pollution as well as CO₂ emissions by saving the carbon-intensive feedstock input. The uncertain carbon price places significant effects on the establishment and operation of the whole supply chain. This study develops a green supply chain model combined with geographic information system (GIS) to account for carbon price uncertainty and evaluate its effects on the closed-loop supply chain (CLSC) of plastic recycling. A two-stage stochastic programming model is constructed, in which the stochastic variable, CO₂ price is modelled as a geometric Brownian motion process. Six scenarios are designed with respect to price expectation and volatility. A case study is performed with the GIS information of the plastic supply chain in Zhejiang province, China. The results illustrate that triggering the establishment of reverse logistics requires a carbon price threshold significantly beyond the current level. Lower price volatility would facilitate the decision-making of investment into the reverse logistics. Mechanisms to alleviate the market variation shall be introduced. A sound market condition is desired to obtain the optimal balance that encourages the CLSC without creating extra pressure on the firms. The proposed modelling framework can be easily applied to other sectors with similar characteristics.     

基于仿真的三级供应链成本优化

针对供应链这种非线性和复杂系统,为了优化供应链的总成本,提高供应链的整体竞争力,提出了一种基于仿真的优化方法。首先建立了一个三级协作供应链的系统动力学模型,供应链上的企业需要求解多个决策变量来降低供应链总成本。然后详细展示了将系统动力学模型转换为Matlab程序模型的过程,并在两个模型中使用了多组随机参数进行对比验证。最后设计了适当的遗传算法进行求解。结果表明,这种基于仿真的优化方法不仅能反映出供应链系统的动态特征,而且求解速度快、精度高,能有效解决供应链系统中的单目标或多目标规划问题。系统动力学模型结构相对固定,也不适合异构的交互环境,该方法给系统动力学模型提供了良好的补充。转换为Matlab程序模型后,可以进行深入的仿真研究,灵活设置目标函数和约束条件,详细分析系统的动态变化过程,还能为其它应用提供交互接口。     

The impacts of sharing production information on supply chain dynamics: A review of the literature

This paper is a review of research into the impacts of sharing production information on the supply chain dynamics. The purpose of the review is to understand the needs to unravel such impacts on the supply chain design and management, to identify the current research frontier and to study the future trend in this field. The scope of the review is somewhat broad in the sense that more than 100 relevant publications resulting from major research projects and originating from some leading research groups are selected for review. In order to maintain the necessary coherence and consistency, this comprehensive review proposes a loose reference framework to reflect major elements commonly involved in this type of research, including the decision level investigated, supply chain structure, research questions addressed, factors involved, supply chain dynamics measured, modelling methodology adopted, data analysis technique used, and findings and insights from the managerial perspective obtained. The framework serves as a skeleton for categorizing literature and research areas into these key elements. It is hoped that this review is valuable and beneficial both to new researchers entering the field, in order to gain their interest, references and focus, and to experienced researchers, in order, perhaps, to identify new research opportunities and/or issues that intersect these researchers' past and current interests.