A Citation Analysis of the Research on Manufacturing and Supply Chain Flexibility

Manufacturing and supply chain flexibility has been discussed extensively in the scientific literature. Given the different definitions, types and dimensions of flexibility, the purpose of this paper is to explore the origins and structure of the scientific research on manufacturing and supply chain flexibility. We identified a sample of 153 internationally published papers and conducted a citation analysis to examine the connections between the many scientific papers and to explore the most influential works and their impact on flexibility research. Our analysis revealed that research on flexibility received the most scientific attention between 1996 and 2005. However, the majority of the currently valid types and dimensions of flexibility have been defined in the late 1980s and the early 1990s. The current paper provides the leading academic journals and papers in the field of flexibility research. Additionally, the results of our citation analysis indicate that flexibility research is significantly influenced by papers that have been presented at subject-specific conferences. Finally, our paper presents a clearly arranged structure of the most frequently cited papers dealing with manufacturing and supply chain flexibility. Furthermore, its contents and findings are briefly discussed to provide an overview of the previous academic research.     

基于价值链的汽车产业供应链协同管理分析

汽车产业供应链协同管理的关键是鉴别供应链的低效及其原因.通过对汽车产业供应链现状的分析,提出了由敏捷性和速度驱动的汽车产业供应链协同管理的价值链模型,并以上海某汽车制造企业为核心的汽车行业供应链为例证明了基于价值链的供应链协同管理在实践中给供应商、制造商以及供应链整体带来的收益和竞争优势,从而说明汽车产业基于价值链的供应链协同管理是一种有效的管理模式.     

Lean versus agile production: flexibility trade-offs within the automotive supply chain

Given the recent dynamics of the automotive industry in the UK, the ability for a firm to be flexible has often taken priority over other performance indicators. Using the notion of distinct business models and trade-offs as our theoretical lens, the purpose of this study was to: (1) Distinguish lean and agile firms based upon production methods; and (2) Compare lean and agile levels of external flexibility (EF) and supply chain flexibility (SCF). Data was obtained from 140 automotive firms in the Midlands (UK) via a survey which was sent by emails. Findings supported the theoretical notion of trade-offs, as firms implementing agile production methods were found to be more flexible in comparison with firms implementing lean production methods. More importantly, the agile firms that possessed high EF levels and SCF levels were predominantly positioned at the lower end of the automotive supply chain, whereas the lean firms were largely found to be operating at the top of the supply chain. First, we provide an innovative way in which lean and agile firms can be conceptualised. Second, as flexibility levels were assessed on actual numerical values, as opposed to using opinion based Likert Scale questions, a methodological contribution is made. Third, as flexibility is in its infancy stage of theoretical development we make an empirical contribution by developing a taxonomy that distinguishes each production concept. Finally, given the supply chain position to where lean and agile firms were found, we invoked a power perspective better understand this phenomenon.     

A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Supply Chain Finance (SCF) is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain. In recent years, with the deep integration of supply chain and Internet, Big Data, Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes. However, with the rapid development of new technologies, the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones. The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains. In this article, a distributed approach of big data mining is proposed for financial fraud detection in a supply chain, which implements the distributed deep learning model of Convolutional Neural Network (CNN) on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly. By training and testing on the continually updated SCF dataset, the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors, so as to enhance the financial fraud detection with high precision and recall rates, and reduce the losses of frauds in a supply chain.     

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.     

Framework for flexibility in dynamic supply chain management

Effective Supply Chain Management (SCM) is a pivotal issue in the dynamically changing business environment. Dynamic SCM requires integrated decision-making amongst autonomous chain partners with effective decision information synchronization amongst them. By exploiting flexibility in supply chain structures, better performance can be achieved. Similarly, by judiciously employing decision flexibility and the associated dynamic control amongst autonomous supply chain nodes, many improvements are possible. The paper presents a study on the role of different flexibility options (i.e. no flexibility, partial flexibility and full flexibility) in a dynamic supply chain model based on some key parameters and performance measures. Supply chain flexibility has significant potential and needs a greater research attention. The paper attempts to advance the knowledge of dynamic control on effective flexibility exploitation in the context of dynamic supply chains. A simulation model of a dynamic supply chain is used for this purpose. The key results are highlighted along with industry implications. Here each supply chain node involves decision-making. Based on the order and sample information available from the immediate buyers or customers, the supplier selection decisions are dynamically made. A seemingly good decision at a stage based on local information often ends up as detrimental not only to the total chain cost, but also to the total costs of the node itself. These observations are important for the designers and managers of the flexible supply chain systems to arrive at appropriate types and a judicious level of flexibility to attain significant improvements in total cost reduction. The modelling of dynamic supply chains with a focus on flexibility can offer enormous potential to the industry. This paper addresses this interesting and challenging domain.     

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.     

Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches

Supply chains are becoming more lengthy and complex due to globalisation and vertical integrations. In this context, adopting proactive approaches is needed for dealing with changing risks and vulnerabilities for securing supply chain systems. Supply chain risks are interlinked and thus, one mitigation strategy can reduce many of other supply chain risks. For example, aggregate or pooling demand reduces forecast risks, capacity risks and inventory risks. Also, some of the risk mitigation strategies have negative influences over certain supply chain risks as adding capacity has a negative influence on capacity risks. Twelve major supply chain risk categories and 21 risk mitigation strategies with typical focus on electronics manufacturing supply chains have been identified. A combination of grey theory and digraph-matrix methodologies has been used for quantifying various supply chain risk mitigation strategies and this approach is not seen in literature till date. The proposed model was also tested taking a case study of an Indian electronics manufacturing company. Obtained results were also subject to sensitivity analysis. The net positive influence values of risk mitigation strategies proposed in this research could effectively be used by top management for ascertaining their risk mitigation strategies for better management of supply chains as a whole.     

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.     

废旧汽车逆向供应链的集成优化分析

为解决现有废旧汽车回收模式中企业优势无法集中而导致的回收成本高的问题,提出了一种价值链集成式回收模式。基于比较优势理论和价值链理论,分别比较分析了传统3种回收模式中制造商、第三方物流企业、专业汽车回收企业和零售商等回收主体企业的优劣势,并且以制造商为主导企业建立逆向物流模式的成本-收益模型,对各主体企业进行成本比较分析,得到各主体企业在逆向供应链中具有相对优势的价值链环节,并将其各自具有相对优势的环节进行集成,构建集成式的逆向供应链模型。最后通过集成式回收模式与传统的回收模式的净收益对比和验证,得出在一定条件下集成式回收模式的净收益较大的结论。     

Development of a hybrid fresh food supply chain risk assessment model

Supply chain managers and scholars recognise the importance of managing supply chain risk, especially in fresh food supply chain due to the perishable nature and short life cycle of products. Supply chain risk management consists of supply chain risk assessment, risk evaluation and formulation and implementation of effective risk response strategies. The commonly adopted qualitative methods such as risk assessment matrix to determine the level of risk have limitations. This paper proposes a hybrid model comprising both fuzzy logic (FL) and hierarchical holographic modelling (HHM) techniques where risk is first identified by the HHM method and then assessed using both qualitative risk assessment model (named risk filtering, ranking and management Framework) and fuzzy-based risk assessment method (named FL approach). The risk assessment results by the two different approaches are compared, and the overall risk level of each risk is calculated using the Root Mean Square calculation before identifying response strategies. This novel approach takes advantage of the benefits of both techniques and offsets their drawbacks in certain aspects. A case study in a fresh food supply chain company has been conducted in order to validate the proposed integrated approach on the feasibility of its functionality in a real environment.     

Operational strategies for seru production system: a bi-objective optimisation model and solution methods

In recent years, the interest in seru production system (SPS) has increased to enhance the flexibility of production systems. Because the worker resource in an SPS is critical for adapting to changes in demand, this study focuses on workforce-related operational strategies rarely considered for SPS. To this end, for the first time in the literature, a bi-objective workforce scheduling problem is addressed by considering the interseru worker transfer in SPS. A novel optimisation model is proposed to achieve two objectives, that of minimising makespan and reducing workload imbalance among workers. Because it is proved that the problem falls within a non-deterministic polynomial-time hardness (NP-hard) class, non-dominated sorting genetic algorithm-II (NSGA-II) is employed to solve large-sized problems. For small-sized problems, the second version of the augmented ε-constrained (AUGMECON2) method is implemented and Pareto-optimal solutions are obtained. A set of evaluation metrics is considered to compare two different operational strategies in terms of the desired objectives. The computational results indicate that allowing worker transfer leads to better results for all metrics. The main contribution of the present study is to provide a novel optimisation model for the addressed problem to compare two operational strategies by considering the heterogeneity inherent of workers.     

目前供应链信息流存在的问题及改进

分析了现有供应链中的信息流存在的问题及其对供应链的影响。这些问题包括信息延滞、信号失真,然后针对总理2提出了改进的供应链模型。分析了信息流在该模型中的流程,阐述了供应链上各节点企业在改进后的模型中功能的变化;从成本,柔性、响应速度、简洁和集成5个角度还说明了改进模型的预期效果,最后阐明了实施此模型可能遇到的问题和困难。     

A strategic decision framework for a value added supply chain

Supply chains are becoming increasingly complex. The structure of the supply chain and the suppliers who participate in it are critical decisions for managers. The supply chain is no longer simply a source of inputs or services but an integral source of value added. It is clear that in some cases strategic supply chain objectives may require trade-offs, for example, between cost and innovation capability or between managing risk and maximising flexibility. These decisions balancing trade-offs between desirable partner characteristics are critical and can make a significant contribution to business performance. A framework is outlined, based on the foundation of extant literature, within which firms can make strategic decisions on supply chain structure by categorising the characteristics they prioritise in their supply chain strategy. There is also a strong foundation in the literature on developing mathematical models that provide insight into the decision-making process. A mixed integer programming model is specified that incorporates the opportunity for diversification and provides a demand allocation decision. The model is robust enough to allow for single or diversified supplier strategies based not only on capacity constraints but also on risk pooling and minimum performance requirements for key characteristics that form the basis of the strategy. A Lagrangian relaxation is proposed and satisfactory performance results are provided.     

Delphi study on supply chain flexibility

The flexibility literature is dominated by research on manufacturing flexibility. More recently, supply chain flexibility has been recognized as an important flexibility construct for meeting customer demand. An Internet-based Delphi study involving a group of expert practitioners is used to enumerate the characteristics, and the importance of those characteristics, in making a supply chain flexible. Expert opinion is one method for gathering information that can be used to develop a model of supply chain flexibility and provide a framework for future research.     

Modelling testing mechanism for mitigating genetically modified wheat contamination risks

Recent discoveries of genetically modified (GM) wheat growing in farm fields across several U.S. states have renewed public worries about GM wheat contamination if GM wheat is commercialised. There would appear to be a need for research designed to identify reactive risk mitigation strategies to maintain the sustainability of grain supply chains (SCs) before any potential GM wheat contamination undermines their integrity. This research attempts to identify cost-effective testing strategies to mitigate the GM wheat contamination risks. We explicitly model the U.S. wheat supply chain in a realistic manner to embrace complexity inherent in the system. The specification of appropriate wheat handling strategies in the SC is formulated as system optimisation problems and solved using simulation. Once solved for a base scenario, sensitivity analysis is conducted on key variables that influence wheat varietal testing strategies.     

Towards optimal configuration of a manufacturer’s supply network with demand flexibility

Demand flexibility exhibits the degree to which customers are often willing to compromise on product features or performance levels for budgetary (reflected in price) or schedule (reflected in delivery) reasons. It is essential for a manufacturer to map demand flexibility into the supply side and investigate its impact on supply network configuration to maximise its total profit. This paper is among the first contributions that seek to address the challenge of optimal configuration of a manufacturer’s supply network that consists of raw material suppliers and contract manufacturers, considering demand flexibility and commonality among different product families. A new mixed integer programming model is developed to describe the characteristics of this problem. The objective was to maximise the manufacturer’s total profit subject to various operating constraints of the supply chain. In view of the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, a hybrid constraint programming and simulated annealing algorithm is proposed to solve the problem optimally. Extensive numerical studies are conducted to validate the effectiveness of the proposed model and the hybrid algorithm.     

Allocation flexibility for agribusiness supply chains under market demand disruption

This paper develops a multi-commodity multi-period optimisation model to analyse market demand disruption risk in agribusiness supply chains. It investigates the role of allocation flexibility and the effectiveness of multiple risk management strategies for achieving allocation flexibility. A robust optimisation formulation is used to obtain risk-averse solutions for an objective combining expected profit and risk. Numerical results are presented for a real-life case study of Zespri’s kiwifruit supply chain. The results show that allocation flexibility is effective for mitigating market demand disruption risk. Three proposed risk management strategies, namely diversified demand market, backup demand market and flexible rerouting, improve both expected profit and risk measures. While diversified demand market and backup demand market strategies are equally important for all decision-makers, flexible rerouting is especially significant for less risk-averse decision-makers.     

The value of updated information on future price behaviour

This paper studies the value of information on future price behaviour. We consider a one-period inventory modelling framework with random period length and two order opportunities. The selling price is determined dynamically and the demand is price-sensitive. The second ordering-pricing decision reflects the updated information on future price behaviour on supply chain flexibility. We consider three models with different levels of flexibility: the static model, the quantity flexible model and the combined quantity and timing (fully) flexible model. We compare between the values of three different features in the supply chain: updated information on price behaviour, dynamic pricing and supply flexibility. And we demonstrate the effect of holding cost and demand uncertainty on these three values. We also consider a specific condition with fixed selling prices. We give explicit analysis on the optimal order decisions, and analytically show the impact of information and quantity flexibility on the optimal order decisions.     

An impact of manufacturing flexibility and technological dimensions of manufacturing strategy on improving supply chain responsiveness: Business environment perspective

The main objective of this research is to investigate the impact of manufacturing flexibility and technological dimensions of manufacturing strategy on responsiveness in the supply chain. Based on the theoretical background of dynamic capability, this study also examines the role of the business environment on the relationship between manufacturing flexibility and supply chain responsiveness. 144 structured surveys were collected and the partial least squares of structural equation modelling approach were utilised for data analysis. The result establishes relationships among various dimensions of manufacturing flexibility. Although the technological dimensions in manufacturing strategy of such advanced manufacturing technology (AMT) and e-procurement do not have any direct impact on new product and market flexibility, they increase supply chain responsiveness, which helps to react quickly against supply chain disruptions. More importantly, the business environment has a moderating effect on the relationship between market flexibility and supply chain responsiveness.