A control engineering approach to the assessment of supply chain resilience

There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and nonlinear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.     

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.     

Quick response strategy with cleaner technology in a supply chain: coordination and win-win situation analysis

Quick response strategy (QRS) has been widely adopted in a supply chain where members collect timely market information for better forecasting, and then respond promptly to the market changes by adjusting initial inventory decision. After adopting the QRS, sustainability issues such as greenhouse gas emission and energy waste may be more serious as production lead time is shorter. In this study, due to this dilemma, we develop a two-stage quick response supply chain with cleaner technology, where the manufacturer determines the cleaner technology investment and afterwards the retailer decides the ordering quantity. Based on Bayesian theory, we depict an information updating process for the QRS with cleaner technology. First, we find that the inventory service level significantly affects both manufacturer’s and retailer’s performance under the QRS with cleaner technology. Moreover, our analytical results indicate that the performance of centralised supply chain system is always better than the decentralised one. As a result, we propose two supply chain contracts, minimum ordering quantity (MOQ) and MOQ with buyback (MOQ-BB) to achieve supply chain coordination. Comparing with the MOQ, the MOQ-BB is more flexible to allow better allocation of the ‘additional’ expected profit between the channel members through the buyback price negotiation.     

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.     

Risks and performance in supply chain: the push effect

A risk, when it occurs, causes negative effects on outputs. Typically risks are not independent, as multiple risks occur simultaneously. These risks have links, creating a ‘push’ effect, thus increasing the severity of each and all risk(s) on outputs. This paper aims to verify the mechanism of the push effect that is a new approach in the supply chain risk management literature. In this study, two models were compared: (1) only exists in direct effects of risks on supply chain performance, i.e. the competitive model. The other, (2), contains relationships among risks that show the mechanism of the push effect, i.e. the hypothesised model. Empirical evidence found in the Vietnam construction sector proved that the hypothesised model is better suited and has greater effect on supply chain performance in terms of each and all risk(s). Comparing 55% variance of the competitive model, the hypothesised one can explain up to 73% variance of supply chain performance. These results confirm our hypotheses of the push effect. Furthermore, findings achieved from this research can be used as ‘a guideline’ for reducing the impact of this mechanism.     

考虑风险规避行为的弹性供应链网络集成优化

基于前景理论对多维风险环境下的弹性供应链网络设计和运作集成优化问题进行了研究,使用了能够反映决策者风险规避行为特征的效用函数,通过随机情境生成技术模拟多维风险环境,并使用非线性缺货惩罚成本限制缺货量,从而确保供应链网络具有应对多维风险环境的弹性,在此基础上建立多周期非线性混合整数随机规划模型。文中给出了模型求解的线性转化方法与求解算法,并通过具体的数值算例与灵敏度分析验证了优化模型的有效性与实际应用价值。     

Leagile supply chain: design drivers and business performance implications

In the context of a dynamic and hypercompetitive business environment, effective supply chain design helps organisations to align resources for improved flow of products and services and satisfy customers’ diverse needs. Scholars have proposed several mutually exclusive supply chain designs such as efficient versus responsive, and lean versus agile. Quantitative testing has revealed that supply chain designs of many firms do not match with what was conceptually expected. To address this mismatch, in this study, a new approach to supply chain leagility is introduced and the impact of uncertainty as the key design driver of supply chains on leagility is investigated. The partial least squares (PLS) was employed to analyse data collected from 299 Australian firms by administering a structured questionnaire. Results indicate that higher performance is achievable on minimising the deviation from a balanced supply chain in which aspects of both leanness and agility are equally embedded. Further, the level of uncertainty directly and positively affects the Deviation from Leagility (DFL) index.     

‘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.     

On the quantification of operational supply chain resilience

Operational disruptions impact a supply chain’s ability to match supply and demand. To remain competitive, supply chains need to be resilient and thus capable of rapidly and effectively recovering from operational disruptions. Supply chain resilience is inherently multidimensional, as it spans across multiple tiers, and thus is difficult to quantify. Extant research has measured the transient response through a single-dimension or single-organisation as a proxy for operational resilience. Whilst this greatly simplifies the analysis, it is also potentially misleading, as an erroneous selection of metric(s) may lead to an inaccurate evaluation of the transient response. This research extends the understanding of operational resilience via quantitative evaluation of multiple transient response measures across multiple tiers; the objective being to construct a multidimensional, multi-echelon operational supply chain resilience metric. The study utilises disruptions as experimental inputs for a serial supply chain simulation model; results are obtained for individual measurements of the transient response across multiple supply chain tiers. Analysis indicates that individual dimensions of resilience can adequately explain the transient response at the single-firm level, whilst aggregation of multiple resilience dimensions across multiple tiers has greater capacity to holistically capture the performance response to supply chain disruptions.     

基于供货量损耗比的鲜活农产品均衡问题研究

从动态网络下的鲜活农产品三级供应链出发,考察了供货量损耗比对供应链的影响。构造了含有供货量损耗比的变分不等式模型,并把价格弹性引入到了模型中,从而得到了鲜活农产品供应链的网络均衡模型,针对不同损耗和不同价格弹性的情况进行了分析,探讨损耗比和价格弹性对网络均衡的影响。分析可知,损耗比的增加,以及价格弹性的增加,都会降低整个网络的总利润。因此,要获得最大利润,生产商和销售商应该努力最小化损耗比和价格弹性这2个参数。     

A systematic review of China’s belt and road initiative: implications for global supply chain management

China’s Belt and Road Initiative (BRI) is one of the world’s largest infrastructure projects, with its potential political and economic impact being widely discussed since its inception in 2013. Yet the phenomenon has received only limited attention in the Supply Chain Management (SCM) literature. In response, we first conduct a broad systematic review of the literature to assess how China’s BRI is portrayed. Using this as a backdrop, we then distil the likely impact of the BRI on location decisions and supply chain flows. Finally, in a broader discussion of the SCM literature, we explore the implications of the BRI for future research in four key areas: supply chain configuration, supply chain resilience, sustainable SCM, and cross border SCM. While these areas are not new, the BRI presents a unique context that can be used to enhance theory and understanding in each area. The BRI reduces time distance independent of geographical distance by diverting supply chain flows from established routes to new routes via far less accessible regions. This introduces new risks and sustainability issues that call for multi-criteria decision support systems. Another important issue is the adoption and diffusion of the BRI since this will ultimately determine project success.     

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

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

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.     

Revealing interfaces of supply chain resilience and sustainability: a simulation study

Dynamics of structures and processes is one of the underlying challenges in supply chain management, where multiple dimensions of economic efficiency, risk management and sustainability are interconnected. One of the substantiated issues in supply chain dynamics is resilience. Resilience has a number of intersections with supply chain sustainability. This paper aims at analysing disruption propagation in the supply chain with consideration of sustainability factors in order to design resilient supply chain structure in regard to ripple effect mitigation and sustainability increase. Ripple effect in the supply chain occurs if a disruption at a supplier cannot be localised and cascades downstream impacting supply chain performance. This simulation-based study helps to identify what sustainability factors mitigate the ripple effect in the supply chain and what sustainability factors enhance this effect. The results indicate that (i) sustainable single sourcing enhances the ripple effect; (ii) facility fortification at major employers in regions mitigates the ripple effect and enhances sustainability; and (iii) a reduction in storage facilities in the supply chain downstream of a disruption-risky facility increases sustainability but causes the ripple effect.     

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.     

区间灰色时滞特征的供应链建模及稳定性分析

供应链的平稳运行是供应链管理的重要基础。针对具有区间灰色时滞特征的供应链建模及其稳定性控制的难题,基于时滞系统模型,结合供应链建模中矩阵的区间灰色特性,建立了具有区间灰色时滞特征的供应链多层模型系统,并提出了一种使用李雅普诺夫(Lyapunov)函数方法,研究多层供应链结构的稳定性问题,获取了一个有效且实用的稳定性判据,给出了证明过程。为了验证该方法的有效性能,以2层区间灰色时滞供应链系统为算例,研究具有区间灰色时滞特征的供应链系统的随机稳定性问题。算例结果表明:使用Matlab 7.0计算工具,在判别矩阵U+V<0时获得一组常数值ε,在给定条件下,供应链系统随机稳定。可得出结论:该算法在保证最优解不会降低的情况下,达到了系统稳定能力增强的目的,可为实际应用提供指导。     

Supply chain models with corporate social responsibility

Currently, corporate social responsibility (CSR) has become a critical issue because more than 88% of consumers think companies should try to achieve their business goals while improving society and the environment. This paper focuses on a CSR supply chain where an Original Equipment Manufacturer’s (OEM’s) sales can be significantly reduced because of its oversea supplier’s social misconduct. Specifically, as in a conventional wholesale price contract the supplier determines its wholesale price and the OEM decides its order quantity, but in a CSR supply chain, the supplier can autonomously change its CSR cost once a minimum requirement is satisfied. A higher CSR cost means that the supplier invests more in its corporate social responsibility and the OEM’s sales will be less likely to be influenced by negative CSR events. The equilibrium solutions show an important dilemma – although the supplier’s profit increases in the basic CSR requirement, the supplier will always use the minimum CSR cost under the conventional wholesale price contract, which eventually leads to a low supply chain profit. Thus, we introduce two different contracts to handle this problem: the flexible quantity contract and the wholesale price incentive contract, which are, respectively, a ‘tough’ way and a ‘beneficent’ way for the OEM to solve the problem. Although the two ways cannot (always) coordinate the supply chain, we show that they both will significantly improve the supply chain performance. Our results also show that in some conditions, one strategy will dominate, whereas in different conditions the other strategy dominates.     

A study on investments in the big data-driven supply chain,performance measures and organisational performance in Indian retail 4.0 context

The use of digital technologies such as ‘internet of things’ and ‘big data analytics’ have transformed the traditional retail supply chains into data-driven retail supply chains referred to as ‘Retail 4.0.’ These big data-driven retail supply chains have the advantage of providing superior products and services and enhance the customers shopping experience. The retailing industry in India is highly competitive and eager to transform into the environment of retail 4.0. The literature on big data in the supply chain has mainly focused on the applications in manufacturing industries and therefore needs to be further investigated on how the big data-driven retail supply chains influence the supply chain performance. Therefore, this study investigates how the retailing 4.0 context in India is influencing the existing supply chain performance measures and what effect it has on the organisational performance. The findings of the study provide valuable insights for retail supply chain practitioners on planning BDA investments. Based on a survey of 380 respondents selected from retail organisations in India, this study uses governance structure as the moderating variable. Implications for managers and future research possibilities are presented.     

Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced and defective supply chain problems considering WEEE/RoHS directives

This research explored problems concerning production and delivery in a green supply chain, and constructed an optimal mathematical model to provide solutions. This model incorporates WEEE and RoHS in EU directives for the selection of green partners when establishing a supply chain. The weight of each component is calculated by fuzzy analytic hierarchy process (fuzzy AHP). Previous studies suggested that a supply chain is a balanced system, however, in actual practice, there may be processing damages or delivering losses. Thus, such a supply chain with production loss is known as a ‘defective supply chain’. This research analysed the defective supply chain system to discuss its supplier selection, production, and distribution. It developed an optimal mathematical model for both balanced and defective models, and adopted particle swarm optimisation (PSO) to obtain solutions for both models. Finally, case studies for both models with quality solutions were discussed to confirm the efficiency and effectiveness of the proposed approach.