Mitigate Supply Chain Research Articles

Life Cycle Analysis of Decentralized Preprocessing Systems for Fast Pyrolysis Biorefineries with Blended Feedstocks in the Southeastern United States

Blending biomass feedstock is a promising approach to mitigate supply chain risks that are common challenges for large‐scale biomass utilization. Understanding the potential environmental benefits of biofuels produced from blended biomass and identifying driving parameters are critical for the supply chain design. Herein, a cradle‐to‐gate life cycle analysis model for fast pyrolysis biorefineries converting blended feedstocks (pine residues and switchgrass) with traditional centralized and alternative decentralized preprocessing sites, so‐called depots, is explained. Different scenarios are developed to investigate the impacts of parameters such as feedstock blending ratios, biorefinery and depot capacities, preprocessing technologies, and allocation methods. The life‐cycle energy consumption and global warming potential (GWP) of biofuel production with depots vary between 0.7–1.1 MJ MJ−1 and 43.2–76.6 g CO2 eq. MJ−1, respectively. The results are driven by biorefinery processes and depot preprocesses. A decentralized design reduces the energy consumption of the biorefinery but increases the overall life‐cycle energy and GWP. Such increases can be significantly mitigated by increasing switchgrass content as the energy consumption at the depot is driven largely by the higher moisture content of pine feedstocks. Allocation methods also have a large impact on the results but do not change the major trends and overall conclusions.

UNCERTAINTY AS AN ANTECEDENT OF THE SUPPLY CHAIN RISK: DOES SUPPLY CHAIN FLEXIBILITY MATTER IN RISK MITIGATION?

Purpose: This study aims to address the existing research gap, as well as to explore the various types of flexibility which could contribute in mitigating supply chain risks.
 Methodology: In this regard, we consider three aspects under SCR namely; manufacturing process risk, delivery risk, and supply risk. The study explores the relationships among supply chain risk and environmental uncertainty, and the moderating role of supply chain flexibility by employing data of 91 manufacturing companies and develop a structural equation modelling.
 Results: Developing economies are contributing well in the global trade and are responsible for 50% of the generated global output. They present future prospects for economic growth and pose challenges mainly because of immature supply chain operations. Considering the critical role of emerging economies in global supply chain, such as India and China, it is also important to observe the moderating and driving supply chain risk factors in these regions. It is assumed that this study will not only fill the existing gap in the literature of SCRM but will assist researchers and practicing managers, by enabling them to fully understand certain types of supply chain flexibility, thus reducing the SCR under business environments.

Identifying and mitigating supply chain risks using fault tree optimization

Although supply chain risk management and supply chain reliability are topics that have been studied extensively, a gap exists for solutions that take a systems approach to quantitative risk mitigation decision making and especially in industries that present unique risks. In practice, supply chain risk mitigation decisions are made in silos and are reactionary. In this article, we address these gaps by representing a supply chain as a system using a fault tree based on the bill of materials of the product being sourced. Viewing the supply chain as a system provides the basis to develop an approach that considers all suppliers within the supply chain as a portfolio of potential risks to be managed. Next, we propose a set of mathematical models to proactively and quantitatively identify and mitigate at-risk suppliers using enterprise available data with consideration for a firm’s budgetary constraints. Two approaches are investigated and demonstrated on actual problems experienced in industry. The examples presented focus on Low-Volume High-Value (LVHV) supply chains that are characterized by long lead times and a limited number of capable suppliers, which make them especially susceptible to disruption events that may cause delays in delivered products and subsequently increase the financial risk exposure of the firm. Although LVHV supply chains are used to demonstrate the methodology, the approach is applicable to other types of supply chains as well. Results are presented as a Pareto frontier and demonstrate the practical application of the methodology.

An examination of the impact of flexibility and agility on mitigating supply chain disruptions

This paper examines the effect of flexibility and agility on improving supply chain responsiveness. We examine a supply chain with multiple sites, multiple transportation channels, and multiple product planning, over multiple periods, under supply risk and demand risk. Using a numerical example, we determine the relationship between three objective functions related to responsiveness, risk, and the cost of new and seasonal products, then discuss the impact of flexibility and agility on mitigating supply chain disruptions. To show a trade-off among objective functions in our numerical example, we solve the multi-objective mixed integer programming (MOMIP) model using three multi-objective optimization methods: the weighted goal programming (WGP) method, the augmented ε-constraint (AUGMECON) method, and the non-dominated sorting genetic algorithm (NSGA-Ⅱ). Our findings help decision makers with these two tasks: anticipate how much improvement in flexibility and agility will lead to an improvement in responsiveness; and create an investment plan to minimize the negative impact of supply chain disruptions by an examination of the trade-offs among responsiveness, risk, and cost. The results show that the NSGA-Ⅱ method outperforms the AUGMECON method in several metric indexes: the spacing metric, the diversity metric, and simple additive weighting.

Supply chain sustainability risk and assessment

Risk on the supply chain from various sustainability-related factors has increasingly become relevant to companies in many industries. There however still lacks a comprehensive method to quantitatively evaluate supply chain sustainability risk taking into account the triple bottom line of sustainability (economic, social, and environmental). This study develops a framework to evaluate supply chain sustainability risk by measuring supply chain-wide operational risk, social risk, and environmental risk to form an aggregate metric. A set of indicators readily available in literature are used to represent various aspects of supply chain sustainability risk. Risk assessment space and materiality analysis are then used to prioritize resource allocation among supply chain stages from two distinct perspectives for mitigating supply chain sustainability risk. Two case studies are provided to demonstrate the application of the developed framework, representing two main types of supply chain structure. The apparel industry example represents the deep-structure supply chain driven by relatively simple products, while the automotive industry example represents the broad-structure supply chain driven by more complex products. While the case studies focus on industry-level assessment for demonstration purpose, the developed framework can be flexibly applied to evaluate supply chain sustainability risk for specific companies in any industry.

A hybrid approach for modelling dynamic flows and systemic risks in supply chains

The uncertainty of supply chain operations is becoming more complex with the growth of globalised business collaborations. Temporal business environment fluctuations cause disruptions that require quantitative risk analysis tools to understand and mitigate supply chain risks. This paper presents the formulation and application of the dynamic flow Bayesian networks (DFBNs) and the optimised dynamic flow Bayesian networks (ODFBNs) for a three-stage supply chain. DFBNs are created by integrating dynamic Bayesian networks (DBNs) and system dynamics (SD) to demonstrate the feedback flows of a supply chain with systemic risks considered. ODFBNs that incorporate mathematical optimisation with the original DFBNs are also developed to generate an optimisation-enhanced risk-influenced dynamic flow analysis. DFBN and ODFBN provide supply chain practitioners with a more effective reference for their business strategy. Comparison between the ODFBN and the DFBN is illustrated with a discussion of preliminary modelling results.

Modelling and analysis of supply chain risks in leather industry

Due to globalised marketing, supply chain of leather industries in India are now facing variety of risks. Hence, modelling and analysis of supply chain risk becomes paramount important. In this article, the major risks that affect the supply chain performance of leather industries in India were identified through systematic literature review and experts opinion. These risks are grouped using exploratory factor analysis (EFA) and the grouped risks are confirmed using confirmatory factor analysis (CFA). Structural equation modelling (SEM) was created and analysed. It is found that demand risks influence the operation risk with high impact. This analysis helps in understanding the relationship and the impact of each risk with other risks which in turn very much helpful in developing guidelines for managerial decision making process to mitigate supply chain risks.

Modelling and analysis of supply chain risks in leather industry

Due to globalised marketing, supply chain of leather industries in India are now facing variety of risks. Hence, modelling and analysis of supply chain risk becomes paramount important. In this article, the major risks that affect the supply chain performance of leather industries in India were identified through systematic literature review and experts opinion. These risks are grouped using exploratory factor analysis (EFA) and the grouped risks are confirmed using confirmatory factor analysis (CFA). Structural equation modelling (SEM) was created and analysed. It is found that demand risks influence the operation risk with high impact. This analysis helps in understanding the relationship and the impact of each risk with other risks which in turn very much helpful in developing guidelines for managerial decision making process to mitigate supply chain risks.

An analysis of premium payments as a mechanism for securing preferential service in cloud manufacturing

Paying a premium in a cloud manufacturing free market environment will benefit a customer during steady state scenarios, however during disruption this benefit diminishes to zero; dependent on the degree of disruption. These customers will recover quicker from disruptions, but the returns are small compared to the relative increase in cost. A cloud manufacturing scenario without contracts or preferential supply acts as a free market, this significantly increases supply chain risk. The predominant mechanism available to mitigate supply chain risk is the willingness to pay a premium. Agent-based simulation modelling experiments investigated this scenario using an extension of anarchic manufacturing for scheduling and control. The experiments induced a disruptive demand-side step change in requirement mix of capabilities and volume demanded by all customers. One customer was willing to pay a significant premium to the others. The waiting time (non-operating time) was analysed as the benefit and indicator for supply chain performance. The job cost for the customer willing to pay a premium relative to other customers and the relative waiting time were analysed during steady state, disruption, and recovery periods.

A hybrid Z-number data envelopment analysis and neural network for assessment of supply chain resilience: a case study

Today’s complex supply chains are increasingly susceptible to the turbulent and fast-changing business environment and their economic implications. Resilience as an effective strategic planning during disturbances is a way to mitigate supply chain vulnerabilities. After reviewing the literature on the topic of a resilient supply chain, this paper extracts a complete series of 16 resilience enablers. These identified enablers form the foundation of a questionnaire distributed among over 150 experts and staffs of a real case associated with an Iranian automotive supply chain. The reliability and validity of the questionnaire are evaluated by statistical tests and Cronbach’s alpha. Then, a hybrid of the Z-number data envelopment analysis and neural network is employed for the efficiency score calculation, separately. Finally, the associated results are combined and the final efficiency scores are obtained. The case study findings indicate that by improving the resilience enablers, especially ones with the greatest influence on the supply chain performance, firms can be less vulnerable in times of supply chain disruptions. The framework proposed in this study may find a broad practical application in all types of supply chains.

Roles of inventory and reserve capacity in mitigating supply chain disruption risk

This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterise four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company.

Supply Chain Risk Management

This article describes how as mankind has been ever-evolving, so are their needs, growing in leaps and bounds. Risk management in supply chains have become a prerequisite as it involves a series of steps like procuring, processing, and distribution, where risk has to be managed. Given the advent of technology and transformation of supply chain management from traditional to modern methods, a lot has changed, and of course with ever-evolving technology, the organisations have become adept at handling risks associated with many factors within the organisation and outside the organisation. In this article, the authors will analyse the percentage impact of external and internal supply chain risk factors on various supply chain decisions, and also project various tools available to mitigate Supply chain risks.

A dynamic model for managing adulteration risks of dairy industry supply chain in Indonesia

Supply chain has a principal role in supporting the development of milk processing industry. The supply chain of the dairy industry in Indonesia involves a number of actors and a variety of dairy handling that may lead into potential risks and failures. One of the risks affecting supply chain performance is the adulteration of raw materials. This condition can possibly result in the occurrence of chain’s effect. Thus, it is necessary to build a dynamic model to handle such problems. Dynamic modeling was used in this study. For the initial phase, determination of supply chain risk factor was investigated. Second, modeling of supply chain mitigation risk was conducted by using dynamic system. Dynamic models were used to analyse complex problems with many variables involved. The problem analysis results were modeled in a dynamic system that has an aim to reduce the risk of adulteration. This research has successfully constructed a model consisted of one main variable that can potentially reduce the risk. The foremost recommended strategy was to improve the transparency of supply chain management.

Suppressing partner opportunism in emerging markets: Contextualizing institutional forces in supply chain management

Drawing on the literature of supply chain opportunism and institutional theory, this study examines the confluence of institutional and efficiency views to assess how institutional forces restrain the impact of exchange hazards (i.e., transaction-specific assets and performance ambiguity) on supply chain opportunism. We predict that legal enforceability and guanxi importance mitigate supply chain opportunism, but their interaction does not necessarily help to curb opportunism. In addition, we propose that legal enforceability and guanxi importance have differential moderating effects on the relationships between exchange hazards and opportunism. The empirical analyses of a dyadic buyer-supplier dataset in China confirm the predicted direct and interactive effects of the institutional forces. In addition, the positive impact of transaction-specific assets on opportunism is attenuated by legal enforceability but not by guanxi importance, whereas the effect of performance ambiguity decreases due to guanxi importance but not legal enforceability.

Understanding and Mitigating Supply Chain Fraud

Understanding and Mitigating Supply Chain Fraud

Logistics Capability, Supply Chain Uncertainty and Risk, and Logistics Performance: An Empirical Analysis of Australian Courier Industry

The purpose of this paper is to examine the applicability of logistics capability for mitigating supply chain uncertainty and risk and improving logistics performance in the Australian courier industry. Based on the resource-based view, a quantitative research study is designed to investigate the relationships among three latent variables: logistics capability, supply chain uncertainty and risk, and logistics performance. Structural equation modelling is used to analyse the data. The results indicate that there are significant relationships between logistics capability, supply chain uncertainty and risk, and logistics performance. The results provide insights into supply chain uncertainty and risk management. This paper provides empirical support for the resource-based view as it applies to mitigating supply chain uncertainty and risk management in the Australian courier industry. These findings suggest that managers might develop and deploy logistics capability to support and enable supply chain risk management strategies.

Supply Chain Risk Management: A Review of Thirteen Years of Research

This paper performs a systematic literature review on supply chain risk management (SCRM). This review analyzes 133 articles published between 2005 and the first quarter of 2018. Its main purpose is to identify the developed strategies used to mitigate risks and improve supply chain performance. It appears that there is heterogeneity in the developed strategies and that quantitative methods simulation/modeling are the most used by researchers to mitigate supply chain risks (SCR). Although emphasis is made on the links between SCRM and performance or resilience, risk prevention strategies remain the least represented in the papers analyzed. We also find that there is no superior approach in the set of various risks management strategies and thus it is difficult to linearly establish, the successive evolutions of the models that would replace others.

Cluster analysis for determining distribution center location

Determination of distribution facilities is highly important to survive in the high level of competition in today’s business world. Companies can operate multiple distribution centers to mitigate supply chain risk. Thus, new problems arise, namely how many and where the facilities should be provided. This study examines a fast-food restaurant brand, which located in the Greater Jakarta. This brand is included in the category of top 5 fast food restaurant chain based on retail sales. There were three stages in this study, compiling spatial data, cluster analysis, and network analysis. Cluster analysis results are used to consider the location of the additional distribution center. Network analysis results show a more efficient process referring to a shorter distance to the distribution process.

Real options approach to evaluate postponement as supply chain disruptions mitigation strategy

The purpose of this paper is to assess the value of postponement as strategy for mitigating supply chain disruptions. To accomplish this objective, we develop a real option computational model that quantifies the value of postponement in mitigating both supply and demand disruptions by taking into account the value of managerial flexibility to decide whether exploiting or not the strategy, if and when disruptions occur, and whenever product differentiation proves valuable based on information available at that time. Numerical experiments show the importance of incorporating an option valuation method when pricing the value of postponement. This ensures managers implement postponement only when it is valuable, thus avoiding burdening the company with its initial sunk costs. By modelling the postponement implementation under different conditions, we identify the situations in which postponement performs better as supply chain disruptions mitigation strategy. We derive the operational configurations, in terms of decoupling point position, and external conditions, in terms of riskiness of the environment, which make the postponement an effective mitigation strategy.

Humanitarian supply chain strategies – a review of how actors mitigate supply chain risks

PurposeThe purpose of this paper is to link humanitarian logistics (HL) and supply chain risk management (SCRM) to provide an understanding of risk mitigation strategies that humanitarian organisations use, or could use, to improve their logistics preparedness.Design/methodology/approachBased on systematic reviews of RMS in SCRM and supply chain strategies (SCS) in HL literature, a framework is developed and used to review published case studies in HL.FindingsThe study finds that humanitarian actors use a number of the strategies proposed in the framework, particularly those related to strategic stocks, postponement, and collaboration. Strategies related to sourcing and procurement, however, especially those on supplier relationships, seem to be lacking in both research and practice.Research limitations/implicationsThe study is based on secondary data and could be further developed through case studies based on primary data. Future studies should explore the generalisability of the findings.Practical implicationsPractitioners can use the framework to identify potential new SCS and how strategies can be combined. Findings can help them to understand the abnormal risks of main concern, how they may impact normal risks, and provide ideas on how to tackle trade-offs between different risks.Social implicationsThe results can support improvements in humanitarian supply chains, which will provide affected people with rapid, cost-efficient, and better-adapted responses.Originality/valueThe paper connects SCRM and HL to develop a framework and suggests propositions on how humanitarian actors can mitigate supply chain risks. Questioning the focus on strategic stock it suggests complementary or alternative strategies for improving logistics preparedness.