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In recent years, the need for achieving an environmentally sustainable supply chain has become a prominent concern among researchers as well as industry practitioners. However, there exists an insufficiency of comprehensive studies on the critical factors for environmental sustainability in the context of an emerging economy. To fill this research gap, the present study aims to determine and analyze the critical success factors (CSFs) for implementing Environmentally Sustainable Supply Chain Management (ESSCM) practices considering the readymade garment (RMG) sector of Bangladesh. Initially, fourteen CSFs were sorted-out based on a review of the existing literature. We then held an interactive discussion session with industry experts to screen these candidate CSFs vis-a-vis the RMG industry of Bangladesh. The experts agreed on ten factors that we then processed further intending to understand the contextual relationships and interdependency among them. For this purpose, we applied the Total Interpretive Structural Modeling (TISM) technique. Moreover, we implemented a Cross-impact Matrix Multiplication Applied to Classification (MICMAC) analysis to classify the CSFs according to the influence and dependence level of each factor. Our findings indicate that all the identified CSFs are interrelated and “Effective government policies” is the most dominating CSF that acts independently but influences other CSFs either directly or indirectly. The findings can help motivate managers in the RMG industry to concentrate on the most relevant CSFs, thereby accomplishing ESSCM more effectively in the context of an emerging economy.

This paper proposes a multi-objective robust-stochastic humanitarian logistics model to assist disaster management officials in making optimal pre- and post-disaster decisions. This model identifies the location of temporary facilities, determines the amount of commodity to be pre-positioned, and provides a detailed schedule for the distribution of commodities and the dispatch of vehicles. Uncertainties in demand, node reachability by a particular mode of transportation, and condition of pre-positioned supplies after a disaster are considered. Another supposition of this paper is the equity in the distribution of commodities. This paper contributes to the existing literature by adding vehicle flow and multi-periodicity into a robust-stochastic optimisation model. A real-life case study of a flood in Bangladesh shows the applicability of our model. Finally, the findings show that the proposed model can aid decision-makers in allocating resources optimally.

Recently, the soft drink industry has been confronted with a rapidly changing business climate and increased competition, creating risks and uncertainties in its supply chain. Thus, immediate actions are required to handle these risks and uncertainties. The purpose of this paper is to identify and classify these risks in the soft drink supply chain and to develop a methodology comprising a fuzzy analytical hierarchy process (FAHP) and fuzzy comprehensive evaluation of these risks. FAHP was used to determine the weights of five major risk categories and 23 sub risks under these major categories. Afterward, a fuzzy comprehensive evaluation method (FCEM) was applied for the assessment of overall risk level, risk levels of the five major categories and relative risk levels of the 23 sub risks. This research has been conducted focusing on the soft drink industry of Bangladesh, a developing country with an emerging economy. In the existing literature, this work contributes by identifying and evaluating risks in the soft drink supply chain in the context of Bangladesh. The outcome of this study indicates that the overall risk level in the supply chain of the soft drink industry is between low and medium, and the risk level of the demand risks is the highest whereas the risk level of infrastructural risks is the lowest. It was also found that loss of reputation and brand image is the most significant sub risk, and natural disasters are the least significant sub risk.

This paper develops an integrated economic model for the joint optimization of quality control parameters and a preventive maintenance policy using the cumulative sum (CUSUM) control chart and variable sampling interval fixed time sampling policy. To determine the in-control and out-of-control cost for both mean and variance, a Taguchi quadratic loss function and modified linear loss function are used, respectively. Imperfect preventive maintenance and minimal corrective maintenance policies were considered in developing the model, which determines the optimal values for significant process parameters to minimize the total expected cost per unit of time. A numerical example is used to test the model, which is followed by a sensitivity analysis. The integration of CUSUM mean and variance charts with the maintenance actions are proven successful to detect the slightest shift of the process. The findings reveal that among all the cost components, process failure due to external causes and equipment breakdown has a noteworthy attribute to the total costs of the optimized model. It is expected that top managers can utilize the suggested combined model to minimize the costs related to quality loss and maintenance policy and achieve economical advantages as well.

This paper provides a methodological overview of service supply chain research through a comprehensive review of published literature, enabling us to describe the service supply chain from a knowle...

Reverse logistics (RL) is gradually becoming more important to manufacturing companies through environmental awareness, competitiveness, and environmental regulations. In the leather footwear industry of Bangladesh, it is possible to recycle and reuse waste, meaning a reverse logistics system could increase return-on-investment and give a competitive advantage. To date, studies on barriers to RL implementation have been conducted in other countries and in other domains, leaving a research gap in RL barrier analyses in the leather footwear industry. There are many obstacles to implementing RL in a developing country like Bangladesh; examining these barriers is a crucial research issue. This paper identifies RL barriers through (1) an extent literature review, (2) advice from Bangladeshi industry experts under the Delphi study, and (3) ranking RL barriers using the fuzzy analytical hierarchy process. The results indicate that, of the barriers investigated, the ‘knowledge and support’ category seems to be most critical. A lack of interest and support from top-level management—related to ‘knowledge and support’ issues—appears to be the major obstacle for RL implementation in the Bangladeshi leather footwear industry. These findings will help the Bangladeshi leather footwear industry, as well as other industries in Bangladesh, to understand the nature of each barrier and overcome the complexity of RL implementation in supply chains. This study will also assist decision-makers in making certain strategic policies. Future studies may contribute to the life cycle assessment and engineering of recycled and reused footwear.

This paper aims to develop a recovery planning approach in a three-tier manufacturing supply chain, which has a single supplier, manufacturer, and retailer under an imperfect production environment, in which we consider three types of sudden disturbances: demand fluctuation, and disruptions to production and raw material supply, which are not known in advance. Firstly, a mathematical model is developed for generating an ideal plan under imperfect production for a finite planning horizon while maximizing total profit, and then we re-formulate the model to generate the recovery plan after happening of each sudden disturbance. Considering the high commercial cost and computational intensity and complexity of this problem, we propose an efficient heuristic, to obtain a recovery plan, for each disturbance type, for a finite future period, after the occurrence of a disturbance. The heuristic solutions are compared with a standard solution technique for a considerable number of random test instances, which demonstrates the trustworthy performance of the developed heuristics. We also develop another heuristic for managing the combined effects of multiple sudden disturbances in a period. Finally, a simulation approach is proposed to investigate the effects of different types of disturbance events generated randomly. We present several numerical examples and random experiments to explicate the benefits of our developed approaches. Results reveal that in the event of sudden disturbances, the proposed mathematical and heuristic approaches are capable of generating recovery plans accurately and consistently.

Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.

The success of an organization or a particular activity is evaluated through the measurement of key performance indicators (KPIs). The aim of this paper is to analyze and predict the indicators of healthcare performance using grey systems theory. Recent advancements in science and technology have made the healthcare industry extremely efficient at collecting data using electronic claims systems such as electronic health records. Therefore, collecting field level primary data becomes easier and accumulate them to generate secondary data for research purpose and to get an insight of the organization performance is absolutely necessary. Our research analyzes the KPIs of a hospital based on a secondary data source. Since, secondary data contains uncertainty and sometimes poor information, grey prediction model suits best to make a prediction model in this regard. Conventional grey model has considerable drawbacks while making a rigorous prediction model. For this, we apply an improved grey prediction model to predict the KPIs of the healthcare performance indicators. Several error measures in our model give a best fit of the data and allow prediction of the KPIs. The prediction model gives good estimates of the quantitative indicators and produced error rate within an acceptable range. We observe that the KPIs of bed turnover rate (BTR) and bed occupancy rate (BOR) have an increasing trend, whereas the KPIs of average length of stay (ALOS), hospital death rate (HDR) and hospital infection rate (HIR) show a decreasing trend over time. The main contribution of this research is a grey-based prediction model that can provide managers with the information they need to evaluate and predict the performance of a hospital. The research indicates that managers should give greater priority to the indicators which will result in better patients’ satisfaction and improved profit margin. Healthcare managers striving towards better performance will now have an empirical basis upon which to formulate and adjust their strategies, after analyzing the predicted value.

This paper proposes a high-performance and robust linear quadratic regulator-proportional integral derivative (LQR-PID) controller for frequency regulation in a two-area interconnected smart grid with a population of plug-in hybrid electric vehicles. Controller robustness is achieved using a linear matrix inequality approach. The proposed control framework is tested in a simulated two-area interconnected smart grid integrated with plug-in hybrid electric vehicles under load disturbances and wind power fluctuations. The performance of the proposed controller is simulated using Matlab and compared with that of a conventional linear quadratic regulator controller. Simulation results show that the proposed controller provides reliable smart grid frequency control.

In this paper, a multi-echelon, multi-period, decentralized supply chain (SC) with a single manufacturer, single distributor and single retailer is considered. For this setting, a two-phase planning approach combining centralized and decentralized decision-making processes is proposed, in which the first-phase planning is a coordinated centralized controlled, and the second-phase planning is viewed as independent decentralized decision-making for individual entities. This research focuses on the independence and equally powerful behavior of the individual entities with the aim of achieving the maximum profit for each stage. A mathematical model for total SC coordination as a first-phase planning problem and separate ones for each of the independent members with their individual objectives and constraints as second-phase planning problems are developed. We introduce a new solution approach using a goal programming technique in which a target or goal value is set for each independent decision problem to ensure that it obtains a near value for its individual optimum profit, with a numerical analysis presented to explain the results. Moreover, the proposed two-phase model is compared with a single-phase approach in which all stages are considered dependent on each other as parts of a centralized SC. The results prove that the combined two-phase planning method for a decentralized SC network is more realistic and effective than a traditional single-phase one.

The current COVID-19 pandemic has hugely disrupted supply chains (SCs) in different sectors globally. The global demand for many essential items (e.g., facemasks, food products) has been phenomenal, resulting in supply failure. SCs could not keep up with the shortage of raw materials, and manufacturing firms could not ramp up their production capacity to meet these unparalleled demand levels. This study aimed to examine a set of congruent strategies and recovery plans to minimize the cost and maximize the availability of essential items to respond to global SC disruptions. We used facemask SCs as an example and simulated the current state of its supply and demand using the agent-based modeling method. We proposed two main recovery strategies relevant to building emergency supply and extra manufacturing capacity to mitigate SC disruptions. Our findings revealed that minimizing the risk response time and maximizing the production capacity helped essential item manufacturers meet consumers’ skyrocketing demands and timely supply to consumers, reducing financial shocks to firms. Our study suggested that delayed implementation of the proposed recovery strategies could lead to supply, demand, and financial shocks for essential item manufacturers. This study scrutinized strategies to mitigate the demand–supply crisis of essential items. It further proposed congruent strategies and recovery plans to alleviate the problem in the exceptional disruptive event caused by COVID-19.

This research investigates the impacts of the novel coronavirus disease, also referred to as COVID-19 pandemic, on the food and beverage industry. It examines both short-term and medium-to-long-term impacts of the pandemic and outlines strategies to reduce the potential consequences of those impacts. To this end, we use a qualitative, multiple-case-study methodology, collecting data from eight sample companies with fourteen respondents in the food and beverage industry in Bangladesh. The findings show that the short-term impacts of this pandemic, such as product expiry, shortage of working capital, and limited operations of distributors, are severe, while the medium-to-long-term impacts promise to be complex and uncertain. In the longer term, various performance metrics, such as return on investment by the firms, the contribution of the firms to the gross domestic product (GDP), and employee size, are all expected to decrease. Moreover, firms may need to restructure their supply chain and build relationships with new distributors and trade partners. The study proposes several strategies that managers in this sector can adopt to improve resiliency in the changing environment during and after the COVID-19 era. While this research is novel and contributes to both theory and practice, it does not consider small and medium-sized companies in the food and beverage industry. Therefore, the impacts and strategies we identify may not apply to smaller companies.

Due to the recent pandemic of coronavirus, known as the COVID-19 outbreak, the supply chains have been impacted most significantly. Manufacturers of certain items have experienced a substantial increase in demand, and on the other hand, raw materials supply, to produce those items, has reduced because of supply failure. To overcome these challenges, this paper proposes some strategies to improve service level during an extraordinary pandemic outbreak, such as COVID-19, for the most wanted products such as toilet paper. This study considers meeting the increased demand of the customers for an essential product of daily life like toilet paper during a pandemic is beyond the traditional economic objective, i.e., increase profit, of the manufacturers. Instead, this should be more about the social responsibility of all the manufactures to ensure that they can serve more customers. Motivated by this and taking toilet paper as an example of the product, we first analyzed the current scenario of the manufacturing and the demand for the product and then proposed some strategies to deal with this unprecedented risk and analyzed the results. We have compared the results, using hypothetical data, between the current scenario and proposed strategies. The result shows that sharing information and resources from all manufacturers to produce under a single brand, emergency sourcing, producing basic quality items, and packing in the smallest sizes have a significant positive impact on the service level. This paper first investigates the strategies for a high-demand and essential item during a pandemic situation and proposes strategies to deal with this unique, extraordinary disruption.

The global spread of the novel coronavirus, also known as the COVID-19 pandemic, has had a devastating impact on supply chains. Since the pandemic started, scholars have been researching and publishing their studies on the various supply-chain-related issues raised by COVID-19. However, while the number of articles on this subject has been steadily increasing, due to the absence of any systematic literature reviews, it remains unclear what aspects of this disruption have already been studied and what aspects still need to be investigated. The present study systematically reviews existing research on the COVID-19 pandemic in supply chain disciplines. Through a rigorous and systematic search, we identify 74 relevant articles published on or before 28 September 2020. The synthesis of the findings reveals that four broad themes recur in the published work: namely, impacts of the COVID-19 pandemic, resilience strategies for managing impacts and recovery, the role of technology in implementing resilience strategies, and supply chain sustainability in the light of the pandemic. Alongside the synthesis of the findings, this study describes the methodologies, context, and theories used in each piece of research. Our analysis reveals that there is a lack of empirically designed and theoretically grounded studies in this area; hence, the generalizability of the findings, thus far, is limited. Moreover, the analysis reveals that most studies have focused on supply chains for high-demand essential goods and healthcare products, while low-demand items and SMEs have been largely ignored. We also review the literature on prior epidemic outbreaks and other disruptions in supply chain disciplines. By considering the findings of these articles alongside research on the COVID-19 pandemic, this study offers research questions and directions for further investigation. These directions can guide scholars in designing and conducting impactful research in the field.

This study develops an inventory model to solve the problems of supply uncertainty in response to demand which follows a Poisson distribution. A positive aspect of this model is the consideration of random inventory, delivery capacities and supplier's reliability. Additionally, we assume supplier capacity follows an exponential distribution. This inventory model addresses the problem of a manufacturer having an imperfect production system with single supplier and single retailer and considers the quantity of product (Q), reorder points (r) and reliability factors (n) as the decision variables. The main contribution of our study is that we consider supplier may not be able to deliver the exact amount all the time a manufacturer needed. We also consider that the demand and the time interval between successive availability and unavailability of supplier and retailer follows a probability distribution. We use a genetic algorithm to find the optimal solution and compare the results with those obtained from simulated annealing algorithm. Findings reveal the optimal value of the decision variables to maximize the average profit in each cycle. Moreover, a sensitivity analysis was carried out to increase the understanding of the developed model. The methodology used in this study will help manufacturers to have a better understanding of the situation through the joint consideration of disruption of both the supplier and retailer integrated with random capacity and reliability.

Rapid environmental depletion and ever-increasing CO2 emission have necessitated an environment-friendly manufacturing practice for industries across the globe. In this perspective, green manufacturing (GM) practices were conceptualized and practiced by large scale enterprises of developed countries. However, small and medium-sized enterprises (SMEs) in developing countries are struggling to adopt GM practices. There are many reasons for this struggle in a developing country like India. To shed light on this issue, this research work intends to identify, analyze and rank the predominant barriers, which restrict implementing of GM practices in Indian manufacturing small and medium-sized enterprises (SMEs). Based on a comprehensive literature review and experts’ opinion by employing the Delphi method (DM), the study revealed 25 barriers, in three broad categories, of GM implementation in Indian SMEs. The identified barriers are ranked, and their interrelationships are explored using a novel integrated multi-criteria decision making (MCDM) framework, with a combination of Decision-Making Trial and Evaluation Laboratory Model (DEMATEL), Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A sensitivity analysis is performed to check the consistency of the results. The results reveal that core category, which include several barriers related to lack of internal abilities and strategies, is the most critical category of barriers for manufacturing SMEs in India. In particular, the three most critical barriers are lack of research and development (R&D), failure in eco-design and lack of accreditation respectively. The study findings, which provide valuable insight for SME practitioners of Indian manufacturing SMEs, can be used to formulate appropriate strategies to overcome the barriers.

Supply chain disruptions management has attracted significant attention among researchers and practitioners. The paper aims to examine the effect of potential market demand disruptions on price and service level for competing retailers. To investigate the effect of potential demand disruptions, we consider both a centralized and a decentralized supply chain structure. To analyze the decentralized supply chain, the Manufacturing Stackelberg (MS) game theoretical approach was undertaken. The analytical results were tested using several numerical analyses. It was shown that price and service level investment decisions are significantly influenced by demand disruptions to retail markets. For example, decentralized decision makers tend to lower wholesale and retail prices under potential demand disruptions, whereas a proactive retailer needs to increase service level with an increased level of possible disruptions. This research may aid managers to analyze disruptions prone market and to make appropriate decision for price and service level. The manufacturer or the retailers will also be able to better determine when to close a market based on the proposed analysis by considering anticipated disruptions. The benefits and usefulness of the proposed approach are explained through a real-life case adopted from a toy supply chain in Bangladesh.

In this paper, a recovery model is developed for managing sudden supply delays that affect retailers' economic order quantity model. For this, a mathematical model is developed that considers fuzzy demand and safety stock, and generates a recovery plan for a finite future period immediately after a sudden supply delay. An efficient heuristic solution is developed that generates the recovery plan after a sudden supply delay. An experiment with scenario-based analysis is conducted to test our heuristic and to analyse the results. To assess the quality and consistency of solutions, the performance of the proposed heuristic is compared with the performance of the generalised reduced gradient method, which is widely applied in constrained mathematical programming. A simulation model is also designed to bring the recovery model closer to real-world processes. Several numerical examples are presented and a sensitivity analysis is performed to demonstrate the effects of various parameters on the performance of the heuristic method. The results show that safety stock plays an important role in recovery from sudden supply delays, and there is a trade-off between backorder and lost sales costs in the recovery plan. With the help of the proposed model, supply chain decision-makers can make accurate and prompt decision regarding recovery plans in case of sudden supply delay.

The purpose of this paper is to develop a framework to identify, analyze, and to assess supply chain disruption factors and drivers. Based on an empirical analysis, four disruption factor categories including natural, human-made, system accidents, and financials with a total of sixteen disruption drivers are identified and examined in a real-world industrial setting. This research utilizes an integrated approach comprising both the Delphi method and the fuzzy analytic hierarchy process (FAHP). To test this integrated method, one of the well-known examples in industrial contexts of developing countries, the ready-made garment industry in Bangladesh is considered. To evaluate this industrial example, a sensitivity analysis is conducted to ensure the robustness and viability of the framework in practical settings. This study not only expands the literature scope of supply chain disruption risk assessment but through its application in any context or industry will reduce the impact of such disruptions and enhance the overall supply chain resilience. Consequently, these enhanced capabilities arm managers the ability to formulate relevant mitigation strategies that are robust and computationally efficient. These strategies will allow managers to take calculated decisions proactively. Finally, the results reveal that political and regulatory instability, cyclones, labor strikes, flooding, heavy rain, and factory fires are the top six disruption drivers causing disruptions to the ready-made garment industry in Bangladesh.

© 2017, Global Institute of Flexible Systems Management. This research examines the moderating effect of market uncertainty on the causal effects from supply chain integration to operational performance of a typical supply chain. Based on an extensive and critical literature review, two exploratory conceptual hypotheses have been developed for the nonlinear relationship between the supply chain integration and operational performance of the original equipment manufacturer, and how may that relationship be moderated by a specific construct of market uncertainty. Empirical survey instrument has been designed and applied to gather the data from a wide spectrum of automotive industry in China. Confirmative factor analysis and threshold regression analysis were used as the primary research methodology to test the hypotheses. We find strong support to the hypotheses from the empirical evidence, which leads to the finding that the relationship between the supply chain integration and operational performance is ‘nonlinear’, and the ‘nonlinearity’ can be significantly moderated by the market uncertainty as one of the key environmental factors for the supply chain. This study extends the current literature by contributing for the first time the discussion of an analytical model that represents the causal effects from supply chain integration to its operational performance with respect to the market uncertainty as a moderating factor.

© 2016 Elsevier B.V. In this paper, a three-stage supply chain network, with multiple manufacturing plants, distribution centers and retailers, is considered. For this supply chain system we develop three different approaches, (i) an ideal plan for an infinite planning horizon and an updated plan if there are any changes in the data, (ii) a predictive mitigation planning approach for managing predictive demand changes, which can be predicted in advance by using an appropriate tool, and (iii) a reactive mitigation plan, on a real-time basis, for managing sudden production disruptions, which cannot be predicted in advance. In predictive mitigation planning, we develop a fuzzy inference system (FIS) tool to predict the changes in future demand over the base forecast and the supply chain plan is revised accordingly well in advance. In reactive mitigation planning, we formulate a quantitative model for revising production and distribution plans, over a finite future planning period, while minimizing the total supply chain cost. We also consider a series of sudden disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions and which consequently require plans to be revised after the occurrence of each disruption on a real-time basis. An efficient heuristic, capable of dealing with sudden production disruptions on a real-time basis, is developed. We compare the heuristic results with those obtained from the LINGO optimization software for a good number of randomly generated test problems. Also, some numerical examples are presented to explain both the usefulness and advantages of the proposed approaches.

Evaluating transportation service providers is an applied and multi-criteria decision-making problem. To ensure supply chain sustainability, it is important to consider all sustainable criteria for assessing and evaluating transport service providers. This paper aims to develop a new decision-making framework to evaluate transport service providers considering sustainable criteria from economic, environmental, social and operational aspects. The decision-making framework integrates both qualitative expert opinion and quantitative best-worst method (BWM) and VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method. The developed framework contributes to the academic literature by expanding the knowledge in supply chain sustainability by considering all possible sustainable criteria and integrating both qualitative and quantitative methods for evaluating transport service providers. This study also contributes to practice by developing a decision support tool, by which decision-makers can make an accurate, systematic and prompt decision to identify and assess sustainable criteria and to evaluate the priority of different transport service providers.

COVID-19 has had a major impact on health, economic, social, and industrial activities. It has disrupted supply chain management and affected the movement of essential supplies to a large extent. This study aims to identify and evaluate the challenges hampering sustainable humanitarian supply chain management (SHSCM). Twenty critical challenges to SHSCM are identified using a comprehensive literature review, and three strategies were developed. The challenges and strategies were verified using expert input. The challenges were evaluated using the neutrosophic analytic hierarchical process (AHP) method. The neutrosophic TODIM (an acronym in Portuguese for interactive multicriteria decision making) method was then used to select the best strategy. The findings reveal that facility location problems, short lead times for emergency supplies, spread of rumors, rapid emergence of new clusters, and doubt concerning the available remedy are five critical challenges in SHSCM during COVID-19. Public–private partnerships are identified as the best strategy in SHSCM. Finally, this paper discusses the implications to sustainable development goals in the post-COVID-19 pandemic era.

In the present competitive business environment and era of globalized marketing, supply chain (SC) of the leather industry is facing a variety of risks. Hence, one of the fundamental concerns in the leather industry supply chain (LISC) is recognizing and prioritizing the various risk factors for attaining sustainability. The present study is an attempt to determine a comprehensive evaluation of SC risk factors considering the case of the leather industry. Based on the literature search and interviews with the domain experts’, forty-four risk factors in the context of LISC are identified. The identified risk factors are further segregated into five-dimensions to sustainability (social, environmental, economic, technical, and institutional). A Pareto analysis is performed to discover the most pertinent risk factors. Further, the best-worst method (BWM) is embraced for evaluating the importance of each pertinent risk factor for the decision-making purpose. The findings from the study reflect that ‘inefficient effluent treatment’, ‘change in consumer preference’, ‘improper dumping of solid waste’, ‘volatility of price and cost’ and ‘fiscal changes’ are the crucial risk factors that are required to be addressed for the successful execution of sustainable supply chain management (SSCM) practices in an emerging economy context. It is expected that the results and findings will assist the leather industry managers in decision-making for better administration and alleviation of supply chain risks to achieve sustainability.

The Resource Constrained Project Scheduling Problem (RCPSP) is a combinatorial optimization problem which is non-deterministic polynomial-time (NP)-hard in nature. Due to the diversified applications of RCPSPs, they have been commonly used as scheduling procedures in real-world problems. Since, in practice, project data are prone to changes or disruptions, this paper introduces a mathematical model for a reactive scheduling approach, called the Event Based Reactive Approach (EBRA). This proposed EBRA approach is employed to examine its recovery performance under both a single disruption and a series of independent resource disruptions. Several simulated disruption data are hypothesized to represent real-world disruption scenarios and, without loss of generality, the proposed reactive approach is proved to be efficient in reducing the number of variables and computational complexity and also to be resilient in realistic changes, such as duration inflation and dynamic resource usages. Along with employing an exact method by LINGO software, this paper also proposes an enhanced iterated greedy (EnIG) approach to meta-heuristically solve larger and computationally expensive benchmark instances taken from the Project Scheduling Library (PSPLIB).

Lean six sigma (LSS), a process improvement tool to achieve operational excellence in any industry, has become popular among practitioners over the last few decades. In this study, a framework for identifying barriers to LSS implementation in supply chains has been developed using the interpretive structural modeling (ISM) method. The ISM technique was used to identify the contextual relationships among the barriers. Barriers were classified based on their dependence power and driving power using MICMAC (Matriced Impacts Croises Multiplication Appliquee a un Classement). This framework will provide a comprehensive understanding of how the barriers of LSS affect each other. The proposed framework has been tested using data from a real-world apparel manufacturing company in Bangladesh. 10 barriers to LSS implementation were identified from a literature review and industrial managers’ feedback. This study is expected to guide practitioners in implementing LSS in supply chains by helping to focus their effort on removing the most important barriers.

Due to an increased pressure to be environmentally sustainable, many manufacturing organizations, especially from developing countries like Bangladesh, are attempting to make necessary changes in practices and supply chains. However, those attempts need to be applied strategically with the objective to be both environmentally sustainable and economically viable. This paper offers a decision-making methodology by integrating a fuzzy cognitive map (FCM) and data envelopment analysis (DEA) for evaluating strategies for environmental sustainability based on their impact on the overall supply chain network of an organization. This paper first identifies 18 generic strategies for environmental sustainability and three supply chain performance measurement (PM) factors. Afterwards, the cause-effect relationships among these strategies and PM factors are utilized to capture the complicated relationships by FCM. The extended delta rule (EDR) learning algorithm was used in association with FCM to quantify the impact of those strategies on supply chain PM factors. Finally, DEA is used to prioritize strategies using these impact values. A real-life case using a fast-moving consumer goods (FMCG) manufacturer from Bangladesh is presented to justify the applicability of the proposed methodology. The results reflect the usefulness of this methodology for evaluating strategies for environmental sustainability in a supply chain (SC), specifically in the FMCG sector of an emerging economy. Thus, other manufacturing organizations from any industry can use this methodology to evaluate strategies for environmental sustainability.

Extant literature on sustainability in procurement and supplier selection suffers from a number of deficiencies. First, studies pertaining to the dynamic nature of stakeholders’ expectations of sustainability and its impact on determining a supply portfolio have not been studied before. Second, there is a genuine lack of study linking the stakeholders’ sustainability requirements, firm procurement strategies, and eventual supplier selection. Third, most of the existing studies address sustainability issues in procurement and supplier selection but fall short of determining an optimal portfolio of suppliers and corresponding optimal order quantities. This study addresses the above research gaps by developing a sustainability-focused multi-criteria decision model for supplier evaluation and determining optimal order allocation among the suppliers linking the stakeholders’ sustainability requirements and firm procurement strategies. Based on dynamic capability theory, we develop a decision support framework integrating multi-phased quality function deployment and dynamic optimization. We apply the decision support framework to a European apparel company which sources apparel from Bangladesh: a country that is a low cost sourcing destination. First, this study identifies the stakeholders’ sustainability requirements. It then explicates the company’s procurement strategies in terms of stakeholders’ requirements followed by translating the procurement strategies to relevant supplier assessment criteria. Finally, a linear optimization model is developed to maximize the suppliers’ sustainability performance in order to determine the optimal supply portfolio. The results identify two distinct groups of suppliers satisfying the overall sustainability performance. However the optimal order quantities among the suppliers vary randomly depending on the variations in demand and priority weights of the suppliers. The paper concludes with a detailed discussion of the results and implications.

This paper studies a joint facility location and inventory model from the viewpoint of partial-disruption risk—i.e., when manufacturing facilities meet the demands of third-party distribution centers with a portion of their capacity, free from any disruptions—while considering substitute products as a disruption risk mitigation strategy. We considered these third-party distribution centers as the customers of the manufacturing facilities. We used a multinomial logit model to rank-order the facilities according to customers’ preferences. Then, a non-linear integer programming model was developed which attempted to assign a sequence of facilities to each customer based on their preferences while at the same time, minimizing the total supply-chain cost. We also considered customers’ decisions for backorders while developing the model. Due to the NP-hard nature of the problem, we developed a particle swarm optimization-based metaheuristic algorithm to solve the model. The efficiency of the modified particle swarm optimization (MPSO) was illustrated through computational tests and systematic comparison with the exact method, a hybrid meta-heuristic algorithm including tabu search (TS) and variable neighborhood search (VNS) from the literature, and its modified form (Modified TS-VNS). A numerical example was used to show the applicability of the model. Finally, we gained useful insight into the role of substitute products and customers’ decisions for backorders through scenario-based analysis. We found that the total supply chain cost could increase in disruption scenarios when customers were more likely to refuse backorder offers. However, the cost-saving from producing a substitute for key products could be significant.

The transportation network plays a vital role in the strategic imperative of automotive parts manufacturing companies. There is a lack of academic and practical studies, which focus solely on transportation disruption analysis in the supply chain of automotive parts manufacturing company. Moreover, very few studies have taken into account the cause and effect relationship between transportation disruption factors. The objective of this study is to analyze the critical transportation disruption factors of the supply chain of automotive parts manufacturing company and to represent the interrelationships using the best-worst (BWM) and rough strength-relation (RSR) analysis methods. The newly integrated BWM-RSR framework considers the vagueness and ambiguity in disruption factor analysis. The applicability and effectiveness of the newly developed BWM-RSR framework are demonstrated at an automotive parts manufacturing company in Oldcastle, Ontario, Canada. The results show that infrastructural bottlenecks/congestion and inadequate skilled labor are the most critical factors to the disruption of the transportation network in the automotive industry. The developed new framework can be used as an effective tool to analyze critical transportation disruption factors and examine the associated interrelationships.

© 2016, Springer Science+Business Media New York. In this paper, we develop a quantitative reactive mitigation approach for managing supply disruption for a supply chain. We consider a three-tier supply chain system with multiple raw material suppliers, a single manufacturer and multiple retailers, where the system may face sudden disruption in its raw material supply. First, we develop a mathematical model that generates a recovery plan after the occurrence of a single disruption. Here, the objective is to minimize the total cost during the recovery time window while being subject to supply, capacity, demand, and delivery constraints. We develop an efficient heuristic to solve the model for a single disruption. Second, we also consider multiple disruptions, where a new disruption may or may not affect the recovery plans of earlier disruptions. We also develop a new dynamic mathematical and heuristic approach that is capable of dealing with multiple disruptions, after the occurrence of each disruption as a series, on a real-time basis. We compare the heuristic solutions with those obtained by a standard search algorithm for a set of randomly generated disruption test problems, which shows the consistent performance of our heuristic. Finally, a simulation model is developed to analyze the effect of randomly generated disruption events that are not known in advance. The numerical results and many random experiments are presented to explain the usefulness of the developed models and methodologies.

The future manufacturing and global value chain will be highly dominated by technological and business innovations to cope with the accelerating pace of changes in consumer behaviour and global business environment. This editorial for the special issue “The future of manufacturing: global value chains, smart specialization and flexibility” enriches the topic of future of manufacturing operations and supply chain management literature. In the line with the theme, this special issue publishes five articles that clearly articulate the emerging thematic discussions.

Assessing sustainability in supply chains is an important task for any organization in the competitive business environment. The process of assessing the sustainability of a supply chain involves incorporating different sources of information, which are normally uncertain, incomplete, and subjective in nature. However, previous studies have failed to incorporate such uncertain, incomplete and subjective information. Therefore, this research proposes a methodology that uses an integrated approach combining the Analytical Hierarchy Process (AHP) and Hierarchical Evidential Reasoning (HER) based on Dempster-Shafer (D-S) theory to develop a supply chain sustainability assessment model. After identifying the sustainability assessment criteria, Analytical Hierarchy Process is used to structure and rate the criteria based on experts' opinion. In this research, subjective judgmental belief data are used to test the model. The information is combined using Dempster-Shafer theory and results are depicted according to the supply chain sustainability index. In the proposed mode, the results from the Dempster-Shafer theory are compared using Yager's recursive rule of combination. The model generates satisfactory results which denotes the condition state of sustainability along with unassigned degree of belief or uncertainty. To assess the sustainability condition in supply chain this methodology can be adopted by the management of the organizations.

Green supply chain management is attracting increasing attention as a way to decrease the adverse environmental effects of industries worldwide. However, considering the context of an emerging economy like Bangladesh, green supply chain management is still in its inception and has not been widely embraced in the textile industry, and therefore barriers hindering its adoption in emerging economy context demand a comprehensive investigation. This research reviews the viewpoints and hurdles in adopting green supply chain management practices in the context of the Bangladeshi textile industry. A questionnaire survey of Bangladeshi textile practitioners of operations and supply chain management division, having a sample size of thirty, was undertaken to identify the barriers, and a hierarchical cluster analysis technique was used in the detailed analysis of this data. Opinions were sought from experts on the significance of the resulting clusters, considering the relative importance of the barriers. Fifteen barriers to the adoption of green supply chain management were identified in the review of the literature, with these barriers then analyzed by using the data collected from Bangladeshi textile industry practitioners. The research indicates that the most important barrier is that there is low demand from customers and financial constraint resulting from short term little financial benefit to businesses, with lack of government regulations also a commonly faced barrier in adopting green supply chain initiatives. This study will provide valuables insights to practitioners and relevant policy makers about the barriers prevailing in the emerging economies towards the adoption of green supply chain management practices, which, in turn, can guide to undertake appropriate steps for alleviating those barriers.

Environmental sustainability is not being practiced in the supply chains of many industries. Previous studies on environmental sustainability have not outlined clear strategies to achieve sustainability across supply chains, particularly in the context of emerging economies, and have been of limited relevance in settings beyond the geographical region of their focus. To address these gaps, we have proposed a best worst method (BWM) as a framework to assess the environmental criteria for sustainability in select industries in Bangladesh. Different industrial activities or criteria affecting the environment in various ways were assessed and weighted using the BWM. To ensure the efficiency and accuracy of this framework, we sought the opinions of 34 experts to specify the most suitable indicators from our initial literature review. Findings from this study revealed that “waste management” was the most important indicator for establishing environmental sustainability in industries in Bangladesh, which was substantiated by a sensitivity analysis. This research will assist industry managers and entrepreneurs to work toward environmental sustainability across supply chains.