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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.

Eco-efficiency and resource optimization for business strategy and the environment can be achieved by the circular economy (CE) practices in supply chains (SCs). The leather industry is a significant industrial contributor to the economic growth of some countries, but at the same time, it leads to tremendous environmental pollution. This research focuses on the identification and evaluation of critical success factors (CSFs) needed in the business strategy development of CE practices as well as to minimize environmental pollution in leather industry SCs. The CSFs are identified via a comprehensive literature review and are validated by experts' opinions. The validated CSFs are further analyzed using the best–worst method (BWM) and the decision-making trial and evaluation laboratory (DEMATEL). The BWM is used to identify the weights of the CSFs, and DEMATEL is used to determine the cause–effect relationship between the CSFs. The findings show that “leadership and top management commitment” is the most important CSF. Six CSFs are classified as causal towards CE practices: “leadership and top management commitment,” “strong legislation towards CE practices,” “ecological scarcity of resources,” “knowledge of CE practices,” “funding support for R&D from the government,” and “competitor pressure on CE practices.” The findings of this study can help managers in the leather industry implement CE practices in their existing SCs to minimize waste.

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.

Green supply chain management (GSCM) has emerged as an important issue to lessen the impact of supply chain activities on the natural environment, as well as reduce waste and achieve sustainable growth of a company. To understand the effectiveness of GSCM, performance measurement of GSCM is a must. Monitoring and predicting green supply chain performance can result in improved decision-making capability for managers and decision-makers to achieve sustainable competitive advantage. This paper identifies and analyzes various green supply chain performance measures and indicators. A probabilistic model is proposed based on a Bayesian belief network (BBN) for predicting green supply chain performance. Eleven green supply chain performance indicators and two green supply chain performance measures are identified through an extensive literature review. Using a real-world case study of a manufacturing industry, the methodology of this model is illustrated. Sensitivity analysis is also performed to examine the relative sensitivity of green supply chain performance to each of the performance indicators. The outcome of this research is expected to help managers and practitioners of GSCM improve their decision-making capability, which ultimately results in improved overall organizational performance.

In this paper, we have considered a multistage decentralized supply chain (SC) with different independent entities where each stage’s decision is made based on more than one objective. To do so, mathematical models are developed based on the concepts of the bilevel planning. Here, a strategy of total SC coordination is proposed as an upper level optimization problem with two objectives which are assumed to be implemented by an independent body. Different multiple objectives are considered in each SC stage as lower level problems. We developed a solution approach and solved the model with a multi-objective genetic algorithm approach using Matlab. From the pareto graph analysis, we considered different scenarios to obtain values that allow the decision-maker to select a suitable solution. Moreover, our proposed model is compared with a single-level centralized approach, with two commonly used objectives from literature. The comparison shows that modelling a complete decentralized SC network with independent entities having individual multiple objectives with our proposed model is more realistic and effective than single-level centralized modelling.

Additional file 1: Table S1. Cultural morphology, colony size and conidia production rate of 15 MoT iolates on PDA, OMA, V8 and CMA media.

Additional file 2: Figure S1. Effect of temperature on mycelial growth of MoT isolates. MoT isolate BTJP 4–1 was grown on PDA media and incubated at various temperatures for 7 days. Radial growth of the isolate at different temperatures was recorded by taking the average of two perpendicular measurements.

The leather industry of Bangladesh is facing considerable amounts of pressure to adopt sustainable supply chain management (SSCM). While there are some studies that have examined barriers to SSCM practices in developed and developing countries in various domains, these are not necessarily applicable to the Bangladeshi leather industry. To bridge this gap, it is crucial to identify most influential barriers to SSCM practices, particularly in the context of developing economies. Therefore, this study identifies such barriers and examines the causal relationships between them with an aim to facilitate the effective implementation of SSCM in the Bangladeshi leather processing industry. Thirty-five barriers to SSCM implementation were identified through a detailed literature review and a survey of leather processing industry experts. Among them, the most common 20 barriers were selected with the help of industry experts. Then, a blended, grey-based Decision Making Trial and Evaluation Laboratory (DEMATEL) approach was utilized to examine their interrelationships. The results demonstrate that nine barriers could be classified as “causal” and eleven as “influenced”. ‘Lack of awareness of local customers in green products’ and ‘lack of commitment from top management’ took high priority in the causal group. ‘Lack of reverse logistics practices’ and ‘Outdated machineries’ were the most influenced barriers. This research uses a leather processing company as a case study for demonstrating the proposed model. The findings aim to support the leather processing industry in a structural way, so that industrial managers can identify the most influential barriers and work to eliminate them. This study may be useful to stakeholders to achieve sustainable development.

Sustainable manufacturing practices and the circular economy have recently received significant attention in academia and within industries to improve supply chain practices. Manufacturing industries have started adopting sustainable manufacturing practices and a circular economy in their supply chain to mitigate environmental concerns, as sustainable manufacturing practices and a circular economy result in the reduction of waste generation and energy and material usage. The leather industry, in spite of it contributing remarkably to a country’s economic growth and stability, does not bear a good image because of its role in polluting the environment. Therefore, the leather industries of Bangladesh are trying to implement sustainable manufacturing practices as a part of undertaking green supply chain initiatives to remedy their image with the buyer and to comply with government rules and regulations. The main contribution of this study is to assess, prioritize and rank the drivers of sustainable manufacturing practices in the leather industries of Bangladesh. We have used graph theory and a matrix approach to examine the drivers. The results show that knowledge of the circular economy is paramount to implementing sustainable manufacturing practices in the leather industry of Bangladesh. This study will assist managers of leather companies to formulate strategies for the optimum utilization of available resources, as well as for the reduction of waste in the context of the circular economy.

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.

This study presents a sustainability performance measurement model integrating the Balanced Scorecard (BSC) perspective and the Fuzzy multiple-criteria decision-making (FMCDM) approach. First, this study proposes a list of twenty-one indexes of sustainability, based on four BSC-based dimensions, in line with triple bottom line sustainability dimensions, derived from the literature and experts’ inputs. Then, the relative weight of each sustainability index was evaluated using the Fuzzy Analytic Hierarchy Process (FAHP). To demonstrate the proposed approach, we practically measure the sustainability performance using three MCDM methods — Simple Additive Weighting (SAW), fuzzy Technique for Order Preference by Similarity to Ideal Situation (TOPSIS), and fuzzy Multi-Criteria Optimization and Compromise Solution (VIKOR). Finally, strategies for improving sustainability performance for a real-word case are suggested. The proposed measurement model can be an appropriate tool for industrial managers seeking to evaluate the efficacy of their sustainability strategies.

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).

Dragon fruit (Hylocereus polyrhizus) is a high value newly introduced fruit crop in Bangladesh. It has drawn considerable public attention due to its appealing flesh color, sweet taste and fruit qualities. Recently, basal rot of dragon fruit plants was observed in several farmer's fields, nurseries and in the research field of Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU) where about 10-15% of plants were infected in each location. Initially, the symptoms appeared in the basal part near the soil as brown lesions which gradually extended to the upper stem and finally becoming soft and watery (Figure 1a). Infected plants were collected from Kapasia of Gazipur district (Latitude 24.266 and Longitude 90.633) to isolate the causal organism. Isolations were carried out following the procedure reported by Briste et al. (2019). Briefly, infected plant parts were surface sterilized in 2% NaOCl for 1 min followed by 70% ethanol for 5 min and rinsed 3 times with sterile double distilled water. A large piece of a surface sterilized plant was cut into small pieces (2 mm × 2 mm) from the margin of the necrotic lesion and placed on half strength potato dextrose agar (PDA) and incubated for 7 days at 25 °C. The BTFD1 and BTFD4 isolates were purified from single spores resulting in white colonies with a growth rate of 1cm/day on PDA (Figure 1b). Colonies produced single celled microconidia from unbranched, short monophialidic conidiophores and septate macroconidia as well as chlamydospores in PDA which is consistent with Fusarium oxysporum (Figure 1c). To confirm the identity of the isolates, the internal transcribed spacer (ITS1, 5.8S rRNA and ITS2) and translation elongation factor-1alpha (EF-1α) were amplified using primers ITS-1/ ITS-4 and EF1-728F/ EF1-986R, respectively (Surovy et al. 2018). The ITS sequences of the isolates BTFD1 and BTFD4 (GenBank accession # MN727096 and MN727095, respectively) showed 100% similarity with the sequence from F. oxysporum strain JJF2 (MN626452). Sequence identity for EF-1α (GenBank accession # MN752123 and MN752124, respectively) was 100% with the sequence from F. oxysporum strain CAV041_EO (MK783088). The isolates (BTFD1 and BTFD4) were identified as F. oxysporum based on the aligned sequences of ITS and EF-1α, molecular phylogenetic analyses by maximum likelihood tree (Figure 2a) and maximum parsimony tree methods (Figure 2b). The isolates were stored at 4°C on dried filter paper as well as in an ultra-low temperature freezer (-80°C) at IBGE, BSMRAU, Bangladesh and are available on request. To ensure pathogenicity, isolate BTFD1 was grown on PDA, incubated at 25°C for 7 days and 250 ml conidial suspension (with 1 × 105 conidia/ml) was prepared. Twelve,three-month-old healthy dragon fruit plants were inoculated. Pathogenicity tests were carried out in two sets using three replications in each set. In one set, only the basal part of the plants was dipped into the conidial suspension and in another set the whole plant was dipped into the conidial suspension for two hours. Sterile distilled water was also used in another set of plants as a control. The inoculated plants were placed on wet tissue in a plastic box (31cm × 24cm × 8cm) covered and incubated at 25°C. After 10 days, all inoculated plants in both sets developed rot symptoms similar to those observed in the field, while the control plants remained healthy (Figure 1d). The pathogen was successfully re-isolated from the inoculated symptomatic parts on half strength PDA medium and had morphology as characterized before, thus fulfilling Koch's postulates. This disease has been reported in Argentina and Malaysia (Wright et al. 2007; Hafifi et al. 2019). To the bet of our knowledge, this is the first report of Fusarium basal rot of dragon fruit in Bangladesh caused by F. oxysporum.

This paper aims to identify the negative impacts of the COVID-19 outbreak on supply chains and propose strategies to deal with the impacts in the context of the readymade garment (RMG) industry supply chain of an emerging economy: Bangladesh. To achieve the aims, a methodological framework is proposed through a literature review, expert inputs, and a decision-aid tool, namely the grey-based digraph-matrix method. A total of 10 types of negative impacts and 22 strategic measures to tackle the impacts were identified based on the literature review and expert inputs. Then, the grey-based digraph-matrix was applied for modeling the strategic measures based on their influence to deal with the impacts. Findings reveal that the strategies “manufacturing flexibility”, “diversify the source of supply”, and “develop backup suppliers” have significant positive consequences for managing the impacts of the COVID-19 pandemic in the RMG supply chain. The findings help industrial managers recover from supply chain disruptions by identifying and classifying the impacts and strategies required to manage the major supply chain disturbances caused by the COVID-19 pandemic. As a theoretical contribution, this study is one of few initial attempts to evaluate the impacts of the COVID-19 outbreak and the strategies to deal with the impacts in the supply chain context.

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.

This paper presents a literature review on risk and disruption management in production-inventory and supply chain systems. The review is conducted on the basis of comparing various works published in this research domain, specifically the papers, which considered real-life risk factors, such as imperfect production processes, risk and disruption in production, supply, demand, and transportation, while developing models for production-inventory and supply chain systems. Emphasis is given on the assumptions and the types of problems considered in the published research. We also focus on reviewing the mathematical models and the solution approaches used in solving the models using both hypothetical and real-world problem scenarios. Finally, the literature review is summarized and future research directions are discussed.

A new real-time disruption recovery plan is developed.An imperfect production-inventory system is considered.A new mathematical and dynamic solution approach is developed for managing disruptions.We solved the problem for the both single and multiple disruptions on a real-time basis.We compared the results for a good number of randomly generated disruption test problems. In this paper, a disruption recovery model is developed for an imperfect single-stage production-inventory system. For it, the system may unexpectedly face either a single disruption or a mix of multiple dependent and/or independent disruptions. The system is usually run according to a user defined production-inventory policy. We have formulated a mathematical model for rescheduling the production plan, after the occurrence of a single disruption, which maximizes the total profit during the recovery time window. The model thereby generates a revised plan after the occurrence of the disruption. The mathematical model, developed for a single disruption, is solved by using both a pattern search and a genetic algorithm, and the results are compared using a good number of randomly generated disruption test problems. We also consider multiple disruptions, that occur one after another as a series, for which a new occurrence may or may not affect the revised plan of earlier occurrences. We have developed a new dynamic solution approach that is capable of dealing with multiple disruptions on a real-time basis. Some numerical examples and a set of sensitivity analysis are presented to explain the usefulness and benefits of the developed model. The proposed quantitative approach helps decision makers to make prompt and accurate decisions for managing disruption.

Supplier selection is one of the most important tasks for supply chain decision making, and there are many quantitative and qualitative factors that affect this process. This paper develops a simple and user-friendly supplier selection process for a supply chain which considers various selection criteria for managing supply risks. A rule-based fuzzy inference system (FIS) model is developed using the fuzzy logic toolbox in MATLAB R2012a to select the most excellent supplier by considering both quantitative and qualitative selection criteria. We identify a total of 18 selection criteria, of which four are quantitative and 14 qualitative. Risk factors are also incorporated in the model by developing fuzzy input and output criteria, and the best supplier is selected based on the aggregated supplier ranking index value. Finally, a numerical example presented to explain the usefulness of the developed model.

We have considered a supplier–retailer system, that operates under an agreed coordinated policy, with an imperfect production process and a possibility of having demand fluctuation. In this paper, a dynamic planning process is proposed to deal with short-term demand fluctuations. To do this, a mathematical model was first developed for a single fluctuation, either for increasing or decreasing demand rate. The model generates a revised plan, after the occurrence of the fluctuation event. We also propose a new and efficient heuristic to solve the developed model. Secondly, multiple fluctuations have been considered, for which a new occurrence may or may not affect the revised plan of earlier occurrences and we extend the heuristic so that is capable of dealing with multiple demand fluctuations on a real time basis. We have generated a good number of random test problems and also solved the model using a genetic algorithm, in order to compare the solutions with our heuristic. The comparison confirmed the consistent performance of our developed heuristic, and also its lower computational time. Numerical examples and sensitivity analysis have been presented to explain the usefulness of the developed model.

Determining ordering policy has incisive impacts on the success or letdown of an organization. This research has considered reliability while developing a method for finding ordering policy for multiple supply chain stages through optimal lot sizing. Setup cost, production cost, inspection cost, rejection cost, interest and depreciation cost, holding cost, etc. are considered for each supply chain stage whereas the demand inputs in the costs are taken from an adaptive neuro-fuzzy inference system generated forecasting method. Later, a genetic algorithm has been applied to find the optimum lot size at multiple levels of supply chain network to minimize total cost. Optimal lot size, reliability and total cost are determined and the costs are accumulated to determine total minimum supply chain cost. To validate the model, a comparison with the current situation clearly indicates the superiority of proposed model over the usual company approach to ordering policy.

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.

Adolescents and young adults, between the ages of 10 to 24, comprise approximately 30 percent of India’s population. These youths, 365 million strong, will shape the future of the nation. Although the Government of India has been spending enormous amount of money in a relentless manner over decades and the current cohorts of youth are healthier and better educated than ever before, the fact of the matter is, vulnerabilities still persist for adolescent girls (World Bank, 2014). We argue in this paper that vulnerability of adolescent girls cannot be dealt with by executing verticals programmes in Education, Health, Nutrition and Protection in isolation rather it needs to be handled by running these programmes in a converged manner. The comprehensive reports and analysis of data across these domains would provide a 360-degree view of individual girls, predicting their potential vulnerability well ahead of time, thereby enabling the stakeholders to intervene and prevent untoward incidents like child marriage, child labour, child pregnancy, and trafficking among others. Furthermore, insights generated from analysing the data from the field can be used to identify the areas of improvement for the programme as applicable to decision makers at different levels of societal structure starting with villages to blocks to districts. This paper describes a digital solution called G-Power that comprehensively addresses the above challenges, and shares insights from a field trial carried out in two districts of West Bengal in India. Keywords: mobility, social welfare, vulnerability assessment

With a view to monitoring and controlling manufacturing processes in industries, control charts are widely used and needed to be designed economically to achieve minimum quality costs. Many authors have studied the economic design of the $$ \overline{X} $$ control chart after Duncan (J Am Stat Assoc 51(274):228–242, 1956) first proposed the economic model of the $$ \overline{X} $$ control chart for a single assignable cause. But, in practice, multiple assignable causes are more logical and realistic. Moreover, the economic design does not consider statistical properties like bound on type I and type II error, and average time to signal (ATS). This paper focuses on evaluating the performance of genetic algorithm (GA) in pure economic and economic statistical design of the $$ \overline{X} $$ control chart for multiple assignable causes. The performances of GA are demonstrated by comparing its result with the previously proposed grid search technique for a numerical example. The Duncan model of multiple assignable causes is adopted to formulate objective function, and the computation is achieved by approximation through a numerical method named Simpson's 1/3 rule. Comparison distinctly shows the superiority of GA over grid search results for economic statistical design.

PurposeManagers encounter many decisions that require the simultaneous use of different types of data in their decision‐making process. A critical decision area for managers is the performance evaluation of personnel, whether individually or as a member of a team. Performance evaluation is critically essential for the effective management of the human resource of an organization and evaluation of staff that help develop individuals, improve organizational performance, and feed into business planning.Design/methodology/approachPerformance evaluations require and often involve disparate types of information that are vague, incomplete, objective, and subjective. This paper proposes a performance evaluation system of employees considering various performance evaluation criteria using fuzzy logic. The main task in the proposed approach involves determining the performance indices of employees considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best employee who holds highest performance index comparing all the indices.FindingsA model is developed for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizational aims, and fostering positive relationships between management and staff. Fuzzy control is used to determine the overall performance index by combining results of the performance in selected criteria and provided it in numerical values which will undoubtedly ensure convenience of the concerned human resource personnel during performance rating calculation.Originality/valueThis is the first time, a performance evaluation model is developed using fuzzy approach for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizational aims, and fostering positive relationships between management and staff.

This paper addresses a problem of an imperfect production system under fuzzy demand and inventory holding cost. Production process reliability is considered because of the imperfect production process. In this problem, reliability of the system in regards to producing defective and non-defective items is considered as a decision variable. The objective is to maximize the graded mean integration value (GMIV) of the expected average profit while considering revenues as well as any other relevant costs. The developed model belongs to the class of a geometric programming. We have developed a simple mathematical methodology to solve the model. Genetic algorithm and simulated annealing algorithms are also applied to solve and validate the results. A numerical example has been presented to interpret the solutions.

This paper addresses the operator assignment in predefined workstations of an assembly line to get a sustainable result of fitness function of cycle time, total idle time and output where genetic algorithm is used as a solving tool. A proper operator assignment is important to get a sustainable balanced line. To improve the efficiency and meet the desired target output within the time limit, a balanced assembly line is a must. Real world lines consist of a large number of tasks and it is very time consuming and crucial to choose the most suitable operator for a particular workstation. In addition, it is very important to assign the suitable operator at the right place as his skill of operating machines finally reflects in productivity or in the cost of production. To verify better assignments of workers, a genetic algorithm is adopted here. A heuristic is proposed to find out the sustainable assignment of operators in the predefined workstations. © 2012 Taylor & Francis.

Content in the digital form can be easily copied and distributed without permission of the owner. As a result, it is of paramount importance to protect content and deter illegal distribution using content protection mechanisms like embedding an imperceptible watermark into the content. Given that consumers want access to content from anywhere using any device, it is necessary to transcode content keeping in mind the limitations of the devices in terms of processing power and network connectivity. However, it is important that the watermark embedded in the content is preserved even after transcoding. The proposed approach embeds in a video, an imperceptible yet robust watermark which is resistant to transcoding. This approach focuses on the H.264 codec because of its widespread use in the industry.

A B S T R A C T In this paper, an artificial neural network (ANN) model is developed to determine the optimum level of finished goods inventory as a function of product demand, setup, holding, and material costs. The model selects a feed-forward back-propagation ANN with four inputs, ten hidden neurons and one output as the optimum network. The model is tested with a manufacturing industry data and the results indicate that the model can be used to forecast finished goods inventory level in response to the model parameters. Overall, the model can be applied for optimization of finished goods inventory for any manufacturing enterprise in a competitive business environment.

As business systems are getting interconnected, the importance of security is growing at an unprecedented pace. To protect information, strong security measures need to be implemented and continuously updated and monitored to ensure their promise against present and future security breaches. However, the growth of networked systems and the increasing availability of sophisticated hacking tools make the task of securing business systems challenging. To enhance the security strength and to justify any investment in security-related products, it becomes mandatory to assess the security measures in place and estimate the level of security provided by them. The existing standards to certify the strength of a security system are qualitative, lack consideration of the countermeasures and do not consider the impact of security breaches. Consequently, there is a need for an alternative approach to estimate the security strength of a system in a quantitative manner. This paper aims to provide an extensible framework called iMeasure Security (iMS) that quantifies the security strength of an enterprise system by considering the countermeasures deployed in its network, analyzes the business impact of the security breaches, and provides insights as to how the level of security can be improved from current levels.