Search

The recent COVID-19 pandemic has caused enormous disruptions to supply chain (SCs). Border restrictions forced countless businesses to close either permanently or temporarily. However, the food industry is an essential sector that needs to be operational during a pandemic. Although the food industry has proactively worked towards fulfilling human needs, the food supply chain (FSC) faced numerous challenges, forcing SC managers to rethink their business strategy to cater to consumer demands effectively. In a pandemic situation, manufacturing operations need to repurpose and adapt to produce different high-demand products. Resilience initiatives help fight disruption phases in an uncertain environment by building capacity to resist and recover to a better position. This study identifies 14 key enablers to develop a resilient FSC and reveals the most significant enablers in India. We used a hybrid Delphi-interpretive structural modeling (ISM) and Fuzzy decision-making trial and evaluation laboratory (Fuzzy-DEMATEL) methodology to achieve these goals. The Delphi technique identified essential enablers, while the ISM analyzed the interrelationship among enablers and level of importance in a hierarchical structural model. Finally, the Fuzzy-DEMATEL categorized the enablers into the cause-effect group. This study helps SC decision-makers recognize the enablers and the contextual and causal relationships to improve resilience initiatives. It also helps them repurpose their manufacturing operations and shift to other highly required and high-demand production.

Selecting and investing in stock market with right proportions is one of the major challenges. Majority of the investors end up losing their invested equity capital due to uncertainty in the market. The present study provides a novel framework for novice investors to construct portfolio based on multicriteria decision making techniques under fuzzy environment. The scores obtained from these techniques were used to introduce two non-dimensional parameters for categorization of risky and non-risky assets. Three perceptions portfolios were constructed based on the proposed non-dimensional parameters along with fractional lion clustering algorithm. In order to demonstrate the proposed framework, an illustrative application is included in equity portfolio selection. The returns and risks of these perception based portfolios are compared to major Index funds for validating the efficiency and are found to overpower the Index funds with significant margins by maintaining the risk comparable to Index funds. Further, Markowitz based efficient frontier is plotted for better understanding of optimal returns and risk for perception based investment.

The COVID-19 pandemic has taught global businesses that a pandemic can put business dynamics in unforeseeable turbulence. The disruptions created by the pandemic in the apparel industry exposed the vulnerabilities of apparel supply chains (SCs). To recover the supply chain impacts (SCIs) during an unprecedented event such as the COVID-19 pandemic, apparel SCs need a robust framework that can identify, measure, and mitigate the severity of SCIs by assessing effective mitigation strategies. This study identifies 12 critical SCIs in apparel SCs during a pandemic and 17 mitigation strategies. To assess SCIs and mitigation strategies, a modified grey-based bi-level analytical network process (ANP) is proposed to deal with the complex relationship between the SCIs and mitigation strategies. A real-life case study is conducted from an apparel supply chain for validation purposes. The findings suggest that policymakers in apparel SCs should prioritize implementing government policies and financial aid to deal with increased material and operational costs, the sudden surge in the unemployment rate, cancellation of orders and delayed payment, and increased transportation costs during a pandemic. This study also contributes to the literature by providing a robust decision-making framework for practitioners to deal with the complexity of SCs during future pandemics.

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.

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

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.

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.

© 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.

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.

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.

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.

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.

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.