A novel decision model based on mixed chase and level strategy for aggregate production planning under uncertainty: case study in beverage industry

The present study proposes a novel decision model to aggregate production planning (APP) decision making problem based on mixed chase and level strategy under uncertainty where the market demand acts as the main source of uncertainty. By taking into account the novel features, the constructed model turns out to be stochastic, nonlinear, multi-stage and multi-objective. APP in practice entails multiple-objectivity. Therefore, the model involves multiple objectives such as total revenue, total production costs, total labour productivity costs, optimum utilisation of production resources and capacity and customer satisfaction, and is validated on the basis of real world data from beverage manufacturing industry. Applying the recourse approach in stochastic programming leads to empty feasible space, and therefore the wait and see approach is used instead. After solving the model using the real-world industrial data, sensitivity analysis and several forms of trade-off analysis are conducted by changing different parameters/coefficients of the constructed model, and by analysing the compromise between objectives respectively. Finally, possible future research directions, with regard to the limitations of current study, are discussed

A Bibliometric Review of Two Decades’ Research on Closed-Loop Supply Chain: 2001-2020

The closed-loop supply chain (CLSC) is generally regarded as an environmentally friendly approach that can help in reducing environmental impacts and achieving sustainable development of society and economics. In recent years, the popularity research of CLSC has been widely concerned by both business and academia practitioners. It is observed that most of the literatures have focused only on a particular journal or field; there is a distinct lack of comprehensive bibliometric review of two decades' research on CLSC. This study contributes in fulfilling this gap. A comprehensive bibliometric analysis was conducted based on 1,155 articles in Web of Science Core Collection Database from 2001 to 2020. In order to track research frontiers and hotspots, visualization software VOSviewer and CiteSpace are used for analysis. Initially, a descriptive analysis was carried out to identify the trends of number of publications, the leading journals, top authors and regions. A thematic cluster analysis was then carried out to identify the research domains. Subsequently, based on the analyses of co-keywords, dominant categories and co-citation, hot issues and research trends are summarized. “game theory”, and “remanufacturing” are emerging research trends for CLSC. “Dual channel”, “quality” and “circular economy” had become hot topics. This review also finds the landmark nodes and pivot nodes in the research of CLSC. Finally, some research gaps are revealed to shed light on future directions

A decision support system for selection and risk management of sustainability governance approaches in multi-tier supply chain

Lower-tier suppliers' sustainability noncompliance and focal company's failure at meeting the expectations of the stakeholders to extend sustainability towards lower-tier suppliers carry multiple risks, tangible and intangible, to the focal company. It is expected that extending sustainability to suppliers at lower tiers through effective sustainability governance approaches (SGAs) can reduce these risks for focal companies. The existing literature lacks research on decision support tools using management science techniques to help decision makers choose the most suitable SGA/SGAs in a given situation and the risk management of SGAs in multi-tier supply chain. The present study develops a model-driven decision support system (DSS) using Bayesian network (BN) that can assist operations managers in selecting the most effective SGA/SGAs in multi-tier supply chain considering each situation. The developed DSS includes contingency factors and risk variables and their relationships which are identified through a systematic literature review and is applied to the multi-tier, sustainable supply chain of a multinational company operating in China to demonstrate its practical applicability. The DSS is then used in the risk management of the SGAs in multi-tier supply chain, which includes core steps such as identification of the contingency factors and risk variables, the prioritisation of the contingency factors and risk treatment. By Prioritising the basic contingency factors, ‘‘Focal company's sustainability knowledge’’ and ‘‘The specific nature of the materials sourced from lower-tier supplier’’, and ‘‘First-tier supplier's possession of internal resources'’ and ‘‘First-tier supplier's sustainability training’’ were identified as the two most important factors regarding their impact on the effectiveness of the direct and indirect approaches respectively. Detailed managerial implications related to the development and implementation of the DSS and the risk management process are also provided

Supplementary Materials

Supplementary Materials, including the systematic literature review process for identifying the contingency factors and risk variables, the references list of the papers selected for review and the node probability tables. </p

Sub-supplier's sustainability management in multi-tier supply chains: a systematic literature review on the contingency variables, and a conceptual framework

Sub-suppliers may violate sustainability standards for a variety of motivations, and focal firms' neglecting of sub-suppliers’ sustainability violation despite stakeholder pressures to establish sustainability compliance at sub-supplier level can bring several tangible and intangible risks to focal firms. Focal firms apply sub-supplier's sustainability management (SSM) approaches to extend sustainability to sub-suppliers. As sustainable supply chain management is fundamentally context-dependent, a set of contingency variables are expected to impact the effectiveness of the SSM approaches. Through an up-to-date, comprehensive review of the literature on multi-tier, sustainable supply chain management (MT-SSCM), 37 contingency variables influencing the effectiveness of the SSM approaches in multi-tier supply chain are identified. These variables are then clustered in two stages based on their similarity in terms of their common themes/points for more efficient analysis. Propositions are formulated to explain the way variation in the contingency variables impacts the effectiveness of each SSM approach, when each SSM approach is an effective approach with regard to the contingency variables, the sub-supplier's motivations in not complying with sustainability requirements and the risks of ignoring sub-supplier's noncompliance with sustainability requirements for focal firm. A conceptual framework is built according to the results and findings of the study. Detailed practical implications are also presented to provide managerial insights for supply chain managers. Finally, possible future research directions, that are linked to identified research gaps, are discussed.</p

Evaluating the performance of aggregate production planning strategies under uncertainty in soft drink industry

The present study is to evaluate the performance of different aggregate production planning (APP) strategies in presence of uncertainty. Therefore, the relevant models for APP strategies including the pure chase, the pure level, the modified chase, the modified level and the mixed chase and level strategies are constructed by using both multi-objective programming and simulation methods.The models constructed for these strategies are run with respect to the corresponding objectives/criteria in order to provide business insights to operations managers about the effectiveness and practicality of various APP strategies in presence of uncertainty. The real world operational data are collected from soft drink industry to validate and implement the models.In addition, multiple criteria decision making (MCDM) methods are used besides multi-objective optimisation to assess the overall performance of each APP strategy. A detailed sensitivity analysis is also conducted by changing the criteria weights in MCDM methods to evaluate the impacts that these weight changes can have on the final rank of each APP strategy.The results of the simulation models are compared to those of multi-objective optimisation models. In general, in both mathematical programming and simulation models, the pure chase and the modified chase strategies presented the best performance, followed by the pure level strategy

A decision support system for selection and risk management of sustainability governance approaches in multi-tier supply chain

Lower-tier suppliers' sustainability noncompliance and focal company's failure at meeting the expectations of the stakeholders to extend sustainability towards lower-tier suppliers carry multiple risks, tangible and intangible, to the focal company. It is expected that extending sustainability to suppliers at lower tiers through effective sustainability governance approaches (SGAs) can reduce these risks for focal companies. The existing literature lacks research on decision support tools using management science techniques to help decision makers choose the most suitable SGA/SGAs in a given situation and the risk management of SGAs in multi-tier supply chain. The present study develops a model-driven decision support system (DSS) using Bayesian network (BN) that can assist operations managers in selecting the most effective SGA/SGAs in multi-tier supply chain considering each situation. The developed DSS includes contingency factors and risk variables and their relationships which are identified through a systematic literature review and is applied to the multi-tier, sustainable supply chain of a multinational company operating in China to demonstrate its practical applicability. The DSS is then used in the risk management of the SGAs in multi-tier supply chain, which includes core steps such as identification of the contingency factors and risk variables, the prioritisation of the contingency factors and risk treatment. By Prioritising the basic contingency factors, ‘‘Focal company's sustainability knowledge’’ and ‘‘The specific nature of the materials sourced from lower-tier supplier’‘, and ‘‘First-tier supplier's possession of internal resources'’ and ‘‘First-tier supplier's sustainability training’’ were identified as the two most important factors regarding their impact on the effectiveness of the direct and indirect approaches respectively. Detailed managerial implications related to the development and implementation of the DSS and the risk management process are also provided

Aggregate production planning under uncertainty: a comprehensive literature survey and future research directions

This is the first literature survey of its kind on aggregate production planning (APP) under uncertainty. Different types of uncertainty, such as stochasticity, fuzziness and possibilistic forms, have been incorporated into many management science techniques to study APP decision problem under uncertainty. In current research, a wide range of the literature which employ management science methodologies to deal with APP in presence of uncertainty is surveyed by classifying them into five main categories: stochastic mathematical programming, fuzzy mathematical programming, simulation, metaheuristics and evidential reasoning. First, the preliminary analysis of the literature is presented by classifying the literature according to the abovementioned methodologies, discussing about advantages and disadvantages of these methodologies when applied to APP under uncertainty and concisely reviewing the more recent literature. Then, APP literature under uncertainty is analysed from management science and operations management perspectives. Possible future research paths are also discussed on the basis of identified research trends and research gaps

An integrated fuzzy QFD and fuzzy goal programming approach for global facility location-allocation problem

Companies pursuing extension of their activities and new companies in establishment phase are using various concepts and techniques to consider location decision, because location greatly affects both fixed and variable costs and on the overall profit of the company. This paper suggests a new use of quality function deployment (QFD) for facility location selection problem instead of applying it to traditional product quality promotion. Fuzzy sets concept is also incorporated to deal with imprecise nature of the linguistic judgments of decision makers. First, fuzzy QFD as a stand-alone approach is presented to address international facility location selection decision. To consider resource limitations and operational constraints, fuzzy goal programming is combined with fuzzy quality function deployment to present a developed approach to deal with global facility location-allocation decision. A demonstration of the applicability of proposed methodologies in a real-world problem is presented