Sustainability Analysis under Disruption Risks

Resilience to disruptions and sustainability are both of paramount importance to supply chains. This paper presents a hybrid methodology for the design of a sustainable supply network that performs resiliently in the face of random disruptions. A stochastic bi-objective optimization model is developed that utilizes a fuzzy c-means clustering method to quantify and assess the sustainability performance of the suppliers. The proposed model determines outsourcing decisions and buttressing strategies that minimize the expected total cost and maximize the overall sustainability performance in disruptions. Important managerial insights and practical implications are obtained from the model implementation in a case study of plastic pipe industry

An integrated model for sustainable supplier selection and multi-period multi-product lot-sizing for packaging film industry in Iran

The emergence of sustainability issues has created increasing interest among those involved in the field of sustainable supply chain management. Companies are motivated to modify their supply chains activities based on sustainability issues to enhance their overall level of sustainability in order to fulfil demanding environmental and social legislation and to deal with increasing market forces from different stakeholder groups. Within supply chain activities, selecting appropriate suppliers based on the criteria of sustainability, e.g., economic, environmental, and societal might help companies move towards sustainable development. Although several studies have been accomplished to incorporate sustainability criteria into supplier selection problem, little attention has been paid to developing a comprehensive mathematical model that allocates the exact quantities of orders to suppliers considering lot-sizing problems. Moreover, the effect of inflation as an important issue for companies in the developing countries has been neglected in studies that examined multi-period multi-product lot-sizing along with supplier selection. In this study, a multi-objective mathematical model for sustainable supplier selection integrated with multi-period multi-product lot-sizing problem under the effects of inflation was developed. The model consists of four objective functions which are minimizing total cost, maximizing total social, total environmental score, and total economic qualitative scores. The mathematical model was developed based on the parameters discovered by preprocessing the social, environmental, and economic data of suppliers using a rule-based-weighted fuzzy approach and fuzzy analytical hierarchy process. The model attempted to simultaneously balance different costs under inflationary conditions to optimize the total cost of purchasing and other objective functions. A comprehensive framework was developed as a road map for procurement organizations in order to facilitate the allocation of optimal order quantities to suppliers in a sustainable supply chain. The proficiency and applicability of a proposed approach was illustrated using a case study of packaging films from the food industry. For each main criterion of sustainability, their related subcriteria and influencing factors were extracted from literature and the most related ones were selected by company’s experts. In this research, green competencies, environmental management system, pollution, occupational safety and health, training and education, contractual stakeholder, economic qualitative, and cost were selected by company’s experts as the main subcriteria of sustainable supplier selection. The consideration of sustainability criteria in the proposed multi-objective model revealed that a higher value of sustainable purchasing can be achieved in comparison with a single objective costbased model. In addition, the results show that the proposed model can provide a purchasing plan for the company while monitoring the effect of inflation and assuaging its concerns regarding sustainability issues

Agribusiness supply chain risk management: A review of quantitative decision models

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management

Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process

Within supply chains activities, selecting appropriate suppliers based on the sustainability criteria (economic, environmental and social) can help companies move toward sustainable development. Although several studies have recently been accomplished to incorporate sustainability criteria into supplier selection problem, much less attention has been devoted to developing a comprehensive mathematical model that allocates the optimal quantities of orders to suppliers considering lot-sizing problems. In this research, we propose an integrated approach of rule-based weighted fuzzy method, fuzzy analytical hierarchy process and multi-objective mathematical programming for sustainable supplier selection and order allocation combined with multi-period multi-product lot-sizing problem. The mathematical programming model consists of four objective functions which are minimising total cost, maximising total social score, maximising total environmental score and maximising total economic qualitative score. The proposed model is developed based on the parameters achieved through the preprocessing of suppliers’ social, environmental and economic data by a rule-based weighted fuzzy approach and fuzzy analytical hierarchy process. The proficiency and applicability of the proposed approach is illustrated by a case study of packaging films in food industry. Considering sustainability criteria in the proposed model reveals that a higher value of sustainable purchasing is achievable in comparison with a single-objective cost-based model

Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks

Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions