Behnam Fahimnia

Framing sustainability performance of supply chains with multidimensional indicators

Purpose – This paper aims to provide a framework which can assist focal companies in the development of sustainable supply chains. Sustainable development from an industrial perspective has extended beyond organisational boundaries to incorporate a supply chain approach. Design/methodology/approach – The literature related to sustainable supply chain management is reviewed by incorporating concepts from four organisational theories, including the resource-based, institutional, stakeholder and social network perspectives, to illustrate key drivers and enablers of sustainability initiatives in the supply chain. A conceptual multidimensional framework is then developed that can be used for the initial assessment of supply chain sustainability. Findings – Development and assessment of sustainability in supply chains are being increasingly incorporated as part of supply chain management today. This paper presents a multidimensional framework which can serve as a tool for research scholars and supply chain prac...

Quantitative models for managing supply chain risks: A review

As supply chain risk management has transitioned from an emerging topic to a growing research area, there is a need to classify different types of research and examine the general trends of this research area. This helps identify fertile research streams with great potential for further examination. This paper presents a systematic review of the quantitative and analytical models (i.e. mathematical, optimization and simulation modeling efforts) for managing supply chain risks. We use bibliometric and network analysis tools to generate insights that have not been captured in the previous reviews on the topic. In particular, we complete a systemic mapping of the literature that identifies the key research clusters/topics, interrelationships, and generative research areas that have provided the field with the foundational knowledge, concepts, theories, tools, and techniques. Some of our findings include (1) quantitative analysis of supply chain risk is expanding rapidly; (2) European journals are the more popular research outlets for the dissemination of the knowledge developed by researchers in United States and Asia; and (3) sustainability risk analysis is an emerging and fast evolving research topic.

Survey: Hierarchical facility location problem: Models, classifications, techniques, and applications

The primary objective in a typical hierarchical facility location problem is to determine the location of facilities in a multi-level network in a way to serve the customers at the lowest level of hierarchy both efficiently (cost minimization objective) and effectively (service availability maximization objective). This paper presents a comprehensive review of over 40years of hierarchical facility location modeling efforts. Published models are classified based on multiple characteristics including the type of flow pattern, service availability, spatial configuration, objective function, coverage, network levels, time element, parameters, facilities, capacity, and real world application. A second classification is also presented on the basis of solution methods adopted to solve various hierarchical facility location problems. The paper finally identifies the gaps in the current literature and suggests directions for future modeling efforts.

Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry

Abstract The incorporation of environmental criteria into the conventional supplier selection practices is essential for organizations seeking to promote green supply chain management. Challenges associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. The development and implementation of practical decision making tools that seek to address these challenges are rapidly evolving. This article contributes to this knowledge area by comparing the application of three popular multi-criteria supplier selection methods in a fuzzy environment. The incorporation of fuzzy set theory into TOPSIS, VIKOR and GRA methods is thoroughly discussed. The methods are then utilized to complete a green supplier evaluation and selection study for an actual company from the agri-food industry. Our comparative analysis for this case study indicates that the three fuzzy methods arrive at identical supplier rankings, yet fuzzy GRA requires less computational complexity to generate the same results. Additional analyses of the numerical results are completed on the normalization functions, distance metrics, and aggregation functions that can be used for each method.

Green supply chain network design with stochastic demand and carbon price

This paper presents a two-stage stochastic programming model to design a green supply chain in a carbon trading environment. The model solves a discrete location problem and determines the optimal material flows and the number of carbon credits/allowances traded. The study contributes to the existing literature by incorporating uncertainty in carbon price and product demand. The proposed model is applied to a real world case study and the numerical results are carefully analyzed and interpreted. We find that the supply chain configuration can be highly sensitive to the probability distribution of the carbon credit price. More importantly, we observe that carbon price and budget availability for supply chain reconfiguration can both have a positive but nonlinear relationship with greening of the supply chain.

Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains

A production plan concerns the allocation of resources of the company to meet the demand forecast over a certain planning horizon and a distribution plan involves the management of warehouse storage assignments, transport routings and inventory management issues. A production–distribution plan integrates the decisions in production, transport and warehousing as well as inventory management. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production–distribution plan and hence one key issue in the performance evaluation of a supply chain is the modelling and optimisation of the production–distribution plan considering its actual complexity. Based on the integration of Aggregate Production Plan and Distribution Plan, this article develops a mixed integer non-linear formulation for a two-echelon supply network (i.e. a production-distribution network) considering the real-world variables and constraints. Genetic Algorithm (GA), known as a robust technique for solving complex problems, is employed for the optimisation of the developed mathematical model due to its ability to effectively deal with a large number of parameters. To demonstrate the applicability of the methodology, a real-life case study will be finally studied incorporating the production of different types of products in several manufacturing plants and the distribution of finished products from plants to a number of end-users via multiple direct/indirect transport routes.

Policy insights from a green supply chain optimisation model

This paper introduces a practical supply chain optimisation model that incorporates both economic and carbon emission objectives. The proposed model is implemented to examine the possible economic and environmental trade-offs for various carbon-pricing and fuel-pricing scenarios in an actual case company representing the discrete, durable parts manufacturing sector. Analysis of the numerical results provides important managerial implications and policy insights. For industry practitioners, the findings can assist in identifying the critical activities along the supply chain on which to focus in order to minimise the cost implications of a carbon-pricing regulation. For related policy-makers, the findings provide insights on how carbon should be priced to make meaningful impacts on emissions reduction while matching variations in fuel prices.

Location-inventory problem in supply chains: a modeling review

A location-inventory problem (LIP) aims to integrate strategic supply chain design decisions with tactical and operational inventory management decisions. This study provides an extensive review of the existing literature of LIP modelling. A mathematical model is presented for a basic LIP, which can be further developed to incorporate additional features for use in real-world scenarios. We also discuss the evolution of LIP modelling literature over the past three decades and provide summary tables outlining characteristics of the published works including key modelling attributes and objective function cost components. Additional classifications are completed based on the solution methods adopted and real-world applications investigated. Our observations provide important insights and identify potential directions for future research in the field.

Integrated aggregate supply chain planning using memetic algorithm – A performance analysis case study

This paper aims to examine how a complex supply chain yields cost reduction benefits through the global integration of production and distribution decisions. The research is motivated by a complex real world supply chain planning problem facing a large automotive company. A mixed-integer nonlinear production-distribution planning model is solved using a customised memetic algorithm. The performance and effectiveness of the developed model and solution approach in achieving the global optimisation is investigated through experiments comparing the numerical results from the proposed integrated approach with those of a typical non-integrated (hierarchical) production–distribution optimisation.

Robust supply chain network design: an optimization model with real world application

This paper presents a robust optimization model for the design of a supply chain facing uncertainty in demand, supply capacity and major cost data including transportation and shortage cost parameters. We first present a base model that aims to determine the strategic ‘location’ and tactical ‘allocation’ decisions for a deterministic four-tier supply chain. The model is then extended to incorporate uncertainty in key input parameters using a robust optimization approach that can overcome the limitations of scenario-based solution methods in a tractable way, i.e. without excessive changes in complexity of the underlying base deterministic model. The application of the approach is investigated in an actual case study where real data is utilized to design a bread supply chain network. Numerical results obtained from model implementation and sensitivity analysis experiments arrive at important managerial insights and practical implications.