Design of reverse and forward supply chain network: a case study

Designing a closed-loop supply chain network for citrus fruits crates considering environmental and economic issues

The current research aims to design a fuzzy multiobjective model of the reverse logistics supply chain network in the automotive industry, taking into account the energy and time reduction approach. The automobile industry is one of the industries with a high demand worldwide. To continue the competition, the manufacturers of the leading car equipment should strive for better product quality by continuously improving their production processes, directing the production of greenhouse gases with low carbon levels, and increasing sustainability. In this regard, reverse supply chain networks and closed-loop chains have unique features that are very useful in the industry under review. The goal is to transform this model into a supply chain of a secure link in the automotive industry. Deterministic methods, genetic algorithm, particle swarm algorithm, and several scenarios with different aspects have been used to solve the model. The results show that the effectiveness of the three ways in terms of solution time is higher in the deterministic solution method. Proper use of the proposed process can help managers effectively manage the flow of recycled products concerning environmental considerations, and this process provides a sustainable competitive advantage for companies.

The design of robust value-creating supply chain networks: A critical review

The ever-increasing concerns of the growth in the volume of waste tires, reduction in natural resources and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. A CLSC is a network with a holistic view of the entire forward and reverse logistic system with the aim of transferring the EOL products up the stream from the end users for value recovery such as reuse, repair, remanufacturing, or recycling.There are many factors that are considered when designing a closed-loop supply-chain network, such as uncertainty in the parameters, costs, environmental impacts, social dimensions, governmental regulations, domestic and international competition, disruption and operational risks, reliability of the network, technological advancements, etc. In the real world, the CLSC network design is subject to a variety of uncertainties which can be random and fuzzy. Designing a reliable and environmentally conscious CLSC which considers the risks and uncertainties of the parameters in the network is necessary for a successful supply-chain network. The government regulations could have a significant impact on the supply chain network design, its management and the long/short-term investment decisions. Ever-changing restrictions, mandates and tax policies can affect corporation's competitive advantage, market share, and operational costs. Tariffs and caps imposed on imported goods, intended to limit the volume of imports, protect domestic employment and reduce competition among domestic industries. One of the challenges facing the recovery of EOL products is the uncertainty associated with the conditions of the EOL products after being returned. Designing intelligent products that can collect valuable information during their life cycles is something that has been studied in the past decade. This information can be used to extend the life span of the products and improve the value recovery of EOL products. In this dissertation, we take a closer look at the challenges facing the tire industry and obtain a solution that creates a win-win situation for companies in the tire network as well as the environment. We evaluate the available recovery options for EOL tires, identify the uncertain parameters in the logistics network and find the solution for dealing with these uncertainties using robust modeling for the logistics network. In the first model, we introduce a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty with the objective of reducing costs, reducing environmental impacts, and increasing the social responsibilities and the reliability of the network. Second, we study the impact of tariffs and government regulations on the profitability of the network. Third, we evaluate the impact of using sensor embedded tires on the design of CLSC network. The analysis is done using a novel Robust Fuzzy/ Possibilistic Stochastics Programming (RFSP) model that controls scenario-based uncertainty (disruption risks) and fuzzy-based uncertainty (operational risks) in the network simultaneously and provides more reliable results for the managers and the decision makers. We evaluate the possibility and feasibility of expanding the existing forward logistic network at different capacity levels to cope with the market demand under various scenarios as well as the feasibility of establishing a new reverse logistic network at different capacity levels to cope with the return rate of EOL tires. A case example is considered to illustrate the implementations and validation of the proposed models. --Author's abstract

Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.

The enormous growth and updating speed in mobile phone consumption have brought tremendous pressure on resources and the environment, so enterprises in supply chain of mobile phones are driven by internal and external forces to develop circular economy (CE). This study aims to optimize reverse supply chain network (RSCN) configuration and effectiveness, which consists of multiple recycling sites, recycling centers, pretreatment centers, detection and reprocessing centers and waste disposal centers, considering uncertainty of the recovery quantity and quality in recycling processes. To achieve this goal, the study establishes an optimization model of RSCN for waste mobile phones to maximize the expected profit. First, through the equivalent transformation of fuzzy chance constrained programming, the study transforms fuzzy variables into clear equivalent forms. Then, the study proposed an optimal particle swarm optimization (PSO) algorithm with adaptive dynamic changing inertia weights as a model solving tool. Finally the study gets the optimal layout of facility nodes and transportation path for a province of China, by programming of MATLAB. Though comparison and robust analysis, the model is verified to be valid and feasibility. The study contributes to provide cycling firms or policy-makers with theoretical and practical directions to configure RSCN of waste phones under uncertainty.

The forward and reverse supply chains have unique characteristics that vary among different organizations. Most organizations manage and quantify risks in the forward supply chain by utilizing various available risk management tools. These risk management tools have been developed solely for the forward supply chain. This research presents the development and implementation of a risk management tool to be used to manage and quantify risks specifically in the reverse supply chain. Implementation of this tool at Company ABC demonstrates how the risk management tool is used in order to be beneficial to the organization. The tool is designed to be easily implemented at minimal cost and serves as a valuable tool for personnel faced with important and costly decisions regarding risk occurrence in the reverse supply chain network.

A tabu search based algorithm for the optimal design of multi-objective multi-product supply chain networks

Today’s competitive business environment has resulted in increasing attention to social responsibilities and customer’s attitudes. Buying and returning have become a common practice for different reasons, including incompleteness or immature failure of the product or its failure to meet the customer’s satisfaction. Before the buying and returning cycle can be handled appropriately, companies need a proper logistics network designed following a proper design strategic. In the present research, a forward and reverse logistics network is proposed for product distribution and collection. The contribution of this paper to the literature is the proposal of a multi-period, multi-echelon, integrated forward and reverse supply chain network design problem with transportation mode selection considered. Different kinds of decisions including the determination of optimum number and locations of facilities, facilities opening time and transportation mode selection among different facilities have been considered in this paper. Due to multi-period nature of the problem, the problem is flexible for future periods. A new mixed integer nonlinear programming model was proposed for the introduced problem considering different levels of facility capacities with the maximum profit objective function. As another contribution, a genetic algorithm was developed to cope with problem’s complexity when the problem size goes large.

Currently, levels of production and disposal for communication devices such as smartphones are continuing to increase. In the life cycle of a smartphone, the majority of greenhouse gas (GHG) emissions are generated in the material production stage. To recover the GHG emissions from end-of-life (EOL) products such as smartphones, manufacturers have to recycle EOL products. However, smartphones on the market undergo little recycling because costs related to recycling, transportation, and facilities are very high. Therefore, the decision maker (DM) has to design a reverse supply chain network for collecting EOL products from users and transporting them to recovery or disposal facilities not only environmentally friendly but also economically feasible. This study applies a bi-objective reverse supply chain network design to material-based GHG volumes and related costs applying a multi-criteria decision-making methods as linear physical programming (LPP) to design a reverse supply chain network in the case of smartphones. First, the reverse supply chain network is modeled for recycling EOL smartphones, and a case study based on literatures and life cycle assessment are prepared. Next, the objective functions are set and formulated to minimize the total volume of material-based GHG volume and the total cost using LPP and integer programming. Finally, numerical experiments on the reverse supply chain are conducted and evaluated.

Optimal supply chain network design with process network and BOM under uncertainties: A case study in toothbrush industry

Risk assessment and management for supply chain networks: A case study

Integration of sale and leaseback in the optimal design of supply chain networks

Managing the trade-offs between financial performance and credit solvency in the optimal design of supply chain networks under economic uncertainty

We observe that between the competitors of technology products, companies such as HP and Xerox or Apple and Google, one competitor (HP and Apple) manages the reverse supply chain to extract value profitably, while the other competitor (Xerox and Google) uses a third-party reverse logistics company to simply recycle consumer returns. In this chapter, we show that, under a given set of supply chain levers, the OEM’s approach to supply chain network design can significantly impact the profitability in the reverse supply chain over the product lifecycle. We propose an integrated multi-period optimization model called the product life cycle optimization model (PLCOM) to design the closed loop supply chain (CLSC) for the OEMs. The PLCOM is a mixed-integer linear program that consists of a demand model based on the extended-Bass’s diffusion model and a pricing model based on a realistic customer willingness to pay (WTP). The PLCOM is applied to a realistic case study using Apple’s iPhone 7 for a product life cycle of 8 years. We show that an integrated approach to designing the supply chain network by the OEMs is more profitable compared to a sequential approach.