Collection channel and production decisions in a closed-loop supply chain with remanufacturing cost disruption

Enterprise risk management (ERM) focuses on managing functions of an entire business, with special attention to supply chain uncertainty. Managing supply chain through effective ERM will mitigate firm exposure to risk in operations and ensure the success of the business. We study collection channel and production decisions in a closed-loop supply chain (CLSC) with one dominant manufacturer and one retailer, from the perspective of both firm profit and system robustness. A robust system is desired as only minimal changes in production plan are necessary when facing disruption, and is valuable for enterprise risk reduction. We find that without disruption the indirect (retailer) collection channel achieves higher profit than that of the direct (manufacturer) channel, which is also true under some disruption cases. However, the direct channel is more robust when facing disruption, and generates more profits for manufacturers when the positive disruption is large. We also find that the revenue-sharing contracts are effective in coordinating members of the CLSC. Finally, we conduct numerical studies to validate the models and derive managerial insights.     

Pricing and warranty decisions in a two-period closed-loop supply chain

For a two-period closed-loop supply chain (CLSC) consisting of a manufacturer and a retailer, Stackelberg game analyses are conducted to examine pricing and warranty decisions under two warranty models depending on who offers warranty for new and remanufactured products and the corresponding benchmark models with a warranty for new products only. Next, we identify the conditions under which warranty for remanufactured products is offered and investigate how this warranty affects the CLSC operations. Subsequently, comparative studies are carried out to examine equilibrium decisions, profitability and consumer surplus of the CLSC between the two warranty models. Analytical results show that offering warranty for remanufactured products does not affect new product pricing in period 2, but influences the pricing of new products in period 1 and remanufactured products in period 2, thereby enhancing remanufacturing, individual and channel profitability, and consumer surplus. Compared to the retailer warranty for remanufactured products, the manufacturer warranty can attain a more equitable profit distribution. If the warranty cost advantage of the manufacturer (retailer) is significant relative to that of the retailer (the manufacturer), the manufacturer (retailer) arises as a natural choice to offer warranty for remanufactured products as this decision enhances both profitability and consumer surplus.     

A two-stage robust programming approach to demand-driven disassembly planning for a closed-loop supply chain system

The closed-loop supply chain system, which integrates forward and reverse logistics, is a desirable policy for retaining recoverable resources and extending the life cycles of products. In this study, we propose a methodology to contend with a demand-driven disassembly planning problem under a closed-loop supply chain system. A two-stage robust programming model is developed correspondingly, such that multiple products with a hierarchical product's structure are disassembled to satisfy uncertain demands in multiple periods. The objective of the model is to determine a robust decision for recycle volume and timing of each type of end-of-life (EOL) product, as well as recovery strategies. The results provide two-stage decisions by considering future scenarios of periodic demands at the beginning of a planning horizon. The first-stage decision is to determine a compromise solution that is close to the optimal solution for every scenario while retaining a certain level of infeasibility of constraints, such as unsatisfied demand. Afterward, when the outcome of a scenario has been realised, the second-stage decision, such as, inventory volume, is conducted to become a buffer for mitigating uncertain impacts. Furthermore, the computational results confirm the trade-off relationship between solution robustness and model robustness, which are core results of the robust model apart from expected profit. The different types of decision makers’ preferences toward risk can be accounted for to determine a compromise robust solution.     

Dual channel closed-loop supply chain coordination with a reward-driven remanufacturing policy

This paper investigates a reward-driven policy, employed in a closed-loop supply chain (CLSC), for acquiring used products earmarked for remanufacture. Under the examined model, a single manufacturer sells products through a retailer as well as directly to end users in a forward supply chain. In the reverse supply chain, three different modes of collection are employed to capture used products for remanufacture: they are through a third party, directly by the manufacturer and from the retailer. Mathematical models for both non-cooperative and centralised scenarios are developed to characterise the pricing decisions and remanufacturing strategies that indicate individual and overall supply chain performance. Optimality of all the proposed models is examined with theory. To coordinate and achieve a win–win outcome for channel members, we proposed a three-way discount mechanism for the manufacturer. Extended numerical investigation provides insights on ways to manage an efficient reward-driven CLSC in a dual-channel environment.     

Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing

When business practices shift from a traditional open supply chain to a closed loop instead, the environmental and societal issues are efficiently integrated in business development. However, even an efficiently integrated shift introduces a number of trade-offs due to the contradictory goals that emerge from that business’s economical, environmental and social dimensions. In this paper, we propose a multi-objective mixed integer mathematical problem for a generic closed-loop supply chain (CLSC) network to rationalise how a system’s product recovery helps to improve manufacturing sustainability. The CLSC network proposed in this study consists of a hybrid manufacturing facility, warehouse, distribution centres, collection centres and a hybrid recovery facility (HRF). The proposed model determines the best location for the HRF and optimal flow of products, recovered parts and material in the network while it simultaneously maximises profit, saves activity costs, helps to decrease the harmful effects of the manufacturing process and makes a positive impact on societal development. To validate the model, a numerical illustration with the help of a case study from an electrical manufacturing industry is offered. The results authenticate the approach of the model towards the fulfilment of various environmental regulations. A sensitivity analysis, completed on demand, and the return rate also assists decision-makers to manage their decisions with a broader insight towards manufacturing sustainability.     

The impact of dual-market on supply chain configuration for new products

Given a firm’s supply chain network, the key objective of supply chain configuration (SCC) is to determine a subset of supply chain partners to be involved in development, sourcing, production, distribution and support of a new product at the highest level of efficiency and expected responsiveness. Current literature on SCC realises the importance of considering the demand dynamics associated with the new product diffusion (NPD). However, these studies assume one-segment market for new products, a single homogenous consumer segment. Recent research in marketing indicates that such simplification might be fatal because a diverse and significant number of product categories may experience a dual-market structure, namely early and main markets, and generate a different demand dynamics. The objectives of this study are to: (i) develop a hybrid optimisation model, capturing both SCC decisions and the demand dynamics of dual-market NPD process; (ii) based on real-world data for a host of electronic product categories, various SCC networks and NPD demand dynamics, examine the new integrated optimisation model under one- and two-segment market; and (iii) present relevant managerial implications and guidelines for supply chain and marketing managers. Our extensive comparative computational experiment with 26 categories of consumer electronic products show that on average the relative net profit may improve significantly, when the market is considered as two-segment.     

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment.     

Incorporating risk measures in closed-loop supply chain network design

This paper considers a location-allocation problem in a closed-loop supply chain (CLSC) with two extensions: first, demand and prices of new and return products are regarded as non-deterministic parameters and second, the objective function is developed from expected profit to three types of mean-risk ones. Indeed, design and planning an integrated CLSC in real-world volatile markets is an important and necessary issue. Further, risk-neutral approaches, which are considered expected values, are not efficient for such uncertain conditions. Hence, this paper, copes with the design and planning problem of a CLSC in a two-stage stochastic structure. Besides, risk criteria are considered through using three types of popular and well-behaved risk measures: mean absolute deviation, value at risk and conditional value at risk (CVaR). Consequently, three types of mean-risk models are developed as objective functions and decision-making procedures are undertaken based on the expected values and risk adversity criteria. Finally, performances of the developed mean-risk models are evaluated in various aspects. Results reveal that the inefficiencies of risk-neutral approaches can be overcome. In addition, in terms of quality of solutions, the acceptability of CVaR is proved when it is compared to other risk measures.     

Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming

Supplier selection is an important strategic design decision in closed-loop supply chain systems. In addition, and after identifying the candidate suppliers, optimal order allocations are also considered as crucial tactical decisions. This research presents a multi-objective optimisation model to select the best suppliers and configure manufacturing and refurbishing facilities with the optimal number of parts and products in a closed-loop supply chain network. The objective functions in this research are formulated as total profit, total defective parts, total late delivered parts and economic risk factors of the candidate suppliers. The proposed multi-objective model is solved by hybrid Monte Carlo simulation integrated with three different variants of goal programming method. The effectiveness of the mathematical model and the proposed solution algorithms in obtaining Pareto-optimal solutions is demonstrated in a numerical example adopted from a real case study.     

Novel model and kernel search heuristic for multi-period closed-loop food supply chain planning with returnable transport items

Closed-loop supply chain (CLSC) is of utmost importance to sustainable development and has received increasing attention in recent decades. However, food CLSC with returnable transport items (RTIs) has been rarely studied although its growing applications in practice. This paper aims to investigate a multi-period CLSC planning problem that coordinates the flows of perishable food products and RTIs considering food quality. The objective is to maximise the total profit of the holistic supply chain over a finite planning horizon. To this end, a novel mixed integer linear programming model is first formulated. As the problem is proven NP-hard, an improved kernel search-based heuristic is then developed. A real case study deriving from a food manufacturer in China shows the applicability of the proposed model and method. The results indicate that the manufacturer’s profit can be improved by more than 10% with our method. Numerical experiments on randomly generated instances demonstrate that the proposed heuristic can yield high-quality solutions with much less computation time compared with the commercial solver CPLEX and an existing heuristic.     

Simulation-based ripple effect modelling in the supply chain

In light of low-frequency/high-impact disruptions, the ripple effect has recently been introduced into academic literature on supply chain management. The ripple effect in the supply chain results from disruption propagation from the initial disruption point to the supply, production and distribution networks. While optimisation modelling dominates this research field, the potential of simulation modelling still remains under-explored. The objective of this study is to reveal research gaps that can be closed with the help of simulation modelling. First, recent literature on both optimisation and simulation modelling is analysed. Second, a simulation model for multi-stage supply chain design with consideration of capacity disruptions and experimental results is presented in order to depict major areas of simulation application to the ripple effect modelling. Based on both literature analysis and the modelling example, managerial insights and future research areas are identified in regard to simulation modelling application to the ripple effect analysis in the supply chain. The paper concludes by summarising the most important insights and outlining a future research agenda.     

Robust supply chain networks design and ambiguous risk preferences

In this paper, a robust stochastic optimisation model for regret minimising is proposed for supply chain networks design. The model emphasises the ambiguity lying in both the risk preference and the probability distribution. A duality theory for the model is derived and the random utility functions are identified as the Lagrange multipliers. In addition, a tractable relaxation based on reformulation-linearisation technique is presented for the computational aspects of the model. The numerical experiments show that our regret minimising model can make a well balance between the conservativeness of a pure robust optimisation model and the optimism of risk-neutrality. We also present a case study to demonstrate the applicability of the proposed model.     

Product complexity and supply chain design

To assemble a product, each and every part is required. Hence, the more parts in the product, the greater the risk of disruption. Compared with retailers or assemblers of simple products, manufacturers of complex products are much more sensitive to supply chain delays. This heightened vulnerability to supply chain disruptions should lead complex product assemblers to design less risky supply chains. Supply chain design should depend on the complexity of the product assembled or manufactured. This paper models how product complexity drives the likelihood of disruption for given component supply chain reliabilities. The paper provides insights for supply chain design that comprehends the impact of product complexity.     

What is the role of value-added service in a remanufacturing closed-loop supply chain?

While the previous literature overlooks value-added service in a closed-loop supply chain (CLSC), we consider new research problems with embedding value-added service into remanufacturing models. In this paper, we mainly study the role of value-added service in a CLSC and investigate how the value-added service influences profits and other decision variables. Then, we evaluate the performance of the CLSC in different service structures, where a manufacturer or a retailer provide the value-added service. Systematic comparisons and numerical studies show that the supply chain is more effective when service is provided by a manufacturer rather than by a retailer. Also, value-added service improves the whole supply chain performance and plays a regulatory role in a CLSC and influences the decisions of supply chain members. We also show that while a service cost-sharing mechanism can only optimise rather than achieve the supply chain coordination, and the degree of optimisation depends on cost-sharing percentage, the two-part tariff contract can be an effective mechanism to coordinate decentralised service scenarios by generating the same supply chain performance in a centralised service architecture model.     

An integrated closed-loop supply chain model with location allocation problem and product recycling decisions

Environmental pollutions caused by improper abandoned cartridges increase dramatically nowadays. In Hong Kong, due to abundant quantity of cartridges being used, producers have to optimise their forward and reverse networks to maximise the recycling rate and their profits. In this paper, a comprehensive closed-loop supply chain (CLSC) model is established. This model contains eight partners in CLSC and describes the existing cartridge recycling situation in Hong Kong. In the literatures, many CLSC models were established and studied, but few of them analysed the delivery activity for different kinds of materials extracted from the used products and also, few papers studied the situation that used products are classified into good and poor quality. In this model, delivery activities of different materials are considered and the used cartridges are classified into good-quality ones and poor-quality ones. Producers will have different methods to process them. This problem is formulated into an Integer programming model. Since both delivery routes and delivery quantities problems are known to be NP hard, a novel modified two-stage genetic algorithm (GA) is proposed. A new two-stage encoding algorithm in the proposed GA reinforces the genetic searching ability in tackling this kind of problem. As the model is new in literature, we used Integer Programming to solve the testing instances and benchmark with the proposed algorithm. The results show that a near-optimal solution can be obtained by the proposed GA in a much shorter computational time.     

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.     

A supply chain network equilibrium model for operational and opportunism risk mitigation

Risk management holds a crucial role in ensuring efficiency, predictability, and coherency in supply chain operations of an enterprise. Risks are associated with every member of a supply chain network. Thus, an end-to-end risk management approach is essential to fortify the entire supply chain network. In this paper, we consider a supply chain network consisting of suppliers, manufacturers, distributors and retailers, as the representative stakeholders. In particular, we take supply chain operational, and opportunism risks into account, and investigate the roles of flexibility, and social relationship, respectively, as a mitigation approach. We develop a multi-period network equilibrium model by considering the stakeholders’ objectives of maximising profit and minimising risk. Further, the finite-dimensional variational inequality formulations are derived for the underlying network optimisation problem. An algorithm, with nice features for computations, is then applied to three simulated examples in order to illustrate the model and computational procedure as well as the types of interventions that can help the strategic decision-makers to explore quantitatively the associated profits and incurred risks in an entire supply chain network.     

Modeling of the resilient supply chain system from a perspective of production design changes

Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.     

Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty

In this study, a general comprehensive model is proposed for strategic closed-loop supply chain network design under interval data uncertainty. The proposed model considers various assumptions such as multiple periods, multiple products, and multiple supply chain echelons as well as uncertain demand and purchasing cost. In addition, bill of materials for each product is considered via a new approach in management of forward and reverse flows of products for producing new products and reusing or disassembling returned products. Uncertainty of parameters in the proposed model is handled via an interval robust optimisation technique. The model assumptions are well matched with decision making environments of food and high-tech electronics manufacturing industries. The factors that make these two industries similar are time-dependent properties of products such as prices and warehousing lifetime period. The computational results of solving the proposed model via LINGO 8 demonstrate efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context.     

A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting

This paper addresses a single-manufacturer single-retailer closed-loop supply chain with stochastic product returns considering worker experience under learning and forgetting in production and inspection of returned items at the manufacturer. Customer demand is assumed to be dependent linearly on the retail price, and it is fulfilled by using both manufactured and remanufactured products. The manufacturer delivers the buyer’s order quantity in a number of equal-sized batches. The optimal number of shipments, the shipment size and the retail price are determined by maximising the average expected profit of the closed-loop supply chain. It is observed from the numerical study that high learning effects in production and inspection lead to high recovery rates of used products, which, besides an economic advantage, may have a positive effect on the environment. Even though forgetting has an adverse effect, the average expected profit of the closed-loop supply chain is much higher than that of the basic model which ignores worker learning.