Multi-stage Supply Chain System Research Articles

A multi-stage supply chain disruption mitigation strategy considering product life cycle during COVID-19.

The global pandemic of COVID-19 has caused severe damage to the supply chain such that manufacturers may face long-term supply disruptions. In this paper, a disruption recovery strategy of a supply chain system is investigated from the perspective of product change, in which the life cycle and design change time of a new product are both considered in order to minimize the losses of manufacturer after disruptions. A mixed-integer linear programming (MILP) model is presented to address the disruption recovery problem for this multi-period, multi-supplier, and multi-stage supply chain system. A two-stage heuristic algorithm is designed to solve the problem. Experimental results show that the proposed disruption mitigation strategy can effectively reduce the profit loss of manufacturer due to supply disruption, and demonstrate the impact of product life cycle in the selection of new product design planning. A sensitivity analysis is performed to ensure the applicability of the model in the actual environment, which illustrates the effect of different parameter changes on the results. This work can help manufacturers establish an optimal recovery strategy whenever the supply chain system experiences supply disruptions.

Optimal Pricing, Advertising, Production, Inventory and Investing Policies in a Multi-Stage Sustainable Supply Chain

In this paper, the study of a sustainable production–inventory model with price and advertisement dependent on demand considering carbon emission reduction technology is investigated. The aim of this paper is to determine the optimal appropriate pricing, advertising, production, inventory, and capital investment decisions under various carbon emission policies to maximize the joint total profit of a multi-stage supply chain system. Various theoretical results and an algorithm are provided to verify and obtain the optimal solution of the problem. Further, the model is verified by numerical examples, and the robustness check of parameter variation is also analyzed. Finally, some management implications for decision makers are drawn from numerical examples. In summary, this study puts forward more realistic modeling hypothesis, which is beneficial to the academic research, and the research results can provide relevant decision makers with a model for managing a sustainable supply chain.

Coordinating a multi-retailer decentralized distribution system with random demand based on buyback and compensation contracts

Purpose: The purpose of this paper is to set up the coordinating mechanism for a decentralized distribution system consisting of a manufacturer and multiple independent retailers by means of contracts. It is in the two-stage supply chain system that all retailers sell an identical product made by the manufacturer and determine their order quantities which directly affect the expected profit of the supply chain with random demand. Design/methodology/approach: First comparison of the optimal order quantities in the centralized and decentralized system shows that the supply chain needs coordination. Then the coordination model is given based on buyback cost and compensation benefit. Finally the coordination mechanism is set up in which the manufacturer as the leader uses a buyback policy to incentive these retailers and the retailers pay profit returns to compensate the manufacturer. Findings: The results of a numerical example show that the perfect supply chain coordination and the flexible allocation of the profit can be achieved in the multi-retailer supply chain by the buyback and compensation contracts. Research limitations: The results based on assumptions might not completely hold in practice and the paper only focuses on studying a single product in two-stage supply chain. Practical implications: The coordination mechanism is applicable to a realistic supply chain under a private information setting and the research results is the foundation of further developing the coordination mechanism for a realistic multi-stage supply chain system with more products. Originality/value: This paper focused on studying the coordination mechanism for a decentralized multi-retailer supply chain by the joint application of the buyback and compensation contracts. Furthermore the perfect supply chain coordination and the flexible allocation of the profit are achieved.

Optimisation of multi-stage supply chain systems by integrated simulation-variable neighbourhood search algorithm

In this paper, multi–stage supply chain systems (SCSs) controlled by kanban system are appraised a new simulation metaheuristics approach. In the kanban system, decision making is based on determination of batch size for each kanban. This paper simulates supply chain system regarding the costs under just–in–time (JIT) production philosophy. Since the adopted model is of backward type, the desired output is given in order to find the parameters and/or the structure of the model producing the output. This backward problem is non–analytic and often seems to be even more complex than the forward one. This paper applies genetic algorithm (GA) and variable neighbourhood search (VNS) to optimise the simulation model. A simple real–coded GA and VNS is presented and used to change the simulation model parameters. With each new set of parameters, a simulation run is performed. From the statistics gathered by running the simulation, a goal function is constructed to measure the quality of these parameters. GA and VNS and GA–VNS successfully provide a parameter set to demonstrate its capability to solve such difficult backward problems even in the area of complex simulation model optimisation specially when there is no prior knowledge of simulation model behaviour.

Modelling JIT supply chains by simulation and hybrid genetic variable neighbourhood search algorithm

The objective of this study is investigating multi-stage supply chain systems which is controlled using a kanban system so as to analyse and optimise it. In this paper, a new optimisation approach based on simulation analysis and metaheuristic methods to optimise a multi-echelon supply chain in a JIT production context is proposed. A hybrid of simulation modelling, genetic algorithm (GA), and variable neighbourhood search (VNS) to solve the above mentioned problem was proposed. In this research paper, GA-VNS is used iteratively to optimise the supply chain. Furthermore, the performance of the GA and VNS compared with their GA-VNS counterpart based on their relative error, and it is illustrated that the GA-VNS has ability to solve NP-hard problems in the area of complicated simulation optimisation models, especially where there is no prior knowledge of the behaviour of the system.

Random network models and sensitivity algorithms for the analysis of ordering time and inventory state in multi-stage supply chains

Supply chains in reality face a highly dynamic and uncertain environment, especially the uncertain end-customer demands and orders. Since the condition of product market changes frequently, the tasks of order management, product planning, and inventory management are complex and difficult. It is imperative for companies to develop new ways to manage the randomness and uncertainty in market demands. Based on the graphical evaluation and review technique, this paper provides a simple but integrated stochastic network mathematical model for supply chain ordering time distribution analysis. Then the ordering time analysis model is extended so that the analysis of inventory level distribution characteristics of supply chain members is allowed. Further, to investigate the effects of different end-customer demands on upstream orders and relative inventory levels, model-based sensitivity analysis algorithms for ordering fluctuations and inventory fluctuations are developed. A detailed numerical example is presented to illustrate the application of the proposed models to a multi-stage supply chain system, and the results of which shows the effectiveness and flexibility of the proposed stochastic network models and algorithms in order and inventory management.

A Fuzzy Model for A Multistage Supply Chain System Controlled By Kanban

This paper deals with a multi-machine supply chain system operating under just-in- time delivery policy with fuzzy demands. For this purpose, the system is modeled as a fuzzy linear programming problem. In JIT delivery policy, kanban has the role of a transporter. By the solution of the related fuzzy linear programming problem, the number of kanbans is determined optimally. Fuzzy demands method enables one to minimize inventory costs under uncertain demand fluctuations. Copyright © 2007 IFAC

A stochastic network model for ordering analysis in multi-stage supply chain systems

In this paper we consider the ordering problem in demand driven multi-stage supply chain system. Although ordering decisions are made independently at different supply chain levels which involve manufactures, wholesalers, retailers and customers, the economic performance of ordering policies of supply chain members are heavily interdependent due to the supply–demand relationship among them. Based on the graphical evaluation and review technique, we first present a novel stochastic network mathematical model for order and cost distribution analysis. Further, to investigate the effects of different sub-orders on total system order and the impact of ordering cost on supply chain economic performance, sensitivity analysis models for order fluctuations and ordering cost changes are developed respectively. Finally, a detailed simulation is presented to promote a better understanding of the model approach. The models and algorithms are stylized; however, they are applicable to complex supply chains. The strategy parameters in simulation are presented for various ordering policy settings. This leads to a decision support system for the multi-stage supply chain under various ordering policy scenarios, and will be helpful in identifying the key links in ordering networks and for designing effective strategies to improve supply chain performance.

Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems–a simulation approach

This research aims to develop a simulation approach based on system dynamics modelling (SDM) and adaptive network based fuzzy inference system (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. The proposed model is comprised of three groups of variables influencing the bullwhip effect, namely the structure of a supply chain network, supply chain contributions (ordering process in regular situation or when a supplier has a promotion or shortage gaming) and supply chain performances (the number of defects and ordering lead time). As a result, a two layer simulation model is developed with three generic models. The flexibility of this proposed approach is its ability to model various types of ordering policies which are basic inventory policies, material requirement planning (MRP) system and just in time (JIT) approach. The supply chain of a beverage company was selected to validate and demonstrate the flexibility of the proposed model. The findings of the proposed simulation model are consistent with the results obtained from the case study. The error magnitude of the bullwhip effect level varied between 0 and 9% resulting in bullwhip effect reductions of up to 92%. Accordingly, the bullwhip effect levels are significantly decreased by using the proposed simulation model.

An efficient search algorithm for obtaining the optimal replenishment strategies in multi-stage just-in-time supply chain systems

In this study, we focus on the coordination of the replenishment activities among all firms in a multi-stage supply chain system in which Just-In-Time (JIT) philosophy applies. Similar to all JIT systems, kanban is used to control information flows and material flows between two adjacent manufacturing plants, which is called a kanban stage. In this supply chain, suppliers provide raw materials to the manufacturing plant in the first kanban stage and end-products are delivered from the plant in the last kanban stage to the warehouses or directly to the customers. Studies in the literature formulated this problem as a Mixed-Integer Non-Linear Program (MINLP). The objective of this study is to propose an efficient search algorithm that solves an optimal solution for this MINLP model. We conduct theoretical analysis on the optimality structure of the model and assert that the optimal cost curve is piece-wise convex. The characteristics of the optimal cost curve, especially the 'junction points'', provide essential foundation to the design of our search algorithm. Also, in order to reinforce the effectiveness of the proposed search algorithm, we utilize a relaxation of the problem to obtain tight bounds on the search range. Using randomly generated instances in our numerical experiments, we demonstrated that the proposed search algorithm outperforms a previously published heuristic.

Determination of number of kanbans in a supply chain system via Memetic algorithm

In this research multi-stage supply chain system which is controlled by kanban system, is evaluated. In kanban system, decision making is based on determination of the number of kanbans as well as batch sizes. This paper attempts to model supply chain system with regard to costs under just-in-time (JIT) production philosophy. Since adopted model is of mixed integer non-linear programming (MINLP) type and solving it by exact algorithm such as branch and bound (B&B) takes a lot of time, a heuristic method via Memetic algorithm (MA) is presented. Some problems are solved by our proposed MA to illustrate its performance.

Optimal models for a multi-stage supply chain system controlled by kanban under just-in-time philosophy

This research studies a multi-stage supply chain system that operates under a JIT (just-in-time) delivery policy. Kanbans play an important role in the information and material flows in a supply chain system. Thus, a kanban mechanism is employed to assist in linking different production processes in a supply chain system to implement the scope of JIT philosophy. For a multi-stage supply chain system, a mixed-integer nonlinear programming (MINLP) problem is formulated from the perspective of JIT delivery policy where a kanban may reflect to a transporter such as a truck or a fork-lifter. The number of kanbans, the batch size, the number of batches and the total quantity over one period are determined optimally. It is solved optimally by branch and bound method. A greedy heuristic to avoid the large computational time in branch-and-bound algorithm is developed for solving a large MINLP. Coupled with plant-wide efforts for cost control and management commitment, a logistic system for controlling the production as well as the supply chain is built, which results in minimizing the total cost of the supply chain system. The results show that the improvements in reduction of inventory, wasted labor and customer service in a supply chain are significantly accomplished through the kanban mechanism.