Replenishment Policy in a Two-Echelon Supply Chain: An Analysis Using Discrete-Event Simulation (original) (raw)
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IFAC-PapersOnLine, 2015
The study of a real-time procurement and production mechanism for a multi-stage supply chain system with multiple suppliers subject to an unexpected disruption is presented in this paper. Specifically, a mathematical model is developed for the problem of optimizing replenishment and production decisions for each node after a supply disruption occurrence. The system considered in this research is a three stage supply chain system that consists of three suppliers, one manufacturer and one retailer. The problem that will be considered is an instance of the class of inventory management problems under disruptions with a finite horizon. The solution approach will utilize a heuristic that we have developed in previous works. In addition, an experiment was conducted to study the effects of disruption on the system using predefined scenarios, where supplier prioritization of disruption mitigation strategies was explored. Various disruption scenarios were predefined by combining different disruption locations, as well as different combinations of suppliers. It can be shown that supply disruption at the suppliers with higher inventory holding costs causes higher recovery costs for the overall system. Therefore, it is important for the manufacturer to focus on the suppliers with higher holding cost when planning appropriate strategies for risk management. This optimization study has enabled an increased understanding of the impacts of random disruption events on the total system behaviour, as well as determining priorities in risk mitigation efforts.
Effect of supply disruption on inventory policy
European J. of Industrial Engineering, 2019
This research examines a two-stage supply chain that comprises a supplier who is subject to stochastic disruption and a retailer who has to deal with supply disruption by holding inventory. Under a periodic-review base-stock inventory policy, the main objectives of this study are to determine the optimal inventory policy in the presence of stochastic supply disruption so as to minimise the total inventory cost as well as to analyse the impact of supply disruption on the optimal inventory policy. In this research, the length of a supply disruption is modelled as a continuous random variable, distinguishing it from previous research which modelled the length of a supply disruption as a discrete random variable that receives values only as multiples of the length of a review period. Numerical experiments have been conducted to illustrate the applicability of the proposed inventory model and to examine the effects of various input parameters on the optimal inventory policy. Furthermore, compared with the optimal inventory policy derived when the length of a supply disruption is considered as a multiple of the length of a review period, the proposed inventory model in this research can help derive a more precise optimal inventory policy.
In this paper, a multiple period replenishment problem based on (s, S) policy is investigated for a supply chain (SC) comprising one retailer and one manufacturer with uncertain demand. Novel mixed-integer linear programming (MILP) models are developed for centralised and decentralised decision-making modes using two-stage stochastic programming. To compare these decision-making modes, a Monte Carlo simulation is applied to the optimization models' policies. To deal with demand uncertainty, scenarios are generated using Latin Hypercube Sampling method and their number is reduced by a scenario reduction technique. In large test problems, where CPLEX solver is not able to reach an optimal solution in the centralised model, evolutionary strategies (ES) and imperialist competitive algorithm (ICA) are applied to find near optimal solutions. Sensitivity analysis is conducted to show the performance of the proposed mathematical models. Moreover, it is demonstrated that both ES and ICA provide acceptable solutions compared to the exact solutions of the MILP model. Finally, the main parameters affecting difference between profits of centralised and decentralised SCs are investigated using the simulation method. 1. Introduction A supply chain (SC) is a network of organisations containing suppliers, manufacturers, distributors, wholesalers and retailers. Each of these components plays its specific role in operations and production to supply needed products and required services to the end consumers. Each entity in this chain seeks its own benefits which can be obtained with or without collaboration with other chain entities. Depending on how decisions are made, there exist two major types of SCs. When each entity of SC only considers its own objectives and constraints, the SC is locally optimised and this is called a decentralised control mode. Nonetheless, when a single decision-maker determines its decision policy with consideration of objectives and constraints of the entire entities in the SC, it is called a centralised control mode (Simchi-Levi, Kaminsky, and Simchi-Levi 2004). Achieved decision policy in the centralised mode may not be optimal for each entity; however, it is optimal for the whole SC. Furthermore, the centralised control mode can also be adopted under special configurations. For example, vendor-managed inventory is a setting in which the centralised control mode should be used. The replenishment policy of each facility in the SC is mostly determined at the tactical stage for periods ahead and decision-makers should decide upon when and how much to order and/or produce for each entity to provide their customers with a proper service level. In an uncertain environment, replenishment planning will be more complex where there is not sufficient information about the parameters related to other chain's entities. Sometimes, these entities become involved in different kinds of contracts to coordinate with each other. It reduces risk in a stochastic situation and causes a kind of sharing information by which the planning issue can be implemented in a more appropriate way for each entity. The information sharing between Wal-Mart and one of its main suppliers, Procter & Gamble, is a successful typical example of this issue (Mason-Jones and Towill 1997). In addition, many world-class companies such as Dell and Cisco share information with their suppliers to maximise their competitive advantages. Therefore, through these ways, a decentralised SC becomes closer to a centralised one.
Modern supply chains are attempting to gain competitive advantages in a fiercely competitive marketplace through adopting new initiatives and practices such as lean and just-in-time. These initiatives are suitable for a stable world but they could make the supply chain more vulnerable to the external disruptions such as natural and man-made disasters. This paper aims at studying the dynamics of a disrupted supply chain under a coordination mechanism that is designed to achieve efficiency and resiliency. The proposed procedure relies on establishing a novel replenishment policy based on an information sharing approach to replace traditional policies. In this policy, replenishment orders will be divided into two streams, transmitting both real demand information and required inventory adjustments to the whole supply chain. A simulation model for a four-echelon supply chain has been considered to evaluate the information sharing policy and to compare it with an order-up-to level policy, determining the dynamics of ordering and inventory before and after the disruption. The results showed how the suggested approach was successful in recovering the disrupted supply chain to a stable performance by reducing effects on inventory and ordering patterns.
Multi-Echelon Inventory Management Policies: A Case Study for a Two-Echelon Supply Chain
Proceedings of the International Conference on Industrial Engineering and Operations Management, 2020
Effective multi-echelon inventory management has been widely recognized for minimizing the average total inventory cost by promoting the coordination and cooperation among supply chain members. This paper presents a spreadsheet simulation of the inventory performance and associated costs obtained by developing a multi-echelon control policy in a real-world two-stage supply chain. Our simulation model is based on a supply chain network of a company located in Colombia and Panama. The results indicate an inventory cost reduction without negatively affecting customer service levels.
International Journal of Engineering & Technology, 2020
Multi Echelon Distribution System (MEDS) is a multifaceted system focusing on integration of all factors involved in the entire distribution process of finished goods to customers. This paper proposes a simulation framework at the operational level of MEDS. The proposed model includes three echelons, based on discrete-event simulation approach, where the performed operations within our system are depending on several key variables that often seem to have strong interrelationships. It is necessary to simulate such complicated system, in order to understand the whole mechanism, to analyze the interactions between various components and eventually to provide information without decomposing the system. The simulation framework is used to evaluate the performance of the considered system at initial conditions and to compare it with different scenarios generated by simulation running. The study concludes with an analysis of system performance and the finding results according to each scen...
International Journal of Management Science and Engineering Management, 2018
Effective response and recovery from disruptions are vital to achieving the supply chain objectives. This study aims to formulate a quantitative model for mitigating disruptions in a supply chain. An inventory model has been developed for a manufacturer with one supplier and one retailer by considering random capacity of the supplier and random availability of both the supplier and the retailer assuming zero delivery lead time. Backorders are allowed and it has two parts-unit dependent and both unit and time-dependent. This study suggests an optimal order quantity and a reordering point so that the average cost per cycle gets minimized. A genetic algorithm is used to solve the proposed inventory model. The applicability of the proposed model has been tested using a numerical example. Finally, sensitivity analysis is performed to examine the robustness of the model.
International Journal of Production Economics, 2009
We consider a two echelon supply chain where a single retailer holds an inventory of finished goods to satisfy an i.i.d. customer demand, and a single manufacturer produces the retailer's replenishment orders on a make-to-order basis. The objective of this paper is to analyse the impact of the retailer's replenishment policy on total supply chain performance. We consider two strategies with regard to the production capacity. In a flexible capacity strategy, the manufacturer invests in excess capacity to guarantee constant lead times in order to keep inventories low. The amount of investment depends on the retailer's order pattern. In an inflexible capacity strategy, the capacity is limited and independent of the retailer's replenishment decision. This results in stochastic lead times, thereby inflating the retailer's inventory requirements. We treat the variability of the order rate of the retailer as the primary decision variable to minimise total supply chain costs. The objective is to find the value of the replenishment parameter (parameter to tune the order variability) that minimises total supply chain costs in a flexible and inflexible capacity scenario.
Discrete Event Simulation in Inventory Management
Encyclopedia of Information Science and Technology, Fourth Edition, 2018
Perishability and substitutability are two key attributes that cannot be ignored in supply chain management. Once produced, perishable products have a finite shelf life. When expired, they are either partially or wholly value-less. The more time that perishable inventory is in storage, the less time it is available for sale to customers. Product substitution is a possibility when considering multiple products. Research indicates that an alternative product is willingly chosen by customers if the preferred one is out of stock. Managers must decide on the replenishment time and replenishment quantity for each item within product subcategory, to maximize expected profits under uncertain demand while minimizing the instances of running out of inventory (i.e., a ‘stock out'). The combination of these factors often requires simulation models to be developed to understand the behavior of the system as the parameters change. Simulation can incorporate stochasticity and complexity while ...