Echelon Supply Chain Research Articles

Pengembangan Model Consignment Stock pada Sistem Rantai Pasok Dua Eselon dengan Permintaan Berfluktuasi dan Perubahan Order Awal

Scheduling changes on the production floor are common in practice to meet the consumer demand and these cause the nervousness. The nervousness in turn will result in increased costs and reduced service level. This research deals with production batch size and buffer stock taking into account changes in a preliminary order. Change in the demand for a given day is announced one day before and this is viewed as it occurs randomly. This research was considered in two echelon supply chain system with a single supplier and single manufacturer. The development of model is transactional relationship and consignment stock contract relationship. This study also considers the backorder and production capacity according to the real condition. Numerical examples are given to demonstrate the performance of the models. From the numerical results, it appears that coefficient variation (CV) of the demand affects the results obtained using method of SM and LUC.

Coordination of Agricultural Products Supply Chain with Stochastic Yield by Price Compensation

In this paper, coordination problem of agricultural products supply chain with stochastic yield is studied based on prices compensation strategy. The agricultural producing is influenced by the natural conditions, and the yield is uncertain. While agricultural products is rigid demand goods, the fluctuations of yield cause greater volatility of prices. The two- echelon supply chain with one supplier and one retailor is studied, and the mathematical model is constructed. The model showed that prices compensation strategy is Pareto improvement for agricultural products supply chain with stochastic yield, and it also incentive agricultural products supplier to rise the production plan and balance the profit allocation of supply chain.

Coordination of supply chains by downward direct rebate under symmetric and asymmetric information

Promotions have been frequently used by the members of a supply chain as a means to increase sales, revenues, and market share by increasing consumer's awareness about the product, to reduce inventories of items/seasonal products. But a growing body of literature on operations management has suggested that a high degree of supply chain integration is needed to improve the performance of the supply chain. In this paper, a new coordination policy is proposed by using instant rebate and consumer rebate to coordinate the supply chain. It is proved that the proposed policy coordinates a two–echelon supply chain where demand depends on selling price of the retailer under news vendor framework. When both the retailer and the manufacturer have private information, we use fuzzy signed distance technique to derive the value of rebate. Numerical examples are provided to get additional insights on the profit gained by the proposed coordination policy.

A Centralized Model Predictive Control Strategy for Dynamic Supply Chain Management

Reducing the Bullwhip effect is one of several crucial problems in supply chain management. In this paper, a centralized Model Predictive Control (MPC) strategy is applied to control inventories in a 4 echelons supply chain. The single MPC controller used in this strategy optimizes globally and finds an optimal ordering policy for each node. The controller relies on a linear discrete-time state-space model to predict process output and the prediction can be approached by two multi-step predictors, which depend on measurability of the controller states. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming. Simulation results show that centralized MPC strategy can track customer demand and maintain a proper inventory position level with reduced Bullwhip effect.

Class of resource allocation problems in supply chain - a review

Resource allocation (RA) involves the distribution and utilisation of available resources in the system. Because resource availability is usually scarce and expensive, it becomes important to find optimal solutions to such problems. Thus, RA problems represent an important class of problems faced by mathematical programmers. Conventionally, such RA problems have been modelled and solved for allocation in single echelon supply chain (SC), single objective allocation, allocation with certainty of static input data, single performance measure driven allocation, disintegrated allocation and routing both in strategic and operational level. This research focuses on detailed review of six new and complex sub-classes of RA problems in SC network focusing bi-objectives, dynamic input data, and multiple performance measures based allocation and integrated allocation and routing with complex constraints. This study considers six set of variants of the RA problems normally encountered in practice but has not been giv...

Applied Repairable-item Inventory Modeling in the Aviation Industry

In the aviation industry, reliable spare parts supply is essential for continuous aircraft operation. Spare parts of high value are repaired and returned to stock after removal from the aircraft, forming a closed-loop supply chain. Current planning methods of maintenance, repair, -and overhaul providers (MRO providers) as well as research findings published to date do not cover the requirements of the commercial aviation industry. This paper presents a spare part inventory planning method that calculates cost-optimal inventory levels for warehouses in a two- echelon supply chain for spare parts supply in the aviation industry and takes into account various input and output planning parameter, such as budget and inventory level limitations. The development and introduction of the planning method is accompanied by a change management process. For validation of the calculated inventory levels a simulation model is used and sensitivity analyses are conducted. Using the discrete- event simulation model, effects of the demand level at local warehouses and its influence on the central warehouse are investigated.

Optimizing a multi-echelon supply chain network flow using nonlinear fuzzy multi-objective integer programming: Genetic algorithm approach

Article history: Received March 19, 2012 Received in Revised form June, 20, 2012 Accepted 22 June 2012 Available online June 24 2012 The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixedinteger programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN) by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs) and customers). The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed. © 2012 Growing Science Ltd. All rights reserved.

Coordination of Cooperative Advertising in Two Echelon Supply Chain with Stochastic Demand

Coordination of Cooperative Advertising in Two Echelon Supply Chain with Stochastic Demand

Centralized replenishment policy for deteriorating items in a three echelon supply chain under stochastic lead time

Abstract One of the significant problems faced in inventory management is how to control and maintain the inventories of deteriorating items. Most of studies developed in this field, assumed supply lead time is fixed while that is hard to be exactly determined in the majority procurement systems. Also, most of integration inventory models for supply chains with a deterioration item have been developed for two-echelon supply chains. In this paper, in order to fill these gaps, we focus on optimally determining replenishment policy for deteriorating items among all the partners in a three echelon supply chain with stochastic supply lead time for the buyer. For modeling the deterioration process, a nonlinear holding cost function is considered. Then numerical examples are solved and sensitivity analysis is done to distinguish the situations which are more cost-effective for centralized decision making and demonstrate the applicability of the proposed model.

Optimal Ordering Policies of a Two Echelon Supply Chain for Deteriorating Items

chain management is a crucial task of managing large organizations. In a decentralized supply chain each member focuses on maximizing his own profit. As a result of it, the conflict between the manufacturer and the retailers will arise. To avoid this sort of situations, coordination model strike a balancing between the profit of manufacturers and retailers. This paper investigates a two echelon supply chain system which consisting of one manufacturer and multiple retailers. Using the mathematical modeling a coordination model which maximizes the total profit is developed and analyzed for deteriorating items. The optimal pricing and ordering policies of the model are also derived. A sensitivity analysis with respect to the parameters and costs is also presented. This model lower down the total cost of supply chain and increases the general profit. It also improves cooperation for both manufacturer and retailer.

Application of particle swarm intelligence algorithms in supply chain network architecture optimization

In today’s globalization, the success of an industry is dependent on cost effective supply chain management under various markets, logistics and production uncertainties. Uncertainties in the supply chain usually decrease profit, i.e. increase total supply chain cost. Demand uncertainty and constraints posed by the every echelon are important factors to be considered in the supply chain design operations. Optimization is no longer a luxury but has become the order of the day. This paper specifically deals with the modeling and optimization of a three echelon supply chain network using the particle swarm optimization/intelligence algorithms.

Applying Agent-Based System and Negotiation Mechanism in Improvement of Inventory Management and Customer Order Fulfilment in Multi Echelon Supply Chain

A supply chain (SC) is a coordinated system involved in moving products or services from supplier to customer. With growing focus on supply chain management, it is realized that inventories across the supply chain should be efficiently managed through greater cooperation and better coordination. Multi-agent systems (MAS) offer a new dimension for cooperation and coordination. MAS consist of a collection of automated agents who can perform a specific task intelligently without any human supervision and communicate with each other cooperatively. In this research, a multi-agent system has been developed to simulate a multi echelon SC. These agents are coordinated to control inventories and minimize the total cost of SC by sharing information, forecasting knowledge and using negotiation mechanism in special situation. The result showed that in comparison to a traditional supply chain network, this system decreases the total cost and bullwhip effect.

Integrated markdown pricing and aggregate production planning in a two echelon supply chain: A hybrid fuzzy multiple objective approach

Given high variability of demands for short life cycle products, a retailer has to decide about the products’ prices and order quantities from a manufacturer. In the meantime, the manufacturer has to determine an aggregate production plan involving for example, production, inventory and work force levels in a multi period, multi product environment. Due to imprecise and fuzzy nature of products’ parameters such as unit production and replenishment costs, a hybrid fuzzy multi-objective programming model including both quantative and qualitative constraints and objectives is proposed to determine the optimalprice markdown policy and aggregate production planning in a two echelon supply chain. The model aims to maximize the total profit of manufacturer, the total profit of retailer and improving service aspects of retailing simultaneously. After applying appropriate strategies to defuzzify the original model, the equivalent multi-objective crisp model is then solved by a fuzzy goal programming method. An illustrative example is also provided to show the applicability and usefulness of the proposed model and solution method.

Two-echelon supply chain with selling price dependent demand under wholesale price index and consumer price index

In recent Indian economic scenario, consumer price index (CPI) at the retailer is escalated very high where-as wholesale price index (WPI) at the manufacturer reduced very low, especially in the case of essential commodities. Under these circumstances, illustrating the effect of reduced WPI and increased CPI on the gross profit of the supply chain as well as at individual echelons is worthwhile in taking inventory decisions. In this paper, a model for a two echelon supply chain under WPI and CPI with price dependent demand is developed to analyse the variation of optimal gross profit, cycle time, average inventory levels and total relevant costs of inventory of both the manufacturer and retailer, with and without coordination. Numerical results are presented to demonstrate the application of the model developed. Results show that gross profit of the retailer increases significantly where as that of manufacturer reduces with decrease in WPI and increase in CPI, while overall gross profit of the supply chain increases.

Two echelon partial trade credit financing in a supply chain derived algebraically

Trade credit financing has become a powerful tool to improve sales & profit in an industry. In general, a supplier/retailer frequently offers trade credit to its credit risk downstream member in order to stimulate their respective sales. This trade credit may either be full or partial depending upon the past profile of the downstream member. Partial trade credit may be offered by the supplier/retailer to their credit risk downstream member who must pay a portion of the purchase amount at the time of placing an order and then receives a permissible delay on the rest of the outstanding amount to avoid non-payment risks. The present study investigates the retailer?s inventory problem under partial trade credit financing for two echelon supply chain where the supplier, as well as the retailer, offers partial trade credit to the subsequent downstream member. An algebraic approach has been applied for finding the retailer?s optimal ordering policy under minimizing the annual total relevant cost. Results have been validated with the help of examples followed by comprehensive sensitivity analysis.

Determination of optimal ordering quantity and reduction of bullwhip effect in a multistage supply chain using genetic algorithm

Allocating optimal ordering quantity and mitigation of bullwhip effect is one of the challenging parts in a modern multi echelon supply chain system. Genetic algorithm is used in this research to reduce the bullwhip effect and to determine optimal ordering quantity in a multistage supply chain consisting of six members. Real demand data of a manufacturing company has been used here to conduct the analyses. This research also pinpointed that genetic algorithm can be applied to reduce the cost of total supply chain. To calculate the total cost, five different costs with varying unit holding cost, in inventory cost is used for each of the members of supply chain. This paper also examines the importance of the limit set of the chromosomes of genetic algorithm and concluded that the lower range chromosomes provide a better result by reducing the total supply chain cost more than the range set in higher limit.

Three echelon supply chain inventory model for deteriorating items with limited storage facility and lead-time under inflation

In this study, we have explored a three echelon supply chain inventory model with limited storage facility under inflation. The supplier experiences the problem of lead-time at his end. So even though the retailer has placed the order, he has to wait for the supplier to deliver. The lead-time of the supplier is a probability density function of his managing cost. The more the supplier is ready to spend as managing expenses, smaller will be the lead time and vice versa. However, there is a constraint upon the amount the supplier can afford to spend; hence there involved capital constraint also. For the retailer, we have considered the two situations according to rented and own warehouse used. Due to the life time in RW, the product has no deterioration but in OW, the product has deterioration. The setup has been explored numerically and the sensitivity has also been analysed.

Production-Distribution and Transportation Planning in Flexible Multi-Echelon Suppy Chains

We develop a model for production-distribution and transportation planning in flexible multi echelon supply chains. When solved, the model produces the optimal quantity of each product to be produced at each plant in each period, transported from each plant to each distribution centre (DC) in each period, shipped from each DC to each retailer in each period, transported directly from some plants to some retailers in each period, kept in inventory at each plant and at each DC in each period, and subcontracted at each DC in each period. It also produces the optimal amount of extensions needed at each DC. The model provides two ways for shipping finished goods to retailers, namely: shipments from plants to retailers via the DCs, and direct shipment from plants to retailers. Three sets of numerical examples were given to test and illustrate the model. The first set of numerical examples show that the model works very well and that it produces good results. The second set show that using a single model to optimize all SC key components simultaneously can greatly enhance supply-chain efficiency. The third set of examples involve small-, medium-, and large-scale problems whose numbers of variables and constraints range from 2791 to 59591 and 1121 to 11306 respectively. The results of the solutions to the problems show that our model will produce good results if applied to any real world SC problem of any size – small, medium, or very large. The results also show that the CPLEX optimizer will be useful for solving the model if applied to solve real world large-scale SC problems.

DINLIP: Model for Integrated Decision, Bound and Time Driven Capacitated Multi Echelon Supply Chain Network

One of the important extensions of the classical resource allocation problem is Integrated Resource Allocation and Routing Problem with Bound and Time Window (IRARPBTW). The base variant of IRARPBTW is vehicle routing problem with backhauls and time windows. IRARPBTW problem focuses on finding the optimal allocation and routing for the vehicles with the objective of minimizing the total distance by considering the constraints on time windows and bound on resource by covering delivery service during the linehaul and the collection/pick-up service during the backhaul. We have developed a unified model Decision Support System based on Mixed Integer Linear Programming (DINLIP) to solve VRPBTW and IRARPBTW. Model DINLIP was tested for benchmark datasets of VRPBTW and derived datasets of IRARPBTW and yielded encouraging results.

CLING: heuristic to solve integrated resource allocation and routing problem with time window

One of the important extensions of the classical resource allocation problem is integrated resource allocation and routing problem with time window (IRARPTW). IRARPTW problem focuses on the time window for the service at the demanding node with the consideration of travel time of vehicle for a varying demand-oriented multi echelon supply chain with the consideration of limitation on the number of supply catalyst resource. We have developed a unified heuristic named clustering inherent genetic algorithm (CLING) to solve vehicle routing problem with time windows and IRARPTW. Heuristic CLING was tested for benchmark datasets of VRPTW and derived datasets of IRARPTW and yielded encouraging results.