A coordination framework for distributed supply chains

Supply chain is a network of cooperating organizations that are involved through upstream and downstream linkages. Such a complex system cannot be modeled simply by mathematical equations without simplification of the problem. Recently, Multi-Agent System (MAS) is proposed as a modeling technique to represent supply chain networks. In fact, many application problems in MASs that are concerned with finding a consistent combination of agent actions can be formalized as Distributed Constraints Satisfaction Problem (DCSP). The main purpose of this paper is to propose a novel coordination framework by adopting the DCSP philosophy for distributed supply chains, which are modeled by MASs, subjected to uncertainties. Simulation results indicate that the proposed mechanism outperforms traditional stochastic modeling in solving supply chain dynamics in terms of total cost and fill rate of the system. © 2004 IEEE.published_or_final_versio

Supply Chain Coordination with Quantity Discount for Seasonal Demand

Coordination between manufacturers and multiple buyers represents an important problem in supply chain management. In this paper, we develop a supply chain coordination mechanism in a system with a dominant manufacturer that delivers seasonal products to a group of buyers. These buyers have common replenishment times and receive delivery through a common delivery channel. A twice-stage ordering and production system is introduced in which the first order is placed at some time in advance of the selling season and a second order is placed closer to the selling period. This reorder strategy allows the buyer to collect additional information about seasonal demand, thereby reducing demand forecast error and simultaneously smoothing out production time. This twice-stage model results in savings for both manufacturer and the buyers. Strategies for developing sustainable cooperation between manufacturers and buyers are discussed in light of the conclusions of this model

An integrated decision making model for dynamic pricing and inventory control of substitutable products based on demand learning

Purpose: This paper focuses on the PC industry, analyzing a PC supply chain system composed of onelarge retailer and two manufacturers. The retailer informs the suppliers of the total order quantity, namelyQ, based on demand forecast ahead of the selling season. The suppliers manufacture products accordingto the predicted quantity. When the actual demand has been observed, the retailer conducts demandlearning and determines the actual order quantity. Under the assumption that the products of the twosuppliers are one-way substitutable, an integrated decision-making model for dynamic pricing andinventory control is established.Design/methodology/approach: This paper proposes a mathematical model where a large domestichousehold appliance retailer decides the optimal original ordering quantity before the selling season and theoptimal actual ordering quantity, and two manufacturers decide the optimal wholesale price.Findings:By applying this model to a large domestic household appliance retail terminal, the authors canconclude that the model is quite feasible and effective. Meanwhile, the results of simulation analysis showthat when the product prices of two manufacturers both reduce gradually, one manufacturer will often waittill the other manufacturer reduces their price to a crucial inflection point, then their profit will show aqualitative change instead of a real-time profit-price change.Practical implications: This model can be adopted to a supply chain system composed of one largeretailer and two manufacturers, helping manufacturers better make a pricing and inventory controldecision.Originality/value: Previous research focuses on the ordering quantity directly be decided. Limited workhas considered the actual ordering quantity based on demand learning. However, this paper considers boththe optimal original ordering quantity before the selling season and the optimal actual ordering quantityfrom the perspective of the retailerPeer Reviewe