Nash Game Model for Optimizing Market Strategies, Configuration of Platform Products in a Vendor Managed Inventory (VMI) Supply Chain for a Product Family

This paper discusses how a manufacturer and its retailers interact with each other to optimize their product marketing strategies, platform product configuration and inventory policies in a VMI (Vendor Managed Inventory) supply chain. The manufacturer procures raw materials from multiple suppliers to produce a family of products sold to multiple retailers. Multiple types of products are substitutable each other to end customers. The manufacturer makes its decision on raw materials’ procurement, platform product configuration, product replenishment policies to retailers with VMI, price discount rate, and advertising investment to maximize its profit. Retailers in turn consider the optimal local advertising and retail price to maximize their profits. This problem is modeled as a dual simultaneous non-cooperative game (as a Nash game) model with two sub-games. One is between the retailers serving in competing retail markets and the other is between the manufacturer and the retailers. This paper combines analytical, iterative and GA (genetic algorithm) methods to develop a game solution algorithm to find the Nash equilibrium. A numerical example is conducted to test the proposed model and algorithm, and gain managerial implications.supply chain management;nash game model;vendor managed inventory

SUPPLY CHAIN RISK MANAGEMENT IN AUTOMOTIVE INDUSTRY

The automotive industry is one of the world\u27s most important economic sectors in terms of revenue and employment. The automotive supply chain is complex owing to the large number of parts in an automobile, the multiple layers of suppliers to supply those parts, and the coordination of materials, information, and financial flows across the supply chain. Many uncertainties and different natural and man-made disasters have repeatedly stricken and disrupted automotive manufacturers and their supply chains. Managing supply chain risk in a complex environment is always a challenge for the automotive industry. This research first provides a comprehensive literature review of the existing research work on the supply chain risk identification and management, considering, but not limited to, the characteristics of the automotive supply chain, since the literature focusing on automotive supply chain risk management (ASCRM) is limited. The review provides a summary and a classification for the underlying supply chain risk resources in the automotive industry; and state-of-the-art research in the area is discussed, with an emphasis on the quantitative methods and mathematical models currently used. The future research topics in ASCRM are identified. Then two mathematical models are developed in this research, concentrating on supply chain risk management in the automotive industry. The first model is for optimizing manufacturer cooperation in supply chains. OEMs often invest a large amount of money in supplier development to improve suppliers’ capabilities and performance. Allocating the investment optimally among multiple suppliers to minimize risks while maintaining an acceptable level of return becomes a critical issue for manufacturers. This research develops a new non-linear investment return mathematical model for supplier development, which is more applicable in reality. The solutions of this new model can assist supply chain management in deciding investment at different levels in addition to making “yes or no” decisions. The new model is validated and verified using numerical examples. The second model is the optimal contract for new product development with the risk consideration in the automotive industry. More specifically, we investigated how to decide the supplier’s capacity and the manufacturer’s order in the supply contract in order to reduce the risks and maximize their profits when the demand of the new product is highly uncertain. Based on the newsvendor model and Stackelberg game theory, a single period two-stage supply chain model for a product development contract, consisting of a supplier and a manufacturer, is developed. A practical back induction algorithm is conducted to get subgame perfect optimal solutions for the contract model. Extensive model analyses are accomplished for various situations with theoretical results leading to conditions of solution optimality. The model is then applied to a uniform distribution for uncertain demands. Based on a real automotive supply chain case, the numerical experiments and sensitivity analyses are conducted to study the behavior and performance of the proposed model, from which some interesting managerial insights were provided. The proposed solutions provide an effective tool for making the supplier-manufacturer contracts when manufacturers face high uncertain demand. We believe that the quantitative models and solutions studied in this research have great potentials to be applied in automotive and other industries in developing the efficient supply chains involving advanced and emerging technologies

Supply Contracts with Financial Hedging

We study the performance of a stylized supply chain where two firms, a retailer and a producer, compete in a Stackelberg game. The retailer purchases a single product from the producer and afterwards sells it in the retail market at a stochastic clearance price. The retailer, however, is budget-constrained and is therefore limited in the number of units that he may purchase from the producer. We also assume that the retailer's profit depends in part on the realized path or terminal value of some observable stochastic process. We interpret this process as a financial process such as a foreign exchange rate or interest rate. More generally the process may be interpreted as any relevant economic index. We consider a variation (the flexible contract) of the traditional wholesale price contract that is offered by the producer to the retailer. Under this flexible contract, at t = 0 the producer offers a menu of wholesale prices to the retailer, one for each realization of the financial process up to a future time . The retailer then commits to purchasing at time a variable number of units, with the specific quantity depending on the realization of the process up to time. Because of the retailer's budget constraint, the supply chain might be more profitable if the retailer was able to shift some of the budget from states where the constraint is not binding to states where it is binding. We therefore consider a variation of the flexible contract where we assume that the retailer is able to trade dynamically between 0 and in the financial market. We refer to this variation as the flexible contract with hedging. We compare the decentralized competitive solution for the two contracts with the solutions obtained by a central planner. We also compare the supply chain's performance across the two contracts. We find, for example, that the producer always prefers the flexible contract with hedging to the flexible contract without hedging. Depending on model parameters, however, the retailer may or may not prefer the flexible contract with hedging. Finally, we study the problem of choosing the optimal timing, of the contract, and formulate this as an optimal stopping problem.Operations Management Working Papers Serie

Competitive Bidding in Supply Chains

This thesis is primarily concerned with the competition between suppliers for a buyer’s procurement business with consideration of subcontracting, commitment and capacity reservation. Under the circumstance where suppliers face diseconomies of scale, it may be cost effective for a buyer to split an order across different suppliers. Even when the buyer chooses only one supplier, the winning supplier may subcontract part of the work to the others subsequently. Motivated by these observations, Chapter 2 studies a supplier bidding game where a buyer requests quotes from two competing suppliers. We consider two procurement scenarios: (1) Order Splitting where each supplier submits a function bid which specifies different payments for different quantities, and the buyer may split the order; (2) Single-Sourcing Commitment where the buyer commits to purchasing from only one supplier before suppliers submit their bids, and the winning supplier may subsequently subcontract with the losing one. The second and third papers investigate the competitive behaviour of suppliers with capacity reservation. To hedge against financial risks, the suppliers often require a buyer to reserve capacity in advance by paying an upfront fee. In Chapter 3, we consider a discrete version of this problem where competing suppliers each choose a reservation price and an execution price for blocks of capacity, and the buyer needs to decide which blocks to reserve. Chapter 4 studies a continuous version of the problem where we allow general cost functions. The suppliers compete by offering the price functions (for reservation and execution) and the buyer decides how much to reserve from each supplier. This thesis sheds light on how suppliers compete with each other by considering a variety of factors. We believe an in-depth look at the competitive behaviour of suppliers will deepen our understanding of a buyer’s procurement process, and hence helps a buyer make a better sourcing decision

Competitive Bidding in Supply Chains

This thesis is primarily concerned with the competition between suppliers for a buyer’s procurement business with consideration of subcontracting, commitment and capacity reservation. Under the circumstance where suppliers face diseconomies of scale, it may be cost effective for a buyer to split an order across different suppliers. Even when the buyer chooses only one supplier, the winning supplier may subcontract part of the work to the others subsequently. Motivated by these observations, Chapter 2 studies a supplier bidding game where a buyer requests quotes from two competing suppliers. We consider two procurement scenarios: (1) Order Splitting where each supplier submits a function bid which specifies different payments for different quantities, and the buyer may split the order; (2) Single-Sourcing Commitment where the buyer commits to purchasing from only one supplier before suppliers submit their bids, and the winning supplier may subsequently subcontract with the losing one. The second and third papers investigate the competitive behaviour of suppliers with capacity reservation. To hedge against financial risks, the suppliers often require a buyer to reserve capacity in advance by paying an upfront fee. In Chapter 3, we consider a discrete version of this problem where competing suppliers each choose a reservation price and an execution price for blocks of capacity, and the buyer needs to decide which blocks to reserve. Chapter 4 studies a continuous version of the problem where we allow general cost functions. The suppliers compete by offering the price functions (for reservation and execution) and the buyer decides how much to reserve from each supplier. This thesis sheds light on how suppliers compete with each other by considering a variety of factors. We believe an in-depth look at the competitive behaviour of suppliers will deepen our understanding of a buyer’s procurement process, and hence helps a buyer make a better sourcing decision

Structuring postponement strategies in the supply chain by analytical modeling

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Nash Game Model for Optimizing Market Strategies, Configuration of Platform Products in a Vendor Managed Inventory (VMI) Supply Chain for a Product Family

This paper discusses how a manufacturer and its retailers interact with each other to optimize their product marketing strategies, platform product configuration and inventory policies in a VMI (Vendor Managed Inventory) supply chain. The manufacturer procures raw materials from multiple suppliers to produce a family of products sold to multiple retailers. Multiple types of products are substitutable each other to end customers. The manufacturer makes its decision on raw materials’ procurement, platform product configuration, product replenishment policies to retailers with VMI, price discount rate, and advertising investment to maximize its profit. Retailers in turn consider the optimal local advertising and retail price to maximize their profits. This problem is modeled as a dual simultaneous non-cooperative game (as a Nash game) model with two sub-games. One is between the retailers serving in competing retail markets and the other is between the manufacturer and the retailers. This paper combines analytical, iterative and GA (genetic algorithm) methods to develop a game solution algorithm to find the Nash equilibrium. A numerical example is conducted to test the proposed model and algorithm, and gain managerial implications

Nash game model for optimizing market strategies, configuration of platform products in a Vendor Managed Inventory (VMI) supply chain for a product family

This paper discusses how a manufacturer and its retailers interact with each other to optimize their product marketing strategies, platform product configuration and inventory policies in a VMI (Vendor Managed Inventory) supply chain. The manufacturer procures raw materials from multiple suppliers to produce a family of products sold to multiple retailers. Multiple types of products are substitutable each other to end customers. The manufacturer makes its decision on raw materials' procurement, platform product configuration, product replenishment policies to retailers with VMI, price discount rate, and advertising investment to maximize its profit. Retailers in turn consider the optimal local advertising investments and retail prices to maximize their profits. This problem is modeled as a dual simultaneous non-cooperative game (as a dual Nash game) model with two sub-games. One is between the retailers serving in competing retail markets and the other is between the manufacturer and the retailers. This paper combines analytical, iterative and GA (genetic algorithm) methods to develop a game solution algorithm to find the Nash equilibrium. A numerical example is conducted to test the proposed model and algorithm, and gain managerial implications. © 2010 Elsevier B.V. All rights reserved.postprin

Improving the coordination in the humanitarian supply chain: exploring the role of options contract

The uncertainty associated with the location, severity and timing of disaster makes it difficult for the humanitarian organization (HO) to predict demand for the aid material and thereby making the relief material procurement even more challenging. This research explores whether options contract can be used as a mechanism to aid the HO in making procurement of relief material less challenging by addressing two main issues: inventory risk for buyers and over-production risk for suppliers. Furthermore, a contracting mechanism is designed to achieve coordination between the HO and aid material suppliers in the humanitarian supply chain through optimal pricing. The options contract is modelled as a stylized version of the newsvendor problem that allows the HO to adjust their order quantity after placing the initial order at the beginning of the planning horizon. This flexibility helps to mitigate the risk of both overstocking and understocking for the HO as well as the risk of overproduction for the supplier. Our results indicate that the optimal values for decision parameters are not “point estimates” but a range of prices, which can facilitate negotiation between the two parties for appropriate selection of contract parameters under an options contract. The results imply that options contract can aid in the decentralized approach of fixing the prices between the HO and the supplier, which in turn would help in achieving systemic coordination