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Soft Order Commitment in Supply Chains: Role of Penalties and Rationing Rules
Author(s) -
Tian Zhili,
Tang Sammi Yu,
Kouvelis Panos Panos
Publication year - 2019
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/deci.12327
Subject(s) - supply chain , production (economics) , order (exchange) , business , schedule , pareto principle , rationing , economic order quantity , industrial organization , microeconomics , operations management , economics , marketing , finance , health care , economic growth , management
In some industries (e.g., semiconductors, electronics, and agribusiness), buyers place noncommitted orders (called soft orders) to manufacturers in an effort to guide the manufacturer's production decisions. After the buyers know their own demand, they place purchase orders (called firm orders). These orders are often placed very close to the production start time or even after the production start time so that it is not possible for the manufacturer to revise its production schedule. The discrepancies between the soft order and the firm order often result in high excess inventories at the manufacturer. Without any commitment related to the soft orders by the buyers and the production by the manufacturer, the soft orders are often not effective. We hence propose a penalty contract, through which the buyers pay a penalty for canceling some portion of their soft orders. This contract can perfectly coordinate the decentralized supply chain of one manufacturer and multiple buyers and can lead to Pareto improvement in profits relative to the wholesale price contract for all firms in the supply chain. Our results indicate that it is more likely for the contract to achieve coordination and Pareto improvement when the manufacturer allocates the supply in proportion to the firm orders than when it allocates the supply according to the soft orders. We also illustrate the contract by using the data from a leading manufacturer in the industry.