Demand and capacity sharing decisions and protocols in a collaborative network of enterprises
Introduction
In modern manufacturing enterprises, the random nature of customer behaviors and dynamic changes of demand patterns are inevitable attributes, and enterprise collaboration is an attractive strategy. A set of enterprises forms a beneficial collaborative network of enterprises (CN), and the impacts of market fluctuation and dynamic operational behavior can be minimized by effective enterprise collaboration and coordination [3], [10]. In the CN, an enterprise is generally a self-operative organization, and a set of enterprises forms a CN when mutual benefits are expected. Therefore, local and global decision making processes need to be designed and controlled by well-defined coordination protocols [12], [14], [15]. In this research, a set of collaborating enterprises, having their own customer orders and limited capacities, is considered. When a customer order from an enterprise cannot be fulfilled by the local capacity, the demand will be shared with other collaborating enterprises which have excess capacities. As a result, the possibly unfulfilled demand can be delivered by the enterprise and the remaining capacity of collaborating enterprises can be utilized, such that mutual benefits can be achieved. Demand and Capacity Sharing Protocols (DCSP) in the CN have been designed to coordinate demand and capacity sharing and allocation decisions.
The rest of the paper is organized as follows. The background of the proposed research is presented in Section 2. The general order acceptance decision model and decision making processes in the CN are presented in Section 3. The details of demand and capacity sharing protocols are presented in Section 4. Finally, numerical examples and analyses are presented and discussed in Section 5.
Section snippets
Background
Manufacturing and supply systems can no longer be viewed in isolation; they must be managed in the context of the total business and the associated key linkages of the business: back through the supplier chain, and forward into the distribution and customer chain. When such business collaboration is formed, formal modeling and performance analysis from economic perspectives need to be studied. For example, three dimensional performance analysis approach for collaborative network organizations
Collaborative order acceptance decision model
Given a set of collaborative enterprises E = {e1, ..., ei} where each enterprise receives its own customer orders, a customer order o is composed of order quantity q and due date td information; o = {q, td}. Suppose kth customer order ok = {qk, tkd} is received. ok needs to be evaluated whether qk can be delivered within tkd, based on the enterprise capacity constraints. If a capacity constraint is violated, ok cannot be accepted by the enterprise. Suppose that ok can be fulfilled by a set of
Demand and capacity sharing protocols (DCSP)
In this research, each enterprise plays the role of either a demand sharing enterprise or a capacity sharing enterprise dynamically since each enterprise is a member of the CN. The demand sharing enterprise requires additional capacity to fulfill a customer order when the customer order cannot be fulfilled by its own capacity before its due date. On the other hand, the capacity sharing enterprise holds excess capacities to be shared with collaborative enterprises. The framework of demand and
Numerical examples and analyses
In this section, numerical examples of enterprise collaboration are given to illustrate and analyze the performance of the proposed demand and capacity sharing decisions and protocols. Three types of enterprise collaboration models have been identified; (1) No collaboration model (M1), (2) Complete collaboration model (M2), and (3) Partial collaboration model (M3).
- 1.
No collaboration (M1): Each enterprise deals with their own customer orders, but there is no demand and capacity sharing with other
Conclusion
To overcome the arbitrary nature of customer orders and dynamic changes of demand patterns, enterprise collaboration is an attractive strategy. Enterprise collaboration needs to be carefully coordinated to achieve mutual benefits. In this research, demand and capacity sharing decisions and protocols in the CN have been designed to determine if and when efficient demand and capacity sharing and allocation decisions can be justified. The objective of demand and capacity sharing decisions and
Acknowledgments
This study was supported by the PRISM Center at Purdue University and by Kimberly Clark's project on distributed decision network and protocols. The authors also wish to thank several colleagues who have given valuable comments to improve this study.
Glossary
Set and element
- E
- Set of collaborative enterprises
- Z
- Subset of collaborative enterprises
- ei
- Demand sharing enterprise (∊ E)
- ej
- Capacity sharing enterprise (∊ Z)
Demand
- ok
- kth customer order at any given enterprise
- qk
- Order quantity of kth customer order
- tkd
- Due date of kth customer order
- xj
- Random demand quantity at ej
- f(xj)
- Probability density function of xj
- F(xj)
- Cumulative density function of xj
- Di
- Demand fulfilled by ei
- DT
- Total demand
Capacity
- CA(t)
- Available capacity at time t
- CSk(t)
- Available capacity for okat time t
- mi(ok)
- Maximum available
Sang Won Yoon is a Research Scientist in the Department of Systems Science & Industrial Engineering at the State University of New York at Binghamton. He received his Ph. D. in Industrial Engineering from Purdue University in August 2009. His research interests are in the areas of enterprise collaboration, production and operations management, information system integration, decision support systems, and healthcare services. He has worked on various industry projects including total quality
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Cited by (0)
Sang Won Yoon is a Research Scientist in the Department of Systems Science & Industrial Engineering at the State University of New York at Binghamton. He received his Ph. D. in Industrial Engineering from Purdue University in August 2009. His research interests are in the areas of enterprise collaboration, production and operations management, information system integration, decision support systems, and healthcare services. He has worked on various industry projects including total quality management, enterprise resource planning, enterprise information management, transportation safety and security, and healthcare systems.
Shimon Y. Nof is a Professor of Industrial Engineering at Purdue University, has held visiting positions at MIT and universities in Chile, EU, Hong Kong, Israel, Japan, and Mexico. Director of the NSF-industry-supported PRISM Center for Production, Robotics and Integration Software for Manufacturing and Management; he is a Fellow of IIE, Secretary General of IFPR, and current Chair of IFAC CC—Manufacturing and Logistics Systems. He has published over 250 articles on production engineering and information/robotics engineering and management, and is the author/editor of nine books in these areas. In 1999 he was elected to the Purdue Book of Great Teachers, and in 2002 he was awarded the Engelberger Medal for Robotics Education. Professor Nof has also had over eight years of experience in industry positions.