Evaluating Variance Control, Order Review/Release & Dispatching: A Regression Analysis
Document Type
Article
Publication Date
1994
Publication Title
International Journal of Production Research
Publisher
Taylor & Francis
Volume
32
Issue
5
First page number:
1045
Last page number:
1061
Abstract
The challenge of improving performance in the job shop can be met in a number of ways. We can rely on variance control to reduce variances created either in the planning system (through uneven or fluctuating loads) or on the shop floor (through varying processing times for released batches). We can rely on Order Review/Release (ORR). Finally, we can turn to one of the various dispatching rules that are available. Each option has been previously examined. However, this study examines all of these options simultaneously within the context of a simulated simple job shop. Based on a regression analysis of the data generated, several interesting results are uncovered. First, the results show that the presence of variance control at both the planning and shop floor levels can greatly enhance the effectiveness of ORR. This result partially helps resolve the controversy surrounding ORR. Second, the results show that Shortest Processing Time (SPT), while extremely effective in an uncontrolled environment, reacts adversely to the presence of variance control. Finally, if used effectively, variance control can greatly reduce the need for complex dispatching rule.
Keywords
Regression analysis; Variance control
Disciplines
Business | Operations and Supply Chain Management
Language
English
Permissions
Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.
Repository Citation
Melnyk, S. A.,
Tan, K.,
Denzler, D. R.,
Fredendall, L.
(1994).
Evaluating Variance Control, Order Review/Release & Dispatching: A Regression Analysis.
International Journal of Production Research, 32(5),
1045-1061.
Taylor & Francis.