Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/44937
DC FieldValue
dc.titleAn analysis of Material Requirements Planning (MRP) benefits using Alternating Conditional Expectation (ACE)
dc.contributor.authorSum, C.-C.
dc.contributor.authorYang, K.-K.
dc.contributor.authorAng, J.S.K.
dc.contributor.authorQuek, S.-A.
dc.date.accessioned2013-10-10T04:38:09Z
dc.date.available2013-10-10T04:38:09Z
dc.date.issued1995
dc.identifier.citationSum, C.-C.,Yang, K.-K.,Ang, J.S.K.,Quek, S.-A. (1995). An analysis of Material Requirements Planning (MRP) benefits using Alternating Conditional Expectation (ACE). Journal of Operations Management 13 (1) : 35-58. ScholarBank@NUS Repository.
dc.identifier.issn02726963
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/44937
dc.description.abstractResponding to regional and international competition, many manufacturing companies have adopted MRP systems to improve their manufacturing operations. Of primary interest to managers and users of MRP is the benefits that can be derived from using the MRP technology. While the literature abounds with MRP implementation studies, there is a dearth of research that examines the determinants of specific MRP benefits. Knowledge of the determinants would enable MRP managers and users to concentrate on key areas to achieve benefits that match their company goals. This paper identifies the organisational, implementational, and technological variables that affect specific MRP benefits as reported by Singapore manufacturing companies in the most extensive MRP survey ever conducted in Singapore. Using Alternating Conditional Expectation (ACE), an advanced statistical modeling technique that increases the model fit by approximating the optimal transformations for the dependent and independent variables, the regression models developed reveal more accurate relationships compared to those in previous MRP studies. Our findings offer several novel and valuable insights into the MRP benefit-determinant relationship. The major finding is that determinant variables such as data accuracy, people support, degree of integration, and company size affect benefits in a nonlinear fashion. Data accuracy was found to be critical in affecting operational efficiency, customer service, and interdepartmental coordination benefits. Another finding suggests that when people support and data accuracy degenerate to a critical level, users might still derive increased benefits by resorting to secondary sources to accomplish their work. Users should strive for a high degree of integration to achieve full operational efficiency and coordination benefits. Partial integration does not appear to provide significant improvements. We also found that increasing company size has a positive, followed by a negative impact on operational efficiency. Lastly, our findings suggest that the pattern of technical complaints can be an indicator of system usage and interdepartmental coordination. Our findings have important implications for managers and users of MRP. © 1995.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentDECISION SCIENCES
dc.description.sourcetitleJournal of Operations Management
dc.description.volume13
dc.description.issue1
dc.description.page35-58
dc.description.codenJOTME
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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