A multi-workstation, multi-product model on allocation of investments in prevention and appraisal activities
A firm usually incurs expenses that can actually be avoided, if not at least minimized, by making additional investment. It is important that utmost prudence be exercised in formulating the existing quality cost models so that return on investment can be obviously manifested. Existing quality cost m...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
1995
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_honors/64 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | A firm usually incurs expenses that can actually be avoided, if not at least minimized, by making additional investment. It is important that utmost prudence be exercised in formulating the existing quality cost models so that return on investment can be obviously manifested.
Existing quality cost models failed to consider multi-processing workstations, that is, the different stages in the manufacture of products. In addition, existing models neglect the simultaneous effect on improving process characteristics, those of setup and processing times, aside from reduced defect levels, of different kinds of products in evaluating investments in appraisal and prevention activities. This study is mainly anchored from the study made by Nandakumar, Akella, and Datar [1993] entitled Measuring and Accounting for Cost of Quality.
An extensive review of related literature was undertaken to determine the standing of the problem in relation to previous studies.
The model is primarily divided into three phases: first, the visualization of the system second, derivation of the cost functions and third, the optimal allocation of investments in prevention and appraisal activities. The objective is to determine the optimal difference between the additional cost to provide quality and the increased benefits as the system is improved.
Two important decision variables are optimized here. First is the optimal commit date which will minimize the total delay and tardiness costs. This is evaluated through the formulation of a non-linear programming model. Second is the optimal allocation of investments in prevention and appraisal activities. This is achieved through the formulation of an N-period dynamic programming model.
To fully comprehend the formulation and purpose of the model presented, a validation of the model is made with the use of an illustrative example. |
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