Production decision support system for batch processes
Aggregate planning acts as a blue print for all operational planning activities. It also determines organisational profitability decisions at a certain point. Despite the substantial amount of research that has been done in determining methods to improve aggregate planning approaches, the industry i...
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my-utm-ep.790722018-09-30T08:17:15Z Production decision support system for batch processes 2017 Chee, Lee Pheng TP Chemical technology Aggregate planning acts as a blue print for all operational planning activities. It also determines organisational profitability decisions at a certain point. Despite the substantial amount of research that has been done in determining methods to improve aggregate planning approaches, the industry is still at lost when it comes to working on the tactical planning aspect, especially in aggregate production. Existing methods are either too sophisticated or too focused on solution algorithms, which make them barely discernible except to mathematical modeling experts. As a result, the gap between the academic and industrial world continues to widen. Therefore, this research work aims to present a comprehensive and generalised model that will formulate a realistic batch production environment using an interactive Production Decision Support System (PDSS). This system consists of an aggregate planning framework that combines a simulation model and a Pinch Analysis graphical approach to improve the effectiveness and efficiency of the decision-making process. The ultimate goal is to allow all operational opportunities to be captured at first sight and in turn, maximise organisational profit. Using this system, a full understanding of the batch multi-product process situation could be achieved without having to first group and then disaggregate it, and thus help in the evaluation of alternative plans and assist in establishing new strategies. The simplicity and practicality of this new PDSS is demonstrated by this research work where industry aggregate planning was investigated via implementation of the system at two different emulsion companies in Johor. The first plant has achieved additional 4.5% compared to original plant capacity as well as avoided the severe stock-out scenario of 11,811 tonne. For the second plant, the calculated effective plant capacity was found to be 34% higher than the claimed plant capacity. This has avoided the unnecessary investment in capacity expansion. As a result, this system is now being regarded as a crucial operational management tool in capacity planning, during budgeting exercise, in the monthly rolling forecast review and coordination, as well as in site strategy formulation. 2017 Thesis http://eprints.utm.my/id/eprint/79072/ phd doctoral Universiti Teknologi Malaysia, Faculty of Chemical & Energy Engineering Faculty of Chemical & Energy Engineering |
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Universiti Teknologi Malaysia |
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UTM Institutional Repository |
| topic |
TP Chemical technology |
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TP Chemical technology Chee, Lee Pheng Production decision support system for batch processes |
| description |
Aggregate planning acts as a blue print for all operational planning activities. It also determines organisational profitability decisions at a certain point. Despite the substantial amount of research that has been done in determining methods to improve aggregate planning approaches, the industry is still at lost when it comes to working on the tactical planning aspect, especially in aggregate production. Existing methods are either too sophisticated or too focused on solution algorithms, which make them barely discernible except to mathematical modeling experts. As a result, the gap between the academic and industrial world continues to widen. Therefore, this research work aims to present a comprehensive and generalised model that will formulate a realistic batch production environment using an interactive Production Decision Support System (PDSS). This system consists of an aggregate planning framework that combines a simulation model and a Pinch Analysis graphical approach to improve the effectiveness and efficiency of the decision-making process. The ultimate goal is to allow all operational opportunities to be captured at first sight and in turn, maximise organisational profit. Using this system, a full understanding of the batch multi-product process situation could be achieved without having to first group and then disaggregate it, and thus help in the evaluation of alternative plans and assist in establishing new strategies. The simplicity and practicality of this new PDSS is demonstrated by this research work where industry aggregate planning was investigated via implementation of the system at two different emulsion companies in Johor. The first plant has achieved additional 4.5% compared to original plant capacity as well as avoided the severe stock-out scenario of 11,811 tonne. For the second plant, the calculated effective plant capacity was found to be 34% higher than the claimed plant capacity. This has avoided the unnecessary investment in capacity expansion. As a result, this system is now being regarded as a crucial operational management tool in capacity planning, during budgeting exercise, in the monthly rolling forecast review and coordination, as well as in site strategy formulation. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Chee, Lee Pheng |
| author_facet |
Chee, Lee Pheng |
| author_sort |
Chee, Lee Pheng |
| title |
Production decision support system for batch processes |
| title_short |
Production decision support system for batch processes |
| title_full |
Production decision support system for batch processes |
| title_fullStr |
Production decision support system for batch processes |
| title_full_unstemmed |
Production decision support system for batch processes |
| title_sort |
production decision support system for batch processes |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Chemical & Energy Engineering |
| granting_department |
Faculty of Chemical & Energy Engineering |
| publishDate |
2017 |
| _version_ |
1747818140167831552 |