A quick efficient neural network based business decision making tool in batch reactive distillation
Over the last decade the western world has seen marked changes in the way the manufacturing companies are operating. Many world-class bulk chemical manufacturers have significantly shrunk their businesses and or set up the businesses in the third world. This is due to increased global competition an...
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| Main Authors: | , , |
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| Format: | Article |
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Computer Aided Chemical Engineering
2003
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| Online Access: | http://eprints.um.edu.my/7068/ http://www.scopus.com/inward/record.url?eid=2-s2.0-77956730980&partnerID=40&md5=b07ae0459a965de671f3dd57cc81ab6f |
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| Summary: | Over the last decade the western world has seen marked changes in the way the manufacturing companies are operating. Many world-class bulk chemical manufacturers have significantly shrunk their businesses and or set up the businesses in the third world. This is due to increased global competition and operating cost (mainly labour cost) and strict environmental legislation. Internet has facilitated customers to choose the same products from many different companies at competitive prices in minutes rather than in days. Many UK companies no longer have the luxury to keep the same customers year after year1. The business planners have to react quickly in such a frequently changing market environment to keep the business running on profit. In this work, a quick and efficient neural network based Business Decision Making (BDM) tool is developed that can be used at all levels of the operations in manufacturing companies. This tool is especially useful at the planning level where ultimate business decision making takes place. It is demonstrated in an environment of manufacturing products using batch reactive distillation. The tool can forecast profitability, productivity, batch time, energy cost and can give optimal operating policy in few CPU seconds for changing product specifications, raw material and energy costs and products prices. |
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