@article{scholars9304,
            year = {2017},
       publisher = {Italian Association of Chemical Engineering - AIDIC},
         journal = {Chemical Engineering Transactions},
           pages = {1267--1272},
          volume = {56},
            note = {cited By 1},
             doi = {10.3303/CET1756212},
           title = {Production decision support system: Real-life emulsion plant case study},
        abstract = {As the world becomes increasingly globalised, chemical manufacturers have to make timely decisions and right strategies in order to stay competitive in marketplace. Numerous formal techniques have been suggested for aggregate planning but industry finds these models complicated and lack practicality. The objective of this paper is to develop an industry adoptable Production Decision Support System (PDSS) for multi-product and multiprocessor batch industries. The construction of this system is initiated with the development of a general batch production planning framework which will be eventually translated into an algorithm tool. This tool will be able to support the investigation of required sales forecasts against plant capacity by providing a complete overview with different scenarios on multi-product batch processes, thus improving effectiveness and efficiency of operational decision-making, as well as assisting in strategy establishment. The production contributory factors that are being considered for plant capacity include cycle time, batch size, plant availability, number of production lines and product mixes. The practicality and beneficial use of this PDSS system is then demonstrated through a real-life emulsion plant where its production capacity has been successfully stretched giving additional volume which has translated into additional revenue of RM 10 M to the company. Copyright {\^A}{\copyright} 2017, AIDIC Servizi S.r.l..},
        keywords = {Artificial intelligence; Batch data processing; Decision making; Emulsification; Production control; Sales, Aggregate planning; Chemical manufacturers; Contributory factors; Effectiveness and efficiencies; Operational decision making; Plant availability; Production capacity; Production decisions, Decision support systems},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019439395&doi=10.3303\%2fCET1756212&partnerID=40&md5=a96f5ceb4ed1f4ebd6737531b2826790},
            isbn = {9788895608471},
            issn = {22839216},
          author = {Chee, L. P. and Alwi, S. R. W. and Lim, J. S. and Siwayanan, P.}
}