Decision support system for continuous production

Abstract

PurposeThe purpose of this paper is to develop energy optimizer (ENEO) – a model‐based decision support system (DSS) for an existing European chemical plant with a multi‐stage continuous production process. The system comprises two modules – energy cost minimization and joined energy cost minimization and output maximization. Following the description of the researched production, the paper presents a gist of the underlying formulations. Then, it tests the DSS on real data instances with a focus on its configuration, practical implications and implementation challenges.Design/methodology/approachThe design of the planning tool is consistent with that of the model‐based DSS and based on the existing information systems. The defined research problems are explored with the use of quantitative methods – the operations research methodology.FindingsThe findings show that ENEO reflects the essence of the researched production process and can provide benefits in practical business operations.Research limitations/implicationsBoth the proposed system configuration and the formulated models lay a foundation to further research within the described industrial setting.Practical implicationsThe system can be utilized in daily operations to provide substantial cost savings, improved capacity utilization and reactivity.Originality/valueThis paper contributes to research by bridging the gap between theory and practice. On the one hand, it describes an unexplored problem and its subsequent solution embodied in the DSS. On the other hand, it emphasizes the importance of applying the operations research methodology to the real‐world issues. Therefore, this work is valuable to both academics and practitioners.