Graphical determination of optimal performance vector

Kiischstein, Integrated Supply Chain Planning in Chemical Industry, Produktion und Logistik, DOI 10.1007/978-3-658-08433-2 5, SpringerFachmedien Wiesbaden 2015 

To model chemical production processes, time series methods are used. Theoretical and empirical indications are given that this class of models is able to capture the characteristic short-term time-dependency patterns of physical and chemical transformation processes. Simultaneously, the models complexity is kept at an acceptable level. To model the change of production modes of chemical production plants in the long run, Markov chains are used. The application of these models is illustrated by real-world case studies. 

The above mentioned models can be used to support operational distribution decisions under consideration of the available capacities. On the tactical and strategical level such capacities are subjects of interest. To be able to find improved SC configurations, the effects of alterations of the system s capacities have to be measured. These effects are hard to anticipate since stochastic processes often complicate theoretical deductions which also affect the planning at the operational level. Therefore, this work advocates simulation models as an appropriate approach to model complex chemical SCs with multiple stochastic processes and multiple objectives. This modelling approach integrates operational planning models in a stochastic environment to accurately reflect the dynamic effects of alternative SC configurations. 

Adhitya, A. and Srinivasan, R. Dynamic Simulation and Decision Support for Multisite Specialty Chemicals Supply Chain. Industrial Engineering Chemistry Research, 49 9917-9931, 2010. 

and Rasmy, M. Supply chain performance measurement approaches Review and classification. Journal of Organizational Management Studies, pages 1-20, 2012. doi 10.5171/2012.872753. 

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Figure 4.19 Graphical determination of optimal performance vector...

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