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            Chapter Business Process Linguistic Modeling: Theory and Practice Part I: BPLM Strategy Creator

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            Author(s)
            Jozef, Stašák
            Jaroslav, Kultan
            Peter, Schmidt
            Abu, Urinbasarovich
            Language
            English
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            Abstract
            The business activities provided within any firm or company should be checked and controlled continuously, while two principal approaches should be applied: (a) qualitative monitoring, (b) quantitative evaluations, while KPI indicators play a role of principle importance within business quantitative evaluation in order to make adequate decisions. However, adequate applications form KPI creation and further processing seem to be very significant and important. We have designed a conceptual model of application denoted as BPLM Strategy Creator in form of expert system (ES) operating based on principles closely related to business process linguistic modeling approach, where linguistic sets and PBPL Equation play a role of principle importance. Our contribution contains such application description from qualitative, quantitative and design point of view. The ES qualitative description contains references to appropriate math relations and algorithms postulated within a subsequent section. Both sections are accompanied by the case study, which indicates how the math relations and algorithms might be applied within BPLM Strategy Creator functionality. However, those sections are accompanied by ES structure and functionality description as well, which represent the BPLM Strategy Creator mean or facility.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/162535
            Keywords
            key performance, indicators, business, process, linguistic, modeling; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general
            DOI
            10.5772/intechopen.95096
            Publisher
            InTechOpen
            Publication date and place
            2020
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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