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Proof obligations as a support tool for efficient process management in the field of production planning and scheduling

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dc.title Proof obligations as a support tool for efficient process management in the field of production planning and scheduling en
dc.contributor.author Hrušecká, Denisa
dc.relation.ispartof Serbian Journal of Management
dc.identifier.issn 1452-4864 OCLC, Ulrich, Sherpa/RoMEO, JCR
dc.date.issued 2016
utb.relation.volume 11
utb.relation.issue 2
dc.citation.spage 235
dc.citation.epage 243
dc.type article
dc.language.iso en
dc.publisher University of Belgrade
dc.identifier.doi 10.5937/sjm11-11135
dc.subject Process management en
dc.subject Production planning and scheduling en
dc.subject Production process en
dc.subject Production system en
dc.subject Proof obligation en
dc.description.abstract Production planning and scheduling is one of the most important business processes that significantly influence the performance of manufacturing companies. There are many information systems supporting production planning and scheduling and some of them are based on very sophisticated planning algorithms. Despite this fact, many companies still face serious problems even while using professional software tools for production planning and scheduling. Obviously, a lot of other changes in form of process innovations are required. This paper deals with the problem of process management in the field of production planning and scheduling. Our study explains reasons for low performance of advanced technologies and provides solution in form of system model of key factors affecting the efficiency of planning software. Research part is based on the study conducted within Czech manufacturing companies in form of questionnaire-based investigation combined with interviews. Proposed solution is extended to the abstract mathematical model based on proof obligations which prove or disprove the correctness of intended algorithms. Our study provides basic example of such an abstract model and describes its functionality and influence to proper production planning and scheduling. It will be processed to the form of complex expert system based on Event B method in the future. en
utb.faculty Faculty of Management and Economics
dc.identifier.uri http://hdl.handle.net/10563/1006819
utb.identifier.obdid 43875114
utb.identifier.scopus 2-s2.0-84994885013
utb.identifier.wok 000443473000007
utb.source j-scopus
dc.date.accessioned 2017-02-28T15:11:29Z
dc.date.available 2017-02-28T15:11:29Z
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Hrušecká, Denisa
utb.fulltext.affiliation Denisa Hrušecká* Tomas Bata University in Zlin, Faculty of Management and Economics, Department of Industrial Engineering and Information Systems, Czech Republic Corresponding author: hrusecka@fame.utb.cz
utb.fulltext.dates Received 27 May 2016; accepted 23 August 2016
utb.fulltext.references Abrial, J.R. (2010). Modeling in Event-B: System and Software Engineering. Cambridge, MA, USA: Cambridge University Press. Belás, J., Bilan, Y., Ključnikov, A., Vincúrová, Z., & Macháček, J. (2015). Actual problems of business risk in SME segment. Case study from Slovakia. International Journal of Entrepreneurial Knowledge, 3 (1), 46–56. Goldratt, E.M. (2000). Necessary but not sufficient. Great Barrington, MA, USA: North River Press. Chen, C., & Hasan, N. (2008). How to succeed with supply chain planning. Supply Chain Management Review, 12, 30-36. Jackson, P. (1998). Introduction to expert systems. Boston, MA, USA: Addison Wesley. Jonsson, P., & Ivert, L.K. (2015). Improving performance with sophisticated master production scheduling. International Journal of Production Economics, 168, 118-130. Jonsson, P., Ivert, L.K., & Rudberg, M. (2007). Applying advanced planning systems for supply chain planning: Three case studies. International Journal of Physical Distribution and Logistics Management, 37, 816-834. Lihong, Q., & Shengping, L. (2012). An improved genetic algorithm for integrated process planning and scheduling. International Journal of Advanced Manufacturing Technologies, 58, 727-740. Métayer, C., Abrial, J.R., & Voisin, L. (2005). Event-B language. Rodin deliverable 3.2. EU Project IST-511599-RODIN. Mu, Ch. (2013). On information flow control in Event-B and refinement. In Proceedings of the International Symposium on Theoretical Aspects of Software Engineering. Birmingham, UK. 225-232. Neely, A., Bourne, M., Mills, J., Platts, K., & Richards, H. (2002). Strategy and Performance: Getting the Measure of Your Business. Cambridge, MA, USA: Cambridge University Press. Noonan, J., & Wallace, M. (2006). Improved Optimisation through advanced relationship planning. Supply Chain Management: An International Journal, 11, 483-490. Nyhuis, P., & Wiendahl, H.P. (2009). Fundamentals of production logistics: Theory, tools and applications. Berlin, Germany: Springer Publishing. Šulová, D. (2009). Methods of planning and scheduling in enterprise information systems and their application in a production process management. Zlín, Czech Republic: Tomas Bata University in Zlín. Vidová, H. (2009). Logistics Controlling. Bratislava, Slovakia: STU. Weiss, S.M. (1988). Empirical analysis and refinement of expert system knowledge bases in selected topics in medical artificial intelligence. New York, NY, USA: Springer Publishing.
utb.fulltext.sponsorship This paper is one of contribution to the RVO project “Modelling of effective production and administration processes parameters in industrial companies based on concept Industry 4.0”, realized by Department of Industrial Engineering and Information Systems, Faculty of Management and Economics, Tomas Bata University in Zlin.
utb.wos.affiliation [Hrusecka, Denisa] Tomas Bata Univ Zlin, Fac Management & Econ, Dept Ind Engn & Informat Syst, Zlin, Czech Republic
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