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IDSS used as a framework for collaborative projects in conglomerate enterprises

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Języki publikacji
EN
Abstrakty
EN
Purpose: of this paper is to summarize the application study of the general framework of intelligent decision support system (IDSS) to collaborative projects in conglomerate enterprises. Today's market competition requires project managers to make correct decisions fast. In some situations, even with the knowledge of how to find right information and which decision making methods to apply, people do not have enough time to make right decisions at the right time. In this paper, the frame work of an IDSS system to support real-time collaboration and enable seamless data exchange is presented. The IDSS system is able to support the information flow between internal and external sources. Data are shared, prepared, and streamlined to appropriate analytical tools. A case study of manufacturing projects in a specific European shipbuilding conglomerate is used to illustrate the process and usefullness of IDSS in combination with Enterprise Resource Planning (ERP) systems. Design/methodology/approach: A model of internal and external information flows is proposed for enterprise information management. The important roles of facilitation and organization that the IDSS plays are demonstrated. In the case study examples of manufacturing projects analysis are given with the known methods, including Analytical Hierarchy Process and Bayes Rule. Findings: It was found that IDSS is an essential component to enhance decision makings with complex information flows in large conglomerates. Main conclusions is to show what we will achieve through IDSS systems, to show how perform analysis in logical way. Research limitations/implications: The functionality of the developed framework is limited by the willingness of management style and culture changes in companies, as well as the level of interoperability between commercial software components. Practical implications: Project engineers and managers need to adapt to the new IT-based working environment. Intensive use of software tools may become the major challenge for those who cannot absorb information based on the new format of information presentation. Originality/value: The proposed new information management model and the framework of IDSS system enable ease of data sharing and processing by internal and external stakeholders in decision makings. The collaborative decision making consists of different parts: the management of information flow, the preparation of data for decision making, and actual decision making supported by several methods.
Rocznik
Strony
89--92
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
autor
autor
Bibliografia
  • [1] C.W. Holsapple, M.P. Sena, ERP plans and decision-support benefits, Decision Support Systems 38 (2005) 575-590.
  • [2] E. Shevtshenko, R. Küttner, Intelligent system for engineering and production planning for collaborative SME-s, Proceedings of "Worldwide Congress of Materials and Manufacturing Engineering and Technology" COMMENT 2005, Gliwice-Wisla. Poland, 2005.
  • [3] C.S. Albright, W.L. Winston, C. Zappe, Data Analysis & Decision making with Microsoft Excel, Third Edition, USA, 2003.
  • [4] M. Muehlen, Workflow-based process controlling, Logos Verlag, Berlin, 2002.
  • [5] N. Bolloju, M. Khalifa, E. Turban, Integrating knowledge management into enterprise environments for the next generation decision support, Decision Support Systems 33 (2002) 163-176.
  • [6] V. Gecevska, F. Cus, F. Lombardi, V. Dukovski, M. Kuzinovski, Intelligent approach for optimal modelling of manufacturing systems, Journal of Achievements in Materials and Manufacturing Engineering 14 (2006) 97-103.
  • [7] M.J. Liberatore, R.L. Nydick. Decision technology: modeling, software, and applications, Wiley, 2003.
  • [8] Z. Sterjovski, M. Pitrun, D. Nolan, D. Dunne, J. Norrish, Artificial neural networks for predicting diffusible hydrogen content and cracking susceptibility in rutile flyx-cored arc welds, Journal of Materials Processing Technology 184/1-3 (2007) 420-427.
  • [9] O. Martin, M. Lopez, F. Martin, Artificial neural networks for quality control by ultrasonic testing in resistance spot welding, Journal of Materials Processing Technology 183/2-3 (2007) 226-233.
  • [10] V. Singh, V. Tathavadkar, S. Mohan ,K.S. Raju, Predicting the performance of submerged arc furnance with varied raw material combinations using artificial neural network, Journal of Materials Processing Technology 183/1 (2007) 111-116.
  • [11] F.V. Jensen, An Introduction to Bayesian Networks, London, 2000.
  • [12] A.F. Guisseppi, N.D. Jatinger, N.D. Manuel, T.Mora, Decision-making Support Systems: foundations, applications and challeges, London, 2006.
  • [13] M. Musztyfaga, B. Skołud, Advisory system assisting selection of project structures and project team, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 551-554.
  • [14] M. Dudek- Burlikowska, D. Szewieczek, Quality estimation of sale process with usage of quality methods in chosen company, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 531-534.
  • [15] M. Dudek-Burlikowska, Analytical model of technological process correctness and its usage in industrial company, Journal of Achievements in Materials and Manufacturing Engineering 15 (2006) 107-113.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS3-0017-0025
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