Business performance measurements in asset management with the support of big data technologies

• Autorzy: Campos J. , Sharma P. , Jantunen E. , Baglee D. , Fumagalli L.

Identyfikatory

DOI

10.1515/mspe-2017-0021

Warianty tytułu

PL

Pomiary wydajności biznesowej w zarządzaniu aktywami ze wsparciem technologii big data

• Języki publikacji: EN

Abstrakty

EN

The paper reviews the performance measurement in the domain of interest. Important data in asset management are further, discussed. The importance and the characteristics of today’s ICTs capabilities are also mentioned in the paper. The role of new concepts such as big data and data mining analytical technologies in managing the performance measurements in asset management are discussed in detail. The authors consequently suggest the use of the modified Balanced Scorecard methodology highlighting both quantitative and qualitative aspects, which is crucial for optimal use of the big data approach and technologies.

PL

W artykule przedstawiono pomiar wydajności w dziedzinie zainteresowań. Następnie omawiane są ważne dane dotyczące zarządzania aktywami. W artykule wymieniono również znaczenie i cechy charakterystyczne dla dzisiejszych technologii informacyjno-komunikacyjnych. Szczegółowo omówiono rolę nowych koncepcji, takich jak big data i technologie analityczne dotyczące wyszukiwania danych w zarządzaniu pomiarami wydajności w zarządzaniu aktywami. Autorzy sugerują zastosowanie metodologii zmodyfikowanej Strategicznej Karty Wyników, która podkreśla zarówno aspekty ilościowe, jak i jakościowe, co jest kluczowe dla optymalnego wykorzystania podejścia i technologii big data.

Słowa kluczowe

EN

business performance measurements; asset management; big data technology

PL

pomiary wydajności biznesowej; zarządzanie aktywami; technologie big data

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2017
• Tom: nr 3 (25)
• Strony: 143--149
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Campos J.

• Informatics Linnaeus University, Faculty of Technology Department of Informatics SE-35195 Växjö, SWEDEN

autor

Sharma P.

• Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi 110016 INDIA

autor

Jantunen E.

• VTT Technical Research Centre of Finland Ltd. P.O. Box 1000, FI-02044 VTT, FINLAND

autor

Baglee D.

• Reader in Advanced Maintenance. Faculty of Engineering and Advanced Manufacturing University of Sunderland, St Peters Campus Sunderland SR6 0DD, UNITED KINGDOM

autor

Fumagalli L.

• Department of Management, Economics and Industrial Engineering Politecnico di Milano Piazza Leonardo da Vinci, 32, 20133 Milano, ITALY

Bibliografia

• [1] D. Baglee, M. Knowles and S. Kinnunen, “A Proposed Maintenance Strategy for a Wind Turbine Gearbox Using Condition Monitoring Techniques”, International Journal of Process Management and Benchmarking, vol. 6, no. 3, pp. 386-403, 2016.
• [2] J. Lee, J. Ni, D. Djurdjanovic, H. Qiu and H. Liao, “Intelligent prognostics tools and e-maintenance”, Computers in Industry, vol. 57, no. 6, pp. 476-489, 2006.
• [3] T. Taticchi, F. Tonelli and L. Cagnazzo, “Performance Measurement and Management: A Literature Review and a Research Agenda”, Measuring Business Excellence, vol. 14, no. 1, pp. 4-18, 2010.
• [4] U. Kumar, D. Galar, A. Parida, C. Stenström and L. Berges, ”Maintenance performance metrics: a state of the art review”, Journal of Quality in Maintenance Engineering, vol. 19, no. 3, pp. 233-277, 2013.
• [5] S. Srimai, J. Radford and C. Wright, “Evolutionary paths of performance measurement”, International Journal of Productivity and Performance Management, vol. 60, no. 7, pp. 662-687, 2011.
• [6] M. Gosselin, “An empirical study of performance measurement in manufacturing firms”, International Journal of Productivity and Performance Management, vol. 54, no. 5/6, pp. 419-437, 2005.
• [7] J. Dearden, “The case against ROI control”, Harvard Business Review, vol. 47, no. 3, pp. 124-135, 1969.
• [8] N. Olve, J. Roy and M. Wetter, Performance Drivers: A practical guide to using the balanced scorecard. Chichester: John Wiley and Sons, 1999.
• [9] R.S. Kaplan and D.P. Norton, “The Balanced Scorecard – Measures That Drive Performance”, Harvard Business Review, vol. 70, no. 1 , pp. 71-79, Jan-Feb 1992.
• [10] D.P. Keegan, R.G. Eiler and C.R. Jones, “Are your performance measures obsolete?”, Management Accounting, vol. 70, no. 12, pp. 45-50, 1989.
• [11] L. Fitzgerald, R. Johnston, T.J. Brignall, R. Silvestro and C. Voss, Performance Measurement in Service Businesses. London: CIMA, 1991.
• [12] G. Azzone, C. Masella and U. Bertelè, “Design of performance measures for time-based companies”, International Journal of Operations & Production Management, vol. 11, no. 3, pp. 77-85, 1991.
• [13] K.F. Cross and R.L. Lynch, “The SMART way to sustain and define success”, National Productivity Review, vol. 8, no. 1, pp. 23-33, 1998.
• [14] U.S. Bititci, A.S. Carrie and L. McDevitt, “Integrated performance measurement systems: a development guide”, International Journal of Operation and Production Management, vol. 17, no. 5, pp. 522-534, 1997.
• [15] A. Neely, C. Adams and M. Kennerley, The Performance Prism: The Scorecard for Measuring and Managing Business Success. London: Prentice Hall Financial Times, 2002.
• [16] I. Nonaka, Harvard Business Review on Knowledge Management, Harvard Business School Press, 1991.
• [17] L. Pintelon, N. Du Preez and F. Van Puyvelde, “Information technology: opportunities for maintenance management”, Journal of Quality in Maintenance Engineering, vol. 5, no. 1, pp. 9-24, 1999.
• [18] A. Neely, J. Mills, K. Platts, H. Richards, M. Gregory, M. Bourne and M. Kennerley, “Performance measurement system design: developing and testing a process based approach”, International Journal of Operations & Production Management, vol. 20, no. 10, pp. 1119- 1145, 2000.
• [19] L. Pintelon and F. Van Puyvdelde, “Maintenance Performance Reporting Systems: Some Experiences”, Journal of Quality in Maintenance Engineering, vol. 3, no. 1, pp. 4-15, 1997.
• [20] R.A. Dwight, “Searching for Real Maintenance Performance Measures”, Journal of Quality in Maintenance Engineering, vol. 5, no. 3, pp. 258-75, 1999.
• [21] R.A. Dwight, “Concepts for measuring maintenance performance”, in New Developments in Maintenance: An International View, H.H. Martin, Ed. Utrecht: Moret Ernst & Young Management Consultants, 1995.
• [22] M. Kennerley and A. Neely, “Measuring performance in a changing business environment”, International Journal of Operations and Production Management, vol. 23, no. 2, pp. 213-229, 2003.
• [23] J.H. Lingle and W.A. Schiemann, “From balanced scorecard to strategy gauges: Is measurement worth it?”, Management Review, vol. 85, no. 3, pp. 56-62, 1996.
• [24] A. Neely, “The performance measurement revolution: why now and where next”, International Journal of Operation & Production Management, vol. 19, no. 2, pp. 205-228, 1999.
• [25] U. Al-Turki and S. Duffuaa, “Performance measures for academic departments”, International Journal of Educational Management, vol. 17, no. 7, pp. 330-338, 2003.
• [26] G. Lawrie and I. Cobbold, “Development of the 3rd Generation Balanced Scorecard. Evolution of the Balanced Scorecard into an effective strategic performance management tool”, 2GC Active Management, Maidenhead, Berkshire, UK, 2002.
• [27] R.S. Kaplan and D.P. Norton, The Balanced Scorecard: Translating Strategy into Actions, Boston (MA): Harvard Business School Press, 1996.
• [28] R.S. Kaplan and D.P. Norton, “Putting the Balanced Scorecard to Work”, Harvard Business Review, vol. 71, no. 5, pp. 134-147, Sept-Oct. 1993.
• [29] R. Newing, “Wake Up to the Balanced Scorecard!”, Management Accounting, vol. 73, no. 3, 1995.
• [30] J.P. Kotter, “Leading Change: Why transformation efforts fail?”, Harvard Business Review, vol. 73, no. 2, pp. 59-67, Mar-Apr. 1995.
• [31] A. Parida and U. Kumar, “Maintenance performance measurement (MPM): issues and challenges”, Journal of Quality in Maintenance Engineering, vol. 12, no. 3, pp. 239-251, 2006.
• [32] A. Parida, “Asset Performance Assessment”, in Asset Management. The State of the Art in Europe from a Life Cycle Perspective, T. Van der Lei, P. Herder and Y. Wijnia, Eds. Houten: Springer Netherlands, 2013, pp. 101-113.
• [33] A. Parida and G. Chattopadhyay, “Development of Multi-Criteria Hierarchical framework for Maintenance Performance Measurement (MPM)”, Journal of Quality in Maintenance Engineering, vol. 13, no. 3, pp. 241-258, 2007.
• [34] J. Fan and Y. Fan, “High dimensional classification using features annealed independence rules”, Annals of Statistics, vol. 36, no. 6, pp. 2605-2637, 2008.
• [35] J. Fan, F. Han and H. Liu, “Challenges of Big Data analysis”, National Science Review, vol. 1, no. 2, pp. 293- 314, 2014.
• [36] X. Wu, X. Zhu, G-Q. Wu and W. Ding, “Data Mining with Big Data”, IEEE transactions on knowledge and data engineering, vol. 26, no. 1, pp. 97-107, 2014.
• [37] H.V. Jagadish, J. Gehrke, A. Labrinidis, Y. Papakonstantinou, J.M. Patel, R. Ramakrishnan and C. Shahabi, “Big Data And Its Technical Challenges”, Communications Of The ACM, vol. 57, no. 7, pp. 86-94, 2014.
• [38] P. Folan and J. Browne, “A Review of Performance Measurement: Towards Performance Management”, Computers in Industry, vol. 56, no. 7, pp. 663-680, 2005.
• [39] N. Venkatraman and J.C. Henderson, ”Business platforms for the 21st Century”, in Mastering Information Management, D.A. Marchand, T.H. Davenport and T. Dickson, Eds. Harlow (FT): Prentince Hall, 2000.
• [40] L. Ward and J. Peppard, Strategic planning for information systems. Chichester: Wiley, 2002.
• [41] G. Wang, C. Liu and Y. Cui, “Clustering diagnosis of rolling element bearing fault based on integrated Autoregressive/Autoregressive Conditional Heteroscedasticity model”, Journal of Sound and Vibration, vol. 331, no. 19, pp. 4379-4387, 2012.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)