Stratejik Maliyet Yönetimi ve İş Zekâsı

Yıl 2017, Cilt: 8 Sayı: 28, 7 - 20, 01.07.2017

Ednan Ayvaz

https://doi.org/10.5824/1309-1581.2017.3.001.x

Öz

İş ortamındaki hızlı değişimin bir sonucu olarak stratejik maliyet yönetiminin rolünde önemli değişiklikler olmuştur. Yeni üretim ve bilgi teknolojilerinin değiştirilmesi, müşteri taleplerine odaklanılması, küresel pazarların değişmesi ve diğer sosyo ekonomik değişiklikler işletmeleri endüstride rekabetçi avantajlarını korumak için stratejik bilgi sistemlerini geliştirmeye zorlamaktadır. Kurumsal kaynak planlama ERP sistemleri gibi kurumsal bilgi sistemleri de, veri depolama hacmini ve analize dayalı problem çözme yeteneğini artırmak suretiyle stratejik maliyet yönetimine önemli katkılar sağlamıştır. Stratejik maliyet yönetimi, doğası ve kapsamındaki hızlı bir değişimin sonucu olarak, tanımlayıcı, tahminci ve normatif analiz tekniklerini kullanmaya başlamıştır. Stratejik maliyet yönetimi iç ve dış veri kaynaklarından elde ettiği büyük verilerle, işletmelerin şimdiki durumunu, gelecekteki durumunu ortaya koymanın yanında, karşılaşılan problemlerle ilgili en iyi çözümünün ne olduğu sorularına cevap bulmak için veri analizi tekniklerini kullanmalıdır. Bu çalışmanın amacı, iş zekâsı bağlamında stratejik maliyet yönetimine dayalı bir veri analizi çerçevesi tasarlamaktır. Stratejik maliyet yönetimi veri analizi, stratejik karar alıcılara sağlıklı bilgi sağlamak için, kapsamlı iş analizlerini iş zekâsı yoluyla yapma becerisini sağlar. Bu çalışma ile kurumsal bilgi sisteminde iş zekâsıyla veri analizinin, stratejik maliyet yönetimi üzerindeki etkisi tartışılarak, stratejik maliyet yönetimi araçlarının stratejik maliyet yönetimi veri analizi ile bütünleştirilmesi yoluyla literatüre katkı yapmak hedeflenmiştir.

Anahtar Kelimeler

Stratejik Maliyet Yönetimi, Kurumsal Kaynak Planlaması, İş Zekâsı, Büyük Data

Strategic Cost Management and Business Intelligence

Öz

As a result of the rapid change in the business environment, the role of strategic cost management has changed significantly. Changing new production and information technologies, focusing on customer demands, changing global markets and operating other socio-economic changes forces the industry to develop strategic information systems in order to maintain its competitive advantages. Enterprise information systems, such as enterprise resource planning ERP systems, also made significant contributions to strategic cost management by increasing problem-solving capability based on data storage capacity and analytics. Strategic cost management has begun to use descriptive, predictive and normative analysis techniques as a result of a rapid change in nature and scope. Strategic cost management should use data analysis techniques to answer questions about what is the best solution to the problems encountered, as well as revealing the current state of the business, the future situation, of the great benefits gained from internal and external data sources. The purpose of this study is to design a data analysis framework based on strategic cost management in the context of business intelligence. Strategic cost management data analysis provides the ability to make comprehensive business analyzes through business intelligence to provide healthy information to strategic decision makers. This study aims to contribute to the literature by integrating strategic cost management tools with strategic cost management data analysis by discussing the impact of business intelligence data analysis on strategic cost management in the enterprise information system.

Anahtar Kelimeler

Strategic Cost Management, Enterprise Resource Planning, Business Intelligence, Big Data

Kaynakça

• Alain, Abran ve Buglione, Luigi (2003). A Multidimensional Performance Model for Consolidating Balanced Scorecards, Advances in Engineering Software, Vol. 34, Issue 6, June 2003, s. 339-349.
• Kaplan, Robert S. ve Atkinson, Anthony A. (1998). Advanced Management Accounting, 3th, Prentice Hall, International Edition, USA.
• Appelbaum, Deniz, Kogan, Alexander, Vasarhelyi, Yan, Miklos Zhaokai, (2017). Impact of business analytics and enterprise systems on managerial accounting, International Journal of Accounting Information Systems (25), s.29-44.
• Blocher, E., Chen, K. and Lin, T. (1999). Cost Management: A Strategic Emphasis. The McGraw-Hill Companies, Inc., New York.
• Chae, B. K., Yang, C., Olson, D., Sheu, C., (2014). The impact of advanced analytics and data accuracy on operational performance: a contingent resource based theory (RBT) perspective. Decis. Support. Systems, 59, s.119–126.
• Chae, B., Olson, D. L., (2013). Business analytics for supply chain: a dynamic-capabilities framework. Int. J. Inf. Technol. Decis. Making 12 (01), 9–26.
• Chaudhuri, S., Dayal, U., Narasayya, V., (2011). An overview of business intelligence technology. Commun. ACM 54 (8), s.88–98.
• Chugh, R., Grandhi, S., (2013). Why business intelligence? SigniŞcance of business intelligence tools and integrating BI governance with corporate governance. Int. J. E- Entrep. Innov. (IJEEI) 4 (2), s.1–14.
• Cokins, G., (2013). Top 7 trends in management accounting. Strategic Financ. 95 (6), 21–30.
• Cooper, R. and Slagmulder, R. (1998): Strategic Cost Management: What is Strategic Cost Management? Management Accounting, Jan. Vol. 79 No. 7, s.14-16.
• Cooper, R.ve Slagmulder, R. (2004). Achieving Full-Cycle Cost Management, MIT Sloan Management Review, Vol.46, Issue 1, s.44-50.
• Davenport, T. H., (2006). Competing on analytics. Harv. Bus. Rev. 84 (1), s.98.
• Davenport, T.H., Harris, J.G., (2007). Competing on Analytics: The New Science of Winning. Harvard Business Press.
• Dechow, N., Mouritsen, J., (2005). Enterprise resource planning systems, management control and the quest for integration. Acc. Organ. Soc. 30 (7), s.691–733.
• Dilla, W., Janvrin, D.J., Raschke, R., (2010). Interactive data visualization: new directions for accounting information systems research. J. Inf. Syst. 24 (2), s.1–37.
• Dirks, Paula van Veen- ve Martin Wijn, (2002). “Strategic Control: Meshing Critical Success Factors with the Balanced Scorecard”, Long Range Planning, Vol. 35, Issue 4, August, s. 407-427.
• Edwards, J. B., (2001). ERP, balanced scorecard, and IT: how do they Şt together? J. Corp. Account. Financ. 12 (5), s.3–12.
• Forslund, H., Jonsson, P., (2007). The impact of forecast information quality on supply chain performance. Int. J. Oper. Prod. Manag. 27 (1), s.90–107.
• Gerald K. DeBusk, Robert M. Brown ve Larry N. Killough, “Components and Relative Weights in Utilization of Dashboard Measurement Systems Like The Balanced Scorecard”, The British Accounting Review, Vol. 35, Issue 3, September 2003, s. 215-231.
• Gorla, N., Somers, T.M., Wong, B., (2010). Organizational impact of system quality, information quality, and service quality. J. Strateg. Inf. Syst. 19 (3), s.207–228.
• Granlund, M., Malmi, T., (2002). Moderate impact of ERPS on management accounting: a lag or permanent outcome? Manag. Account. Res. 13 (3), s.299–321.
• Hansen, R. and Mowen, M. (2000): Cost Management: Accounting and Control. 3rd ed., South- Western College Publishing, Ohio.
• Haug, A., Stentoft Arlbjİrn, J., (2011). Barriers to master data quality. J. Enterp. Inf. Manag. 24 (3), s.288–303.
• Hilton, R., Maher, M., Selto, F. and Sainty, B. (2001): Cost Management: Strategies for Business Decisions. 1st ed., The McGraw-Hill Ryerson, New York.
• Hogan, K. M., (2000). A multi-criteria model for predicting corporate bankruptcy using the analytical hierarchy process. Appl. Manag. Sci. 10, s.85.
• Holsapple, C., Lee-Post, A., Pakath, R., (2014). A uniŞed foundation for business analytics. Decis. Support. Syst. 64, s.30–141.
• Holton, C., (2009). Identifying disgruntled employee systems fraud risk through text mining: a simple solution for a multi-billion dollar problem. Decis. Support. Syst. 46 (4), s.853–864.
• IBM, (2013). Descriptive, predictive, prescriptive: transforming asset and facilities management with analytics. In: Thought Leadership White Paper, (October 2013).
• Jourdan, Z., Rainer, R., and Marshall, T. E. (2008) “Business Intelligence: An Analysis of the Literature”, Information Systems Management, 25(2), s.121-131.
• Kallunki, J., Laitinen, E.K., Silvola, H., (2011). Impact of enterprise resource planning systems on management control systems and Şrm performance. Int. J. Account. Inf. Syst. 12 (1), s.20–39.
• Kaplan, R.S., Norton, D.P., 1992. The balanced scorecard: measures that drive performance. Harv. Bus. Rev. 70 (1), 71–79.
• Kaplan, R.S., Norton, D.P., 1996. Using the Balanced Scorecard as a Strategic Management System .
• Kaplan, R.S., Norton, D.P., 2001. Transforming the balanced scorecard from performance measurement to strategic management: part I. Account. Horiz. 15 (1), 87–104.
• Kaplan, Robert S. ve David P. Norton, (1992). “The Balanced Scorecard – Measures That Drive Performance”, Harvard Business Review, January-February, s. 71-79.
• Kaplan, Robert S. ve David P. Norton, (2001). “Transforming the Balanced Scorecard from Performence Measurement to Strategic Management: Part: 1”, Accounting Horizons, Vol. 15, Issue 1, March, s. 93-94.
• Kaplan, Robert S., 2009. Conceptual foundations of the balanced scorecard. Handbooks of management accounting research. 3. pp. 1253–1269.
• Kwon, O., Lee, N., Shin, B., (2014). Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manag. 34 (3), s.387–394.
• Laney, D., (2001). 3D data management: controlling data volume, velocity and variety. In: META Group Research Note. 6. s.70.
• McNair, C., (2000). Defining and Shaping the Future of Cost Management. Journal of Cost Management, September/October, s.28-32
• Nielsen, E. H., Nielsen, E.H., Jacobsen, A., Pedersen, L.B., (2014). Management accounting and business analytics. Dan. J. Manag. Bus. 78 (3-4), 31–44.
• Oracle, 2015. Big data analytics with oracle advanced analytics. In: Oracle White Paper/July 2015.
• Peters, M. D., Wieder, B., Sutton, S.G., WakeŞeld, J., (2016). Business intelligence systems use in performance measurement capabilities: implications for enhanced competitive advantage. Int. J. Account. Inf. Syst. 21, s.1–17.
• Ranjan, Jayanthi, (2005). Busıness Intellıgence: Concepts, Components, Technıques And Benefıts, Journal of Theoretical and Applied Information Technology, Vol 9, No 1, s.60-70.
• Redman, T. C., 1998. The impact of poor data quality on the typical enterprise. Commun. ACM 41 (2), s.79–82.
• Redman, T., 1996. Improve data quality for competitive advantage. Qual. Control Appl. Stat. 41, s.49– 52.
• Redman, Thomas C., (2013). Data Driven: ProŞting From Your Most Important Business Asset. Harvard Business Press (ISBN 978-1-4221-6364-1).
• Rud, O. P., (2009). Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy. John Wiley & Sons.
• Scapens, R.W., Jazayeri, M., 2003. ERP systems and management accounting change: opportunities or impacts? A research note. Eur. Account. Rev. 12 (1), 201–233.
• Van der Aalst, W., Weijters, T., Maruster, L., (2004). Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16 (9), 1128–1142.
• Warren Jr., J.D., Moffitt, K.C., Byrnes, P., (2015). How big data will change accounting. Account. Horiz. 29 (2), s.397–407.
• Watson, H.J., Wixom, B.H., (2007). The current state of business intelligence. Computer 40 (9), s.96–99.
• Wu, X., Zhu, X., Wu, G., Ding, W., (2014). Data mining with big data. IEEE Trans. Knowl. Data Eng. 26 (1), s.97–107.
• Xu, H., Horn Nord, J., Brown, N., Daryl Nord, G., (2002). Data quality issues in implementing an ERP. Ind. Manag. Data Syst. 102 (1), s.47–58.
• Yeoh, W., Koronis, A., (2010). Critical success factors for business intelligence systems. J. Comput. Inf. Syst. 50 (3), s.23–32.
• Zhang, J., Yang, X., Appelbaum, D., (2015). Toward efective big data analysis in continuous auditing. Account. Horiz. 29 (2), s.469–476.