Controlling Production Variances in Complex Business Processes
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Products can consist of many sub-assemblies and small disturbances in the process can lead to larger negative effects downstream. Such variances in production are a challenge from a quality control and operational risk management perspective but also it distorts the assurance processes from an auditing perspective. To control production effectively waste needs to be taken into account in normative models, but this is complicated by cumulative effects. We developed an analytical normative model based on the bill of material, that derives the rejection rates from the underlying processes without direct measurement. The model enables improved analysis and prediction. If the rejection rate is not taken into account the function of the bill of material as a reference model deteriorates and therefore output measures become more opaque and harder to verify. As a consequence it is extremely difficult or even impossible to assess efficiency and effectiveness of operations. Secondly it is impossible to judge whether net salable assets represent the correct amount and finally it is impossible to assert whether the operations do comply to company standards and applicable laws.
The research in this paper was supported by the SATIN research project, funded by NWO.
- 3.Christiaanse, R., Griffioen, P., Hulstijn, J.: Adaptive normative modelling: a case study in the public-transport domain. In: Janssen, M., Mäntymäki, M., Hidders, J., Klievink, B., Lamersdorf, W., van Loenen, B., Zuiderwijk, A. (eds.) I3E 2015. LNCS, vol. 9373, pp. 423–434. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25013-7_34 CrossRefGoogle Scholar
- 4.Christiaanse, R., Griffioen, P., Hulstijn, J.: Reliability of electronic evidence: an application for model-based auditing. In: Proceedings of 15th International Conference on Artificial Intelligence and Law, ICAIL 2015, pp. 43–52. ACM, New York (2015)Google Scholar
- 5.COSO: Internal control - integrated framework. Report, Committee of Sponsoring Organizations of the Treadway Commission (1992)Google Scholar
- 6.COSO: Enterprise risk management - integrated framework. Report, Committee of Sponsoring Organizations of the Treadway Commission (2004)Google Scholar
- 7.COSO: Guidance on monitoring internal control systems. Report, Committee of Sponsoring Organizations of the Treadway Commission, USA (2009)Google Scholar
- 14.Merchant, K.A.: Modern Management Control Systems, Text and Cases. Prentice Hall, Upper Saddle River (1998)Google Scholar
- 15.Merchant, K.A.: The control function of management. Sloan Manag. Rev. 23(Summer), 43–55 (1982)Google Scholar
- 16.Moffitt, K.C., Vasarhelyi, M.A.: Accounting information systems in an age of big data. J. Inf. Syst. 27(2), 1–19 (2013)Google Scholar
- 17.Rachuri, S.: Smart manufacturing systems design and analysis. Report, National Institute of Standards and Technology (NIST) (2014)Google Scholar
- 18.Simons, R.: Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal. Harvard Business School Press, Boston (1995)Google Scholar
- 19.Starreveld, R.W., de Mare, H.B., Joëls, E.J.: Bestuurlijke informatieverzorging, volume deel 1: Algemene grondslagen, 2nd edn. Samson Uitgeverij, Aplhen aan den Rijn/Brussel (1988). (in Dutch)Google Scholar