Evaluating Enterprise Risk in a Complex Environment

ABSTRACT

This paper examines the relationship between operational risk management and knowledge learning process, with an emphasis on establishing the importance of statistical and mathematical approach on organizational capability to forecast, mitigate and control uncertain and vulnerable situations. Knowledge accumulation reduces critical situations unpredictability and improves organizational capability to face uncertain and potentially harmful events. We retain mathematical and statistical knowledge is organizational key factor in risk measuring and management process. Statistical creativity contributes to make quicker the innovation process of organization improves exploration capacity to forecast critical events and increases problem solving capacity, adaptation ability and learning process of organization. We show some important features of statistical approach. First, it makes clear strategic importance of risk culture within every level of organization; quantitative analysis support the emergence of latent troubles and make evident vulnerability of organization. Second, innovative tools allow to improve risk management and organizational capability to measure total risk exposition and to define a more adequate forecasting and corrective strategy. Finally, it’s not so easy to distinguish between measurable risk and unmeasurable uncertainty, it depends on quantity and quality of available knowledge. Difficulty predictable extreme events can bring out crisis and vulnerable situations. Every innovative approach which increases knowledge accumulation and improves forecasting process should be considered.

This paper examines the relationship between operational risk management and knowledge learning process, with an emphasis on establishing the importance of statistical and mathematical approach on organizational capability to forecast, mitigate and control uncertain and vulnerable situations. Knowledge accumulation reduces critical situations unpredictability and improves organizational capability to face uncertain and potentially harmful events. We retain mathematical and statistical knowledge is organizational key factor in risk measuring and management process. Statistical creativity contributes to make quicker the innovation process of organization improves exploration capacity to forecast critical events and increases problem solving capacity, adaptation ability and learning process of organization. We show some important features of statistical approach. First, it makes clear strategic importance of risk culture within every level of organization; quantitative analysis support the emergence of latent troubles and make evident vulnerability of organization. Second, innovative tools allow to improve risk management and organizational capability to measure total risk exposition and to define a more adequate forecasting and corrective strategy. Finally, it’s not so easy to distinguish between measurable risk and unmeasurable uncertainty, it depends on quantity and quality of available knowledge. Difficulty predictable extreme events can bring out crisis and vulnerable situations. Every innovative approach which increases knowledge accumulation and improves forecasting process should be considered.

Cite this paper

nullI. Noni, L. Orsi and L. Pilotti, "Evaluating Enterprise Risk in a Complex Environment,"*Journal of Service Science and Management*, Vol. 3 No. 3, 2010, pp. 352-362. doi: 10.4236/jssm.2010.33041.

nullI. Noni, L. Orsi and L. Pilotti, "Evaluating Enterprise Risk in a Complex Environment,"

References

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[16] P. Davidson, “Money and the Real World,” The Economic Journal, Vol. 82, No. 325, 1972, pp. 101-115.

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[22] D. Smith, “Business (not) as Usual: Crisis Management, Service Recovery and the Vulnerability of Organizations,” Journal of Services Marketing, Vol. 19, No. 5, 2005, pp. 309-320.

[23] P. M. Allen and M. Strathern, “Evolution, Emergence and Learning in Complex systems,” Emergence, Vol. 5, No. 4, 2003, pp. 8-33.

[24] D. Smith, “Beyond Contingency Planning: Towards a Model of Crisis Management,” Industrial Crisis Quarterly, Vol. 4, No. 4, 1990, pp. 263-275.

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[37] M. J. Machina and D. Schmeidler, “A More Robust Definition of Subjective Probability,” Econometrica, Vol. 60, No. 4, 1992, pp. 745-780.

[38] J. Neslehová, P. Embrechts and V. Chavez-Demoulin, “Infinite-Mean Models and the LDA for Operational Risk,” Journal of Operational Risk, Vol. 1, No. 1, 2006, pp. 3-25.

[39] P. Embrechts and G. Samorodnitsky, “Ruin Problem and How Fast Stochastic Processes Mix,” The Annals of Applied Probability, Vol. 13, No. 1, 2003, pp. 1-36.

[40] S. Iacus and D. L. Torre, “Approximating Distribution Functions by Iterated Function Systems,” Journal of Applied Mathematics and Decision Sciences, Vol. 2005, No. 1, 2005, pp. 33-46.

[41] M. Moscadelli, “The Modelling of Operational Risk: Ex- perience with the Analysis of the Data Collected by the Basel Committee,” Temi di discussione, No. 517, 2004.

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[44] A. J. McNeil, R. Frey and P. Embrechts, “Quantitative Risk Management: Concepts, Techniques, and Tools,” Princeton University Press, Princeton, 2005.

[1] D. Foray, “Economics of Knowledge,” MIT Press, 2004.

[2] S. Vicari, “Conoscenza e impresa,” Sinergie rivista di studi e ricerche, Vol.76, 2008, pp. 43-66.

[3] D. J. Teece, “Managing Intellectual Capital,” Oxford Uni- versity Press, Oxford, 2000.

[4] E. Altman, “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy,” Journal of Finance, Vol. 23, No. 4, 1968, pp. 569-609.

[5] L. Pilotti, “Le Strategie dell’Impresa,” Carocci, Roma, 2005.

[6] R. M. Grant, “Toward a Knowledge-Based Theory of the Firm,” Strategic Management Journal, Vol. 17, No. Spe- cial Issue, 1996, pp. 109-122.

[7] J. A. Schumpeter, “Business Cycles,” McGraw-Hill Book Company, New York, 1939.

[8] A. Pagani, “Il nuovo imprenditore,” Franco Angeli, Mila- no, 1967.

[9] M. Haller, “New Dimension of Risk: Consequences Management,” The Geneva Papers on Risk and Insurance, Vol. 3, 1978, pp. 3-15.

[10] G. Dickinson, “Enterprise Risk Management: Its Origin and Conceptual Foundation,” The Geneva Papers on Risk and Insurance, Vol. 26, No. 3, 2001.

[11] E. Rullani, “Economia del rischio e seconda modernità,” In S. Maso, Ed., Il rischio e l’anima dell’Occidente, Cafoscarina, Venezia, 2005, pp. 183-202.

[12] G. M. Mantovani, “Rischio e valore dell’impresa. L’ap- proccio contingent claim della finanza aziendale,” Egea, Milano, 1998.

[13] G. M. Golinetti, L. Proietti and G. Vagnani, “L’azione di governo tra competitività e consonanza,” In G. M. Golinelli, Ed., Verso la scientificazione dell’azione di governo. L’approccio sistemico al governo dell’impresa, Cedam, Padova, 2008.

[14] L. Proietti, “Rischio e conoscenza nel governo dell’impre- sa,” Sinergie rivista di studi e ricerche, Vol. 76, 2008, pp. 191-215.

[15] BIS, “International Convergence of Capital Measurement and Capital Standards,” Basel Committee on Banking Su- pervision, 2004.

[16] P. Davidson, “Money and the Real World,” The Economic Journal, Vol. 82, No. 325, 1972, pp. 101-115.

[17] P. Streeten, “The Cheerful Pessimist: Gunnar Myrdal the Dissenter (1898-1987),” World Development, Vol. 26, No. 3, 1998, pp. 539-550.

[18] K. J. Arrow, “Alternative Approaches to the Theory of Choice in Risk-Taking Situations,” Econometria, Vol. 19, 1951, pp. 404-437.

[19] H. M. Markowitz, “Foundation of Portfolio Theory,” Jour- nal of Finance, Vol. 46, No. 2, 1991, pp. 469-477.

[20] U. Beck, “Risk Society: Towards a New Modernity,” Sage, London, 1992.

[21] D. Elliott and D. Smith, “Football Stadia Disaster in the UK: Learning from Tragedy,” Industrial and Environ- mental Crisis Quarterly, Vol. 7, No. 3, 1993, pp. 205-229.

[22] D. Smith, “Business (not) as Usual: Crisis Management, Service Recovery and the Vulnerability of Organizations,” Journal of Services Marketing, Vol. 19, No. 5, 2005, pp. 309-320.

[23] P. M. Allen and M. Strathern, “Evolution, Emergence and Learning in Complex systems,” Emergence, Vol. 5, No. 4, 2003, pp. 8-33.

[24] D. Smith, “Beyond Contingency Planning: Towards a Model of Crisis Management,” Industrial Crisis Quarterly, Vol. 4, No. 4, 1990, pp. 263-275.

[25] D. Smith, “On a Wing and a Prayer? Exploring the Human Components of Technological Failure,” Systems Research and Behavioral Science, Vol. 17, No. 6, 2000, pp. 543-559.

[26] J. T. Reason, “The Contribution of Latent Human Failures to the Breakdown of Complex Systems,” Philosophical Transactions of the Royal Society of London, Series B, Vol. 327, No. 1241, 1990, pp. 475-484.

[27] J. T. Reason, “Managing the Risks of Organizational Acci- dent,” Ashgate, Aldershot, 1997.

[28] C. Sipika and D. Smith, “From Disaster to Crisis: The Failed Turnaround of Pan American Airlines,” Journal Contingencies and Crisis Management, Vol. 1, No. 3, 1993, pp. 138-151.

[29] L. G. Epstein, “A definition of Uncertainty Aversion,” Review of Economic Studies, Vol. 66, No. 3, 1999.

[30] F. Knight, “Risk, Uncertainty and Profit,” Houghton Mifflin, Boston and New York, 1921.

[31] D. Pace, “Economia del rischio. Antologia di scritti su rischio e decisione economica,” Giuffrè, 2004.

[32] J. V. Neumann and O. Morgenstern, “Theory of Games and Economic Behavior,” Princeton University Press, 1944.

[33] J. M. Keynes, “The General Theory of Employment, Interest and Money,” Macmillan, New York, 1936.

[34] J. M. Keynes, “A Treatise on Probability,” Macmillan, New York, 1921.

[35] N. N. Taleb and A. Pilpel, “On the Unfortunate Problem of the Nonobservability of the Probability Distribution,” 2004. http://www.fooledbyrandomness.com/knowledge.pdf

[36] R. Lowenstein, “When Genius Failed: The Rise and Fall of Long-Term Capital Management,” Random House, New York, 2000.

[37] M. J. Machina and D. Schmeidler, “A More Robust Definition of Subjective Probability,” Econometrica, Vol. 60, No. 4, 1992, pp. 745-780.

[38] J. Neslehová, P. Embrechts and V. Chavez-Demoulin, “Infinite-Mean Models and the LDA for Operational Risk,” Journal of Operational Risk, Vol. 1, No. 1, 2006, pp. 3-25.

[39] P. Embrechts and G. Samorodnitsky, “Ruin Problem and How Fast Stochastic Processes Mix,” The Annals of Applied Probability, Vol. 13, No. 1, 2003, pp. 1-36.

[40] S. Iacus and D. L. Torre, “Approximating Distribution Functions by Iterated Function Systems,” Journal of Applied Mathematics and Decision Sciences, Vol. 2005, No. 1, 2005, pp. 33-46.

[41] M. Moscadelli, “The Modelling of Operational Risk: Ex- perience with the Analysis of the Data Collected by the Basel Committee,” Temi di discussione, No. 517, 2004.

[42] M. Barnsley and L. Hurd, “Fractal Image Compression,” AK Peters. Ltd, Wellesley, MA, 1993.

[43] B. Forte and E. Vrscay, “Inverse Problem Methods for Generalized Fractal Transforms,” In: Y. Fisher, Ed., Fractal Image Encoding and Analysis, Springer-Verlag, Trond- heim, 1995.

[44] A. J. McNeil, R. Frey and P. Embrechts, “Quantitative Risk Management: Concepts, Techniques, and Tools,” Princeton University Press, Princeton, 2005.