Operations risk management by optimally planning the qualified workforce capacity

Emmanuel Fragniere, Jacek Gondzio, Xi Yang

Research output: Contribution to journalArticlepeer-review

Abstract

Operational risks are defined as risks of human origin. Unlike financial risks that can be handled in a financial manner (e.g. insurances, savings, derivatives), the treatment of operational risks calls for a "managerial approach". Consequently, we propose a new way of dealing with operational risk, which relies on the well known aggregate planning model. To illustrate this idea, we have adapted this model to the case of a back office of a bank specializing in the trading of derivative products. Our contribution corresponds to several improvements applied to stochastic programming techniques. First, the model is transformed into a multistage stochastic program in order to take into account the randomness associated with the volume of transaction demand and with the capacity of work provided by qualified and non-qualified employees over the planning horizon. Second, as advocated by Basel II, we calculate the probability distribution based on a Bayesian Network to circumvent the difficulty of obtaining data which characterizes uncertainty in operations. Third, we go a step further by relaxing the traditional assumption in stochastic programming that imposes a strict independence between the decision variables and the random elements. Comparative results show that in general these improved stochastic programming models tend to allocate more human expertise in order to hedge operational risks. Finally, we employ the dual solutions of the stochastic programs to detect periods and nodes that are at risk in terms of the expertise availability. (C) 2009 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)518-527
Number of pages10
JournalEuropean Journal of Operational Research
Volume202
Issue number2
DOIs
Publication statusPublished - 16 Apr 2010

Keywords

  • Operational risk management
  • Manpower planning
  • Bayesian network
  • Endogenous stochastic programming
  • STOCHASTIC PROGRAMS
  • MODEL

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