Operations risk management by optimally planning the qualified workforce capacity

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.

Workforce planning incorporating skills: State of the art

“…When higher skilled workers perform tasks that are usually performed by lower skilled workers, the resulting efficiency tends to be higher. Some papers therefore assume that the speed of work is higher when substitution is allowed [42,44,58,78,85,86,92,100,105,108,109,115,116,117,118]. Tiwari et al [108] even assume that the quality of work will be higher in this case.…”

“…Some of them assume that temporal workers have less skills and therefore entail lower labor costs than permanent workers [28,38,58,106]. Others mention the lower efficiency or the slower learning rate of temporal workers as their main disadvantages [24,38,42,58,105]. Some authors assume no difference in performance between temporal and permanent workers and are only concerned with task restrictions in their model [11,83,84].…”

“…The research papers shown in the first row of Table 7 assume that temporal workers are inferior to permanent workers in several ways. Some of them assume that temporal workers have less skills and therefore entail lower labor costs than permanent workers [28,38,58,106]. Others mention the lower efficiency or the slower learning rate of temporal workers as their main disadvantages [24,38,42,58,105].…”

“…In [28,38,44,58,78,85,86,105,109,115,116,117,118], the authors model the skills in the objective function, but not as soft constraints. Instead of skill constraints, these papers consider skill based performance/efficiency measures with a direct impact on the objective (such as speed of work and the quality of the finished products).…”

“…Hierarchical skills are usually defined on a continuous scale but are sometimes discretized in several skill levels. Some researchers even only use two levels: skilled or unskilled [42,58,65,83,84,106].…”