Outplacement time and probability estimation using discrete event simulation

In today’s rapidly changing technological scenario, tech giants revise their strategic alignment every couple of years. As a result, their workforce has to be adapted to the organization’s strategy. Members of the workforce who are neither relevant to the strategic alignment, nor can be made relevant by reskilling, have to be either outplaced (i.e. placed in an another job within organization) or separated from the organization. In geographies like Europe, where the cost of separation is very high, it becomes very important to make the right decision for each employee. In this paper, we describe a simulation based methodology to find the probability and time of outplacement of an employee. These numbers are inputs to a global problem of making the optimal decision for the entire workforce.

Outplacement means placing a candidate in an another job within organization. Estimation of outplacement time (OT) and outplacement probability (OP) of candidates in jobs is an analytically intractable problem due the inherent nature of the problem. Big technology organizations need to often solve this problem, due to frequent realignment of strategic directions in the rapidly changing tech landscape. Every time an organization decides to focus on certain technical domains, they need to make some tough decisions regarding their workforce. Each employee is tagged as being in one of the following categories:

  1. Suitable to the strategic alignment with some investment in reskilling (potentially zero for many employees).
  2. Not suitable to the strategic alignment, but can be outplaced.
  3. Not suitable to the strategic alignment, can not be outplaced.

Determining an appropriate category for each candidate, given the organization’s budget constraints is the umbrella problem to be solved. In order to solve the problem, every employee’s fitment to the strategic alignment has to be quantified. Those below a threshold should be considered for outplacement. Employees in category three have to be separated from the organization. In some geographies such as Europe, the cost of separation is very high. Hence it is imperative that the organizations be very careful in tagging employees in the last category. Technically, employees who have a very large expected OT and or very small OP are candidates in the third category. It is this problem of finding a candidate’s outplacement time and probability that we are addressing here.

With an approximate reskilling cost vs relevance curve, and outplacement time/probability numbers available, a global problem to maintain an aligned headcount while staying close to the budget can be formulated and solved. The outplacement time can be monetized by multiplying it with an employee’s salary, and adding the actual placement process cost to it. Those with outplacement cost more than severance cost might be separated instead of being chosen for outplacement. The overall problem is that of making one of the three decisions for each employee, keep with (potentially zero) reskilling cost, outplace or separate, with corresponding costs. The objective is to minimize the deviation from realignment budget (with more weight for deviation on higher side) with headcount constraints.

Entities/Events and Model Logic

Entities/Events and Model Logic.

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