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.

Falling Off the Cliff? Increasing Economic Security for Low Income Adults as the Safety Net Shrinks

The public assistance system is supposed to offer a bridge between poverty and self-sufficiency. Families receive benefits such as Temporary Assistance for Needy Families (TANF) or Supplemental Nutrition Assistance Program (SNAP) to soften the impact of loss of income. The programs are intended to be limited in duration and provide a very modest amount of financial support. Some families are fortunate to also receive a housing voucher or a child care subsidy to help offset basic expenses. Eligibility for benefits varies by program and is based on different criteria, most of which are linked to personal income. This study asks: what happens when benefits are cut before individuals reach economic stability? This is frequently called the “benefits cliff.”