Resource Optimization During Merger and Acquisitions Transactions

Abstract

Mergers and Acquisitions have become means of a quick transformation for companies while basic guidelines related to resource allocation during a transaction are not available. Therefore, this capstone project set out to determine a mathematical approach with the aim to estimate the number of human resources required to create a stable supply chain operation during the sequential merging and separating of subsidiaries. We approached the problem in two steps. First, we used Mixed Integer Linear Programming (MILP) to calculate the optimal resource allocation number after divesture of the business units. The optimization was helpful to find the baseline resource requirement, but the result still generated backlog as the calculation was made under deterministic conditions. In step two we added flexibility to our model through functional simulations to capture the effect of uncertainties. Allowing us to adjust the center of amplitude related to backlog (performance metric for our system) as close to '0' as possible. After conducting the simulation-based optimization, we revealed the most advantageous resource allocation options while simultaneously providing beneficial insights for strategic decision making by the executive management. As a result, we were able to reduce the absolute number of required resources from 13.22 to 11.71 while enabling stable post-merger operation through a scalable and adaptable resource-allocation model.