Framework for the selection of acquisition targets applied in the commercial aerospace industry

Abstract

Acquisitions are costly, even the ones that never happen. They require a significant commitment of resources involving multi-disciplinary teams examining all aspects of a business. This can come at a significant cost if either the acquisition does not produce the value that was originally expected or if the acquisition process is aborted all together. Clearly, it is critical to be confident in the initial selection, when investing capital and resources to carry out the lengthy investigation process. However, while there is exhaustive research on the detailed evaluation of a target, there is very little published about the preliminary selection process. In this thesis, we demonstrate a methodology for acquisition target selection. We propose a method of metric-based ranking of targets for criteria defined in 3 dimensions. The first is a measure of how a target meets the Strategic Goals of the acquiring company. This is critical not only to measure a target, but to clarify and create alignment among the leadership of the company for the purpose of the acquisition. The second dimension is a measure of Acquisition Fit. This represents a rough measure of likelihood of integration success of a target. The metrics in this dimension are based on research into attributes of acquisition failures. The final dimension is a Financial Impact measure, which represents a rough business case for the acquisition. In the second half of this thesis, we introduce a case study of this methodology being applied in the large commercial aircraft (LCA) industry at Spirit AeroSystems, Inc. This case study demonstrates the application of this methodology with the necessary industry analysis, internal and external technology evaluation and implementation challenges. During this case study, the LCA industry is undergoing a period of technological disruption and re-distribution of engineering responsibilities. These shifts in the industry structure require additional rigor in evaluation of technological and engineering needs and capabilities. Experience is statistically a strong indicator of success in M&A. We hope to lower the learning curve costs and associated risk by capturing research of best practices in a manageable process for M&A target selection.