The increasing complexity of aerospace products and programs and the growing competitive pressure is facilitating the aggregation of small, medium and large enterprises of certain geographical regions into more integrated and collaborative entities (clusters). Clusters are by their same nature formed by heterogeneous companies, with huge differences not only in size but also for their core competences: such a diversity is a strength of the cluster, but it also increases its complexity.
The purpose of this paper is to describe a benchmarking methodology that can be adopted to assess the performances of companies belonging to a cluster from different perspectives: economics and financials, competitive differentiators, specific know how, business strategies, production and logistic effectiveness, quality of core and supporting processes. The methodology is based upon a questionnaire organized in 11 sections and more than 380 questions; over 75% of such questions require a “discrete” answer (boolean, number, percentage, pick list, vote) while only a minority are in free text format.
The assessment has been proposed to companies belonging to a cluster of aerospace companies in the North Western area of Italy (Piedmont), the Torino Piemonte Aerospace (TPA) cluster. The number of participants to the survey was limited so no statistically sound conclusion could be drawn on the basis of the data collected to date, however the methodology was validated in several critical aspects: effort required to companies to provide the information requested, availability within the company of (most of) the required data, clarity of the questions, relevance of the key performance indicators calculated on the basis of the raw data supplied. Some key findings of this survey already supported the cluster management board in the decision process and the project is now facilitating specific benchmarking processes between cluster members, thus acting as a stimulus for the improvement of each company performances. Its adoption by other clusters would further enrich the knowledge base allow an even more effective analysis and a comparison between different clusters.