Leveraging financial management performance of the Spanish aerospace manufacturing value chain

Abstract

We study financial management performance during 2008–2013 for the Spanish aerospace manufacturing value chain and the links with managerial decisions. Data from company financial statements is analysed with Principal Component Analysis, Data Envelopment Analysis and an Artificial Neural Network. Top financial performers focus on liquidity management rather than on returns: both in the short term, by increasing levels of current assets and funding them with short-term liabilities, as well as increasing asset turnover; and in the long term, by aligning equity to non-current assets, while reducing asset and debt intensity levels. Only the manufacturing value chain is analysed, showing the potential for future research in related fields (e.g. Value chain, country). Benchmarking and forecasting financial performance yields information and enables agility and accuracy in the strategy setting process. This study makes a unique contribution because it applies the scientific method where no previous related studies have done. It offers the novelty of using a single metric while Ratio Analysis requires multiple unweighted measures. We contribute by: (a) providing a method based on publicly information to benchmark and predict financial performance, thus offering benefits for aerospace stakeholders and academia; and (b) employing a big data sample that closely represents the population.

Keyword : market performance, market structure, aerospace, factor analysis, neural networks, data envelopment, financial management