Why is your company not robotic? The technology and human capital needed by firms to become robotic

The impact of companies' adoption of robotics is increasingly interesting. This study aims to elucidate how the adoption of these technologies will affect companies and society. Companies that use these technologies expect to gain a competitive advantage, but robotization implies risks that must be managed by companies and governments. This research focuses on one of the most sensitive elements of this transformation process-the workforce. First, we analyze the characteristics of the workforce and the degree of adoption of robotics using a sample of 4,640 firms with 26 years of observation. We develop a predictive model using a supervised artificial neural network multilayer perceptron (ANN-MLP) to evaluate a company's readiness to make this transformation according to its workforce's characteristics. Second, we focus on the characterization and segmentation of the companies for which the ANN-MLP is unable to correctly predict the degree of adoption of robotics. This classification failure means that there are unidentified factors that determine why a company has a workforce composition and structure that do not correspond to its expected degree of robotization. For this analysis, we investigate the main business indicators of these companies and cluster them using an unsupervised artificial neural network, specifically the Kohonen selforganizing map. Our findings will enable companies to understand the importance of transforming to robotics at the right moment, considering factors such as the optimum structure and composition of the workforce. The combination of technology and human capital is the key to boosting the efficiency of the transformation process toward robotics. At this point, a recommendation model to determine whether the company has sufficient maturity to make the transition is crucial for decision makers.

Automated summary

This study aims to investigate the impact of companies' adoption of robotics on the workforce and provide decision support. The research identifies factors influencing the degree of adoption of robotics and develops a predictive model based on these factors. The study also conducts a characterization and segmentation analysis of companies that fail to correctly predict the degree of adoption.