Digital technologies are improving the capability of organizations and individuals of accessing, storing and analyzing information (cloud computing, big data and artificial intelligence), of making things in a smarter, more efficient and safer way (augmented and virtual reality, additive manufacturing), and of connecting things (Internet of Things - IoT). There are certain dimensions in which digital technologies transform industries and processes in ways that replicate and extend transformations of the past. For example, IoT, big data and artificial intelligence offer new ways of implementing continuous improvement and lean thinking methods. However, there are other ways in which digital technologies transform industries that are fundamentally new (e.g. smart connected products and product-service systems), create new approaches to design and management, expose organizations to new sources of uncertainty, risk and competition, and require new approaches to decision-making, design and engineering. Both the “old” revisited approaches and the more radical transformations have in common the need to develop job positions characterized by technical skills on data science and managerial skills and attitudes needed to understand with a business and an operational engineering lens how big data can be used. Such T-shaped profile of skills will allow data scientists to work in cross-functional teams and have effective coordination with business domain experts.
The course intends to explore the main challenges and perils produced at three levels. At the macro level, the course provides an interpretation of the social and economic implications of big data and it will offer a comparative analysis of its characteristics and effects across industries, within and outside the manufacturing world. Contextual factors will be considered. While digital transformation and big data enable global innovations, economic regulations and institutions are still at a local level. This implies an understanding of the factors that can facilitate or hinder the success and the replicability of digital transformation initiatives across different industries.
At the meso level, big data raise fundamental questions on the underlying processes, routines, capabilities and structures by which organizations innovate and build their organizational learning mechanisms, in processes like product and service development, manufacturing and customer relationship management. At the micro level, the simultaneous introduction of Artificial Intelligence, Big Data, algorithms and virtual reality challenges existing skills and capabilities into the organization. This raises several points for organizations related to how the new digital skills should be acquired and combined with “analog” legacy skills of an organization.