The transition to predictive quality in high-knowledge manufacturing industries: A case study of obstacles and facilitators for digital innovations in quality management for lithium-ion battery manufacturing
Examensarbete för masterexamen
Management and economics of innovation (MPMEI), MSc
The rise of industry 4.0 facilitates many opportunities for the manufacturing industry, turning some into a digitalized, high-knowledge industries and enabling a whole new scale, customization, and optimization. Quality 4.0 captures the same benefits as Industry 4.0 but is still fairly underutilized. The purpose of this thesis is to provide insights on the implementation of predictive quality innovations, which are data-based quality assurance methods that utilize statistical patterns to predict future outcomes, in a high-knowledge manufacturing company. This was done by investigating obstacles to the implementation and how these could be reduced by drawing upon three constructs from innovation implementation literature; management support, information- and knowledge diffusion, and implementation climate. To investigate this, an eight-week, longitudinal single-case study of a European lithium-ion battery manufacturing was performed. Seven predictive quality projects were observed, which spanned from software-based innovations aiming to develop a machine learning algorithm for process optimization, to digital technology innovations, which aimed for enabling quality data analytics. Data collection also included observations of company documents, and 16 interviews with company employees from different departments, such as the Quality department, Digitalization department, and other departments involved in the projects. Four obstacles to the implementation of predictive quality innovations were identified, a technological obstacle, a resistance obstacle, a deliverable obstacle, and a supportive obstacle. Further, all constructs had an impact on each of the obstacles, for instance through bridging new cross-departmental dependencies that emerged from the combined digital- and quality characteristics of predictive quality methods in a complex manufacturing setting. The insights from this thesis contribute to the literature on digital transformation by providing a detailed empirical account of implementing data-driven innovation in a complex context. For practitioners, the findings are useful in providing guidance to mitigate challenges in the transition towards Quality 4.0.
Predictive Quality , Predictive Analytics , Data Analytics , Quality Management , Quality 4.0 , Industry 4.0 , High-knowledge Manufacturing Industry , Lithium-Ion Battery Manufacturing