PurposeManufacturers seek to innovate and improve processes using new digital technologies. However, knowledge about these new technologies often resides outside a firm's boundaries. The authors draw on the concept of absorptive capacity and the literature on open innovation to explore the role of external search in the digitization of manufacturing.Design/methodology/approachThe authors developed and distributed a survey to manufacturing firms in Switzerland, for which 151 complete responses were received from senior managers. The authors used multiple linear regressions to study the relations among the breadth and depth of external search, firms' adoption of digital technologies and operational performance outcomes.FindingsExternal search depth was found to relate positively to higher adoption of computing technologies and shop floor connectivity technologies. No significant correlation was found between external search breadth and firms' adoption of digital technologies. Regarding performance outcomes, there is some evidence that increased adoption of digital technologies relates positively to higher volume flexibility, but not to increased production cost competitiveness.Practical implicationsManufacturing firms that aim to digitize their processes can benefit from inbound open process innovation, but its utility varies for different clusters of digital technologies. Generally, the findings suggest that firms should build strong ties with a few external knowledge partners rather than surface relations with many.Originality/valueThis study contributes to the growing literature on the digitization of manufacturing with an analysis of the relation between firms' external search and their adoption of digital technologies. It adds early empirical insights to the literature on open process innovation.

Lean has been the dominant production paradigm for the past few decades. With its focus on reducing complexity, lean suggests to limit the use of digital technologies on the shop floor. Recent advancements in digital technologies, however, promise significant improvements through its ability to manage complexity. This apparent conflict raises the question as to whether these two paradigmslean and digitalizationcontradict or complement each other. Furthermore, there is ambiguity about whether or not firms should excel in lean before investing in digitalization. This paper contributes to this discussion through an empirical investigation of this relationship. It draws on survey data from Swiss manufacturers as well as consecutive interviews with selected firms. The analyses indicate a positive correlation between the digital maturity and the lean maturity of firms. This relationship is discussed from two perspectives: first, how digitalization can support lean and, second, how lean can support digitalization. Furthermore, the different characteristics of companies of different maturities in lean and digitalization are examined. It is concluded that a favorable organizational culture and some specific continuous improvement practices help the mature implementers of lean and digitalization to achieve superior operational performance.

This paper proposes a data-driven procedure to improve productivity in make-to-stock manufacturing. By leveraging recent developments in information systems research, the paper addresses manufacturing systems with high process complexity and variety. Specifically, the proposed procedure draws upon process mining to dynamically map and analyse manufacturing processes in an automated manner. This way, manufacturers can leverage data to overcome the limitations of existing process mapping methods, which only provide static snapshots of process flows. By bridging data and process science, process mining can exploit hitherto untapped potential for productivity improvement. The proposed procedure is empirically validated at a leading manufacturer of sanitary products. The field test leads to three concrete improvement suggestions for the company. This research contributes to the literature on production research by demonstrating a novel use of process mining in manufacturing and by guiding practitioners in its implementation.

Lean production is best taught on the factory floor. Yet, in higher education, it is almost exclusively taught in classrooms. We want to keep and proliferate the learning experience of exploring a real factory's "Gemba" and, at the same time, to remove limitations to factory visits. Due to recent developments in virtual reality (VR) technologies, VR offers excellent opportunities to achieve this. In this paper, we present an innovative way to teach lean production with VR. We show how we implemented a solution to let students be immersed in the factories of Toyota, ABB, and other world-renowned companies without having to travel. We also report on our experiences and provide other teachers the information needed to adopt "Gemba VR" in their own teaching.

The continuous improvement process (CIP) enables companies to increase productivity constantly by sourcing ideas from their employees on the shop floor. However, shorter production cycles require manufacturing companies to also adapt their production processes in a faster manner and reduce resources for CIP activities. Traditional CIP approaches fall short in such a fastpaced environment characterized by uncertainty. This study proposes a novel approach for increasing the efficiency and speed of the CIP by using data of previous improvements and predict current potentials. This results in a prescriptive model supporting the employees how to improve their processes.