Industry 4.0 with a Lean perspective - Investigating IIoT platforms' possible influences on data driven Lean

Abstract: Purpose: To investigate possible connections between an Industrial Internet of Things (IIoT) system, such as Predix, and data driven Lean practises. The aim is to examine if an IIoT platform can improve existing practises of Lean, and if so, which Lean tools are most likely influenced and how this is.Design/Methodology: The paper follows a phenomenon-based research approach. The methodology contains of a mix of primary and secondary data. The primary data was obtained through “almost unstructured” interviews with experts, while the secondary data comprises of a comprehensive review of existing literature. Moreover, a model was developed to investigate the connections between the concepts of IIoT and Lean.Findings: Findings derived from expert interviews at General Electric (GE) in Uppsala have led to the conclusion that Predix fulfils the necessary requirements to be considered an IIoT platform. However, the positive effects of the platform on the selected Lean tools could not be found. Only in one instance improved Predix the effectiveness of a Lean tool. Overall, data analytic efforts are performed and let to better in-process control. However, these efforts were independent from the Lean efforts carried out. There was no increase in data collection or analytics due to the Lean initiative and Predix is not utilised for data collection, storage, or analysis. It appears that the pharmaceutical industry is fairly slow in adapting new technologies. Firstly, the high regulatory requirements inherent within the pharmaceutical industry limit the application of cutting edge technology by demanding strict in-process control and process documentation. Secondly, the sheer size of GE itself slows down the adoption of new technology. Lastly, the pragmatic approach of the top management to align the digital strategies of the various industries and thereof resulting allocation of resources to other more technologically demanding businesses hinders the use of Predix at GE in Uppsala.