In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing industry. The collection and use of real-time position data of logistics, tools and load carriers are already standard procedure in entire branches of the industry today. In addition to asset tracking, the technologies used also offer new possibilities for collecting and evaluating position and biometric data of employees. Thus, these technologies can be used for monitoring performance or for tracking worker behaviour, which can lead to additional burdens and stress for employees. In this context, the collection and evaluation of employee data can influence the workplace of the affected employee in the company to his or her disadvantage. The approach of Privacy by Design can help to benefit from all the advantages of these systems, while ensuring that the impact on employee privacy is kept to a minimum. Currently, there is no survey available that reviews tracking and tracing systems supporting this important and emerging field. This work provides a systematic overview from the perspective of the impact on employee privacy. Additionally, this paper identifies and evaluates the techniques used with regard to employee privacy in industrial tracking and tracing systems. This helps to reveal new privacy preserving techniques that are currently underrepresented, therefore enabling new research opportunities in the industrial community.

In the context of smart manufacturing, the technical development of monitoring systems has made it possible to track employees with the same systems that are used to track assets. This study contributes to our understanding of the acceptance of location-based monitoring of employees and investigates how the perceived privacy risk regarding monitoring can be tackled by examining the role of transparency and the perceived value of monitoring. We designed an experimental setting in which students assembled a 3D printer and manipulated transparency with two conditions: a detailed explanation of monitoring during the task vs. monitoring without any explanation. The results show that the higher the privacy concerns and perceived risks were, the lower was the acceptance for monitoring. However, the negative effect of perceived risk diminishes when both, transparency and the value of monitoring are high, but becomes even stronger when only transparency is high and perceived value is low.