# Indoor Positioning Systems Can Revolutionise Digital Lean
## 5 Conclusions  
In this paper, the possible applications of indoor positioning systems in Lean 4.0 are explored. The proposed IPS incorporates different kinds of sensors to acquire not only positional data but also other data such as vibration, which enables them to recognize motion and transportation activities. Along with this IPS architecture, the traditional set of \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Workplace** & \
\begin{tabular}{l} **Average Cycle Time** \\ **[min]** \\ \end{tabular} &
\begin{tabular}{l} **Queueing Time [min]** \\ \end{tabular} & **Produced Tasks** \\ \hline Waiting time & 119.47 & - & 54 \\ \hline Waste of transportation & 2.73 & - & 27 \\ \hline AO2 & 77.73 & 102.86 & 56 \\ \hline DAEWOO & 84.71 & 84.05 & 49 \\ \hline DHOLE & 88.69 & 5.09 & 163 \\ \hline HELER1 & 99.36 & 91.80 & 72 \\ \hline HELER2 & 228.53 & 46.74 & 34 \\ \hline HELER3 & 197.16 & 59.56 & 32 \\ \hline HELER4 & 124.82 & 42.18 & 146 \\ \hline M4 KTK & 61.91 & 84.61 & 145 \\ \hline PACK2 & 30.30 & 47.72 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The cycle and queueing times calculated with the help of the positional data. Figure 7: The distribution of the average times calculated based on the IPS and MES data. Lean KPIs are redefined and redesigned to be derived automatically from IPS-based data. The process mining-based analysis of the collected data can provide insight into the key factors that determine the productivity and efficiency of production systems. The proposed method of data acquisition enables further system optimization, which assists managers in monitoring their system effortlessly and in a stress-free manner. In the trend of Lean 4.0, the use of such a system is expected to soon be dominant due to its hardware maturity, as well as the readiness of data and the need from the manufacturer. The framework for process analysis can provide the basis for further optimization and enhancement of human-machine activity cooperation, which will constitute our future research. A case study is conducted in a mechanical manufacturing firm to show the possible output of Lean 4.0 KPIs, and improvements can be made based on activity data. The accuracy of the result from the system is much dependent on the hardware characteristics. The most frequent error occurs when the location sensor cannot recognize which area is between two adjacent ones. Due to the current technology limitation, the defined space of workstations needs to be separated with a distinct distance. Fortunately, with process mining tools, meaningless noise and error can be excluded. However, the authors believe that this problem can be mitigated soon, with advancements in the new hardware system. According to the intensive use of data in monitoring a smart factory, one particular concern is personal privacy. When a tag is attached to an operator, then every movement can be tracked. To ensure personal privacy, the tag is only active in the production zones. Besides, with a large amount of operation data and production monitoring parameters from the system, the management dashboard needs to be discussed and adjusted at a managers' meeting, not by any single person. As the system can improve the facility operation--through line speed changes, human assignment and dynamic line balancing--it is unwise to teach it in the wrong way. The consultation of an LM expert in setting the KPIs and allowable adjustment is necessary. We believe that the proposed framework and the presented results provide a practical starting point for lean management practitioners and can initiate further research projects. Conceptualization, T.-A.T., Supervision, J.A. ; Code development: T.R. ; Writing--original draft preparation, T.-A.T. and T.R. ; funding acquisition, J.A. All authors have read and agreed to the published version of the manuscript.