# Indoor Positioning Systems Can Revolutionise Digital Lean
## 4 Application to the Monitoring of a Flexible Manufacturing Process
### Calculation of the IPS-Based Indicators  
This section shows how the data from MES and IPS can be utilised based on the relevant KPIs.  
The IPS sends signals to the MES when the actual cart arrives at the pre-defined station. The applied process mining algorithm determines the process model of the production flow. An illustrative result is presented in Figure 5. Due to the flexibility of the manufacturing process, the extracted model is not trivial and varies over time; therefore, the model is continuously updated based on the real-time positional data.  
The discovered process flows serve as input for work standardisation projects. According to the most frequently conducted steps, a pattern of main material flow is recognized in Figure 5a, where the main workstations and machines are highlighted in blue. We note that there is no leading process flow. Along with the material flow map, the average cycle times are recorded, as illustrated in Figure 5b. The thickness of the arrows represent the time delay between the two stations. The result can be compared to the manufacturing processes  
Figure 4: The distribution analysis of the positional data supports the identification of the states of the internal inventories, and the waiting and cycle times. (**a**) Tracked path of one product on the shop-floor. The rectangles define the workstations and the dots represent the positional data. The timeline is presented by the colours of the dots. (**b**) The discovered status is based on positional data. The blue stars are the transportation, while the orange markers are the queueing positions.  
standard times so that non-conformance stands out. The main process mining-based KPIs are summarized in Table 2.  
A Gantt diagram has been developed to further study the reason for the long transition times of the semi-finished products (see Figure 6a). The rows of the Gantt chart show the orders (_Tasks_) and colours represent the workstations. We defined the _Unknown_ station to show the period where we have no data (from MES). These periods are denoted in  
Figure 5: A production flow model discovered by process mining based on IPS and MES data. (**a**) The frequency of the material flows. The colours of stations represent utilisation of the workstations. (**b**) The discovered average cycle and transition times. The colours of stations represent the cycle times. We can notice that the _HELLER_ stations are the bottlenecks of the process. The transition times are represented by the arrows, which highlight the possible hidden wastes.  
Figure 6a with red lanes and these periods could be the source of the long time period between two stations on the results of process mining and could be the hidden wastes of the manufacturing process. The _Unknown_ period is the 19.74% of the studied time period.  
In the next step, we discovered the status of thee _Unknown_ periods based on the positional data. We analysed 22 days of production data for this purpose. Based on the positional data from IPS, the velocity is calculated to determine the _Waste of transportation_ periods. An example for that period is shown in Figure 4b with the blue stars. Temporary storage is the positions that are closely located out of the pre-defined zones (workstations). When the carts with the products are located in a pre-defined zone (but it is not logged to the MES, so it is not under production), we assumed these products are queueing before the actual workstation (see the orange points on Figure 4b). Figure 6b shows the new Gannt chart with three more defined stations related to queueing, temporary storage, and transportation.