# Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case
## 4 Industrial Pilot Case
### Validation and Lessons Learnt  
The solution presented in chapter 3 has been validated in the industrial pilot case. As presented in Figure 6 the digital solution has been deployed on the shop floor as follows:  
* Two client workstations (PCs) to monitor the process execution by retrieving information from the operators of the machines close to each of the workstations. A touch screen was installed in each client workstation to allow for interaction with the opera  
All HW components, namely: server, client PCs and gateway were connected to the same LAN through Ethernet cabling. In Figure 7 the deployment of the solution in an operational shop-floor environment is presented.  
Both estimated measurements during the experiments and as well as experts' judgment have verified that the solution can bring a considerable impact on several business processes (production scheduling, shop-floor operators' activities/tasks and monitoring of the production process). The validation tests have verified that the proposed DT approach for supporting the operators on the shop floor and especially the interaction of the operator with the product/part as well as the interaction with the shopfloor's production plan is technically feasible and the required modification in the established business process is acceptable. The required modifications, which introduce additional verification steps taken by the operator through the solution, have been received by the operators and engineers without major concerns. Finally, the expected impacts of the solution to the mold-shop that have been identified in the early phases of the digital transformation project, such as increased resource utilization, decrease extra working hours spent on scheduling/rescheduling, decrease non-added value activities for engineers, reduce delays in projects' delivery time, percentage of total time spent on documenting efforts and time spent on a project and  
Figure 6: Solution deployment on the shop-floor.  
Figure 7: Installation of HW/SW on the shopfloor during the experiments.  
percentage of erroneous data records have been verified through the experiments. An exception was the human-errors reduction that had initially been overestimated, however during the experiments it was evident that the solution marginally contributes to quality improvements and that is mainly achieved by increasing the awareness of the shop-floor operators on the sequence of the production steps. Table 3 below summarizes the expected benefits for mold producers by applying the proposed DT solution.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Business Process (BP)** & **Business Objectives (BO)** & **Business Process Indicator (BPI) & **BPI (As Is” Value Value Value Value & **BPI (As Is” Value Value & **Total Value Measured** & **Comments** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Business Performance Indicators (BPIs).  
From a technical and software systems perspective, it was clear to the development team that the open-source components used (ORION and IDAS IoTAgent) are mature solutions with straightforward integration steps within the backend system of the system. Moreover, the execution performance of the FIWARE tools was also within the expectation with no major issues identified in aspects like response times or stability.  
On a technical scale, the solution demonstrates how a traditional manufacturing system can be transformed into a CPS system by integrating IoT technology with smart capabilities such as dynamic scheduling. In Table 4 key findings from the qualitative analysis from the technical point of view are presented.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Business Process** & **Business Objectives** & **Business Process** & **BPI Process** & **BPI** & **BPI** & **Comments** \\