# Vision System Experimentation in Furniture Industrial Environment
## 5 Results  
As described earlier, the impact of the solution is measured by the OEE indicator, focusing on the use of the selected edge banding machine. This indicator varies depending on the type of piece produced. Therefore, this has been defined through 3 different scenarios (optimistic, base and pessimistic) which cover an open range of situations in the factory. Before the deployment of the solution, the OEE in the company for the three scenarios has been setup as: 74% in the optimistic scenario, 68% in the base scenario and 59% in the pessimistic scenario. On the one hand, as can be seen in Table 1, the values in the left column (Optimistic) have been calculated from an ideal working day in which all the pieces processed are homogeneous (one single lot), and the setup time as well as the maintenance and stop time have been the shortest. On the other hand, the values in the far-right column (Pessimistic) represent a hard working day, covering a heterogeneous group of pieces (eight lots) with considerable time spent on setup, maintenance and stops in the machine. From both situations, the middle column (Base) is taken as reference for an ordinary working day. Figure 9: Asynchronous Web Dashboard to provide Work Instructions. The classification time is reduced because of the level of automation in the interpretations of the codes for each piece. Three different hypotheses on the reduction of the processing time have been defined in Table 2 below. Hypothesis 1 (H1) implies 75% of reduction in classification time, while in Hypothesis 2 (H2) the reduction is 50%, and in Hypothesis 3 (H3) the reduction is just 25%. The results are also affected by the size and shape of piece manufactured: in small pieces the time reduction is higher as per H1, but when large pieces are involved, the time reduction is lower as per H3, due to the difficulty in handling these pieces. According to the initial estimation of OEE (before deploying the solution), and the prototype test carried out to estimate the solution's impact, a 'What If' analysis was conducted to estimate the final impact on the OEE of the edge banding machine. The three initial scenarios with the current OEE were cross-referenced with the three hypotheses of classification time reduction, obtaining the results shown in Table 3 below. As commented earlier, the values in the far-left column (Optimistic H1), represent an ideal working day, while the values in the far-right column (Pessimistic H3) represent a hard-working day. Therefore, the middle column (Base H2) should be taken as reference for the actual impact in the factory environment. It should be noted that these values have been measured through ad-hoc monitoring in the real production environment of the number of lots and time dedicated to setup, maintenance, and eventual stops. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Optimistic** & **Base** & **Pessimistic** & \\ \hline
**OEE** & **74.0\%** & **67.8\%** & **59.2\%** & \\
**A (Availability)** & **95.3\%** & **92.0\%** & **84.0\%** & \\ TT (Total Time) & 480 & 480 & 480 & min./day \\ PPT (Planned Production Time) & 450 & 450 & 450 & min./day \\ RT (Run Time) & 429 & 414 & 378 & min./day \\ ST1 (Stop Time) & 21 & 36 & 72 & min. \\ Maintenance & 20 & 30 & 40 & min./day \\ Number. Lots & 1 & 3 & 8 & lots \\ Setup Time & 1 & 2 & 4 & min./lot \\ \hline
**P (Performance)** & **80.0\%** & **77.6\%** & **75.8\%** & \\ \hline ICT (Ideal Cycle Time) & 0.43 & 0.63 & 0.83 & min./piece \\ TC (Total Count) & 792.0 & 506.9 & 343.6 & pieces \\ RT (Run Time) & 429 & 414 & 378 & min./day \\ CT (Classification Time) & 0.10 & 0.17 & 0.23 & min./piece \\ MST (Micro-stops Time) & 0.008 & 0.017 & 0.033 & min./piece \\ \hline
**Q (Quality)** & **97.0\%** & **95.0\%** & **93.0\%** & \\ \hline GC (Good Count) & 768.2 & 481.6 & 319.6 & pieces \\ TC (Total Count) & 792.0 & 506.9 & 343.6 & pieces \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Initial scenarios for calculation of current OEE in the edge banding machine.