# Vision System Experimentation in Furniture Industrial Environment
## 6 Concluding Remarks  
The technical solution deployed in the industrial environment has been described, along with how the classification process is improved by the use of the vision system, and the approach followed to measure the impact on the overall production process. The impact of the solutions developed in the production line has been measured by OEE: a standard KPI used by the companies to improve manufacturing operations. Further improvement of this KPI could be achieved through a standardized process with big batches, manufacturing the same piece. Otherwise, the set-up times increase with the number of lots and some activities, such as the classification tasks, increase the dedicated time which also reduces the overall performance. However, the market demands customization, together with an agile and efficient supply chain. A furniture producer like LAGRAM wants to face this challenge by adapting its production processes and adopting a Lot Size 1 manufacturing strategy. The solution \begin{table}
\begin{tabular}{c c c} \hline \hline
**Absolute OEE** & **Optimistic** & **Base** & **Pessimistic** \\ \hline
**Initial situation** & **74\%** & **68\%** & **59\%** \\ H1 & 85\% & 78\% & 69\% \\ H2 & 80\% & 74\% & 65\% \\ H3 & 77\% & 71\% & 62\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Final OEE values obtained according from the different scenarios and hypothesis. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Scenario** & **Optim.** & **H1** & **H2** & **H3** & **Base** & **H1** & **H2** & **H3** & **Pessim.** & **H1** & **H2** & **H3** & \\ \hline
**OEE** & **74.0\%** & **84.6\%** & **80.3\%** & **76.8\%** & **67.8\%** & **78.4\%** & **74.0\%** & **70.5\%** & **59.2\%** & **68.6\%** & **64.6\%** & **61.6\%** & \\
**A (Availability)** & **95.3\%** & **95.3\%** & **95.3\%** & **95.3\%** & **92.0\%** & **92.0\%** & **92.0\%** & **92.0\%** & **84.0\%** & **84.0\%** & **84.0\%** & **84.0\%** \\ TT (Total Time) & 480 & 480 & 480 & 480 & 480 & 480 & 480 & 480 & 480 & 480 & 480 & min./day \\ PPT (Plan. Prod. Time) & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & 450 & min./day \\ RT (Run Time) & 429 & 429 & 429 & 429 & 414 & 414 & 414 & 414 & 378 & 378 & 378 & 378 & min./day \\ ST (Stop Time) & 21 & 21 & 21 & 21 & 36 & 36 & 36 & 36 & 72 & 72 & 72 & min. \\ Maintenance & 20 & 20 & 20 & 20 & 30 & 30 & 30 & 30 & 40 & 40 & 40 & 40 & min./day \\ Number. Lots & 1 & 1 & 1 & 1 & 3 & 3 & 3 & 3 & 8 & 8 & 8 & 8 & lots \\ Setup Time & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 4 & 4 & 4 & 4 & min./lot \\ \hline
**P (Performance)** & **80.0\%** & **91.5\%** & **86.8\%** & **83.1\%** & **77.6\%** & **89.7\%** & **84.6\%** & **80.7\%** & **75.8\%** & **87.8\%** & **82.7\%** & **78.8\%** \\ \hline ICT (Ideal Cycle Time) & 0.43 & 0.36 & 0.38 & 0.41 & 0.63 & 0.51 & 0.55 & 0.59 & 0.83 & 0.66 & 0.72 & 0.78 & min./piece \\ TC (Total Count) & 792.0 & 1095.3 & 971.3 & 872.5 & 506.9 & 730.6 & 636.9 & 564.5 & 343.6 & 504.0 & 36.2 & 384.4 & pieces \\ RT (Run Time) & 429 & 429 & 429 & 414 & 414 & 414 & 414 & 378 & 378 & 378 & 378 & min./day \\
**CT (Class. Time)** & **0.10** & **0.03** & **0.05** & **0.08** & **0.17** & **0.04** & **0.08** & **0.13** & **0.23** & **0.06** & **0.12** & **0.18** & **min/piece** \\ MST (Micro. stops Time) & 0.008 & 0.008 & 0.008 & 0.017 & 0.017 & 0.017 & 0.017 & 0.033 & 0.03 & 0.03 & min./piece \\ \hline
**Q (Quality)** & **97.0\%** & **97.0\%** & **97.0\%** & **97.0\%** & **95.0\%** & **95.0\%** & **95.0\%** & **95.0\%** & **93.0\%** & **93.0\%** & **93.0\%** & **93.0\%** \\ \hline GC (Good Count) & 768.2 & 1062.5 & 942.2 & 846.4 & 481.6 & 694.1 & 605.1 & 536.3 & 319.6 & 468.7 & 405.6 & 357.5 & pieces \\ TC (Total Count) & 792.0 & 1095.3 & 971.3 & 872.5 & 506.9 & 730.6 & 636.9 & 564.5 & 343.6 & 504.0 & 436.2 & 384.4 & pieces \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation of OEE in pilot machine because of factory connectivity implementation.