# Vision System Experimentation in Furniture Industrial Environment
## 3 Applied Methodology  
In order to get a reliable and comprehensive vision of the components and mechanisms put in place to improve the performance in the production plant, the solution for Factory Connectivity and the deployment in the industrial premises are described in the following subsections.  
As a general overview, the steps involved in the research are shown in Figure 1. Firstly, the Overall Equipment Effectiveness (OEE) indicator in the machine considered in the experimentation has been measured before deploying the solution. Then, the Factory Connectivity solution has been implemented and tested. Finally, once the solution has been deployed, the new OEE value has been calculated.  
This situation is measured in this experimentation by the OEE manufacturing KPI [10]. This indicator, usually used in companies with Lean Manufacturing methodology implanted, measures the use of equipment by the composition of three basic indicators: availability, performance and quality.  
\[OEE\ (\%)=Availability\times Performance\times Quality \tag{1}\]  
The availability is quantified as the time available for the machine to work respect the planned time:  
\[Availability=\frac{Run\ Time}{Planned\ Production\ Time} \tag{2}\]  
Figure 1: Steps involved in the methodology for experimentation and evaluation.  
The performance is strongly related to the speed of the process. This can be affected by some non-productive tasks (i.e., the classification activities carried out around the bending machine area):  
\[Performance=\frac{(\textit{Ideal Cycle Time}\times\textit{Total Count})}{Run\;Time} \tag{3}\]  
Finally, the quality depends on the number of right and wrong pieces that the process is able to manufacture:  
\[Quality=\frac{\textit{Optimal Count}}{\textit{Total Count}} \tag{4}\]  
Traditionally, the OEE indicator decreases because of the following 6 parameters that affect the measurement of its value in the industrial environment [11]:  
1. the equipment failure/breakdown losses;
2. the set-up time;
3. the idling and minor stops;
4. the reduced speed;
5. the reduced yield;
6. the quality defects.  
The losses of the list above decrease the Availability, Performance, and Quality of the machine, and therefore OEE value.  
Automating the calculations of the OEE indicator becomes difficult in the scope of SMEs [12]. Manufacturing Execution Systems (MES) generally allow companies to obtain a machine's OEE, however in the presented experimentation the manufacturer does not manage a MES system that supports the automatic acquisition of this information. Therefore, in order to obtain an accurate OEE estimation for the edge banding machine in LAGRAMA, several scenarios have been defined, with the support of the operational manager of the company. The possible types of waste have been evaluated and those related to the focus of this research have been fully considered. Through this methodology, the initial OEE value (based on different scenarios) has been calculated, as well as the corresponding impact on the OEE, according to the savings achieved from the experimentation.  
The Edge Solution--that supports the vision camera system--is the main asset deployed in the factory environment. In order to provide a comprehensive technical overview of the solution, the Edge Factory Connector, as well as its deployment in the factory, are described below.