What are the Training Considerations For Automated Data Collection
The addition of new technology to a manufacturing process is usually carefully planned. Decisions on the type of equipment, its placement in the process, and its expected benefits are based on factual analysis: time and material savings, greater capacity, and new capabilities. These considerations are certainly true when purchasing an automated data collection system. An additional consideration, especially significant in the implementation of a new SPC data collection system, is training for all members of the production team. DataMyte systems provide people with the process information they need to make better decisions on process control. If this process information cannot be interpreted and implemented by the person controlling the process, the entire system breaks down.
Figure 18.1.1 shows the elements and interactions of an effective automated SPC system. Missing elements or a failure between elements stops the process. People are key to the system’s success. They must be able to use a system effectively to gather process data, interpret SPC charts and indices, and adjust operating levels of the process.
Unique Training Requirements
Each data collection system is a unique combination of standard components: computers, software, data collectors, cables, and gauges. A careful analysis of data collection and possible network needs is completed, and a system configuration is proposed. The same is true for the training requirements for each system. An analysis of the knowledge and skill needs of all people involved in the project results in a “training prescription.’’ The training prescription may include data collector training, software training and SPC training. It is essential that this training needs analysis be completed. In some cases, it may reveal that many of the required skills are already in place and the system may be implemented with minimal on the job training. In other cases, it may show that a series of structured training courses are critical for the success of the system. In either case, the training decision is based on fact.
The actual process of assessing training needs parallels the complexity of the data collection system: a simple system with one or two data collectors and one or two operators and a project coordinator will have easily-defined training requirements. A more complex system would consist of multiple data collectors of different models, many operators who may be spread across more than one shift, networking hardware and software, and a centralized management system for collected information. A complex system requires a carefully-considered training assessment.