Computer integrated quality data collection and management systems allow manufacturers to maintain consistently high levels of quality and nip potentially expensive problems in the bud for tangible bottom-line improvements. Metrology and computer systems hardware and software developers can provide the tools needed to devise a system that will perform well in a manufacturing environment. However, before beginning meaningful discussions with these external resources, at least six sets of issues that will govern subsequent choices need to be resolved.
1. Needs and concerns. Hold a meeting or series of meetings with all of the stakeholders in this initiative and have everyone identify their needs and concerns. At a bare minimum the stakeholders will include actual decision makers representing corporate management, production, quality assurance and IT. Do not even begin to make system architecture decisions until there is buy-in from each of these groups of stakeholders.
Particularly important is IT because they are responsible for the integrity and security of data throughout the organization. If they don’t buy, it won’t fly. After global concerns have been addressed, start mapping out what will be done with the data collected.
What type of data is currently collected? What other information should be collected? What outputs are needed and to whom should various levels of information be accessible? It will probably be discovered that management, production and QA people have remarkably different quality information needs. That is why it is important to involve key stakeholders from the beginning.
2. Data acquisition. How is the data going to get into the system? Pencil and paper with subsequent keyboard entry affords the lowest initial cost for hardware and software, but it is the most expensive choice long term due to on-going costs for data entry labor and recovering from the effects of human error.
Networks, wired and wireless, are the best way to go. To make a meaningful justification of this route to management it is necessary to have a good understanding of the up front data collection hardware, software and infrastructure costs. It is wise to reserve presentation of this information until estimating future cost savings such as the elimination of scrap and rework and improvement of deliveries.
3. Formatting of data. There are many ways to get data into a QA analysis and reporting software such as hand entry, ASCII Text via RS 232 and importation of spreadsheets as CSV (comma separated values). Keep in mind that the format of the data the devices and spreadsheets put out must match up precisely with the format the analysis and reporting software can accept.
For example, many electronic measurement products output data that includes both the measurement value and the units: 2.0175 inches or 37 millimeters. That can avoid a lot of confusion on the shop floor but creates a data importation problem because mixed (alpha and numeric) data are not allowed together in individual fields. Figure out how to get the data into analytical programs cleanly and automatically to avoid expensive and error-prone manual data handling.
4. Manipulation. This is where to decide what types of statistical number crunching and limit settings will be performed with the data. It is time to start evaluating software packages. Most QA people are comfortable with this and want to start here. That would be putting the cart before the horse.
5. Storage. What are the storage criteria? Online storage? Memory stick? File server? Where will the data be saved? How will the data be maintained? Who gets access? How much of it has to be public?
6. Distribution. This may include electronic reports, e-mail updates, real-time updates on the shop floor using a big display everyone can see, audible and/or visible alarms at the computer numerical control (CNC) machines and work cells, or any combination of the above.
Acquisition, storage and distribution may have special requirements if the work performed is regulated by federal agencies such as the FDA or FAA. Government regulations (21CFR part 11) dealing with signing and storage of electronic files may apply and these considerations should be addressed early in the process of devising the system architecture.
This, of course, is not a comprehensive list of everything needed to address to launch down the road of computer integrated quality data collection. However, if beginning in earnest to address each of the six major issues, considerably more of the subconsiderations will reveal themselves in short order.