by Jim Connett, on August 18, 2020
Quiz time! What common element exists across the following items?
The answer is that in all of these scenarios, some amount of DATA is being collected. With each digital tidbit we share, we enable interested parties to collect and aggregate data to learn about us and make decisions about how to engage with us. The same can be said for manufacturing. Each step of a manufacturing process has the potential to generate a tremendous amount of data regarding the process itself, the product being manufactured, the equipment being used, and the operator involved. Leveraging this data provides opportunities for manufacturers to learn important details. This data illustrates how any variety of factors throughout the production process influence product quality and, ultimately, profitability.
When approaching data collection, three initial questions must be considered. First, “how will the data be collected?” Second, “when will data be collected?” Third, “where will the data be stored? The “how” question focuses on the mode of data collection. The “when” question involves identifying points in the process where data collection would be most beneficial to your end goals. The “where” question addresses data storage solutions. A general understanding of these three questions establishes good boundaries that mark a path toward implementing a useful data collection model (or improve the existing model in use).
These questions cannot be answered in isolation. Answers to one question will inform and direct decisions in the others. Eventually, answers to the “how”, the “when” and the “where” questions will come together.
The number of ways data can be collected are as numerous as the hex codes in a color palette. Some facilities manually write down the required data on a run card or lot traveler. Some use complex and numerous spreadsheets where data is entered at the time of measurement. Others use text files or modify PDF records created by an engineer to record the results from the fab floor. An automated manufacturing site will almost always prefer automation-driven data upload and collection systems where data is pushed into storage so that users can report on the data using statistical process control or business intelligence solutions. Each method has benefits and drawbacks which must be considered, and every method is most effective when the data collection process is uniform and consistent.
Many points of data collection such as adaptive control system data, tool performance data, critical dimension data, and sort data are required to control complex processes. As you can imagine, data collection on a widget configured with 50 required data collection points becomes rather untenable with a paper-traveler or spreadsheet model. Ultimately, as the volume and complexity of products and processes increases, the number of data collection points and frequency of data collection will also increase. Therefore, the volume and complexity of products and processes will offer some indication regarding the suitability of manual versus automated methods for the collection of manufacturing data.
Of the three questions to consider, the “where” question may be the easiest to answer. Over the past several years, costs associated with server hardware, disk space, and data duplication have dramatically dropped. The number of solid and reputable free, open-source, and commercial database engines have dramatically increased. As a result, a properly structured database is the only viable and sustainable solution for data storage. While all database engines have their particular strengths that may distinguish one from the other, the purpose of the database—for the most part—remains the same: store relevant data in an organized, efficient, and secure way and make that data available for use in other processing and reporting systems. When it comes to where data should be stored, there is only one answer.
Data collection helps engineers, process teams, and quality control staff to understand how the material is being processed, the process itself, how a prior step can improve a current step, how a current process can impact a future step, and where improvements can be made to the product being created. The more complex the product or process, the more robust the data collection method. While we may shy away—or even actively resist—data collection that exists in our personal lives, the importance of intentional, targeted and organized data collection is critical to quality, productivity, and profitability in manufacturing.
To learn more about improving manufacturing efficiency, check out SYSTEMA's guide to digital transformation.
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