With some of the auto makers leading the 5 o’clock news headlines these days (not in a good way, I’m afraid), I asked our industry expert Ed Scott how some of these problems might have been averted. Here is his take on taking the right steps to a solution… Mike T.
Managing the quality of product is paramount to a supplier’s profitability and even survival. If you turn on the news you can see the effects of poor quality across many companies and industries. Often supplier quality is dependent not only on parts that are designed and produced by the supplier but also the parts purchased from external suppliers. Even though the root cause of quality issues may not be their responsibility the supplier could still be responsible for identifying defective units delivered to the final customer. This can be a daunting task without an automated system that can track the work done internally to produce a part and also linking the supplier’s data of purchased parts. The ability of being able to identify quickly which parts are defective can have huge financial and brand ramifications. It’s amazing that small parts can cost a company millions of dollars in recall and containment actions not to mention the damage done to the brand image. So how do you mitigate the risk? From my perspective I would take a multi-prong approach. Obviously you need to design in the quality but you also need the information to detect and contain quality upsets faster. The first step is the implementation of a traceability system that can track when and where you produced your parts linking this information with other critical characteristics that affect quality like process information and supplier lot information. This would allow you to respond more quickly to quality upsets and help reduce the size of product recalls.
Ed Scott
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Michael Tay is Manager of Sales Engineering at Pavilion Technologies, a Rockwell Automation company, specializing in biofuels, ag processing, drying, energy and other manufacturing solutions. He has extensive experience in identifying and deploying innovative solutions across a broad range of industries. Michael has over 30 years of experience working in the process industries in the areas of model predictive control and optimization.
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