My February 28 column introduced the idea that supplier collaboration would be easier with supporting software. The purpose of that column was to give visibility to manufacturers—OEMs and their suppliers—that such tools do exist. It was also to enlighten supply-management-focused tool developers that there is a market for apps that facilitate customer-supplier collaboration. This column is a follow-up to that article.
I became aware of the tool described in this article when I spoke at a January conference in Palo Alto that focused on—and was attended by representatives of—the consumer electronics industry. This industry is a bit unique in that many of the OEMs who design and market products don’t manufacture them. Rather they depend on first and second tier contract manufacturers for this, most of them located overseas.
Back in the day when I was a materials manager, one of the biggest difficulties I faced was the management of and collaboration with distant suppliers. This problem has increased in magnitude since then, due to the increased complexity of products and their production, overall relationship management including language and cultural differences, the cost of travel, the lag time associated with travel, etc. The problem magnifies when a contract manufacturer is relied upon for producing a large part of or all of a product.
In the old days when my people traveled to a supplier, regardless of where it was located, they carried a Polaroid camera (talk about dating myself!) since it is true that pictures do speak a thousand words. They used the resulting pictures to increase my organization’s ability to collaborate more effectively with their suppliers, all the way from product development through production.
At this conference I learned of a manufacturing platform using a similar strategy to provide collaboration capability that is light years ahead of what we could accomplish. Called Instrumental, the platform takes images at key stages of product assembly, in development and production, transforms them with AI, and then presents data that facilitates communication between and input from all involved parties, including suppliers. I’ll illustrate this with the following example of some of the collaboration this tool facilitates:
The best products are those based on input from OEM and supplier functions that are involved in design and/or order fulfillment to the end-use customer. Design reviews are generally the basis for such collaboration. Their purpose is to ensure customer safety; that designs take into account manufacturability and quality as well as to reduce time and cost in the product and in its manufacture. In other words, all factors that contribute to a product’s time-to-market.
In the past, since design reviews were generally face-to-face, they were difficult to schedule, costly to conduct and introduced a significant amount of non-value added time i.e. lag. You may ask “why did they need to be face-to-face?” The answer is that there was a dearth of tools that allowed effective electronic collaboration.
The visibility this tool provides can make communication between OEMs and suppliers more effective and reduce or eliminate the need for face-to-face interaction, cutting cost and lag time. This is accomplished by providing holistic product images of every unit as it gets built—in real time—which enables teams to pinpoint where in the process issues arise and provide tangible evidence for their causation, whether it be upstream or downstream. Suppliers have also used the tool as a data record system to meet regulatory requirements such as PPAP (Part Production Approval Process) or to protect themselves against downstream customer poor quality claims.
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On the production side, the software provides suppliers and customers with real-time defect rate data. This feature has proven particularly effective in ensuring that operations teams are alerted to shifts in processes or unexpected upstream changes, thus increasing understanding of manufacturing processes and enabling proactive mitigation. This means that suppliers and customers can align on root cause of defects and work to solve them together.
I must admit that my interest in this tool was first sparked by the overall strategy behind its development: specifically, reducing the time-to-market of new or revised products. In other words, to reduce their “true” lead-times in the various areas of product development and order fulfillment.
As regular readers of this column know, reduction of “true” lead-times is something I tend believe is the most effective strategy in prioritizing continuous improvement efforts, i.e., reducing cost. Bottom line: companies that can develop and deliver quality products to the customer faster than their competition will generate financial return on investment sooner and are more likely to succeed in the markets they enter.
The second feature that interested me in this product is its capability to transform production data using flexible AI algorithms. It can do this with as little as data from 30 units.
From my time in inspection management, I learned that a 30-piece sample size gives +/- 6 Sigma probability, i.e., delivers 99-plus accuracy in predicting process capability. The difference between this product and other AI-derived products is that while AI alone can be a tool to improve quality and increase yield, this product has been optimized for manufacturing datasets and environments. It also provides a drop-in solution (requiring zero system integrators), integrates with MES, augments engineering workflows, and has the potential to ease supplier relationships.
Companies that limit collaboration with suppliers and/or within their own organizations; i.e., operate in functional smokestacks, increase the risks associated with not hitting deadlines and targeted costs. This product, and others like it, can help significantly reduce the chance of that happening.
Paul Ericksen is IndustryWeek’s supply chain advisor. He has 40 years of experience in industry, primarily in supply management at two large original equipment manufacturers.
