Since the time of Covid there has been continuous dialogue about challenges manufacturers face when trying to grow or diversify (multi-source) their supply chain. At Michigan Manufacturing Technology Center (MMTC), we spend a LOT of time on the topic. In fact, the Technology Adoption Team often discusses these challenges with our colleagues in Market Research. Initially, we reviewed the impact of matchmaking sites. Perhaps the biggest challenge is getting enough participation and data from manufacturers to make the site useful. That discussion led to an idea: using AI scrapers and analytics to collect data from public websites to build a large enough database to be immediately viable. Unfortunately, this approach is also challenging because many websites are abandoned and floating adrift on the web. For example, if a given site wasn’t generating a lot of traffic, or if the original person responsible moved on, up, or out, no one may be watching it, and the information may be obsolete or incorrect. Whatever the reason, an unkept or outdated website means bad data, and as a longtime Lean Six Sigma practitioner, “Garbage In/Garbage Out” still applies. Data collected from weed-filled data fields isn’t going to be very useful.
If supplying manufacturers wish to diversify or manufacturers wish to improve their supply chain, they will need to do certain things to be “found” digitally.
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