Corporations are engaged in some form of AI strategy to enhance their business operations primarily focused on customer service and some form of digesting documents that have been sheltered from direct access. However, there is a gap between automating customer service or traversing thousands of previously untapped documents and actually apply AI to the detailed information that profiles what they engineer as products they deliver to market.
This gap exists because AI can’t process information about engineered products until those engineered products are fully profiled. Profiling engineered products must start will a fully defined Part Classification structure. Some companies feel that Engineers will make use of a generic description field or attribute that will have consistency: meaning clear definition that includes no typos or missing information. Description fields are not sufficient because there is no guarantee that everything needed to be in that field will be there for Part Searching, let along AI processing.
When a bolt or screw needs to have head type, thread count, diameter, and length to even generally describe its profile, it is critical to ensure that all of this be applied to that bolt part and it’s not going to be ensured simply through a description field/attribute. Instead, the company has to invest in establishing a part classification structure that ensures that a bolt has the fields / attributes specific to its profile, just as it must have a different profile to describe a washer, such as its type, thickness, outer diameter and inner hole diameter.
Once a company has formed this detailed Part Classification structure, this information can then be posted for AI analysis and insight development of which products are made up of specific parts and subassemblies. This can then be joined with sales and return rates by market or region to determine what products to alter or remove from market due to engineering variations of the product line.
If a given product is not performing in hot / humid weather conditions in the southeast region of the US, causing product failure / returns, then altering the reengineering of the product (creating a new variance to select parts or subassemblies) may be required. AI can identify other products composed of similar parts or subassemblies that will have similar results in geographical locations with similar weather conditions. That then informs the business as to whether a broader reengineering is required or whether simply not offering affected products in those regional areas is a more prudent approach (cost/benefit analysis done also by AI).
This one example could be followed with dozens of other examples but suffice it to say that the key to being able to have unprecedented visibility to these types of situations is more easily resolved by establishing a Part Classification structure. Digital Solution Group is the industry expert in working with companies to establish a fully defined Part Classification profiling of its Engineering information and has also been able to perform this activity “in place” without disrupting the current Engineering programs.
Contact Digital Solution Group via the contact form below or simply calling +1-603-566-5382. Leverage AI beyond its current limited value offering by unmasking the engineered products as they are positioned for sale in your target markets. Act NOW!