By Neil Stickels, Infoglide Senior Solutions Architect
Product and inventory management (or Product Information Management) across an enterprise is an increasingly widespread problem. There are countless dollars being wasted on ordering, cataloging, and warehousing similar and, worse yet, duplicate parts. However, Product Information Management can be an arduous task because it involves combing through thousands, sometimes millions, of products and parts, and then grouping them appropriately. Typically this process has been a manual one, requiring thousands of hours of intensive labor by a large services organization. The cost of this type of solution is usually too large to provide an acceptable solution for small to medium-sized businesses.
A much more cost-effective solution is to create an automated way of aggregating all of the product data into a Master Product Index. To efficiently create a Master Product Index, a software solution can be used to take in the product list as well as a product taxonomy, if available. Next, the product list is classified using the taxonomy. Any products not classified result in an update to the taxonomy, and the process is repeated. This iterative process results in a Master Product List. The Master Product List is then coupled with business rules to create a Master Product Index. This Master Product Index then ties into all of the existing supply chain management services to return the most appropriate part.
Using similarity searching while performing the classification would improve the results even more. An auto-classification system using similarity searching technology applies sophisticated similarity algorithms to multiple sources of data in real time to provide not just exact matches, but similar or “fuzzy” matches as well. This approach alleviates different representations of identical data, as well as misspellings and typos that might occur during data transcription. For example, when comparing two chips, a 250 nm central processing unit produced by AMD is recognized as the same thing as a .25 µm CPU made by Advanced Micro Devices. By using similarity searching, the Master Product Index determines the closest match to a product requested, even if the exact match isn’t available.
Once the Master Product Index is created, it can be used by all facets of the supply chain management. For example, purchasing can use it in order to provide the most appropriate part number and vendor to use when ordering a given part. By coupling the Master Product List to business rules, the Master Product Index not only returns the best matching part, but also the most cost-effective part, saving costs associated with ordering duplicate parts and ensuring the best price for a part. Additionally, it can be used by an inventory system to combine inventories across multiple sites and consolidate similar groupings of products down into a single view.
Bottom line, an auto-classification system that utilizes a Master Product Index coupled with similiarity searching can efficiently solve the time- and resource-consuming problems faced within Product Information Management by providing a relatively hands-off, automated process.
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