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<p>ABC Analysis is an inventory categorization technique used to classify and prioritize inventory items in an effort to better allocate business resources. A items are defined as the inventory items considered extremely important to the business, requiring strict oversight and control. B items are important to the business, but don’t require the tight controls and oversight required of the A items. C items are marginally important to the business. ABC Analysis aims to ensure the business-driving inventory items are effectively and efficiently managed. There are numerous single- and multiple-criteria approaches to implementing ABC Analysis. This thesis presents an analysis and comparison of multiple approaches, as they relate to Navy Weapons Systems Support (WSS) Command’s large National Item Identification Number (NIIN) inventory. Additionally, random forests are grown from the inventory metadata to identify and/or verify the attributes most strongly affecting fleet readiness goals. The model will allow WSS to focus resources not only on the correct NIINs, but in the correct areas of NIIN management. Better WSS resource allocation will result in higher fleet readiness, WSS’s primary goal.
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<br>Subjects: Clustering; Random Forest; Bootstrap Forest; Regression Trees; Supply Chain Management; Inventory Control; Spare Parts; Navy Enterprise Resource Planning; ABC Inventory Classification.</div>
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May, Benjamin</div>
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