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VQ (Vendor Qualification) and IOQ (Installation and Operation Qualification)audits are implemented in warehouses to ensure all equipment being turned overin the fulfillment network meets the quality standards. Audit checks are likelyto be skipped if there are many checks to be performed in a short time. Inaddition, exploratory data analysis reveals several instances of similar checksbeing performed on the same assets and thus, duplicating the effort. In thiswork, Natural Language Processing and Machine Learning are applied to trim alarge checklist dataset for a network of warehouses by identifying similaritiesand duplicates, and predict the non-critical ones with a high passing rate. Thestudy proposes ML classifiers to identify checks which have a high passingprobability of IOQ and VQ and assign priorities to checks to be prioritizedwhen the time is not available to perform all checks. This research proposesusing NLP-based BlazingText classifier to throttle the checklists with a highpassing rate, which can reduce 10%-37% of the checks and achieve significantcost reduction. The applied algorithm over performs Random Forest and NeuralNetwork classifiers and achieves an area under the curve of 90%. Because ofimbalanced data, down-sampling and upweighting have shown a positive impact onthe models' accuracy using F1 score, which improve from 8% to 75%. In addition,the proposed duplicate detection process identifies 17% possible redundantchecks to be trimmed.

Applying Machine Learning for Duplicate Detection, Throttling and Prioritization of Equipment Commissioning Audits at Fulfillment Network