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