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$900K
savings due to ML and actionable intelligence.
90%
accuracy after two rounds of training.
50%
efficiency gains for FTEs using the ML model and tools.
Back
A global manufacturer struggled with fulfilling the demand for an array of necessary parts due to the impacts offree form text fields in the purchase order process.
By applying rigorous research to product classification, executing process improvements and implementing machine learning to enriched and connected data, the resulting solution matched requested parts with the master catalog and known supplier data. The manufacturernow has access to actionable intelligence and spend analytics for improved efficiencies and savings, with the machine learning and business logic solution improving over time for long-term value.
Free form text fields used to request parts unlisted in a manufacturer’s material master made tracking, fulfilling and anticipating demand for these products difficult, and impacted reporting accuracy and ultimately the ability to negotiate prices. The manufacturer wasunsure of the scale of the issue or how to tackle it within their system.
They wanted to understand:
Aligned Automation applied deep product research and extracted detailed classification, working with the manufacturer to categorize products and develop business logic. An AI/ML algorithm librarywas inserted into the material master to extract part numbers, check for similarities and ultimately recommend the best matches. This led to a PartExtraction Engine with the ability to continuously learn and improvise.
Utilizing Natural Language Processing, the Part Extraction Engine “read” short and long free form text descriptions, matching requested parts with those in the material master as well as with connected supplier data. With this machine learning-driven triangulation,automatically detecting and ordering the correct part at the best possible price could be achieved.
In 90% of the cases the ML engine was ableto correctly identify the part from FFT, verify stock and pricing with suppliers, add the item to the material master and order. Manual effort was cutby 50%, with constant improvement to speed and quality.
The team used the data to create a monthly reporting dashboard to track FFT requests and spend. The manufacturer canregister spending and ensure category managers are able to control the fullvolume of customer demand, while freeing employees for higher-value work.
With accurate spend reporting andactionable intelligence, the company can better predict customer needs and negotiate consistently with OEMs and vendors. Over time, the machine learningand business logic solution will become faster and more effective at accountingfor FFT field, leading to long-term value savings.
All three categories where Aligned Automation applied ML have seen thousands of free form text instances optimized, resulting in nearly $900K of savings.
