CYLAD and Ekimetrics utilize big data technologies to reduce purchasing costs for a client in the aerospace industry.
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CYLAD Consulting together with Ekimetrics, a partner company specialized in data science, were awarded a project to support ongoing purchasing cost reduction for an Aerospace company. The main challenge was to achieve an additional 2% savings on the recurrent purchasing cost base for 2017, and an additional 4% for 2018.
The challenge was major due to specific factors:
The short project duration – 5 months
Two years of prior effort by the client to reduce the purchasing cost, leveraging mainly suppliers’ negotiations and short-term improvement levers
An industry characterized by the difficulty to create supplier competition due to qualification and industrialization barriers
The project’s target was achieved as a result of several levers:
The development of a smart data analytics tool facilitating an analysis of the annual spend which rapidly detected additional short-term negotiation levers
The prioritization and negotiations based on a full knowledge of suppliers and business opportunities
The automation of the preparation for negotiation with suppliers
The automation of the forecasting of savings, tracking of commitments, and monitoring of execution
The analytics tool developed by the team could scan more than 2000 suppliers, 100K articles, and 3M purchasing order lines over a three-year period with identifying opportunities for cost savings. Such savings included gaps between contracts and purchase orders, internal price comparisons, and serial-to-spare price ratios.
A total of 150 suppliers were identified with significant potential savings. Once put into perspective of each specific OEM/supplier relationship and context, 50 of those suppliers were selected for a new round of negotiations. These negotiations were prepared with fact-based optimization levers identified from automated outputs of the analytics tool. This created drastic time savings between analysis and negotiation while enhancing the preparation phase.
With the help of the tool, the team automated the otherwise time-consuming tasks of forecasting savings, tracking commitments, and monitoring execution.
The complete work package required the use of Big Data technologies to aggregate various voluminous data sources. In specific terms, it created a bridge between purchase-to-pay data (contracts, orders, and invoices) and technical data covering the full purchased products life cycle (original parts, spares, and repair).
It was the combined business expertise and data science skills of the team that generated tangible results for the client. The business experts defined proper axes for identifying savings while the data scientists implemented these with a reduced timeline and cost. The project thus achieved the fixed annual target of recurring cost gains over a purchasing scope that was reduced to the last five months of the year. The ROI of this engagement can be estimated to a rough order of magnitude of 1 to 50.
