Increasing Cost Savings through Data-Driven Cost Estimation

The procurement function is taking centre stage in areas of risk mitigation, cost reduction, innovation, and guaranteeing overall business continuity as the COVID-19 Epidemic affects every facet of the business landscape. Supply cost reduction is proving essential for protecting and growing operating margins as businesses around the world look for ways to obtain a competitive advantage in this difficult market.

What Level of Artificial Intelligence Should Procurement Adopt?​

To save money from these strategic partners, businesses need to adopt a more data-driven, strategic technique. Machine learning and data-driven methodologies can then rejuvenate the function and boost efficiency.

As supplier networks expand, traditional cost-reduction strategies are becoming less and less successful.

Data-Powered Purchasing​

Three ways that data can improve performance

  • Calculate the price of a new good or service with almost flawless precision.
  • Find potential for cost savings across all product categories.
  • Provide current, anonymous benchmarks for particular categories.

Cost estimation using random forest and drivers

  • A random forest’s 1,000 decision trees are used to evaluate cost.
  • When compared to conventional statistical methods, the method increases accuracy by 60% or more.
  • In order to find discrepancies and improvement opportunities, the software examines any relationships between the actual purchase prices and the “cost drivers” of the good or service.
  • Data collection Our off-shore data team, situated in Chennai, India, builds a database that contains all cost drivers for each component number by extracting attributes from client drawings.

Cost factors are identified by family members utilising technical knowledge.

  • A database is built that includes cost drivers by part for each family.
  • To improve the model, cost drivers are normalised.
  • To complete the project, the client is given access to the complete list of attributes.
  • Machine learning estimates forecast the most competitive pricing that important strategic suppliers should be able to provide for currently available parts that are currently supplied by transactional suppliers.

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