Data Driven Cost Estimation : Taking Cost Savings To The Next Level

Companies need to adopt a more data-driven, strategic methodology to derive savings from these strategic partners. This is when Machine Learning and data driven approaches can renew the function and accelerate efficiency.

Traditional cost reduction measures are becoming less and less effective, as supplier networks become more strategic in nature. Value within the supply chain is no longer solely a function of the lowest product or service provided.

Therefore, it should not come as a surprise to see supplier leverage moving away from the purchasing organization and becoming more neutral (a zero-leverage scenario based on the give and take of vendor/supplier dynamics).

Let’s assume we are comparing two screws within your Fasteners purchase category.

Both screws have identical raw material, head type, and finishing, and very comparable length, width, heat tolerance, and thread length, and are purchased in similar quantities over the year, however the price of one screw is 25% more expensive than the other. That 25% difference in price cannot be explained based on its attributes, and therefore becomes the basis for cost reduction at the SKU level.

This simple example, which most likely exists several times over within your purchase portfolio, describes precisely what easyKost uncovers for its users. It is a fast, accurate, easy-to-use tool that delivers serious results.

Mr Jason Mallory
KEPLER N-America

EasyKost is cost modeling software utilizing machine learning and your own purchase data to identify pricing inconsistencies at the SKU/PN level. With KEPLER, they are working to help the client :

• establish cost savings,
• consolidate their supplier base
• reduce the number of Part Numbers in their overall portfolio.

Turbo-Charging Cost-Reduction With Data

Kepler Consulting has partnered with easyKost, a leading Costing software Company that utilizes Machine Learning to identify savings opportunities within a company’s existing purchase portfolio.

Using a Random-Forest based algorithm, the solution compares your own internal purchasing data against the attributes of the products you purchase to identify inconsistencies in your supplier’s pricing.

The discovered inconsistencies are then used as the basis for renegotiating pricing with suppliers.

This thoughtful, quantitative approach is helping numerous companies around the world turbo-charge their savings achievement, streamline the supplier base and create an overall more efficient supply chain.

Possible Use of Cost Estimating & Data Mining Softwares

• Estimation of the cost of a new product/service with near-perfect accuracy
• Identification of savings opportunities across your product categories
• Provision of real time & anonymous benchmarks on specific categories

Random Forest : Cost Estimation Based on Drivers

• Cost is estimated through a thousand decision trees in a random forest.
• The method leads to a 60%+ increased accuracy compared to traditional statistical methods.

By extracting attributes from Client drawings, our off-shore data team based in Chennai India, creates a database that captures all cost drivers per part number.

• Cost drivers are defined by family using technical expertise
• For each family, a database is created capturing cost drivers by part
• Cost drivers are normalized to improve the model
• The full catalog of attributes is provided to Client to close the project