The purpose of this article is to share our experience in the use of machine learning to generate quick saving opportunities and homogenize purchased pricing across business units / regions in large groups.
Context and challenges within a multi-BU group
It could rapidly become cumbersome for a large group and its supply management team to ensure consistency of prices on a specific category.
Those gaps can be normal but need to be controlled and understood so that suppliers or R&D can be challenged.
Even with a corporate organization and systems, Business Units are often independent or even worse, siloed. They could implicitly refuse to play it global because they need to go fast and stay independent. But, beyond organization & cultural behaviors, main barriers are technical & IT related:
For sure, the traditional should cost approach and solutions can help solve these issues.
However, they are often complex to implement on a large scale, because they require a lot of manufacturing process related information, strong technical expertise and deep & time-consuming cooperation with suppliers (open book policy is mandatory).
Refer to our article “Supply Chain optimization: a total cost approach”
Many firms have developed an innovative & effective approach combining advanced analytics and predictive algorithms (derived from Artificial Intelligence) to generate quick savings opportunities, only by processing your existing data.
The methodology is based on 5 pillars:
The following benefits have been observed:
Machine learning based costing solutions
Machine learning based costing solution which estimates the price of a new product or service by processing current/historical data through a sophisticated algorithm, such as “Random Forrest”. Random Forrest is a nonparametric statistical method that performs learning on multiple decision trees driven on slightly different subsets of data generated by Bootstrap techniques (Ref. Breiman, L., Random Forests. Machine Learning. 45, 5-32 (2001)).
This type of methods allows to estimate the price of a product/service based on pre-identified parameters called “cost-drivers”. The estimation is very quick and accurate (30% of increased accuracy in comparison to traditional statistical methods).
The main advantages of machine learning based costing compared to traditional methods are:
For all these reasons machine learning based software helps create a very robust and ready-to-use costing model.
Beyond the above, machine learning based costing solution is processing all current purchasing prices and identifying inconsistencies / gaps versus estimates, which makes it easy to identify savings opportunities, including negotiations with suppliers.
Finally, some solutions have integrated benchmark functionalities which allow to compare each BU/Region for a specific category (even through products that have different design & characteristics).
They are increasingly utilized in industry and therefore is, every day, adding external benchmark knowledge for each commodity (IP and confidentialities being respected).
This allows to create benchmarking communities and share further (life sciences, automotive…)..
Examples of software
Statistical Parametric Model
Non-Parametric Statistical Model
« Random Forests »