Automated Costs Allocation Based on Historical Inputs of Financial Controllers

Problem

A prominent company in the semiconductor industry was facing challenges in optimizing the cost allocation processes for wafer production and material costs. These costs are crucial in semiconductor manufacturing, and the existing manual methods were inefficient and prone to errors. The company needed a solution to accurately classify these costs into multiple categories, leveraging the vast amounts of historical financial controller data.

Solution

The solution was an advanced machine learning-based system designed to automate the cost allocation process. This system was developed to analyze financial controller data, specifically focusing on wafer production and material costs, and classify these costs accurately using a multi-class classification approach. The project involved a detailed analysis of the financial data, followed by the application of several machine learning techniques to find the most effective method for cost classification.

Tools Used

The project employed a variety of machine learning tools and techniques to achieve its objectives:

• Regression Analysis to understand the relationships between different factors and cost allocations.
• Decision Trees for their simplicity and ease of interpretation, providing clear decision paths.
• Naive Bayes for its probabilistic approach, suited for handling high-dimensional data.
• Neural Networks to model complex, non-linear relationships inherent in the financial data.
• Ensemble Methods to enhance predictive accuracy by combining multiple models.

Integration with the SAP Data Intelligence platform was a crucial step, ensuring that the machine learning algorithm could be directly applied within the company’s existing procurement processes.

Outcome

The implementation of the machine learning-based cost allocation system marked a significant improvement for the client. The system achieved a 90% accuracy rate in automatically allocating costs, significantly streamlining the process. This automation allowed financial controllers to focus on the 10% of cases that required manual intervention, thus optimizing both time and resources. The project not only resulted in cost savings but also set a new standard for financial process efficiency in the semiconductor industry.