Leveraging AI for Demand Forecasting and Inventory Optimization

Introduction

Manufacturing organizations across various industries rely on the efficient procurement of essential components and materials to produce their specialized products. This crucial process not only ensures the quality and reliability of the final output but also impacts the competitiveness and sustainability of these organizations in today's global market. MTE-THOMSON, a prominent manufacturing organization, today manages the acquisition of a diverse range of materials necessary for the production and distribution of their specialized automotive components, by leveraging data and AI/ML. 

Challenges Faced

MTE-THOMSON was grappling with two interconnected issues that were impacting its operational efficiency and profitability. 

1. The first was accurately predicting the demand for its diverse range of products. Inaccurate demand forecasts could lead to overproduction or stockouts, both of which have significant cost implications. 

2. The second challenge was managing inventory at optimal levels. Without accurate demand forecasts, maintaining the right balance of inventory becomes a daunting task. Overstocking results in increased holding costs and risk of obsolescence, while understocking could lead to missed sales opportunities and customer dissatisfaction.

Solution Implemented

The team from RapidCanvas was able to finish the project in a span of just three months. The implementation was carried out systematically, making progress at every step:

‍Data collection and cleaning 

The next step involved gathering relevant data from various sources. This data was then cleaned to ensure its accuracy and reliability, which is crucial for the subsequent stages.

‍Automated modeling for demand forecasting 

With the cleaned data, the team then built a machine learning model specifically designed to forecast the demand for MTE- THOMSON's products. This model uses historical data and patterns to predict future demand, providing a more accurate and dynamic forecast than traditional methods.

‍Developing the inventory optimization system 

Once the demand forecasting model was in place, the team developed an inventory optimization system. This system uses the demand forecasts to determine the optimal stock levels for each product, taking into account factors such as lead times, order cycles, and safety stock levels.

‍Creating dashboards with insights 

The final step was the creation of a user-friendly dashboard. This tool provides an easy way for MTE-THOMSON to monitor key metrics, view demand forecasts, and manage inventory, enabling more informed and effective decision-making.

Results and benefits

‍Improved forecast accuracy

The Mean Absolute Percentage Error (MAPE), a measure of forecast accuracy, improved by 9%. This indicates a more accurate prediction of demand, reducing the likelihood of overproduction or underproduction.

‍Optimized stock levels

In 67% of cases, the average stock levels were reduced without an increase in stockouts. This means the company was able to maintain a leaner inventory without compromising on product availability.

Increased visibility and control

The new system provided greater visibility into the inventory management process. This allowed for more effective monitoring and decision-making, leading to better control over inventory levels.

‍Boosted Productivity

The supply chain team's productivity was enhanced as they could shift their focus from managing spreadsheets to more strategic tasks. This not only improved efficiency but also allowed for more strategic and proactive inventory management.