Leveraging AI for Demand Forecasting and Inventory Optimization

Discover how MTE-THOMSON uses data-driven insights and AI solutions to revolutionise the way they maintain and manage their inventory.

In the ever-evolving automotive aftermarket, we recognized the need to embrace the digital revolution and harness the power of AI. Our vision was to integrate AI into our demand prediction and inventory management processes, anticipating significant gains in these areas. RapidCanvas has been instrumental in turning this vision into reality. The transition from spreadsheet-based models to RapidCanvas's sophisticated AI model has been a game-changer. We've seen a 35% improvement in operational efficiency and a 50% reduction in time spent on manual adjustments, eliminating operational bottlenecks and streamlining our processes.

Arthur Strommer

Vice President, MTE-THOMSON

No items found.

‍

Introduction

MTE-THOMSON, founded in 1957 as Metalúrgica Termo-Elétrica, is a top Brazilian manufacturer of automotive temperature control and engine management systems. With over 3,000 products, including sensors and thermostats, they serve customers in more than 100 countries. Their focus on quality and innovation has made them the only producer of PTC and NTC thermistors in Latin America.

To stay competitive in the global market, MTE-THOMSON efficiently procures essential materials using data and AI/ML. This approach ensures the quality and reliability of their specialized automotive components, driving their success in the industry.

‍

Challenges Faced

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

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.

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 team began with 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

Increase in operational efficiency

Reducing the time spent on manual adjustments and allowing the supply chain team to focus on strategic tasks results in savings of over $200K.

Reduction in order suggestion errors

With fewer errors, the risk of stockouts and lost sales has decreased, potentially recovering $200K in missed sales annually, and also easing up nearly $500K with optimal stocking.

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.

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.

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.