Context: Why is anticipating demand now strategic?
In the retail and distribution sector, logistics and supply chain management play a crucial role in the overall performance of companies.
The ability to anticipate demand has thus become a key factor for competitiveness.
For Saint-Gobain, a leader in the distribution of construction materials, operational efficiency relies on precise anticipation of future demand.
- Stockouts harm customer satisfaction.
- Costly overstocks directly impact profitability.
Faced with these challenges, Saint-Gobain is modernizing its forecasting processes to increase accuracy and efficiency.
Operational Challenges: Improving sales forecasting and product availability
The challenge is to provide demand forecasts for each item that are more reliable and accurate than those generated by existing business tools.
This objective is twofold:
1 – Reduce stockouts:
Guarantee optimal product availability to meet customer expectations.
2 – Optimize storage costs:
Limit overstocks and their financial impacts.
Metrics used to evaluate prediction performance and improvement
1. Bias
- Measurement of the average overestimation or underestimation of forecasts compared to actual values.
- Example: A reduction from +5.8 units (existing) to +2.2 units (model co-developed with MARGO).
2. WAPE (Weighted Absolute Percentage Error)
- A weighted error measurement used to calculate forecast accuracy while accounting for the relative importance of each product or category.
- Example: An improvement in accuracy from 44% (existing) to 38% (model co-developed with MARGO).
These indicators are particularly suitable for evaluating forecast quality in a logistics context where the impacts of prediction errors (stockouts or overstocks) can have significant operational implications.
These are essential for limiting critical errors and their operational repercussions.
MARGO's Response: Forecasts optimized by machine learning
MARGO developed a custom machine learning solution, integrated directly into Saint-Gobain's business tools.
This solution relies on advanced predictive models capable of providing more accurate forecasts than those generated by existing algorithms.
Main stages of the intervention:
1. Historical data analysis:
Evaluation of the existing system and identification of biases in current forecasts.
2. Construction of predictive models:
Implementation of machine learning algorithms capable of better interpreting historical data to produce more reliable forecasts.
3. Integration into business tools:
Predictions from the machine learning models are integrated directly into the replenishment systems, enabling flow automation and adjustable forecast maintenance by pilots.
4. Monitoring and validation:
Comparison of new model performance with existing forecasts to validate their effectiveness on key metrics such as WAPE (Weighted Absolute Percentage Error).
ROI
MARGO's intervention generated significant benefits:
Productivity gain:
Automation of long-term forecasts, allowing teams to focus on strategic tasks.
Expanded coverage:
Over 100,000 monthly forecasts produced, covering a wide range of goods.
Improved forecasting performance:
Bias reduced by 50%: Decreased from +5.8 units to +2.2 units.
In Conclusion: Optimized logistics for sustainable performance
Thanks to MARGO's expertise, Saint-Gobain has transformed its forecasting processes by leveraging advanced technologies.
This collaboration has optimized storage costs and strengthened product availability, a crucial element in maintaining customer satisfaction.
By relying on cutting-edge machine learning technologies, MARGO has enabled Saint-Gobain to reach a new milestone in managing its logistics flows, while offering a sustainable competitive advantage in a constantly evolving sector.
Are you facing similar challenges? Contact us!
MARGO supports you in solving complex problems, combining technical expertise, operational challenges, and strategic objectives. Thanks to our expertise in Data Science and a methodical approach, we design tailor-made solutions that meet the specific expectations of each client.
Our areas of intervention:
- Exploitation of heterogeneous data
- Process automation
- Supply chain optimization
Transform your challenges into opportunities with innovative and tailor-made solutions!
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What are the consequences of poor demand forecasting in distribution?
Poor forecasting can lead to stockouts, harming customer satisfaction, or overstocks, increasing storage costs and reducing profitability.
How did Saint-Gobain modernize its logistics forecasting?
Saint-Gobain collaborated with MARGO to implement a machine learning solution integrated into its business tools, allowing for more accurate demand anticipation and limiting critical errors.
What are the key indicators used to evaluate forecast quality?
- Bias, measuring the average overestimation or underestimation.
- WAPE (Weighted Absolute Percentage Error), an error measurement weighted according to the importance of the products.
What are the benefits of using machine learning for logistics forecasting?
- Improve forecast accuracy
- Reduce stockouts and overstocks
- Automate flow management
- Help teams focus on higher value-added tasks
Why use WAPE to measure forecasting performance?
WAPE takes into account the relative importance of each product, making the evaluation more representative in contexts where volumes and stakes vary significantly from one item to another.
