Demand forecast prediction and orders prediction for 700k points of sales

‍Problem description

How to forecast and organise demand for products in 700ths point of sales?

Solution

• Based on historical data (transactions, inventory, transport time), including possible promotions/advertising campaigns/holidays/events in the area, there  was created an initial predictive Machine Learning model.
• In addition to the main model, several versions of additional models (champion-challengers, shadow models) were prepared.
• In SLS Studio was defined process:
  
  1. process estimating the predictive model, run according to a schedule (daily/weekly/monthly),
  2. Integration with the client's trading systems
  3. Integration with the client's warehouse systems
  4. Integration with the client's logistics systems
  5. Integration with systems containing promotion data
  6. Integration with systems containing data on advertising campaigns
  7. the process query additional prediction/forecast models that is used alongside the main model.

Key Results

• Efficient supply chain operations: integration with the client's trading, warehouse, and logistics systems can lead to smoother and more efficient supply chain operations. This integration ensures that inventory levels are aligned with demand, reducing unnecessary transportation and storage costs.
• Improved demand forecasting accuracy by 50% with ML algorithms: the initial predictive machine learning models, along with champion-challengers and shadow models, likely led to more accurate demand forecasts. By analysing historical data and considering various factors such as transactions, inventory levels, transport times, promotions, holidays, and events, the models provided more precise predictions of future demand and orders prediction.
• Enhanced inventory management: with better demand forecasting, the company optimized its inventory management. This means having the right number of products at each point of sale to meet customer demand without overstocking or understocking.

Why Choose SLS?

The SLS platform offers manufacturers:  

• Predictive capabilities to plan reference changes based on real-time data.  
• Automated alerts and reporting to ensure efficient production schedules.  
• Enhanced decision-making that minimizes downtime and improves resource utilization.  
  

With SLS, manufacturers can optimize production line efficiency and reduce costs, ensuring smoother operations. 

Questions Answered:

How can we avoid downtime caused by component shortages during reference changes?  

• Answer: By using SLS’s forecast algorithms to predict and manage component availability proactively.  

How can we reduce waste and optimize production schedules?  

• Answer: SLS provides real-time insights and automated workflows to align production schedules with available resources, minimizing waste and maximizing efficiency.