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How to Approach Demand Forecasting Using AI

How to Approach Demand Forecasting Using AI

We will revise the forecasting program to match the sales trends of your products!

First, we will provide the daily and item-specific sales data that is the subject of the issue, conduct forecasts using the IPS demand forecasting program, and analyze the discrepancy between the forecasted values and the actual values as the forecast error to verify and analyze the level of accuracy achieved. Next, we will analyze the trends in the discrepancies between the output forecasted values and actual values, and set the goals and KPIs that should be achieved in this initiative. Finally, we will modify the forecasting program to match the sales trends of the customer's items. 【Approach to Demand Forecasting】 1. Simple analysis using the demand forecasting AI (prototype) 2. Accuracy trend analysis and setting of goals and KPIs for each item 3. Program modifications for accuracy improvement for each customer 4. Achievement of goals and KPIs *For more details, please refer to the related link page or feel free to contact us.

  • Other production management systems
  • Other contract services

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[Examples of Demand Forecasting Initiatives] Optimization of Inventory and Standardization of Production Planning

[Examples of Demand Forecasting Initiatives] Optimization of Inventory and Standardization of Production Planning

We will support you in considering how to manage the standard inventory and reflecting it in the production plan!

The initial consultation brought in was about the low accuracy of monthly sales forecasts prepared by the sales team, which resulted in frequent stockouts. We proposed calculating the standard inventory and safety stock based on sales forecasts and forecast accuracy, and conducting planned production based on the derived production quantities. It is important not only to predict demand but also to change inventory management methods and production planning approaches. We will support you in considering how to hold standard inventory based on sales rank and forecast accuracy, and reflecting this in your production plans. 【Features】 1. Daily data is decomposed into four components: "trend," "weekly seasonality," "annual seasonality," and "monthly seasonality" (additional factors for holidays and events can be included). 2. An error term is added to the elements from step 1, combined linearly to create a forecasting model. 3. The model's output is aggregated monthly. * This will serve as an example of forecasting on a monthly basis using item-specific and daily sales data. * For more details, please refer to the related link page or feel free to contact us.

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  • Other contract services

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