Demand Forecasting


The challenge

The company has a focus on customer satisfaction. An important indicator for this is a quick delivery of the ordered product. The success of this process depends on accurate forecasts and related human resource planning. A bias in the current demand from the forecasted demand can lead to lower productivity, less effective use of resources or loss of customers.

At the moment, the forecast is made by taking the sales of last year and adding a markup of 5%. In practice, this deviates on average about 15-20% and in external cases up to 80%. This leads to inefficiency in the operation and a possibly lower customer satisfaction due to a delayed delivery. 

The Solution

The expected demand depends on various factors such as weather, promotions,  seasonality and time. Through a combination of internal expertise, local data and Artificial Intelligence, an accurate forecasting algorithm can be developed to calculate the orders and  the optimal amount of (flex)forces that are needed to process the orders.

Often, AI systems make decisions, but end users are in the dark about how conclusions were reached, and if challenged the systems themselves can’t provide any reasoning retrospectively. The Genius Platform provides much-needed clarity to understand how and why a decision was made. The end user can follow the decision making process of how the predicted orders are calculated and why the amount of calculated forces are needed.

The Outcome

When using the demand forecasting tool:

  • business analysts are able to make a more accurate forecast by using the model that includes all relevant parameters in the decision;
  • warehouse managers are able to make more accurate predictions how many workers they need to process the orders efficiently;
  • marketeers get insights what drives customer demand and how this demand can be influenced;
  • the company has an integrated solution where the forecast of the platform is connected to the clients’ ERP systems;

Genius’ ability to explain its own actions is particularly important to the demand forecasting project, because all the different stakeholders should understand why it’s a good decision to hire more or less employees to process the orders.

This new tool is expected to increase the productivity by approximately 15% while reducing the inefficiency costs.

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