Forecasting is the process of statistical forecasting whereas Demand Planning is the process of overriding the statistical forecasts. This article covers the forecasting process in Advanced Demand Planning.
The Forecasting Hierarchy
Every product based company has the same basic selling structure. It sells many items that are sold to many customers typically out of many locations. For some companies, it is quite OK to forecast an item based on its total sales. However, if you are a company that sells to retail, manages promotions, or is one to two tiers away from an OEM customer, you should consider forecasting by the context of your key customer, channel, product category, location, or other demand definition in order to improve the accuracy of your forecasts. Without context, going to sales and asking how a specific item or product family is going to sell guarantees a non commital response.
The planning hierarchy helps establish the context to which item forecasts are generated and managed. It is made up of two (3 if DRP is enabled) independent yet related hierarchies which for simplicity sake we will call the who and the what. The table that ties these elements together is the sales register in your ERP - the record of who bought what when, how much, and from where.
Without a proper hierarchy, the ability to roll up data using the sale record from the item level to any hierarchical level above becomes more complex. Once the hierarchy is built, it is significantly easier to pivot, present, and edit data.
The planning hierarchy may be left in its default state in DemandCaster which is Company (Overall) >> Customer >> Item. Most companies choose to add additional hierarchical levels between Overall >> Customer (the customer hierarchy) or Customer >> Item (the product hierarchy). They do this to better identify patterns in sales to help improve forecasting.
In demand planning, the forecast is typically generated at the customer level or below. As such, it is important that the "who or where the demand is created" level be correctly defined.
In DemandCaster, the Customer Hierarchy typically defines the who. This is who the items are sold to and is commonly composed of elements such as the end customer, ship from location, market, channel, region, or other top down one to many definition. The key comes to sales marketing and in turn how you sell and promote. This is something that the front end of your company understands and responds to.
For companies that sell to thousands of one off customers such as those in brick and mortar retail or internet retail, the definition of customer is singular i.e. there is one customer and it is their direct internet and retail channel..
Ask sales or marketing how they define and plan their customers. This will help define the customer for demand planning
When building the forecast hierarchy as described in the article Building a Forecast or Demand Plan Hierarchy, consider what grouping will provide the clearest picture of market behaviour and will be able to be easily reviewed by persons that have accountability. For simplicity sake, we recommend only two forecasting levels. The top aggregate level that sets the context to which forecasts will be calculated, and the bottom level where the forecast will be statistically calculated when middle-out approach is applied. The objective is to set the levels where the grouping is significant enough to generate a meaningful statistical forecast at an aggregate level i.e. defines seasonality and trend.
To view the hierarchy, click on the hierarchy button. In the example below, the context is item by customer.
The term customer is used to describe who/what creates demand. This could be a channel, discrete customers, or group of customers. The term location is typically used to describe a ship from location but it could also be a geography market location.
Once the forecast hierarchy is created, we recommend not changing it to ensure there is consistency from plan to plan.
Running a Forecast
Once the forecast hierarchy is determined, click "Run Forecast" to commence the forecasting process.
The Forecast Settings opens with the following options:
- Aggregation Type: Select either top-down or middle out to select the type of aggregate forecasting process. See below to understand how the different options produce a forecast.
- Bucket Type: Select monthly or weekly buckets.
- Forecasting Level: If top down is selected, only this option may be selected. If middle out is selected, this will be the top forecasting level.
- Bottom Forecasting Level: If middle out is selected, this will be the bottom forecasting level.
If Top-Down is selected, the forecast at an upper level of the hierarchy will superimposes the shape of the aggregate forecast down to the item by the weighted distribution of the items history within the context of the top level. For example if an item makes up 20% of the overall history of the customer, the items forecast will be 20% of the overall customer forecast. You will notice in the image below.
- The Middle-Out approach is the best of both worlds since the overall shape of the aggregate forecast is combined with the shape and trend of the item level forecast. Though the middle-out approach takes longer to process since it is running twice the numer of forecasts, we recommend it due to it generating the best possible forecast.
Once the options are selected, click "Run Forecast" to commence the forecasting process. The process runs the statistical forecasts, drives the forecasts down to the item level, sums up forecasts at each level of the hierarchy, calculates sales dollars and average sales prices as well as calculates performance metrics.
The time required to generate the demand plan is dependent on numerous factors including the number of customers, levels, and items being forecasted.
Please note that the forecast UI only presents the forecast results and the measures related to forecast. Any overrides to the forecast values will be performed in the Demand Planning section.
Access to the Forecast tab may be managed via permissions. We recommend only providing access to personnel who are responsible for generating and evaluating statistical forecasts.