The power of data in demand planning: facts, plans, indicators

By Bregje van der Staak

In one of our previous blogs, we introduced the various types of data that are essential to enable smart-touch demand forecasting: facts, plans, and indicators. To shed more light on what these types of data can bring in a smart-touch forecasting set-up, we will dive deeper into this topic in this blog.  

Accurate data is essential within a smart-touch demand planning set-up (including machine learning). Data provides the foundation for building accurate models and thus increasing performance. The accuracy of the models used depends on the selection and quality of demand drivers. By using various types of data, such as facts, plans and indicators, these models learn to recognize patterns and make more accurate predictions. As the availability of structured data within large multinationals grows, the need arises to investigate what data archetypes to use for what purpose. 


Demand planning data archetypes 

Data available for demand planning can be classified into three categories: facts, plans and indicators. 

The role of facts, plans and indicator data in demand planning.

To make this a bit more tangible, we will touch on each data archetype in the next paragraphs. We will indicate typical examples of each type. To make it easier for you to recognize these types in your own organization, we will touch on some characteristics per data type. We will look at: 

  • Data ownership: who owns the data, who unlocks the data or where can you find the data 
  • Data availability: how frequently is the data available 
  • Data granularity: what is the level of detail in the data type 
  • Time horizon: for which horizon (short, mid or long) and in which direction (backward, forward) is the data available  



The first data archetype essential for demand planning is factual data. This resembles data that is available within your company and can be taken off the shelf usually (e.g., from your ERP system, data received from your customer). Usually, this type of data is very structured, with a lot of detail and available in high frequency. 

Factual data in demand planning

Factual data can both be historical data, such as historical sales or growth numbers. It, however, can also be future data, such as orderbook and contracts. Let’s say you have an agreement with one of your customers to deliver 100 tons of a certain product; you can already consider this in your demand plan. In the overview below, you can find some examples of factual data. 

Many companies already use some factual data in their demand planning process. Historical sales are often fed into a statistical forecasting engine to predict future demand. We also see more and more that other types of factual data are also integrated into the demand planning process. For example, order book can easily be included as a driver in a machine learning model (as our client Cargill did, see here). Overall, factual data is the data archetype that is most easily available and can be a great stepping stone towards smart-touch forecasting. 



The second data archetype is related to plannable data. This is data of activities that you are foreseeing for the (near) future. This data is typically related to future events, portfolio changes or activities that are specially generated as part of plans to influence demand.  

Next to internal customer data, it could also be related to customer activities or changes that your customers plan to execute. In general, this data is updated regularly but less frequently than factual data. Updates come from review moments, new information that is processed or because of business planning processes such as S&OP / IBP. 

The essential role of plannable data in demand planning.

This type of data is often already included in the demand planning process for many companies. The most obvious example is related to portfolio changes. You can think about including ‘phase-in and phase products’ as the first step in your historical data cleaning process. If you perform this in the right way, you can take the history of the preceding product and turn that into your succeeding product. This will make your baseline model much more accurate.  

Overall, plannable data is the data archetype that requires good alignments internally between different parties such as marketing and sales. Think carefully about your business planning framework in which you combine making the plans with a data integration set-up that brings efficiency and transparency. It could also require collaboration with your customers on the activities that they are performing.  


The last data archetype is indicator data. This concerns all forms of data that give an indication of the direction demand is likely to go in. Often these can be % indicators or expectations that determine a position versus a specific situation. Indicator data can be extremely varied. Typically, this data is more aggregated and less frequently available. 

Indicator data in demand planning

Looking at market data, you could think about oil prices for customers in the process industry, but also about the evolution of the flu for the life sciences industry. One of the most common examples is weather data to predict the number of ice creams sold. If you had this external data available, you would be better able to predict possible peaks and dips in your demand. However, the biggest issue with this type of data is the lagging effect. This means that external data is not always available when you need it. For example, weather data is only accurate as of 14 days prior. If your supply chain has a lead time longer than 14 days, this data is less interesting to include. 

The role of data in demand planning: operational, tactical and strategical level.


Where do these data archetypes add value to demand planning? 

The data archetypes mentioned above have very different characteristics. Because of this they will add value at different time horizons within your planning horizon. Factual data is typically adding value on the short horizon in which you perform demand sensing. E.g. using forward looking order book data to determine what your likely sales will be in combination with your forecast data. This can help to sense your demand and tune the short-term demand signal, thus impacting service and optimizing inventory. To really get the value out of these types of data, a certain level of automation is required, thus allowing the focus to go taking decisions with high speed.   

On the other end of the spectrum, long-term indicator data is mostly focused on more strategic decisions or mid-term decisions. Growth outlooks in your customers’ industry can give indication of demand changes you will see further out. Using such indicator data to anticipate can drive key decisions on network design. Integrating indicators is best organized by means of a scenario planning approach, which is reviewed at planned moments as part of your IBP process.  

In the mid-term, we tend to see that plannable data has the biggest impact. It is mainly focused on the tactical horizon, which is aimed at the improvement of demand planning and forecasting. Applying information on sales and marketing plans makes your forecast more realistic and thus allows to steer supply and inventory management decisions. Regular review of the forecast with the latest information is the best approach here. Avoid continuously reviewing the forecast because of plan changes, as plans tend to change multiple times. But also avoid never reviewing, as initial plans are highly unlikely to stay stable.  


Ready to elevate supply chain forecasting and unlock the full potential of demand planning?

Despite the steps made in the road towards smart-touch demand planning, many organizations do not unlock the full potential of the data they have available. To unlock this data, you need to know what you can do with it.  

That’s why we’ve created the smart-touch forecasting roadmap. 

In this roadmap, we’ll dig deeper into the hype surrounding complete forecast automation, advocating the crucial role of human expertise when applying statistical and machine-learning models. And we’ll provide you with practical steps to accelerate progress in your supply chain forecasting maturity level. Get your copy here.

If you would like to further discuss the data you have available and how to use it to make better predictions, please reach out to our forecasting specialists: Erik de Vos or Bregje van der Staak. 


future proof demand forecasting technique: smart touch forecasting

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