Intelligent Forecasting

Using real-time data and advanced analytics to understand and predict customer demand, Intelligent Forecasting enables companies to optimise supply chain and inventory management.

Intelligent Forecasting differs from traditional demand planning, which relies on historical sales data and assumes that future demand will follow the same patterns as the past.

Intelligent Forecasting is designed to be more agile and responsive, incorporating up-to-date information on factors such as changes in consumer behaviour, weather patterns, and economic conditions.

Intelligent Forecasting is becoming increasingly important in today’s market context as companies face various challenges that make traditional demand planning less effective, such as rapidly changing consumer preferences, increased value chain complexity, market disruptions, and social factors.

Get a demo

How Intelligent Forecasting works?

First, the exogenous data sources must be identified and analysed. It is crucial to dedicate sufficient time to this phase, as it not only narrows down the potentially vast field of variables of interest but also requires finding data sources that provide values for these variables in the format and timing needed for the forecasting process.

Next, select the ML algorithms to apply to estimate the effect that one or more exogenous variables, or a combination, have on sales.

Then, we will train and test the ML algorithms on historical data to select the exogenous variables whose effect on demand forecasting can be predicted with sufficient accuracy and is significant.

Finally, it enables data flows to systematically acquire the predicted values of these variables so they can be actively used to support the forecasting process.

Intelligent Forecasting by sedApta

Machine Learning algorithms are used to analyse the impact of one or more exogenous variables on past sales to estimate the impact these variables might have on future sales.

Once the exogenous variables of interest are identified, whether they are independent of the sector in which the company operates (e.g., macroeconomic indicators such as GDP) or sector-specific (e.g., trends in dietary habits in the F&B sector), the next step is to train and test specific Machine Learning algorithms. This is done to determine if, and to what extent, the forecast generated by the sedApta Sales Analysis module should be adjusted to account for external phenomena.

Depending on the type of forecasting process and the nature of the exogenous variables, these can be used to fine-tune the forecast both in the short term (days and weeks) and in the medium term (months).

Using ML algorithms in combination with ‘traditional’ forecasting algorithms available in the sedApta suite to consider exogenous variables offers numerous advantages, including:

  • More accurate and reliable forecasts
  • Increased ability to quickly and efficiently revise the forecast in response to changes in the context in which the company operates
  • Superior demand analysis capability, leading to a better understanding of the factors that most significantly influence sales, thereby supporting budgeting and rolling forecast processes.

Discover more

FAQ

  • Intelligent Forecasting uses real-time data and advanced analytics to understand and predict customer demand. Unlike traditional planning, which relies on historical sales data and assumes future demand will follow the same patterns as the past, this approach is more agile and factors in up-to-date information about external factors.

  • Exogenous variables include changes in consumer behavior, weather patterns, economic conditions, and other factors external to historical sales data. They can be general macroeconomic indicators, like GDP, or specific to your industry.

  • The first step is identifying the exogenous data sources to analyze, narrowing down the variables of interest and looking for sources that provide data in the format and timing the forecasting process requires. It's a time-consuming phase, but a decisive one for the quality of the model.

  • Machine Learning algorithms estimate the effect that one or more exogenous variables had on past sales, to calculate how much the forecast generated by the Sales Analysis module should be adjusted to account for external phenomena.

  • The algorithms are trained and tested on historical data, to select the exogenous variables whose effect on demand forecasting is predictable with sufficient accuracy and turns out to be significant. Only variables that pass this test make it into the model.

  •  Yes: depending on the type of forecasting process and the nature of the exogenous variables, Intelligent Forecasting corrects forecasts both in the short term (days and weeks) and the medium term (months). 

  • Macroeconomic variables are independent of the industry you operate in, like GDP; industry-specific variables relate to dynamics unique to your market - for example, trends in eating habits in Food & Beverage. Both can enter the model, depending on how significant they turn out to be.

  • Intelligent Forecasting works on top of sedApta's Sales Analysis module: the ML algorithms calculate whether, and to what extent, the forecast generated by Sales Analysis should be adjusted to account for the exogenous variables identified.

  • The model can include factors such as changes in consumer behavior, weather conditions, and economic conditions, as well as industry-specific indicators, based on how relevant they turn out to be for your sales.

  • Traditional planning assumes future demand will replicate past patterns - an assumption that holds up less and less against rapidly shifting consumer preferences, more complex value chains, and frequent market disruption. Intelligent Forecasting is built to be more agile and responsive in exactly these conditions.

About Supply Chain Planning

Value creation across the planning horizon

Optimizing Sales & Operational Planning is key to enable companies to reach their business goals. If your company aims to improve performance and surpass the industry competition, it must be able to implement an efficient business S&OP process, which will only be as effective as the technology it utilizes. 

Discover more

Talk to us about what operational intelligence could mean for your operations

Contact us