Demand Sensing
Using real-time data and advanced analytics to understand and predict customer demand, Demand Sensing enables companies to optimise supply chain and inventory management.
Demand sensing differs from traditional demand planning, which relies on historical sales data and assumes that future demand will follow the same patterns as the past.
Demand sensing 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.
Demand sensing 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.
How Demand Sensing 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.
Demand Sensing 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.
Related Products
About Supply Chain Planning
Value creation across the planning horizon
Optimizing Sales & Operational Planning is, in today’s demand-driven markets, key to enable companies to reach their business goals. If your company aims to achieve “best-in-class” 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.