Is demand sensing and shaping a key component of your company’s digital supply chain transformation?


Depending on who you speak with you will get varying definitions and opinions regarding demand sensing and shaping from sensing short-range replenishment based on sales orders to manual blending of point-of-sales (POS) data and shipments.       

Most companies think that they are sensing demand when in fact they are sensing replenishment, and shaping inventory safety stock. Or at best, they are forecasting POS or syndicated scanner data, and manually blending it with sales orders or shipments. Also, if you're using basic time series methods like exponential smoothing you can only forecast demand, not shape demand.

To truly sense and shape demand, you need more advanced predictive analytics methods to model those factors that influence demand other than trend and seasonality.  For example, price, sales promotions, in-store merchandising (feature, display, feature & display, temporary price reductions), store distribution, advertising, and more.

According to Oliver Wight’s book, Demand Management: Best Practices (2003), true demand is POS/syndicated scanner data, not sales orders or shipments.  Also, if you are upstream in operations planning, then most likely you are too far removed from all the downstream marketing activities to understand those key sales/marketing programming tactics that influence demand.  If you don’t have the capability to model those demand drivers that influence demand, then it’s impossible to shape future demand using what-if analysis.  Demand sensing isn’t just forecasting the demand signal it’s about sensing those demand drivers that influence the demand signal, and then, using what-if analysis to shape future demand.

If you are manually blending POS/syndicated scanner data with sales orders, or shipments then the accuracy of your replenishment forecast (supply plan) will most likely not be very accurate.

To gain the most value from demand sensing and shaping, it's important to connect demand to replenishment or shipments using the Multi-Tiered Causal Analysis (MTCA) process.  MTCA uses analytics methods, like multiple regression and/or ARIMA(X) as well as other advanced algorithms to sense and model the demand drivers.  It's a two-step process.

  1. Model the key demand drivers that influence POS/syndicated scanner data, and then, run what-if analysis to shape future demand.
  2. Take the demand history and the shaped future demand forecast, and use it as a leading indicator in a sales orders, or shipment model.

This process provides a framework to model the push/pull effects of a company's business, and has been proven to significantly improve the accuracy of the supply plan, which is normally referred to as the shipment forecast.

In other words, you link the two-data series together through the data using advanced analytics, not manually blending them together.  We all know any manual blending and/or overrides add personal bias whether intentional or unintentional increasing error.

The key is being able to do consumption based modeling using the MTCA process for thousands of products automatically up and down the company’s business hierarchy across markets, channels, brands, product groups, SKUs, demand points, and/or customers.  It requires large-scale automatic hierarchical forecasting technology, that's driven by artificial intelligence (AI).  AI uses a rule-based production system that automatically builds and updates the models as more data and causal factors are added to the system.

So, beware of software vendors who say that they can do consumption-based forecasting – demand sensing and shaping – as well as AI/Machine Learning. Only a vendor with robust experience in forecasting and advanced analytics, like SAS, can pull this off.

If you can only sense and forecast historical demand and sales orders or shipments six weeks into the future you need to ask yourself if that is really demand sensing and shaping?  It sounds to me like replenishment forecasting and inventory safety stock optimization.  I’ll let you decide.

So, are you truly sensing those demand drivers that influence the demand signal, and shaping future demand, or are you just forecasting demand and manually blending demand with sales orders to sense replenishment for short-range (1-6 weeks) supply planning?


About Author

Executive Industry Consultant/Trusted Advisor, SAS Retail/CPG Global Practice

Charles Chase is the author of Next Generation Demand Management: People, Process, Analytics and Technology, author of Demand-Driven Forecasting: A Structured Approach to Forecasting, and co-author of Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation, as well as over 50 articles in several business journals on demand forecasting and planning, supply chain management, and market response modeling. He is the executive industry consultant and trusted advisor for the SAS Retail/CPG global practice, and writes a quarterly column entitled, “Innovations in Business Forecasting” in the Journal of Business Forecasting. Author page

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