
Consumer packaged goods (CPG) companies are no strangers to disruption. But traditional statistical forecasting methods mainly rely on past sales data and are struggling to keep up with today's unpredictable market conditions.
As a result, companies need a more responsive approach to forecasting. This is where demand planning and sensing come into play, shifting supply chain management from reacting to issues as they arise to anticipating and addressing them before they become problems.
Demand sensing, fully integrated into a machine learning framework, leverages near real-time data to help companies adapt quickly to market changes. By incorporating detailed sales channel data, such as retailer point-of-sale (POS) data, assortment changes, promotions, local events and weather patterns, CPG companies can generate more accurate forecasts and automate responses to fluctuations. This enables faster reactions to market shifts, enhances forecast accuracy, reduces costs and ensures better product availability, which ultimately provides a competitive edge in today’s dynamic market.
The Importance of Demand Sensing
Demand sensing optimizes forecasting by moving beyond historical data. Statistical forecasting methods base their predictions on past sales patterns and on an aggregate level. This manual forecasting often fails to account for real-time shifts in consumer behavior or unexpected external events. In contrast, demand sensing processes large amounts of data from diverse sources, including internal, external and retailer-specific—at the sales channel level, such as a specific retailer banner on an SKU level— to offer a more accurate, real-time view of consumer demand.
For CPG companies, this granular visibility is invaluable. Demand sensing allows for daily forecasting updates, automatically adjusting based on the latest data. Whether it's a weather event, sports tournament, local holiday festival, sudden market shifts or unexpected promotions, demand sensing ensures supply chains remain agile. This significantly reduces manual processes and improves forecast accuracy.
One of the key distinctions of demand planning and sensing is speed. Both Machine learning-based demand forecasting and demand sensing identify trends for long-term planning and demand sensing is optimized for rapid response.
Challenges Limiting Statistical Forecasting Potential
Statistical forecasting has long been a cornerstone of supply chain planning, but its limitations are becoming more apparent. Historical data can only predict so much, especially when external disruptions like economic crises, global pandemics and price fluctuations create major shifts in consumer behavior. Traditional models are not designed to handle these rapid changes, which often result in overstocking, stockouts, and other operational inefficiencies.
Moreover, traditional statistical forecasting adjustments are often labor-intensive and slow. By the time planners adjust forecasts based on new data, the market may have shifted again. This lag in response time can lead to significant losses in both sales and customer satisfaction. The manual nature of these processes means that by the time adjustments are made, they are often too late to be effective. Maintaining traditional statistical models also requires continual manual oversight to ensure they remain up-to-date and function properly. Manual processes also increase the likelihood of human error, increasing the risk of making the wrong decisions in the supply chain.
Demand sensing mitigates these challenges by automatically integrating real-time data, freeing up planners to focus on higher-value tasks and ensuring forecasts remain up-to-date, even in the face of sudden changes. Machine learning forecasting should be applied across all demand planning, as it seamlessly integrates with demand sensing inputs. By using a single, unified technology, there are no additional integration requirements or new processes needed.
The Benefits of Demand Sensing
The advantages of demand sensing extend across the entire supply chain. By incorporating real-time data and machine learning, CPG companies can seamlessly integrate real-time data and demand sensing inputs to improve forecast accuracy, reduce waste and optimize inventory levels.
Three key benefits stand out:
- Improved Availability and Reduced Lost Sales: Demand sensing provides a more accurate picture of current consumer demand by incorporating real-time data, such as POS information from retailers. This allows companies to better align inventory with demand, reducing stockouts and ensuring that the right products are available when customers need them. For promotions or peak seasons, this level of accuracy is especially critical. With demand sensing, companies can immediately see how new products are performing through POS data and adjust plans accordingly, eliminating the delay typically caused by waiting for the next retailer order. This minimizes the risk of lost sales and maximizes revenue.
- More Accurate Supply During Promotions: Promotions often create spikes in demand that traditional forecasting methods struggle to accommodate. Demand sensing uses external data and machine learning to anticipate these fluctuations, dynamically adjusting inventory levels to match real-time demand during promotions. This means retailers can avoid both overstocking and stockouts, which often plague promotional periods.
- Reduced Waste and Spoilage: For companies dealing with perishable goods, such as food and beverages, waste is a major concern. Demand sensing helps mitigate this by continuously updating forecasts based on real-time data, ensuring that stock levels are aligned with actual consumer demand. This leads to better inventory turnover and less waste, contributing to both cost savings and sustainability goals.
Preparing for the Future with Demand Sensing
As supply chains continue to face disruption, demand sensing offers a solution that equips CPG companies to thrive, not just survive. The ability to process and react to real-time data provides a crucial edge in a market where agility is key. By shifting from reactive forecasting to a proactive, data-driven approach, companies can optimize their operations, improve service levels, and drive profitability—even in uncertain times.
For companies looking to adopt demand sensing, success starts with three key factors:
- Building a granular foundation: Companies need the ability to forecast at the banner and day level, ensuring their system can scale to handle the high volume of transactions while maintaining accuracy.
- Leveraging a powerful machine learning tool: A robust ML solution that automates most of the forecasting work is essential. The tool should account for seasonality, events, promotions, and other variables to ensure accurate and timely forecasts.
- Incorporating relevant external data: Identifying and integrating external data variables, such as weather or retailer (POS) data, is critical. Understanding how to access and enrich forecasts with these regressors will further enhance accuracy and responsiveness.
Demand sensing represents the future of supply chain management for CPG companies. By leveraging real-time data and machine learning, businesses can not only enhance forecast accuracy but also ensure that their supply chains are prepared for whatever disruptions may come their way. For companies still relying on statistical forecasting methods, adopting demand sensing is not just a competitive advantage—it’s a necessity.