Retailers and consumer goods suppliers are urgently trying to determine how changes in consumer behavior will affect their regions, channels, categories, brands and products during and beyond the COVID-19 crisis. The key to success is in finding new ways to adapt to the shifting consumer behaviors in response to the pandemic and to adjust strategies immediately to improve business performance while preparing for the new normal.
The length of time it takes for the demand forecasting process to incorporate rapid changes or short-term spikes in demand is a challenge for most companies. Short-term spikes can occur with retail store promotions, sudden changes in weather conditions, social media ambiances, and, of course, pandemics.
The concept of rapid demand response (RDR) forecasting is based on updating demand forecasts to reflect real and rapid changes in demand, both during and between planning cycles. In this case, reacting to a pandemic – responding, recovering and reimagining the new normal.
CPG companies and grocers are among the few types of business that have seen unprecedented increases in sales during the pandemic. They are urgently trying to determine how changes in consumer behavior will them and what actions they can take now.
For instance, A global CPG company has been working with SAS to understand the impact of COVID-19 on its sales forecasts and production plans across key products by market. They were particularly interested in understanding which factors were affecting the markets that had already peaked and were now in the recovery stage of the COVID-19 pandemic. They have been challenged to replenish products on store shelves in order to meet spikes in consumer demand, and to properly manage the movement and storage of raw materials and finished goods from the point-of-origin to the point-of-consumption.
Similarly, Grocers also have struggled with the effects of pantry-loading and predicting how the spread of the virus will cascade across their regions and stores. For instance, a major regional online grocer and SAS customer experienced an increase in online grocery purchases of more than 200% virtually overnight! This generated substantial supply chain bottlenecks and product substitution challenges, resulting in order cancellations as consumers turned to digital channels to purchase food and other goods.
According to Progressive Grocer, e-commerce grocery sales for home delivery and/or store pickup reached $5.3 billion in April, a 37% increase over March sales, which were already a new record. It's likely that this shift to digital consumer experiences will continue to grow in popularity even after the coronavirus subsides and companies that act quickly and modernize their delivery models to help consumers navigate the pandemic safely and effectively will have an advantage over their competitors.
Key challenges and SAS’ Approach:
In both situations, understanding true demand – and those disruptive events that impact each product and location – is critically important. Sales orders alone will not account for the changing demand patterns resulting from the pandemic. It requires investing in new data, advanced analytics and technology to make the necessary adjustments both now and in the future.
SAS’ approach was to implement an RDR forecasting process using advanced analytics and machine learning, paired with internal and external data to predict detailed weekly demand across the product portfolio.
It was necessary to build models using more advanced time series and machine learning technologies. These models integrate additional information like epidemiological data, exchange rates, Google trends and stringency index to account for causal factors that influence future demand and improve the predictability of the models.
By applying machine learning to product attribute data and other external data in combination with historical demand, the approach proved to be the most accurate way to predict demand by product and location. Implementing a hierarchical forecasting approach allowed additional external data and casual factors to be deployed at different levels of data aggregation. Additionally, it allowed the estimation of internal cannibalization effects from product out-of-stocks across categories and products.
SAS delivered a real-time feed and automation of data, providing the demand planners an up-to-date view of global and regional patterns. SAS also delivered additional “what if” scenarios to adjust product forecasts based on key consumer sentiment. SAS technology was able to estimate event impacts and quantify the unique effects for each product and location up and down the hierarchy. In addition, using robust, real-time, what-if capabilities, the company was able to evaluate multiple scenarios regarding the outcome of the disruptive events on future demand.
Retail and CPG executives should plan to rapidly adapt their marketing and demand plans to reflect changing consumer demand patterns and sentiment by quickly optimizing their e-commerce channels, re-calibrating product demand patterns, and emphasizing the fastest-selling SKUs. Retailers should expect daily resets to their demand forecasts, shifting online assortments accordingly, and adjusting supply chain logistics and distribution centers to meet online consumer demands.
Learn more by downloading the white paper, "Using Advanced Analytics to Model, Predict and Adapt to Changing Consumer Demand Patterns Affected by COVID-19" , Retail forecasting through a pandemic and by exploring SAS Retail & Consumer Goods Solutions in the Age of COVID-19.