Why Demand Forecasting Is Crucial for a QSR Food Supply Chain 

QSRs operate in a competitive and highly complex business space, so one of the highest priorities for these organizations is creating strategic plans that will reduce food waste to avoid lost revenue and wastage disposal fees. According to Rethink Food Waste Through Economics and Date, the U.S. restaurant sector generates 11.4 million tons of food waste annually, equating to more than $25 billion in losses. 

There are many factors that impact consumer demand that QSRs must consider in their planning process – severe weather, school and college sessions and holidays, large events as well as pandemic restrictions. Unfortunately, accounting for these external demand influences is not simple given the volume and range of demand catalysts, and there is no “one size fits all” solution for QSRs to completely eliminate food waste. The good news is while it can be difficult, it is not impossible. There are strategic ways that companies can leverage data and analytics to optimize their planning to better predict demand anomalies, which will lead to more accurate predictions,  reducing food waste, protecting the environment, and reducing revenue loss. 

Historical Data Alone Won’t Cut It Anymore  

As we are now at the mercy of two years of abnormal dining conditions and restaurant restrictions, relying on historical data to predict demand leaves businesses in an extremely vulnerable position that could lead to massive over or under forecasting. Given the fluidity of our world and the likelihood of ongoing changes such as new variants likely on the horizon, having access to data to pinpoint the impact of events so you can prepare for similar events in the future is practically non-negotiable.

While there can be similar trends year to year, historical data neglects to consider potential demand surges and troughs that impact business outcomes. Historical data on its own does not explain why these anomalies took place. Rather than making QSR forecasting a guessing game that involves massive amounts of work and risk, leveraging more granular and predictive technologies will unlock insights that allow for better decision making across inventory management, staffing and marketing campaigns.

Understanding What Drives Demand 

The key to unlocking accurate forecasting is making it real-world aware: understanding each location's environment and how it affects the store’s demand patterns. While multiple QSRs may be under the umbrella of one large corporation, this in no way means each location drives the same amount of demand. 

QSRs in locations with clusters of venues or attractions and a highly dynamic demand environment could lead to rushes of customers on certain days or times. Therefore it is crucial to know about these events to prepare in advance and prevent overstocking the following day or week. That said, it’s important to remember that not all events are created equal – a sporting event may not increase demand as much as a business conference, and visa versa. More regional stores are more vulnerable to unscheduled events such as hurricanes, tornados and similar disrupting supply chains. So if you’re only analyzing data from a singular event to inform food supply decisions, you could end up with a whole lot of wasted food. That’s why it’s critical to identify which types of events impact your business and take a nuanced approach to demand forecasting.

Understanding what drives the highest impact in demand will unlock opportunities to predict the amount of food needed for each individual location, leaving little room for overestimation that results in waste. Whether it be surrounding businesses, events, weather, or other relevant demand indicators, being able to identify those drivers and avoid unexplained demand surges will lead to better food supply chain forecasting capabilities. 

External Data Intelligence and Solutions 

Investing in an external solution that can help identify demand drivers and access critical data points will not only allow your organization to prevent food waste, but it can improve your overall predictions holistically and decrease your carbon footprint. 

To utilize these insights, you don’t need to be a data scientist – you simply need to look at the bigger picture that includes the aggregate impact of the hundreds of events that could be taking place in a given location at any time. For example, there are currently over 225 events scheduled to take place across the U.S. in the first quarter of this year that will draw crowds of over 200,000 people, along with thousands of other smaller scale events will take place throughout the quarter. That’s why it is critical to understand that proper forecasting encompasses much more than just historical data. It requires understanding of real world, constant drivers that can be measured with smart data and analytics. Review your models frequently, eliminate guessing from the equation, and let the data lead the way.  

It is crucial that QSRs begin to implement this kind of in-depth analysis of demand drivers, both for optimal business outcomes as well as social and environmental benefits. Given the competitive nature of the QSR industry and increasingly data-driven nature of demand forecasting and planning technologies, this kind of data can no longer be considered a luxury, but rather a necessity.