Role of FORECAST Squared in QSR

The challenge

Quick Service Restaurants (QSRs) are exposed to very high levels of demand volatility. This impacts many facets of the business. Most notable outcome is very high food wastage levels. Some research suggest that this can be as much as 40% on average.

On the other hand, efforts to curb wastage can lead to unavailability of menu items which can ultimately affect customer satisfaction and retention.

Owing to demand volatility, QSRs also struggle to maintain optimal staffing across the business. This too ultimately leads to either excessive costs or low customer satisfaction, sometimes even both!

To address these challenges, highly accurate and dynamic demand forecasts at outlet-item level is essential. However, the traditional methods tend to be inaccurate, static, and un-scalable.

 

What leads to poor demand forecasting?

Demand in QSRs can be influenced by multiple drivers such as advertising, price and promotional activities, weather, holidays and events. This multidimensional view is lacking in most traditional methods as they are primarily based on trend and seasonality observed over years. This simplistic understanding of demand can lead to erroneous estimates.

Also, any adjustments based on subjectivity and intuition fails the test of scale required for outlet-item level forecasting

The other main drawback is the static nature of traditional forecasting methods. They fail to learn and update at the rate required for QSR business.

What is FORECAST Squared?

FORECAST Squared is a forecasting system that can build 1000s of highly accurate forecasts within a few minutes!  Moreover, it does not assume any forecasting or data science background for users. Developing 1000s of highly accurate forecasts can be a matter of just a few clicks.

FORECAST Squared can generate monthly, weekly or daily forecasts for long time horizons.  It also continuously updates forecasts based on latest data.

How does FORECAST Squared work?

It considers many factors that can influence the forecast. As an example, it considers factors such as advertising spend, price, weather and inflation. Then it automatically considers many derivatives of these factors, which is called feature engineering in machine learning. Then the algorithm sweeps through 1000s model classes and distinct parameter values and selects the best. All of this custom model building happens without any human intervention

The Outcome?

Highly accurate forecasting across the supply chain ensures proactive synchronization. Proactive synchronization leads to reduced production costs, reduced inventory across the supply chain and better availability for consumers.

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