How the implementation of SMART Demand Forecast improved the accuracy of demand forecasting at McDonald’s Georgia

McDonald’s Georgia has been maintaining a high level of service and customer satisfaction for 25 years, making a significant contribution to the reputation of one of the largest and most famous fast-food chains in the world. Thanks to the Experience of the Future initiative, aimed at improving the visitor experience by implementing the latest technologies, McDonald’s Georgia provides them with quality service. That’s why visitors always want to come back here to once again make sure that “I’m really loving it!”. Digital boards with products and offers, self-order terminals, table service, a modern mobile app – a lot has changed since the opening of the first restaurant in Tbilisi in 1999. At the same time, McDonald’s Georgia has managed to preserve the fundamental values of the establishment and give it its own identity. It is the world-famous hospitality of Sakartvelo that distinguishes this chain in its segment.

The company continues to implement new methods to improve service. A significant step for McDonald’s Georgia was the implementation of SMART Demand Forecast. This comprehensive tool is aimed at increasing the accuracy of demand forecasting thanks to innovative technologies based on machine learning algorithms and artificial intelligence. This solution is a proprietary development of SMART business, which has been promoting digital business transformation in more than 60 countries for 15 years. And today we talk about the challenges, process and benefits of implementing this solution in a new success story with McDonald’s Georgia.

Prerequisites of searching for an implementation partner and how the demand was forecasted before the integration of SMART Demand Forecast

The need to implement a new solution for demand forecasting at McDonald’s Georgia was caused by several reasons. But before we talk about them, for a better understanding, remember how crowded it is in one McDonald’s restaurant and how free it is in another, located literally a block away. At the same time, on other days the situation can be the other way around. For visitors, this is a matter of just a few extra minutes, since McDonald’s establishments are known for their fast service even at peak times. But from the restaurant’s standpoint, such fluctuations in demand cause more serious challenges. The McDonald’s Georgia team allowed us to have a look at their forecasting “kitchen” and shared how the process was organized before the implementation of SMART Demand Forecast and why the old approach needed to be transformed.

The company had a well-established demand forecasting process, but at the level of the entire chain. With this approach, it was difficult to take into account all the necessary factors with granularity to any McDonald’s Georgia restaurant. Accordingly, this did not provide the necessary detailing and led to the frequent need for the team to instantly respond to a shortage or excess of components for the products. In addition, this entailed unpredictable logistics costs and difficulties in inventory management. Let’s add here peak periods, mass events in the city and the proximity of retail outlets to them, as well as various promotional factors. As a result, due to unpredictable increased demand, some restaurants were forced to attract more personnel, while in other establishments, on the contrary, there was an excess of man-hours. The above factors were triggers that gave an understanding that it is more effective to make forecasts not for the entire chain at once, but for each restaurant.

McDonald’s Georgia also wanted to reduce the forecast intervals. Before collaborating with SMART business, forecasts were made for the next 3 months with aggregation at the level of each month. Such a wide forecast interval did not take into account shorter-term changes in demand, such as pronounced seasonal dependence, local events or changes in the competitive environment.

The company representatives noted that since the forecast accuracy indicator was calculated for the entire chain, this did not reflect the real situation in terms of individual restaurants. Let’s assume that in summer, at the level of the entire McDonald’s Georgia chain, the demand for cold drinks is predicted to increase by 20%. This forecast is based on historical data for the past few years and general trends. In the restaurant in the coastal city of Batumi, the forecast is justified due to the large number of tourists and vacationers who want to cool down on hot days. But in the central areas of Tbilisi, especially near office centers, the demand for cold drinks may increase by only 10%, since most visitors prefer coffee to perk up. Accordingly, the Tbilisi location is faced with an excess of cold beverage stock.

Requirements for a new forecasting system and why the solution from SMART business was chosen

There were several key needs that McDonald’s Georgia sought to meet through future implementation:

• The new solution had to provide forecasting with aggregation down to the weekly level. Such an approach would allow for more accurate consideration of short-term changes in demand, such as seasonal fluctuations, local events and other factors that can have a significant impact on sales in a particular week.
• Granulation down to product and end location was also important for the customer. Since, as noted above, each restaurant in the chain may have a different level of demand for specific products. Forecasting at the level of each restaurant and individual products allows us to take these differences into account. This would provide more accurate forecasts and help avoid situations where one restaurant lacks a product, while another has a surplus. As a result, granulation increases the efficiency of inventory management and reduces the company’s costs.
• McDonald’s Georgia was looking for a solution that would allow them to switch to a centralized forecasting system. In this way, the company wanted to ensure consistency of data and forecasts across the entire restaurant chain to avoid discrepancies and errors that arise during manual forecasting.

Learn more

How was SMART Demand Forecast implemented for McDonald’s Georgia?

SMART Demand Forecast was implemented into the business processes of McDonald’s Georgia in three stages: diagnostics, modeling and implementation.

• Diagnostics
  First, the SMART business team analyzed in detail how the customer’s business processes were organized “as is”. At the next stage, a clear vision of how forecasting would work at McDonald’s Georgia with SMART Demand Forecast was defined. The value of this step was that SMART business offered the customer a modern approach and tools for building accurate forecasts without the need to change the company’s existing business processes.

Next, a process diagram with a detailed flow was built for a clear vision by the McDonald’s Georgia team of future steps and the possibilities of planning their further actions in accordance with the plan. Additionally, roles and responsible persons were assigned, terms were defined, and priorities were set. All this provided a structured approach to introducing changes and guaranteed that all project participants understood their tasks and areas of responsibility. Thanks to this, the implementation was as smooth and productive as possible.

At the diagnostic stage, a unified data structure was approved by the customer, which will be used by SMART Demand Forecast to build demand forecasts. It is important to note that for solutions based on ML & AI algorithms, a unified data structure is the key to achieving efficient model operation and high forecast accuracy at the output. The SMART business project team analyzed the customer’s key business processes and came to McDonald’s Georgia with a ready-made template. The customer only had to collect and transfer the necessary data to the vendor.

In addition, SMART business developed technical documentation describing all aspects of the implementation and use of the solution by McDonald’s Georgia.

Indeed, the SMART business team analyzed and highlighted those points of influence on the forecast that the customer may have been unaware of. This is a painstaking and multi-stage process consisting of a thorough audit and observation, briefing key employees, using advanced methods of analyzing large volumes of data and machine learning algorithms. The team uses data visualization tools for a more transparent understanding of the results obtained. Thus, at the diagnostic stage, it was possible to identify many local challenges of McDonald’s Georgia that needed to be taken into account when implementing the solution. We would like to highlight the most interesting of them:

• Anomalies – that is, unusual or unexpected spikes or drops in sales that stand out from standard patterns. At McDonald’s, this could be a bus passing by with hungry tourists who visit a specific restaurant. In this case, this event is not systemic and, accordingly, it is not taken into account in standard data and is labelled as an anomaly. SMART Demand Forecast identifies such incomprehensible changes and cleans the data so that they are not involved in forecasting.
• Dependence of individual SKUs on seasonality – the customer has many different product items that have a seasonal dependency. For example, demand for McFlurry usually increases in the summer when it is hot. Therefore, the system automatically calculates special seasonal factors and coefficients.
• Forecasting demand for new products introduced during promotional campaigns – the difficulty is that new items do not have historical sales data. But SMART Demand Forecast uses an integrated approach that takes into account multiple factors, such as similar products, common sales patterns, seasonal trends, and other factors. This approach differs significantly from traditional manual forecasting, which is often limited in terms of processing large volumes of data. The automated solution allowed for faster and more accurate data processing, providing more accurate forecasts for new products.

It is important to note that if the customer has a non-standard request that needs to be taken into account in forecasts, SMART Demand Forecast supports manual demand forecasting. This feature allows them to quickly enter additional clarifying data and flexibly respond to changes in the conditions of market volatility. At the same time, the possibility of automating any specific needs at the customer’s request was discussed as a nice to have for the future.

The final step of diagnostics was preparing for the transition to the next stage – modeling.

• Modeling
  The main task of this stage in McDonald’s Georgia was to train the artificial intelligence model so that its algorithms were as accurately oriented to the needs of the business as possible. Since the “fit once = fit every time” approach does not work for different companies, even from the same industry, the SMART business team customized the model for McDonald’s Georgia:

What did McDonald’s Georgia gain from implementing SMART Demand Forecast?

After identifying the key factors, the model began to produce stable forecasting results. An optimal number of factors was formed, by which McDonald’s Georgia can receive the most accurate indicators with minimal acceptable fluctuations.

The success of the project was determined by achieving a forecast accuracy of 70 to 80%:

1. on a horizon of 4 calendar weeks from the start date of the planning period,
2. for each calendar week,
3. for each of the 21 declared restaurants of the network.

The project started in October 2023 and was fully implemented in July 2024. The success of the implementation was evident already at the pilot project stage. It started in the summer, when there are many different seasonal factors and, in general, the demand of the McDonald’s Georgia chain grows the most.

As a result, it was possible to achieve the following target indicators that even exceeded expectations:

• 83% accuracy of sales forecasting for each restaurant based on weekly data aggregation for a period of 4 weeks.
• 80% accuracy of sales forecasting for each restaurant based on weekly data aggregation for a period of 12 weeks.
• Average deviation of up to 5% from forecast fulfillment, which is the norm among the global business community.

But even such high indicators are not the limit for forecasting improvement. McDonald’s Georgia together with SMART business are going to continue to develop the functionality of SMART Demand Forecast, which will cover omnichannel forecasting.

The mission of SMART business is to help entrepreneurs make the future more understandable and transparent to reduce risks and be ready for any challenges. The company prides itself on contributing to the success of its customers around the world and empowering them to achieve their key goals.