Don't confuse forecasts with budgets, plans or assumptions. Forecasts describe expectations, while budgets are a source of aspirations. A plan is a set of planned actions that, together with past data and future assumptions, are used to create forecasts
Discrepancies between forecasts and actual demand at the end points of sale were negatively affecting inventory management, which increased our costs. At the same time, manual forecasting did not allow us to effectively take into account all factors, even with an understanding of all causal relationships. Having analyzed the market, we realized that we needed an AI-based solution that could process large volumes of data and automatically take into account multiple factors.
When choosing a new demand forecasting system, we found out that SMART Demand Forecast can forecast both regular and promotional sales. We were especially interested in the system's ability to work with analogs and anomalies. The vendor outlined the implementation process in detail, which included launching a pilot project with the ability to assess the accuracy of forecasts at the initial stage. In addition, a clearly designed product development map gave us confidence in long-term cooperation, which together was the decisive factor in choosing a solution from SMART business
For us, the value is that the vendor independently determined what data is needed to build a high-quality demand forecast, focusing on its own expertise and the specifics of our business processes. Accordingly, we, for our part, simply provided information according to the universal data structure (sales, product hierarchy, promotional campaigns, external factors, etc.). And the SMART business team adapted them and built ML models that are used in the system and take into account cannibalization aspects and additional calculation factors that significantly influenced demand.
First, based on basic factors affecting demand, such as the day of the week, sales data for previous months, product prices, etc., an initial model was built that provided a certain level of forecast accuracy. Then we moved on to optimizing the model by adding new factors: cannibalization, seasonal coefficients, elasticity factors, etc. We observed what improved and what did not. The main thing here is not to overdo it and not to overload the system with unnecessary things, because an overtrained model can make false forecasts. Our team has deep knowledge and all the necessary tools to determine the importance of the influence of each factor. This allowed us to filter out the least significant factors, reduce the “noise” of the data and improve the accuracy of forecasts for each individual McDonald’s Georgia restaurant.
We are very pleased with the results of the SMART Demand Forecast implementation. Thanks to this project, we were able to significantly improve the accuracy of demand forecasting in each of our restaurants. And when we received the first results, we clearly understood that the return on this investment will be quick. We are grateful for the high professionalism and well-coordinated work of the entire SMART business team, which helps us feel and understand our business processes at a new level.
Implementation of solutions with artificial intelligence requires constant cooperation, analysis of the steps taken and close communication between the vendor and the customer, which in tandem brings high results, which we managed to achieve with McDonald’s Georgia. Our teams were always in touch, responded promptly to the issues, which positively affected the productivity and success of this project and laid a solid foundation for further cooperation.
