Stacked ensemble ML models incorporating Harvard Data Science methodology outperformed traditional forecasting approaches, delivering 20% improvement in accuracy for a multi-location restaurant group.
A company operating multiple restaurant locations needed to determine whether machine learning could improve upon their existing time series sales forecasting models. Traditional forecasting methods were falling short — limiting the ability of decision-makers to plan accurately, respond to demand shifts, and make informed business decisions with confidence.
Leveraged Harvard Data Science concepts and skills across several key phases: Data Collection and Preparation, Exploratory Data Analysis, Feature Engineering, Model Development, and Model Evaluation. Utilized R Programming, data wrangling, data visualization, algorithm building, statistics, and machine learning — including a variety of time series models alongside newer stacked ensemble techniques.
New sales forecasting models demonstrated significant enhancements compared to existing methods. Through the incorporation of additional relevant predictor variables and exploration of various time series and machine learning models, the identified models outperformed the original approach — offering enhanced accuracy and effectiveness in predicting sales trends, enabling more informed business decisions across the portfolio.
Food & Beverage operators trust Precision Wave to turn complexity into competitive advantage.
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