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Data-Driven Forecasting & Peak Demand Prediction for Afficionado Coffee Roasters

Year: 2026

Type: article

Abstract: This research presents a data-driven forecasting framework for coffee retail operations to predict sales demand and identify peak demand periods across multiple store locations. The study applies time-series forecasting techniques, including baseline models and Gradient Boosting regression, to predict daily revenue and hourly demand. The model achieves high forecasting accuracy and improves operational planning. An interactive Streamlit dashboard is used to support real-time decision-making.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19437137

Author: Singh, Kundan Kumar

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Climate action

Open Access status: green

OpenAlex ID: W7150723876

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