Learn how to differentiate and coordinate sales forecasting, demand planning, and procurement planning—understanding who owns each process, the data sources, key steps, and practical tips—to keep the supply chain smooth, delivery reliable, and inventory lean.
This article demonstrates how to build a basic mathematical model that separates natural sales from marketing‑driven increments, estimates each component using assumptions about target audiences, giveaway impact, and word‑of‑mouth effects, and combines them with sensitivity analysis to forecast the first‑month sales of the upcoming book “Smart Use of ChatGPT for Mathematical Modeling.”
The article details a comprehensive presentation on Alibaba Health's pharmaceutical e‑commerce sales forecasting, covering supply‑chain challenges, the evolution of time‑series prediction methods, a full data‑to‑model pipeline, change‑point detection, handling imbalanced data, multi‑model fusion, and specialized seasonal and long‑sequence forecasting techniques.
This article demonstrates how to use Python's data‑analysis ecosystem (NumPy, pandas, statsmodels, etc.) together with Flask, MySQL, and ECharts to create a web‑based sales forecasting tool that automates data extraction, modeling, prediction, and visualisation.
This article summarizes a AAAI 2021 paper that introduces a deep meta‑learning framework with an amortization network to generate spatio‑temporal representations, enabling accurate retail sales predictions across regions and time periods, especially during high‑volume shopping festivals.
The paper proposes a deep meta‑learning framework that generates spatio‑temporal representations for retail sales forecasting, especially during large shopping festivals, by combining amortization networks, shared statistical structures, and alternating spatial‑temporal training to achieve robust and accurate predictions despite scarce historical data.
This article walks through a comprehensive analysis of a 3.43 GB sales dataset from a Brazilian automotive service chain, covering data loading, cleaning, exploratory visualizations, time‑series forecasting with ARIMA, RFM customer segmentation, product clustering, and key business insights using Python and Tableau.
This article presents a comprehensive analysis of a global supermarket's four‑year sales dataset, covering data collection, preprocessing, exploratory visualizations, sales, quantity, profit, market segmentation, product performance, customer segmentation, RFM modeling, and actionable recommendations to improve revenue and customer retention.
