The article explains ten core supply‑chain formulas—from inventory turnover and safety stock to cash conversion cycle—showing how mis‑calculations can lock cash, cause stockouts, and damage customer satisfaction, and why continuous, system‑driven monitoring is crucial for modern operations.
The article analyzes why fast‑moving consumer goods sales have stalled, how most firms mis‑start AI projects by focusing on content creation, and how data‑driven AI applications—illustrated by case studies achieving 210% campaign lift, 98% forecasting accuracy, and 30% inventory turnover reduction—can truly transform the industry.
The article explains how excessive inventory ties up cash flow, identifies root causes such as inflated safety stock, poor supplier analysis, and inadequate material classification, and offers a data‑driven, step‑by‑step framework—including classification, demand forecasting, supplier management, and EOQ—to achieve lean, precise purchasing.
The article examines how cutting‑edge technologies such as machine learning and operations research can reshape consumer‑retail supply chains, outlining a four‑part framework that covers company background, the evolution from basic to intelligent supply chains, acceleration strategies, and real‑world best‑practice case studies.
This article shows how forecasting, linear programming, EOQ inventory, pricing elasticity, and location‑selection models can be applied to a coffee shop to predict foot traffic, optimize menus, reduce waste, set optimal prices, and choose the best site, ultimately boosting profitability.
This article explores how causal inference techniques, including dynamic Bayesian networks and time‑series models, can be used to improve demand forecasting and replenishment strategies in inventory management, offering both theoretical concepts and practical case studies for operational decision‑making.
This article presents a comprehensive overview of Hema's supply chain, detailing its business model, logistics‑inventory trade‑offs, algorithmic positioning, and the design, modules, and achievements of its fresh‑food automatic replenishment system, which leverages demand forecasting, graph neural networks, and dynamic inventory control.
Effective e‑commerce supply‑demand coordination requires accurate SKU‑level forecasting, optimized replenishment under MOQ and lead‑time constraints, and dynamic post‑sale traffic control, using a blend of time‑series, tree‑based and deep learning models together with expert knowledge to minimize inventory costs and avoid stock‑outs.
The paper presents a graph convolutional network approach that leverages multi‑task learning and spectral graph convolutions to forecast shared‑bike inflow, outflow, and demand gaps across a city’s non‑Euclidean parking network, demonstrating improved accuracy over traditional time‑series baselines while noting scalability and directional graph limitations.
JD.com's Smart Supply Chain Y Business Management team collaborated across divisions to implement AI‑driven demand forecasting, automated replenishment, micro‑service architecture, and real‑time monitoring, enabling precise inventory control, cost reduction, and seamless 11.11 promotion fulfillment through integrated planning, pricing, and fulfillment innovations.
This article outlines Alibaba Retail's demand forecasting workflow, describing the evolution from simple mean and time‑series models to machine‑learning and deep‑learning approaches, the incorporation of feature engineering, operational plans, and methods for estimating prediction uncertainty to support intelligent replenishment.
This article explains how Alibaba’s supply‑chain algorithms and data‑driven operations enable rapid order processing, accurate demand forecasting, dynamic inventory allocation, and efficient warehouse fulfillment during the massive traffic of Double 11, highlighting the challenges faced and the solutions implemented.
