Latest industry insights, tutorials, best practices and case studies.
7 articles
For seasonal and promotional goods the hard part is rhythm, not formulas: build-up, peak and clearance phases each have different objective functions. Seasonal indices, uplift estimation, phased replenishment and clearance tactics.
Reverse cash flows from refunds, returns and exchanges are the most error-prone part of e-commerce reconciliation. This article maps the funds impact of the three aftersales forms, how platforms surface them in statements, and provides a reverse-reconciliation checklist.
The hard part of multi-platform reconciliation is not volume but model divergence. This article presents a three-layer statement model — raw layer, standard fact layer, reconciliation layer — with field specifications, an accounting-item dictionary and bridge-table lineage.
Based on the Qeasy intelligent reconciliation platform running in production at a large-scale e-commerce company, this case study breaks down how the AI agent matrix — bill parsing, reconciliation scripting, expense allocation plus a general assistant — compresses monthly reconciliation from days to hours at the 100k-document scale.
A practical comparison of moving average, exponential smoothing (SES/Holt/Holt-Winters), Croston and machine-learning methods for e-commerce replenishment, with SKU-tiered selection guidance.
Reading platform statements is the first step of reconciliation. Using JD POP, Douyin Shop, Pinduoduo and Amazon as examples, this article breaks down how gross merchandise, service fees, commissions, advertising and shipping insurance are composed and calculated, and recommends a unified field mapping.
E-commerce financial reconciliation has evolved through four stages: manual Excel checks, semi-automated scripts, iPaaS integration, and AI Agents. This article maps the capability boundaries of each stage and offers a decision framework for large-scale e-commerce businesses choosing an automation path.
