購買 AI 工具前，先指定物料主檔負責人
Before You Buy an AI Tool, Name the Owner of Your Item Master
購買 AI 工具前，企業應先指定物料主檔負責人，確保有人審批新增貨品編碼並修正錯誤資料。文章建議匯出貨品清單，檢查描述相近的重複項目及計量單位，並記下由誰跟進。印度一間食品服務分銷商採用 Microsoft Dynamics NAV，改善數據可靠性後，易腐貨品浪費減少 18%，訂單填充率達 96%，增加 Rs 1.2 crore 利潤。
Ask a room of managers who owns the item master and watch what happens. Finance points at operations. Operations points at IT. IT says it only keeps the server running.
That silence tells you more about your AI readiness than any vendor demo.
The quiet file that runs everything
The item master is the list of every product you buy, make or sell. Each line has a code, a unit of measure, a supplier, a lead time. Forecasts read it. Reorder logic reads it. Dashboards group by it. If one product has three codes, the forecast sees three weak products instead of one strong one. If one plant counts in boxes and another in units, every total is wrong before any model runs.
Nobody notices this while people plan in spreadsheets, because people fix it by hand. They know that "SNK-01" and "SNACK 1" are the same thing. An AI system does not know that. It takes the data at its word.
Why ownership comes before tools
Clean data is not a one-time project. New items arrive every month. Suppliers change. Someone creates a duplicate code on a busy Friday. Without a named owner, the clean-up decays and the AI layer slowly starts giving confident wrong answers.
So the first readiness question is not "which model?" It is "who approves a new item code, and who fixes a bad one?" Give each master, items, customers and suppliers, one person with the authority to say no.
A small test you can run this week
- Export your item list.
- Sort by description and look for near-duplicates.
- Check that each item has one unit of measure.
- Write down who would fix what you find.
If step four is hard, you have found your first project. It is usually smaller than an AI project, and it pays back faster.
What changes once the backbone is in place
We saw this with a food-service distributor in India on Microsoft Dynamics NAV. Once live stock data could be trusted, forecasts could raise reorders inside the ERP. The distributor saw 18% less perishable wastage, a 96% fill rate and Rs 1.2 crore in added margin. The ERP was not replaced. The data in it was made reliable.
The full post covers the rest: a readiness checklist, how to tell a system of record from a system of conversation, and when to extend, upgrade or replace.
Read the full guide: ERP Readiness for AI: Why ROI Needs a Data Backbone
Abhijit Singh is CEO of Seven Billion Analytics, which builds enterprise AI, analytics and ERP systems for mid-size manufacturers and distributors.
來源：DEV Community · dev.to