How Durian Importers Use ChatGPT for Supply Reports

Step 1: Lock the Raw Import Data Sources Before You Prompt

You cannot feed ChatGPT vague “market conditions” and get a supply report. Start with the actual files your operation already generates:

– Packing house summary from Raub or Bentong (Musang King) — includes daily harvest weight, grade A/B/C split, and reject rate.

– Cold chain logger export from the reefer container or chilled van (Sensitech or Berlinger loggers, often attached to the Bill of Lading for Port Klang or KLIA Cargo).

– Customs declaration (e-form or myInvois export) with HS code 0811.90 for frozen durian paste and whole fruit.

– Purchase order confirmation from the supplier, normally sent from Pahang Fruit Valley or similar stations.

Create a folder in your Google Drive shared with your ChatGPT Plus or Team account. The name of each file must include date and origin, e.g., `PLU-Supplier-KG-2025-06-14.csv`. ChatGPT reads these directly in its data analysis mode, but only if the file structure is clean. Test the CSV columns first — your KTM / Port Klang trucking partner might export timestamps in UK format. Fix that manually before you upload, or ChatGPT’s forecast will be off by half a day.

Step 2: Normalize the CSV Structure Inside ChatGPT

Put the raw files into one master spreadsheet before generating anything. Use a prompt chain that does the same steps every week:

First prompt: “Merge all CSV files in the drive folder. Match columns: PackingHouseDate, ArrivalAtPortKlang, ContainerSealNumber, SKUCode, Grade, NetWeightKg, TemperatureAtOffloadPct. Remove columns that are empty over every row, like ‘ConsigneeContact2’.”

Second prompt: “Add a calculated column ‘TransitHours’ = ArrivalAtPortKlang minus PackingHouseDate, plus a ‘ColdChainBreach’ flag for any row where TemperatureAtOffloadPct is above -18°C for more than 2 minutes.”

This gives you a structured dataset where every internal team member — warehouse supervisor in Selayang, sales rep in Cheras — sees the same number for a given batch. ChatGPT’s file-reading is deterministic enough for this as long as you do one clean merge per session. Do not mix frozen puree (HS 0811.90) and fresh whole fruit (HS 0810.10) in the same merge; their temperature protocols differ.

Step 3: Build a Weekly Supply Report Template in Markdown

You only write the template once. Save it as a custom GPT instruction or paste it into every new chat. The template must include:

– Headline stats per SKU: total net volume (MT), number of pallets or cartons, average transit hours, reject rate (grade B to C downgrade).

– Supplier performance: comparing this packing house’s average reject rate against the trailing six-week average.

– Stock projection for the next 21 days based on average daily offload volume and current cold storage inventory at either Westports’ bonded warehouse in Pulau Indah or an external cold room in Bukit Raja.

– Permission flags: which container loads still lack a completed phytosanitary certificate — pull this from the status column in your customs export.

One example prompt: “Use the merged table. Group by SupplierID and WeekOfYear. Output a Markdown table with columns: TotalVolumeMT, AvgTransitHours, RejectRatePct, Vs6WeekAvgRejectRate. Then write 20 words of risk commentary only for suppliers whose reject rate rose above 5%.”

This keeps the output from hallucinating numbers — every figure you cite traces back to a provided CSV row.

Step 4: Automate the Data Refresh With Pabbly Connect or Zapier

The trick is not to re-run the prompt manually every Monday. You can connect ChatGPT to the report pipeline with:

– Google Sheets as the live master ledger that receives new rows from your warehouse’s barcode scanner system or a manual WhatsApp relay from the Selayang cold room.

– Pabbly Connect deep-link (used widely in KL because of its lower cost per operation) to trigger a ChatGPT API call every Monday at 07:30 MYT.

– The API call invokes your saved prompt template and posts the resulting Markdown table to a shared WhatsApp broadcast list or your Tencent Docs board for your Chinese distributor partners.

Example API payload structure: `model=”gpt-4o”`, `temperature=0` so the report does not generate creative wording. You want raw deterministic output, not a stylized summary.

Alternatively, run a scheduled prompt in ChatGPT’s Projects feature with `Data Sources` set to your Drive folder. This works if your operation is under 5 suppliers and 40 line items per week. The API route is necessary when you handle 20+ container equivalents at peak season (July–September). During peak, your cold room’s in/out log changes 10 times a day.

Step 5: Cross-Check Output Against Customs and Smart Tags

A supply report is only useful if it reaches the person who books cargo space. Before the report is broadcast:

1. Export the completed report as a `.txt` or Markdown file from ChatGPT.

2. Paste the key volumes into your LHDN e-invoice portal (myInvois) to verify the weight fields match the GD (customs declaration).

3. Compare the report’s reject rate with your RF-tagged box data from the cold room. If the supplier’s digital packing list says 1,200kg and the cold room’s scale says 1,150kg, correct the master file first — ChatGPT cannot detect physical shrinkage without the scale log.

This step is where most importers fail: they normalize the supplier’s word rather than the receiver’s scale. ChatGPT will repeat a 4% shrinkage error all the way into the cash flow projection. Fix the input file, then re-run the prompt.

Table of the Workflow