Delivery Order Issue Analysis — AI workflow for Malaysian SMEs
Delivery Order Issue Analysis turns delivery complaints/refunds into issue themes. AI writes the first draft. You check it before it goes out.
Ready-to-use prompts for Delivery Order Issue Analysis
Create prompt
Create prompt
Create a first-pass deliverable
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
The job: delivery order issue analysis. I will give you delivery complaints/refunds. Turn it into issue themes. Keep it practical for restaurants, cafes, bakeries, cloud kitchens and food manufacturers. Use short sections, end with one recommended next action, and give me a version I can paste straight into the workflow.
Replace “delivery complaints/refunds” with your real data.
Analyse prompt
Analyse prompt
Analyse the input and identify the highest-impact insights
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
The task is analysis: delivery order issue analysis. Material: delivery complaints/refunds. Give me the five most important findings, ranked. Say what each does to the business. Build issue themes from what you find. Close with three actions that improve refund rate.
Replace “delivery complaints/refunds” with your real data.
Optimise prompt
Optimise prompt
Improve an existing version for better business results
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Optimise an existing delivery order issue analysis setup. Material: delivery complaints/refunds and the current output with results. Diagnose the weaknesses first. Then deliver an improved issue themes. Present changes as issue, change, expected refund rate effect. Skip cosmetic edits.
Replace “delivery complaints/refunds” with your real data.
Automate prompt
Automate prompt
Turn the task into a repeatable AI-assisted workflow
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Turn delivery order issue analysis into an automated workflow. It starts when delivery complaints/refunds arrives. It must end with issue themes. Available systems: POS, inventory, procurement, scheduling, delivery platforms. Map out: trigger, fields to capture, AI step, decision rules, where a human approves, what happens on failure, data to store, and refund rate tracking. Keep the whole thing maintainable by a small team.
Replace “delivery complaints/refunds” and the listed systems with what you actually run.
New here? How the four prompts work together
They are one sequence, not four options. Run them in order on the same piece of work, feeding each answer into the next.
Create
Paste your real data. You get a first draft. Most people never need to go further than this.
Analyse
Paste that draft back in. It tells you what is weak, missing or wrong before a customer sees it.
Optimise
Paste the draft again with what Analyse found. You get a stronger version.
Automate
Only once the output has been good five times running. This is the one people reach for too early.
Nothing forces you through all four. A good Create draft that you edit yourself is a finished job. The longer version.
Specification
- You give it
- Delivery complaints/refunds
- You get back
- Issue themes
- Moves
- Refund rate
- Run it
- Daily/Weekly
- Priority
- P3
- Works with
- POS, inventory, procurement, scheduling, delivery platforms
- Tool guides
- POS systems, Delivery platforms
Is this for you?
- Pick it for
- Restaurants, cafes, bakeries, cloud kitchens and food manufacturers. No setup needed. If you can copy and paste, you can run it.
- What you get
- Drafted in minutes, not built from scratch. It aims at cost control. Watch one number: refund rate.
- Reach for it when
- Most SMEs run it daily to weekly. If you are doing it by hand more often, automate it first.
How to run it
- 01Collect the input: delivery complaints/refunds.
- 02Copy the Create prompt and paste it into ChatGPT, Claude or Gemini.
- 03Replace “delivery complaints/refunds” in the prompt with your real data, then run it.
- 04Read the output against what you already know and correct anything the model guessed at.
- 05Track refund rate. Only wire up the Automate prompt after five useful runs in a row.
The tools you will need
The prompts run in any chat assistant — ChatGPT, Claude or Gemini. These are what you need around them to get the input in and the output somewhere useful.
- inventory
- inFlow Inventory
- KatanaAimed at makers and light manufacturing.
- SortlyFree tier for a small catalogue.
- Zoho Inventory
- procurement
- Any tool you already use is fine.
- scheduling
- Any tool you already use is fine.
- delivery platforms
- Any tool you already use is fine.
Check the rules yourself
This workflow touches rules set by someone else. Check the current requirements yourself before you act on the output.
- JAKIM
- Halal status depends on the whole supply chain, not just the recipe. Changing a supplier can affect it, and logo use is controlled.
Links go to each authority’s own site. See all Malaysian authorities.
Data & privacy
If you paste customer or staff details into an AI tool, your business stays responsible for them under the PDPA. Replace names and numbers with initials or references first.
Using customer data with AI tools in Malaysia covers what to check with any provider before you paste.
For this website, it works differently.
Nothing you type here is sent to us or to an AI model. The prompts are static content, and the copy button works directly inside your browser.
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Maintained by Naven Pillai