Root Cause Analysis — AI workflow for Malaysian SMEs
Root Cause Analysis turns failure details and process data into root-cause hypotheses. AI writes the first draft. You check it before it goes out.
Ready-to-use prompts for Root Cause 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 task is root cause analysis. Input: failure details and process data. Output I expect: root-cause hypotheses. Make it work for small manufacturers, workshops, fabricators and production businesses. Short sections only. Wrap up with one next action and a version ready to paste into the workflow.
Replace “failure details and process data” 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
Run an analysis of root cause analysis. Input: failure details and process data. Identify five key findings. Quantify what can be quantified. Explain the impact in plain terms. Produce root-cause hypotheses. Finish with the three actions most likely to improve first-pass yield.
Replace “failure details and process data” 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
Task: optimise root cause analysis. You will see failure details and process data together with the current version. Be blunt about what is not working. Return a stronger root-cause hypotheses. Structure the changes as: issue found, recommended change, expected effect on first-pass yield.
Replace “failure details and process data” 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
Design an AI-assisted automation around root cause analysis. Starting point: failure details and process data. End result: root-cause hypotheses. Connected systems: ERP/MRP, quality logs, maintenance logs, spreadsheets. I need the trigger, field list, AI instruction, decision rules, human approval points, failure handling, stored records and a first-pass yield tracking method. Keep maintenance light.
Replace “failure details and process data” 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
- Failure details and process data
- You get back
- Root-cause hypotheses
- Moves
- First-pass yield
- Run it
- Daily/Weekly
- Priority
- P3
- Works with
- ERP/MRP, quality logs, maintenance logs, spreadsheets
- Tool guides
- Excel & Google Sheets
Is this for you?
- Pick it for
- Small manufacturers, workshops, fabricators and production businesses. 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: first-pass yield.
- 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: failure details and process data.
- 02Copy the Create prompt and paste it into ChatGPT, Claude or Gemini.
- 03Replace “failure details and process data” 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 first-pass yield. 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.
- ERP/MRP
- Any tool you already use is fine.
- quality logs
- Any tool you already use is fine.
- maintenance logs
- Any tool you already use is fine.
- spreadsheets
Check the rules yourself
This workflow touches rules set by someone else. Check the current requirements yourself before you act on the output.
- DOSH
- Workplace incidents carry reporting duties with deadlines, and some plant needs a certificate of fitness before it runs. A safety document generated by AI is a draft for your safety officer, never the record itself.
- SIRIM
- Some products need certification or a registered mark before they may be sold here. Whether yours does is decided by product category, so check before you print the packaging.
- 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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- Finished Goods Ageing and Disposal DecisionTurns your finished goods ageing report into an ageing analysis with sell, rework or dispose calls.
All manufacturing & quality workflows: Manufacturing & Quality
Maintained by Naven Pillai