Complete AI Training
Sign inGet my AI kit

Your job's AI kit

Get your AI kit

Tell us who you are and what you do. We show you your kit right away and email you the link: skills, prompts, AI agents, MCP servers and courses for your job.

500+ jobs ready, and we make a kit for any other job. No payment needed to look.

Share

AI agent for production planners

Planning Parameter Audit Agent

Planning data that matches reality and a plan that does not break when it is updated

DecisionsAnalysisOperations
See how it worksGet access
Planning Parameter Audit Agent: what goes in, what the agent does and what you get

What it does

Stale lead times, lot sizes and yields distort plans and cause shortages. This agent compares actual lead times, yields and run rates with the master data. It proposes updates where the gap is large and consistent. Before anything changes, it simulates the effect on planned orders and checks for unwanted spikes or new shortages. Changes that create problems are revised and retested. After approval, it checks the next planning run to confirm that the plan improved. The planner approves the updates. Edge case: a lead time has grown because of one bad month, so the agent suggests a temporary value instead of a permanent one.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedYes, continueNoNo 1 STARTS WHEN Monthly parameter audit 2 USES A TOOL Load master data and actual history 3 DOES Compare actual lead times, yields and run rates withmaster data 4 DOES List parameters that differ beyond the tolerance 5 DOES Propose new values 6 USES A TOOL Simulate the effect on planned orders 7 CHECKS THE RESULT Does the plan stay free of new shortages and spikes? If not: adjust the values or phase in the change, thenresimulate. Back to step 5. 8 YOU APPROVE Planner approves the updates 9 USES A TOOL Check the next planning run 10 CHECKS THE RESULT Did the plan improve as expected? If not: revise the values and retest. Back to step 4. 11 RESULT Parameter audit filed
Read the steps as a list
  1. Monthly parameter audit
  2. Load master data and actual history
  3. Compare actual lead times, yields and run rates with master data
  4. List parameters that differ beyond the tolerance
  5. Propose new values
  6. Simulate the effect on planned orders
  7. Does the plan stay free of new shortages and spikes?If not: adjust the values or phase in the change, then resimulate. Back to step 5.
  8. Planner approves the updatesThe agent waits here for your OK.
  9. Check the next planning run
  10. Did the plan improve as expected?If not: revise the values and retest. Back to step 4.
  11. Parameter audit filed

How it decides

A parameter is updated when actuals differ from master data by the tolerance across enough orders. Changes are accepted only if the simulation shows no new shortage or spike.

  • Update when actuals differ by over 10 percent across 10 orders
  • Use a temporary value for one-off swings
  • Phase in changes that create spikes over 20 percent
  • Never change master data without approval

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Tolerance (default 10 percent)
  • Minimum orders (default 10)
  • Parameters covered
  • Phase-in rule

What keeps you in control

It always asks you first

  • Master data updates

Hard limits

  • Never update master data itself
  • Never use too few orders as proof

It stops when

  • Done: updates approved and plan verified
  • Stop: history is too short

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensA resin's lead time was set at 14 days but the last 12 orders averaged 22. The agent proposed 22. The simulation showed a spike of 40 percent in week 3, so the check failed. It phased the change over two weeks and retested. The planner approved. The next run had no shortage.

More agents for production planners

Decisions · Operations

Supplier Delay Recovery Agent

Production keeps running with the least disruption when a supplier is late.

9 steps2 checks5 jobs
Data cleanup · Automation

Inbound Shipment Notice Exception Agent

Close each shipping notice exception and keep the plan accurate.

11 steps2 checks2 jobs
Risk management · Monitoring

Tooling and Mold Maintenance Agent

Service tools before they cause defects and prove that the service worked.

11 steps2 checks4 jobs
Risk management · Reporting

Daily Production Meeting Preparation Agent

Start each morning meeting with the facts and a clear list of today's risks.

11 steps2 checks4 jobs