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Brisbane, Australia
February 2026
8 min readWorkflow Demo

FactoryOps: Maintenance Work Packs, OEE & Production Scheduling

How GetPost Labs would build the maintenance planner's workflow: work requests turned into draft work packs by AI agents, parts and permits checked by rules, and every scheduling decision left with your planner. OEE and the production schedule sit on the same view.

Conceptual Prototype — Illustrating our approach

Executive Summary

The Problem: Every week the maintenance planner turns work requests into work packs — scope, parts, labour, tools, hazards — fits them around the production schedule and keeps SAP up to date. The information sits in SAP, old work orders, spreadsheets and the production plan, so each pack is assembled by hand and the downtime it costs is hard to see.

The Solution: FactoryOps gives the reading and drafting to AI agents: the work pack from past jobs, the suggested production window, the SAP close-out. Parts availability, permits and OEE calculations are fixed rules in software. The planner decides what runs and when, with the production schedule and OEE losses beside each job.

The Outcome: The planner reviews drafted work packs instead of building each one from scratch, maintenance and production plan from the same view, and every job keeps its record from request to close.

1

The Challenge

Understanding the problem space

"Prepare comprehensive project work schedules including scope, hazards, tools, labour, materials, durations, and sequencing."

— A duty listed in a current Australian maintenance planner job ad (SEEK, September 2026)

Across 36 current maintenance planner and production planner ads, the job is scheduling: plan and schedule work orders and production runs, prepare work packs, line up parts, labour and materials, and keep the maintenance system accurate. Several name SAP, and most ask for strong Excel.

Most of that work is cross-checking what already exists — the last time this job was done, which parts it used, what hazards it carried, when the line can come down. When that lives across SAP, spreadsheets and the production plan, each work pack is rebuilt by hand and maintenance and production negotiate over the same hours by email.

94%
of ads mention scheduling
50%
of ads ask for Excel or spreadsheets
50%
of ads mention spare parts or materials

Source: 36 maintenance planner and production planner job ads in Australia on SEEK, September 2026. Employers not named.

2

The Solution

What GetPost Labs would build

Core Capabilities

Work Pack Drafting (AI agent)
Reads the request and past work orders and drafts the scope, parts list, labour, tools and known hazards
Parts & Permit Rules
Stock on hand, lead times, isolation and permit-to-work requirements checked by fixed rules, not AI
Scheduling Stays With the Planner
The agent suggests a window; the planner decides what runs and when, and agrees it with production
OEE on the Same View (Rules)
Availability, performance and quality calculated by fixed rules from run and downtime records, beside the production schedule
SAP Close-Out (AI agent)
Writes hours, parts used and notes back to the SAP work order from the technician's job card, once the job is signed off
Backlog Record
Every request, pack, scheduling decision and close-out kept together for backlog and schedule-compliance reporting
3

Where the Time Comes Back

Checking, not assembling

The planner stops building each work pack from scratch and starts reviewing what the agent has drafted:

Work Packs

Before

Find the last similar work order, copy the scope, check the parts in SAP, re-type the hazards and isolation steps.

After

The agent has drafted the pack from past jobs and flagged the part that isn't in stock. The planner checks and adjusts it.

Production & Maintenance Windows

Before

Compare the production plan spreadsheet with the maintenance backlog and negotiate the shutdown window by email.

After

Planned jobs sit on the production schedule with the OEE loss each one causes, so both planners agree windows from one view.

SAP Records

Before

Collect paper job cards at the end of the shift and key hours, parts and notes into SAP.

After

The agent writes the close-out from the technician's notes; the planner confirms it and the asset history is complete.

4

BPMN Workflow

The business process modelled

Work Request: Raised to Closed

Operations / TechnicianAI AgentRules in SoftwareMaintenance PlannerFault foundRaise RequestDraft Work PackParts & PermitsSuggest WindowSchedule JobComplete WorkUpdate SAPClosed
Operations / Technician
AI Agent
Rules in Software
Maintenance Planner
AI agent task
Rule in software
Person
5

User Journey

A Work Request, Raised to Closed

Scenario: A food manufacturer running two packing lines. An operator reports a noisy conveyor bearing on Line 2 during the morning shift.

1
Line OperatorRaise Request

Logs the noisy bearing on Line 2 from the tablet at the line, with a short video

2
AI AgentDraft Work Pack

Finds past work orders for the same conveyor and drafts the scope, parts, labour, tools and the isolation steps used last time

3
Rules in SoftwareParts & Permits

Confirms the bearing is in stock and reserves it. Marks the job as needing an isolation permit before work starts

4
AI AgentSuggest Window

Reads the production schedule and suggests Wednesday's planned changeover on Line 2, when the line is already stopped

5
Maintenance PlannerSchedule Job

Agrees the Wednesday window with the production planner and schedules the job with the technician

6
Maintenance TechnicianComplete Work

Isolates the conveyor under the permit, replaces the bearing and records notes and parts on the job card. The supervisor signs it off

7
AI AgentUpdate SAP

Writes the close-out, hours and parts used to the SAP work order from the job card, for the planner to confirm. The stop is recorded against Line 2's OEE as planned downtime

Outcome: The bearing is replaced in a planned window instead of a breakdown, and SAP holds the full history. The agent prepared the work; the planner decided when it ran.

6

Interactive Prototype

Functional dashboard demonstrating the concept

FactoryOps
FactoryOpsOEE Dashboard

OEE Dashboard

Overall Equipment Effectiveness

87%
OEE
↑ was 72%
94%
Availability
96%
Performance
96.2%
Quality

© 2026 GETPOST Labs. Full Stack Engineering Solutions.

Functional prototype. Click on cells and entries to see interactions.

7

System Context

Where FactoryOps fits in the ecosystem

SYSTEM INTEGRATIONSAPWork ordersAsset historyStockAPIFactoryOps• AI work packs• Parts & permit rules• OEE & schedule viewAPIProduction LinesRun & downtime dataOperator requestsJob cardsAPIProduction PlanScheduleChangeoversShift rosters

Have a Similar Problem?

This is the kind of workflow automation GetPost Labs builds. If your organisation has similar challenges, we'd love to discuss how a custom solution might help.