How to automate production planning

An agent that puts orders, materials, machine capacity and changeovers side by side every day, proposes a production schedule with the reasons for it, and publishes only what your planner approves. Below: real examples, the steps to set it up, how it connects to the tools you use, and the pitfalls.

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Public examples

Built by other companies and vendors, with figures exactly as their sources publish them. They are not our clients; follow each source for the full story.

STARTEAM · PCB manufacturer, country not stated; named in the source with its AI scheduling tool PAILOT: AI scheduling.; Result: 43% reduction in delays; 10% increase in capacity utilization; 15% drop in set-up time; 24% decrease in energy use (as presented in the source).; Source: productronica trade fair talk, year not stated
Contract manufacturer · country not stated: C3 AI production schedule optimization across six production lines, with data from 10 sources.; Result: estimated 2.8% revenue uplift (an estimate, not a measured result). Deployment took 12 weeks.; Source: C3 AI, year not stated
Vietnamese consumer goods maker · country Vietnam, name not stated: AI-powered production planning platform built by Synodus.; Result: factory planning time cut by 90%; planners had spent up to 10 days a month organizing shifts and lines (vendor-reported).; Source: Synodus, year not stated

How it is set up

Data pull: take open orders, bill of materials, routings with standard times, machine calendars and the changeover matrix from the ERP or MES, at least daily.
Normalize: convert all durations to one unit, map machines to work centres, and flag routings whose times differ from the last 30 days of actual data.
Optimize: a scheduler (constraint solver or heuristic) builds the sequence under capacity, due date, material and changeover rules; the LLM step only explains trade-offs in plain language.
Planner review: the production planner sees the schedule, the late orders and the reasons, and approves or edits it. Nothing is published unapproved.
Publish: write the approved sequence to the ERP or MES and push work orders to the shop floor.
Rescheduling: on events (machine down, rush order, material short), re-run the plan for the affected area only and show the planner the changes.
Monitoring: track on-time delivery, utilization, changeover time and schedule changes per week.

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We build this agent around your own process, connect it to your tools, and keep it working as things change. You approve what matters; every action is logged.

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Connecting your tools

SAP S/4HANA: OData APIs for production orders and work centres (PP). Docs
Odoo: external API for manufacturing orders, bills of materials and work centres. Docs
Microsoft Dynamics 365 Business Central: REST API v2.0 for items and production orders. Docs
Oracle Fusion Cloud: REST APIs for manufacturing work orders. Docs
OPC UA: standard protocol for machine status and counters on the shop floor. Docs
Google Sheets: API for planner review tables. Docs

Questions people search for

AI agents in production

On the production floor, agents suit scheduling support, quality-check summaries, maintenance requests and spare-parts tracking. They read MES and ERP data through APIs or OPC UA and write suggestions for a person, not machine commands. Docs

Industrial AI agents

Industrial agents combine sensor data with ERP records. They should not control machines directly. Start with one line and one KPI, a human approval step, and a connection through OPC UA and the MES API. Docs

Pitfalls

Wrong standard times in routings produce a schedule that looks correct and fails on the floor. Teams compare routing times with actuals and block scheduling for routings with large gaps.
Planners reject a schedule they cannot explain. Teams show the constraint that drove each late order, not only the result.
Frequent full re-plans confuse the shop floor. Teams limit rescheduling to the affected area and freeze the next shift's sequence.
Forecast errors spread through the plan. Teams plan firm orders first and keep forecast-driven work as a separate, smaller block.

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