Global Shop + scheduling
AI for Global Shop Solutions scheduling
Short answer
AI on Global Shop Solutions reads work orders, routing operations, work center load, and material status so a scheduler can ask why a job is late and what a rush order would push out. It explains and compares; it does not replace the finite scheduler. Access is read-only, with on-prem hosting for shops that need it.
- ERP
- Global Shop Solutions
- Industries
- Job shops, Industrial equipment, Aerospace and defense suppliers, Discrete manufacturing
- Written for
- Production Scheduler and Operations Manager
Global Shop Solutions is used by a broad range of make-to-order and engineer-to-order manufacturers, including aerospace and defense suppliers and industrial equipment builders. In those shops the schedule is a living document. Orders change, material arrives late, a machine goes down, and the scheduler has to say what happens next before the morning meeting.
The system holds the data a scheduler needs: sales orders, work orders, routing operations, work center calendars, purchased material and its due dates, and the finite schedule. Pulling it together to answer a specific question is still a manual job, often done by the one person who knows where to look.
An AI layer lets the scheduler, the operations manager, and customer service ask questions of that data in plain language. Which jobs are at risk this week? What is holding up work order 4471? If we start the rush order on the mill first, which jobs slip? Each answer shows the data behind it.
The assistant does not become a second scheduler. Global Shop's own scheduling remains the reference, and any change is made in the system by a person. This page describes the approach, what the built-in tools already cover, and how to host it privately for shops under ITAR or customer confidentiality rules.
What usually gets in the way
The problems we hear most from production scheduler and operations manager teams running Global Shop Solutions.
Too many screens for a simple question
Finding out why a job is late means checking the work order, the operation queue, purchasing, and inventory in turn.
Rush orders without a clear price
The cost of starting a hot job is the other jobs that slip, but that is not visible when the decision is made.
Material surprises
Purchased material and outside processing dates change, and the schedule is not always revisited.
Capacity based on standards, not reality
Routing times are standards. Actual performance differs by operator and machine, and the schedule drifts as a result.
Key-person dependency
One scheduler knows the shop's quirks. When they are out, quality of the schedule drops.
Where AI earns its place in Global Shop Solutions
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Late job explanation
The scheduler asks about a work order. The assistant identifies whether the delay comes from material, a queued work center, an outside operation, or an earlier operation not completed.
Touches: Work orders, routing operations, work center queue, purchase orders and receipts, outside processing
Outcome: Root cause in one question instead of several lookups, with the supporting records listed.
Rush order impact analysis
Given a rush job and a work center, the assistant lists which scheduled jobs would move, by how much, and which customers they belong to.
Touches: Scheduled operations, work center capacity, due dates, customer orders
Outcome: The decision to rush is made with the trade-off visible. The scheduler makes the change in Global Shop.
Weekly at-risk job list
Each Monday the assistant ranks jobs likely to miss their dates, with the reason, based on remaining hours, load, and material status.
Touches: Open work orders, remaining operation hours, work center load, material availability
Outcome: An exception list that replaces manual scanning. How well it works depends on timely shop floor reporting.
Material and outside service watch
The assistant compares upcoming operations with purchase order due dates and flags those that will start before material or outside processing returns.
Touches: Purchase order lines, due dates, outside processing operations, work order requirements
Outcome: Buyers and schedulers deal with shortages earlier, which gives time to expedite or resequence.
Customer date inquiry assistant
Customer service can ask for the likely ship date of an order. The assistant answers from the current schedule and flags uncertainty.
Touches: Sales orders, work orders, scheduled completion, finished goods
Outcome: Consistent answers with less interruption of the scheduler.
Standards versus actuals review
The assistant lists operations where recorded time regularly differs from the routing, ranked by hours of impact, to fix the standards that distort scheduling.
Touches: Labor reporting, routing standards, completed operations
Outcome: More realistic schedules once standards are updated, through your normal change process.
Morning production meeting brief
A short summary of what shipped, what is late, what is blocked, and what needs a decision, with links to the underlying work orders.
Touches: Shipments, open work orders, late operations, blocked material
Outcome: A consistent starting point for the meeting that managers can correct before it is used.
Reference architecture
A read-only assistant is placed beside Global Shop Solutions. It uses curated queries and a transparent what-if routine, shows its sources, and leaves schedule changes to a person working in the system.
- 1
Source access
A read-only path to the Global Shop database, commonly through ODBC reporting access or the integration options available for your version. Confirm what is enabled in discovery, since installs differ.
- 2
Scheduling query layer
Tested queries for late jobs, load, material gaps, and outside processing, with parameters filled by the model rather than free-form SQL.
- 3
What-if comparison
A simple, explainable routine that works on a copy of schedule data to compare options. It is meant to inform, not to replace the finite scheduler.
- 4
Model serving
An open-weight model on your GPU or in a private cloud, so schedule, customer, and part data stay in your boundary.
- 5
Controls and audit
User role mapping, question and query logging, and a clear boundary between suggestions and system entries.
Integration notes for your ERP team
- Global Shop installs vary in database and hosting. Confirm whether you have direct reporting access or only vendor-provided interfaces before designing the read path.
- If the vendor hosts your system, request a read-only reporting connection in writing and check the contract for data access terms.
- Finite schedule output is the reference. Reproduce a known schedule view before relying on new answers.
- Shop floor data collection completeness sets the quality of late job and remaining-hours answers. Audit it early.
- Outside processing is often tracked through purchase orders linked to operations. Map that link explicitly.
- Keep the query layer in version control and retest it after Global Shop upgrades.
Deployment options
On-prem or private server beside the Global Shop Solutions database
Shops that will not let job, customer, or drawing data leave the building, including ITAR and CUI work.
An open-weight model (Qwen, Llama, Mistral, or gpt-oss class) is served with vLLM or Ollama on a single GPU server on the plant network. Retrieval and query tools run on the same box. If your Global Shop is hosted by the vendor, we agree an approved read path first and keep the AI stack in your own environment.
Sovereign or private cloud tenant
Multi-site manufacturers that want managed GPUs without sending data to a shared public AI API.
The model and the vector index run in a private cloud account or a regional sovereign provider, reached from the plant over a private link. Data stays in a tenant you control and you choose the region.
Read-only pilot against a replica
Teams that want to prove value on real data before committing hardware or touching production.
A nightly restore or replica of the Global Shop Solutions data feeds a small model and a handful of approved questions. Nothing writes back. Most of the learning is about data quality and which questions people actually ask.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR
Private hosting keeps controlled part and program data under customer control. The customer maintains its own registration and handling procedures.
AS9100 and customer flow-downs
Answers cite work order and operation records. Quality records stay under document control and are changed only by authorized staff.
CMMC and NIST SP 800-171
Where CUI is present, the AI stack sits inside the same enclave as the ERP, with access control and logging that support the customer's assessment.
Commercial confidentiality
Role-based limits keep customer and pricing data scoped to the people who should see it.
How an engagement runs
Phase 1 . 1 to 2 weeks
Discovery and data map
- -Inventory of the Global Shop Solutions tables, views, and reports behind scheduling and work orders
- -Ranked list of 15 to 25 real questions from the people who do the work
- -Decision on hosting, access method, and which roles may see which data
- -Written read-only boundary and approval rules for any later write-back
Phase 2 . 2 to 4 weeks
Grounding and retrieval build
- -Read-only connection with a service account scoped to the approved objects
- -Curated query layer and document index for scheduling and work orders
- -Evaluation set of questions with checked answers from your own data
- -Visible query or source citation on every answer
Phase 3 . 3 to 4 weeks
Pilot with a small group
- -Pilot with 5 to 15 estimators, planners, supervisors, or quality staff
- -Weekly review of wrong or unhelpful answers and fixes
- -Usage log showing which questions were asked and which saved time
Phase 4 . 2 to 3 weeks
Harden and hand over
- -Role mapping, audit logging, and backup of the AI stack
- -Runbook for model updates and index refresh after Global Shop Solutions upgrades
- -Go or no-go note on any gated write-back actions
Questions to ask any vendor, including us
A short list that separates real Global Shop Solutions AI work from a chatbot demo.
- Is our Global Shop installation self-hosted or vendor-hosted, and what read access can we get?
- Does the assistant replace the finite scheduler or explain its output?
- How is the what-if routine validated against real outcomes?
- How are user roles and customer visibility handled?
- What happens when routing standards are wrong?
- Can it combine ERP data with machine monitoring or shop floor systems?
- Who approves any change to the schedule?
- Where does the model run and what leaves our network?
Frequently asked questions
Does Global Shop Solutions have its own scheduling tools?
Yes, it includes scheduling capability, and many shops are well served by it. An assistant is useful when schedulers need to answer ad hoc questions across work orders, purchasing, and capacity quickly, or when customer service needs dates without interrupting the scheduler.
Will the AI change my schedule?
No. It is read-only by default and offers explanations and comparisons. The scheduler makes any change in Global Shop. That keeps accountability clear and avoids unexpected moves.
Can we use this if Global Shop is hosted by the vendor?
Possibly. We need an approved read-only reporting path and contract terms that allow it. The AI model and index run in your environment, and only the approved data is pulled.
How is accuracy measured?
We build a set of real scheduling questions with verified answers from your data and score the assistant on them before users see it. In production every answer cites its records so errors are easy to catch.
What if shop floor reporting is late?
Then answers about remaining hours and late operations will lag. This is usually the first thing the pilot reveals, and improving reporting discipline helps both the schedule and the assistant.
Does it need a lot of hardware?
Not for a typical shop. A single GPU server running an open-weight model is enough for a team of schedulers and customer service users. We size it to your user count and chosen model.
How long until it is useful?
A pilot with real users typically runs eight to twelve weeks from data mapping. The first useful answers, such as late job explanations, often come in the first month once access is in place.
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Talk it through with an engineer who knows Global Shop Solutions
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.