ChatGPT Enterprise training for operations & supply chain teams in Singapore
ChatGPT Enterprise training for operations teams in Singapore: one hands-on day, onsite, on your own files. Operating procedures and handovers.
ChatGPT Enterprise for operations & supply chain teams
Operations teams hold their process knowledge in documents nobody has time to maintain. This programme uploads that material, builds a procedure assistant on it, and covers how to keep it current.
Where the work happens
- File analysis
- Custom GPTs
- Projects
- Canvas
The distinguishing exercise is building a custom GPT on your own files that a whole team can reuse, plus the verification standards that keep its answers trustworthy.
Who this programme is for
Operations, procurement, quality and supply chain teams. Teams building shared internal tools.
- Operations managers and supervisors writing procedures and shift handovers
- Procurement teams comparing supplier proposals and preparing clarifications
- Quality and maintenance teams summarising deviations and records for review
- Operations leaders deciding where AI may support decisions on the floor and where it must not
Not the right fit
- Teams looking for a vendor demo rather than hands-on practice
- Anyone expecting AI to approve suppliers, release stock or close quality deviations without human sign-off
- ERP, MES or planning-system implementation projects — this is applied assistant training
What the day covers
Module 1: Operating procedures and handovers
Process notes and shift records are uploaded and turned into clear, reviewable procedures.
Exercise. Turn process notes and shift records into clear procedures, production summaries and handover checklists.
What you leave with. A reviewed procedure and shift handover template.
Module 2: Procurement and suppliers
Supplier proposals are compared against agreed criteria, with missing terms flagged rather than inferred.
Exercise. Compare supplier proposals against agreed criteria and prepare clarification requests without inventing missing terms.
What you leave with. A source-backed supplier comparison and correspondence pack.
Module 3: Quality and planning
An exception-reporting GPT is built for recurring deviation and inventory summaries.
Exercise. Summarise deviations, maintenance records and inventory exceptions for accountable human review.
What you leave with. An exception report and action-tracking workflow.
A worked example: Late-delivery analysis from a CSV
One exercise from the day, as it runs on your own material in ChatGPT Enterprise.
- What goes in
- Six months of purchase order and goods receipt data as a CSV.
- What is asked
- Show late deliveries by supplier and by month, and list the ten worst orders with the days late.
- What comes out
- A summary table and chart for the supplier review.
- What the reviewer checks
- The totals are re-run in a pivot table you control before the figures go to a supplier meeting.
Singapore rules the exercises respect
Operations documents carry legal and safety weight, and the review step reflects that.
- Procedures with safety-critical steps are signed off by the responsible person under your Workplace Safety and Health obligations, not by a draft.
- Supplier and contract comparisons flag missing terms for negotiation rather than assuming them.
- Supplier and employee personal data in records is anonymised before it is used in an exercise.
What this day does not cover
Scope is agreed before the session so the day stays hands-on.
- API work, function calling or anything that requires writing code. This is a day for the people doing the business task.
- Admin configuration, workspace provisioning or SSO setup.
- Fine-tuning. The programme builds custom GPTs on your own files, which is a different thing and the one most teams actually need.
Before the session
- A licensed ChatGPT Enterprise seat for each participant
- Working knowledge of your own procedures, supplier process and quality records
- Anonymised or sample working files — a procedure, two supplier proposals, a deviation log extract
- No coding background required
Format and schedule
- Delivery
- On site in Singapore or remote, delivered live. Cohorts are kept small enough that every participant works on their own files rather than watching a screen share.
- Duration
- Typically one full day, or two half-days scheduled around shift and production patterns. Module order is adjusted to your calendar and tool stack.
- How each module runs
- Each module follows the same pattern: a short walkthrough of the technique, a hands-on exercise on your own data, then a review of what the assistant got wrong and how to catch it.
- Afterwards
- Participants leave with the prompt patterns, templates and review checklists produced during the session, so the work continues after the training day.
How your data is handled
- Exercises run inside your own tenant and your own licences, so your data does not leave your environment for the training.
- Files shared with the trainer in advance are anonymised extracts; supplier pricing, contracts and production records stay inside your own tenant and licences.
- Every module includes verification: checking output against the source, confirming what the assistant left out, and marking what still needs a human read.
- Approvals stay with your people. The programme teaches AI-assisted preparation and review, never unattended supplier, stock or quality decisions.
Who runs the session
Abhijit Ghosh
Founder, Eagentix · Data & Generative AI Practitioner · Singapore
The programme is delivered by Abhijit Ghosh, who has spent 25 years in data and AI, including roles at Accenture, IBM, Deloitte, Cloudera, Tibco and Wipro. Sessions are run by the practitioner, not handed to a junior facilitator.
Questions about ChatGPT Enterprise for operations & supply chain
Our procedures are out of date. Does that break this?
It makes the first module more valuable. Uploading what exists and turning it into reviewable procedures is usually the fastest route to current documentation, provided an owner verifies each one.
What is a custom GPT and why does the day centre on building one?
It is a reusable assistant configured once with your own files and instructions, then shared with the team. It matters because it turns a good prompt one person wrote into a repeatable standard everyone uses — including the review checkpoints written into its instructions.
Is our uploaded material used to train the model?
Enterprise plans are sold on the basis that business data is not used for training, and your administrators can confirm the terms that apply to your workspace. We cover what to upload and what to keep out regardless, because access scope inside your own organisation matters as much as the vendor terms.
Can AI-drafted procedures be used for safety-critical work?
Only after the responsible person has verified and signed them off. The assistant shortens drafting; it does not replace the competent review your safety obligations require.
Do we need clean data before the session?
No. Real, messy shift notes and records are better material, because finding the gaps and contradictions is part of the exercise.