How to Customize GPT-5 for Supply Chain
Turn GPT-5 into a practical copilot for procurement, logistics, and S&OP in one week
Most teams still use GPT-5 like a search box. Quick questions. Generic answers. Then back to spreadsheets. You can do much better. With a simple setup, GPT-5 can learn your products, suppliers, policies, and targets. It can draft plans, challenge assumptions, and speed up decisions.
Below is a step by step playbook to make GPT-5 think like your supply chain. You will get copy ready templates, a starter data pack, and a tested prompt library.
Why customization beats ad hoc chats
Consistent answers that match your policies and KPIs
Less context repeating and fewer copy paste errors
Faster reviews because outputs arrive in your house format
Better adoption across teams because the copilot speaks your language
Step 0: Switch on memory and set guardrails
Turn on memory in Settings so GPT-5 can remember stable facts: company name, network footprint, SKU families, service targets
Save only durable context. Do not save sensitive one off numbers
Add a short “red lines” list: do not send emails, do not place POs, draft only, cite sources
Guardrails snippet to paste in your first message
Guardrails: draft only, never execute transactions, never log into systems, cite assumptions, list data freshness, highlight confidence limits, flag PII.
Step 1: Write custom instructions that make GPT-5 think like your team
Paste the template below into Custom Instructions. Update the brackets. Keep it crisp.
About our supply chain:
- Company: [Name], regions: [NA, EU, APAC], channels: [retail, D2C, B2B]
- Network: [plants], [DCs], [3PLs], main lanes: [list]
- Product mix: [top 10 families], shelf life: [if relevant]
- Systems: ERP [..], WMS [..], TMS [..], planning [..]
Policies and targets:
- Service: [OTIF 95% key accounts, 92% base]
- Inventory: [DIO target], safety stock method: [..]
- Cost to serve: track [transport %, handling %, duty]
- Expediting: cap per order [$/%], approval levels [..]
How to respond:
- Prefer tables and one page briefs
- Always state assumptions and data as of dates
- Offer 3 options with pros and cons for big calls
- Use our formats: S&OP summary, supplier brief, risk memo
Voice and tone:
- Direct, plain language, no hype, no jargon
Step 2: Connect lightweight data that powers great answers
You do not need a data lake to start. Share a small “starter data pack” as CSV or Excel. Refresh weekly.
Starter data pack columns
demand_history.csv: sku, location, week, units
inventory.csv: sku, location, on_hand, on_order, safety_stock
open_pos.csv: po, supplier, sku, qty, promise_date, incoterm
suppliers.csv: supplier, region, lead_time_days, otif_90d, risk_flag
lanes.csv: origin, destination, mode, lead_time_days, avg_cost
customers.csv: account, service_target, priority_flag
Tell GPT-5 the file names you will upload. Ask it to remember schema names.
Step 3: Teach safe actions
If you have APIs, restrict actions to read only. If not, keep GPT-5 in draft mode.
Allowed: draft emails to suppliers, build scenario tables, create S&OP summaries, write decision memos
Not allowed: sending messages, booking freight, changing orders
Step 4: Create role presets for key teams
Procurement preset
Role: strategic sourcing partner
Priorities: continuity of supply, landed cost, supplier risk, contract compliance
Playbooks: dual source thresholds, MOQ overrides, expedite rules
Output: supplier brief, negotiation plan, risk heatmap
Logistics preset
Role: transportation and DC flow optimizer
Priorities: OTIF, dwell, cost per shipment, carbon
Playbooks: mode shift rules, port avoidance, peak surge plan
Output: route proposal, carrier scorecard, recovery plan
S&OP preset
Role: plan challenger and summarizer
Priorities: forecast error, bias, inventory health, revenue at risk
Playbooks: consensus guardrails, exception thresholds
Output: 1 page exec summary with 3 decision asks
Step 5: Use a simple 30 day rollout
Week 1: load custom instructions, upload starter data, run two small pilots
Week 2: adopt standard outputs for S&OP summary and supplier risk brief
Week 3: add exception triage for late POs and at risk orders
Week 4: measure impact, prune unused prompts, set a weekly cadence
The prompt library you will actually use
Copy, paste, replace bracketed fields.
1. Exception triage
You are our exception desk. Using inventory.csv and open_pos.csv, list the top 15 at risk customer orders for the next 14 days. Show: customer, sku, qty, promise date, risk reason, revenue at risk, 3 recovery options with cost and OTIF impact. End with the one page action plan.
2. Supplier risk brief
Build a risk brief for [Supplier X]. Use suppliers.csv and any public signals I paste. Rate probability and impact next 90 days. Propose mitigations inside our expedite and dual source policies. Output: one page brief plus a table of triggers.
3. Negotiation plan
Draft a negotiation plan with [Supplier Y] for a 3% cost down without hurting continuity. Include fact base, give get list, walk away points, and a 5 email sequence. Match our contract terms with Incoterms [..].
4. Forecast sanity check
Load demand_history.csv for [top 50 SKUs]. Detect bias and seasonality. Flag SKUs where proposed forecast exceeds p90 by more than 15%. Suggest guardrail adjustments and show expected impact on DIO.
5. Safety stock tune up
Using demand_history.csv and lead_time_days from suppliers.csv, recompute safety stock for [family A] with target service [95%]. Compare current vs proposed, show units and working capital delta by location.
6. Route optimization quick win
From lanes.csv and current freight prices I will paste, propose 3 alternative routings for [origin to destination] that reduce cost while holding lead time within +2 days. Include carbon estimate and risk notes.
7. S&OP executive summary
Create a one page S&OP summary for the next 13 weeks. Sections: demand highlights, supply constraints, inventory health, revenue at risk, 3 decisions needed with P&L impact. Use our tone and format.
8. Root cause on fill rate dip
We saw fill rate drop from [96%] to [92%] last week. Use inventory.csv and open_pos.csv to attribute the dip by cause: late inbound, forecast miss, DC capacity, allocation rules. Recommend two fixes per cause with effort and time to value.
9. Cost to serve lens
Build a cost to serve view for top 20 customers. Combine lanes.csv and handling assumptions: pick [x], pack [y], linehaul [z]. Rank customers by gross margin after logistics and handling. Suggest 3 policy changes.
10. New product ramp
For [SKU launch], propose a 12 week ramp plan. Include initial safety stock, DC deployment, supplier readiness checks, and a watchlist of leading indicators. Provide a weekly review template.
11. Peak readiness
Create a peak season checklist for our network. Buckets: demand shaping, labor, carrier capacity, inventory positioning, cut-off times, comms. Add dates and owners. Keep to one page.
12. Board memo draft
Draft a 2 page board memo on supply chain resilience. Cover current risk posture, time to recover for top 10 SKUs, scenario results, and investment asks. Plain language, numbers up front.
Formats that speed reviews
Ask GPT-5 to default to these outputs.
One page brief
Risk heatmap table
Options with pros and cons
Assumptions box with data as of date
Action plan with owners and deadlines
Measure what matters
Set a tiny scorecard. Track weekly.
Planning cycle time saved
Expedite spend change
OTIF and fill rate change
Inventory health change
Number of exceptions auto drafted by GPT-5
User satisfaction quick pulse
ROI sketch
ROI = (hours saved x blended rate) + (expedite reduction) + (working capital benefit) + (revenue protected) - (AI license + setup time)
Security and compliance checklist
Keep sensitive data out of memory. Use session files for volatile numbers
Mask PII. Share only what is needed for the task
Review outputs before sending to partners
Log key decisions and assumptions for audit
Rotate shared prompt libraries quarterly
Common pitfalls and simple fixes
Vague prompts. Fix with clear tasks, files, and constraints
Wall of text outputs. Fix with table first then short narrative
Stale data. Fix with a weekly upload routine and “data as of” tags
Overreliance. Fix with a human review step and a short checklist
Quick FAQs
Do we need full system integration to start
No. Begin with CSVs and docs. Standardize later.
Will this replace planners
No. It removes low value grunt work. Humans still own decisions, trade offs, and trust.
How long to see value
Most teams see time savings in week one and cleaner decision packs by week two.
Key takeaways
Custom instructions and a tiny starter data pack unlock most of the value
Role presets keep answers aligned with procurement, logistics, and S&OP needs
A small prompt library plus guardrails gives speed without risk
Measure cycle time, service, cost, and adoption
Got a prompt that works better than mine, or a format your execs love? Share it in the comments. Then join the discussion with supply chain peers inside Chain.NET. Joining is free and only takes a few minutes: https://mygs.cc/chain
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