Guide: Transparent Supply Chain with the AI Assistant
Introduction
With this guide, you will build a fully transparent supply chain step by step – supported by the AI Assistant.
The assistant helps you visualize material flows, uncover risks, identify transparency gaps, and derive actionable improvement measures.
Click any prompt to run it directly in the AI Assistant – from supply chain overview to the final transparency report.
Step 1 – Overview of the Supply Chain
📋 Example Output
Raw Materials
- Brewing Water Hamburg — Treated deep well water (Elbe basin)
- Brewing Water Munich — Soft alpine spring water for wheat beer
- Barley Malt — Base/specialty malts (DE/NL)
- Hops — Aroma/bitter hops (Hallertau, DE/PL)
- Yeast — Top-/bottom-fermenting strains (DE/NL/PL)
- Glass Bottles — Returnable/single-use (DE/NL/FR)
- Cans & Labels — Aluminum cans, lids (DE/UK/NL)
Suppliers
- Hallertau Hopfen GmbH — Primary hop supplier (DE)
- AgroTrans Malz KG — Reliable malt supplier (DE)
- EuroMalt BV — Secondary malt supplier (NL)
- HopMaster Polska — Alternative hops (PL)
- YeastSupply NL — Wheat beer yeast strains (NL)
- YeastPro Polska — Backup yeast with cold chain issues (PL)
- BottleWorks BV — Glass bottles, capacity constraints (NL)
- LabelPrint GmbH — Labels with short lead times (DE)
Logistics Partners
- NordLine Logistics — Main northern distribution/export (DE)
- CityShip — Rail/intermodal network (EU)
- EuroTrans BV — Rail logistics for malt/hops (EU)
- NL Trans Freight — NL inbound, delay risk (NL/DE)
- PolFreight Logistics — Packaging components (PL/DE)
- BalticRail Polska — Rail for glass/packaging (PL/DE)
- CoolBrew Express — Cold chain transports (South DE)
Production Sites
- ElbeBräu Hamburg — Brewhouse: kettle, 2 fermenters, combined filling line
- Alpenquell Munich — Large facility: 2 kettles, 3 fermenters, can/bottle filling
Warehouses
- Hamburg Raw Materials Warehouse — Malt, hops, labels
- Hamburg Finished Goods Warehouse — Outbound hub, NordLine connection
- Munich Raw Materials Warehouse — Water, malt, yeast; FEFO capable
- Munich Finished Goods Warehouse — Distribution/Export hub
Distributors
- NordGetränke GmbH — Northern wholesale (focus: ElbeBräu)
- SüdBier Handel — Southern regional distributor
- EuropaBrew Import — EU importer with high capacity
👉 Goal: Build a complete supply chain map.
AI Assistant Logic
- Automatically categorizes by role (raw material, supplier, logistics, site, distributor).
- Detects missing or duplicate location data.
- Builds a full end-to-end supply chain map.
Step 2 – Visualize Material Flow
📋 Status Legend:
✔️ ok ⏱️ delay ❗ missing ❔ unclear ⚠️ contradiction
📋 Example Output (two products):
Material Flow – Elbe Hell (SKU001)
| Stage | Status | Details |
|---|---|---|
| Raw Materials | ❔ unclear | Malt, yeast, hops |
| Delivery | ⏱️ delay | NL Trans Freight |
| Production | ✔️ ok | Brewhouse HH → Fermenter → Filling |
| Warehouse | ✔️ ok | Hamburg Finished Goods |
| Distributor | ⏱️ delay | NordGetränke, CoolBrew Express |
Material Flow – Alpen Hell (SKU004)
| Stage | Status | Details |
|---|---|---|
| Raw Materials | ❔ unclear | Malt, yeast, hops |
| Delivery | ⏱️ delay | EuroTrans BV, NL Trans Freight |
| Production | ✔️ ok | Kettle → Fermenter → Filling |
| Warehouse | ✔️ ok | Munich Finished Goods |
| Distributor | ⏱️ delay | EuropaBrew Import via CityShip |
👉 Goal: Establish end-to-end material traceability.
AI Assistant Logic
- Analyzes each SKU across all stages.
- Highlights systematic delays (e.g., NL Trans Freight).
- Detects contradictory data (raw materials unclear but production ok).
Step 3 – Identify Risks & Bottlenecks
📋 Example Output
Top Risks (High)
- Inbound delays (NL Trans Freight / EuroTrans)
- SPOF outbound Munich (CityShip)
- SPOF filling line Hamburg
- SPOF brewhouse Hamburg
- Yeast cold chain (YeastPro)
Medium Risks
- BottleWorks supply issues
- Outbound delays Hamburg
- Fermenter MUC3 in maintenance
- Rail dependency
- Data quality: raw materials “unclear”
Low Risks
- Water sites stable
- Hops/malt sufficiently dual sourced
👉 Goal: Build a prioritized risk management view.
AI Assistant Logic
- Combines material flow + supply chain data + machine data.
- Rates criticality by impact × probability.
- Automatically detects single points of failure.
Step 4 – Identify Transparency Gaps
📋 Example Output
Origin
- Water HH/MUC without source ❗
- Raw materials with generic origin (“Hamburg”)
Transport
- No shipment IDs, ETA, milestones
- Outbound missing slot/capacity data
Inventory
- No on-hand/allocated/ATP
- No lot/WIP visibility
Quality
- No COAs
- No yeast temperature curves
Risk
- No owners/scores
- SPOFs without failover documentation
👉 Goal: Identify and prioritize critical data gaps.
AI Assistant Logic
- Compares target data model vs. actual graph.
- Highlights critical missing fields.
- Groups gaps by transport, quality, risk, inventory.
Step 5 – Define Improvements (P1–P3)
📋 Example Output
P1 – Immediate (0–6 weeks)
- ASN + ETA tracking — Supplier messages with precise ETAs
- Inventory status + DOS — Distinguish available/blocked/QC
- Yeast cold chain — Temperature loggers + auto QC hold
- Control Tower — Daily exception board
- Carrier performance — OTIF baseline + secondary carrier tests
P2 – Build-out (6–12 weeks)
- Batch traceability — QC approvals visible
- WIP visibility — Live kettle/fermenter/filling status
- Supplier collaboration — 13-week forecast & commit
- Slot booking — Yard management
P3 – Structural (12+ weeks)
- End-to-end traceability — SSCC serialization
- CO₂ transparency — Emissions per shipment/lane/material
- Risk register — Full SPOF documentation + tests
👉 Goal: A structured roadmap from quick fixes to maturity improvements.
AI Assistant Logic
- Prioritizes based on impact on transparency & risk.
- Links measures to quick wins (ETA, DoC, COA).
- Ensures a clean roadmap: Immediate → Build-out → Structural.
Step 6 – Additional Transparency Data Points
📋 Example Output
Transport
- EPCIS milestones
Standardized event tracking for traceability - ETA with confidence score
Predictive accuracy improves interventions - Sensor data (temp./humidity)
Cold chain monitoring for yeast/hops - Geofence events
Reveals bottlenecks at nodes
Inventory
- In-transit inventory with ETA
Improves ATP planning - Lot/batch & FEFO status
Prevents aging, enables recalls - ATP/CTP reservations
Reduces stockouts via visibility - Quarantine status per lot
Prevents accidental shipping
Quality
- COA/LIMS results per batch
Links quality directly to availability - OOS events with CAPA ID
Speeds up resolution - CIP/SIP protocols
Ensures hygienic production - Cold chain excursions
Connects temperature issues to quality
Risk
- Single sourcing index (HHI)
Shows dependency levels - Tier-2/3 visibility
Uncovers hidden bottlenecks - Supplier credit score
Indicator for supply risk - Climate risk at origin
Assesses hops/barley climate sensitivity
ESG
- CO₂e per transport leg
Enables Scope-3 reporting & optimization - Energy mix & renewable share
Shows decarbonization progress - Water consumption per hl beer
Supports efficiency measures - Recycling rates
Proof of circularity
👉 Goal: A prioritized list of additional data points with justification.
AI Assistant Logic
- Detects missing or incomplete fields in the graph.
- Recommends minimal viable sets (ETA, COA, DoC).
- Groups by impact on planning, quality, and risk.
Step 7 – Processes & Systems for Transparency
📋 Example Output
A) Real-time Monitoring & Alerts
- Cold-chain monitoring (P1): Install IoT sensors → Configure MQTT gateway → Activate temp alerts | Data: temp loggers, MQTT
- Carrier events → ETA (P1): Build API → Normalize events → Compute ETA + push alerts | Data: TMS API, GPS
- Quality exception handling (P2): Detect COA deviations → Auto-ticket → Escalation path | Data: LIMS, QM system
B) Master Data & Traceability
- Lot-to-lot traceability (P1): Standardize batch IDs → Build consumption matrix → Run recall simulation | Data: ERP, WMS, MES
- Supplier master data sync (P2): Unified schema → API sync → Deduplicate entries | Data: SRM, ERP
- Material provenance (P3): Add origin tags → Store blockchain hash → Verification workflow | Data: Blockchain, supplier portal
C) KPIs & Reporting
- OTIF dashboard (P1): Activate delivery tracking → Compute OTIF → Weekly reporting | Data: TMS, ERP
- Supplier scorecard (P2): Define KPIs → Build scoring logic → Monthly reviews | Data: ERP, QM, TMS
- Carbon footprint tracking (P3): Add emission factors → Compute CO2 → ESG report | Data: TMS, emission DB
D) Automation & Integration
- EDI-to-API migration (P2): Analyze EDI → Develop REST APIs → Migrate stepwise | Data: EDI gateway, API management
- Exception-based planning (P3): Define triggers → Adjust planning logic → Test auto replanning | Data: APS, MES, ERP
👉 Goal: Implementation-ready systems and process enhancements.
AI Assistant Logic
- Groups actions by impact & effort.
- Links directly to data gaps (Step 6).
- Turns transparency goals into concrete A→B→C steps.
Step 8 – Summarize Quick Wins
📋 Example Output
- ASN + ETA tracking — Yeast/hops with YeastSupply + NL Trans (Logistics)
- DOS dashboard — Days-of-supply Hamburg/Munich (Planning)
- Yeast cold chain — IoT logger + auto QC hold (Quality)
- Control tower — Daily 15-min exception board
- Carrier performance — OTIF baseline + EuroTrans pilot
- WIP board — Live kettle/fermenter status
- QC traffic light — Visible batch release status
👉 Goal: Actionable quick wins with clear owners.
AI Assistant Logic
- Selects measures with the biggest impact < 14 days.
- Focuses on transport, inventory, quality, and risk.
- Maps each win to responsible team + next action.
Step 9 – Business Case
📋 Example Output
Efficiency
- Plan-to-ship lead time
Typical effect: −10–20%
Benefit formula: reduced days × daily process cost/volume - Tracking effort (manual → automated)
Typical effect: −50–70% hours in logistics/planning
Benefit formula: saved hours/year × fully loaded hourly cost - OEE line availability
Typical effect: +3–5 pp
Benefit formula: additional runtime × output/h × margin
Cost
- Expedite / express costs
Typical effect: −20–40%
Formula: baseline × reduction % - Inventory cost (working capital)
Typical effect: −10–15% average inventory
Formula: avg. inventory × cost of capital × % reduction - Write-offs / obsolescence
Typical effect: −15–30% (via FEFO)
Formula: baseline × reduction %
Quality / Service
- OTIF
Typical effect: +5–10 pp
Formula: ΔOTIF × revenue impacted × margin - First-pass yield
Typical effect: +2–4 pp - Complaint / recall rate
Typical effect: −15–30%
Resilience
- Out-of-stock days
Typical effect: −20–35% - Dual sourcing coverage
+20–30 pp reduction in exposure - Time to recall
Typical effect: −50–70%
ESG
- CO₂e per hl (transport)
Typical effect: −5–12% - Water usage per hl beer
Typical effect: −3–6% - Audit/compliance overdue items
Typical effect: −40–60%
Example Calculation
- Expedite: €250,000 × 30% = €75,000 p.a.
- Inventory: €5.0M × 12% × 12% ≈ €72,000 p.a.
- Complaints: €120,000 × 20% = €24,000 p.a.
- Total (excerpt): ≈ €171,000 p.a.
- Payback: typically 6–12 months
👉 Goal: Quantified business case for decision-making.
AI Assistant Logic
- Links KPIs directly to quick wins and P1–P3 measures.
- Uses realistic benchmark ranges.
- Supports management-level prioritization.
Step 10 – Generate PDF Report
👉 Goal: A compact, management-ready summary report.
Conclusion
With these prompts, the AI Assistant guides you from supply chain analysis to management-ready transparency reports:
- Steps 1–3: Map your supply chain, understand material flows, and identify risks
- Steps 4–6: Close transparency gaps, prioritize improvements, and develop data strategy
- Steps 7–10: Implement concrete systems, execute quick wins, and build the business case
🎯 Result: A fully transparent, controllable, and resilient supply chain – with prioritized measures, quantified benefits, and a management-ready implementation plan.
👉 Get started now: Open the prompts in the AI Assistant, apply them to your supply chain – and gradually achieve greater transparency, control, and resilience in your material flows.