Agentic AI Supply Chain Control Tower using BIG-AI
Imagine this: It’s 7 a.m. Your factory floor is just waking up. Overnight customer orders have already triggered fresh production plans. Trucks are routed, raw materials are en route, and suppliers have been notified — all without a single email, spreadsheet, or urgent phone call.
This isn’t a futuristic dream. It’s what we built using an Agentic Multi-AI Supply Chain Control Tower powered by BIG-AI, transforming days of coordination into minutes of autonomous action. And yes — this works today, in live production systems.
The Supply Chain Chaos We Wanted to Fix
Every enterprise faces the same painful truth — systems don’t talk to each other.
Executives repeatedly tell us:
“Our ERP is here.”
“Our MES is somewhere else.”
“Logistics runs on another platform.”
“Forecasting is done in Excel.”
“Production planning lives inside SAP.”
By the time teams align everything, the market has already shifted. This misalignment leads to overstocks, stockouts, delays, and inflated operational costs.
Researchers have long warned that fragmented supply chains slow down decision-making and increase risk (Christopher 112; Ivanov and Dolgui 45). Modern supply chains need autonomy, speed, and integrated intelligence.
We wanted a supply chain that could think, act, and adapt in real time.
Enter Agentic AI — A Digital Operations Team
Traditional analytics tell you what happened.
Agentic AI figures out what to do next — and executes it.
Our solution deploys specialized agents:
Demand Agent
Production Agent
Inventory Agent
Logistics Agent
Procurement Agent
Control Tower Orchestrator
Each agent performs its role independently but collaborates through a shared intelligence layer — much like a digital operations team. Research shows that multi-agent systems are effective for coordinating distributed decisions in complex supply chains (Wooldridge 57).
The system does four things continuously:
Acts • Communicates • Executes • Explains
How the Multi-Agent Control Tower Works (Pilot Example)
We deployed this with a mid-sized consumer goods manufacturer.
Here is the complete autonomous cycle — Forecast → Plan → Ship → Deliver.
1. Sales Forecasting Agent
This agent ingests:
POS data
E-commerce signals
Promotion data
Weather and event data
Every six hours, it generates a probabilistic forecast with confidence bands.
During the pilot, the system detected a sudden demand spike two days earlier than legacy forecasting methods — aligning with research showing machine learning improves forecast accuracy in dynamic markets (Makridakis et al. 798).
2. Production Planning Agent
It evaluates:
Machine availability
Maintenance windows
Labor constraints
Then optimizes schedules to minimize changeovers. Frequent product switching kills efficiency — something well-documented in operations literature (Nahmias and Olsen 212).
Our agent reduced unnecessary line stops and increased continuous run time significantly.
3. Inventory Optimization Agent
This agent automatically:
Balances stock across warehouses
Applies safety stock rules
Considers supplier lead times
Evaluates carrying cost
During deployment, we reduced safety stock for slow SKUs and increased it for fast movers — fully automated.
4. Route & Shipping Agent
Using traffic, driver availability, and vehicle load data, the logistics agent optimizes delivery routes and ETAs.
When shipments delay, it signals the production and inventory agents instantly, allowing the entire system to re-sync.
5. Procurement Agent
When materials drop below threshold, it:
Creates purchase orders in SAP
Checks vendor performance
Compares suppliers
Can even negotiate automatically
6. Control Tower Agent — The Brain
This orchestrator pulls data from:
SAP
ERP
OT systems
IoT sensors
It uses a RAG (Retrieval-Augmented Generation) layer to fetch SOPs, SLAs, and policy documents — ensuring every decision is evidence-backed. This approach aligns with current research on the reliability of RAG-enhanced LLM systems (Lewis et al. 7).
Every action is logged for audit, review, and human oversight.
The Technology Backbone
Our architecture includes:
Multi-agent framework: Python, LangChain, Semantic Kernel
RAG layer: Evidence-grounded decision making
Vector DB: Pinecone for operational memory
Orchestrator: LLM + Policy Engine
Integrations: SAP, MES, WMS via secure APIs
Reinforcement learning continuously tunes the agents to maximize KPIs such as service levels, machine utilization, and working-capital efficiency.
The Results — In Just 3 Months
Our pilot delivered measurable impact:
+18% Forecast accuracy
−22% Inventory cost
+10 percentage point improvement in on-time delivery (86% → 96%)
- −30% Production changeovers
The highlight?
A regional sales spike hit unexpectedly. Within 90 seconds, the system:
Detected demand
Updated production
Reordered raw materials
Re-routed trucks
Rebalanced inventory
No human intervention required.
Human + AI: A Partnership, Not Replacement
Agentic AI doesn’t replace planners — it empowers them.
AI handles repetitive coordination
Humans approve strategic or high-cost actions
Every decision has a full audit trail
Overrides are always possible
This is the future researchers envision: collaborative, human-centered AI systems (Shneiderman 22).
Implementation Roadmap — How You Can Start
We follow a structured three-phase program:
1. Discovery (2 weeks)
Data readiness, KPIs, and risk review.
2. Pilot (8–12 weeks)
Connect 1–2 sites in shadow mode and measure performance.
3. Scale (3–6 months)
Roll out across sites, integrate deeply with SAP, enable governance workflows.
Fast start • Measurable ROI • Enterprise-grade security
Contact: contact@turilytix.ai
Demo: https://turilytix.ai/contact-us/
Risk, Governance & Trust
We designed governance from day one:
Role-based access
Encryption across all layers
Auditable decision logs
Rollback controls
Manual override
MLOps evidence for regulated sectors
Trust is not an afterthought — it’s embedded.
The Future: Self-Driving Supply Chains
The next evolution will include:
Supplier ESG scoring
Carbon-aware decision making
Integrated sustainability metrics
Autonomous procurement ecosystems
The self-driving supply chain isn’t ten years away — it’s already happening.
So… would you trust an AI to run your supply chain?
Share your thoughts in the comments.
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REFERENCES
- Christopher, Martin. Logistics & Supply Chain Management. 5th ed., Pearson, 2016.
- Ivanov, Dmitry, and Alexandre Dolgui. “Artificial Intelligence in Operations and Supply Chain Management.” International Journal of Production Research, vol. 58, no. 23, 2020, pp. 710–720.
- Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474.
- Makridakis, Spyros, et al. “Statistical and Machine Learning Forecasting Methods: Concerns and Ways Forward.” PLOS ONE, vol. 13, no. 3, 2018, pp. 1–26.
- Nahmias, Steven, and Tava Olsen. Production and Operations Analysis. 8th ed., Waveland Press, 2015.
- Shneiderman, Ben. Human-Centered AI. Oxford UP, 2022.
- Wooldridge, Michael. An Introduction to MultiAgent Systems. 2nd ed., Wiley, 2009.
