Platform Capabilities at a Glance
End-to-End Visibility
Single pane of glass across every supplier, plant, warehouse, carrier, and customer — from raw material to last-mile delivery.
360° · supply chain view
Supply Chain Digital Twin
Live virtual replica of your network. Run what-if scenarios in seconds before committing to any operational decision.
<2s · scenario simulation
Generative AI & Agents
AI narratives, root-cause explanations, and autonomous agents that resolve 40–60% of exceptions without human touch.
60% · touchless exceptions
Conversational Interface
Ask questions in plain English. "Which orders are at risk this week?" — the platform answers with data, charts, and recommended actions.
NLQ · natural language queries
Real-Time Data Fabric
Streams from ERP, WMS, TMS, IoT sensors, carrier APIs, weather, and risk feeds — all reconciled into one operational truth.
<30s · data latency
Resilience Intelligence
Proactively detect disruptions 7–21 days before impact. Scenario modelling, supplier risk scoring, and business continuity simulation.
21d · advance disruption warning
Core Architecture & Building Blocks
EPS Supply Chain Control Tower — 4-Layer Composable Architecture
Layer 1 — Unified Data & Integration
Single source of operational truth across all SC systems
- REST / GraphQL APIs
- EDI X12 / EDIFACT
- Kafka Event Streaming
- SAP BAPI / IDocs
- Oracle Fusion APIs
- SFTP Flat Files
- Webhook Subscriptions
- Data Quality Engine
Layer 2 — Supply Chain Digital Twin
Live virtual replica of the entire network — nodes, links, flows, and risk scores
- Node & Link Network Model
- Real-Time Sync Engine
- Simulation Sandbox
- Disruption Path Analysis
- Capacity & Inventory States
- Lead-Time Graph
Layer 3 — AI / ML & Generative AI
Always-on reasoning engine — predictive, prescriptive, and generative intelligence
- Demand Sensing (0–14d)
- Inventory Risk Scoring
- Supplier Lead-Time Prediction
- Transportation Delay Model
- Anomaly Detection
- Root-Cause Narratives
- Prescriptive Recommendations
- Document Intelligence (Gen AI)
Layer 4 — Conversational & Agentic Interface
Natural language queries, autonomous agents, and multi-agent orchestration
- NLQ Chat Interface (RAG)
- Executive Briefing Engine
- Autonomous Exception Agents
- Escalation Router
- Multi-Agent Orchestration
- Action Audit Log
Outputs
- Supplier Intelligence
- Demand Command
- Inventory Intelligence
- Logistics Visibility
- Manufacturing Sync
- Customer Fulfilment
- Resilience & Risk
- Sustainability Command
1. Unified Data & Integration Layer
The foundation is a single source of operational truth assembled from every system that touches the supply chain:
- Ingests data from ERP, WMS, TMS, supplier portals, 3PL systems, and external feeds (weather events, port congestion indices, traffic data, geopolitical risk signals)
- Breaks down application silos by normalising data into a unified supply chain ontology — every order, shipment, inventory position, and supplier record speaks the same semantic language
- Supports historical + real-time data correlation: batch feeds for master data and transactional history sit alongside sub-second streaming event data from IoT sensors and carrier APIs
- A data quality engine continuously scores, cleanses, and reconciles inbound data — flagging anomalies at ingestion rather than at decision time
Connectivity patterns supported: REST/GraphQL APIs, EDI (X12/EDIFACT), Kafka event streaming, SAP BAPI/IDocs, Oracle Fusion APIs, flat-file SFTP, and webhook-based event subscriptions.
2. Digital Twin of the Supply Chain
Built on the unified data layer, EPS's Supply Chain Digital Twin creates a live, navigable virtual model of the entire network:
- Node & link model — every supplier, plant, DC, 3PL node, and transport lane is modelled with its current state: capacity, inventory, lead times, and risk score
- Real-time synchronisation — the twin updates continuously as transactions flow; a goods-receipt in the WMS instantly moves inventory in the twin; a vessel departure event from a maritime API updates the in-transit position
- Simulation engine — planners run forward-looking scenarios on the twin: "If the Kaohsiung port closes for 10 days, which customer orders are at risk and what is the cost of re-routing through Singapore?" — without touching the live system
- Disruption path analysis — the twin traces the downstream impact of any disruption through the dependency graph, surfacing affected orders, revenue at risk, and recovery options ranked by cost and lead-time impact
3. AI / ML & Generative AI Layer
The third layer embeds intelligence directly into operational workflows — not as a separate analytics tool but as an always-on reasoning engine acting on the live digital twin.
Predictive Models
| Model | Horizon | Output | Trigger |
|---|---|---|---|
| Demand Sensing | 0–14 days | Daily SKU-level demand update | Daily POS / order data refresh |
| Inventory Risk Scoring | 1–8 weeks | Stockout probability per node/SKU | Weekly re-planning cycle |
| Supplier Lead-Time Variability | 2–12 weeks | Predicted delivery date vs. committed date | Supplier shipment confirmation events |
| Transportation Delay Prediction | 0–7 days | Expected delay probability per shipment lane | Weather, port congestion, carrier AIS feeds |
| Demand Forecasting | 3–18 months | Statistical + ML consensus forecast | Monthly S&OP cycle |
Generative AI Capabilities
- 01
Anomaly detection narratives — when the AI flags an anomaly (e.g., a supplier's on-time rate drops 15 points), Gen AI drafts a plain-English root-cause hypothesis with correlated upstream signals and historical pattern matching.
- 02
Prescriptive action recommendations — for each flagged exception, the AI generates 2–4 ranked resolution options with projected cost, lead-time, and service-level impact for each option.
- 03
Natural-language executive briefings — leaders receive a daily AI-written supply chain briefing: top risks, performance highlights, and recommended decisions — pulled live from the digital twin.
- 04
Document intelligence — the platform ingests supplier contracts, quality certificates, and shipping documents; Gen AI extracts key obligations, flags deviations from standard terms, and answers compliance questions in natural language.
4. Conversational & Agentic Interface
The fourth layer is how humans and automated agents interact with the Control Tower.
Conversational Interface (Natural Language Query):
Supply chain planners and executives interact via a chat-style interface backed by a retrieval-augmented generation (RAG) pipeline grounded on the live digital twin:
- "What is my current inventory cover for Product A in the EMEA region?"
- "Show me all POs from Supplier B that are more than 5 days late and flag which ones have customer orders at risk."
- "Simulate the impact of a 2-week shutdown of our Hamburg DC on European OTIF."
- "Which of my top-20 suppliers have a financial distress signal in the last 30 days?"
Responses are delivered as natural-language narrative + auto-generated charts + drill-down data tables — no SQL or BI tool required.
Agentic Workflows (Autonomous Decision Execution):
Beyond answering questions, EPS Control Tower agents can act:
- Autonomous exception resolution — for pre-approved exception classes (routine reorder triggers, carrier substitution within approved list, safety stock top-ups below threshold), AI agents execute corrective actions end-to-end and write a log entry for human review
- Escalation routing — exceptions exceeding the agent's authority are routed to the right human with a pre-built decision package: context, ranked options, recommended action, and risk if no action taken
- Multi-agent orchestration — complex disruptions trigger coordinated responses across procurement, logistics, planning, and customer service agents running in parallel within guardrails set by supply chain leadership
EPS Control Tower — Functional Modules
| Module | Core Capability | Key KPIs Managed | Primary Users |
|---|---|---|---|
| Supplier Intelligence | Real-time scorecards, lead-time prediction, financial health monitoring, sub-tier risk mapping | OTIF by supplier, quality PPM, lead-time vs. committed | Procurement, Supplier Quality |
| Demand Command | Demand sensing (0–14d), ML forecasting (1–18m), consensus planning, promotion modelling | Forecast MAPE/WMAPE, bias, fill rate risk | Demand Planning, Commercial |
| Inventory Intelligence | Multi-echelon optimisation, stockout prediction, excess flagging, reorder automation | Days of Supply, stockout events, inventory turns | Inventory Planning, Finance |
| Logistics Visibility | End-to-end shipment tracking, ETA prediction, carrier performance, exception alerting | OTIF, shipment delay %, freight cost vs. budget | Logistics, Customer Service |
| Manufacturing Sync | Schedule adherence, WIP visibility, quality hold tracking, capacity risk | Schedule adherence %, OEE, first-pass yield | Supply Planning, Operations |
| Customer Fulfilment | Order promising, ATP/CTP check, proactive delay notification, returns intelligence | Perfect Order %, OTIF, return rate | Customer Service, Sales |
| Resilience & Risk | Disruption scenario modelling, supplier concentration risk, geopolitical overlay, BC simulation | Resilience Index, single-source exposure %, revenue at risk | Executive, Risk Management |
| Sustainability Command | Scope 3 tracking, carbon per shipment, supplier ESG scoring, circular flow tracking | Scope 3 tCO₂e, carbon intensity per $ revenue | Sustainability, Procurement |
Deployment Options
- Cloud-native SaaS — hosted on AWS/GCP/Azure (customer choice), fully managed by EPS, ISO 27001 and SOC 2 Type II certified; 99.9% SLA
- Private cloud / on-premise — containerised (Kubernetes/Helm charts) for data residency requirements (financial services, defence, regulated pharma)
- Hybrid — sensitive master data on-premise; AI inference and visualisation layer in cloud with encrypted data streams
Integration Accelerators — Pre-Built Connectors
| System | Connector Type | Data Exchanged |
|---|---|---|
| SAP S/4HANA / ECC | SAP certified BAPI/RFC + Change Data Capture | Orders, inventory, production, finance |
| Oracle Fusion SCM | REST API | POs, inventory, shipments, demand plans |
| Microsoft Dynamics 365 | Dataverse API | Orders, inventory, suppliers |
| Manhattan WMS | REST + event webhook | Inventory positions, shipment confirmations |
| SAP TM / Oracle TMS | API + EDI 214 | Shipment milestones, ETA updates |
| Carrier networks (FedEx/UPS/DHL/Maersk) | Direct carrier APIs + project44/FourKites | Real-time tracking events |
| Supplier portals (Ariba/Coupa) | API + EDI 856/855 | PO acknowledgements, ASNs |
| Weather / Risk feeds | REST (Tomorrow.io, Dataminr, Resilinc) | Disruption signals, risk scores |
Business Value & Outcomes
15–25% Inventory Reduction
Multi-echelon optimisation and demand sensing eliminate over-stocking while maintaining service levels.
18% · avg. inventory reduction
3–5pt OTIF Improvement
Proactive exception management allows intervention 7–21 days before a missed delivery.
+4pt · avg. OTIF gain
60–80% Faster Exceptions
AI decision packages cut disruption-to-resolution time from hours to minutes.
70% · faster resolution
20–40% Better Forecast
Daily ML demand sensing replaces lagging statistical forecasts where decisions are made.
30% · avg. MAPE improvement
EPS Control Tower Implementation Roadmap
- Month 1–2Phase 1: Connect
Discovery & Data Foundation — stakeholder alignment, data audit, ERP/WMS/TMS connector deployment, data quality baseline established
- Month 2–3Phase 1: Connect
Digital Twin MVP — network model built, key nodes and lanes populated, first live inventory visibility dashboard operational
- Month 3–4Phase 2: Visibility
Logistics Visibility live — end-to-end shipment tracking activated, carrier API integrations complete, OTIF alerting running
- Month 4–5Phase 2: Visibility
Supplier Intelligence module — supplier scorecards live, lead-time prediction models trained on 12+ months of history, financial health monitoring active
- Month 5–6Phase 3: Intelligence
Demand Sensing & Inventory Intelligence — ML demand models deployed, safety stock optimisation running, stockout probability alerts active
- Month 6–8Phase 3: Intelligence
Generative AI & Conversational Interface — NLQ interface launched, AI anomaly narratives and recommendation engine live, executive briefings automated
- Month 8–10Phase 4: Autonomy
Agentic Workflows — first autonomous exception classes defined, agent guardrails configured, pilot autonomous PO top-up and carrier substitution agents deployed
- Month 10–12Phase 4: Autonomy
Resilience & Sustainability Modules — Resilience Index live, scenario simulation library built, Scope 3 baseline published, sub-tier risk mapping complete
- Month 12+Phase 5: Optimise
Continuous optimisation — model retraining, new category/region roll-outs, agentic scope expansion, integration of new data sources and market signals
EPS Control Tower vs. Traditional Approach
Traditional Control Tower
- Point dashboard — visualises data from one or two systems
- Reactive alerting — notifies after the exception has occurred
- Static reports refreshed daily or weekly in batch
- Requires BI analysts to build reports; planners consume static views
- No simulation — decisions made on intuition and experience
- Integration project takes 12–24 months; rigid connectors
- AI is an add-on layer, disconnected from operational workflows
- Each exception resolved manually; no automation or learning
EPS Control Tower
- Composable platform — unified data from all SC systems + external feeds
- Proactive intelligence — predicts disruptions 7–21 days before impact
- Live digital twin updated in near-real-time from streaming events
- Conversational NLQ — any user asks questions in plain English
- Built-in simulation engine — test scenarios before committing
- Pre-built connectors; full integration live in 60–90 days
- AI and Gen AI are native — every insight and alert is AI-generated
- Agentic workflows resolve 40–60% of routine exceptions autonomously