End-to-End Supply Chain AI & Analytics Services

Six specialised services — from descriptive dashboards to autonomous AI agents. Explore capabilities, problem-solution flows, and real case studies.

📊 Analytics
📈 Forecasting
🤖 Agentic AI
🧠 ML Engineering
⚙️ Optimisation
📋 S&OP
AnalyticsPower BI

SC Analytics & Dashboards

Turn raw ERP data into strategic clarity. We build executive dashboards that track 15+ KPIs in real-time — OTIF, fill rate, inventory turns, forecast accuracy, supplier performance — with SKU-level drill-down and automated threshold alerts.

Request a Demo →See Dashboard Product →
DASHBOARD — KPI OVERVIEWOTIF94.2%FILL RATE97.8%INV TURNS8.4xWEEKLY OTIF TRENDDecision Latency3 days (was 12)Reports Consolidated23 Excel → 1
80%
Faster Reporting
15+
KPIs Tracked
3 days
Decision Time Saved
23→1
Reports Consolidated
Problems We Solve → How We Solve Them
No real-time visibility — teams make decisions on data that is days or weeks old. By the time reports arrive, the opportunity has passed.
23+ disconnected Excel reports taking 3–5 days to compile monthly. Manual copy-paste, formula errors, no version control.
Cannot find root cause of OTIF failures. "We missed target" — but which SKU? Which supplier? Which region? No drill-down capability.
Live Power BI dashboards updated daily. OTIF, fill rate, inventory turns, forecast accuracy — all in one view. Threshold alerts notify before problems escalate.
Automated data pipeline pulls from ERP/WMS, transforms, and publishes. Zero manual effort. Reports appear in seconds, not days.
SKU-level drill-down with root cause waterfall: forecast error? Lead time? Production? Supplier? The dashboard shows exactly where to act.
📊
Power BI Dashboard Design

KPI cards, trend lines, heatmaps, drill-through pages, mobile-responsive layouts.

🔌
ERP/WMS Data Integration

SAP, Oracle, D365, custom databases. SQL-based ETL pipelines with Power Query.

🔔
Automated Alerting

Threshold-based alerts via email/Teams when KPIs breach targets.

🔒
Row-Level Security

Multi-region access control. Each manager sees only their data.

Case Study

Pharmaceutical Distributor

Replaced 23 manual Excel reports with a single live Power BI dashboard. Decision latency dropped from 12 days to 3 days. OTIF improved 6 percentage points within first quarter as teams could see and act on exceptions in real time.

12→3 days
Decision Speed
+6pp
OTIF Gain
23→1
Reports

Still running on Excel reports?

See a live demo built on your KPI structure. Free.

Book Live Demo →
AI / MLDemand Planning

SC Forecasting & Demand Sensing

Full ML forecasting pipelines — from ARIMA through XGBoost, LSTM, Prophet, and foundation models. Automated bias correction, demand sensing with external signals, tracking signal monitoring, and uncertainty quantification.

Request a Demo →See Forecasting Product →
FORECAST ACCURACY — BEFORE vs AFTERSKU-ASKU-BSKU-CSKU-DBefore (38% MAPE)After (19% MAPE)
35%
Avg MAPE Reduction
6x
Faster Reforecast
22%
Safety Stock Savings
Zero
Undetected Bias
Problems We Solve → How We Solve Them
High MAPE (30–55%) inflating safety stock and locking working capital. Every 5% error = 10–15% excess cost.
Undetected forecast bias compounding monthly. Over-forecast bias of 12% creates $120K excess/month on a $1M SKU.
Management overrides degrade accuracy in 60–70% of cases. No FVA measurement to prove it.
Ensemble ML models auto-select best performer per SKU. XGBoost, LightGBM, Prophet, LSTM compete — accuracy improves 20–40%.
Automated tracking signal monitors CFE and triggers recalibration when |TS|>4. Bayesian bias correction applied monthly.
FVA analysis measures every process step's contribution. Steps that degrade accuracy are flagged and eliminated.
Case Study

FMCG Distributor — 14,000 SKUs

Reduced MAPE from 38% to 19% in 4 months. Bias-corrected forecasting eliminated $2.4M in annual excess inventory. Auto-recalibration triggers when tracking signal exceeds ±4.

38→19%
MAPE
$2.4M
Capital Freed
4 months
Time to Value

Where is your forecast bias hiding?

Get a free accuracy audit — we'll show you exactly where the errors are.

Request Free Audit →
Agentic AIAutomation

Supply Chain Agentic AI

Multi-agent systems for procurement automation, inventory replenishment bots, supplier risk agents and S&OP copilots. Built on LangGraph, AutoGen & CrewAI with human-in-the-loop governance.

Request a Demo →See Copilot Product →
EXCEPTION HANDLING — COPILOT IMPACT55%Auto-ResolvedAuto-Resolved (55%)Copilot-Assisted (25%)Human Required (20%)PLANNER TIME FREED40%
55%
Auto-Resolved
4x
Faster Response
40%
Planner Time Freed
24/7
Continuous Monitoring
Problems We Solve → How We Solve Them
Planners spend 70% of time firefighting — chasing exceptions, expediting, responding to alerts manually.
Decision latency 10–21 days because exceptions queue. By the time someone acts, the disruption has cascaded.
200+ alerts/day — planners can't distinguish critical from noise. Important signals get buried.
55% of exceptions auto-resolved by pre-approved playbooks. Reorders, escalations, alerts — handled autonomously.
Response in minutes, not days. Copilot detects anomaly, generates action, executes or routes for one-click approval.
Priority-ranked exception queue with revenue impact scoring. Top 10 critical actions, not 200 noise alerts.
Case Study

Electronics OEM — 6,200 SKUs, 4 Regions

55% of supply exceptions auto-resolved. Planner capacity freed 40%, redirected to strategic sourcing. Decision latency dropped from 12 days to under 4 hours for critical alerts.

55%
Auto-Resolved
12d→4h
Response Time
40%
Capacity Freed

Let AI handle the exceptions.

See how the Copilot resolves your top 10 supply chain exceptions.

Request Copilot Demo →
EngineeringMLOps

SC ML Engineering & MLOps

Production-grade ML systems purpose-built for supply chain. Feature engineering on transactional data, model deployment, monitoring, retraining. MLflow, Prefect, Docker and FastAPI pipelines that deliver value in production — not just notebooks.

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ML PIPELINE — SUPPLY CHAINData IngestionERP / WMS / CSVFeature EngineLag, rolling, calendarModel TrainingXGBoost / LSTM / ProphetModel RegistryMLflow trackingMonitoringDrift / accuracy alertsAPI ServingFastAPI / DockerPrefect Orchestration · CI/CD · Auto-Retrain on Drift · 99.5% Uptime
10x
Faster Deployment
99.5%
Pipeline Uptime
CI/CD
Auto-Retrain
0
Manual Retraining
Case Study

Logistics Company — 3 ML Models in Production

Deployed demand forecasting, ETA prediction, and carrier allocation models via FastAPI + Docker. MLflow tracks 50+ experiments. Prefect orchestrates daily retraining. Models serve 15,000 predictions/day with 99.5% uptime. Zero manual intervention for 8 months.

15K
Predictions/Day
99.5%
Uptime
8 months
Zero Manual

Your ML models should run in production, not in notebooks.

Let us build the pipeline. You focus on the business.

Discuss Your ML Pipeline →
OptimisationOperations Research

Supply Chain Optimisation

Mathematical and AI-driven optimisation across inventory, transport, production, warehouse and procurement. OR-Tools, PuLP, Pyomo for large-scale combinatorial problems.

Request a Demo →See Inventory Product →
OPTIMISATION IMPACT ACROSS DOMAINSInventory SS-28% (optimal)currentTransport Cost-12% (VRP optimised)Production OEE+18% (scheduling)Warehouse Pick+25% (slotting)Procurement-15% spend (allocation)
28%
SS Reduction
12%
Transport Cost Down
$3.2M
Capital Freed
5
Domains Optimised
Case Study

Auto Parts + FMCG — Multi-Domain Optimisation

Inventory: 31% SS reduction ($3.2M freed), fill rate 93%→98.5%. Transport: 12% cost reduction ($890K/year) across 8 DCs. Production: changeover time cut 22%, plan adherence 81%→94%. All using OR-Tools, PuLP, and custom Python optimisation.

$4.1M
Total Savings
5
Domains
6 months
Implementation

How much working capital is trapped in your inventory?

Free inventory health assessment with savings quantified.

Request Free Assessment →
PlanningConsulting

S&OP Analytics & Planning

End-to-end S&OP process redesign: demand review, supply review, pre-S&OP, executive S&OP. Integrated with AI-driven scenario planning, consensus forecasting, and FVA analysis.

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S&OP CYCLE — MONTHLY CADENCEWeek 1DemandReviewWeek 2SupplyReviewWeek 3Pre-S&OPAlignmentWeek 4ExecutiveS&OPAI-POWERED SCENARIO PLANNINGBest CaseBase CaseDisruption Case
15%
Accuracy Gain
6pp
OTIF Improvement
30%
Planning Time Saved
70%
Ad-Hoc Decisions Reduced
Case Study

Industrial Manufacturer — Multi-Site

Implemented structured S&OP across 3 manufacturing sites. Forecast accuracy improved 15% through FVA-driven cleanup. Executive S&OP reduced ad-hoc decision-making by 70%. OTIF improved from 88% to 94% in two quarters.

88→94%
OTIF
-70%
Ad-Hoc Decisions
2 quarters
Time to Value

Is your S&OP process driving decisions or just generating slides?

We'll assess your current S&OP maturity and design a roadmap.

Request S&OP Assessment →