Command Center
Supply Chain Intelligence Overview — All Modules
Service Level
—
Awaiting data
Risk Index
—
Awaiting data
Forecast Accuracy
—
Awaiting data
Inventory Turnover
—
Awaiting data
Global SC Network Globe
Demand vs Supply
Live Alerts 0
No Alerts
Load data to detect issues
Top Risk Nodes
No data
ABC Distribution
Module Health
No data
Visual Library
Data Ingestion
CSV / Excel — Any supply chain dataset, any industry
Drop file or click to browse
Supports .csv, .xlsx, .xls — any structure
Expected Schema (flexible)
The platform auto-detects columns. Map your columns below after upload.
| Column | Type | Required |
|---|---|---|
| node / location / sku | text | yes |
| demand / qty / actual / forecast | number | yes |
| supply / stock / inventory_level | number | rec |
| service_level / otif / fill_rate | 0–1 | rec |
| lead_time / transit_time / delivery_lead_time | days | opt |
| transport_cost / freight_cost / cost_per_unit | number | opt |
| risk / risk_score / supplier_risk_score | 0–100 | opt |
| oee / capacity_utilization / throughput | number | opt |
| return_rate / recovery_value / refurb_rate | number | opt |
| cash_to_cash / cost_to_serve / gmroi | number | opt |
| complaints / nps / sla_adherence | number | opt |
| latitude / longitude | number | opt |
No file? Use sample data
Live Backend Ingestion
Upload through the live backend engine for secure pipelines, tenant isolation, and real-time analytics.
Backend not queried yet
How to load data and get KPI results
1. Upload a .csv or .xlsx file, or use sample data if you want a working example.
2. Review the preview and confirm the detected column names.
3. Map the key fields: node, demand, supply, service_level, lead_time, risk.
4. Click Apply & Analyse to transform the data and populate the Analytics tabs.
5. Visit Command Center or any module tab to see KPIs and charts update.
2. Review the preview and confirm the detected column names.
3. Map the key fields: node, demand, supply, service_level, lead_time, risk.
4. Click Apply & Analyse to transform the data and populate the Analytics tabs.
5. Visit Command Center or any module tab to see KPIs and charts update.
Best practice: numeric values should not include units or commas.
Recommended fields: node, demand, supply, service_level/otif, lead_time, cost, nps, lat/lon.
Tip: map the most important fields first, then add optional fields for richer insights.
Data Management
Manual input or import Excel/CSV in segregated sheets format
Data Sheets
Demand_Forecast — Forecast KPIs, accuracy, bias
| SKU | Date (YYYY-MM-DD) | Actual_Demand | Forecast | Accuracy (%) | Bias | MAPE |
|---|
Inventory — Stock, safety stock, reorder logic
| SKU | Location | Current_Stock | Safety_Stock | Reorder_Point | Lead_Time (Days) |
|---|
Logistics — Shipment, cost, transit metrics
| Shipment_ID | Origin | Destination | Transit_Time (Days) | Cost | Volume (CBM) | Weight (KG) |
|---|
Production — OEE, capacity, downtime
| Plant | Date (YYYY-MM-DD) | OEE (%) | Capacity_Utilization (%) | Downtime_Hours | Throughput |
|---|
Procurement — Supplier performance
| Supplier_ID | SKU | Lead_Time (Days) | On_Time_Delivery (%) | Quality_Score (0-100) | Cost_per_Unit |
|---|
Customer — Service level, NPS, returns
| Customer_ID | Order_ID | Service_Level (%) | NPS | Return_Rate (%) | SLA_Adherence (%) |
|---|
Risk — Node-level risk + geo coordinates
| Node | Risk_Score (0-100) | Latitude | Longitude | Risk_Type |
|---|
Network — Supply chain links
| Source_Node | Target_Node | Flow_Volume | Lead_Time (Days) | Cost |
|---|
Financial — Cost-to-serve, GMROI
| SKU | Cost_to_Serve | GMROI | Cash_to_Cash (Days) | Revenue |
|---|
AI_Control — Decision engine + scenario tracking
| Scenario_ID | Decision | Outcome | Confidence (%) | Timestamp |
|---|
Demand & Forecasting
Module 1 — Demand patterns, bullwhip, forecast accuracy
Demand vs Forecast
Forecast Error Distribution
Bias Trend Line
Seasonality Decomposition
Forecast Value Add
Bullwhip Effect
MAPE / MAD / Bias by Node
Demand Pattern Classification
Inventory Intelligence
Module 4 — ABC/XYZ, DOI, stockouts, dead stock
Inventory Aging
Stock Level Trend
ABC Classification
XYZ Volatility
SKU vs Location Heatmap
ABC Summary
Inventory Item Detail
Sourcing & Procurement
Module 3 — Supplier OTIF, lead times, PPV, defect rates
Supplier OTIF Performance
Spend Pareto
Supplier Risk Heatmap
Lead Time Trend
Supplier Scorecard
Logistics & Distribution
Module 5 — OTIF, transport cost, reverse logistics
Route Map Visualization
Delivery Performance Trend
Cost Breakdown
Transit Time Distribution
Return Rate & Reverse Logistics
Production & Operations
Module 6 — OEE, capacity, throughput, schedule adherence
OEE & Utilization
Throughput Trend
Production Schedule (Gantt)
Machine Utilization Heatmap
Downtime Pareto
Work-in-Progress
Customer Service Metrics
Module 6 — Fill rate, cycle time, NPS, complaints
Order Fulfillment Funnel
Satisfaction Trend
Return Reasons Pareto
Reverse Logistics
Module 7 — Returns, recovery, refurbishment, scrap
Returns Flow
Recovery Value Trend
Financial Supply Chain
Module 10 — Cash cycle, cost-to-serve, GMROI, logistics cost
Cash Flow Trend
Cost Breakdown
Profitability Analysis
GMROI by Category
Control Tower
Module 11 — AI alerts, decision latency, scenario success, autonomous planning
AI vs Actual
Scenario Success
Predictive Risk Curve
Scenario Simulation Tree
Alert Stream
Advanced Visuals
Advanced chart gallery: box plot, sankey, choropleth, Monte Carlo, waterfall, Gantt and Pareto
Checking...
Input parameter guide
Box Plot: category/node, demand/cost/supply distribution
Sankey: supplier → node flows using supply or demand values
Choropleth: lat/lon points with service_level or risk intensity
Monte Carlo: scenario_success_rate, forecast_value_add, demand sensing impact
Waterfall: revenue_per_unit, spend, logistics_cost_pct, return_rate
Pareto: supplier/node spend or transport cost contributions
Gantt: node/task lead_time and delivery_lead_time durations
Box Plot Pending
Hover for required inputs.
Sankey Pending
Hover for required inputs.
Choropleth Pending
Hover for required inputs.
Monte Carlo Pending
Hover for required inputs.
Waterfall Pending
Hover for required inputs.
Pareto Pending
Hover for required inputs.
Gantt Pending
Hover for required inputs.
Box Plot Distribution
Monte Carlo Simulation
Waterfall Analysis
Pareto Optimization
Sankey Diagram
Choropleth Map
Project Timeline (Gantt)
Risk Engine
Module 7 — OSINT-style risk scoring, disruption matrix, resilience
Risk Matrix (Likelihood × Impact)
Low
Medium
Critical
Monte Carlo Risk Distribution
Critical Alerts — High Risk Nodes
Risk Register
Network Graph
Module 2 — Supply chain topology, connectivity, visibility
● Low Risk
● Medium
● High Risk
Global Supply Chain Map
Geo-tagged nodes with risk heat overlay
Node Geo Table — lat/lon required in data
Digital Twin Simulation
Module 8 — What-if scenarios, disruption modelling, capacity planning
Scenario Parameters
Demand Surge %
20%
Supply Disruption %
10%
Lead Time Increase %
0%
Cost Inflation %
5%
Safety Stock Multiplier
1.5×
Disruption Type
Simulation Output — Service Level Impact
Projected Service Level
—
Stockout Risk Nodes
—
Excess Cost Impact
—
Safety Stock Calculator
All 7 methods — basic, Z-score, variability-combined, dynamic
Method 1 — Basic Formula
SS = (Max Daily Use × Max LT) − (Avg Daily Use × Avg LT)
Method 2 — Z-score × σ × √LT
Service level driven. Z × σ_demand × √Lead Time
All Method Results
Enter values and click Calculate
Data-Driven Safety Stock — From Loaded Dataset
Load data to compute safety stock per node
KPI Tracker
All 30+ supply chain KPIs across all modules