Supply Chain Management: balance service, cost, and inventory.
Here are 10 numeric, per-item/per-site (or per-lane) variables that are high-value in Supply Chain Management (e.g., SAP IBP, Oracle SCM, Kinaxis, Blue Yonder, o9, Manhattan, E2open) and well suited to visualize using Immersion Analytics:
| # | Variable | What it is (numeric) | Why it matters | Good IA mapping (suggestion) |
|---|---|---|---|---|
| 1 | Forecast Error (MAPE, %) | % deviation vs actual demand | Drives over/under-stock and service | Color (hotter = higher error) |
| 2 | Supplier/Lane Lead Time (days) | Avg days from order to receipt | Core planning horizon | X-axis (longer → right) |
| 3 | Lead-Time Variability (CV% or σ days) | Volatility of lead time | Predictability & safety stock sizing | Pulsation (faster = more volatile) |
| 4 | OTIF / Service Level (%) | On-time, in-full deliveries | Customer promise reliability | Y-axis (higher → up) |
| 5 | Days of Supply (days) | Inventory ÷ demand rate | Stockout vs excess risk | Z-depth (closer = fewer days) |
| 6 | Inventory Value ($) | On-hand value for SKU/site | Working capital tied up | Size (bigger = more $) |
| 7 | Backorder Rate (%) | % demand unmet at promise date | Direct service pain & penalties | Transparency (higher = more hollow) |
| 8 | Capacity Utilization (%) | Plant/DC/transport utilization | Bottlenecks vs idle capacity | Glow (brighter = higher) |
| 9 | Logistics Cost per Unit ($/unit) | Transportation + handling cost | Margin & network efficiency | Shimmer (intense = higher) |
| 10 | Supplier Breadth (# active suppliers) | Count serving this SKU/site | Concentration risk & resilience | Satellites (more satellites = more suppliers) |
What service levels, working capital, and freight savings could you unlock by seeing all ten—simultaneously—across your entire network?
Logistics: de-bottleneck flow and improve OTIF.
Here are 10 numeric, per-shipment/per-lane (or per-route/per-carrier) variables that are high-value in logistics (e.g., SAP TM, Oracle OTM, Manhattan TMS, FourKites, project44) and well suited to visualize using Immersion Analytics:
| # | Variable | What it is (numeric) | Why it matters | Good IA mapping (suggestion) |
|---|---|---|---|---|
| 1 | OTIF (%) | On-Time In-Full rate | Core service KPI for customers | Y-axis (higher → up) |
| 2 | Cost per Shipment ($) | Total door-to-door cost | Direct impact on margins | X-axis (right = higher) |
| 3 | Transit Time (hrs) | Actual origin→destination time | Speed & reliability signal | Z-depth (closer = shorter) |
| 4 | Facility Dwell (min) | Median dwell at nodes | Bottleneck and detention driver | Color (hotter = longer) |
| 5 | Capacity Utilization (%) | Used capacity vs available | Indicates waste or overload | Size (bigger = higher) |
| 6 | Exception Rate (%) | Shipments with exceptions | Operational volatility to manage | Pulsation (faster = higher rate) |
| 7 | Tender Acceptance Rate (%) | Carrier accepts first tender | Reduces re-tendering delays | Glow (brighter = higher) |
| 8 | ETA Risk (%) | Probability of late arrival | Proactive expediting/alerts | Shimmer (stronger = higher risk) |
| 9 | Damage/Claim Rate (%) | Shipments with damage/claims | Hidden cost & CX impact | Transparency (more hollow = worse) |
| 10 | Stops / Handoffs (#) | Stops, cross-docks, handoffs | Each handoff adds risk/time | Satellites (more satellites = more stops) |
What cost savings and OTIF gains could you unlock by seeing all ten—simultaneously—across your lanes, carriers, routes, and facilities?
