Supply Chain Brain — Live
ExlAthena
The Supply Chain Brain
Decision intelligence for the people who live inside OTIF, fill rate, cover days, MPS, and lane risk — not another dashboard they already ignore.
If your Monday still starts in Excel because ERP, MES, and the control tower disagree — this brochure is for you. GIS & geospatial intelligence is not optional: without place and lane physics, every plan invents OTIF misses.
From dashboards to decisions. GIS is crucial — built for operators, not slideware.
Part 1Forecast — Demand — Inventory — Production
CrucialGIS & geospatial intelligence
Part 2PdM — Diagnostics — AI Decisions
OutcomeExplainable next-best action
Inside this brochure
Two halves of the same operating week
Part 1 is the planning spine every supply chain leader owns. Part 2 is the live network and asset reality that breaks the plan — plus the AI decision layer that closes both into action. GIS & geospatial intelligence is crucial: it is the spatial fabric that makes both halves honest.
GIS is crucial — not a nice-to-have map
Without place, lane, port, and plant↔DC physics, demand/inventory/production plans invent OTIF misses. Geospatial intelligence sits between Part 1 and Part 2 so every recommendation knows where the problem is.
Part 1 — Plan the network
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Part 2 — Run & decide
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You speak OTIF
We speak next action when OTIF is about to miss — with working shown.
You own cover days
We flag cover < lead time before the customer call, not after.
You need the map
GIS is crucial — lane ETA and plant↔DC reality inside every decision.
Demo hook: Bring one painful SKU family, one late lane, and one chronic line. We show the recommendation on your language — not a generic slide.
Part 1 — Plan
03 — Sales forecasting
Stop forecasting in a parallel universe from the plant.
Sales builds the number. Operations inherits the firefight. ExlAthena ties causal and ML forecast signals to the same ERP/MES history your planners already trust — so the number is honest before S&OP freezes it.
Monday morning you already know
Bias by region. Baseline vs. promo noise. A “locked” forecast that died when the supplier slipped two days — and nobody updated the sell-side story.
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Signal from live order & shipment history
Not a month-end extract. Continuous feeds so forecast drift shows up while you can still act.
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Causal + ML, constrained by reality
Lead-time risk, delay scores, and capacity shortfalls sit next to the demand curve — not in a side deck.
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One number for sales and supply
Handoff into demand planning, inventory, and production as the same object — so consensus isn’t theatre.
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Explainable exception to a human
When the model moves the number, you see signals and thresholds — ready for AI decision write-back.
AI decision layer (Part 1): Forecast exceptions become governed recommendations — revise, lock, or escalate — with planner approval.
Part 1 — Plan
04 — Demand planning
Demand that reads your ERP and MES — not a twin spreadsheet.
Forecasts become executable plans only when they respect live BOM, routing, and work-center capacity. ExlAthena closes the demand’plan gap so procurement and production stop rebuilding the week in Excel.
You feel this every freeze
Signal lag. Constraint blindness. Broken handoffs. By Thursday the “consensus plan” is already fiction — and customer promises still assume it is true.
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Constrained demand on real fabric
Orders, inventory, open POs, BOM, routing, work centers, supplier history, MES downtime — continuous, not month-end.
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Lead-time & capacity shortfall scoring
Buy / make decisions see delay risk before the PO is raised or the shift is loaded.
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Analytics ? Prediction ? Planning
One pipeline so demand, procurement, and production stay synchronized as constraints move.
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Ready for L5 decisions
Plan breaks become explainable next actions — not another red cell in a dashboard.
AI decision layer (Part 1): Material risk and plan gaps become approve / expedite / reallocate — with signals, model, and threshold visible. GIS is crucial here: supplier and DC location must sit inside the same recommendation.
Part 1 — Plan
05 — Inventory optimization
From stockout risk to an explainable replenishment.
Safety stock set in 2019 does not know today’s open POs, MES downtime, or supplier fill-rate drift. ExlAthena recomputes cover versus lead time on plant’DC’line reality — then recommends what to do.
Inventory’s quiet failure mode
Days-of-cover without replenishment lead time. Excess in the wrong DC. Expedite air for a problem that was visible three weeks ago on the knowledge graph.
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Multi-echelon on manufacturing-first surfaces
Plant / DC / line as the honest network. Safety stock and reorder points from live history.
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Cover vs. lead-time risk
Delay and capacity signals participate in every tradeoff — finite supply, open POs, alternates.
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Allocation that can ship
Warehouse, slotting, and supplier allocation when everything cannot be protected equally.
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L5: expedite — reallocate — backup
Working shown before write-back. Planners stay in the loop by default.
GIS is crucial here: Which DC, which lane, which ETA risk — inventory decisions inherit geospatial context, not a flat table. Without the map, cover math lies.
Part 1 — Plan
06 — Production planning
MPS / MRP that knows the line will not be there.
Production plans die when MES downtime, routing change, or PdM risk is invisible until the shift starts. ExlAthena plans on live line load — and resequences before you burn the promise date.
Shift-start déjà vu
Frozen schedule. Hot job inserted by phone. Press #4 vibration ignored by planning. Customer OTIF still assumes 100% Line B.
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Live capacity & routing
Work centers, MES signals, and constraint sets update the plan object — not a twin Gantt in a shared drive.
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PdM risk inside MPS
Predicted degradation reshapes available hours before you overload the week.
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Resequence with impact
See OTIF, cost, and capacity tradeoffs when you pull a batch forward or park a SKU.
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Decide before the horn
AI decision layer proposes resequence / split / buy — planner approves into the system of record.
GIS is crucial here: Production planning consumes PdM diagnostics and geospatial lane delays as first-class constraints — same brain, same week. No map, no honest MPS.
Part 2 · Run · Crucial
07 — GIS & geospatial intelligence · Crucial
GIS is crucial. Your network is not a table.
Without geospatial intelligence, Part 1 plans are fiction the moment a lane slips or a DC is in the wrong place. ExlAthena's GIS layer puts ETA risk, exceptions, and cover on a living map — so control-tower decisions inherit where the problem actually is.
Why GIS is crucial for supply chain
A red KPI with no port, no lane, no mile marker. Expedite decisions that ignore congestion, weather corridors, and multi-hop plant↔DC reality. Geography is not decoration — it is constraint physics for OTIF.
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360° network on one graph
Nodes and edges for suppliers, plants, DCs, ships, fleet, and assets — spatially aware, not a slide map.
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Ship & fleet tracking in the plan
Ocean lane ETA risk and plant↔DC exceptions feed inventory cover and production promises.
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Bird’s-eye enterprise control tower
From ocean lane to MES line — every exception in geographic and operational context.
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Spatial decisions, not just pins
Reallocate across DCs, reroute fleet, or pull alternate port — as explainable L5 actions.
GIS is crucial — bottom line: Demand, inventory, and production decisions that ignore location and lane physics invent OTIF misses. Geospatial intelligence is the bridge that makes Part 1 plans survivable in Part 2 reality — and every AI decision knows the map.
Part 2 — Run
08 — Predictive maintenance & diagnostics
Asset risk is not a CMMS side quest.
Vibration alerts that never touch MRP still break customer promises. ExlAthena ties RUL and plant diagnostics into capacity, sequencing, and the same decision loop as inventory and demand.
The split-brain plant
Rich PdM in maintenance. Optimistic capacity in planning. Promise dates that assume Press #4 will run forever.
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Diagnostics cockpit for plant & network
Critical / risk / plan alerts in one stream — asset, cover, and schedule — not three portals.
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RUL tied to capacity & promises
Failure windows reshape available hours and which WOs you protect this week.
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Physical AI on the floor
Vision, robotics, and MES signals feed the knowledge graph alongside classic PdM.
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Open WO — resequence — spare PO
Maintenance action becomes a supply-chain decision with OTIF impact visible.
AI decision layer (Part 2): Same engine as planning — propose open WO, resequence, or expedite spare — human approves, audit retained.
Parts 1 & 2 — Decide
09 — AI decision layer
Dashboards report. We recommend — on both halves of the week.
The AI decision layer is not a separate product. It is the closer for Part 1 planning and Part 2 geospatial / PdM reality: explainable next-best actions with planner approval and write-back.
On Part 1 — Plan
Forecast — Demand — Inventory — Production
When the plan object breaks, you get an action — not another chart.
- Revise forecast / freeze exception
- Expedite alternate supplier
- Reallocate stock across DCs
- Resequence MPS before the shift
On Part 2 — Run
GIS — PdM — Diagnostics — Network
When the physical world moves, the same engine proposes the fix.
- Reroute lane / pull alternate port
- Open WO from RUL window
- Protect promises under capacity loss
- Fleet exception ? cover action
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Signals — model — threshold
Every recommendation shows why — so a planner can overrule it without guessing.
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Human in the loop by default
AI proposes. You approve. Nothing silent unless policy explicitly allows it.
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Write-back that learns
Approved actions return to ERP/MES so the next pass measures what changed.
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Governed for the enterprise
Decision rights, stewardship, and audit — aligned to how you already run SCOR / S&OP.
Next step
See the brain on your fabric.
Bring one painful SKU family, one late lane, and one chronic asset. We run Part 1 planning and Part 2 geospatial / PdM through the AI decision layer — on your language.
