CONTROLTOWEROrchestrate · Supply chain digital twin & disruption response

Your operation, running twice. Test the fix in the twin, then fire it for real.

ControlTower keeps a live digital twin of your whole chain, built from the systems you already run. When a disruption hits, it traces the orders, customers and money at stake, tests costed responses in the twin, and fires the one a named planner approves into the real systems, with a rollback plan on every action.

Run the demo Discuss your disruption-response challenge

Operations control room with banks of monitors showing process screens and large wall displays above

ControlTower at a glance

1Reads

Orders, stock and POs from your ERP
Shipments and lanes from your TMS
Warehouse stock and transfers

2Decides

ControlTower

Which response to fire
Who must approve it
Which signals to mute, on the record

3Produces

PO expedites in the ERP
Stock transfers in the WMS
Freight re-bookings in the TMS

↺ Learns Outcome against the twin's prediction, recorded in the value ledger back to 1 · Reads

The business problem

Everyone sees their own slice. Nobody owns the trade-off.

Monday, 4 a.m. Congestion at the inbound port adds five days to six containers of bottles and caps. The carrier's portal shows the new arrival date. Nobody connects it to the plant schedule, the two distribution centers those bottles feed, or the retail orders promised for next week.
By Wednesday the plant has found out, logistics is quoting air freight, sales is apologizing to a retail chain, and finance is asking who approved the expedite bill. Each team made a sensible call for its own slice. Nobody priced the options against each other while the cheap ones were open, and nobody could test them before trying them on real orders.
The disruption cost more than it needed to. Three weeks later nobody can say what the response saved, or what it should have been.

Why the usual fixes fall short

Dashboards and alert-only control towers show the problem and stop there. Working out the response still takes calls, spreadsheets and a lost day.
Each planning or execution system optimizes its own function. None of them prices an expedite in one against a broken promise date in another.
Response plans built by hand are tested on the real chain, so the side effects (a stock transfer that starves another DC) show up a week later.

Disruptions keep getting handled one function at a time, at the most expensive moment, with no record of what the response cost or saved.

Customer servicePromise dates slip without warning, and the customer often hears before your account team does.
Freight and expeditingAir freight and premium expedites get booked under pressure, after the cheaper options have expired.
Working capitalAfter each scare, safety stock creeps up everywhere because nobody trusts the network to rebalance in time.
AccountabilityAfterwards there is no record of the options weighed, who chose, or what the choice saved.

In plain terms

What ControlTower is, and how it fits

What it is: A live digital twin of your whole supply chain, built for decisions. It mirrors your suppliers, plants, DCs, lanes, stock and open orders from the systems you run, so every response can be tested in the twin before it is fired in the real world.
Who uses it: COOs and heads of supply chain at mid-size manufacturers. Day to day: supply and demand planners, the inventory manager and the S&OP lead, with the CFO signing the value ledger.
What it does: Turns signals (a port delay, a supplier slip, a demand jump) into a case, runs the case forward in the twin to find the orders and money at risk, drafts responses and tests each one in the twin, sends the approved one to the systems that own each action, and records what it saved.
How it fits your systems: It reads from the systems you run (SAP, Oracle, Microsoft Dynamics or Tally, plus your WMS and TMS) and, where you use them, from other FLX⁴ products. Connectors are configured per deployment. It writes nothing until a person approves, and then only through the system that owns the record.
What is different: Your operation runs twice: once in the twin, once for real. Most control towers stop at the alert. ControlTower carries the case through: a costed response, tested in the twin before anyone sees it, approved by a named person, fired through the owning systems with a rollback plan, and checked afterwards against what the twin predicted. Solvers do the arithmetic; the AI explains it.

See it work

A port slows down and next week's orders are at risk

A simulated scenario with representative data. Move the delay, test three responses in the twin, and approve one as the named approver.

A simulated scenario on representative data. You choose the option and give the approval; the numbers follow your choices.

End-to-end workflow

From data to decision to outcome

What goes in, what ControlTower works out, what gets decided, what happens next, and how the result comes back.

InputsDATA

Orders, stock and POs from your ERP
Shipments and lanes from your TMS
Warehouse stock and transfers
Port, weather, carrier and supplier signals
In India: GST e-invoice and e-way bill data as an independent check

IntelligencePREDICT

A live digital twin of suppliers, plants, DCs, lanes, SKUs and orders
Impact tracing: which orders, customers and how much money a delay touches
Every drafted response run in the twin before anyone sees it
Optimization solvers for quantities, costs and feasibility
AI that summarizes signals and explains options, never the arithmetic

DecisionsDECIDE

Which response to fire
Who must approve it
Which signals to mute, on the record

ExecutionEXECUTE

PO expedites in the ERP
Stock transfers in the WMS
Freight re-bookings in the TMS
Tasks for account teams
A rollback plan on every write

FeedbackLEARN

Outcome against the twin's prediction, recorded in the value ledger
Value entries signed by the head of supply chain or finance
Decision memory: similar past cases and what worked
Twin inputs corrected from what actually happened

Modules

Capabilities, organized by the work they do

11 modules in 4 groups. Open a module for its inputs, outputs, users and an example.

Your operation, running twice2 modules Sense and respond3 modules Approve, fire and prove3 modules Plan ahead on the twin3 modules

Your operation, running twice

A live mirror of your chain, so every decision is tested in the twin before it is fired in the real world.

Network twinKeeps a live digital twin of your suppliers, plants, DCs, lanes, SKUs, stock and open orders, refreshed from your systems, so any event can be traced to everything it touches.

Challenge: When something slips, nobody can quickly say which orders, customers or how much money are affected. The answer takes a war room of spreadsheets.
Inputs: ERP orders, POs and stock; WMS and TMS data; in India, GST e-invoice and e-way bill data; FLX⁴ product data where you run them.
Outputs: One model of every node and lane, and answers to "what does this delay touch?" with the orders, SKUs, customers and money at stake.
Used by: Supply and demand planners, logistics.
Value: The reach of a disruption is known while there is still time to act.
Works with: Transactions stream in continuously through connectors configured per deployment. Planners check the twin against reality before any response is drafted from it.
In action · illustrative: A five-day delay on six containers is traced through Plant 1 to two DCs and 32 orders for 7 customers, with the revenue at stake on each lane.

Twin simulation and what-if branchesRuns any decision in a copy of the twin first (a re-route, a stock transfer, an expedite, a new promise date or a whole S&OP scenario) and compares the branches in cost and service.

Challenge: Response plans built by hand are tried on the real chain, so side effects, such as a stock transfer that starves another DC, show up a week later.
Inputs: The current twin, the decision or scenario to test, carrier, supplier and stock options, penalty terms.
Outputs: Branches side by side, with cost, orders still late, stock left at each site and revenue at risk, ready to attach to an approval as evidence.
Used by: Planners, S&OP lead, head of supply chain.
Value: Decisions are tested before they touch a real order, so surprises happen in the twin instead of at the customer.
Works with: No response is proposed without a twin run. Simulation inputs such as port dwell, lead times and receiving windows are corrected from actual outcomes.
In action · illustrative: Before a 2,100-unit transfer out of DC South is approved, the planner runs it in the twin and sees DC South stays above safety stock through its next replenishment.

Sense and respond

Turn signals into cases, and cases into costed responses, while the cheaper options are still open.

Sensing engineJoins outside signals with your own data and opens a ranked case, with a severity, an exposure in money and an owner, when something will hurt.

Challenge: Port, weather and supplier news arrive in different inboxes and are noticed when a customer calls.
Inputs: External signals (weather, ports, freight, supplier news) and internal ones (orders, stock, shipments, e-way bills).
Outputs: Disruption cases with a severity, the part of the twin affected, the money at stake and an owner.
Used by: Planners, logistics.
Value: Days of warning instead of a surprise, and less noise: one case instead of three alerts.
Works with: Signal feeds are configured per deployment. Severity thresholds are tuned per customer. Mute rules are logged and reviewable.
In action · illustrative: A port feed, a carrier ETA change and a weather warning arrive within an hour and become one high-severity case.

Response agentsThe moment a case opens, agents draft action bundles (re-route, expedite, rebalance, re-sequence, re-promise), test each one in the twin and attach a rollback plan.

Challenge: By the time a team agrees what to do, the cheaper options have expired.
Inputs: The case, the twin, carrier and supplier options, stock positions, penalty terms.
Outputs: Two or three action bundles, each with its cost, penalties avoided, orders still late and a rollback plan, ready for approval.
Used by: Supply planners.
Value: Trade-offs across freight, stock and customer dates are priced side by side, with the evidence to defend the choice.
Works with: Quantities and costs come from optimization solvers. Every bundle goes to a named approver.
In action · illustrative: Re-routing four containers, expediting one PO and moving 2,100 units between DCs is set side by side with air freight and with re-promising.

Solver-checked numbersComputes every quantity, route and allocation with optimization solvers. The language model only explains the trade-off and drafts the message.

Challenge: An AI that invents numbers cannot be trusted with a supply chain, and planners will not defend a figure they cannot trace.
Inputs: Capacities, lead times, stock positions, costs and constraints from the twin.
Outputs: Feasible quantities and costs for each bundle, every number linked to its evidence, and a plain-language summary of the trade-off.
Used by: Planners, finance reviewers.
Value: Recommendations a planner can defend line by line.
Works with: Every number in a decision is solver output.
In action · illustrative: The solver confirms the second supplier can ship 18,000 bottles within six days and that the transfer leaves DC South above safety stock. The AI writes the two-line summary the approver reads.

Approve, fire and prove

Put each tested response in front of the right person, fire it into the real systems, and record what it was worth.

Approval gates and rollbackPuts each bundle in front of a named approver and, once signed, fires each action into the system that owns it, with a rollback plan ready if a write fails.

Challenge: Approved responses get carried out piecemeal by email, with no way to undo half a plan, and agents acting on their own make a board nervous.
Inputs: The approved bundle, approval limits, escalation rules and the rollback plan.
Outputs: PO expedites in the ERP, stock transfers in the WMS, re-bookings in the TMS or TransNest, tasks for account teams, and automatic reversal if a write fails.
Used by: Supply planning manager, head of supply chain.
Value: Speed with clear accountability. Detection and analysis run on their own; every action that moves money waits for a person, and autonomy widens one action type at a time after a measured track record.
Works with: Writes only through the owning system. High-value or customer-facing moves always escalate.
In action · illustrative: A bundle within the planner's limit is approved by the supply planning manager. One that changes customer dates goes to the head of supply chain.

Value ledgerRecords the value of each executed response (expedites and penalties avoided, inventory released, orders kept on time), linked to its twin run and outcome, for finance to sign.

Challenge: Control towers are bought on a promise and renewed on faith, because nobody can show what they saved.
Inputs: Executed bundles, actual freight and penalty costs, twin results, stock released.
Outputs: Signed value entries, the outcome against the twin's prediction for each case, and a value statement for the CFO.
Used by: CFO, head of supply chain.
Value: A value record finance can audit, case by case.
Works with: A baseline agreed in writing. A value entry counts only once it is signed.
In action · illustrative: A case closes with its response cost, penalties avoided and late orders set against the twin's prediction, and goes to the head of supply chain to sign.

Decision memoryRemembers what the agents proposed, what people changed and what actually happened, and brings up similar past cases when a new one opens.

Challenge: Every disruption is handled as if it were the first one.
Inputs: Cases, drafted bundles, approver edits and overrides, outcomes.
Outputs: Similar past cases and what worked, shown on each new case, and better-tuned proposals.
Used by: Planners, head of supply chain.
Value: Responses that improve with each case.
Works with: Memory stays inside each company. Nothing is pooled across customers.
In action · illustrative: A new delay on the same lane opens with last quarter's case beside it. The re-route worked, but the alternate port ran a day slower than quoted, so the twin now uses the observed dwell.

Plan ahead on the twin

Carry the same twin and the same evidence into demand, inventory and S&OP decisions.

Demand sensingAdjusts the short-horizon forecast from fresh signals, with the driver behind each change.

Challenge: The monthly forecast can't see this week's demand shift, which is already visible in invoices and dispatches.
Inputs: The current forecast, GST e-invoice and e-way bill signals in India, point-of-sale data where available.
Outputs: Forecast adjustment proposals in the demand planner's queue, each with its driver.
Used by: Demand planners.
Value: Stock positioned for the demand that is actually arriving, with each change explained.
Works with: Runs on the PlanningNest forecast engine. Adjustments above a threshold wait for the demand planner.
In action · illustrative: E-way bills for one region jump two weeks before the forecast expects it; a raised forecast is proposed with that evidence attached.

Multi-echelon inventory optimizerRe-runs safety stock and rebalancing across plants and DCs continuously instead of once a quarter, and tests each transfer in the twin.

Challenge: Safety stock is set once a quarter and drifts away from reality in between.
Inputs: Stock positions by location, demand variability, lead times, service targets.
Outputs: Safety-stock changes and stock-transfer proposals.
Used by: Inventory manager.
Value: Stock where it is needed, and cash released where it is not.
Works with: Every proposal waits for the inventory manager.
In action · illustrative: Two DCs hold more cover than they need while a third runs short; a transfer is proposed.

S&OP workspaceRuns the monthly cycle from data lock to executive sign-off on one timeline, with scenarios run in the twin and stated in money, and a plan that executives sign.

Challenge: S&OP cycles end in slides, and the reasoning behind the agreed plan is lost.
Inputs: Demand and supply plans, scenarios, financial assumptions.
Outputs: Base, upside and constrained scenarios compared in revenue and margin, and a signed plan promoted to the planning systems.
Used by: S&OP lead, executives.
Value: A faster cycle, and an S&OP record that keeps its reasoning from one cycle to the next.
Works with: Plan promotion is always signed by an executive.
In action · illustrative: Base, upside and constrained scenarios are compared in revenue and margin; the agreed plan is signed and promoted.

Intelligence and automation

What the intelligence actually does

Five levels, from seeing what is happening to learning from it.

Visibility

The twin shows your whole chain in one view, with open disruption cases and the orders, customers and money each one touches.

Prediction

Runs a delay forward in the twin to show which orders will miss their promise dates and what that will cost; demand sensing corrects the short-horizon forecast.

Decision

Drafts two or three responses, each tested in the twin and costed by solvers, with cost set against the penalties it avoids.

Execution

After approval, fires each action into the system that owns it, with a rollback plan. Money-moving actions never run without a named approver.

Learning

Compares each outcome with the twin's prediction, corrects the twin's inputs, and keeps a decision memory of similar cases.

Runs automatically

Collecting and classifying signals
Opening a case and tracing its impact through the twin
Drafting responses and testing them in the twin
Reversing a bundle if one of its writes fails

Always waits for a named person

Every response bundle (supply planning manager; above a value limit, or when customer dates change, the head of supply chain)
Any change to a customer promise date
Mute rules for signals
Value ledger entries (head of supply chain or finance)
Letting any action type run with less oversight, which needs a measured track record and your agreement, and can be withdrawn

Business value

What it helps you work toward, and how we measure it

We agree the baseline with you in writing before a pilot starts. These are the outcomes we measure.

Lower cost per disruption

Freight, expedite and penalty costs weighed together while the cheaper options are still open.

Response cost plus penalties per case, against what planners did in shadow mode, on a baseline agreed in writing at the start.

Earlier, steadier response

Cases open from signals instead of from the customer calling.

Hours from first signal to an approved response, from case timestamps.

Fewer customer orders hit

Responses chosen for the customers who would feel the delay first.

Orders delivered after their promise date per disruption, before and after.

A twin you can trust

Responses behave in the real chain the way they behaved in the twin.

Gap between the twin's prediction and the actual outcome per case, tracked month by month.

Value finance can audit

Every saving linked to the case, the twin run and the approver.

Share of value ledger entries with linked evidence and a signature.

Illustrative example

A five-day port delay touches 32 orders for 7 customers. Left alone, 17 orders arrive late, with about $88K in penalties and lost sales. Tested in the twin, re-routing four containers, expediting one purchase order and moving stock between two DCs costs about $21K and leaves one order late. Illustrative figures, not a customer result.

Enterprise readiness

Integration, control and deployment

ERP & system integration

Reads SAP, Oracle, Microsoft Dynamics or Tally, plus WMS and TMS data, read-only first, and writes approved actions only through the owning system. Connectors are configured per deployment.

Data sources & connectivity

Each customer's twin and decision memory stay separate, with no pooling across customers. Outside signal feeds (port, weather, freight, news) are configured per deployment.

Deployment & implementation

Rolls out in stages: connect read-only, check the twin against reality with your planners, run in shadow mode (it drafts while planners decide as usual), then approve the first live bundles.

Configuration & business rules

Approval limits, escalation rules, signal thresholds and mute rules are settings, with every change logged.

Roles, access & approvals

Planners (cases and bundles), inventory manager, S&OP lead, head of supply chain (escalations, ledger sign-off) and finance (value ledger).

Security, governance & audit

Single sign-on, role-based access and a full audit log.

Human oversight & exceptions

Every response is tested in the twin before it is shown, every write waits for a named approver, every executed bundle carries a rollback plan, and autonomy is extended one action type at a time on measured results.

Scale & ongoing optimization

Sized for networks of tens of thousands of SKUs, with impact questions answered fast enough to use during a live case.

Straight answers

Questions about ControlTower

Something else? Ask a specialist.

Is this a digital twin or a dashboard?

A twin built for decisions. It holds your sites, lanes, stock, orders, constraints and costs, so a response can be run in it and its cost and service effect measured before anything changes in the real chain. It refreshes continuously from your systems, and planners check it against reality before it drafts anything.

Do we need the other FLX⁴ products?

No. It reads your ERP, warehouse and transport systems directly. Where you run other FLX⁴ products, such as TransNest or PlanningNest, it reads them too and hands actions to them.

Will it act on its own?

Detection, impact tracing and drafting run on their own. Every action that moves money waits for a named planner, and high-value or customer-facing actions go to the head of supply chain. An action type runs with less oversight only after a measured track record, with your agreement, and that can be withdrawn.

Where do the numbers come from?

From optimization solvers and the twin. The AI summarizes signals and explains the options in plain words. It does not do the arithmetic.

How do we start?

Connect read-only, check with your planners that the twin matches reality, then run in shadow mode, where ControlTower drafts and tests responses while your planners decide as usual. The two are compared before anything is written. We agree the baseline and success measures with you in writing first.

Next step

Discuss your disruption-response challenge

Disruptions are where working in slices costs the most, all at once. ControlTower runs your operation twice: it tests the response in the twin, puts one costed choice in front of the right person while the cheap options are still open, fires it for real, and proves afterwards what it saved.

Talk to a specialist

For mid-size manufacturers with several plants or DCs, imported or long-lead inputs, and customers who charge for late delivery.

In the same loop