Five operating assumptions and one investment figure, all of them yours. The model returns an annual value, a break-even month, and a link your team can check line by line. Nothing is calculated until you enter a number, and nothing you enter leaves your browser.
Maintenance sets the operating assumptions. Operations validates the impact. Finance approves the inputs and owns the decision.
The whole model, before you scroll
Downtime value = events x avoidable hours x cost per hour Labor value = events x coordination hours x loaded rate Total modeled value = downtime value + labor value Break-even months = 12 x investment / total modeled value Modeled ROI = (total modeled value - investment) / investment That is the entire model. There is no hidden multiplier, no benchmark, and no assumption we did not ask you for.
We supply no cost per downtime hour, no loaded labor rate, and no price. A number you cannot defend in a budget review has no business in the model.
Two multiplications, one division, one break-even. Read them, argue with them, and reproduce the whole model in your own spreadsheet in five minutes.
Every input and output remains visible as your arithmetic on your assumptions. Our measured results are stated separately, further down.
EQUA AIMMS customer-specific value model
Prepared on the date shown by your browser. Model published at equa.work/roi. Every input below was supplied by the reader, not by EQUA AI.
Enter the operating assumptions your team can defend. The formulas stay visible, finance controls every input, and no value appears until you supply the numbers.
This model runs entirely in your browser and needs JavaScript to calculate. The five formulas above are the complete model, so you can reproduce it in a spreadsheet without running anything from this site.
Modeled annual value from your inputs
Total modeled value per year
$0
Break-even at these assumptions
Enter investment
Enter your assumptions to produce a result. Nothing is calculated until you do.
This is your arithmetic on your assumptions, not a measurement, forecast, guarantee, or AIMMS result. A measured first deployment replaces the assumptions with your operation's baseline and measured change. Finance controls every input.
The six inputs above are enough on their own. This layer exists only to help you defend the avoidable-hours figure. Nine of the twelve delay categories are digital coordination AIMMS can move. Three are physical work that stays with people, so the model never counts them. Change nothing and the pre-selection stands.
Hatched figures show the modelled structure. Delivery counts only the ordering and coordination time before a shipment moves, never transit time.
Selected scope suggests 5.0 avoidable hours per event.
Inputs supplied by the reader
Model published at equa.work/roi. Every figure above uses assumptions supplied by the reader; EQUA AI supplied none of the inputs. A first deployment replaces those assumptions with a real baseline and a measured change.
The model counts downtime avoided and coordination capacity recovered. It deliberately ignores procurement leakage, expert load, record quality, and retained knowledge, because those are easier to claim than to defend. If two levers do not carry the case, the case is not ready.
Put integration work, data cleanup, security review, training, and internal time into the investment input. A break-even that excludes your own effort is not a break-even.
The model assumes no change to who operates the plant. AIMMS moves permitted digital work. It does not write to PLC, DCS, or SCADA, and it does not execute setpoints, restarts, interlocks, or actuator commands. SCADA and historian context stays read-only.
The only way to replace these assumptions is to measure them. A first deployment reconstructs your baseline, runs the workflow for a focused operating period, and reports the change against the number you entered here.
Reconstruct recent repairs and quantify where time is spent.
Target a customizable two-week setup for one consequential workflow, subject to data, security, and user readiness.
Run historical cases and confirm evidence, parts, workflow, and control quality.
Use AIMMS on the approved live workflow for a focused 30-day operating period after readiness.
Compare downtime, ready-work time, expert load, supplier cycle, approvals, and closeout.
Produce a finance-ready rollout case, control model, and expansion scope.
Detection is instant. What follows is not. People search for context, verify safety, identify the right part, coordinate suppliers, find authority, and reconstruct the record. The repair is the short part. Draw the same repair twice and you can see where the days actually go.
Fault detected
The repair starts here instead
Fault detected
These are the gaps the drawing above is made of. Each one is a place the work is ready and the decision is not.
Your team is not slow. The work around the repair is. Put in your last critical repair and see the split. We do not project a result onto your numbers.
Drag both controls. Use your own last repair.
This section is a calculator. Without JavaScript the controls cannot compute, so the figures beside this paragraph are a worked example on a 38-hour repair rather than anything you entered. The point it makes does not depend on the arithmetic: on a critical repair, most of the elapsed time is spent waiting on coordination rather than on the asset.
The ledger uses only the two values you enter and applies no assumed AIMMS effect.
32 of those hours were waiting. 84% of that repair was coordination, not repair. Your crew was ready for 6 of those 38 hours.
What we measured
In one anonymized industrial production deployment, MTTR fell 14.2 h 9.7 h, a 31.7% reduction.
The assessment classifies every elapsed hour of your recent repairs into twelve delay categories. The baseline shows exactly which delays AIMMS can remove, which must remain, and where the first deployment should focus.
Before the categories comes the harder question of what the clock runs between. Our measurement protocol sets out the clock boundaries, denominators and exclusions that decide whether any pilot number means anything, including the conditions attached to ours.
Digital coordination AIMMS can remove or compress Physical work that stays with people
The line between the two halves of this page
Your model ends here. Nothing below it was used to produce anything above it.
Every figure above is your arithmetic on assumptions you supplied, with no AIMMS effect applied. Every figure below is measured, in one anonymized industrial production deployment, and it is not a forecast of your result.
Everything above is your arithmetic on your assumptions. The figures below are ours. They are measured, they are reported at their real scope, and they are not fed into the model, not used as a default, and not projected onto your operation. Read them as evidence that the workflow moves, then decide for yourself what it would move at your site.
One anonymized industrial production deployment
Six levers move when the digital work moves. The value model on the ROI page counts only the first two, because they are the two a finance approver can defend without argument.
Counted in the model
Counted in the model
Deliberately not counted
Deliberately not counted
Deliberately not counted
Deliberately not counted
Four of these six are real and none of them is in the arithmetic. A model that counts everything it can imagine is not a stronger case, it is an easier one to reject.
Maintenance and reliability
Choose the recurring failure, validate the current delay, and judge whether the workflow becomes easier to execute.
Operations and the COO
Confirm the control boundary, return-to-service evidence, and effect on continuity and team capacity.
Finance and the CFO
Approve the economic inputs and compare measured operating value with the cost and risk of rollout.
In 20 minutes we will map the current delay, the customer control boundary, and a first-deployment measurement plan that reports against the numbers you just entered.
One operating problem. One focused working session. No obligation.