Make the trade-offs visible.
Explore how a decision engagement is framed, what it produces and how value can be evaluated. These are illustrative business scenarios based on AEVO capabilities, not published customer case studies or verified performance claims.
Live Solver
Three planning problems, one real optimization solver.
Pick a problem: a production mix, purchase orders against an uncertain forecast, or a battery trading on tomorrow’s prices. HiGHS solves each one in your browser, reports how close the plan is to the best possible, and shows which limit is worth relaxing.
Production mix
Four products, three lines, setup costs and limited line hours. The solver decides what to make on which line.
Contribution margin after setups
$64,969
| Line 1 | Line 2 | Line 3 | |
|---|---|---|---|
| Product A | — | — | — |
| Product B | 278 | 409 | — |
| Product C | — | — | 480 |
| Product D | — | — | 182 |
Line hours
- Line 1 Bottleneck
250.2 of 251 h
- Line 2 Bottleneck
396.7 of 397 h
- Line 3 Bottleneck
396 of 396 h One more hour adds $68.67
Inventory and purchasing
Eight weeks of forecast demand, with a promotion in weeks 4 to 6. Every order has a fixed cost, stock costs money to hold, and the supplier can ship only so much a week. The solver decides when to order and how much.
Total cost: ordering plus holding
$9,591
Orders: 6 · holding cost: $2,391
- Supplier capacity: 700 units/week Bottleneck
One more unit of weekly supplier capacity saves $4.50
Orders, stock and safety stock by week (units)
| Week | Forecast demand | Safety stock | Order | Stock at week end |
|---|---|---|---|---|
| Week 1 | 420 | 69 | At capacity — | 180 |
| Week 2 | 380 | 72 | At capacity 272 | 72 |
| Week 3 | 460 | 99 | At capacity 487 | 99 |
| Week 4 | 520 | 123 | At capacity 544 | 123 |
| Week 5 | 610 | 161 | At capacity 652 | 165 |
| Week 6 | 580 | 168 | At capacity 700 | 285 |
| Week 7 | 450 | 141 | At capacity 700 | 535 |
| Week 8 | 400 | 135 | At capacity — | 135 |
Battery arbitrage
A 1 MW grid battery and an hourly price forecast for tomorrow. Every charge loses some energy and every cycle wears the battery. The solver decides when to buy power and when to sell it.
Margin for the day
$383
Sold: $742 · bought: $277 · wear: $82
- Battery capacity: 4 MWh Bottleneck
One more MWh of capacity earns $39.40 a day
- Power: 1 MW Bottleneck
One more MW of power earns $225.08 a day
Price forecast ($/MWh)
Charging (down) and discharging (up), MW
Energy stored (MWh)
Hourly schedule (MWh)
| Hour | Price | Charge | Discharge | Stored |
|---|---|---|---|---|
| 00:00 | $62 | — | — | 0.0 |
| 01:00 | $58 | — | — | 0.0 |
| 02:00 | $55 | 1.0 | — | 0.9 |
| 03:00 | $54 | 1.0 | — | 1.9 |
| 04:00 | $56 | 1.0 | — | 2.8 |
| 05:00 | $63 | — | — | 2.8 |
| 06:00 | $78 | — | 0.6 | 2.1 |
| 07:00 | $95 | — | 1.0 | 1.1 |
| 08:00 | $88 | — | 1.0 | 0.0 |
| 09:00 | $70 | — | — | 0.0 |
| 10:00 | $45 | — | — | 0.0 |
| 11:00 | $32 | 1.0 | — | 0.9 |
| 12:00 | $24 | 1.0 | — | 1.9 |
| 13:00 | $20 | 1.0 | — | 2.8 |
| 14:00 | $26 | 1.0 | — | 3.8 |
| 15:00 | $38 | 0.3 | — | 4.0 |
| 16:00 | $64 | — | — | 4.0 |
| 17:00 | $98 | — | 0.8 | 3.2 |
| 18:00 | $142 | — | 1.0 | 2.1 |
| 19:00 | $165 | — | 1.0 | 1.1 |
| 20:00 | $128 | — | 1.0 | 0.0 |
| 21:00 | $96 | — | — | 0.0 |
| 22:00 | $80 | — | — | 0.0 |
| 23:00 | $70 | — | — | 0.0 |
Uncertainty → decision
A forecast is not an order quantity.
Demand is uncertain. When running out costs more than leftovers, the best order sits above the median forecast, by an amount the costs decide.
- Critical ratio
- 75%
- Median forecast (P50)
- 1,000 units
- Best order quantity
- 1,121 units
- Saving from the decision model
- $117
| Order quantity | Expected cost of uncertainty | |
|---|---|---|
| Order the P50 forecast | 1,000 | $574 |
| Order the best quantity | 1,121 | $458 |
Illustrative model: normally distributed demand with mean 1,000 units and standard deviation 180; one ordering decision.
Manufacturing decisions
Step inside the decision.
Explore one factory, from receiving to dispatch. Change demand or production capacity, then see how the same quantities move through the scene and the calculation below.
Drag to rotate; pinch to zoom. Page scrolling remains available. Use view buttons for the same camera positions.
Selected station
Line A
The lower-cost production line is allocated first, within its available capacity.
- Allocated units
- —
- Effective capacity
- —
- Capacity used
- —
Starting scenario
The full order fits the stated capacity.
- Delivered units
- 1,200 Baseline 1,200
- Unfilled units
- 0 Baseline 0
- Variable cost · USD
- $31,500 Baseline $31,500
Cost per delivered unit · USD $26.25
Stage quantities and limits
| Select a station | Allocated units | Effective capacity | Capacity used |
|---|---|---|---|
| Receiving | 1,200 | 1,500 | 80% |
| Line A | 700 | 700 | 100% |
| Line B | 500 | 600 | 83.3% |
| Warehouse | 1,200 | 1,500 | 80% |
| Dispatch | 1,200 | 1,600 | 75% |
Calculation rules
Quantities are units per batch, with one received unit yielding one finished unit. Fixed limits: receiving 1,500; warehouse 1,500; dispatch 1,600. Each delivered unit includes USD 12 material, 2 handling and 3 dispatch, plus production: 8 on Line A or 11 on Line B. Total cost = A units × USD 25 + B units × USD 28. Unfilled units incur no modeled cost; a lower total after a stoppage may simply reflect fewer units delivered.
Supply network laboratory
When one link changes, see the whole decision.
Select a supplier, change capacity or interrupt supply. The network reallocates quantities within the stated capacity and delivery limits, so you can compare the routes and their costs.
On a small screen, swipe the network horizontally. Select a supplier to edit its assumptions.
Line width shows allocated quantity. Supplier-to-plant labels show units; downstream lines show the total passing through each link.
The full order can be delivered by the deadline.
Compare with the fixed baseline
- On-time delivery
- 1,200 units Baseline 1,200 units · Change 0 units
- Unfilled demand
- 0 units Baseline 0 units · Change 0 units
- Variable supply cost
- $32,900 Baseline $32,900 · Change $0
| Measure | Baseline | Current scenario |
|---|---|---|
| On-time delivery | 1,200 units | 1,200 units |
| Unfilled demand | 0 units | 0 units |
| Variable supply cost | $32,900 | $32,900 |
| Cost per delivered unit | $27.42 | $27.42 |
| Longest used delivery route | 6 days | 6 days |
| Feasible network capacity | 1,300 units | 1,300 units |
Route quantities and constraints
| Supply route | Baseline | Current scenario | Route status |
|---|---|---|---|
| Supplier A → Plant 1 | 600 | 600 | Allocated |
| Supplier A → Plant 2 | 0 | 0 | Available, not allocated |
| Supplier B → Plant 1 | 100 | 100 | Allocated |
| Supplier B → Plant 2 | 0 | 0 | Available, not allocated |
| Supplier C → Plant 1 | 0 | 0 | Available, not allocated |
| Supplier C → Plant 2 | 500 | 500 | Allocated |
| Plant 1 → Distribution hub | 700 | 700 | Allocated |
| Plant 2 → Distribution hub | 500 | 500 | Allocated |
| Distribution hub → Customer orders | 1,200 | 1,200 | Allocated |
| Supply route | Fixed route capacity | Variable cost / unit | Delivery days |
|---|---|---|---|
| Supplier A → Plant 1 → Distribution hub → Customer orders | 600 | $28 | 4 |
| Supplier A → Plant 2 → Distribution hub → Customer orders | 350 | $29 | 7 |
| Supplier B → Plant 1 → Distribution hub → Customer orders | 400 | $31 | 5 |
| Supplier B → Plant 2 → Distribution hub → Customer orders | 450 | $40 | 5 |
| Supplier C → Plant 1 → Distribution hub → Customer orders | 500 | $35 | 6 |
| Supplier C → Plant 2 → Distribution hub → Customer orders | 500 | $26 | 6 |
Model assumptions
One period, one interchangeable product, no opening inventory, split orders allowed and no fixed costs. Supplier capacities are shared across both plants. Plant 1 receives 55% of the combined production limit and Plant 2 receives 45%; the hub accepts up to 1,500 units. Route capacities, unit costs and delivery days are fixed and listed below. Delivery days add transport, processing and distribution time; queues are excluded. A route is unavailable when its supplier is interrupted, its capacity is zero or its delivery time exceeds the deadline.
How allocation is calculated
A deterministic minimum-cost flow calculation first finds the largest quantity that can reach the customer before the deadline, then the least variable cost for that delivered quantity. It respects supplier, plant, route and hub capacities. Unfilled demand is shown explicitly; no penalty cost, stochastic uncertainty or real-time scheduling is modeled.
Decision laboratory
Change an assumption. See the trade-off.
Explore three operating decisions with transparent calculations. Compare your assumptions with a fixed baseline before opening the decision engine.
Illustrative data and bounded formulas, calculated in your browser. This is not live customer data, model inference, an optimization solver or a claimed business result. All monetary values are USD.
Supply allocation
A larger order competes for inventory and production capacity. Compare the cost of external supply with the service you can actually deliver.
Model assumptions
Opening inventory: 400 units. Internal capacity: 800 units. Inventory and internal production: USD 40/unit; external supply: USD 65/unit. One period, identical products, no delivery delay or fixed costs.
Calculation rule
Use unprotected stock first, then internal production, then external supply up to its limit. Unfilled demand = order − delivered quantity. No claim of global optimality.
The order fits the stated capacity and protected inventory.
- Delivered quantity
- 1,200 units
- Supply cost
- $53,000
- Unfilled demand
- 0 units
Baseline and scenario comparison (units)
Equivalent results in a table
| Measure | Baseline | Your scenario |
|---|---|---|
| Delivered quantity | 1,200 units | 1,200 units |
| Supply cost | $53,000 | $53,000 |
| Unfilled demand | 0 units | 0 units |
| Inventory used | 200 units | 200 units |
| Internal production | 800 units | 800 units |
| External supply | 200 units | 200 units |
| Ending inventory | 200 units | 200 units |
90-day cash runway
Growth can absorb cash before it generates receipts. Change collection and inventory timing to reveal the lowest cash point.
Model assumptions
Opening cash: USD 2m; no opening receivables. Daily sales: USD 40,000 before growth; purchases: 68% of sales; daily operating cash cost: USD 6,000. Supplier credit: 30 days. Constant sales, no tax, interest or financing inflows.
Calculation rule
Day 0 commitment = max(inventory days − 30, 0) × daily purchases. Receipts start after collection delay; supplier payments start after max(30 − inventory days, 0). Daily cash = prior cash + receipts − payments − operating cost. Funding need covers the lowest deficit.
Cash stays non-negative throughout the 90-day example.
- Cash on day 90
- $1,412,000
- Lowest cash balance
- $1,004,000
- Additional funding needed
- $0
Baseline and scenario comparison (USD)
Equivalent results in a table
| Measure | Baseline | Your scenario |
|---|---|---|
| Cash on day 90 | $1,412,000 | $1,412,000 |
| Lowest cash balance | $1,004,000 | $1,004,000 |
| Additional funding needed | $0 | $0 |
| Initial cash commitment | $0 | $0 |
| Receipts over 90 days | $2,400,000 | $2,400,000 |
| Supplier payments | $2,448,000 | $2,448,000 |
| Operating cash costs | $540,000 | $540,000 |
| Day | Baseline | Your scenario |
|---|---|---|
| 0 | $2,000,000 | $2,000,000 |
| 15 | $1,502,000 | $1,502,000 |
| 30 | $1,004,000 | $1,004,000 |
| 45 | $1,106,000 | $1,106,000 |
| 60 | $1,208,000 | $1,208,000 |
| 75 | $1,310,000 | $1,310,000 |
| 90 | $1,412,000 | $1,412,000 |
Energy cost and carbon
A renewable target meets a real supply limit. Compare energy cost and modeled emissions against your carbon ceiling.
Model assumptions
Renewable contract capacity: 1,100 MWh/period at USD 90/MWh; grid supply: USD 72/MWh. Illustrative factors: renewable 0.035 tCO₂e/MWh, grid 0.55 tCO₂e/MWh. Unlimited grid backup; no storage, time-of-use tariffs or certificate accounting. These are example factors, not a carbon inventory.
Calculation rule
Renewable supply = min(demand × requested share, 1,100). Grid supplies the balance. Cost and emissions are each the sum of source volume × source factor. A scenario is feasible only when the requested share is available and emissions meet the ceiling.
The requested share is available and modeled emissions meet the ceiling.
- Energy cost
- $112,140
- Modeled emissions
- 445.6 tCO₂e
- Delivered renewable share
- 45%
Baseline and scenario comparison (MWh)
Equivalent results in a table
| Measure | Baseline | Your scenario |
|---|---|---|
| Energy cost | $112,140 | $112,140 |
| Modeled emissions | 445.6 tCO₂e | 445.6 tCO₂e |
| Delivered renewable share | 45% | 45% |
| Renewable supply | 630 MWh | 630 MWh |
| Grid supply | 770 MWh | 770 MWh |
| Renewable supply shortfall | 0 MWh | 0 MWh |
| Emissions above ceiling | 0 tCO₂e | 0 tCO₂e |
Illustrative engagement
An urgent order. A constrained factory.
A manufacturer needs to assess a new order without disrupting existing commitments. Compare margin, material availability, machine capacity and delivery feasibility together. The output is a reviewable acceptance and scheduling recommendation, measured against delivery and contribution-margin baselines.
How value is measured
On-time delivery · contribution margin · schedule adherence · capacity utilization
Illustrative engagement
Inventory decisions are cash decisions.
A multi-location business needs service levels without unnecessary stock. Model demand and lead-time uncertainty, compare replenishment policies, and trace their impact on stockouts, inventory days and the cash conversion cycle before changing the operating policy.
How value is measured
Fill rate · inventory days · stockouts · procurement cost · recovery time
Illustrative engagement
Energy assets. One economic picture.
An operator considers coordinating solar, storage and flexible loads. Compare dispatch scenarios under tariffs, asset degradation and carbon objectives. Separate operating savings from investment assumptions, and validate feasibility before connecting asset controls.
How value is measured
Energy cost · peak demand · emissions · asset degradation · project cash flow
Start with a decision that matters.
Tell us where cost, capacity, risk or cash is limiting performance. We will help define a focused evaluation before committing to a wider transformation.