① Guide · retail and seasonal estates
Sub capacity monthly license charges are driven by the peak rolling four hour average, and retail demand spikes hard and on schedule. A few hours of holiday trading can decide your software cost for the month, every year. Here is how the R4HA turns seasonality into cost, a worked MSU example, and the levers that flatten the peak without starving the business.
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Get expert help →On a sub capacity model, the system measures CPU utilization continuously, averages it over a rolling four hour window, and reports the single highest four hour average in the month. That peak, not the monthly average, sets the charge for the monthly license charge products running on the capacity. The mechanic is unforgiving for a seasonal business: a retailer can run comfortably under capacity for eleven months and still pay against the one peak that lands on a holiday trading day. The work that stacks up in that window, customer transactions plus whatever batch, reporting, and housekeeping happens to run at the same time, all counts toward the four hour average that becomes the bill. The lesson is that what you do during the peak hours is what you pay for.
Take an illustrative retailer whose estate sits comfortably below capacity for most of the year. The figures below are made up to show the mechanic, not benchmarks. Notice that November alone sets the cost profile, and that avoidable work inside the peak is what makes it worse:
| Month | Typical peak R4HA (MSU) | What drives the peak | Bill basis |
|---|---|---|---|
| Feb to Sep | ~850 | Steady trading plus overnight batch | Charged against ~850 |
| October | ~1,000 | Pre season ramp begins | Charged against ~1,000 |
| November | ~1,500 | Holiday peak plus batch stacking on top | Charged against ~1,500 |
| December | ~1,250 | Sustained holiday trading | Charged against ~1,250 |
| January | ~1,050 | Returns and post season clearance | Charged against ~1,050 |
Now apply one lever. Of November's 1,500 MSU peak, suppose 250 MSU is reporting and housekeeping batch that does not need to run during the trading window. Move it out of the four hour peak and the reported peak R4HA falls toward 1,250 MSU, the same as December, while the customer facing capacity is untouched. The shopper sees no difference; the month's bill basis drops by the work you relocated. That is the entire game: protect the revenue earning load, evict the avoidable load from the hours that set the charge.
Illustrative figures to demonstrate the R4HA mechanic, not benchmarks or guaranteed outcomes. Actual peaks, eligible work, and savings depend entirely on your workload mix. Model soft capping and rescheduling against real data, because mishandled capping can harm service at peak.
Three levers matter most for a retailer. First, peak management: move non urgent batch, reporting, and housekeeping out of the trading window so it does not stack on the revenue load, and use intelligent scheduling and soft capping where service levels allow. Second, the renewal: a seasonal estate should negotiate caps and consumption protections that account for the predictable spike, so a known peak does not become an annual surprise. Third, the model decision: a consumption based approach such as Tailored Fit Pricing removes the peak shaving pressure entirely, but only pays if the negotiated baseline is good, so model both positions against your own demand curve before switching. The constant across all three is that the peak is predictable, which means it can be planned for rather than absorbed. This is the work of our mainframe cost optimization engagements. For the mechanics, see peak shaving and scheduling around the R4HA, and for a neighboring seasonal sector, mainframe licensing in logistics.
Because the peak rolling four hour average drives the monthly charge, and retail produces sharp, predictable peaks. A few hours of holiday trading can set the software cost for the whole month.
Often yes. Move non urgent work out of the trading window and manage the four hour average with scheduling and soft capping, so avoidable load stops inflating the peak that sets the charge.
Worth modeling. Consumption pricing removes peak shaving pressure but only pays if the baseline is good. Model both the R4HA and the Tailored Fit position against your own data first.
Identify which work runs inside the seasonal peak and which of it is avoidable. The relocatable batch and reporting in the trading window is where the cheapest savings sit.