Sharing Ratio

The sharing ratio is the most consequential number in most workplace strategies and the most frequently guessed. It determines capacity, cost, experience and change intensity simultaneously — and when it is imported from a benchmark rather than derived from this organisation's measured rhythm, it fails in one of two expensive directions: wasted capacity or Tuesday crisis. This method derives the ratio from evidence, differentiates it where the evidence differentiates, stress-tests it against risk scenarios, and prices its change implications honestly.

• In Develop, once the concept direction is set and the presence logic is decided
• Whenever a benchmark ratio ('everyone does 0.7 now') enters the room and needs replacing with evidence
• Re-run in Recalibrate when attendance assumptions breach their triggers

• Strategist and spatial analyst running the model
• CRE and Finance for capacity and cost consequences
• HR and change lead for the change implications
• Team leads of populations at differentiated ratios — they must understand and be able to explain their number

• Presence logic and rhythm redistribution from Hybrid Balance
• Temporal rhythm model: daily curves, weekly structure, demand cliffs
• Peak occupancy structure from the Occupancy Assessment
• Ratio bands and population differentiation from Taxonomy Alignment
• Dedicated-setting requirements: roles, equipment or regulation demanding assigned positions

1. Model workforce demand against the rhythm structure: not average attendance, but the distribution of simultaneous presence per population per day type.
2. Derive the ratio per population from peak structure with an explicit service level: what percentile of demand days does the capacity serve, and what happens on the days beyond it?
3. Separate dedicated from shared settings on evidence: which roles hold requirements (equipment, regulation, continuous presence) that assignment genuinely serves — and which hold history.
4. Stress-test against risk scenarios: the all-anchor-day collision, the post-policy attendance rebound, the growth scenario's headcount, the seasonal peak. Record where each ratio breaks and what the overflow protocol is.
5. Model the interaction with rhythm redistribution: a ratio that fails under current anchor days may hold under the redistributed rhythm Hybrid Balance designed — the ratio and the rhythm are one system.
6. Price the change implications per ratio step: the behavioural distance between 1:1 and 0.8 is small; between 0.8 and 0.6 it is a different workplace. Change cost is part of the ratio decision, not a footnote.
7. Document each ratio with its evidence chain, service level, break conditions and overflow protocol; register the attendance assumptions in Calibration.

• Average-based ratios that die on Tuesdays — the peak distribution, not the mean, sets the number
• Dedicated-desk claims that are seniority in requirement costume
• Ratios tightened to make a business case close, with the experience cost booked to nobody
• Uniform ratios flattening evidence-based differentiation for administrative comfort
• Overflow protocols that don't exist: every ratio has break days, and pretending otherwise converts a known condition into a crisis

• Evidence-derived sharing ratios per population with service levels
• Dedicated-versus-shared setting register with evidence per dedication
• Risk scenario results and break conditions per ratio
• Overflow protocol
• Change implication assessment per ratio step
• Registered attendance assumptions with triggers

The ratios drive capacity in Stacking and Blocking and setting quantities in the Kit of Parts. The change implications enter the Deliver-phase change programme. The registered assumptions and break conditions become Recalibrate's monitoring targets: when observed attendance breaches the trigger, the platform regenerates the ratio model rather than the organisation discovering the breach through Tuesday queues.

The model produces the distribution; judgement sets the service level — how many break days per year the organisation accepts is a leadership decision about cost versus experience, not an output. So is the call on which dedication claims are genuine and which are status, a judgement requiring political courage the arithmetic cannot supply.

• Importing a benchmark ratio and reverse-engineering the justification
• Modelling on average attendance
• One ratio for all populations against differentiated evidence
• Deciding the ratio without pricing its change distance
• No overflow protocol, so the first break day becomes the ratio's public failure

A consultancy's finance team demanded 0.6 across 1,400 staff to close the business case. The peak-distribution model showed 0.6 breaking 47 days a year for client-facing teams under their required overlap rhythm — but holding at 11 break days under the redistributed rhythm, with a defined overflow protocol into bookable project space. The decision became explicit: 0.6 with rhythm redistribution and overflow provision, accepted by leadership with the break-day count on the record. Observed break days in year one: nine.