Facilities Process

How does the function actually run, decision by decision?

The Matrix

Seven recurring workflows hold the function together. Each has a trigger, an owner, an input, an output, an SLA, an AI augmentation, and (where it exists) a crypto instrument that changes how it runs. The Head of Facilities sits as the apex decision maker — the owner column names who runs the workflow day to day; the Head of Facilities ratifies or overrides.

W1. New-space decision (the four-step flow)

• Trigger: Growth signal (FTE outgrowing space), lease expiry < 18 months out, operating-model shift (new sector, new modality, new geography).
• Owner: Head of Facilities, with the buyer-side player network assembled before Step 1.
• Input: Operating-model spec, headcount projection, financial envelope.
• Output: Shortlist of options with completed per-option evaluation memo (W2).
• SLA: 6–12 weeks from trigger to shortlist.
• AI-augmentation: comparable-rent synthesis, due-diligence sweep, density audit.
• Crypto-instrument: none mature today.

Run in order. Do not skip the early steps to get to the property tour faster — every shortcut compounds.

Step 1 — Clarify the operating model and the role of space

The question is not "what office do we need." The question is "what must the space enable in our operating model."

Collect:

• Revenue model (SaaS, manufacturing, services, retail, mixed)
• Customer interaction model (on-site, remote, hybrid)
• Operational constraints (production, lab, warehousing, regulatory)
• Talent strategy (where the people are, hybrid ratio, headcount ramp)

Output: explicit list of space functions — production, logistics, customer-facing, collaboration, brand, regulatory. Each function is a line item the space must serve.

Step 2 — Translate to capacity and location

Turn the operating model into numbers.

Collect:

• Headcount by function (current + projected over the lease horizon)
• Production capacity targets, machinery, inventory footprints
• Customer catchment zones, service radius
• Required adjacencies (port, CBD, motorway, airport)

Output: a quantified spec — m² range, power, ceiling height, loading, parking, connectivity, accessibility, zoning. Without this you cannot filter listings; you can only react to them.

Step 3 — Set the financial envelope

Before viewing any property, decide what "good" looks like.

Collect:

• Projected revenue and margin from the location
• Rent or purchase budget
• Fit-out and capex
• Operating expenses (utilities, rates, maintenance, insurance)
• Incentives available (rent-free, landlord works, council grants)

Output: target ranges for occupancy cost as % of revenue (per Performance G1), payback on fit-out (G7), acceptable lease term and break options. This is the envelope. Deals outside it never get viewed.

Step 4 — Filter options against the spec

Scan the market — filter hard. For each candidate: address, size, condition, spec, asking rent or price, incentives, term, zoning, planning restrictions. Many options die on power, access, or term alone before any financial analysis runs.

Output: shortlist for W2 per-option evaluation. Side-by-side comparison table — one row per option, one column per spec attribute.

W2. Per-option evaluation (the four lenses + two gates)

• Trigger: Shortlist produced by W1.
• Owner: Head of Facilities, with input from Finance, Legal, QS, workplace strategist.
• Input: Shortlist + W1 spec + envelope.
• Output: Per-option scoring with RAG status per cell; recommendation memo.
• SLA: 2–4 weeks from shortlist to recommendation.
• AI-augmentation: AI NPV builder, due-diligence sweep, negotiation prep brief.
• Crypto-instrument: none mature.

For every shortlisted option, run all four lenses. Skip a lens and the decision is partial.

• Lens A — Total cost of occupancy. NPV or 5–10 year cost profile. Per FTE, per m², per unit throughput. Not rent — full cost.
• Lens B — Revenue and capability impact. Does this address amplify the operating model, or just absorb cash? Catchment, talent, logistics, regulatory proximity, brand.
• Lens C — Operational fit and resilience. Reliability, redundancy, growth headroom, sub-lease rules, business continuity, environmental risk. Know the exit before signing the entry.
• Lens D — Legal and governance. Lease vs own, covenant strength, options to renew / expand / assign, make-good, governance thresholds. Lease abstraction structured so finance and ops can track triggers.

Then two gates the framework usually misses:

• Wage-Rent Substitution. Can $X of annual rent buy more revenue as one extra senior hire instead? Force the conscious trade.
• Joint Reversibility Cost. Cost to exit in 12 / 24 / 36 months — including break + make-good + move + lost productivity — for the rent AND the headcount plan at each option. The lowest joint cost wins on uncertain demand curves.

W3. Lease negotiation

• Trigger: Preferred option selected from W2.
• Owner: Head of Facilities + tenant rep broker + property lawyer.
• Input: Landlord's draft lease, W2 evaluation, negotiation prep brief.
• Output: Signed lease with documented redlines.
• SLA: 4–8 weeks.
• AI-augmentation: lease-clause analysis flags every non-standard / tenant-unfriendly clause against the redline playbook.
• Crypto-instrument: smart-contract escrow for fit-out contribution and break-clause triggers — emerging.

W4. Fit-out execution

• Trigger: Lease signed; access date set.
• Owner: Head of Facilities + QS + project manager.
• Input: Approved fit-out spec, budget, contractor list, landlord works commitments.
• Output: Operational space, on time, on budget, snag-list closed.
• SLA: Per fit-out plan; payback target ≤ 50% of remaining lease term (Performance G7).
• AI-augmentation: AI takeoff for cost reality-check; AI scope-tracking against contractor invoices.
• Crypto-instrument: DeFi lending against the lease could finance fit-out without personal guarantee — emerging.

W5. Lease renewal / break decision

• Trigger: 18 months from lease expiry OR 12 months from break date — whichever comes first.
• Owner: Head of Facilities.
• Input: Performance gauges G1–G6, comparable-rent refresh, operating-model projection over the next horizon, joint reversibility cost.
• Output: Renew / renegotiate / break / sublease / move decision with memo.
• SLA: Decision routed 12+ months before expiry.
• AI-augmentation: counterparty refresh (any change in landlord posture?), comparable-rent refresh, density audit refresh.
• Crypto-instrument: smart-contract escrow could automate break-clause notice and funds release.

W6. Exit / sublease / assign

• Trigger: Decision from W5 to vacate before lease end OR to sublease surplus space.
• Owner: Head of Facilities + property lawyer + tenant rep broker (acting as listing broker for the sublease).
• Input: Sublease market scan, assignment terms in the lease, make-good obligation.
• Output: Sublease signed OR assignment complete OR lease terminated with documented obligations met.
• SLA: Depends on market — typically 3–9 months for sublease.
• AI-augmentation: sublease market scan, prospective sub-tenant due diligence, negotiation prep.
• Crypto-instrument: on-chain tenant reputation could shorten sub-tenant due diligence — does not exist yet.

W7. Ongoing facilities management

• Trigger: Continuous, post-occupancy.
• Owner: Head of Facilities + building coordinator + vendor management.
• Input: Maintenance schedule, incident log, vendor contracts, regulatory calendar, safety compliance.
• Output: Building runs. Incidents tracked. Vendors paid on terms. Safety obligations current.
• SLA: Per maintenance and compliance calendar; incident triage same business day.
• AI-augmentation: maintenance-agent runs predictive scheduling against incident history; vendor-agent tracks SLA adherence.
• Crypto-instrument: none mature for ongoing FM; sensor + DePIN integration emerging — see Real Estate Industry DePIN.

By Operating Model — Where the Flow Tilts

Same workflows. Different weights.

Model	Space role	Data to prioritize	Decision tilt
SaaS / digital-first	People + collaboration	Headcount ramp, remote/on-site ratio, team topology, talent catchment	Short, flexible leases. Reconfigurable space. Cost per FTE over per m².
Manufacturing / industrial	Core production asset	Throughput targets, material flows, shift patterns, freight, machine footprints, regulatory	Optimize layout and flow first, then pick buildings that host that flow. Longer terms justified — moving is expensive.
Logistics / warehousing / e-commerce	Network node	Order density, delivery SLAs, carrier costs, vehicle routing, picks/hour, dock utilisation	Locations are network nodes. Optimize inventory positioning, delivery time, cost — over rent per m².
Professional services / consulting	Relationship + culture infrastructure	Client clusters, in-person/remote ratios, secure project space, confidentiality	Flexibility + right-sized space. Evaluate impact on utilisation, recruiting, client experience.
Retail / customer-facing	Demand capture + brand	Pedestrian / vehicle counts, competitor locations, demographics, conversion by location, local spend	Each site = mini-P&L with location-driven sales assumptions. Rent and incentives weighted against projected uplift.

Pick the wrong tilt and the four lenses still pass — they just measure the wrong things.

The Decision Memo Template

When W2 is complete, the output collapses to a one-page memo:

1. Operating model + role of space (W1 Step 1 in one paragraph)
2. Spec (W1 Step 2 quantified)
3. Envelope (W1 Step 3 — occupancy cost ceiling, term, break)
4. Options table — one row per shortlisted property, columns for each spec attribute
5. Per-option scoring — four lenses + two added gates, RAG status per cell
6. Recommendation — chosen option + the constraint or flex that makes it the right call as the operating model evolves, not just as it looks today
7. Open risks + mitigations

A memo at this shape can be reviewed by an accountant, a lawyer, and a board in one sitting. A binder of broker brochures cannot.