Simulating Work Patterns

– understanding instead of observation

William Fawcett and Paul Bartlett

Cambridge Architectural Research Ltd

Workplace Trends

24 October 2013

Content

• The problem

• The theory

• Solution

• Proving it works

• Business benefits

Background Economic Drivers

• Global competition increasing

• Unrelenting pressure to reduce business costs

UK at Sharp End

• UK has service sector at heart of economy

• Property costs are typically second highest (to

employment)

• UK property costs (especially London) amongst

highest in the world

• West end approaching £15,600 per workstation

(ref DTZ 2013 GOCO Survey)

Consequences for property

• Expectation of lowering accommodation cost per

employee

• Limited scope for reduced space allocation per

workstation

Getting it right for the future workplace

• Consensus that 1:1 allocation is wasteful

• Average utilisation is below 50%

• But pervading anxiety from users about

insufficient number of shared desks

• How much workspace is optimum?

• Deliver enhanced productivity through

understanding work patterns

Empty is bad ...

... and congested is bad ... where is the

balance?

How do we understand the workplace –

PLATT, S. (2008) Graduate Accommodation Survey, Cambridge Architectural Research Ltd.

observation or simulation?

Using space to ‘nudge’ behaviour– settings for interaction

–

Seating choice – observation

Suppose that analysis of

observational data

shows that:

2/3 of the time people use

adjacent seats, and

1/3 of the time opposite

seats –

Example suggested by Lionel March

Seating choice – observation

THEREFORE, you

confidently conclude from

the observations:

people prefer sitting

at adjacent seats

Seating choice – simulation

How do 2 people sit at

a 4-seat tables?

We can simulate 2 people sitting at

a 4-seat table: there are 12 cases –

and no more

Seating choice – simulation

How do 2 people sit at

a 4-seat tables?

In 2/3 of cases people use adjacent

seats

We can simulate 2 people sitting at

a 4-seat table: there are 12 cases –

and no more

Seating choice – simulation

How do 2 people sit at

a 4-seat tables?

In 2/3 of cases people use adjacent

seats, and

in 1/3 of cases the use opposite seats

– simply because of the available

spatial opportunities

We can simulate 2 people sitting at

a 4-seat table: there are 12 cases –

and no more

Seating choice – observation or simulation

So simulation reveals

something that observation

misses:

the real conclusion is

that people choose

seats randomly

Observed facts in isolation are not very revealing ...

There are many questions about change to

working environments that cannot be observed

They are best answered by simulation

For example, with flexible working how many

shared workstations are needed?

In airlines, high utilisation is the core business – not an overhead

profitable bankrupt

– efficient airlines use techniques of yield management

Yield management – balancingsupply and demand to maximise benefit

Consider an airline flight – say, Cambridge to Rotterdam

on Thursday 24 October 2013 – in a plane with a

capacity of 12 seats, with flexible booking

How to keep the cabin full of passengers?

RISK-AVERSE* MANAGEMENTaccept 12 bookings,

but on average 25% of people don’t turn up

– the plane flies with empty seats

9 seats income

3 seats WASTAGE

* there can never be

more passengers than

seats

3 no-shows

12 bookings accepted

capacity on the

flight = 12 seats

9 people fly

= $ loss

How to keep the cabin full of passengers?– overbooking

SIMPLISTIC OVERBOOKINGaccept 16 bookings, allowing for 25%

no-shows

– on average, the plane flies with a full cabin

4 no-shows= maximum

revenue

12 seats income

16 bookings accepted

capacity on the

flight = 12 seats

based on 25% average

no-shows

12 people fly

BUT when you overbook, you don’t know how many people will actually turn up

1. 10 people come: empty seats

= $ loss

2. 12 people come: cabin full – perfect!

3. 14 people come: bumping

= $$$ loss

= maximumrevenue

10 seats income

2 seats WASTAGE

12 seats income

12 seats income

2 seats PENALTY

6 no-shows

4 no-shows

2 no-shows

16 bookings accepted

capacity on the

flight = 12 seats

based on 25% average

no-shows

Optimal overbooking – minimise the combined cost of wastage and queueing

Fine-tuning needs good data – US airlines’ overbooking in 2007:

1 passenger per 1,000 finds there is no seat

90% of those accept compensation

1 passenger per 10,000 is dissatisfied (99.99% satisfaction)

Analysis of overbooking based on –

1. probability of no-shows

2. cost penalty of wastage

3. cost penalty of bumping

wastagepenalty

too little overbooking too much overbooking

co

mb

ined

pen

alt

y c

ost

*optimum

bumpingpenalty

Workstation sharing/overbooking –prudent level of overbooking

Get close to but do not cross the queueing threshold

Use simulation to identify prudent level of overbooking/sharing

Analysis based on –

1. numbers of staff

2. time at the office

3. workstyle at the office

prudent

wastagepenalty

too little overbooking too much overbooking

co

mb

ined

pen

alt

y c

ost

*optimum

queueingpenalty

Observation Simulation

Simplified and clear

Future ‘what-ifs’

Strong explanations

‘Real’ and complicated

Backward looking

Weak explanations

Turning Theory into Practice –

Produce a workable tool that adds value

• Keep it simple

• Key planning parameter is number of workstations

– Total space is derived from this

– Meeting and third space are also calculated

• Forecast of future behaviour

• Inputs need to be simple and readily available

• Outputs need to be numeric and reveal optimum

• Develop a consensus on level of risks

The answer - ST Simulation

• Feeds straightforward data about staff numbers and

workstyles into

• Computer based simulation of workstation use

• Produces individual scenarios, each represents a half-

days attendance

• How many people are in the office?

• How many need a full workstation that period?

2. What percentages of the employees are 'static', 'flexible' and 'mobile'?

A. Static (more than 70% of their working

time at the premises)

B. Flexible (30% - 70% of their working time

at the premises)

The percentages in

C. Mobile (less than 30% of their working

time at the premises)

boxes A, B and C should add to 100%

Input Form I

Input Form II

3. When employees work in the employer's premises, what percentages

are 'territorial', 'task-focused' and 'interaction-focused'?

A. Static B. Flexible C. Mobile Average

(only if individual

not

accessible)

A. Territorial (always work at own workstation)

B. Task-focused (majority of time work at a

desk, minority in social/meeting areas)

C. Interaction-focused (minority of time work

at a desk, majority in social/meeting

The percentages in each column should add to 100%

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

nu

mb

er

of

req

uests

half-days (ranked)

Number of requests for Allocated and Bookable workstations

0

10

20

30

40

50

60n

um

ber

of

req

uests

200 half-days (ranked)

Number of requests for Allocated and Bookable workstations

Proof that it works

• CAR commissioned to apply ST Simulation at 9

units

• 3 GSK sites and single sites at AAT and Mott

MacDonald.

• Sites selected to enable comparison with

observed occupancy

Results

• Demonstrated feasibility of obtaining data from occupier team managers

• The accuracy of current occupancy simulation was higher than expected

– For 7 of the 9 floors studied simulated average utilisation is within 4% of the observed outcome

– One anomaly has exceptionally high ‘in use unoccupied’

– Maximum simulated occupancy is within 1-6 desks of observed outcome

• Simulation of target workgroups suggest workstation reductions of around 15% could be achieved without risking significant queuing

Comparison of Results

Lessons Learned

– 90+% of occupier management were able to provide

the input data easily

– Most effective mechanism was short (10 min approx)

interview with work unit manager

– Explanation of the simplicity of the input task, the

value of the outputs and management commitment

needed

– Without engagement with the business groups, the

results would have varied

Mott MacDonald Case Study

• Aviation Consultancy needed to accommodate growth

in numbers

• Decided to consider flexible working

• Proposal to accommodate 50% increase in staff with

5% increase in workstations

• Concerns about over-occupation

• ST Simulation provided range of scenarios and gave

quantified forecast of risks and definition of tipping-point

• Delivered shared confidence to move to 7:5 sharing

ratio

Benefits to occupying business of

simulation

• The CAR technique can identify opportunities to:

– Target existing workspaces for observation

– Simulate future use of space to identify key ‘tipping points’

• Enhance user acceptance and engagement with agile working initiatives

• Inform dialogue with occupiers by answering their ‘what if’ questions

• Increasing the accuracy of forecasting future needs and resilience eg to increased staff numbers

• Sustainability – reduced CO2 per person

Key Issue

STS can deliver workstation numbers

closer to the optimum than any alternative

Gets users nearest to the ideal office

which is both efficient and

productive!

Questions?

Contact: paulbartlett@sbssol.co.uk

Review of Utilisation Techniques Example: Multi Floor Site, 400 wkstns

1 Generic estimate 2 Existing or future capital investment may be required

Criteria

Observed

Utilisation

(2 wks)

Continuous

Utilisation

Project

Workplace

Sensors

CAR Work

style Model

Type of Study Observational Data Driven ObservationalUser input &

simulations

Cost Up to £10k1 £20k2 £80k2 Less than £5k

Programme 8 weeks 4 weeks 3 weeks 2 weeks

Business

EngagementNo Input No Input No Input

Business input

driven

Frequency of

DataTwo weeks Real Time Real Time Project Based

Scope

Desk

Meeting Rooms

Support Space

Laboratories

Site Data by

Business Group

Workstations

Meeting Rooms

Support Space

Laboratories

Workstations

Forecast

ReportingNo No No Yes

Financial Appraisal

• Cost of typical outer London workstation is approx

£10,000 pa

• Full study of 300 workstation unit would be less than

£4,000 from CAR

• Potential saving from better understanding of optimum

allocation = 1%

• Equates to annual saving of £30,000

• Thus payback better than 2 months