workplace trends 2013: paul bartlett & william fawcett, simulating work patterns
DESCRIPTION
Techniques of computer-based mathematical simulation and yield management, taken from the airline and hotel industries, have been applied to workspace forecasting. Simple data about staff and workstyle patterns can generate a forecast of average and peak workstation use. This presentation will describe the approach developed by Cambridge Architectural Research and the field study results that validate their ‘Space Time Simulation’ of desk occupancy. It will show how simulation can estimate the risk of overcrowding and can engage occupant management in exploring ‘what if’ questions about future workplace change. The presenters will argue that simulation has advantages over observed occupancy studies and can improve the efficiency and effectiveness of workplace planning.TRANSCRIPT
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: [email protected]
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