Stimulating Work Patterns


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Presentation by Paul Bartlett and William Fawcett, Cambridge Architectural Research
at the Workplace Trends Conference 2013, Royal College of General Practitioners, London

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Stimulating Work Patterns

  1. 1. Simulating Work Patterns – understanding instead of observation William Fawcett and Paul Bartlett Cambridge Architectural Research Ltd Workplace Trends 24 October 2013
  2. 2. Content • The problem • The theory • Solution • Proving it works • Business benefits
  3. 3. Background Economic Drivers • Global competition increasing • Unrelenting pressure to reduce business costs
  4. 4. 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)
  5. 5. Consequences for property • Expectation of lowering accommodation cost per employee • Limited scope for reduced space allocation per workstation
  6. 6. 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
  7. 7. Empty is bad ...
  8. 8. ... and congested is bad ... where is the
  9. 9. How do we understand the workplace – observation or simulation? PLATT, S. (2008) Graduate Accommodation Survey, Cambridge Architectural Research Ltd.
  10. 10. Using space to ‘nudge’ behaviour – settings for interaction –
  11. 11. 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
  12. 12. Seating choice – observation THEREFORE, you confidently conclude from the observations: people prefer sitting at adjacent seats
  13. 13. 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
  14. 14. 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 In 2/3 of cases people use adjacent seats
  15. 15. 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 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
  16. 16. Seating choice – observation or simulation So simulation reveals something that observation misses: the real conclusion is that people choose seats randomly
  17. 17. 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?
  18. 18. In airlines, high utilisation is the core business – not an overhead profitable bankrupt – efficient airlines use techniques of yield management
  19. 19. Yield management – balancing supply 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
  20. 20. How to keep the cabin full of passengers? capacity on the flight = 12 seats RISK-AVERSE* MANAGEMENT accept 12 bookings, but on average 25% of people don’t turn up – the plane flies with empty seats 9 people fly 9 seats income 3 seats WASTAGE 3 no-shows 12 bookings accepted * there can never be more passengers than seats = $ loss
  21. 21. How to keep the cabin full of passengers? – overbooking capacity on the flight = 12 seats SIMPLISTIC OVERBOOKING accept 16 bookings, allowing for 25% no-shows – on average, the plane flies with a full cabin 12 people fly 12 seats income 4 no-shows 16 bookings accepted based on 25% average no-shows = maximum revenue
  22. 22. BUT when you overbook, you don’t know how many people will actually turn up capacity on the flight = 12 seats 1. 10 people come: empty seats 6 no-shows 10 seats income 2 seats WASTAGE = $ loss 2. 12 people come: cabin full – perfect! 16 bookings accepted 4 no-shows 12 seats income = maximum revenue 3. 14 people come: bumping based on 25% average no-shows 2 no-shows 12 seats income 2 seats PENALTY = $$$ loss
  23. 23. combined penalty cost Optimal overbooking – minimise the combined cost of wastage and queueing bumping penalty Analysis of overbooking based on – 1. probability of no-shows 2. cost penalty of wastage 3. cost penalty of bumping wastage penalty too little overbooking * too much overbooking optimum 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)
  24. 24. combined penalty cost Workstation sharing/overbooking – prudent level of overbooking queueing penalty Analysis based on – 1. numbers of staff 2. time at the office 3. workstyle at the office wastage penalty too little overbooking prudent * too much overbooking optimum Get close to but do not cross the queueing threshold Use simulation to identify prudent level of overbooking/sharing
  25. 25. Observation Simulation ‘Real’ and complicated Backward looking Weak explanations Simplified and clear Future ‘what-ifs’ Strong explanations
  26. 26. 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
  27. 27. 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 halfdays attendance • How many people are in the office? • How many need a full workstation that period?
  28. 28. Input Form I 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) C. Mobile (less than 30% of their working time at the premises) The percentages in boxes A, B and C should add to 100%
  29. 29. 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%
  30. 30. Number of requests for Allocated and Bookable workstations 60 number of requests 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 half-days (ranked)
  31. 31. Number of requests for Allocated and Bookable workstations 60 number of requests 50 40 30 20 10 0 200 half-days (ranked)
  32. 32. Proof that it works • CAR commissioned to apply ST Simulation at 7 units • 3 GSK sites and single sites at AAT and Mott MacDonald. • Sites selected to enable comparison with observed occupancy
  33. 33. 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
  34. 34. Comparison of Results
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 38. 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!
  39. 39. Questions? Contact:
  40. 40. Review of Utilisation Techniques Example: Multi Floor Site, 400 wkstns Observed Utilisation (2 wks) Continuous Utilisation Project Workplace Sensors CAR Work style Model Observational Data Driven Observational User input & simulations Up to £10k1 £20k2 £80k2 Less than £5k Programme 8 weeks 4 weeks 3 weeks 2 weeks Business Engagement No Input No Input No Input Business input driven Frequency of Data Two weeks Real Time Real Time Project Based Desk Meeting Rooms Support Space Laboratories Site Data by Business Group Workstations Meeting Rooms Support Space Laboratories Workstations No No No Yes Criteria Type of Study Cost Scope Forecast Reporting 1 Generic estimate 2 Existing or future capital investment may be required
  41. 41. 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