Service design om ppt @ DOMS

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Service design om ppt @ DOMS

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Service design om ppt @ DOMS

  1. 1. Service Design
  2. 2. Lecture Outline <ul><li>Service Economy </li></ul><ul><li>Characteristics of Services </li></ul><ul><li>Service Design Process </li></ul><ul><li>Tools for Service Design </li></ul><ul><li>Waiting Line Analysis for Service Improvement </li></ul>
  3. 3. Service Economy
  4. 4. U.S. Economy
  5. 5. Characteristics of Services <ul><li>Services </li></ul><ul><ul><li>acts, deeds, or performances </li></ul></ul><ul><li>Goods </li></ul><ul><ul><li>tangible objects </li></ul></ul><ul><li>Facilitating services </li></ul><ul><ul><li>accompany almost all purchases of goods </li></ul></ul><ul><li>Facilitating goods </li></ul><ul><ul><li>accompany almost all service purchases </li></ul></ul>
  6. 6. Continuum From Goods to Services Source: Adapted from Earl W. Sasser, R.P. Olsen, and D. Daryl Wyckoff, Management of Service Operations (Boston: Allyn Bacon, 1978), p.11.
  7. 7. Characteristics of Services <ul><li>Service are inseparable from delivery </li></ul><ul><li>Services tend to be decentralized and dispersed </li></ul><ul><li>Services are consumed more often than products </li></ul><ul><li>Services can be easily emulated </li></ul><ul><li>Services are intangible </li></ul><ul><li>Service output is variable </li></ul><ul><li>Services have higher customer contact </li></ul><ul><li>Services are perishable </li></ul>
  8. 8. Service Design Process
  9. 9. Service Design Process <ul><li>Service concept </li></ul><ul><ul><li>purpose of a service; it defines target market and customer experience </li></ul></ul><ul><li>Service package </li></ul><ul><ul><li>mixture of physical items, sensual benefits, and psychological benefits </li></ul></ul><ul><li>Service specifications </li></ul><ul><ul><li>performance specifications </li></ul></ul><ul><ul><li>design specifications </li></ul></ul><ul><ul><li>delivery specifications </li></ul></ul>
  10. 10. Service Process Matrix
  11. 11. High vs. Low Contact Services <ul><li>Facility location </li></ul><ul><li>Convenient to customer </li></ul>Design Decision High-Contact Service Low-Contact Service <ul><li>Near labor or transportation source </li></ul><ul><li>Facility layout </li></ul><ul><li>Must look presentable, accommodate customer needs, and facilitate interaction with customer </li></ul><ul><li>Designed for efficiency </li></ul>
  12. 12. High vs. Low Contact Services <ul><li>Quality control </li></ul><ul><li>More variable since customer is involved in process; customer expectations and perceptions of quality may differ; customer present when defects occur </li></ul>Design Decision High-Contact Service Low-Contact Service <ul><li>Measured against established standards; testing and rework possible to correct defects </li></ul><ul><li>Capacity </li></ul><ul><li>Excess capacity required to handle peaks in demand </li></ul><ul><li>Planned for average demand </li></ul>
  13. 13. High vs. Low Contact Services <ul><li>Worker skills </li></ul><ul><li>Must be able to interact well with customers and use judgment in decision making </li></ul>Design Decision High-Contact Service Low-Contact Service <ul><li>Technical skills </li></ul><ul><li>Scheduling </li></ul><ul><li>Must accommodate customer schedule </li></ul><ul><li>Customer concerned only with completion date </li></ul>
  14. 14. High vs. Low Contact Services <ul><li>Service process </li></ul><ul><li>Mostly front-room activities; service may change during delivery in response to customer </li></ul>Design Decision High-Contact Service Low-Contact Service <ul><li>Mostly back-room activities; planned and executed with minimal interference </li></ul><ul><li>Service package </li></ul><ul><li>Varies with customer; includes environment as well as actual service </li></ul><ul><li>Fixed, less extensive </li></ul>
  15. 15. Tools for Service Design <ul><li>Service blueprinting </li></ul><ul><ul><li>line of influence </li></ul></ul><ul><ul><li>line of interaction </li></ul></ul><ul><ul><li>line of visibility </li></ul></ul><ul><ul><li>line of support </li></ul></ul><ul><li>Front-office/Back-office activities </li></ul><ul><li>Servicescapes </li></ul><ul><ul><li>space and function </li></ul></ul><ul><ul><li>ambient conditions </li></ul></ul><ul><ul><li>signs, symbols, and artifacts </li></ul></ul><ul><li>Quantitative techniques </li></ul>
  16. 16. Service Blueprinting
  17. 17. Service Blueprinting
  18. 18. Elements of Waiting Line Analysis <ul><li>Operating characteristics </li></ul><ul><ul><li>average values for characteristics that describe performance of waiting line system </li></ul></ul><ul><li>Queue </li></ul><ul><ul><li>a single waiting line </li></ul></ul><ul><li>Waiting line system </li></ul><ul><ul><li>consists of arrivals, servers, and waiting line structure </li></ul></ul><ul><li>Calling population </li></ul><ul><ul><li>source of customers; infinite or finite </li></ul></ul>
  19. 20. Elements of Waiting Line Analysis <ul><li>Arrival rate (λ) </li></ul><ul><ul><li>frequency at which customers arrive at a waiting line according to a probability distribution, usually Poisson </li></ul></ul><ul><li>Service rate (μ) </li></ul><ul><ul><li>time required to serve a customer, usually described by negative exponential distribution </li></ul></ul><ul><li>Service rate must be higher than arrival rate (λ < μ) </li></ul><ul><li>Queue discipline </li></ul><ul><ul><li>order in which customers are served </li></ul></ul><ul><li>Infinite queue </li></ul><ul><ul><li>can be of any length; length of a finite queue is limited </li></ul></ul>
  20. 21. Elements of Waiting Line Analysis <ul><li>Channels </li></ul><ul><ul><li>number of parallel servers for servicing customers </li></ul></ul><ul><li>Phases </li></ul><ul><ul><li>number of servers in sequence a customer must go through </li></ul></ul>
  21. 22. Operating Characteristics <ul><li>Operating characteristics are assumed to approach a steady state </li></ul>
  22. 23. Traditional Cost Relationships <ul><li>As service improves, cost increases </li></ul>
  23. 24. Psychology of Waiting <ul><li>Waiting rooms </li></ul><ul><ul><li>magazines and newspapers </li></ul></ul><ul><ul><li>televisions </li></ul></ul><ul><li>Bank of America </li></ul><ul><ul><li>mirrors </li></ul></ul><ul><li>Supermarkets </li></ul><ul><ul><li>magazines </li></ul></ul><ul><ul><li>“ impulse purchases” </li></ul></ul>
  24. 25. Psychology of Waiting <ul><li>Preferential treatment </li></ul><ul><ul><li>Grocery stores: express lanes for customers with few purchases </li></ul></ul><ul><ul><li>Airlines/Car rental agencies: special cards available to frequent-users or for an additional fee </li></ul></ul><ul><ul><li>Phone retailers: route calls to more or less experienced salespeople based on customer’s sales history </li></ul></ul><ul><li>Critical service providers </li></ul><ul><ul><li>services of police department, fire department, etc. </li></ul></ul><ul><ul><li>waiting is unacceptable; cost is not important </li></ul></ul>
  25. 26. Waiting Line Models <ul><li>Single-server model </li></ul><ul><ul><li>simplest, most basic waiting line structure </li></ul></ul><ul><li>Frequent variations (all with Poisson arrival rate) </li></ul><ul><ul><li>exponential service times </li></ul></ul><ul><ul><li>general (unknown) distribution of service times </li></ul></ul><ul><ul><li>constant service times </li></ul></ul><ul><ul><li>exponential service times with finite queue </li></ul></ul><ul><ul><li>exponential service times with finite calling population </li></ul></ul>
  26. 27. Basic Single-Server Model <ul><li>Assumptions </li></ul><ul><ul><li>Poisson arrival rate </li></ul></ul><ul><ul><li>exponential service times </li></ul></ul><ul><ul><li>first-come, first-served queue discipline </li></ul></ul><ul><ul><li>infinite queue length </li></ul></ul><ul><ul><li>infinite calling population </li></ul></ul><ul><li>Computations </li></ul><ul><ul><li>λ = mean arrival rate </li></ul></ul><ul><ul><li>μ = mean service rate </li></ul></ul><ul><ul><li>n = number of customers in line </li></ul></ul>
  27. 28. Basic Single-Server Model <ul><li>probability that no customers are in queuing system </li></ul><ul><li>probability of n customers in queuing system </li></ul><ul><li>average number of customers in queuing system </li></ul><ul><li>average number of customers in waiting line </li></ul>( ) P 0 = 1 – λ μ ( ) ( )( ) P n = ∙ P 0 = 1 – λ n λ n λ μ μ μ L = λ μ – λ L q = λ 2 μ (μ – λ)
  28. 29. Basic Single-Server Model <ul><li>average time customer spends in queuing system </li></ul><ul><li>average time customer spends waiting in line </li></ul><ul><li>probability that server is busy and a customer has to wait (utilization factor) </li></ul><ul><li>probability that server is idle and customer can be served </li></ul>1 L μ – λ λ W = = λ μ (μ – λ) W q = λ μ ρ = I = 1 – ρ = 1 – = P 0 λ μ
  29. 30. Basic Single-Server Model Example  = 24  = 30
  30. 31. Basic Single-Server Model Example
  31. 32. Service Improvement Analysis <ul><li>Waiting time (8 min.) is too long </li></ul><ul><ul><li>hire assistant for cashier? </li></ul></ul><ul><ul><ul><li>increased service rate </li></ul></ul></ul><ul><ul><li>hire another cashier? </li></ul></ul><ul><ul><ul><li>reduced arrival rate </li></ul></ul></ul><ul><li>Is improved service worth the cost? </li></ul>
  32. 33. Excel Single-Server Solution D4/(D5-D4) (1/(D5-D4))*60 (D4/D5)*(D5-D4)*60
  33. 34. Advanced Single-Server Models <ul><li>Constant service times </li></ul><ul><ul><li>occur most often when automated equipment or machinery performs service </li></ul></ul><ul><li>Finite queue lengths </li></ul><ul><ul><li>occur when there is a physical limitation to length of waiting line </li></ul></ul><ul><li>Finite calling population </li></ul><ul><ul><li>number of “customers” that can arrive is limited </li></ul></ul>
  34. 35. Advanced Single-Server Models
  35. 36. Advanced Single-Server Model Probability of zero customers
  36. 37. Basic Multiple-Server Model <ul><li>Single waiting line and service facility with several independent servers in parallel </li></ul><ul><li>Same assumptions as single-server model </li></ul><ul><li>s μ > λ </li></ul><ul><ul><li>s = number of servers </li></ul></ul><ul><ul><li>servers must be able to serve customers faster than they arrive </li></ul></ul>
  37. 38. Basic Multiple-Server Model <ul><li>probability that there are no customers in system </li></ul><ul><li>probability of n customers in system </li></ul>1 λ n s!s n – s μ P 0 , for n ≤ s ∑ + 1 λ n n! μ ( ) { ( ) P 0 , for n > s P n = 1 λ n 1 λ s sμ n! μ s! μ sμ - λ ( ) ( )( ) n = s – 1 n = 0 1 P 0 =
  38. 39. Basic Multiple-Server Model <ul><li>probability that customer must wait </li></ul>( ) 1 λ s sμ s! μ sμ – λ P w = P 0 λμ (λ/μ) s λ (s – 1)! (sμ – λ) 2 μ L = P 0 + L λ W = L q = L – λ μ 1 L q μ λ W q = W – = ρ = λ sμ
  39. 40. Basic Multiple-Server Model Example <ul><li>Three-server system </li></ul>
  40. 41. Basic Multiple-Server Model Example
  41. 42. Basic Multiple-Server Model Example
  42. 43. Basic Multiple-Server Model Example <ul><li>To cut waiting time, add another service rep </li></ul><ul><li>Four-server System </li></ul>
  43. 44. Multiple-Server Waiting Line in Excel
  44. 45. Multiple-Server Waiting Line in Excel

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