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Service design om ppt @ DOMS

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Service design om ppt @ DOMS

Service design om ppt @ DOMS

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