Chapter 18 - Management of Waiting Lines.pptx

Production and Operations Management

1. CHAPTER 18: MANAGEMENT OF WAITING LINES JOHANAH P. ALISEN MBA-104

2. LO18.1 What imbalance does the existence of a waiting line reveal? LO18.2 LO18.3 LO18.4 LO18.5 What causes waiting line to form, and why is it possible to eliminate them completely? What metrics are used to help managers analyze waiting lines? What are some psychological approaches to managing waiting lines and why might a manager want to use them? What very important lesson does the constant service time model provides for managers? LEARNING OBJECTIVES:

3. The action of staying where one is or delaying action until a particular time or until something else happens. WHAT IS WAITING? In operations management, the wait time is the period of time in which a work item, like a document, is waiting for further processing. Waiting may be caused by a person or machine required for the process that is working at full capacity.

4. Occurs when there is temporary imbalance between supply (capacity) and demand. 01 02 One of the seven waste in lean system. 03 04 Non-value added occurrences. -Add to the cost of operation and reflect negatively on customer service - Commonly found whenever customers arrived randomly for service WAITING LINES

5. ¨FOR CUSTOMERS can be acceptable (short waiting) can be annoying (long wait) can be a matter of life and death (emergencies) ¨FOR BUSINESS ­cost of waiting comes from low productivity leads to a competitive disadvantage ¨FOR SOCIETY costs are wasted resources leads to reduced in quality of life Waiting affects different sectors

6. Take note that: Reducing waiting lines both benefit the customers and the company From a managerial perspective, the key is to determine the balance that will provide an adequate level of service at a reasonable cost.

7. Designers must weigh the cost of providing a given level of service capacity against the potential (implicit) cost of having customers wait for service. This planning and analysis of service capacity frequently lend itself to queuing theory. mathematical approach to the analysis of waiting lines directly applicable to a wide range of service operations including call centers, banks, post offices, restaurants, theme parks, telecommunications systems, and traffic management. used for planning and analysis of service capacity rely on the use of formulas and tables it is important to assess customer satisfaction QUEUING THEORY

8. How does queuing theory starts? Is based on studies about automatic dialing equipment made in the early part of the 20th century by Danish telephone engineer A.K. Erlang. He used queuing theory to determine how many phone lines (no cellphones in those days) and operators companies needed to provide adequate service. After World War II, few attempts were made to apply queuing theory to business problem.

9. Waiting lines are commonly found whenever customers arrive randomly for services. Some examples of waiting lines we encounter in our daily lives include: Lines at supermarket checkouts Fast-food restaurants Airport ticket counters Theaters Post offices Toll booths In many situations, the “customers” are not people but orders waiting to be filled, trucks waiting to be unloaded, jobs waiting to be processed, or equipment waiting for repairs.

10. Why is there waiting? Or why do we need to wait? The keyword is average. Waiting lines tend to form even when a system is not fully loaded. Variability Arrival and service rates are variable. And because services cannot be completed ahead of time and stored for later used, the system becomes temporarily overloaded, giving the rise of waiting lines.

11. MANAGERIAL IMPLICATIONS OF WAITING LINES Cost to provide waiting space. Possible loss of business: should customers leave the line before being served or refuse to wait at all. Possible loss of goodwill. Possible reduction in customer’s satisfaction. Resulting congestion that may disrupt other business operations or customers.

12. Manager Marketing Business Head Manager QUEUING SYSTEM customers enter a waiting line of a service facility, receive service when their turn comes, and then leave the system.

13. GOAL OF WAITING LINE MANAGEMENT minimize the sum of two costs; customer waiting cost and service capacity cost Capacity Cost are the costs of maintaining the ability to provide service Customer Waiting Cost includes salaries paid to employees while they wait for the service cost of the space for waiting any loss of business due to customers refusing to wait possibly going to others in the future Total Cost sum of capacity cost and customer waiting cost TC = customer waiting cost + capacity cost

14. as the capacity increases, its cost increases (upward slope) as capacity increases, the number of customers waiting and the time they wait tend to decrease, thus decreasing waiting cost (downward slope) total cost can be represented as a u-shape curve

15. GOAL OF WAITING LINE MANAGEMENT In situations where those waiting in line are external customers (as opposed to employees), the existence of waiting lines can reflect negatively on an organization’s quality image. Consequently, some organizations are focusing their attention on providing faster service. This is through speeding up the rate at which service is delivered rather than merely increasing the number of servers. The effect of this is to shift the total cost curve downward if the cost of customer waiting decreases by more than the cost of the faster service.

16. CHARACTERISTICS OF WAITING LINES There are numerous queuing models from which an analyst can choose. Naturally, much of the success of the analysis will depend on choosing an appropriate model. Model choice is affected by the characteristics of the system under investigation. The main characteristics are: Population Source Number of Servers (channels) Arrival and service patterns Queue discipline (order of service)

17. SIMPLE QUEUING SYSTEMS

18. CHARACTERISTICS OF WAITING LINES 1. POPULATION SOURCE Approach to use in analyzing a queuing problem depends on whether the potential number of customers is limited. ­ Infinite source situation- customer arrivals are unrestricted ­ Finite source situation- the number of potential customers is limited 2. NUMBER OF SERVERS (CHANNELS) Channel- a server in a service system ­Single Channel System- a group of servers working together as a team ­Multiple Channel System- more than one server

19. FOUR COMMON VARIATIONS OF QUEUING SYSTEMS

20. ARRIVAL AND SERVICE PATTERS Remember, waiting lines are a direct result of arrival and service variability. They occur because random, highly variable arrival and service patterns cause systems to be temporarily overload. In many instances, the variability can be described by theoretical distribution by: ­Poisson Distribution- often provides a reasonably good description of customer arrivals per unit of time ­ Negative Exponential Distribution- often provides a reasonably good description of customer service times

21. POISSON ARRIVALS AND EXPONENTIAL SERVICE TIMES

22. POISSON ARRIVALS AND EXPONENTIAL SERVICE TIMES

23. RESEARCH HAS SHOWN THAT THESE ASSUMPTIONS ARE OFTEN APPROPRIATE FOR CUSTOMER ARRIVALS BUT LESS LIKELY TO BE APPROPRIATE FOR SERVICE. ¨ In these situations where the assumptions are not reasonably satisfied, the alternatives would be to: 1. to develop a more suitable model 2. search for a better and usually more complex existing model 3. or to resort to computer simulation OTHER POSSIBILITIES CUSTOMERS CAN DO: 1. waiting customers grow impatient and leave the line (reneging) 2. customers switch to another line (jockeying) 3. upon arriving, customers decide the line is too long and therefore, do not enter the line (balking)

24. CHARACTERISTICS OF WAITING LINES QUEUE DISCIPLINE Refers to the order in which customers are processed. First come first served basis - most commonly encountered rule. There is a first-come service at banks, stores, theaters, restaurants, four-way stop signs, and registration lines.

25. The average number of customers waiting, either in line or in the system. The average time customers wait, either in line or in the system. System utilization, which refers to the percentage of capacity utilized. The implied cost of a given level of capacity and its related waiting line. The probability that an arrival will have to wait for service. MEASURES OF WAITING LINE PERFORMANCE

26. MEASURES OF WAITING LINE PERFORMANCE The implication is that under normal circumstances, 100 percent utilization is not a realistic goal. Even if it were, 100 percent of service personnel is not good; they need some slack time. Thus, instead, the operations manager should try to achieve a system that minimizes the sum of waiting costs and capacity costs.

27. the models pertain to a system operating under steady state conditions ; that is, they assume the average arrival and service rates are stable. (e.g., the opening rush at a store is over). QUEUING MODELS: INFINITE- SOURCE FOUR MODELS DESCRIBED ARE: Single server, exponential service time Single server, constant service time Multiple server, exponential service time Multiple priority service, exponential service time

28. QUEUING MODELS: INIFINITE SOURCE SYMBOLS

29. BASIC RELATIONSHIPS System Utilization: degree to which any part of the service system is occupied by an arrival.

30. BASIC RELATIONSHIPS

31. BASIC RELATIONSHIPS

32. EXAMPLE

33. SOLUTION

34. the simplest model involves a system that has one server or a single crew. the queue discipline is first come first served basis SINGLE SERVER, EXPONENTIAL SERVICE TIME

35. EXAMPLE

36. SOLUTION

37. SINGLE SERVER, CONSTANT SERVICE TIME Waiting lines are a consequence of random, highly variable arrival and service rates. If a system can reduce or eliminate the variability of either or both, it can shorten waiting lines noticeably. A case in point is a system with constant service time. The effect of a constant service time is to cut in half the average number of customers waiting in line.

38. EXAMPLE SOLUTION

39. MULTIPLE SERVER, EXPONENTIAL SERVICE TIME exists whenever two or more servers are working independently to provide service to customer arrivals. use of model involves the following assumptions: 1. a poisson arrival rate and exponential service time 2. servers all work at the same average rate 3. customers form a single waiting line

40. TABLE 18.3 MULTIPLE SERVER QUEUING FORMULA

41. EXAMPLE

42. SOLUTION

43. COST ANALYSIS The design of a service system often reflects the desire of management to balance the cost of capacity with the expected cost of customers waiting in the system. C omputing system costs- simplest approach to a cost analysis

44. COST ANALYSIS

45. EXAMPLE SOLUTION

46. amount of space to allocate for waiting lines with an infinite population source, the waiting line can become infinitely long. This implies that no matter how much space is allocated for waiting lines, one can never be completely sure that the space requirements won’t exceed that amount. MAXIMUM LINE LENGTH

47. MAXIMUM LINE LENGTH FORMULA

48. EXAMPLE SOLUTION

49. MULTIPLE PRIORITIES Customers are processed according to some measure of importance Useful for describing customer waiting time. Arriving customers are assigned to one of several priority classes or categories according to a predetermined assignment method Customers are then processed by class, highest class first. Within each class, processing is first come first served basis

50. To increase the number of servers Attempt to increase the service rate, say by introducing new methods if any of the waiting time computed is deemed too long, there are two options: REVISING PRIORITIES

51. QUEING MODEL: FINITE SOURCE is appropriate for cases in which the calling population is limited to a relatively small number of potential calls arrival rates are required to be Poisson and service times exponential For instance, one (1) person maybe responsible for handling breakdowns on 15 mins; thus, the size of the calling population is 15. However, there may be more than 1 server or channel. For example, due to a backlog of machines awaiting repairs, the manager might authorize an additional person to work on repairs.

52. EXAMPLE SOLUTION Suppose that operators are paid $10 per hour, and machine downtime costs $16 per hour. Should the department add another operator if the goal is cost optimization? M- N umber of server/s N- Number of potential customers J- Average number of customers not in line or in service

53. Use temporary workers Using temporary or part-time workers during busy periods may be possible. Shift demand The variable pricing strategies can be effective in smoothing demand more evenly on the system. S tandardized the service The more service can be standardized, the greater the impact on the waiting line. L ook for a bottleneck One aspect of the process may be largely responsible for a slow service rate. CONSTRAINT MANAGEMENT

54. THE PSYCHOLOGY OF WAITING Steps can be taken in certain situations that make the situation more acceptable to those whose in waiting line. If those waiting in line have nothing else to occupy their thoughts, they often tend to focus on the fact that they are waiting in line and they usually perceive the waiting time to be longer than the actual waiting time. If something else occupies them while they wait, their perception of waiting time is often less than their actual waiting time.

55. Consider the possibility of reducing variability in processing times by increasing the degree of standardization of the service being provided. E ffort to shift some arrivals to “off times” by using a reservation system, early bird specials, senior discounts, etc. OPERATIONS STRATEGY

56. Analysis of waiting lines can be an important aspect of the design of service systems. Waiting lines have a tendency to form in such systems even when, in a macro sense, the system is underloaded. The arrival of customers at random times and variability of service times combine to create temporary overloads. When this happens, waiting lines appear. By the same token, at other times the servers are idle. A major consideration in the analysis of queuing systems is whether the number of potential customers is limited (finite source) or whether entry to the system is unrestricted (infinite source). Five basic queuing models are described, four dealing with infinite-source populations and one dealing with finite-source populations. In general, the models assume that customer arrival rates can be described by a Poisson distribution and that service time can be described by a negative exponential distribution. SUMMARY

57. CHOOSING THE APPROPRIATE MODEL Infinite-Source Model. Use when entry to the system is unrestricted (open to the public). The basic relationship formulas can be used with any infinite-source model. There are formulas for system utilization, the average number or average time waiting for service, the average number of being served, and the average number or time in the system. Single-channel model: Use when there is one server, team, or crew. Multiple-channel model. Use when there are two or more independent servers, teams, or crews. Multiple-priority model. Use when service order is based on priority class. Finite-Source Model. Use when entry to the system is restricted to system members.

58. Waiting line occurs when there is an imbalance between supply and demand in service system. One cause of imbalances is variability in service times and or customer arrival times. Two important approaches to managing lines are reducing variability where possible by standardizing a process and altering the perceived waiting time .

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