Service Management Managing Capacity

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1 Service Management Managing Capacity Univ.-Prof. Dr.-Ing. Wolfgang Maass Chair in Economics Information and Service Systems (ISS) Saarland University, Saarbrücken, Germany WS 2011/2012 Thursdays, 8:00 10:00 a.m. Room HS 024, B4 1

2 General Agenda 1. Introduction 2. Service Strategy 3. New Service Development (NSD) 4. Service Quality 5. Supporting Facility 6. Forecasting Demand for Services 7. Managing Demand 8. Managing Capacity 9. Managing Waiting Lines 10. Capacity Planning and Queuing Models 11. Services and Information Systems 12. ITIL Service Design 13. IT Service Infrastructures 14. Guest Lecture Dr. Roehn, Deutsche Telekom 15. Summary and Outlook Slide 2

3 Agenda Lecture 8 Overview Strategies for Managing Demand and Capacity Managing Capacity Necessity Scheduling work shifts Increasing customer participation Cross-training employees & Using part-time employees Managing Demand and Capacity: Yield Management Slide 3

4 Overview Strategies for Managing Demand and Capacity Managing service operations Strategy Level capacity: Managing demand Chase demand: Managing capacity 1) Customer-induced variability 2) Segmenting demand & Offering price incentives 4) Scheduling work shifts 5) Increasing customer participation 3) Reservation systems and overbooking 6) Cross-training employees & Using part-time employees 7) Yield Management (Fitzsimmons & Fitzsimmons, 2011) Slide 4

5 Agenda Lecture 8 Overview Strategies for Managing Demand and Capacity Managing Capacity Necessity Scheduling work shifts Increasing customer participation Cross-training employees & Using part-time employees Managing Demand and Capacity: Yield Management Slide 5

6 Managing Capacity: Necessity Service industry: Large variations in demand levels (e.g., in transportation, lodging or restaurant industry) Problem of some service companies: Demand cannot be allocated easily (e.g., 24-hour call center) Demand varies in short-terms Demand varies due to time of day, week or season Demand cannot be adapted to capacity, but capacity needs to be adapted to demand. Financial success depends on management of productive capacity (e.g., personnel and equipment) to use them as efficiently as possible Low demand: Productive capacity is wasted High demand: Demand cannot be met as productive capacity is not Demand needs to be forecasted and capacity adopted accordingly. (Buffa et al., 1976; Lovelock, 1984; Fitzsimmons & Fitzsimmons, 2011) Slide 6

7 Agenda Lecture 8 Overview Strategies for Managing Demand and Capacity Managing Capacity Necessity Scheduling work shifts Increasing customer participation Cross-training employees & Using part-time employees Managing Demand and Capacity: Yield Management Slide 7

8 Managing Capacity: Scheduling work shifts Work shift scheduling: Scheduling employees to adopt to variable demand pattern (e.g., daily, weekly or seasonal variability). Example: 24 h telephone operator Service level: In 89% of the time incoming calls must be answered within 10 seconds. Difficulty: Strong variability of demand Peak volume: 10:30-11:00 A.M. (2560 calls) Minimum volume: 5:30 A.M. (20 calls) Steps of creating a work shift schedule Forecasting demand Convert to operator requirements Schedule shifts Assign operators to shifts Forecasting Demand per day: Forecasted in half-hour intervals including seasonal and weekly variations (lower demand at the weekend and in summer, high demand on Christmas) Exponential smoothing with seasonal adjustment used (e.g., last weeks forecasting error is included) FIGURE 3 Typicat half-hourly call distribution (Bundy D A). CALLS 1 Demand curve calls per minute indicates that the standard deviation is equal to the square ro of the mean, a reasonable, practical test for randomness, and that the arriv rates are described by the Poisson distribution. For the sample of Figure R = calls per minute; SD = 4.85 calls per minute; and = 3.9 Therefore, the variation within the hour is taken as random. This variation ca not be accommodated by planning and scheduling, thus sufficient capacity mu be provided to absorb the random variations. (Buffa et al., 1976) Slide 8

9 Managing Capacity: Scheduling work shifts Convert to operator requirements Number of operators required is calculated per half-hour Schedule shifts Shifts need to be assigned to the demand (adopt to demand line): Computerized Restrictions (shift is 7-8 hours, lunch break, days off, laws) Di = Number of operators needed in period i Wi = Number of operators assigned in period i Objective: Result: List of optimum distribution of shifts & schedule of break during shifts Assign operators to shifts (in exercise) (Buffa et al., 1976) is used to choose shifts at each step. Conceptually, as the schedule of Wi values is built up, the distance is minimized between the demand and operators supplied curves as illustrated by Figure 9. At each stage in building up the schedule, there exists some remaining distance between Di and Wi. The criterion for choice of the next shift is the following test on each alternate shift: add the contributions of the shift to Wi (1 for all working periods and 0 for idle periods such as lunch and rest pauses), and recalculate expression (1). Choose the shift which minimizes (1). In order to negate the preference of the preceding choice rule to favor shorter length shifts, the different shift lengths are weighted in the calculation. The longest shift is given a weight of 1.0, and shorter shifts are weighted by the ratio of the working Total day operator hours must be allocated to the half-hour demand Conventional queuing model is used (including exponential smoothing) FIGURE 9 Result: Number of operators required per half-hour taking into account the response time (demand) Concept of the schedule building process, using the criterion, minimize E : In 5 30 Y P Y 0 a 20 m , DEMAND. b-[ W, AT FINAL STAGE t-93 7-m TIME INTERVALS Slide 9

10 Agenda Lecture 8 Overview Strategies for Managing Demand and Capacity Managing Capacity Necessity Scheduling work shifts Increasing customer participation Cross-training employees & Using part-time employees Managing Demand and Capacity: Yield Management Slide 10

11 Managing Capacity: Increasing Customer Participation Customer participation: Customer participates actively in the service process. Objectives: Cost reduction (less personnel is needed) Customer participates exactly at the time when needed: Capacity becomes more variable, according to demand Example: Fast food restaurant Customer orders himself Customer brings the food to the table Customer cleans the table after the meal Example: Microsoft Fix it Center Tools for easily fixing computer problems (customer support) Based on problem reporting of customers Customers can help themselves Disadvantages: Customer expects quicker service (expects meal to be prepared quickly) Customer expects low prices (compensation for his help) Quality of customers work cannot be controlled by company (e.g., customer can leave his waste on the table) (Fitzsimmons & Fitzsimmons, 2011) Slide 11

12 Brainteaser 10 Minutes Think of 3 examples (besides fast-food restaurant) of customer participation in the service industry. How has the internet influenced customer participation in service processes? Please write your answers down (papers will be collected for discussion in exercise). Slide 12

13 Agenda Lecture 8 Overview Strategies for Managing Demand and Capacity Managing Capacity Necessity Scheduling work shifts Increasing customer participation Cross-training employees & Using part-time employees Managing Demand and Capacity: Yield Management Slide 13

14 Managing Capacity: Cross-training employees & Using part-time employees Cross-training: Training employees to be able to do several different tasks Demand peaks: Each employee performs his specialized work (e.g., cashier in a supermarket) Low demand: Employee performs additional tasks: Job is enlarged (e.g., filling the shelves in a supermarket) Advantages: Capacity (number of employees) can be better adopted to demand: Increased flexibility Job is more varied for employees Using part-time employees: Employing temporary staff When demand peaks can be foreseen: Additional staff can be employed for these times (e.g., lunchtime in restaurants) Skills needed low: Students can be taken (e.g., bakery) Standby personnel: Staff is ready to go to work if additional people are needed during some part of their free time (e.g., nurses in a hospital) Advantage: Increased flexibility Lower costs of personnel (when employing students) (Fitzsimmons & Fitzsimmons, 2011) Slide 14

15 Agenda Lecture 8 Overview Strategies for Managing Demand and Capacity Managing Capacity Necessity Scheduling work shifts Increasing customer participation Cross-training employees & Using part-time employees Managing Demand and Capacity: Yield Management Slide 15

16 Managing Demand and Capacity: Yield Management Definition yield management: ( ) Yield management guides the decision of how to allocate undifferentiated units of capacity to available demand in such a way as to maximize profit or revenue. (Kimes, 1989). Yield management allows the airlines to allocate their fixed capacity of seats in the most profitable manner possible. (Kimes, 1989) ( ) forecast demand and calculate the number of seats to be made available to each fare type, with the goal of maximizing total flight revenues. (Belobaba et al., 1997) Problems of airlines: Airline s inventory (seats) are of perishable nature: Empty seat is opportunity costs (high fixed capacity with high fixed costs). Airlines prefer filling their planes with full-fare customers, but often there are not enough of them (seats would remain empty). Low-fare passengers might take all of the seats and there will not be any left for fullfare passengers who book lately. Solution: Determination of how much of the capacity to sell at what price and to which market segment, especially amount of discount fares. Leaving enough capacity for late-booking full-fare customers (using yield management). First Class Business Class Economy Class (Kimes, 1989, Belobaba et al., 1997) Slide 16

17 Managing Demand and Capacity: Yield Management Airline industry: First industry to apply yield management (yield per available seat mile or yield per revenue passenger mile), Reason: Deregulation of airline industry. Different types of tickets are offered, e.g., full-fare, maxi-savers and supersavers. Seats are sold at different prices within one plane: first, business & economy class Booking early: Low prices might be available. Book last-minute: Often high prices. Objective: Increase the revenue (= profit) by optimizing capacity utilization and pricing. Maximization of the revenue or yield of the firm. Decision of how much of each type of inventory (e.g., seats on an airplane, rooms in a hotel, or cars in a rental car fleet) to allocate to different types of demand. Revenue gains of usually 2-5%. (Kimes, 1989, Belobaba et al., 1997) Slide 17

18 Managing Demand and Capacity: Yield Management Trade-off: Between high capacity utilization and selling seats at maximum price. Especially used in capital-intensive service industries (airlines, hotels, car-rentals & cruise lines) with high fixed costs and much lower variable costs. If fixed costs are covered: Marginal costs of selling another seat are small compared to the marginal revenue. Characteristics of industries convenient for yield management: Relatively fixed capacity (high fixed costs for a hotel & employees) Demand can be segmented into clearly-identified partitions (e.g., business customers vs. tourists in a hotel) Inventory is perishable (e.g., empty seats on a plane) Product is sold some time in advance (e.g., tickets for a cruise) Demand fluctuates strongly (e.g., according to day of the week or season) Marginal sales costs and production costs are low, but capacity costs are high (e.g., costs of a cruising ship & crew are high, but variable costs for 1 additional passenger are low). Managers need to detect the different customer s needs, segment the market accordingly & identify the patterns in demand s fluctuation. Yield management system: Combines seat inventory management with pricing tools and demand forecasting methods. (Kimes, 1989; Lovelock, 1984) Slide 18

19 Managing Demand and Capacity: Yield Management Demand-capacity conditions of service companies: 1. Demand exceeds maximum available capacity: Potential business may be lost (e.g., overbooking of hotels) 2. Demand exceeds the optimum capacity level: No customer is turned away, but quality of delivered service is reduced for all customers (e.g., crowded bus, restaurant where every table is occupied). 3. Demand and supply are well balanced at the level of optimum capacity (optimum, objective that should be reached). 4. Demand is below optimum capacity and resources are under-utilised: Waste of resources (e.g., staff at a car repair shop is bored as there are too few customers). Example: Hotel allocates a certain percentage of its rooms to different customer segments during different seasons (charges multiple prices) Percentage of capacity allocated to different segments First class Standard Budget 30% 50% 10% Peak (30%), e.g., summer 20% 20% 20% 50% 30% 1st shoulder (20%), e.g., spring 30% 50% Off-peak (40%), e.g., winter 50% Percentage of capacity 30% allocated to different seasons 2nd shoulder (10%), e.g., fall (Lovelock, 1984) Slide 19

20 Managing Demand and Capacity: Yield Management Problem of airlines: Often capacity is larger than demand (Q), sold at one single price (P): Traditional fixed price Revenue: PQ Large part of capacity is empty (lost revenue) Large consumer surplus (some people would be willing to pay a higher price: lost revenue) Prices with yield management: Multiple prices Different prices for different segments (P1-P3) Different quantity sold of each price class Q1: Full-fare tickets (purchased at any time, fully refundable) Q2: Advanced purchased tickets (purchased 2 months in advance, non-refundable) Q3: Internet special tickets (e-ticket sold via the web site 1 week in advance, non-refundable) Revenue: P1Q1 + (Q2 Q1)P2 + (Q3 Q2)P3 Customer surplus is reduced, airlines revenue is increased Number of empty seats (lost revenue) is reduced (Kimes, 1989; Fitzsimmons & Fitzsimmons, 2011) P1 P2 P3 Airline revenue Price Consumer surplus P Traditional fixed price Demand curve Airline revenue Lost revenue Seats available (empty) Q Quantity Price Multiple prices (yield management) Demand curve Q1 Consumer surplus Q2 Q3 Lost revenue Seats available Quantity Slide 20

21 Managing Demand and Capacity: Yield Management Example Blackjack Airline: Airline offers two types of ticket at different prices (Ticket 1 special offer, non-refundable at p1: 49$, ticket 2 full-fare at p2: 69$). Number of special offer tickets is supposed to be reduced to keep enough seats for full-fare late-booking passengers. Plane capacity: 95 seats Demand for full-fare tickets: normally distributed, mean (m): 60, standard deviation (SD): 15 x = Number of seats reserved for full-fare passengers d = Demand for full-fare tickets P = Probability Cu = Lost revenue, too few seats were reserved for full-fare passengers (underestimated demand), lost opportunity costs (passenger who would have purchased full-fare ticket at P2 now purchases only a special offer ticket at P1) Cu = p2 p1 = 69$ 49$ = 20$ Co = Costs of reserving too many seats for full-fare tickets (overestimated demand), seats stay empty instead of being sold at special offer price p1 Co = p1 = 49$ P(d>x) = Underestimated demand P(d<x) = Overestimated demand Question: What is the optimum number of seats reserved for full-fare passengers x? (Fitzsimmons & Fitzsimmons, 2011) Slide 21

22 Managing Demand and Capacity: Yield Management Marginal analysis: Critical fractile value for overestimated demand P(d<x) Business Class P(d<x) = 20$/(20$+49$) = 0,29 z-transformation (from normal distribution to standard normal distribution: Given value) m = Mean SD = Standard diviation z-value for P(d<x) = 0,29: z = -0,55 Economy Class Empty full-fare seat (overestimated demand) Seats reserved for full-fare tickets Optimum number of reserved full-fare seats: x = 60 + (-0,55) * 15 = 51 Seat sold at special offer price that could have been sold at fullfare (underestimated demand) Special offer seats (Fitzsimmons & Fitzsimmons, 2011) Slide 22

23 Managing Demand and Capacity: Yield Management Improvement of calculation of optimum number of reserved full-fare seats: Percentage of customer segments is known (e.g., 90% of customers are special offer seekers = shoppers) Some of the passengers are looking for a special offer ticket, but just get a full-fare one 2 values for Co If passenger is a shopper (customer who is lost if special offer is not available, 90%) If passenger is a non-shopper (customer who buys the full-fare ticket if special offer is not available): Airline profits as he buys the expensive ticket, 10%. Co = 0,9 * 49$ - (1-0,9) * (69$ - 49$) = 42,10$ Cu = 20$ (as before) Critical fractile value: P(d<x) = 20$/(20$ - 42,10$) = 0,32 z-value: z = -0,47 Number of reserved full-fare seats: x = 60 + (-0,47) * 15 = 53 (Pfeifer, 1989) Slide 23

24 Outlook 1. Introduction 2. Service Strategy 3. New Service Development (NSD) 4. Service Quality 5. Supporting Facility 6. Forecasting Demand for Services 7. Managing Demand 8. Managing Capacity 9. Managing Waiting Lines 10. Capacity Planning and Queuing Models 11. Services and Information Systems 12. ITIL Service Design 13. IT Service Infrastructures 14. Guest Lecture Dr. Roehn, Deutsche Telekom 15. Summary and Outlook Slide 24

25 Literature Books: Fitzsimmons, J. A. & Fitzsimmons, M. J. (2011), Service Management - Operations, Strategy, Information Technology, McGraw Hill. Papers: Belobaba, P.P. & Wilson, J.L. (1997), Impacts of Yield Management in Competitive Airline Markets, Journal of Air Transport Management, 3(1), pp Buffa, E.S., Cosgrove, M.J. & Luce, B.J. (1976), An Integrated Work Shift Scheduling System, Decision Sciences, 7(4), pp Kimes, S.H. (1989), Yield Management: A Too for Capacity-Constrained Service Firms, Journal of Operations Management, 8(4), pp Lovelock, C.H.(1984), Strategies for Managing Demand in Capacity-Constrained Service Organisations, The Service Industries Journal, 4(3), pp Pfeifer, P.E. (1989), The Airline Discount Fare Allocation Problem, Decision Sciences, 20(1), pp Slide 25

26 Univ.-Prof. Dr.-Ing. Wolfgang Maass Chair in Information and Service Systems Saarland University, Germany Univ.-Prof. Dr.-Ing. Wolfgang Maass

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