Managing Capacity and Demand Learning Objectives Describe the strategies for matching capacity and demand for services. Recommend an overbooking strategy. Use Linear Programming to prepare a weekly workshift schedule. Prepare a work schedule for part-time employees. Use yield management. Strategies for Matching Supply and Demand for Services DEMAND STRATEGIES Developing complementary services Developing reservation systems SUPPLY STRATEGIES Partitioning demand Sharing capacity Establishing price incentives Crosstraining employees Promoting off-peak demand Using part-time employees Yield management Increasing customer participation Scheduling work shifts Creating adjustable capacity Segmenting Demand at a Health Clinic Percentage of average daily physician visits 140 130 120 Smoothing Demand by Appointment Scheduling 110 Day Appointments 100 Monday Tuesday Wednesday Thursday Friday 90 80 70 60 1 2 3 Day of week 4 5 84 89 124 129 114 Discriminatory Pricing for Camping Experience type 1 2 3 4 Days and weeks of camping season Saturdays and Sundays of weeks 10 to 15, plus Dominion Day and civic holidays Saturdays and Sundays of weeks 3 to 9 and 15 to 19, plus Victoria Day Fridays of weeks 3 to 15, plus all other days of weeks 9 to 15 that are not in experience type 1 or 2 Rest of camping season No. of days 14 Daily fee $6.00 23 2.50 43 0.50 78 free EXISTING REVENUE VS PROJECTED REVENUE FROM DISCRIMINATORY PRICING Experience type 1 2 3 4 Total Existing flat fee of $2.50 Campsites occupied Revenue 5.891 $14,727 8,978 22,445 6,129 15,322 4,979 12,447 25,977 $ 64,941 Discriminatory fee Campsites occupied (est.) Revenue 5,000 $30,000 8,500 21,250 15,500 7.750 …. …. 29,000 $59,000 Hotel Overbooking Loss Table Number of Reservations Overbooked NoProbshows ability 0 .07 1 .19 2 .22 3 .16 4 .12 5 .10 6 .07 7 .04 8 .02 9 .01 Expected loss, $ 0 0 40 80 120 160 200 240 280 320 360 121.60 1 2 3 4 5 100 200 300 400 500 0 100 200 300 400 40 0 100 200 300 80 40 0 100 200 120 80 40 0 100 160 120 80 40 0 200 160 120 80 40 240 200 160 120 80 280 240 200 160 120 320 280 240 200 160 91.40 87.80 115.00 164.60 231.00 6 7 8 9 600 700 800 900 500 600 700 800 400 500 600 700 300 400 500 600 200 300 400 500 100 200 300 400 0 100 200 300 40 0 100 200 80 40 0 100 120 80 40 0 311.40 401.60 497.40 560.00 Daily Scheduling of Telephone Operator Workshifts 2500 30 Number of operators Topline profile Calls 2000 1500 1000 500 0 12 2 4 6 8 10 12 Time 2 4 6 8 10 12 25 20 Scheduler program assigns tours so that the number of operators present each half hour adds up to the number 15 10 required 5 012 Tour 2 4 6 8 10 12 Time 2 4 6 8 10 12 LP Model for Weekly Workshift Schedule with Two Days-off Constraint Objective function: Minimize x1 + x2 + x3 + x4 + x5 + x6 + x7 Constraints: Sunday Monday Tuesday Wednesday Thursday Friday Saturday Operator 1 2 3 4 5 6 7 8 Total Required Excess Su x … … … … … … x 6 3 3 x2 + x3 + x4 + x5 + x6 x3 + x4 + x5 + x6 + x7 x1 + x4 + x5 + x6 + x7 x1 + x2 + x5 + x6 + x7 x1 + x2 + x3 + x6 + x7 x1 + x2 + x3 + x4 + x7 x1 + x2 + x3 + x4 + x5 xi 0 and integer 3 6 5 6 5 5 5 Schedule matrix, x = day off M Tu W Th x … … … x x … … ... x x … ... x x … … … … x … … … x … … … x … … … … 6 5 6 5 6 5 6 5 0 0 0 0 F … … … … x x x … 5 5 0 Sa ... … … … … … … x 7 5 2 Tellers required 5 6 2 3 4 7 Scheduling Part-time Bank Tellers Tellers required 0 1 2 3 4 5 Decreasing part-time teller demand histogram 0 1 Two Full-time Tellers Mon. Tues. Object ive Minimize Wed. Thurs. 5 4 3 2 1 4 3 2 1 1 5 2 Fri. Mon. Wed. Thurs Fri. funct io n: x1 + Co nst raint s: Sunday Mo nday x2 +x3 +x4 +x5 +x6 +x7 x2 +x3 +x4 +x5 +x6 x3 +x4 +x5 +x6 +x7 b1 b2 DAILY PART-TIME WORK SCHEDULE, X=workday Teller 1 2 3,4 5 Mon. x x x …. Tues. …. …. …. …. Wed. x …. …. x Thurs. …. x …. …. Fri. x x x x Tues. Ideal Characteristics for Yield Management Relatively Fixed Capacity Ability to Segment Markets Perishable Inventory Product Sold in Advance Fluctuating Demand Low Marginal Sales Cost and High Capacity Change Cost Percentage of capacity allocated to different service classes Seasonal Allocation of Rooms by Service Class for Resort Hotel First class 30% 20% 50% Standard 20% 30% 20% 50% 60% Budget 10% Peak (30%) Summer 30% Shoulder (20%) Fall 50% 30% Off-peak (40%) Winter Shoulder (10%) Spring Percentage of capacity allocated to different seasons Demand Control Chart for a Hotel 300 Expected Reservation Accumulation 2 standard deviation control limits 200 150 100 50 Days before arrival 86 81 76 71 66 61 56 51 46 41 36 31 26 21 16 11 6 0 1 Reservations 250 Yield Management Using the Critical Fractile Model Cu ( F D) P(d x ) Cu Co p F Where x = seats reserved for full-fare passengers d = demand for full-fare tickets p = proportion of economizing (discount) passengers Cu = lost revenue associated with reserving one too few seats at full fare (underestimating demand). The lost opportunity is the difference between the fares (F-D) assuming a passenger, willing to pay full-fare (F), purchased a seat at the discount (D) price. Co = cost of reserving one to many seats for sale at full-fare (overestimating demand). Assume the empty full-fare seat would have been sold at the discount price. However, Co takes on two values, depending on the buying behavior of the passenger who would have purchased the seat if not reserved for full-fare. if an economizing passenger D Co ( F D) if a full fare passenger (marginal gain) Expected value of Co = pD-(1-p)(F-D) = pF - (F-D) Topics for Discussion What organizational problems can arise from the use of part-time employees? How can computer-based reservation systems increase service capacity utilization? What possible dangers are associated with developing complementary services? Will the widespread use of yield management eventually erode the concept of fixed prices? What possible negative effects can yield management have on customer relations? Interactive Exercise Watch the PowerPoint presentation concerning the overbooking experience at the Doubletree Hotel in Houston, Texas. How could this situation been handled differently?