Managing Capacity and Shin Ming Guo Demand NKFUST Managing - PDF document

Managing Capacity and Shin ‐ Ming Guo Demand NKFUST • Managing dynamic demand • Service capacity is perishable • Yield Management Case: Disneyland Paris  Established in 1992  Overestimate the demand  Too many hotel rooms lead to high operating cost  Need more space for bus parking  Unbalanced workforce scheduling 1

Service Capacity Capacity: amount of output over a period of time  Participation: Need to be near customers  Simultaneity: Inability to transport services  Perishability: Inability to store services  Heterogeneity: Volatility of demand Often use resource input to measure capacity Focus: Matching Capacity with Demand • Demand can vary and is unpredictable. • Capacity is inflexible and maybe costly. • Demand < Capacity  Impossible to stock service • Demand > Capacity  Customers may not wait for service 4 2

Economic Consequences of Mismatch Air travel Emergency Room Retailing Supply Seats on specific Medical service Consumer flight electronics Travel for specific Urgent need for Kids buying video Demand time & destination medical service games Supply Empty seat Doctors, nurses, High inventory Exceeds and infrastructure costs Demand are under ‐ utilized Overbooking; Demand Crowding and delays Foregone profit; Profit loss Exceeds in the ER, Deaths Consumer Supply dissatisfaction 5 Capacity Utilization vs. Service Quality Optimal operating level  70% of Design capacity 3

Matching Supply and Demand for Services DEMAND Capacity Strategies Strategies 7 Increasing 2 Partitioning customer demand 8 1 Managing participation Franchising Variability 4 Promoting 9 Cross ‐ Off Peak training 3 Differential 11 Demand employees Pricing Scheduling 6 Developing 10 Using work shifts reservation part ‐ time 5 Developing systems employees complementary services 12 Yield Management 7 1. Managing Customer-induced Variability Type of Accommodation Reduction Variability Arrival Provide generous staffing Require reservations Capability Adapt to customer skill Target customers based on levels capability Request Cross ‐ train employees Limit service breadth Effort Do work for customers Reward increased effort Subjective Diagnose expectations Persuade customers to adjust Preference and adapt expectations 8 4

2. Segmenting Demand Too many walk ‐ in patients on Mondays at a health clinic. 140 Smoothing Demand by Appointment 120 Scheduling 100 Day Appointments Before 80 Smoothing 60 Monday 84 After Smoothing Tuesday 89 40 Wednesday 124 20 Thursday 129 Friday 114 0 Mon. Tue. Wed. Thur. Fri. 9 3. Differential Pricing 10 5

4. Promoting Off-Peak Demand  Different sources of demand Hotel: conventions for business or professional groups during the off ‐ season.  Avoid waiting times Department store: shop early and avoid the rush. 5. Developing Complementary Services • A new service is the complementor if customers value your service more when they already have purchased the existing service. • Movie theaters offer popcorns and soft drinks. • A new service is the complementor if it results in a more uniform demand. • Restaurants offer the “afternoon tea” service. • Travel agency: Australia and New Zealand Tours 12 6

6. Reservation and Overbooking • Taking reservations is like preselling the service. • Reservations may benefit consumers by reducing waiting and guarantee service availability. • Approximately 50% of reservations get cancelled. • Multiple reservations, late arrivals, no ‐ shows. • Customers can cancel or postpone reservations— with a penalty • Airlines and hotels can overbook reservations— with a penalty 13 Overbooking to Protect Revenue Overbooking—accept more reservations than supply Example: On average there would be 10 cancellations or no ‐ shows. So the hotel can accept 10 more reservations. Too much overbooking: some customers may have to be denied a seat even though they have a confirmed reservation. Too little overbooking: waste of capacity, loss of revenue 14 7

Example: Surfside Hotel expected number of no ‐ shows = 0(0.07)+1(0.19)+…+9(0.01)=3.04 Expected opportunity loss = 3.04 × $40 = $121.60 15 Cost of too many overbooking: C o =$100 for accommodation at some other hotel and additional compensation. Cost of not enough overbooking: C u =$40 per room. 16 8

Overbooking Solution C 40   u 0 . 286 • Critical ratio   C C 40 100 u o • Find x such that x is the largest number that satisfies P(number of no ‐ shows < x) ≤ 0.286 • Optimal number of overbooking = 2 • There is about a 26% chance that the hotel will have more customers than rooms. 17 Strategies for Managing Capacity 18 9

7. Customer Participation Customer participates actively in the service process. Objectives: • Cost reduction (less personnel is needed) • Capacity becomes more “variable”, according to demand Disadvantages: • Customer expects quicker service • 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) 19 8. Franchising Benefits to the Franchisor Less financial investment Quick expansion to other markets Economies of Scale Problems Franchisee does not receive proper training Franchisee fails to follow the contract or regulations Franchisor does not have new product development Franchisor fails to provide support 10

Economies of Scale for Service Industry • Chain stores lead to buying power. • Travel agency buy airline tickets and hotel rooms in bulks to get deeper discount. • Small business can form an alliance to increase the bargaining power against big suppliers.  Competing retail stores or restaurants located in the same area may attract more consumers.  Commuter cleaning  Economies of scale may hurt service quality 9. Cross-training & Part-time Employees Training employees to be able to do 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) Using part ‐ time employees • 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) 11

10. Adjustable Capacity • Airlines: Different aircrafts • Rental Cars: ability to move cars around. Workshift Scheduling • The peak to valley variation is 125 to 1. • Carefully schedule the workforce so that the required service level can be maintained with the minimal cost. 23 Convert Demand and Schedule Shifts 24 12

Scheduling Consecutive Days Off Mon Tue Wed Thu Fri Sat Sun forecast 4 3 2 4 3 1 2 4 3 2 4 3 1 2 A 3 2 1 3 2 1 2 B 2 1 0 2 2 1 1 C 1 1 0 1 1 0 0 D 25 12. Revenue Management • Return = Revenue – Operations Cost = Throughput  Price – Fixed Costs –Throughput  Variable Costs – Reduce fixed costs – Reduce variable costs – Increase price – Increase throughput • If capacity is fixed and perishable, fixed costs are high and variable costs are low, increasing price and/or throughput to improve profitability. 26 13

Case: Increase Revenue with Fixed Capacity • The Park Hyatt Philadelphia, 118 King/Queen rooms. • Regular fare is r H = $225 (high fare) targeting business travelers. • Hyatt offers a r L = $159 (low fare) discount fare for a mid ‐ week stay targeting leisure travelers. • Demand for low fare rooms is abundant. • Most of the high fare demand occurs only within a few days of the actual stay. 27 Booking Limits and Yield Management • Choice 1: Do not accept low fare reservation. Hope that high fare customers will eventually show up. • Choice 2: Accept low fare reservations without any limit. • Choice 3: Accept low fare reservations but reserve rooms for high fare customers • Objective: Maximize expected revenues by controlling the sale of low fare rooms. 28 14

Yield Management: Airline Pricing • Carriers typically fill 72.4% of seats and have a break ‐ even load of 70.4%. • Very high fixed costs and perishable capacity. 29 Example: Blackjack Airline d = demand for full fare ($69) ~ N (60, 15 2 ) Expected revenue=69  60=$4140 95 seats Demand for “gamblers fare” ($49) is abundant Expected revenue=49  95=$4655 Decision: x = seats reserved for full fare passengers 30 15

Optimal Booking Solution Cost of too many full fare seats reserved: C o =$49 Cost of not enough full fare seats reserved: C u =$20 C 20     P ( d x ) u 0 . 29   C C 20 49 u o   d  d 60   z ~ N ( 0 , 1 )  • 15 •  (z)=P( d < x )=0.29  z= -0.55  x 60    z 0 . 55 15  x     60 ( 0 . 55 ) 15 51 31 Optimal Revenue for Blackjack Airline • Z= ‐ 0.55  Normal Loss Function L(z) =NORMDIST(z,0,1,0) ‐ z*(1 ‐ NORMSDIST(z)) =0.7328 • For full fare customer expected loss (due to not enough seats reserved) =L(z) ∙  =0.7328  =10.99 expected sales + expected loss = expected full fare demand  expected sales=expected demand ‐ expected loss =60 ‐ 10.99=49.01 • Expected total revenue=49.01*69+(95 ‐ 51)*49 =$5537 16