Value Chain
Management
Session 2
S. Alex Yang
S. Alex Yang – Value Chain Management
Last Session
• VCM is about matching supply with demand
− Mismatch due to uncertainty
• Trade-off “too much” vs. “too few”
− The newsvendor model offers a useful benchmark to balance this tradeoff
• Investing in (uncertain) demand estimation
− A/F ratio
− People, Process, and Technology means
S. Alex Yang – Value Chain Management
2
In this Session
Investment
under
Uncertainty
(Session 1)
Supply
Management
(Session 3)
Demand
Management
(Session 2)
Supply
Demand
Inflexible
Uncertain
Information Management (Session 4)
&
Incentive Alignment (Session 5)
S. Alex Yang – Value Chain Management
3
Harrah’s Entertainment
S. Alex Yang – Value Chain Management
© Las Vegas Review Journal
Timeline
• 1937
Company founded
• 1990s
Launched Winner’s Information Network (WINet)
• 1997
Launched loyalty program Total Gold
• 1998
Appointed Gary Loveman as COO
• 1999
Total Gold upgraded to Total Rewards (different tiers)
• ..
• 2007
Largest casino network, most profitable
− 400,000 sq. meters gamble space, 40,000 hotel rooms
− 40 million TR members, 8 million active over the last 12 months, tracked 80% gaming
revenues
S. Alex Yang – Value Chain Management
5
Harrah’s Value Chain
Stimulate
customers to
visit properties
How to allocate
rooms among
customers?
On-property
experience
User Data Collection and Analytics
S. Alex Yang – Value Chain Management
6
Hotel Revenue Management
• Resource (Supply): hotel rooms
− Fixed (at least in the short run)
− Perishable
• Demand: customers
− Uncertain
− Arrive sequentially over time
− Can be segmented
Data Collection (e.g.,
Loyalty Programs) and
Predictive Analytics
• Key trade-off: sell now or wait until later
(potentially at a high price)?
• Resolution: Pricing
S. Alex Yang – Value Chain Management
Prescriptive Analytics
7
Harrah’s Unique Features
• An integrated view
• Primary purpose is not running a hotel but supporting the
gaming business
• Customer tiers: the average daily theoretical (ADT)
− More than 20 in practice
− Let’s consider 3 tiers in this case
S. Alex Yang – Value Chain Management
Tier
Average Daily
Theoretical
(ADT, $)
0
1
2
882
285
69
8
Labor Day: 1st Monday in September
• Today is 15 August
• We have three rooms left for 1 September
• From now until 1 September, the forecasted demand for a
September 1 room is 2 for each tier.
• One Tier 0 request
− Do we accept the request?
− What is the quoted room rate?
• What about a Tier 2 request?
S. Alex Yang – Value Chain Management
Tier
Average Daily
Theoretical
(ADT, $)
Average
Demand
0
1
2
882
285
69
2
2
2
9
Labor Day: 1st Monday in September
• Customer requests in practice
Request
Number
Arrival Time of
Request
Date of
Arrival
LOS Requested
Customer
Tier
1
10:03am
1 Sep
1
0
2
11:26am
1 Sep
2
2
3
2:19pm
1 Sep
3
1
4
2:47pm
1 Sep
3
2
5
4:10pm
1 Sep
1
0
6
7:28pm
1 Sep
1
1
S. Alex Yang – Value Chain Management
10
Harrah’s Current Approach
• Clearing price:
− opportunity cost of a marginal room, i.e., amount of additional profit you would
make if you had one more room
• Quoted room rate = clearing price – customer’s
revenue potential;
• Makes Harrah’s indifferent between booking now
versus waiting
S. Alex Yang – Value Chain Management
11
Implementation Steps
Step 1: Obtain initial forecasts and estimates
a. Determine remaining room capacity
b. Work out demand by customer tier; arrival date; length of stay (LOS)
Step 2: Determine Clearing price for a given night
a. Expand each tier’s demand forecast to tally total demand per night
b. Allocate rooms to high-value demand, and then to low
[Value = ADT (Average Daily Theoretical); daily gaming revenue of a tier]
c. Clearing price is ADT of tier at which free rooms sell out
Step 3: When potential customers ask for room rates
a. quoted room rate = clearing price – customer’s value (ADT)
b. customer then decides whether to accept or reject the offer
S. Alex Yang – Value Chain Management
12
Step 1a: Estimate remaining room capacity for forecast
dates (Table 3 in Case)
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
Physical
Inventory
1,570
Out of order
Mgr. Held
Group Block
222
Avail to Book 1,348
Overbook
0
Booked
187
Cancel
19
Free
1,180
1,570
269
1,301
0
266
27
1,062
1,570
2
260
1,308
1
251
25
1,082
1,570
2
227
1,341
1
990
99
450
1,570
4
163
1,403
0
1,011
101
493
Avail. to Book = Physical Inventory – Out of order – Mgr. Held – Group Block + Overbook
Free = Avail. to Book – Booked + Cancel
S. Alex Yang – Value Chain Management
13
Step 1b: Detailed Demand Forecast
• More than 20 customer tiers by Average Daily Theoretical (ADT)
value of gaming activities
−
for simplicity, we will use 3: Tier 0 = $881 ADT, Tier 1 = $285 ADT, Tier 2 = $69 ADT
• Forecast demand for rooms by segment, arrival date, length of
stay (LOS) (Table 5 in Case)
Arrival
Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
−
LOS 1
432
486
655
20
20
Tier 0
LOS 2
149
147
189
7
8
LOS 3
107
84
98
6
7
LOS 1
280
251
321
60
44
Tier 1
LOS 2
213
168
210
48
35
LOS 3
157
118
146
36
26
LOS 1
369
333
310
372
352
Tier 2
LOS 2
148
47
170
215
164
LOS 3
49
16
57
72
55
Total customer requests = 7,257
S. Alex Yang – Value Chain Management
14
Step 2a: Expand each segment to find total demand for
each night
31 Aug; LOS = 3
+
31 Aug; LOS = 1
S. Alex Yang – Value Chain Management
+
1 Sep; LOS = 1
2 Sep; LOS = 1
15
Step 2a: Expand each segment to find total demand for
each night (Table 6 in Case)
Arrival
Date
31 Aug
31 Aug
31 Aug
1 Sep
1 Sep
1 Sep
2 Sep
2 Sep
2 Sep
3 Sep
3 Sep
3 Sep
4 Sep
4 Sep
4 Sep
LOS
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
Total
S. Alex Yang – Value Chain Management
ADT Tier 2 Demand for Each Occupancy Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
369
148
148
49
49
49
333
47
47
16
16
16
310
170
170
57
57
57
372
215
215
72
72
352
164
55
566
593
649
902
915
16
Step 2b: Demand forecast by night and customer tier
(Table 7 in Case)
Tier
0
1
2
Total
Gaming
Revenue
(ADT)
$881
$285
$69
Demand by Tier per Occupancy Date
31 Aug 1 Sep
2 Sep
3 Sep
4 Sep
688
650
566
1,904
146
335
915
1,396
973
907
593
2,473
1280
1120
649
3,049
404
618
902
1,924
• Total room-nights of demand = 10,746
S. Alex Yang – Value Chain Management
17
Step 2c: Determining Clearing Price
• Clearing price = Gaming Revenue (ADT) of tier at which free
rooms sell out (Table 8 in Case)
Gaming Revenue
(ADT)
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
0
$881
688
973
1,280
404
146
1
$285
1,338
1,880
2,400
1,022
481
2
$69
1,904
2,473
3,049
1,924
1,396
Free Rooms
1,180
1,062
1,082
450
493
Clearing Price
$285
$285
$881
Tier
S. Alex Yang – Value Chain Management
Cumulative Demand by Tier per Occupancy Date
?
?
18
Steps 3a & 3b: What happens when customers go to book
rooms?
Customer
Requests
(Demand)
Request
Tier
Number
Request
1 Sep
Request Request
2 Sep
3 Sep
Free
Rooms
1 Sep
Free
Rooms
2 Sep
Free
Rooms
3 Sep
2
2
1
1
1
0
1
0
0
3
2
2
1
1
1
0
1
1
1
3
Clearing
Price
0
1
1
1
0
0
0
$285
$881
$285
Room
Availability
(Supply)
Revenue from accepted customers using clearing prices (CP) from case (Table 8):
Offered room rate by tier = max{0, CP – ADT}
Gaming Gaming Gaming
Revenue Revenue Revenue
1 Sep
2 Sep
3 Sep
Room
Revenue
1 Sep
CP = $285
Room
Revenue
2 Sep
CP = $881
Room
Revenue
3 Sep
CP = $285
Total
By
Request
Request
Number
Tier
1
0
$881
0
0
0
0
0
$881
2
1
$285
$285
0
0
$596
0
$1,166
3
0
$881
$881
$881
0
0
0
$2,643
Total by day
$2,047
$1,166
$881
$0
$596
$0
$4,690
S. Alex Yang – Value Chain Management
19
Limitations and Improvements
• Demand is uncertain
− Forecast is not accurate
− Solution #1: re-calculate clearing prices based on actual customer arrivals and
updated demand forecast.
− Solution #2: booking limit and protection level (see supplemental slides)
• Customers’ willingness to pay (WTP) for hotel rooms
− Especially for non-gamers
• Customers may stay for multiple days
S. Alex Yang – Value Chain Management
20
Improvement:
Including Customers’
willingness to pay
S. Alex Yang – Value Chain Management
Incorporating Customers’ WTP
•
Customers’ Willingness to Pay for Rooms
Revenue Potential = Gaming Revenue (ADT)
+ Willingness to Pay (WTP)
Tier
Gaming
Revenue
(ADT)
Willingness
To Pay for Room
(WTP)
Revenue
Potential
0
$881
0
$881
1
$285
$250
$535
2
$69
$250
$319
S. Alex Yang – Value Chain Management
22
Adjusted Clearing Price (ACP)
• Adjusted Clearing Price (ACP) is determined by the Revenue
Potential of the tier which leads to full utilization of rooms: Tier
1 on Sep. 1, Tier 0 on Sep. 2, Tier 1 on Sep. 3
Offered room rate to Tier i = max{WTP of Tier i, ACP - ADT of Tier i}.
Date
SupplyExhausting
Tier
Adjusted
Clearing
Price
(ACP)
Tier 0
Offered Room
Rate
Max{0,ACPADT(0)}
1 Sep
1
$535
2 Sep
0
3 Sep
1
Tier 1
Offered Room Rate
Max{$250,ACP-ADT}
Tier 2
Offered Room Rate
Max{$250,ACP-ADT}
0
$250
$466
$881
0
$596?
$812
$535
0
$250
$466
S. Alex Yang – Value Chain Management
23
Adjusted Clearing Price (ACP)
• Adjusted Clearing Price (ACP) is determined by the Revenue
Potential of the tier which leads to full utilization of rooms: Tier
1 on Sep. 1, Tier 0 on Sep. 2, Tier 1 on Sep. 3
Offered room rate to Tier i = max{WTP of Tier i, ACP - ADT of Tier i}.
Date
SupplyExhausting
Tier
Adjusted
Clearing
Price
(ACP)
Tier 0
Offered Room
Rate
Max{0,ACPADT(0)}
1 Sep
1
$535
2 Sep
0
3 Sep
1
Tier 1
Offered Room Rate
Max{$250,ACP-ADT}
Tier 2
Offered Room Rate
Max{$250,ACP-ADT}
0
$250
$466
$881
0
$596
$812
$535
0
$250
$466
S. Alex Yang – Value Chain Management
24
High Profit Customer Protection
Tier
0
1
2
Revenue
Potential
(ADT+WTP)
881+0=881
285+250=535
69+250=310
Cumulative Demand by Tier per Occupancy Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
688
1,338
1,904
973
1,880
2,473
1,280
2,400
3,049
404
1,022
1,924
146
481
1,396
Free Rooms
Adjusted CP
1,180
$535
1,062
$535
1,082
$881
450
$535
493
$535
• Applies to other non-gaming revenues (e.g., restaurant and
theater)
• How to protect Tier 0 customers arriving on 1 Sept?
– Booking Limit/Protection level
S. Alex Yang – Value Chain Management
25
Improvement:
Multiple Night Stays
S. Alex Yang – Value Chain Management
Multiple Night Stays and ACP
+
31 Aug; LOS = 3
Tier
0
1
2
Arrival
Date
31 Aug
1 Sep
31 Aug; LOS = 1
+
1 Sep; LOS = 1
2 Sep; LOS = 1
Revenue
Potential
(ADT+WTP)
881+0=881
285+250=535
69+250=319
Cumulative Demand by Tier per Occupancy Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
688
1,338
1,904
973
1,880
2,473
1,280
2,400
3,049
404
1,022
1,924
146
481
1,396
Free Rooms
Adjusted CP
1,180
$535
1,062
$535
1,082
$881
450
$535
493
$535
LOS 1
432
486
Tier 0
LOS 2
149
147
S. Alex Yang – Value Chain Management
LOS 3
107
84
LOS 1
280
251
Tier 1
LOS 2
213
168
LOS 3
157
118
LOS 1
369
333
Tier 2
LOS 2
148
47
LOS 3
49
16
27
Multiple Night Stays and ACP
Tier
0
1
2
Arrival
Date
31 Aug
1 Sep
Revenue
Potential
(ADT+WTP)
881+0=881
285+250=535
69+250=319
Cumulative Demand by Tier per Occupancy Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
688
1,338
1,904
973
1,880
2,473
1,280
2,400
3,049
404
1,022
1,924
146
481
1,396
Free Rooms
Adjusted CP
1,180
$535
1,062
$535
1,082
$881
450
$535
493
$535
LOS 1
432
486
Tier 0
LOS 2
149
147
LOS 3
107
84
LOS 1
280
251
Tier 1
LOS 2
213
168
LOS 3
157
118
LOS 1
369
333
Tier 2
LOS 2
148
47
LOS 3
49
16
Question: Should Tier 2 customers arriving on 31 August with LOS = 1 be
accepted?
Based on ACP ($535 > $319), we should not!
S. Alex Yang – Value Chain Management
28
Multiple Night Stays and ACP
Tier
0
1
2
Arrival
Date
31 Aug
1 Sep
Revenue
Potential
(ADT+WTP)
881+0=881
285+250=535
69+250=319
Cumulative Demand by Tier per Occupancy Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
688
1,338
1,904
973
1,880
2,473
1,280
2,400
3,049
404
1,022
1,924
146
481
1,396
Free Rooms
Adjusted CP
1,180
$535
1,062
$535
1,082
$881
450
$535
493
$535
LOS 1
432
486
Tier 0
LOS 2
149
147
LOS 3
107
84
LOS 1
280
251
Tier 1
LOS 2
213
168
LOS 3
157
118
LOS 1
369
333
Tier 2
LOS 2
148
47
LOS 3
49
16
Sept 1 constraint says Tier 1 customers arriving on 31 August cannot all be
accepted.
31 August ACP ($535) is too high!
Should accept some Tier 2.
S. Alex Yang – Value Chain Management
29
Multiple Night Stays and ACP
Tier
0
1
2
Arrival
Date
31 Aug
1 Sep
Revenue
Potential
(ADT+WTP)
881+0=881
285+250=535
69+250=319
Cumulative Demand by Tier per Occupancy Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
688
1,338
1,904
973
1,880
2,473
1,280
2,400
3,049
404
1,022
1,924
146
481
1,396
Free Rooms
Adjusted CP
1,180
$535
1,062
$535
1,082
$881
450
$535
493
$535
LOS 1
432
486
Tier 0
LOS 2
149
147
LOS 3
107
84
LOS 1
280
251
Tier 1
LOS 2
213
168
LOS 3
157
118
LOS 1
369
333
Tier 2
LOS 2
148
47
LOS 3
49
16
Question: Should Tier 1 customers arriving on 1 Sept with LOS = 1 be
accepted?
Based on ACP, we should!
S. Alex Yang – Value Chain Management
30
Multiple Night Stays and ACP
Tier
0
1
2
Arrival
Date
31 Aug
1 Sep
Revenue
Potential
(ADT+WTP)
881+0=881
285+250=535
69+250=319
Cumulative Demand by Tier per Occupancy Date
31 Aug
1 Sep
2 Sep
3 Sep
4 Sep
688
1,338
1,904
973
1,880
2,473
1,280
2,400
3,049
404
1,022
1,924
146
481
1,396
Free Rooms
Adjusted CP
1,180
$535
1,062
$535
1,082
$881
450
$535
493
$535
LOS 1
432
486
Tier 0
LOS 2
149
147
LOS 3
107
84
LOS 1
280
251
Tier 1
LOS 2
213
168
LOS 3
157
118
LOS 1
369
333
Tier 2
LOS 2
148
47
LOS 3
49
16
Tier 1 customers arriving on 31 August with LOS = 2 are more profitable.
Should not accept any Tier 1 with LOS = 1.
S. Alex Yang – Value Chain Management
1 September ACP ($535) is too low!
31
Multiple Night Stays
• Simply breaking multiple night stays into several single
night stay is problematic
• Multiple night stays require multiple resources (hotel
rooms of different nights) simultaneously
• The mechanism (e.g., clearing prices) need to take
such connections into consideration
• The solution: Network Linear Program (LP)
S. Alex Yang – Value Chain Management
32
Network Linear Program
• Decision Variables: how many rooms to sell for each itinerary?
− Each itinerary: customer tiers x LOS x arrival dates
• Objective: maximize revenue
− Revenue from an itinerary equals number of rooms sold for this itinerary times revenue
from booking one such itinerary (ADT + WTP)
• Constraints:
− Rooms sold for each itinerary cannot exceed demand for that itinerary
− Rooms sold for each date cannot exceed number of free rooms for that date
Link to Supplemental Slides
S. Alex Yang – Value Chain Management
33
Network LP Solution
Segment
0
1
2
Rate
0
250
250
Maximize Total Value =
Seg
Arrival
Date
ADT
Value
per Day
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
31-Aug
31-Aug
31-Aug
1-Sep
1-Sep
1-Sep
2-Sep
2-Sep
2-Sep
3-Sep
3-Sep
3-Sep
4-Sep
4-Sep
4-Sep
31-Aug
31-Aug
31-Aug
1-Sep
881
881
881
881
881
881
881
881
881
881
881
881
881
881
881
285
285
285
285
Daily
Room
Rate
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
250
250
250
250
S. Alex Yang – Value Chain Management
Net Rooms
31-Aug
1180
<=
1180
1-Sep
1062
<=
1062
Demand
31-Aug
1-Sep
432
149
107
486
147
84
655
189
98
20
7
6
20
8
7
280
213
157
251
1
1
1
Total Admitted
3526230
LOS
Total
Value
Over
LOS
Num to
Admit
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
881
1762
2643
881
1762
2643
881
1762
2643
881
1762
2643
881
1762
2643
535
1070
1605
535
432
149
107
486
147
84
457
189
98
20
7
6
20
8
7
280
89
0
0
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
1
1
1
1
1
1
1
1
1
1
1
2-Sep
1082
<=
1082
3-Sep
450
<=
450
4-Sep
493
<=
493
5-Sep
295
<=
1000000
Capacity Use Incidence Matrix
2-Sep
3-Sep
4-Sep
5-Sep
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
34
1
1
Shadow Prices
• The solution to this Linear Program (e.g., using Excel Solver)
also gives Shadow Prices for hotel rooms for each night
− Shadow price is a better estimate of the opportunity cost of a marginal room, can be used
as clearing prices.
Final
Shadow
Constraint
Allowable
Allowable
Cell
Name
Value
Price
R.H. Side
Increase
Decrease
$J$5
Rooms 8/31
1180
319
1180
246
123
$K$5
Rooms 9/1
1062
751
1062
123
89
$L$5
Rooms 9/2
1082
881
1082
198
457
$M$5
Rooms 9/3
450
535
450
21
10
$N$5
Rooms 9/4
493
535
493
23
21
Offered room rate = max{WTP, ACP - ADT}
S. Alex Yang – Value Chain Management
35
Financial Performance
• Can the multi-night stay clearing price based on the network
linear program generate higher revenue?
• Monte Carlo Simulation
− 10,000 sample paths
• Demand distribution is assumed to be Poisson
− Commonly used to capture arrival over time
• Demand and capacity scaled down by a factor of 100
− e.g., demand 73  7,257/100
S. Alex Yang – Value Chain Management
36
Simulated Revenue
• Under Adjusted clearing prices
Aug 31
Sep 1
Sep 2
Sep 3
Sep 4
535
535
881
535
319
S. Alex Yang – Value Chain Management
37
Simulated Revenue
• Multi-night stay (LP) clearing prices;
Aug 31
Sep 1
Sep 2
Sep 3
Sep 4
319
751
881
535
319
+ 3% increase
S. Alex Yang – Value Chain Management
38
Financial Impact of RM
Percentage change in profit for different gross margins, revenue increases and net profits as a
percentage of revenue.
Net profit % = 2%
Revenue increase
Gross
margin 1%
2%
5%
8%
100% 50% 100% 250% 400%
90% 45% 90% 225% 360%
75% 38% 75% 188% 300%
50% 25% 50% 125% 200%
25% 13% 25% 63% 100%
15% 8% 15% 38% 60%
S. Alex Yang – Value Chain Management
Net profit % = 6%
Revenue increase
Gross
margin 1% 2% 5%
8%
100% 17% 33% 83% 133%
90% 15% 30% 75% 120%
75% 13% 25% 63% 100%
50% 8% 17% 42% 67%
25% 4% 8% 21% 33%
15% 3% 5% 13% 20%
39
Financial Impact of RM
Impact of 1% improvement on
operating profit
McKinsey
(1992)
A.T. Kearney
(2000)
Price Management (Revenue)
11.1%
8.2%
Variable Cost
7.8%
5.1%
Sales Volume
3.3%
3.0%
Fixed Cost
2.3%
2.0%
S. Alex Yang – Value Chain Management
40
Revenue Management
in General
S. Alex Yang – Value Chain Management
Environments Suitable for RM
• Resources (e.g., hotel rooms)
− Fixed (at least in the short run)
− Perishable
• Demand (Customers)
− Uncertain
− Arrive sequentially over time
− Can be segmented
Data Collection (e.g.,
Loyalty Programs) and
Predictive Analytics
• Not illegal or morally irresponsible to charge different prices to
different customers
S. Alex Yang – Value Chain Management
42
Environments Suitable for RM
• Hotels, airlines, cruise, theater, sports events, car rental
• Display advertising
• Cloud computing
• Retail liquidation and inventory-based lending
• Real estate sales
S. Alex Yang – Value Chain Management
43
RM in Airlines
1978: Airline Deregulation Act
1981: PeopleExpress starts cost-efficient operations and
offers fares 50% to 70% lower than major carriers
1984: PeopleExpress revenues $1B, profit $60M
1985: American Airlines’ Vice President of Marketing (Robert
Crandall) implements Dynamic Inventory Allocation and
Maintenance Optimizer (DINAMO) system
1986: PeopleExpress files for bankruptcy, sold to Continental
Airlines
S. Alex Yang – Value Chain Management
44
“We were a vibrant, profitable company from 1981 to 1985, and
then we tipped right over into losing $50 million a month. We
were still the same company. What changed was American’s
ability to do widespread Yield Management in every one of our
markets. We had been profitable from the day we started until
American came at us with Ultimate Super Savers.”
---Donald Burr, CEO, PeopleExpress
S. Alex Yang – Value Chain Management
45
The Power of RM
• American Airlines estimated a benefit of $1.5B over 3 years
(Boyd 1998).
• National Car Rental avoided liquidation in 1993 using yield
management techniques (Geraghty and Johnson 1997).
• Marriott Hotel credits revenue management with $100M in
additional revenue annually (Cross 1997).
S. Alex Yang – Value Chain Management
46
RM in Sports
• Leverage on big data to
estimate demand
− Secondary market, opponents, etc.
• Special pricing for fans to
enhance experience
• Flexible pricing and secondary
market
• Significant revenue gain
− 30% for high demand events and 5-10%
for low demand ones.
S. Alex Yang – Value Chain Management
© Deloitte 2014
47
Retail Liquidation
• Bankrupt or distressed retailers need to liquidate their inventory
− Fixed supply
• Specialized retailer liquidators (e.g., Gordon Brothers Group)
maximize the liquidation value of the inventory through
sophisticated markdown strategies
− 76% retail value for CompUSA, 70% for Circuit City…
• Expertise in retail liquidation enables inventory-based lending
− In July 2011, JC Penney received a US $1.25 billion line of credit from JP Morgan Chase
solely secured by its inventory.
− Loan terms depend on the net orderly liquidation value (NOLV) of the inventory
S. Alex Yang – Value Chain Management
48
Retail Liquidation
• “Aunt Qian”
− No overnight meat/vegetables
• Committed price schedule
10% Off
20% Off
30% Off
40% Off
50% Off
60% Off
70% Off
80% Off
90% Off
Free
S. Alex Yang – Value Chain Management
49
Demand Management beyond RM
• Time
− Time of use tariff: do your laundry at night
− “Flatten the curve”
• Location/Market
• Products
− Opaque selling
− ``1 day old donuts”
− Change configuration
S. Alex Yang – Value Chain Management
50
Key Takeaways
• Under fixed supply, actively manage demand
• Revenue management (RM) is a solution:
− Segment customers based on profitability
− Maximize profit by charging a different price to each segment and/or limit the amount sold
at low profitability
• Shifting demand over time, location, and products
• Scope for huge gains
• Prescriptive analytics enables the monetization of predictive
analytics
S. Alex Yang – Value Chain Management
51
After Class
• Problem Set 2 (on Canvas) due before Session 3
− One question based on the Hewlett-Packard case
• Prepare the Hewlett-Packard case (under Session 3)
• Sign up groups for the Group Simulation (between S4 and S5)
S. Alex Yang – Value Chain Management
52
Supplemental
Slides
S. Alex Yang – Value Chain Management
Booking Limit /
Protection Level
S. Alex Yang – Value Chain Management
An Airline Example
• Single cabin aircraft, capacity C=100 seats
• ‘Business’ (full fare) and ‘Leisure’ (discount) customers book the
same seats
• Ticket prices: Business pB = £120, leisure pL=£80
• On average E[DB] = 36 business customers and E[DL] = 223
leisure customers arrive over 15 weeks to request bookings
− (Demand DB: approximately normal with mean 36 and stdev. 6)
• How can the airline maximize revenue?
S. Alex Yang – Value Chain Management
Booking Limit / Protection Level
• The booking limit X is the maximum number of seats sold to
leisure customers (set upfront)
• The protection level is Q = C – X: number of seats protected
for business customers
• What is the best booking limit?
S. Alex Yang – Value Chain Management
Maximizing Profit
• Key trade-off: benefit of selling a leisure seat now versus saving
it for later when a business customer might show up
• Solution – similar to the newsvendor model
− Increasing the booking limit X such that the gain of selling one leisure seat (pL=£80) equals
to the potential loss of selling this seat to business customers at pB = £120 with probability
Prob(DB >= C – X)
or:
S. Alex Yang – Value Chain Management
Optimal Booking Limit
D ~ N(36, 6)
P(D > Q)
1.20
0.07
1.00
0.06
0.80
0.05
0.67
0.04
0.60
0.03
0.40
0.02
2/3
1/3
0.01
0.20
0.00
0
10
20
33.5
30
Q
40
50
60
70
0.00
0
10
20
30 33.5
40
50
Q
2
𝑃 𝐷𝐵 ≥ 𝑄 = 𝑠𝑜 𝑄 = 𝑁𝑜𝑟𝑚. 𝐼𝑛𝑣 0.33,36,6 = 33.5
3
S. Alex Yang – Value Chain Management
60
Optimal Booking Limit
S. Alex Yang – Value Chain Management
Network Linear Program
for Multiple Night Stays
S. Alex Yang – Value Chain Management
Problem
• Consider Harrah’s hotel room revenue management problem for
the Labor Day Weekend
• Three Customers tiers: Tier 0, 1, and 2.
• Three Length of Stays: LOS = 1, 2, 3.
• Five Arrival Date: 31 Aug, 1 – 4 Sept
• In total, 45 itineraries (3 x 3 x 5)
• Decisions:
− How many rooms to allocate to each itinerary?
− What are the quoted room rates for each itinerary?
S. Alex Yang – Value Chain Management
61
Network Linear Program
rj = revenue from booking itinerary j
45 itinerary types
(3 tiers * 3 LOS’s * 5 arrival dates)
max r1 x1 + r2 x2 +  + r45 x45 
maximize contribution
s.t.
xj  d j
(j = 1,..45)
book up to forecast demand
ai1 x1 + ai 2 x2 +  + ai 45 x45  ci
(i=1,...5)
book up to free rooms
xj  0
(j=1,..45)
non-negativity
5 nights of rooms
(31-Aug…4-Sep)
1 if customer type j requires a room on night i
aij = 
0 otherwise
S. Alex Yang – Value Chain Management
62
Definitions
• j = index for an itinerary, i.e., combination of tier, arrival date
and length of stay
• rj = revenue obtained from selling itinerary j
• dj = forecast of demand for itinerary j = max. number of
itinerary j that can be sold
• i = index for a particular night. i = 31-Aug,…,4-Sep
• ci = free rooms on night i
• aij = 1 if itinerary j requires a room on night i; 0 otherwise
• xj = number of itinerary j to sell; these are the decision
variables
S. Alex Yang – Value Chain Management
63
Example Constraint
• Constraint: Book up to Free Rooms for 31 August
number of itinerary 1 to sell
Free rooms on 31 AUG
a31 AUG,1  x1 + a31 AUG, 2  x2 +  + a31 AUG, 45  x45  c31 AUG
1 x1 + 1 x2 +  + 0  x45  1180
1 if itinerary 1 requires a room on 31 AUG
a31AUG,1 = 
0 otherwise
S. Alex Yang – Value Chain Management
64
Excel Function: SUMPRODUCT
• Multiplies corresponding components in the given arrays, and
returns the sum of those products
• Syntax: SUMPRODUCT(array 1, array 2)
• Example
=SUMPRODUCT(B2:B11, C2:C11)
=1*2 + 2*4 + 3*6 +…+10*20 = 770
S. Alex Yang – Value Chain Management
65
Optimal Solution to LP
Segment
0
1
2
Rate
0
250
250
Maximize Total Value =
Seg
Arrival
Date
ADT
Value
per Day
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
31-Aug
31-Aug
31-Aug
1-Sep
1-Sep
1-Sep
2-Sep
2-Sep
2-Sep
3-Sep
3-Sep
3-Sep
4-Sep
4-Sep
4-Sep
31-Aug
31-Aug
31-Aug
1-Sep
881
881
881
881
881
881
881
881
881
881
881
881
881
881
881
285
285
285
285
Daily
Room
Rate
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
250
250
250
250
S. Alex Yang – Value Chain Management
Net Rooms
31-Aug
1180
<=
1180
1-Sep
1062
<=
1062
Demand
31-Aug
1-Sep
432
149
107
486
147
84
655
189
98
20
7
6
20
8
7
280
213
157
251
1
1
1
Total Admitted
3526230
LOS
Total
Value
Over
LOS
Num to
Admit
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
881
1762
2643
881
1762
2643
881
1762
2643
881
1762
2643
881
1762
2643
535
1070
1605
535
432
149
107
486
147
84
457
189
98
20
7
6
20
8
7
280
89
0
0
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
<=
1
1
1
1
1
1
1
1
1
1
1
2-Sep
1082
<=
1082
3-Sep
450
<=
450
4-Sep
493
<=
493
5-Sep
295
<=
1000000
Capacity Use Incidence Matrix
2-Sep
3-Sep
4-Sep
5-Sep
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
66
1
1
Sensitivity Analysis
Final
Shadow
Constraint
Allowable
Allowable
Cell
Name
Value
Price
R.H. Side
Increase
Decrease
$J$5
Rooms 8/31
1180
319
1180
246
123
$K$5
Rooms 9/1
1062
751
1062
123
89
$L$5
Rooms 9/2
1082
881
1082
198
457
$M$5
Rooms 9/3
450
535
450
21
10
$N$5
Rooms 9/4
493
535
493
23
21
S. Alex Yang – Value Chain Management
67
Shadow Prices
The Shadow Price obtained from the solution to the Linear Program is a better estimate of the
opportunity cost of the room. For example, the calculation above shows that if Harrah’s had 1
more room on 31 Aug, then it could generate an additional profit of $319 = Shadow Price for
room capacity on 31 Aug = Opportunity cost of room capacity on 31 Aug.
Current Optimal Solution:
Maximize Total Value =
3526230
Total
Total Admitted
Net Rooms
31-Aug
1180
<=
1180
1-Sep
1062
<=
1062
2-Sep
1082
<=
1082
3-Sep
450
<=
450
4-Sep
493
<=
493
5-Sep
295
<=
1000000
6-Sep
88
<=
1000000
1181
<=
1181
1062
<=
1062
1082
<=
1082
450
493
295
88
493
<=
1000000
<=
1000000
Now, add 1 room on 31-Aug
Maximize Total Value =
3526549
Total
Total Admitted
Net Rooms
<=
<=
450
Difference in Revenue = $3,526,549 - $3,526,230 = $319
S. Alex Yang – Value Chain Management
68
LP-based Capacity Control
•
No room for 500 Tier 1 arriving on 31 Aug → Some (Tier 2, 31 Aug, LOS 1)
should be accepted!
31 August ACP ($535) is too high!
•
No (Tier 1, 1 Sep, LOS 1) should ever be accepted because (Tier 1, 31 Aug,
LOS 2 and 3) are more profitable!
1 September ACP ($535) is too low!
Final
Shadow
Constraint
Allowable
Allowable
Cell
Name
Value
Price
R.H. Side
Increase
Decrease
$J$5
Rooms 8/31
1180
319
1180
246
123
$K$5
Rooms 9/1
1062
751
1062
123
89
$L$5
Rooms 9/2
1082
881
1082
198
457
$M$5
Rooms 9/3
450
535
450
21
10
$N$5
Rooms 9/4
493
535
493
23
21
Offered room rate = max{WTP, ACP - ADT}
S. Alex Yang – Value Chain Management
69
Network Linear Program
for Airline Itineraries
S. Alex Yang – Value Chain Management
Passenger Airline Network
S. Alex Yang – Value Chain Management
Network Capacity Management
SEA
BOS
100 seats
LAX
100 seats
SEA-LAX (Ticket Price: $110) (Avg. Demand = 65)
LAX-BOS (Ticket Price: $90) (Avg. Demand = 30)
SEA-BOS (Ticket Price: $180) (Avg. Demand = 60)
• How to manage the booking process for the different itineraries?
S. Alex Yang – Value Chain Management
72
The Network LP
• Assume deterministic demand:
DSEA-LAX=65 ; DLAX-BOS=30 ; DSEA-BOS=60
• Decision variables: # tickets sold
XSEA-LAX; XLAX-BOS ; XSEA-BOS
• Maximize Revenue = 110XSEA-LAX + 90XLAX-BOS + 180XSEA-BOS
• Subject to constraints:
XSEA-LAX + XSEA-BOS ≤ 100
XLAX-BOS + XSEA-BOS ≤ 100
Limited seats
XSEA-LAX ≤ DSEA-LAX
XLAX-BOS ≤ DLAX-BOS
Limited demand
XSEA-BOS ≤ DSEA-BOS
S. Alex Yang – Value Chain Management
73
Optimal Solution
SEA-LAX ($110)
LAX-BOS ($90)
SEA-BOS ($180)
SEA
BOS
QuickTime™ and a
decompressor
are needed to see this picture.
LAX
100 seats
QuickTime™ and a
decompressor
are needed to see this picture.
100 seats
Network LP:
Solution:
Max 110XSEA-LAX + 90XLAX-BOS +180XSEA-BOS
s.t.
XSEA-LAX + XSEA-BOS ≤ 100
XLAX-BOS + XSEA-BOS ≤ 100
0 ≤ XSEA-LAX ≤ 65
0 ≤ XLAX-BOS ≤ 30
0 ≤ XSEA-BOS ≤ 60
S. Alex Yang – Value Chain Management
X*SEA-LAX = 40
X*LAX-BOS = 30
X*SEA-BOS = 60
Bid prices (shadow prices):
CSEA-LAX = 110
CLAX-BOS = 0
Bid Price Capacity Control
Bid prices = opportunity cost of seats
SEA-LAX ($110)
LAX-BOS ($90)
SEA-BOS ($180)
SEA
BOS
CSEA-LAX = $110
CLAX-BOS = $0
QuickTime™ and a
decompressor
are needed to see this picture.
LAX
100 seats
QuickTime™ and a
decompressor
are needed to see this picture.
100 seats
Network profit = revenue – opportunity cost
RSEA-LAX - CSEA-LAX = 110 – 110 = 0
RSEA-BOS - CSEA-LAX - CLAX-BOS = 180 - 110 – 0 = 70
RLAX-BOS - CLAX-BOS = 90 – 0 = 90
Booking limits to protect:
- SEA-BOS against SEA-LAX tickets
- (LAX-BOS against SEA-BOS tickets)
S. Alex Yang – Value Chain Management