advertisement

Revenue Management Peter C. Bell 2005

0 %
100 %
advertisement
Information about Revenue Management Peter C. Bell 2005

Published on January 13, 2009

Author: sgehrels

Source: slideshare.net

Description

Additional presentation on Revenue Management
advertisement

Revenue Management Vision 2020: Ahmedabad 2005 Peter C. Bell Ws PBELL@IVEY.CA 1 w © 2005 by Peter C. Bell

My Objective To introduce you to the practice and theory of Revenue Management Since this is a large and fast growing field, this will be a broad-brush survey. 2 w © 2005 by Peter C. Bell

AGENDA: • Introduction • What is revenue management, who uses it and what has been the impact? • The five pillars of RM – Pricing, discount allocation, overbooking, trading up and re-planing • Integrating the tools • Conclusions 3 w © 2005 by Peter C. Bell

AGENDA: • Introduction • What is revenue management, who uses it and what has been the impact? • The five pillars of RM – Pricing, discount allocation, overbooking, trading up and re-planing • Integrating the tools • Conclusions 4 w © 2005 by Peter C. Bell

REVENUE MANAGEMENT: Definition Revenue management (RM) is the science and art of enhancing firm revenues while selling essentially the same amount of product. 5 w © 2005 by Peter C. Bell

REVENUE MANAGEMENT: History The first reference to RM is: Taylor, C.J. (1962) The determination of passenger booking levels. Proceedings of the Second AGIFORS Symposium, American Airlines, New York. This work recognizes the value of selling more airline seats than capacity in anticipation of “no-shows.” We now call this “overbooking.” 6 w © 2005 by Peter C. Bell

REVENUE MANAGEMENT: History The first major users of RM were American Airlines and Delta Airlines starting about 1985. Both Tom Cook (American) and Robert Cross (Delta) have been cited as the “fathers” of corporate RM. 7 w © 2005 by Peter C. Bell

OR developments in “revenue enhancement” since 1985 have led to innovative new methods of pricing and delivering products. We call these methods “revenue management” tools. 8 w © 2005 by Peter C. Bell

“MANAGEMENT SCIENTISTS HAVE WRECKED THE AIRLINE INDUSTRY” Joseph F. Coates (California futurist) 9 w © 2005 by Peter C. Bell

However the impact of revenue management has been dramatic … and the use of RM continues to expand to new products. 10 w © 2005 by Peter C. Bell

“We estimate that yield management has generated $1.4 billion in incremental revenue in the last three years” by “creating a pricing structure that responds to demand on a flight-by-flight basis” R.L. Crandall, Chairman and CEO of AMR, 1992 11 w © 2005 by Peter C. Bell

“We have estimated that the yield management system at American Airlines generates almost $1 billion in annual incremental revenue” Tom Cook, President SABRE Decision Technologies, June 1998 12 w © 2005 by Peter C. Bell

quot;Ford Motor Co. has quietly been enjoying a huge surge in profitability... 1995 and 1999, U.S. vehicle sales rose just 6 percent, from 3.9 million units to 4.1 million units. But revenue was up 25 percent, and pretax profits soared 250 percent, from about $3 billion to $7.5 billion. Of that $4.5 billion growth, Ford's Lloyd Hansen, controller for global marketing and sales, estimates that about $3 billion came from a series of revenue management initiatives.” CFO Magazine, August, 2000 13 w © 2005 by Peter C. Bell

“(revenue management) basically saved National Car Rental. And you can go from the CEO of National on down, and they will all say: ‘just applying these OR models made the life or death difference for this company’” Kevin Geraghty, Aeronomics Inc. 15 w © 2005 by Peter C. Bell

and .. on the other side: RM used by the competition has bankrupted several corporations .. the clearest example being Peoples’ Express Airlines. 16 w © 2005 by Peter C. Bell

Donald Burr, Founder and CEO of People Express “believes that major carrier’s use of sophisticated computer programs to immediately match or undercut his prices ultimately killed People Express” 17 w © 2005 by Peter C. Bell

WHO USES RM? • AIRLINES (All?) • HOTELS (Hyatt, Marriott, Hilton, Sheraton, Forte, Disney ..) • VACATIONS (Club Med, Princess Cruises, Norwegian ..) • CAR RENTAL (National, Hertz, Avis, Europcar ..) • WASHINGTON OPERA • FREIGHT (Sea-Land, Yellow Freight, Cons. Freightways ..) • TELEVISION ADS (CBC, ABC, NBC, TVNZ, Aus7 ..) • UPS, SNCF • RETAIL (Retek, Khimetrics) • REAL ESTATE (Archstone) • NATURAL GAS • TEXAS CHILDREN’S HOSPITAL 18 w © 2005 by Peter C. Bell

AGENDA: • Introduction • What is revenue management, who uses it and what has been the impact? • The five pillars of RM – Pricing, discount allocation, overbooking, trading up and re-planing • Integrating the tools • Conclusions 19 w © 2005 by Peter C. Bell

THE 5 PILLARS OF RM • New approaches to pricing • Discount allocation • Trading-up • Overbooking • Re-planing • + markdown optimization, purchase loans, etc 20 w © 2005 by Peter C. Bell

NEW APPROACHES TO PRICING PRODUCTS AND SERVICES 21 w © 2005 by Peter C. Bell

THE NEW PRICING CONCEPT • Use product price as a management control variable. • Set this price “optimally” to various customer groups or “clusters”. • Be prepared to change prices often. 22 w © 2005 by Peter C. Bell

THE “CONCEPTUAL LEAPS” • “Products” do not have a value, rather the value of a product depends on the point in time of purchase. • “Products” have different values to different clusters of customers. 23 w © 2005 by Peter C. Bell

DEMAND AT A POINT IN TIME BY A CUSTOMER CLUSTER DRIVES RM PRICING MARKET SEGMENTATION IS THE KEY TO ENHANCING REVENUES THROUGH RM PRICING 24 w © 2005 by Peter C. Bell

A MARKET Q = F(P,……..) Price Quantity Sold 25 w © 2005 by Peter C. Bell

SEGMENTING A MARKET Q = F(P,……..) becomes qi = Fi(pi,…..) for i = 1,…N Price Market Segmentation Quantity Sold 26 w © 2005 by Peter C. Bell

MARKET SEGMENTATION: METHODS • Time of purchase • Customer characteristics (seniors, others) • Sales channel (“clicks” and “bricks”) • Offer a discount to large customers • Offer a discount for slow delivery ++ 27 w © 2005 by Peter C. Bell

Example of market segmentation Coke price rises with heat Vending machine that alters price with temperature change is possible NEW YORK (CNNfn) - It's not the New Coke, but it may be the Smart Coke. Soft-drink giant Coca-Cola Co. is working on a vending machine that automatically raises the price of a soda whenever the weather grows hot. Coke chairman Doug Ivester said the machine was designed to reconcile supply and demand by raising the price when demand increased. quot;Coca-Cola is a product whose utility varies from moment to moment,quot; he was quoted as saying. 28 w © 2005 by Peter C. Bell

“CLUSTERING” CUSTOMERS • Different groups of customers value a product differently – Example: • “Business class” air customers value convenience, comfort, flexibility • “Economy class” customers value low prices, (and perhaps longer stays, advance reservations) • “Clustering” means assigning customers to “clusters” or “market segments” 29 w © 2005 by Peter C. Bell

An example of “clusters” • E-Bivalent Newbies (5% of online shoppers) – Newest to Net, somewhat older, spends least online, likes online least • Time Sensitive Materialists (17%) – Most interested in convenience, less likely to read reviews or compare prices • Clicks and Mortar (23%) – Browse online, prefers to buy offline, more likely female, has privacy and security concerns, goes to malls often • Hooked, Online and Single (16%) – More likely young, single males with high incomes, have been on Net longest, most likely to play games, download, bank online • Hunter-Gatherers (20%) – Typically 30-49 years old, two kids, most likely to visit sites that provide information and comparison • Brand Loyalists (19%) – Most likely to bypass search engines to go directly to sites they know, most satisfied with shopping online, spend most online -- Harris Interactive study of 3,000 Internet Shoppers, 2000 30 w © 2005 by Peter C. Bell

CHANGING PRODUCT VALUE OVER TIME (“time segmentation”) • Perishable products that age, • Seasonality, • Some customers will pay for the security of early purchase, or for the flexibility of late purchase, • Suppliers may attach value to the security of early sales………. 31 w © 2005 by Peter C. Bell

CHANGING DEMAND OVER TIME Example: PERISHABLE PRODUCTS Demand Segment 1 S2 S3 Time 32 w © 2005 by Peter C. Bell

CHANGING DEMAND OVER TIME Example: EVENT or TRIP TICKETS Demand Time Event Date 33 w © 2005 by Peter C. Bell

CHANGING DEMAND OVER TIME Example: EVENT or TRIP TICKETS Demand Time Event Date 34 w © 2005 by Peter C. Bell

Common Demand Models Q = Quantity sold p = price/unit Linear demand: Q=A–Bp Usually maximum and minimum prices are specified Constant elasticity demand: Q = A p-e where e is the price elasticity of demand 35 w © 2005 by Peter C. Bell

LINEAR APPROXIMATION OF DEMAND CURVE Demand Curve Price Linear Approximation Quantity Sold 36 w © 2005 by Peter C. Bell

MODEL CHOICE MAY NOT BE OBVIOUS DEMAND EQUATION ESTIMATION 70 60 50 Quantity 40 30 20 10 0 $100.00 $150.00 $200.00 $250.00 Price Q = 92.1 - .37 P R2 = 0.95 Q= 2170037377 P-3.59 R2 = 0.95 37 w © 2005 by Peter C. Bell

General Demand Function In general, for any time period and cluster: Q = F(P, Ps, Pc, X1, X2, ….) • where: • P = price of product • Ps = price of substitute products • Pc = price of complement products • Xi are exogenous variables (weather, economic factors, advertising…etc) 39 w © 2005 by Peter C. Bell

Estimating Demand Demand estimation is a craft: firms tend to keep this part of their RM confidential. Two steps are required: 1. Build a demand model (off-line). Common techniques include regression, curve fitting, and cluster analysis. 2. Develop an on-line demand model updating procedure to respond immediately to unexpected observed demand. 40 w © 2005 by Peter C. Bell

“LEAKAGE” • Demand “leakage” (from a high priced segment to a low priced segment) occurs when segmentation is not perfect. Q1 = F1(p1…) – L(p1 – p2) Q2 = F2(p2…) + L(p1 – p2) where p1 > p2 41 w © 2005 by Peter C. Bell

“FENCES” • Revenue will disappear unless market segments are kept separate to limit “leakage” from high priced segments to low priced segments. • Tools to maintain segment separation are called “fences”. Examples of fences: – The fee airlines charge to modify a low fare ticket (usually $150-200). – The requirement for a Saturday night stopover for a low fare ticket. – Booking and paying 90 (or 60, or 30) days in advance. Look for examples of creative fences! 42 w © 2005 by Peter C. Bell

Pricing Approaches to Revenue Maximization – Traditional fixed pricing – Variable pricing – Optimum dynamic pricing • Computing optimum prices 43 w © 2005 by Peter C. Bell

Single price: 2 market segments Revenue = p (Q1 + Q2) There is usually a p* that Price optimizes revenue. p Q1 Q2 Quantity Sold 44 w © 2005 by Peter C. Bell

Two prices: 2 market segments Revenue = p1Q1 + p2Q2 Price If p1* and p2* maximize revenue then: p1 p1*Q1 + p2*Q2 ≥ p*(Q1 + Q2) p2 Q1 Q2 Quantity Sold 45 w © 2005 by Peter C. Bell

Formal statement of deterministic ODP problem (time segments) Let: pi be the product price in period i, qi be the quantity sold in period i, qi = fi (pi) be the demand curve for period i, and I be the inventory to be sold over N pricing periods. then: ∑ N Max Z = pi qi i =1 Subject to: qi = fi (pi) for i = 1, ......N ∑ N qi = I i =1 46 w © 2005 by Peter C. Bell

Deterministic ODP problem with forecast errors Let: pi be the product price in period i, qi be the quantity sold in period i, qi = fi (pi) be the demand curve for period i, and I be the inventory to be sold over N pricing periods. then: For each period, k, k = 1,2,...N ∑ N Z= piqi Max i=k Subject to: qi = fi (pi) + error for i = k, k+1, ......N ∑ ∑ k −1 N qi = I − qi i= k i =1 49 w © 2005 by Peter C. Bell

ACCEPT/REJECT DECISION MAKING RM algorithms are mostly “accept/reject” rules. If a customer appears and offers to buy a unit for $P, do you accept (in which case you give up the opportunity to sell the same unit to a later arriving customer who may pay more than $P), or do you reject (in which case you give up $P in the hope of selling the unit later >$P but may not sell the unit). The issue is one of balancing “yield loss” and “spoilage”. 52 w © 2005 by Peter C. Bell

Revenue optimization drove the entry of RM into services. - non-replenishable inventories, - low (zero?) variable cost of providing product. For restockable items, contribution optimization can be more difficult. - role of inventory and replacement policies, - need for “profitable market share”. 53 w © 2005 by Peter C. Bell

The Single-Period Stochastic Optimum Pricing Problem Define: • p - the price (a decision variable), • c - the variable production cost (c < p), • I - the quantity available for sale (which may be a decision variable), • h - holding cost per unit of inventory unsold at the end of the period, • u - shortage cost per unit of unsatisfied demand, • q = D(p, ξ) be the demand function where ξ is a random variable with density function f(ξ). The contribution, π, depends on the random demand: qp − cI − h ( I − q ) q<I π ( p, I ) =   I ( p − c) − u (q − I ) q≥I The firm must chose values for the decision variables (p and I) based on expected contribution, E(π), which depends on the forecast of the random demand q: 54 w © 2005 by Peter C. Bell

Inventory/SC models vs. RM models • Most inventory and supply chain models assume demand (Q) is given. • RM models replace given demand (Q) with a given demand curve [F(p)]. The firm must optimize p (and hence determine Q) and simultaneously optimize inventory. • Many research opportunities. 55 w © 2005 by Peter C. Bell

56 w © 2005 by Peter C. Bell

THE 5 PILLARS OF RM • Optimum dynamic pricing • Discount allocation • Trading-up (or planned upgrades) • Overbooking • Re-planing (or short selling) • + markdown optimization, purchase loans, etc 57 w © 2005 by Peter C. Bell

Discount allocation • Multiple prices for the same units – Multi-day packages (hotels, rental cars) – Singles and 10-packs – A ski hill had 22 different packages covering the same ski session • Issue: how many units to allocate to each discount package price (the “reservation level”)? 58 w © 2005 by Peter C. Bell

Discount allocation: Solver example 59 w © 2005 by Peter C. Bell

Discount allocation: hotel example 60 w © 2005 by Peter C. Bell

DYNAMICS OF MULTIPLE DISCOUNT PACKAGES AND PRICE CATEGORIES Issue: If a customer appears and demands the low fare or discount package, when do you say “No” in order to preserve product for the higher paying customers? Example: How many “full fare economy” seats on the plane? 61 w © 2005 by Peter C. Bell

Reservation rule (Littlewood 1972) Issue: How many units (seats) to reserve for low price sale? Continue to sell discount product at time t until: r ≥ (1 - Pt) R or (1 - Pt) ≤ r / R Where: r = low price (marginal revenue) R = high price (marginal revenue) Pt = probability of selling at least the remaining number of units. 62 w © 2005 by Peter C. Bell

EMSR Heuristic (Expected Marginal Seat Revenue) Belobaba 1987 Apply Littlewood’s rule sequentially to fare classes in increasing fare order. Let µi ,σi be the estimates of mean and std. deviation of demand for product class i with price pi Set a reservation (or protection) level of Li so that pi+1 = Pi P(Xi > Li) Where Pi is the weighted average fare for classes 1,2,,,,,I And Xi is a normal random variable convoluting demand for classes 1,2,….i 64 w © 2005 by Peter C. Bell

Protection levels: Expected MR curves 65 w © 2005 by Peter C. Bell

Overbooking (aka overselling) Issue: Should you take more orders than you have product in the expectation that some customers who have ordered will not collect the product? • If so, how many extra orders should you take and when? – P{cancel order} declines as delivery date approaches 66 w © 2005 by Peter C. Bell

OVERBOOKING: EXAMPLES • airline seats, and passenger train seats, are usually booked in advance of the date of travel, • rental cars can be reserved ahead of the day of rental, • hotel rooms and campsite spaces, seats for stage shows, sports events, and concerts are sold in advance, • fresh turkeys can be ordered for delivery at thanksgiving, • package vacations and cruises are usually booked in advance, • tuxedos, cut flowers, some baked goods, etc. are commonly booked for future delivery 67 w © 2005 by Peter C. Bell

Managing the overbooked customer A key cost in all the models • Airlines offer cash or travel coupons to ticketed customers in order to persuade them to take an alternate flight when space is needed for overbooked passengers. • Hotels will trade up overbooked customers to rooms on the executive floor, • Rental car companies will substitute a higher class of car at no extra charge. In all these cases, the cost is well know. 68 w © 2005 by Peter C. Bell

Minimizing the “no show” rate If all customers with reservations showed up, there would be no benefit from overbooking • Require payment at the time the booking is made, perhaps offering an early payment price reduction. • Require payment in full at some prearranged time in advance of the delivery date. If payment is not received, the supplier cancels the booking. • Fees may be charged if a booking is changed. 69 w © 2005 by Peter C. Bell

Overbooking: the basic model M = the amount of product B = the booking limit (B ≥ M) RN = the value of sale of unit N with RN+1 ≤ RN, Ci = cost of satisfying the ith overbooked customer if no product is available, Ci+1 ≥ Ci P[Q|B] = probability that Q customers will show up if we sell B units. The expected cost of unsold product is: {(RQ+1 + RQ+2 +...+ RM) P[Q|B] } summed over all values of Q < M The expected cost of handling oversold customers is: {(C1 + C2 +...+ CQ-M) P[Q|B] } summed over all values of Q > M Find B that minimizes the sum of these two expected costs. 70 w © 2005 by Peter C. Bell

Overbooking: Determining the optimal level 71 w © 2005 by Peter C. Bell

Trading up (planned upgrades) Issue: If a customer appears demanding a product that is sold out, should you trade the customer up to a higher valued product (at your expense)? Issue B. If you adopt this as policy, what does this do to your inventory management? 73 w © 2005 by Peter C. Bell

Re-planing Issue: If a plane is sold out several days before flight date and a customer appears prepared to pay a “high” price for that flight, can you profitably “incentivize” a low-fare paid customer to change flights? 2001 McKinsey, CALEB Technologies, American Airlines 74 w © 2005 by Peter C. Bell

Other RM tools • “Markdown optimization” is common in retail (Retek, Manugistics, Spotlight): This is really price optimization with non-increasing prices. – The benefits are claimed to be very substantial • “Bottleneck optimization” (Maxager Tech.) is price optimization where “inventory” is production time on the scarce production unit. • There are others but all seem to be variations on the 5 basic tools. 75 w © 2005 by Peter C. Bell

AGENDA: • Introduction • What is revenue management, who uses it and what has been the impact? • The five pillars of RM – Pricing, discount allocation, overbooking, trading up and re-planing • Integrating the tools • Conclusions 76 w © 2005 by Peter C. Bell

Integration • Although these tools are almost always discussed separately, they all function within a highly integrated system 77 w © 2005 by Peter C. Bell

PRICES RESERVATION LEVELS 78 w © 2005 by Peter C. Bell

CAPACITY PRICES RESERVATION LEVELS 79 w © 2005 by Peter C. Bell

CAPACITY OVERBOOKING LEVEL PRICES RESERVATION LEVELS 80 w © 2005 by Peter C. Bell

CAPACITY OVERBOOKING LEVEL PRICES RESERVATION LEVELS PLANNED UPGRADES 81 w © 2005 by Peter C. Bell

CAPACITY OVERBOOKING LEVEL PRICES REPLANE LEVEL RESERVATION LEVELS PLANNED UPGRADES 82 w © 2005 by Peter C. Bell

CAPACITY OVERBOOKING LEVEL PRICES REPLANE LEVEL RESERVATION LEVELS PLANNED UPGRADES 83 w © 2005 by Peter C. Bell

Update Real time Database Historical Data Product Sales Sales Data Revise Demand Model Current Inventory Build The Demand Levels Demand Model Market Forecasts Off-line Support Activities Pricing Posted Prices System Revenue Management System 84 w © 2005 by Peter C. Bell

REQUIREMENTS FOR SUCCESS • SUPERIOR INFORMATION TECHNOLOGY, • SUPERIOR OPERATIONS RESEARCH SKILLS, and • THE ABILITY TO MANAGE DYNAMIC PRICES. 85 w © 2005 by Peter C. Bell

AGENDA: • Introduction • What is revenue management, who uses it and what has been the impact? • The five pillars of RM – Pricing, discount allocation, overbooking, trading up and re-planing • Integrating the tools • Conclusions 86 w © 2005 by Peter C. Bell

BUSINESS OPPORTUNITIES A great number of products seem ripe for RM. Some examples: Cinemas, golf courses, electricity, taxis, public transit, newspapers and magazines, advertising, fashion clothing, blue jeans, vegetables, fast food, supermarkets, sports events, theatre, pop concerts,…… 87 w © 2005 by Peter C. Bell

RESEARCH OPPORTUNITIES Academic Almost every “inventory” model can be extended to include a demand curve (some already have been!) Accept/reject decision heuristics (Bayesian) Application The merger of SC optimization and RM (“EPO”) Clustering New and improved “fencing” Demand modeling (how good does it need to be?) 88 w © 2005 by Peter C. Bell

CONCLUSIONS RM tools have become very important for both business and OR There are opportunities for many new revenue maximizing models and pricing/inventory models Heuristics to aid implementation are a priority Competitive models provide an opportunity The competitive customer Competition among RM firms 89 w © 2005 by Peter C. Bell

CONCLUSIONS RM techniques raise many business issues: winning firms will be able to implement these ideas while keeping customers (and regulators) happy. Everyone gains from the efficiencies that RM produces, but some individuals lose. Successful implementation usually requires taking good care of the few who lose. 90 w © 2005 by Peter C. Bell

91 w © 2005 by Peter C. Bell

Add a comment

Related pages

Stowe Shoemaker, Ph.D

... and strategic pricing and revenue management. ... Management. Raab, C., Shoemaker, ... Operations Management-Peter Jones Editor. Shoemaker ...
Read more

MANAGING CUSTOMER RELATIONSHIPS - Dr. Ruth N. Bolton ...

The customer relationship management ... Thomas, and Peter C. Verhoef. (2002) “Linking Customer Assets to ... (2005) “Why Do Customer Relationship ...
Read more

Peter Drucker - Wikipedia, the free encyclopedia

... 1909 – November 11, 2005) was an ... John C . Drucker: The Man ... Weber, Winfried W. Kulothungan, Gladius (eds.) Peter F. Drucker's Next Management ...
Read more

Cleversafe - About

Jeffrey Giannetti is responsible for accelerating revenue growth and expanding Cleversafe’s customer ... and general management. ... Peter Barris ...
Read more

Management team – Company – Google

David C. Drummond Senior Vice President, ... Omid oversees all revenue and customer operations, as well as ... from 2005 to 2008.
Read more

Verizon Communications - Wikipedia, the free encyclopedia

What eventually became Verizon was founded as Bell ... raise revenue for Verizon ... 720 times between 2005 and 2007. Verizon won a ...
Read more

Stowe Shoemaker, Ph.D. Employment History

Revenue Management: ... Management. Forthcoming! Raab, C., Mayer, K., ... Operations Management – Peter Jones Editor: Shoemaker, ...
Read more

Internal Revenue Bulletin - November 28, 2005 ...

November 28, 2005 ... Metro Wildlife Management Base ... the Internal Revenue Service will issue a ruling or determination letter with ...
Read more