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Revenue Management and Strategic Pricing for Service Enterprises. Center for Service Enterprise Engineering The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering Pennsylvania State University. Revenue Management and Pricing. Introduction
Center for Service Enterprise Engineering
The Harold and Inge Marcus Department
Industrial and Manufacturing Engineering
Pennsylvania State University
Examples and Challenges
Our Research and Proposal
Pioneer application to American Airlines contributed $1.4 billion in revenue over a 3 year period with associated profits of $0.892 billion (for 1989-1991)
Customers are statistically distinguished from one another according to their propensity/ability to pay
Pricing is by customer class or even by individual as well as by service class
Pricing is dynamic
Computer-automated and applicable to large transaction volumes
The new frontier: strategic pricing, wherein the price response of competitors is explicitly considered
Might be loss
Source from http://www.continental.com/
Following this slide are slides that give an overview of our relevant research and proposed effort
Different types of products
Basic Burglary (4), Value (2), Cellular (2), Expanded (2)
Burglary, Fire, Video surveillance, Integrated system, Access Control
Price-sensitive product demand
Demand-dependent quality (“congestion” at the monitoring center)
Strategic Pricing: The Need to Set Prices Considering Competitors’ Responses
Maximize profit in light of competitors’ strategic actions
Develop multi-year plan to displace rivals
We propose to develop software for dynamic and strategic pricing for security service companies.
In the event service companies have already invested in revenue management, we can add a strategic pricing capability not available elsewhere.
Following this slide are several slides that give an overview of our proposed effort.
Analysis, and Implementation
Perfect information (imperfect information)
Demand is deterministic (uncertainty via learning, robust optimization, data driven)
Single product (network)
Single resource (network)
Single period (multiple period, continuous time)
Sellers optimization, pricing (resource allocation)
Single seller (game)
Complete market, risk neutral (incomplete market, risk averse)
How should sellers price the product and allocate resource with competition?
What are the equilibrium prices in the market?
How to handle demand uncertainty?
How to handle demand learning?
How to handle risk preference?
Demand distribution and parameters
Revealed over time
High frequency of data
Simultaneously forecast the demand and optimize the pricing strategy.
Sellers Maximize profit over whole time horizon
Allocation of capacity
Constraint Demand dynamics
Bounds on price
Bounds on capacity
Bounds on demand