Revenue management (RM) is the umbrella term for a set of strategies, tactics and
instruments aiming at the maximization of yield by allocating a company’s capacity to
different customers at different prices. Due to its great success, the application of
revenue management is widespread nowadays. But as the origin of RM lies in the
airline industry, this is still the sector of its main application. Service industries such
as hotels, car-rentals or internet service providers which share the same characteristics
as the airline industry (e.g. fixed capacity and a highly uncertain demand) discovered
quite early the potential of RM. Consequently, they were the first to adopt
RM strategies.1 Retailers, broadcasting industries and companies of the energy sector
have followed lately.
The core concept of RM becomes clear, considering the economics of RM (Cross
1997, p.73ff): The downward-sloping demand curve (figure 1) shows the number of
units of a certain product which are sold at a certain price. [...]
Table of Contents
1 Introduction
1.1 Problem statement
1.2 Objective
2 Basics of Revenue Management
2.1 Definition and Historical Background
2.2 Price- vs. Quantity-based Revenue Management
2.3 Instruments
2.4 Forecasting
2.5 Measuring RM performance
3 Requirements for quantity-based RM
3.1 Market
3.2 Company
4 Application to the Manufacturing Industry
4.1 Definition of manufacturing
4.2 Applicability in Service Industries vs. Manufacturing Industries
4.2.1 Market
4.2.2 Company
4.2.3 Differences between the service industry and manufacturing
4.3 In-depth look at MTO and ATO
4.3.1 Implications for the use of RM methods
4.3.2 Static models for bid-price generation
4.3.3 Self-adjusting bid-price
4.4 Current use of RM in manufacturing industries
5 Case Study
5.1 Setting of the case study
5.2 RM-fitness
5.3 Introduction of the Test Case
5.4 Implementation
6 Conclusion
7 Future Trends and Challenges
Objectives and Core Topics
The primary objective of this study is to examine the applicability of Revenue Management (RM) strategies within the manufacturing industry. The research aims to identify whether manufacturing enterprises meet the necessary requirements for RM, specifically focusing on quantity-based approaches like Make-to-Order (MTO) and Assemble-to-Order (ATO) systems, and to demonstrate practical implementation through a real-world case study in the steel industry.
- Theoretical foundations of Revenue Management and its core instruments.
- Evaluation of requirements for implementing RM in manufacturing environments.
- Comparison between service industry characteristics and manufacturing production models.
- Advanced mathematical approaches for bid-price generation, including static and self-adjusting models.
- Case study application demonstrating capacity allocation and order acceptance processes in the steel sector.
Excerpt from the Book
3.1 Market
The requirements concerning the market - advance sales, stochastic demand, demand segmentation and that the price should not serve as a signal of product value or quality - are listed and explained below:
To successfully apply RM in a company, the company's marketing must be coined by advance sales. This means that the product (capacity) is sold before it is actually produced (used) (Swann 1999, p.8). In this context Kimms and Klein (2005, p.6) introduce the term "external factor" to explain how, in the considered companies, only some kind of input/contribution from the customer can enable the service provider or producer to perform his service. Therefore, the advance sale of the service or product is a mandatory requirement as without it the service cannot be performed. For example, it is impossible for a transportation company to begin with the loading of goods as long as the customer has not made these goods available to the transportation company. In addition to that Rehkopf (2006, p.47) mentions that auctions can be used to efficiently regulate demand for a service (for example: a seat on certain flight) if all customers are willing to bid for the service at the same time.
If a company facing a shortage of production capacity knew all the incoming orders for the production period it has to plan, it would be relatively easy to decide which orders should be accepted and which orders should be rejected. However, when this static determination is impossible due to stochastic demand for a product, the use of revenue management is required. RM enables the company to make very accurate forecasts of future orders which provides them with the knowledge to make close to optimum decisions within the order acceptance process (Kniker and Burman 2001, p.300).
Summary of Chapters
1 Introduction: Provides a definition of RM, discusses the problem statement, and outlines the primary objectives of the research.
2 Basics of Revenue Management: Introduces the historical development, distinguishes between price- and quantity-based RM, and reviews common instruments like capacity control and forecasting.
3 Requirements for quantity-based RM: Identifies the fundamental market and company-related conditions necessary for the successful deployment of quantity-based RM systems.
4 Application to the Manufacturing Industry: Analyzes the suitability of different manufacturing production models (MTS, MTO, ATO) for RM application and explores mathematical bid-price models.
5 Case Study: Presents a practical application of quantity-based RM within the iron and steel industry, including setup, implementation, and simulation results.
6 Conclusion: Summarizes the key findings regarding the applicability of RM in manufacturing and highlights the potential for further research.
7 Future Trends and Challenges: Discusses the integration of RM into organizational structures and addresses future challenges regarding system integration and human factors.
Keywords
Revenue Management, Manufacturing Industry, Quantity-based RM, Bid-price Control, Make-to-Order, Assemble-to-Order, Capacity Allocation, Stochastic Demand, Order Acceptance, Performance Measurement, Steel Industry, Forecasting, Resource Optimization, Profit Maximization, Production Planning.
Frequently Asked Questions
What is the core focus of this research?
The research explores the potential and limitations of applying Revenue Management (RM) strategies, originally developed for the service industry, to the manufacturing sector.
What are the central themes of the work?
Key themes include the structural requirements for RM, the classification of manufacturing types (MTS, MTO, ATO), and the mathematical modeling of capacity allocation.
What is the primary goal of the study?
The goal is to determine if and how quantity-based RM can be successfully implemented in manufacturing to increase revenue, validated through a case study in the steel industry.
Which scientific methods are employed?
The work utilizes empirical study reviews, descriptive analysis of production models, and quantitative simulation of bid-price strategies compared to first-come, first-served (FCFS) methods.
What does the main part of the book cover?
The main part covers the theoretical framework of RM, detailed requirements for application, an analysis of MTO/ATO scenarios, and the technical implementation of network-based capacity control.
What are the defining keywords of the work?
The most important keywords include Revenue Management, Manufacturing Industry, Bid-price Control, Make-to-Order, and Capacity Allocation.
Why is the "fixed capacity" requirement important for RM?
Fixed capacity is necessary to justify RM; if capacity were infinitely flexible, companies could simply adjust to demand without needing complex optimization systems to accept or reject orders.
What is the role of the Ford Motor Company example in the text?
The Ford example serves as a rare industry case study demonstrating how an automotive manufacturer utilized custom RM tools to align production with market demands, resulting in significant profit improvements.
How does the "self-adjusting bid-price" approach differ from conventional models?
Unlike static models that require frequent recomputation, self-adjusting bid-prices use a linear function to adapt thresholds dynamically based on time and resource usage, reducing the need for constant re-optimization.
- Quote paper
- P. Blumenthal (Author), I. Petersen (Author), T. Schubert (Author), 2008, Application of Revenue Management to the Manufacturing Industry, Munich, GRIN Verlag, https://www.grin.com/document/126374
