Revenue Management and Pricing
This workshop programme will provide basic and advanced methodologies for revenue management problems. The course aims at integrating different disciplines to solve both theoretical and practical concerns that arise in the design and development of systems for network revenue management, pricing, and operation.
The lectures will cover operation research methods, optimization algorithms and econometric models and machine learning. Keynotes will be given on timely topics by well-known researchers in the field. This workshop is open to Master, PhD. and post-doctoral students, as well as researcher and practitioners. This opportunity will facilitate professional networking and exchange of ideas about the theory and practice of research in revenue management.
- Network Revenue Management
- Pricing models
- Choice-based optimization
- Discrete and large-scale optimization
- Decision support systems for revenue management
- Case studies
- Dr. Kalyan Talluri
- Dr. Ilker Birbil
- Dr. Arnoud de Boer
- Dr. Shadi Sharif Azadeh
- Dr. Luce Brotcorne
- Dr. Christiane Barz
- Dr. Catherine Cleophas
- Dr. Arnoud de Boer
- Dr. Joern Meissner
- Dr. Shadi Sharif Azadeh
There are four time slots available for students’ presentations. We highly encourage PhD students or postdocs who are working on the related topics to present their research in this workshop. Interested students should send their abstracts (1 page, Times New Roman with 1.5 space between lines), and titles to firstname.lastname@example.org no later than 1 August.
Time Program 09:15-09:30 Coffee 09:30-09:45 Welcome and introduction 09:45-10:45 Dr. Kalyan Talluri 10:45-11:00 Break 11:00-12:00 Gianluca Antonecchia 12:00-13:30 Lunch on Campus 13:30-14:30 Session (Student presentation) 14:30-14:45 Break 14:45-15:45 Dr. Shadi Sharif Azadeh 15:45-16:00 Break 16:00-17:00 Session (Student presentation) 19:00 Dinner venue Time Program 08:45-09:00 Coffee 09:00-10:00 Dr. Ilker Birbil 10:00-10:15 Break 10:15-11:15 Dr. Arnoud de Boer 11:15-11:30 Break 11:30-12:30 Session (Student presentation) 12:30-13:00 Final remarks
Capacity sharing is arguably one of the best approaches to obtain sustainable and cost-effective use of resources. There exist various mathematical programming tools for optimal resource allocation. However, we still need to convince multiple parties to agree upon sharing their capacities. Even if they give their consent for collaboration, they also rightfully raise their concerns regarding the privacy of their sensitive data used in optimization models. In this work, we present the first model that considers the data privacy in bid-price control for network revenue management. Our analysis makes use of several previous privacy studies based on random transformations of the problem data. However, our focus on the bid-prices allows us to present new results about the privacy of the dual solutions. It is well-known that the transformed problem takes a long time to solve due to the loss of sparsity structure of the original problem. To overcome this issue, we propose a new solution approach that produces a random transformation which is likely to result in a sparse problem. Finally, we support our results with a computational study on a set of revenue management problems where the network structure is taken from a major European firm.
Cookie-Cutter Competition? Non-Price Strategies of Multiproduct Firms under Uniform Pricing
In this paper, we study how multiproduct firms compete using non-price strategies - namely, quantity, promotions, and rationing - in an industry where all firms charge the same price. Prior literature observes uniform pricing in retail chains (Gentzkow and DellaVigna, 2017), online music (Shiller and Waldfogel, 2011), movie industry (Orbach and Einav, 2007), soft drinks (McMillan 2007), and rental cars (Cho and Rust, 2010). Under classical uniform pricing, prices do not vary across regional markets. But significant price variation exists between firms in a market (Gentzkow and DellaVigna, 2017). We observe uniform pricing to be not a merely within-firm phenomenon but an industry-level one, which, to our knowledge, has not been examined in the previous literature.
Our study is set in India’s biscuit industry. We use Nielsen’s monthly data on the prices, quantities, sales revenues, and promotions of nearly 15,000 SKUs and 800 firms during April 2014 to March 2015. The data are disaggregated into 12 market segments - such as Cream, Crackers, and Marie - and 40 urban and rural regional markets. We augment these SKU-level data with CMIE Prowess data on firm financials. The richness of our data allow us to examine non-price strategies in detail.
We find that products with one standard deviation higher productivity offer, on average, 1.5% more quantity for the same price. Firms also compete by offering volume promotions for more productive products. We find greater levels of product availability and productivity-induced competition in urban areas compared to rural areas, implying uneven welfare effects. We show that deviating from uniform pricing can improve welfare of rural consumers. Overall, our results indicate that using non-price strategies, more productive products appear to gain market share, implying that competition thrives under the veil of uniform pricing. In the paper we propose a quantity-based measure of product-level productivity that controls for the biases related to input measurement, simultaneity and product scope of the firm. Our results are robust to alternative methods of estimating product-level productivity (De Loecker et al., 2016; Dhyne et al., 2017).
Our paper primarily contributes to the marketing literature in emerging economies, where differences in culture, socioeconomic conditions, institutions, and infrastructure result in novel marketing strategies (Prahalad, 2005; Narasimhan et al., 2015; Sudhir and Talukdar, 2015; Qian et al., 2015). Uniform pricing is usually motivated by consumer’s perceived fairness (Kahneman et al., 1986a and 1986b), and can, in theory, increase producer surplus (Chen and Cui, 2013). The potential negative welfare effects for rural consumers relative to urban consumers indicate that bottom-of-the-pyramid strategies combined with fairness-based pricing strategies do not always benefit the poor.
Co-Author : Ajay Bhaskarabhatla
Ilker Birbil is a faculty member in Erasmus University Rotterdam at the Econometric Institute, where he serves as an endowed professor for the Chair in Data Science and Optimization. He received his PhD degree from North Carolina State University, Raleigh, USA. He then worked for two years as a postdoctoral research fellow in Erasmus Research Institute of Management, Rotterdam, The Netherlands. His research interests include parallel and distributed optimization in machine learning, algorithm development for large-scale optimization problems, data science, revenue management, stochastic dynamic programming. Lately, he is very much interested in data privacy in decision making.
Venue on 28 August 2019
There is a fee of 40 euros, and registration is necessary for participation. Please register here. Deadline for registration is 19 August 2019.
Bank Transfer: reference: 12020001.001.111 Ectrie – Workshop Revenue Management & Pricing and your name.
Erasmus Universiteit, Erasmus School of Economics
P.O. Box 949, 3000 DD Rotterdam, the Netherlands
Interested students should send their abstracts and titles to email@example.com no later than 1 August 2019.