Abstract
Revenue management is a complex operational planning process involving predictive and prescriptive analytics. As real-world implementations strongly rely on the joint outcomes from both algorithms and analysts, we consider the revenue management system as an example of symbiotic analytics systems. This paper presents insights from a field study observing a natural experiment in revenue management. As a firm updates its automated revenue management systems, it also updates the related processes and the corresponding organizational structure. We use this opportunity to examine the multilevel use of symbiotic analytics systems based in a field study and explore the implications for the design of future systems. Specifically, we identify two different perspectives on the revenue management process. In the functional view, jobs are organized sequentially with a high degree of system-oriented specialization. The process view organizes jobs in a parallel structure, differentiating two perspectives on demand. Depending on what view the firm implements, different structural fault lines turn the communication and training of analysts into keystones of the planning process. Furthermore, as we point out, even implementing more sophisticated algorithms and redesigning planning processes and organization do not seem to reduce the relevance of human analysts.
Highlights
This paper presents insights from a field study observing a natural experiment in revenue management
While these research topics open up an interesting research opportunity when applied to revenue management (RM), we focus on a different perspective
Instead of measuring the fit of RM systems to existing organizational variables, we focus on different possible interconnections of information systems (IS), users, and organizational structure that can be implemented when switching to a new RM system
Summary
The importance of analytics and the role of algorithms in general and artificial intelligence for business operation in particular is growing (Choi et al 2018). As an exemplary area of analytics, this paper analyzes the revenue management (RM) process in a field study featuring an airline updating its systems and processes. In that, it focuses on the combination of automated algorithms and analyst interventions. Considering the advanced state of RM from the perspective of operations research, one could conclude that the topic is firmly in the hands of automated IS Firms implement such automated RM systems at a significant financial and organizational effort. Further examples from the domain of operational planning that combines algorithms and analysts to implement predictive and prescriptive analytics are inventory management (Wild 2017), work-shift scheduling (Lodree et al 2009), and assortment optimization (Hart and Rafiq 2006). We conclude the paper and give an outlook on further research in the final section
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