INFORMS Advances in Decision Analysis Conference (ADA 2024) was held on July 10-12 at Aalto University, Espoo. It was chaired by Professor Ahti Salo, the Honorary President of FORS. The conference was the fifth in the series of INFORMS ADA conferences and it brought together 140 international scientists working and interested in decision analysis. ADA 2024 provided a platform for interdisciplinary discussions through talks by researchers in decision analysis, behavioral economics, judgment and decision-making, machine learning, statistics, and other related disciplines with a prescriptive focus.

FORS board members Jussi Leppinen and Topias Terho participated in the ADA Conference, which FORS member Leevi Olander was organizing. They will now share their experiences and thoughts about the event.

Topias’s thoughts
For me, ADA was a great experience. In larger conferences such as EURO, I tend to attend only sessions that are close to my field of research. However, since ADA only had three parallel sessions, I was forced to step outside of my habitat and get some exposure to other fields of operations research. My highlights included the Kahneman panel, where esteemed scholars like Don Kleinmuntz reflected on the importance of Daniel Kahneman’s work to their careers and society, and the keynote by Kari Renko, where he presented how decision analysis was used in the Finnish fighter aircraft procurement process.

The overall atmosphere of the conference was warm and exciting, and it was truly a pleasure to participate in the evening activities. It was especially nice to get to know many colleagues from Finland whom I had not previously encountered.

The doctoral thesis of M.Sc. (Tech.) Marjut Hirvonen, “Leveraging the impact of analytics on organizational performance – a multi-method study,” will be publicly examined at the Aalto University School of Business on Friday, October 25, 2024.

The defence will take place in Ekonominaukio 1, Espoo (Lecture hall U006) at 12:00.

The management of analytics and the development of employees’ analytics skills are needed to maximize the benefits derived from analytics

In the current business environment, companies must optimize their increasingly complex internal processes and supply chain operations to achieve a competitive advantage. Utilizing analytics offers solutions to challenging problems and enables the maximization of business value. Although analytics methods have been extensively studied, practical benefits have been less addressed. Marjut Hirvonen examines the impact of analytics on organizational performance, the benefits of analytics, and best practices for deploying analytics in her doctoral thesis.

‘The results show that in companies, it is important to consider not only technical issues but also the management of analytics and the training of staff in the use of analytics to fully benefit from analytical solutions,’ says Marjut Hirvonen.

Three studies – from general insights to specific details

The doctoral thesis includes three separate studies, progressing from a general level to a more detailed level. The first study is a comprehensive meta-analysis of previous literature, examining the relationship between the use of analytics and organizational performance. The importance of analytics skills and management is emphasized in the results to achieve the best possible benefits from analytics.

The second study of the doctoral thesis focuses on the most advanced form of analytics, namely prescriptive analytics, which provides decision recommendations based on data. The study identifies key enablers for the successful deployment of prescriptive analytics based on interviews conducted in several organizations. These key enablers include leadership, sufficient resourcing, and user engagement. In addition to the importance of management support, it is essential to understand that merely implementing analytics technically does not yield the greatest benefits for organizations. Instead, investment should also be made in developing the skills of analytics users.

The third study analyzes the benefits of advanced analytics in optimizing the supply chain for renewable fuels. It identified both monetary and less tangible benefits, such as improved data management. This study encourages the use of analytics across various applications, and similar benefits can be expected to be gained in other industries as well.

The doctoral thesis demonstrates the usefulness of analytics for organizations and helps to succeed in the deployment of prescriptive analytics. The results support the integration of analytics into the company’s strategy and emphasize the importance of developing employees’ analytics skills.

More information about the defence

News about the thesis

The doctoral thesis of M.Sc. (Tech) Olli Herrala, “Mixed-integer formulations for large-scale energy-environmental optimization”  will be publicly examined at the Aalto University School of Science on Friday, September 6, 2024.

The defence will take place in Otakaari 1, Espoo (hall H304) at 12 noon.

During the past 10 years, we have seen stronger and more frequent impacts of climate change. It is increasingly evident that action should be taken to at least slow down this change. However, making decisions about what exactly should be done is challenging, largely because of the substantial uncertainty in many parts of the problem.

Decision-making under uncertainty has been researched widely in the past 70 years, but most of the research assumes the uncertainty to be independent of our decisions. This is however not the case when making decisions in researching new climate change mitigation techniques such as carbon capture and storage, as these decisions have an impact on the highly uncertain future costs of climate change mitigation. To address this research gap, this thesis presents results on solving problems with decision-dependency in both probabilities and information structures.

This thesis also considers a hierarchical decision-making setting where an international policymaker wants to reduce emissions from electricity production by setting a carbon tax, while avoiding significant decreases in the total production that would increase electricity prices. However, the problem also includes transmission system operators and electricity producers, each with their own goals. The interactions between these players result in a complex problem where a carbon tax might have unexpected consequences such as shifting production from one country to another or simply increasing the price of electricity.

The methods presented in this dissertation allow decision-makers to model and anticipate the effects of decision-dependent uncertainty and hierarchical decision-making processes. The solution methods are based on mixed-integer optimization, leveraging the substantial developments in solving such models during the past 20 years. The case studies presented in the dissertation illustrate the capabilities of the proposed methods and show how they could be used to support decision-making in these complex systems.

More information about the defence