Forecasting Impact Forecasting Impact

In this episode, we spoke to Joannes Vermorel, founder and CEO at Lokad, a quantitative supply chain software company. 

Joannes discussed how supply chain theory is broken down, and that we need to think in terms of paradigms and modules rather than models for solving supply chain problems. He talked about issues in time series forecasting and judgmental forecasting. He emphasized how critical it is to have a holistic view of the problem, to aim for optimization of the entire system. and to acknowledge that we often don’t know the metric to be optimized and it requires some experimentation. We also discussed how Lokad is deploying AI pilots to address some of the important problems in supply chain.

To learn more about Lokad, visit https://www.lokad.com/ or check them out on YouTube. 

In this episode, we spoke to Laurent Ferrara, Professor of International Economics at SKEMA Business School. Laurent discussed the role of nowcasting, particularly in the realm of macroeconomic nowcasting. He delved into the details of the models and methods that have been proven effective in this domain. Laurent also talked about GDP nowcasting using Google data and shared some intriguing results from his recent research.

Laurent is the program chair of the 44th International Symposium on Forecasting, which will be held in Dijon, France. He provided an overview of the conference program and explained why we should attend!

In this episode of our podcast, we delve into the intricate world of machine learning (ML) deployment with Dr. Eric Siegel, author of the book AI Playbook, Mastering the Rare Art of Machine Learning Deployment. 
Dr. Siegel, once an avid advocate of ML, now approaches the field with a disciplined yet optimistic perspective. He shares invaluable insights on how businesses can effectively implement ML strategies. Our discussion revolves around a range of compelling topics, from the inspiring story of Jack from UPS, who leveraged his psychology background to revolutionize parcel delivery, to the common pitfalls that cause many ML projects to fail. 

Eric elucidates the six crucial steps for ML deployment, emphasizing the importance of ethical considerations in this rapidly evolving field. Whether you're a student, a business leader, or just an AI enthusiast, this episode offers a treasure trove of knowledge and strategies to navigate the complex landscape of machine learning deployment. 

Our guests are Michele Trovero, leader of the Forecasting R&D group at SAS, and Spiros Potamitis, Data Scientist and Product Marketing Manager at SAS. We delved into the intriguing intersection of Language Model-based AI (LLMs) and forecasting software.

We explored the openness of forecasting software providers to embrace LLMs and discussed the profound impact these models could have on the industry. Michele and Spiros shared insightful examples of LLM applications. They elaborated on the way code generation capabilities powered by LLMs would enhance the development of forecasting software and the user experience. Additionally, they explored how LLMs could democratize forecasting, and discussed other tools and technologies that could contribute to this goal. We also discussed the typology of models behind LLMs, and their applicability in forecasting, as well as the limitations and enablers in using AI-pretrained models in forecasting.   The discussion extended to SAS Visual Forecasting and Model Studio, shedding light on their functionalities and workings.

Michele and Spiros speculated on the areas of focus for forecasting software companies, enhanced automation in forecasting, shifts in user consumption patterns, and anticipated integrations between forecasting systems and other technologies.

They recommended the following for further study:

1. How Will Generative AI Influence Forecasting Software? by Michele Trovero and Spiros Potamitis, Foresight: The International Journal of Applied Forecasting.
2. A Glimpse into the Future of Forecasting Software, by Spiros Potamitis, Michele Trovero, Joe Katz, Foresight: The International Journal of Applied Forecasting. 

Martie-Louise Verreynne is a Professor in Innovation and Associate Dean (Research) in the Faculty of Business, Economics and Law, at the University of Queensland.

Prof. Martie-Louise Verreynne joined us to discuss the evolving partnership between academia and industry. While not a new concept, it has significantly transformed over the years, with over 40% of global patents now stemming from this collaboration, underscoring its growing importance in innovation. We examined the keys to success and common barriers such as differing priorities, resources, IP, paperwork, and objectives.
Martie-Louis discussed her recent work on collaborations between Small and Medium-sized Enterprises (SMEs) and universities, highlighting the complexities of balancing diverse interests. 
We also explored successful university-industry collaborations in forecasting and their significant impacts. The pandemic has emphasized the importance of agility, which has influenced collaboration dynamics. Looking ahead, she envisions universities playing a central role in shaping the future of university-industry collaboration, continuing to drive innovation for the benefit of society and industries.

In this episode, we delved into the dynamic realm of transportation forecasting, exploring a wide array of ideas and questions. 
Our discussion with David began by examining the primary data sources and methodologies that drive modern transportation forecasting. We continued by highlighting the pivotal role of real-time data, GPS technology, and advanced algorithms in providing accurate insights into traffic patterns, public transit ridership, and the trajectory of mobility trends.  We also discussed the integration of emerging technologies like autonomous and electric vehicles, showcasing their transformative potential in shaping transportation models and infrastructure. 

From a consulting and practical perspective, we explored the challenges of ensuring the accuracy and reliability of transportation forecasts and contemplated the influence of AI and machine learning on the future of transportation forecasting.