INFORMS Journal on Applied Analytics

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PepsiCo Deploys AI-Driven Pricing and Promotion Optimization at Scale
- Aleix Llenas
  Corresponding Author
  Aleix Llenas
  [email protected]
  https://orcid.org/0000-0002-3436-0165
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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  ,
- Eduardo Salazar-Treviño
  Eduardo Salazar-Treviño
  https://orcid.org/0009-0006-9872-4825
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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- Francisco Leskovar
  Francisco Leskovar
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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- Francesc Pons-Llopis
  Francesc Pons-Llopis
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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- Federico Todeschini
  Federico Todeschini
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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- Deepika Gowda
  Deepika Gowda
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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- David Bofill
  David Bofill
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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- Laxmi Anish
  Laxmi Anish
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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- Michael Cleavinger
  Michael Cleavinger
  [email protected]
  Data Science & Advanced Analytics Department, PepsiCo, Inc., Purchase, New York 10577
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Published Online:June 11, 2026
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Abstract
AbstractPepsiCo deployed two AI-driven systems, PromoAI and PricingAI, that combine machine learning with mathematical optimization to automate promotional calendar generation and base price recommendations at enterprise scale. Both tools have been ...
Editorial Statement—Harnessing the Power of Large Language Models Responsibly in Applied Analytics
- Segev Wasserkrug
  Segev Wasserkrug
  [email protected]
  https://orcid.org/0000-0002-8314-0222
  IBM Research, Haifa 31905, Israel
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Published Online:June 11, 2026
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Abstract
Editorial statement to ensure the ethical usage of Large Language Models as part of analytics applications.
Leveraging Geospatial Analysis and Machine Learning for Optimal Green Vehicle Assignment
- Josué C. Velázquez-Martínez
  Corresponding Author
  Josué C. Velázquez-Martínez
  [email protected]
  https://orcid.org/0000-0001-8863-6609
  Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142; and AI for Manufacturing & Supply Chain Institute, Tecnologico de Monterrey, Monterrey, Nuevo León 64700, Mexico
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- Laura Palacios-Argüello
  Laura Palacios-Argüello
  [email protected]
  Luxembourg Center for Transportation and Supply Chain Management, University of Luxembourg, L-1359 Luxembourg, Luxembourg
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- Ade Barkah
  Ade Barkah
  [email protected]
  Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
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- Jan C. Fransoo
  Jan C. Fransoo
  [email protected]
  https://orcid.org/0000-0001-7220-0851
  Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, Netherlands
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- Karla M. Gamez-Perez
  Karla M. Gamez-Perez
  https://orcid.org/0000-0002-9761-8768
  Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142; and School of Engineering & Science, Tecnológico de Monterrey, Monterrey, Nuevo León 64700, Mexico
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Published Online:June 8, 2026
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Abstract
AbstractThis paper presents a data-driven approach that uses geospatial analysis and machine learning to assign the right vehicles to the right regions, reducing fuel consumption, costs, and transportation-related CO2 emissions. Applied to a large retail ...
Analytics for Resource Allocation in Pandemic Management
- Jarmo Haferkamp
  Corresponding Author
  Jarmo Haferkamp
  [email protected]
  https://orcid.org/0000-0002-5914-7539
  Management Science, Otto-von-Guericke-Universität Magdeburg, 39106 Magdeburg, Germany
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- Martin Bicher
  Martin Bicher
  [email protected]
  https://orcid.org/0000-0002-1362-6868
  Institute for Information Systems Engineering, Technische Universität Wien, 1040 Vienna, Austria
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- Peiman Ghasemi
  Peiman Ghasemi
  [email protected]
  https://orcid.org/0000-0002-1085-0776
  Business Decisions and Analytics, Universität Wien, 1010 Vienna, Austria
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- Marlin W. Ulmer
  Marlin W. Ulmer
  [email protected]
  https://orcid.org/0000-0003-2499-6570
  Management Science, Otto-von-Guericke-Universität Magdeburg, 39106 Magdeburg, Germany
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- Nikolas Popper
  Nikolas Popper
  [email protected]
  https://orcid.org/0000-0003-4615-2774
  Institute for Information Systems Engineering, Technische Universität Wien, 1040 Vienna, Austria
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- Jan Fabian Ehmke
  Jan Fabian Ehmke
  [email protected]
  https://orcid.org/0000-0001-8474-7483
  Business Decisions and Analytics, Universität Wien, 1010 Vienna, Austria
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Published Online:June 8, 2026
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Abstract
AbstractThe COVID 19 pandemic highlighted the importance and difficulty of allocating scarce containment resources based on accurate predictions of disease spread. While extensive research has focused on descriptive and predictive analytics to support ...
Geiger Uses Machine Learning to Reduce Customer Churn in the Promotional Products Industry
- Jan Schemm
  Corresponding Author
  Jan Schemm
  [email protected]
  https://orcid.org/0009-0001-2167-179X
  Faculty of Business Studies, Hochschule Düsseldorf - University of Applied Sciences, 40476 Düsseldorf, Germany
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- Christian Schwarz
  Christian Schwarz
  [email protected]
  https://orcid.org/0000-0002-5310-0035
  Faculty of Business Studies, Hochschule Düsseldorf - University of Applied Sciences, 40476 Düsseldorf, Germany
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- Marc Strickrodt
  Marc Strickrodt
  [email protected]
  Geiger GmbH, 48308 Senden, Germany
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Published Online:June 5, 2026
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Abstract
AbstractTo reduce customer churn in its drop-shipment business, Geiger GmbH implemented a machine-learning-based prediction system that helps sales representatives identify customers at risk of leaving. Integrated into a Power BI dashboard, the solution ...

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