INFORMS Journal on Applied Analytics

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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 ...
Standing Up Business Analytics in the Space Force
- Jeffrey Williams
  Corresponding Author
  Jeffrey Williams
  [email protected]
  https://orcid.org/0000-0003-1990-474X
  United States Air Force, Air University, Maxwell AFB, Alabama 36112
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- Andy Burrow
  Andy Burrow
  [email protected]
  https://orcid.org/0009-0003-3457-6825
  United States Space Force, Space Operations Command, Peterson Space Force Base, Colorado 80914
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Published Online:June 1, 2026
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Abstract
The Space Force stands up business analytics at a field command to do more with less across operational and administrative missions.
When Perfect Data Meet Imperfect Thinking: A Delivery Optimization Story
- Michael F. Gorman
  Michael F. Gorman
  [email protected]
  https://orcid.org/0000-0003-1295-8147
  School of Business Administration, Department of MIS, Operations and Decision Sciences, University of Dayton, Dayton, Ohio 45469
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Published Online:May 26, 2026
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Abstract
AbstractThis optimization story, in the spirit of Gene Woolsey’s “Fifth Column” series, describes the very real non-modeling challenges faced in many modeling projects.
Hierarchical Parameter Calibration of Digital Twins at Ford Motor Company
- Jaeshin Park
  Jaeshin Park
  [email protected]
  https://orcid.org/0009-0002-4347-5914
  Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109;
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  ,
- Chenfei Li
  Chenfei Li
  [email protected]
  https://orcid.org/0000-0001-7473-7417
  Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109;
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  ,
- Eunshin Byon
  Corresponding Author
  Eunshin Byon
  [email protected]
  https://orcid.org/0000-0002-2506-1606
  Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109;
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- William Bailey
  William Bailey
  [email protected]
  Vehicle and Propulsion Systems, Research and Advanced Engineering, Ford Motor Company, Dearborn, Michigan 48124
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- Quazi Hussain
  Quazi Hussain
  [email protected]
  Vehicle and Propulsion Systems, Research and Advanced Engineering, Ford Motor Company, Dearborn, Michigan 48124
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- Yashodeep Lonari
  Yashodeep Lonari
  [email protected]
  https://orcid.org/0000-0001-7432-2086
  Vehicle and Propulsion Systems, Research and Advanced Engineering, Ford Motor Company, Dearborn, Michigan 48124
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Published Online:May 22, 2026
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Abstract
AbstractWe propose a hierarchical calibration framework for digital twins that accounts for parameter structures. Applied to real-world electric vehicle digital twin from Ford Motor Company, the method improves calibration accuracy and enables prediction ...
Final Exam Scheduling at Bucknell University: A Case Study and Open-Source Tool
- Clara Chaplin
  Clara Chaplin
  [email protected]
  Department of Mathematics & Statistics, Bucknell University, Lewisburg, Pennsylvania 17837
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- Stanley Gai
  Stanley Gai
  [email protected]
  Department of Analytics and Operations Management, Bucknell University, Lewisburg, Pennsylvania 17837
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  ,
- Samuel C. Gutekunst
  Corresponding Author
  Samuel C. Gutekunst
  [email protected]
  https://orcid.org/0000-0002-3516-2996
  Department of Computer Science, Mathematics & Statistics, Bucknell University, Lewisburg, Pennsylvania 17837
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  ,
- Tsugunobu Miyake
  Tsugunobu Miyake
  [email protected]
  Department of Computer Science, Bucknell University, Lewisburg, Pennsylvania 17837
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- Thiago Serra
  Thiago Serra
  [email protected]
  https://orcid.org/0000-0001-8096-9804
  Department of Business Analytics, University of Iowa, Iowa City, Iowa 52242
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  ,
- Luke Snyder
  Luke Snyder
  [email protected]
  Department of Computer Science, Bucknell University, Lewisburg, Pennsylvania 17837
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  ,
- Vy Tran
  Vy Tran
  [email protected]
  Department of Computer Science, Bucknell University, Lewisburg, Pennsylvania 17837
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  ,
- Lucas Waddell
  Lucas Waddell
  [email protected]
  Department of Mathematics & Statistics, Bucknell University, Lewisburg, Pennsylvania 17837
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Published Online:May 8, 2026
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Abstract
We present a case study for final exam scheduling at Bucknell University. We worked in close collaboration with the Registrar’s office, ultimately designing integer programming models and heuristics. We implemented our approach in open-source, user-...

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Alexandra M. Newman

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ISSN: 2644-0865 (Print)

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2024 Impact Factor: 1.8

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