HCell and E-Waste Collection: An Analytics Case for Business Decision Making

Nazli Turken et al.

INFORMS Transactions on Education2025https://doi.org/10.1287/ited.2024.0106caarticle
AJG 2ABDC C
Weight
0.50

What the paper says

Prescriptive analytics has emerged as a powerful tool in decision-making processes across various industries. We present a detailed case study that explores a specific decision-making problem encountered by a cell phone manufacturer. The objective of this study is to demonstrate how case-based learning can enhance relatability and effectiveness in problem-solving, particularly in the realm of prescriptive analytics. The case utilizes prescriptive analytics methodologies and involves modeling and solving the problem using Excel, General Algebraic Modeling Language, or Python. The case study has been successfully integrated into graduate-level courses and has received positive student reviews. Feedback indicates that the case enhances students’ understanding of prescriptive analytics and its real-world applications, fostering improved engagement and learning outcomes. Supplemental Material: The Teaching Note and Excel/GAMS/Python files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

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https://doi.org/https://doi.org/10.1287/ited.2024.0106ca

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@article{nazli2025,
  title        = {{HCell and E-Waste Collection: An Analytics Case for Business Decision Making}},
  author       = {Nazli Turken et al.},
  journal      = {INFORMS Transactions on Education},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1287/ited.2024.0106ca},
}

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HCell and E-Waste Collection: An Analytics Case for Business Decision Making

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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