Optimization of On-Time and Complete Deliveries in Logistics through Multi-Criteria Classification and Intelligent Cycle Counting with Machine Learning
Published: 16 June 2026| Version 1 | DOI: 10.17632/8w95c966yn.1
Contributors:
, Jair BroncanoDescription
This dataset contains the anonymized transactional history and multi-criteria classification data (Average Daily Turnover, Unit Value, Annual Frequency, and Days with Available Stock) used to train a Machine Learning Decision Tree model for inventory optimization and Smart Cycle Counting.
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Institutions
- Peruvian University of Applied SciencesLima Province, Lima
Categories
Industrial Engineering, Machine Learning, Logistics, Accuracy Analysis, Inventory Management