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At Datup.ai, we believe trust is as important as the data itself. Discover the technical rigor behind the "Supply Chain Trends 2026" study.
Validated Responses (N)
Convenience sampling across professional networks.
Geographic Scope
Priority focus on Latin America.
Data Collection
11/24/2025 to 01/15/2026. Captures fiscal planning.
Quality Control
Manual identity filtering and Python-based cleaning.
The instrument was distributed through a multi-channel strategy focused exclusively on specialized profiles:
Corporate credentials (Name, Email) were requested exclusively to verify professional experience.
Contact data will not be used for sales. The report is fully anonymous and aggregated.
Manual filtering ensures insights from real actors in the logistics ecosystem, eliminating bots or duplicates.
Before ingestion, records were removed to ensure relevance:
Manual normalization of obvious spelling errors in open fields that would hinder subsequent automatic categorization.
explode() to separate multiple options.
Exploration of frequencies and distributions to identify dominant trends ("The current snapshot").
Variable cross-referencing (e.g., Company Size vs. AI Adoption) to understand segmented impact.
Transparency includes the tools that made this analysis possible. Learn about the technical workflow behind the report.
Dynamic forms with conditional logic for fluid and structured data capture.
Centralized repository for raw data, ensuring information integrity and backup.
Script execution in Google Colab for deep cleaning, normalization, and statistical calculation.
Advanced assistant for code generation and qualitative synthesis.
The use of Generative AI Chatbots was limited to programming assistant functions for generating and debugging Python cleaning scripts, as well as support in drafting and preliminary synthesis of qualitative findings, ensuring efficiency without compromising human oversight.
