A python-based record-keeping framework for data accuracy and operational transparency in logistics

Abstract

Accurate record-keeping and operational transparency are critical for optimizing logistics performance, reducing errors, and enhancing stakeholder trust. With the increasing digitization of supply chains, there is a pressing need for frameworks that ensure reliable data capture, storage, and traceability. This paper proposes a Python-based record-keeping framework designed to enhance data accuracy and operational transparency within logistics operations. The framework leverages Python's versatility and open-source libraries to provide automated data validation, real-time record updates, and audit trail generation. Through a comprehensive review of current technologies and methodologies, the paper highlights the gaps in existing logistics record-keeping systems and justifies the suitability of Python as a core tool. The proposed framework's design components, operational workflow, and potential impact on logistics efficiency are discussed. This contribution offers logistics practitioners and researchers a scalable, adaptable solution that aligns with modern data governance principles.