| dc.description.abstract | In the dynamic landscape of business operations, the optimization of processes remains
a critical challenge for enterprises. Startups often face unique challenges with their
distinct workflows that conventional tools fail to adequately address. Limited resources
hinder the development of custom tools, leaving companies reliant on manual processes.
The purchasing order workflow operates under time constraints while grappling with a
significant manual workload involved cleaning, integration, and data computation.
Consequently, this thesis aims to leverage Python automation to streamline manual
tasks, reducing the workload for the procurement department and minimizing human
errors. The primary objective of this research is to develop and implement a Python based tool to automate key aspects of the purchasing order process. Through the
application of the time-stop method and empirical research, a comparative analysis of
the existing manual process and the proposed automated system will be conducted to
measure its effectiveness. The study seeks to demonstrate not only the efficiency gains
but also the potential for error reduction afforded by the automated system. The findings
of this research are expected to contribute to the field of supply chain optimization,
particularly for small and medium-sized enterprises (SMEs). The proposed tool aims to
enhance the overall efficiency of purchasing order processes, offering a practical
solution for businesses with limited resources. This thesis provides insights into the
adoption of Python automation tools in supply chain management and offers a model
for other SMEs seeking to optimize their operations. | en_US |
