Enhancing MRP Adoption in SMEs: A Python-Based Algorithmic Approach

Implementing Enterprise Resource Planning (ERP) and Material Requirements Planning (MRP) systems enables companies to gain a competitive advantage in current dynamic and uncertain markets. Although implementing these systems offers several benefits, it encounters major barriers, especially in Small and Medium Enterprises (SMEs). They constitute a significant share of the industrial sector globally but often face challenges such as limited access to finance, technology, and markets, which can hinder their growth and sustainability. Addressing these barriers is vital for leveraging the full potential of SMEs and fostering a more dynamic and resilient global economy. In this context, adopting Python-based solutions could be a promising solution. Python’s open-source nature indeed empowers users with unrestricted application development, distribution, and commercialization. This flexibility fosters platform-independent development and eliminates vendor lock-in. Furthermore, its clear syntax, powerful interpreter, and extensibility enable efficient and versatile programming across various paradigms, making it a very useful language for resource-limited contexts such as SMEs. To this concern, the objective of the present work was to investigate the benefits offered by implementing a Python-based algorithm to facilitate the adoption of an MRP system in SMEs. To this end, a Python algorithm has been developed based on a sequence of four logical steps: exploding, netting, lot sizing, and offsetting, and was numerically applied. The results obtained provided a clear perspective on the usefulness of Python and the benefits derived from its use due to its ease of use, flexibility, and reduced costs.