نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
In today's business landscape, outsourcing has emerged as a crucial process within organizations, presenting various challenges in contractor selection. Even with the appropriate contractor chosen and contracts signed, numerous factors can influence the contractor's performance throughout the project lifespan, potentially disrupting the operations of the client organization. This study seeks to enhance, integrate, and streamline the outsourcing process by leveraging machine learning algorithms based on historical data from the Energy Industries Design and Engineering Company (EIED).
Employing a quadratic multi-parameter regression algorithm grounded in quality management system principles, the study identifies and evaluates the status of contracts at the end of the operational phase through the systematic periodic assessment of contractors. The regression model utilized in this research is a multivariable quadratic regression model that analyzes past data within the organization to effectively assess and categorize contractors. This model not only enables a thorough evaluation but also facilitates the revision of contracts based on the company’s standards by monitoring the model's output.
The analysis generated by this model empowers organizations to anticipate future contractor performance, thereby informing and guiding necessary adjustments to improve the evaluation of contractors. By harnessing machine learning and historical insights, the study ultimately aims to provide organizational leaders with the tools necessary for optimizing their outsourcing strategies, ensuring that contractor performance aligns more closely with organizational objectives. This approach is expected to mitigate risks associated with contractor performance variability and contribute to more successful project outcomes through informed decision-making processes.
In summary, the integration of advanced analytics and historical data analysis offers a promising pathway for enhancing the efficacy of outsourcing in various organizational contexts.
کلیدواژهها English
