Industrial Innovations

Industrial Innovations

Supplier Evaluation in the Outsourcing Process of Energy Industry Projects Using Data-Driven Approaches via Machine Learning Algorithm (Case Study: Energy Industries Engineering & Design Company)

Document Type : Original Article

Authors
Matin Mottaghi 1
Omid Poursabzi 2
Mohammad Mohammadi 3
1 Department of Industrial Engineering, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
2 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran. - QHSE System Management Department of the Design and Engineering Company in the energy industry.
3 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
10.61882/jii.2.4.436
Abstract
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.
Keywords
Artificial Intelligence
Oil &‌‌‌‌‌‌‌‌ Gas &‌‌‌‌‌‌‌‌ Petrochemical
Machine Learning
Process Mining
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Industrial Innovations
Volume 2, Issue 4 - Serial Number 8
Autumn 2024
Pages 436-451

  • Receive Date 20 August 2025
  • Revise Date 25 October 2025
  • Accept Date 01 November 2025