Mehdi Abdollahi Kamran, Samira Afsharfar, Fatma Al Mawali, Reza Babazadeh, Marya Al Balushi,
Volume 0, Issue 0 (10-2024)
Abstract
One of the most critical concerns in supply chain management is supplier selection, which significantly impacts an organization's efficiency and market agility. Balancing ordinal and basic criteria in supplier selection has become increasingly crucial in recent years within SCM. This research presents three MCDM methods including Fuzzy AHP and Fuzzy TOPSIS methods to assess and select suppliers in oil and gas industry. The critical criteria for supplier selection in the oil and gas sector have been reviewed in the literature and validated by experts actively working in the field. Initially, the Fuzzy AHP technique determines criterion weights and ranks suppliers. Subsequently, the Fuzzy TOPSIS approach is applied to rank prospective suppliers identified through objective evaluation. The findings show the capability of the utilized approaches in supplier selection procedure in oil and gas industry.
Hessam Nedaei, Seyed Gholamreza Jalali Naini, Ahmad Makui,
Volume 32, Issue 1 (1-2021)
Abstract
Data envelopment analysis (DEA) measures the relative efficiency of decision-making units (DMU) with multiple inputs and multiple outputs. In the case of considering a working team as a DMU, it often comprises multiple positions with several employees. However, there is no method to measure the efficiency of employees individually taking account the effect of teammates. This paper presents a model to measure the efficiency of employees in a way that they are fairly evaluated regarding their teammates’ relative performances. Moreover, the learning expectations and the effect of learning lost due to operation breaks are incorporated into the DEA model. This model is thus able to rank the employees working in each position that can then be utilized within award systems. The capabilities of the proposed model are then explored by a case study of 20 wells with 160 distinct operations in the South Pars gas field, which is the first application of DEA in the oil and gas wells drilling performance analysis.
