|
Providing a Model for Assessing the Health of Automotive Production Lines’ Quality through Relative Indicators Using Network Data Envelopment Analysis
|
Mehdi Komijani    , Farhad Hoseinzadeh Lotfi , Amir Gholamabri , Naghi Shoja , Seyed Ahmad Shayannia |
| Islamic azad university,tehran,iran , farhad@hosseinzadeh.ir |
|
|
Abstract: (1700 Views) |
| This research uses Network Data EnvelopmentAanalysis (NDEA) by undesirable factors to analyze and evaluate the performance of automotive industry. The modeling used is applied to five production lines of an automobile company by 16 indicators. The data used are for the year 2019. The main purpose is to provide a model to improve the quality of the product by evaluating the performance of quality health in production lines able to rank by providing appropriate quality indicators to identify, formulate and achieve corrective measures. Accompanied with accurate problem solving and operational scheduling according to the most efficient organization/production line and so investigating the source of the problem and preventing the occurrence of the problem. Because determining the direction of performance and key performance indicators (KPI) of the organization and measuring them to increase its health efficiency requires an efficient and integrated system. On the other hand, creating a homogeneous and orderly development process between the elements of the organization as a common language to solve the quality problems by aiming the improvement of the performance, customer satisfaction, sustainable production and cost management has been proposed. |
|
| Keywords: Data Envelopment Analysis, Quality Health Model, Performance Evaluation, Efficiency, Network Data Envelopment Analysis |
|
|
Full-Text [PDF 1286 kb]
(7114 Downloads)
|
Type of Study: Original |
Subject:
Mathematical Modeling and Applications of OR
Received: 2022/03/15 | Accepted: 2021/06/12 | Published: 2021/06/12
|
|
|
|
|
|
|
| Send email to the article author |
|
|
|
| Add your comments about this article |
|
|
|
