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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.

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This study explores the implementation of virtual platforms in supply chain management, emphasizing online production, procurement, and distribution without traditional factory infrastructures. Using a qualitative descriptive-survey approach with inductive reasoning, the research aims to enhance supply chain performance through advanced digital technologies. Rapid advancements in Information and Communication Technologies such as Internet of Things and Artificial Intelligence challenge conventional models by enabling real-time data exchange, improving forecasting accuracy, and reducing delays. Digital integration facilitates seamless communication among suppliers, manufacturers, distributors, and customers, enhancing coordination and cost efficiency. Semi-structured interviews with industry experts were analyzed through thematic analysis, yielding 139 initial codes refined into 25 categories and 5 key themes. These highlight critical dimensions: Digital Integration, Stakeholders Coordination, Edge Computing, Data Analytics and Agility Management. Advanced analytics, leveraging mathematical models and Intelligence algorithms, provide actionable insights for demand forecasting and inventory optimization, strengthening decision-making. The findings underscore the importance of flexibility and agility in addressing market disruptions, with edge computing and real-time data processing identified as vital for operational resilience. Practical recommendations include deploying simulation tools, developing logistics optimization algorithms, and implementing robust cybersecurity protocols. Overall, virtual platforms offer a transformative approach to supply chain management, improving efficiency, reducing costs, and enhancing competitiveness in dynamic markets.