A Systematic Framework for Integrating Digital Twin Technology and Deep Transfer Learning to Enhance CRM Performance in Pakistan’s Textile Industry

Abstract

The numerous stages of manufacturing must be coordinated, information must be freely exchanged, and real-time data feedback must be employed in order to meet Pakistan's enormous demand for mass-produced yarn and overcome these issues. In response this paper proposes a framework built around Model-Based Systems Engineering (MBSE). Low rejection rates and good quality products are critical for the companies to stay competitive, not just locally but internationally as well. Therefore, the shift from traditional approaches to digitized prediction and product quality control is necessary. Additionally, this paper will discuss sufficient model performances to determine how the proposed DT-driven framework can be implemented to reduce defects, optimize operations, improve resource management and lead to optimal quality products. In total, CRM data innovation empowers reliable client cooperation across various channels of correspondence, includes versatile and web advances, client information the board and examination, process robotization however workflow innovations, combination with different frameworks and innovations, and an expansive set-up of applications, deals, administration, and accomplices, which may all be altered or on the other hand pre-configured for industry-specific requirements.