Textile Supply Chain Optimization through Artificial Intelligence and Prescriptive Analytics Capability

Abstract

In the context of a rapidly changing landscape of the international market, high competition, and short product life cycle, highly unpredictable demand, Pakistan's Textile sector is increasingly facing problems in its supply process. To gain clarity in the supply chain, textile companies are turning to display innovative digital technology, enabling digital materials integration and predictions analysis. However, coming from these investments in technologies, the performance of the supply chains is still uneven. These reveal a Non-Simultaneous Significant relationship between creating analytical sites and effective decision making, emphasizing the need for advanced decision-oriented skills, in this study it is the functional prescriptive analytics capability (PAC) which comes between the integration of AI in the digital supply chain, predictive analytics capability and textile supply chains performance Pakistan from dynamic capability and resource-based view. Prescriptive analytics are a higher order analytical capability that combines optimization logarithms, simulation models and decision support system that would both predictive and intelligence based to recommend the best approach to the operational constraint. The study adopts quantitative cross sectional source approach to collect the information on medium size and big textile manufacturing companies from professional who work on production in relation to supply chain procurement and planning of Pakistan. The link and the mediation among the variables of prescriptive analytics capability are tested with partial least square equation modeling (PLS-SEM). The proposed model states that only when technical and analysis skills are transformed into data based decisions focused on optimization of the supply chain; the performance of the supply chain will be improved. The results should demonstrate how they drive digital supply chain integration, and predictive analytics, boost prescriptive analytics capability which in turn greatly enhance supply chain performance. This research contributes much to the literature of supply chain analytics particularly in the framework of emerging economies in doing so, the prescriptive analytics capabilities are empirically validated as mediators.