Abstract:
In today’s era of globalization and consumerism, manufacturing industries are moving towards adopting mass personalization, which refers to production of personalized products with high efficiencies. This shift from push-type production to pull type mass personalization will result in critical operational challenges for manufacturing organizations, ranging from eliciting customer requirements, designing and manufacturing products to commissioning, installation and after sales support. This research project addresses these operational challenges by identifying key technological capabilities for manufacturing organizations in order to enable flexible yet resilient engineering management systems for Mass Personalization. These capabilities identified have been synthesized into a robust manufacturing automation framework which organizations can adopt. Central to this operational framework is the digital thread, which streamlines information flow across different departments of the organization such as design, manufacturing, after sales and field service. A digital replica of the product or digital twin is created at the beginning of the product co-designing process and is updated with relevant product and process information as it passes through different engineering stages such as manufacturing, installation, commissioning and after sales support. At each stage, efficient data exchange and communication across the digital thread can be enabled through increased automation in business processes such as automatic integration of PDM and ERP packages and improved real-time manufacturing operations management through superior machine to machine communication. The different digital twins are realized as physical products through self-organized shop-floor consisting of self-aware and cognitive manufacturing entities. Manufacturing entities need to be equipped with decision making capabilities and be capable of handling different scheduling algorithms to facilitate dynamic decision making in shop-floor, aided by artificial intelligence and extensible shop-floor control beyond traditional PLC/ SCADA based control paradigms. Unique product installation procedures and consistency in product servicing can be ensured through a robust after sales service request management engine which capture human expertise in servicing through artificial intelligence and natural language processing based methodologies. Finally, a real world case study of an engineer to order industry in the construction sector has been presented to validate the information flows between different digital threads. Finally the challenges faced by the proposed automation framework and areas of future work are also discussed.