Integrated Product-Process Design: Conceptual Framework for Data Driven Manufacturing Resource Selection

Abstract

With the extensive development of new technologies and the application of information in the manufacturing industry, immense volumes of distinct data are being generated and collected daily. However, this data is largely unusable as its not meticulously cleaned and processed. The effective utilization of such complex data is the cornerstone of data analytics, as successful analysis leads to useful, relevant, and actionable knowledge, which in the long run can prove to be revolutionary for any field and open new avenues. Although the application of data analytics in areas such as sales & marketing, healthcare, cybersecurity, and climate change is largely prevalent, the implementation of data analysis and its tools for efficient product & process design is an unexplored opportunity with large volumes of data generated by major stakeholders throughout the manufacturing and product-process design activity remaining underutilized. This thesis, therefore, defines a novel conceptual framework that applies data analysis to integrated product-process design (IPPD) for weighted data driven IPPD that amalgamates data generated from multiple streams. Primarily from the user perspective, supply chain network, current & upcoming technological processes, and competitor process and product designs, will be utilized. The proposed framework can be further used to create new products better aligned with customer requirements, enhance the overall quality of the product, improve production efficiency through new technological advancements, support the supply chain network, and give the applicant industry a competitive advantage against its competitors