## 3 Research Methodology  
This research aims to supplement traditional methods of defining KPIs by integrating DDA techniques for business optimization and sustainability. The focus is on analysing business data generated by systems integrated through PDA to identify patterns, correlations, and trends within the data. This will enable the creation of SKPIs that can adjust dynamically to changing business conditions. The methodology depicted in Figure 1 encompasses two segments. The first segment involves the development of the DDA model through a four-step process of performing data sourcing; data sampling; ML (data validation, decision engines, and predictive analytics); and AI (patterns and correlations, KPI significance, and SKPIs). The second segment involves evaluating the developed model using case study methods, considering the complexities of an ecosystem that generates substantial amounts of performance-relevant data. In addition to the studies reviewed in Section 2.1, [PERSON] et al. [29] used a data-driven method to diagnose multiplicative key performance degradation in automation processes. This method was aimed at identifying low-level components that increase the variability of process variables and consequently cause degradation in performance. [PERSON] et al. [34] applied data-driven metadata inference to learn from building automation systems (BASs) to reduce energy consumption in residential and commercial building stocks in the US. [PERSON] et al. [31] used ML and AI to achieve automation and a data-driven design of polymer therapeutics. Their research was meant for gene delivery, drug delivery, bioactive polymers, drug design, antimicrobial polymers, and organic synthesis. These studies extracted data produced by process-driven systems in various industries and applied data-driven methods to achieve further automation and to draw engineering insights. These studies extensively employed various techniques, including advanced statistics, ML, and AI, to realize data-driven models. This validates the effectiveness of data-driven methods in supplementing process-driven systems by achieving further automation, extracting and interpreting meaningful patterns and trends from data. Therefore, this study borrows some of the data-driven techniques exhibited by the respective studies to create a data-driven model for smart KPIs to optimize and sustain businesses. This research is buttressed by an integrated ecosystem that leverages the transformative power of the 4 IR integrations, specifically, vertical, end-to-end, and horizontal integrations, through the lens of PDA. Building on the foundational work from previous studies [35, 36, 37, 38], this study adopts a comprehensive framework that defines and operationalizes these three 4 IR integrations. The vertical integration, a critical component of this framework, enables real-time connectivity between physical machinery on the shop floor and sensors, control systems, manufacturing systems, and Enterprise Resource Planning (ERP) systems. This networked manufacturing system facilitates seamless communication and information flow between different sections and processes, ensuring that data flow effortlessly from the shop floor to  
Figure 1: Research process. the top floor (ERP) and back. This integration enables organizations to respond rapidly to changing market conditions, optimize production processes, and improve overall efficiency. End-to-end digital integration, another vital component of this framework, enables organizations to track products throughout their entire lifecycle, from inception and design, manufacturing and distribution, usage by customers, and eventual end of life. This integration facilitates real-time monitoring and management of products, ensuring optimal performance and efficiency. By leveraging end-to-end digital integration, organizations can improve product quality, reduce waste, and enhance customer satisfaction.