# Smart Manufacturing Application in Precision Manufacturing
## 4 Discussion  
In this section, the results of the smart manufacturing application implementation are discussed, as is its impact on operational performance (productivity, machine utilization, and cost savings), economic benefits (financial investment opportunity), and environmental sustainability (energy and emission reductions). The summary of the impacts achieved by the precision manufacturing company over the years 2020 to 2022, compared with the base year 2019, are displayed in Table 1.  
This success can be attributed to the adoption of smart manufacturing practices and the implementation of the connectivity, integration, and analytics stages of the six-gear smart factory roadmap. The connectivity stage focused on the factory floor's connectivity. It was achieved by connecting the CNC machines in the production lines through secure and resilient network to a condition monitoring system to ensure the real-time visibility of the machines and the machining process. As a result, this improved the machines' utilization through better production time management and improved maintenance management via the early identification of deviations to enable diagnostics. The integration stage focused on the vertical integration of IT and OT. It was achieved by integrating the production management system, the ERP system, and the machine condition monitoring system through digital threads, which allowed production data to be exchanged relatively in real time across different functions at the office and on the factory floor. As a result, relevant processes that were predominantly managed by humans were digitized and automated, enabling full visibility of the production process and the factory environment and improving the capability to identify deviations and diagnostics related to the production process. All these contributed to productivity improvements, downtime reductions, and the minimizing of no-value-added activities, leading to time and cost savings. The analytics stage focused on building enterprise intelligence. This was achieved by digitizing the data collection and reporting capabilities using a BI tool, where the business and production data collected from multiple sources across the enterprise and the factory floor were analyzed and reported relatively in real time. As a result, this enabled the visibility of KPIs relevant to the business to be accessed and monitored from one place as the source of truth as well as provided insights to enable quick problem identification and actions to be taken in order to adapt to changing needs, leading to agility improvements. The impact of this implementation is evidenced in the improvements seen in the production machines' utilization, the reduction in the total downtime waste in the factory, the cost savings as a result of the total downtime reduction (i.e., lost productivity recovered), the financial investment returns with respect to return on investment (ROI) and the opportunity with respect to internal rate of return (IRR) of the smart manufacturing implementation, and the reductions in energy consumption and emissions in the operations over the three-year period. The KPIs that were chosen in this study to evaluate the operational performance, economic benefits and environmental sustainability evaluation, are commonly used in the manufacturing industry and in the literature. In fact, the study by [PERSON], et al. [25] on the real-life implementation of digital technologies within manufacturing and how such implementations have been evaluated in terms of operational performance and economic viability highlighted that the frequently adopted non-case-specific operational performance KPIs were related to process times efficiencies, resource utilization, cost, emission, energy consumption, and quality; however, their use was still quite low. That study also highlighted the scarcity of KPIs that can be used to evaluate industrial performance of industry 4.0 implementation in manufacturing and the need for academic attention. This study