# Smart Manufacturing Application in Precision Manufacturing
## 1 Introduction
For example, [PERSON] and [PERSON] [26] investigated the impact of smart manufacturing applications across 234 organizations across Europe, covering original equipment manufacturers (OEMs) as well as Tier 1 and Tier 2 suppliers from the automotive industry, on operational performance benefits (such as quality, cost, productivity, delivery, and flexibility) and financial performance elements related to profit margins. The findings highlighted that the adoption of smart manufacturing results in improvements in operational performance and supports a firm's financial performance in maintaining competitiveness. Another study by [PERSON], et al. [27] investigated the impact of the smart factory adoption of 240 organizations in Korea, which covered a wide range of firm sizes from multiple important Korean industrial segments including automobile, electronics, semiconductors, electrical, communication, chemicals, metals, and machinery on manufacturing performance elements related to productivity and sustainable manufacturing (such as waste reduction, energy consumption). The findings showed a significant and positive contribution that led to productivity improvements and sustainability enhancements. Such empirical studies have confirmed the scarcity of research measuring the actual performance of manufacturing firms that have already adopted smart manufacturing applications, and this work seeks to address such gap. Another study by [PERSON], et al. [25] investigated the real-life adoption of digital technologies within industrial plants and how such implementations have affected both performance and economic viability. The study was based on 299 case studies produced in the academic literature between 2015 and 2022. The findings highlight that out of all the investigated case studies, 41% provided a form of performance evaluation, and only 13% included a form of economic assessment. The study also indicated that the academic literature focused more on the technical implementation aspect of digital technologies but lacked an evaluation of the application with specific key performance indicators (KPIs) and economic indices. This finding was also pointed out in a previous study by [PERSON], et al. [20], who examined the state-of-the-art research in industry 4.0 based on an analytical literature review and a global survey of 238 studies from different industry 4.0-related disciplines. The findings indicated a scarcity of academic works that dealt with operational and real-life aspects, such as performance measurements and cost-benefit analysis. The lack of clarity of the real economic benefits of the implementations of digital technologies within industrial plants requires urgent academic attention, which can be addressed through case studies. These findings confirm the research gap in terms of the performance and economic benefit analyses of smart manufacturing implementation case studies in industry, and this work seeks to address this gap. Another interesting finding by [PERSON], et al. [25] highlighted that the most frequent digital technologies applied in industrial plants were related to smart manufacturing applications and that such applications involved the use of multiple groups of digital technologies integrated together, with those at the plant level having the most impact. However, the lack of strategic vision and the lack of understanding of the nature industry 4.0. were two of the most significant limitations that inhibited its adoption [20]; therefore, there is a need for a simplified, less complex framework to assist manufacturing companies with an implementation strategy for the technology integration of smart manufacturing applications in a plant. This work seeks to address this gap. According to reports that gauged the readiness of the manufacturing industry for smart transformation, the industries in the bottom five were all dominated by SMEs, and the precision manufacturing industry scored the lowest on the maturity scale [21].