# Smart Manufacturing Application in Precision Manufacturing
## 1 Introduction
Recent industrial reports stress that some of the critical challenges facing the manufacturing industry are related to supply chain constraints, inflation, economic pressure and uncertainty, cybersecurity risks, shortage of skills and talent acquisition, rising energy costs, climate change, and dramatic changes in geopolitics [6; 7; 8; 9]. The recent pandemic has already highlighted the importance of a robust, transparent, and more resilient supply chain [10; 11; 12]. The emerging environmental, social, and governance (ESG) sustainability goals to mitigate the internal risks are major considerations for driving digital transformation and industry 4.0 adoption [6; 13]. Further calls are now driving an evolution of industry 4.0 to be more intelligent, green, and human-centric, aligning it with the vision of industry 5.0 [14; 15]. To address the global challenges facing the manufacturing industry, governments have been setting strategies to develop and implement advanced manufacturing technologies to build resilience and sustainability into manufacturing and its supply chains. Industrial reports have continually recommended digital transformation and the adoption of industry 4.0 across the manufacturing value chain to protect long-term profitability by improving resilience and agility, driving productivity, and focusing on corporate social responsibility. Manufacturers that have accelerated their digital transformation and the adoption of industry 4.0 have shown greater resilience and agility to supply chain challenges by having increased visibility and being able to pivot faster [12]. Smart manufacturing implementations have also been progressing in laying the technology foundations and connectivity as companies tend to expand their capabilities and build new ones [16]. But, despite the rapid increase in the literature on industry 4.0, its general adoption is still immature and has been progressing slowly [17]. Industrial studies suggest that only one-third of digital transformation initiatives are considered successful and have met their organizational objectives [13]. As a result, the impact on global productivity and economic growth has been less pronounced than anticipated. Several factors that have contributed to this slower-than-expected progress are external due to the industry challenges mentioned earlier and others are internally based. Amongst the top internal barriers reported by industry and in the academic literature are budget resources constraints [6; 7], the high cost of technology investment and digital transformation [6; 7; 18], the lack of technical skills to implement/optimize smart manufacturing adoption [6; 7; 18; 19], the complexity of the deployment and integration of new technologies associated with industry 4.0 [6; 18], and the lack of clear and well-defined strategies and clarity of economic business benefits that drives value [7; 13; 17; 20; 21; 22; 23]. Small and medium enterprises (SMEs), in particular, share common struggles regarding the high cost of implementation, unclear return on investments, and the lack of digital skills to benefit from industry 4.0 [24]. To address such barriers, study findings from industry and the academic literature have recommended practical and comprehensive strategic frameworks to support manufacturing companies with its implementation, showcasing the impact on business objectives, performance, and economic indicators [7; 13; 17; 20; 25]. This work seeks to address this gap. Recent empirical analytical studies have investigated the impact of smart manufacturing on operational performance, financial performance, and sustainability; such investigations showed a positive contribution leading to a competitive advantage.