Smart manufacturing, also known as Industry 4.0, represents the digital transformation that modern manufacturing is undergoing. Rather than employing individual technologies for traditional automation, firms try to make the whole manufacturing process more integrated and flexible to meet fast-changing demand. Collecting and utilizing data plays a crucial role in the integration of production.

As digital capabilities become more critical in firm survival and industry competitiveness, major countries put forward strategies to promote and support firms' digitization. The Korean government has also pursued policies to encourage firms, small and medium-sized in particular, to adopt smart manufacturing. The goal is to have 30,000 smart factories by 2022.

Despite its importance and difference from traditional automation, there has been little effort to understand the economics of smart manufacturing. This report is the first comprehensive study to measure the level of smart manufacturing, examine its economic impacts, and identify the determinants of adopting the smart factory. Based on the concrete quantitative evidence, this study provides policy suggestions for the effective digital transformation of manufacturing.

From a technical point of view, a smart factory is defined as "Digitization and networking of all processes, products, and resources." As the smart factory integrates the factory system to digitize and network all production activities―and thereby go beyond simple automation―it can improve the efficiency of the production process, flexibly cope with breakdowns and sudden changes in supply and demand, and even help to develop new products and predict future demands.

We first measure the level of the smart factory by two factory-specific characteristics: (1) the degree in which all the production activities in the factory are interconnected systemically (system integration) and (2) the degree to which the factory collects, shares and utilizes data for decision-making (data share and use). To collect necessary information, we surveyed about 1,000 domestic plants in the Korean manufacturing industry. From the survey result, we find that the sample factories were generally low in the smart factory level, but there was a modest improvement between 2015 and 2017. We also find a significant gap between factories in both levels and change.

Chapter 2 examines the effects of factory smartization on the three key performance indicators (KPI) of the factory: productivity, cost efficiency, and product variety. For productivity, we find that daily production increases when the smart factory level increases. We find similar effects on other KPIs, which are heterogeneous by the type of production process. For example, we find a significant reduction in the lead time only from continuous-process factories, while we observe a reduction in the defect rates, a cost-efficiency indicator, only from assembly line-process factories. For product variety, we find the most substantial positive effect from the batch (and job shop) process.

Chapter 3 explores the employment effect of factory smartization and compares it to that of automation. While economists find negative impacts of individual technologies, such as robots and AIs, engineers argue that smartization, a system-wide change distinct from automation, creates new labor demand for constant reconfiguration and training of workers. This chapter tests the hypothesis by examining the relationship between planned smartization and predicted labor demand by worker groups (production workers, technical engineers, and office workers). The results support the engineers' prediction. While automation is expected to decrease the demand for all three classes, improvement in the production process is not likely to decrease the demand for technical engineers. Smartization in even greater scale - integration of all business activities - is expected to have no adverse effects on all three worker groups. Having said that automation and smartization are different in their job impact, this chapter also emphasizes that there is considerable heterogeneity across firms and workers. Production workers and office workers, young and old workers, and workers at low-level smart factories are those more vulnerable to production smartization. Policymakers should encourage firms to provide them with proper retraining and reallocation program.

Given the effects of the factory smartization on performance and employment, it is important to understand what factors determine the level of smartization. Chapter 4 investigates the determinants including the adoption of technologies relevant to the smart factory with a focus on the role of complementary organizational features. We find that there is a complementarity between human resource management practices and technology adoption in driving smartization. Technology adoption leads to smartization, and such effect magnifies disproportionately with the level of structured human resources management. We also find other magnifiers, such as the presence of an ICT division and CEO's willingness to upgrade the manufacturing system.

The complementarity between technology and organizational practices suggests the need to reconsider the current policy direction. First, before providing support, there needs to be an accurate diagnosis of the target firm's organizational practices, needs, and expectations for the effects of the smartization. This “pre-consulting & post-support” approach will be more effective than merely helping firms to introduce specific technologies. Second, we propose to build a platform that provides one-stop services for SMEs. Third, we make suggestions to make the government's support system smart (government smartization) to increase the effectiveness of the support.

Chapter 5 concludes this study by proposing a plan to reform the governance for manufacturing innovation. The key message is that the private sector should play a central role in both policy-making and implementation process for policies to be effective in this transforming digital era. We suggest to organize a council by benchmarking the Germany’s Industrie 4.0 platform. The council is a network of representatives from business, science, government ministries and trade unions. The council is expected to present the economy- wide digitization strategy. We also argue that cooperation between SMEs and large corporations is crucial in pursuing smart manufacturing in the Korean business environment. We discuss possible policy measures to facilitate their cooperation and make the environment more favorable to SMEs.

Preface
Executive Summary

Chapter 1 Introduction
　References

Chapter 2 Smartization of Factories and Firm Performance (Sunghoon Chung, Minho Kim)
　Section 1 What Is a Smart Factory?
　Section 2 Measuring Smartization and Data Collection
　Section 3 Impact of Factory Smartization on Firm Performance
　Section 4 Summary and Conclusion
　References

Chapter 3 Impact of Smart Manufacturing on Employment (Changkeun Lee)
　Section 1 Introduction
　Section 2 Technology and the Labor Market: Existing Studies and Key Issues
　Section 3 Research and Measurement Strategy
　Section 4 Employment Effects of Smart Manufacturing
　Section 5 Conclusion and Policy Implications
　References

Chapter 4 Determinants of Smartization and Strategies for Smart Transformation (Minho Kim, Sunghoon Chung)
　Section 1 Level of Smartization and Key Determinants in Korean Manufacturing Firms
　Section 2 Adoption and Diffusion of Smart Factories: Support Systems
　Section 3 Policy Recommendations for Corporate Smartization
　Section 4 Conclusion
　References

Chapter 5 Conclusion and Policy Recommendations for a "Smart Manufacturing Innovation Strategy"
　References

Appendix
ABSTRACT