Industry 4.0 revolution offers smart manufacturing; it systematically incorporates production technology and advanced operation management. Adopting these high-state strategies can increase production efficiency, reduce energy consumption, and decrease manufacturer costs. Simultaneously, small and medium-sized enterprises (SMEs) were the backbone of economic growth and development. They still lack both the knowledge and decision-making to verify this high-stage technology’s performance and implementation. Therefore, the research aims to define the readiness indicators to assess and support SMEs toward Industry 4.0. The research begins with found aspects that influence the SME 4.0 readiness by using Bibliometric techniques. The result shows the aspects which were the most occurrences such as the Industrial Internet, Cloud Manufacturing, Collaborative Robot, Business Model, and Digital Transformation. They were then grouped into five dimensions by using the visualization of similarities (VOS) techniques: (1) Organizational Resilience, (2) Infrastructure System, (3) Manufacturing System, (4) Data Transformation, and (5) Digital Technology. Cronbach’s alpha then validated the composite dimensions at a 0.926 level of reliability and a significant positive correlation. After that, the indicators were defined from the dimension and aspects approach. Finally, the indicators were pilot tested by small enterprises. It appeared that 23 indicators could support SMEs 4.0 readiness indication and decision-making in the context of Industry 4.0.

Hospitals play a crucial role in delivering high-quality patient care, and the healthcare industry is experiencing significant changes driven by the integration of advanced technology. This integration aims to enhance patient care, streamline operations, and minimize errors. Therefore, this study seeks to propose a comprehensive decision-making framework for selecting appropriate technologies, focusing on the medication dispensing process within hospital settings. Through an extensive literature review and expert input, seven critical criteria for technology adoption in hospitals have been identified. Additionally, the study considers the Pick-to-Light system, Carousel Storage System, and Robotic Dispensing System as alternative technologies for integrating medication dispensing processes, with a focus on enhancing labeling, picking, and packing activities. The evaluation involves participants from five tertiary care hospitals in Thailand. The Analytic Hierarchy Process (AHP) is used to assess the relative importance of criteria, followed by the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) for technology selection. The analysis results, including criteria weights and technology selection, are presented separately for each hospital case and overall. The top-three criteria identified as having high priority weights across hospitals are budget and funding support (BF) (0.329), staff technological proficiency levels (TP) (0.147), and leadership support (LS) (0.128). These findings can guide healthcare organizations in making informed decisions during technology implementation, particularly in improving medication dispensing processes. The study identifies the “Pick-to-Light” technology as a suitable solution across all hospital cases, as it can streamline dispensing processes, improve accuracy, and enhance overall operational efficiency. This research, along with the proposed decision-making framework, contributes valuable insights for healthcare institutions seeking to effectively select and integrate technologies, ultimately leading to improved outcomes in hospital medication services.

In the Industry 4.0 revolution, advanced manufacturing capabilities integrate technology and data to create intelligent production systems, such as automation, cloud computing, the Internet of Things and cyber-physical systems. Small and medium-sized enterprises, which are the backbone of economic growth, especially must apply the advanced technology in their business and operations so as to increase productivity. This paper empirically proposes the Smart SMEs 4.0 maturity model and its implementation for assessing the readiness of an organisation to enter the realm of smart manufacturing. The model is categorised into five dimensions as well as 43 sub-dimensions for evaluating SMEs 4.0 maturity. These dimensions are mainly composed of “manufacturing and operations”, “people capability”, “technology-driven process”, “digital support” and “business and organisation strategies”. Moreover, the model is implemented in two case studies for two companies in Thailand. The results imply that the model can evaluate an organisation’s readiness and also can guide companies to implement the Smart SMEs 4.0 efficiently.

This chapter examines how the SMEs 4.0 concept can be implemented. The objective is to map organizational requirements for movement from current state to smart SMEs. The toolkit begins with the readiness assessment and gap analysis of an organization. The assessment guideline composes of four dimensions, i.e., Information Technology, Operations Management, Automation, and Human Resource modules. Then, the readiness of each organization is analyzed. The result suggests organization on how to implement module by considering products, processes, organization infrastructure, and financial status of the organization. Four case study companies in Northern Thailand are used to preliminarily test the toolkit. Results are presented and discussed. Keywords Industry 4.0 Small-and medium-sized enterprise SME Implementation Toolkit Smart Factory