Suchergebnisse

Triggered by the ongoing transformation of Singapore's manufacturing industries towards 'smart(er)' manufacturing with a focus on digitising and automating production processes and more competitive business models, this study contributes to the limited Asian management literature about the readiness and impact of 'Industry 4.0' (I4.0) on the business models of Singapore's Small and Medium-sized manufacturing Enterprises (SMEs). I4.0 encompasses adopting opportunities from end-to-end digitalisation with connected computers and increasingly autonomous automation systems equipped with intelligent machine learning algorithms that control robotics with minimal human input. As the traditional manufacturing model is increasingly replaced by advanced, high-value manufacturing technologies such as the Industrial Internet-of-Things (IIoT), cloud computing, real-time data processing and insights etc., adopting the right enabling technologies in a phased manner with proper planning (based on a value-added business cum operating model) is critical for the successful adoption of I4.0 solutions. To examine the readiness and impact of I4.0 on the business models of Singapore's manufacturing SMEs and associated challenges such as digitalisation and business model innovation, a (grounded theory)qualitative case study research approach was adopted based on A. Osterwalder's Business Model Canvas (BMC) framework and semistructured interviews with experts and owner-managers of local manufacturing SMEs. During phase one of the study, eight key decision-makers across Singapore's I4.0 ecosystem comprising government agencies, Institutes of Higher Learnings (IHLs), suppliers/providers of technology, business associations and the local SME sector were interviewed. During phase two, a multiple case studycovered four manufacturing SMEs (medical technology, engineering equipment, machine vision and imaging) via inductively analysed indepth interviews with their owner-managers. The expert interviewshelped to identify five key drivers (Government's technology push with funding and training support, labour dependence, productivity and efficiency issues, pressure to innovate business models due to increased competition, impact of Covid-19) and four main barriers (ROI concerns, capability and mindset issues, ecosystem limitations) for adopting and implementing Industry 4.0 approaches. Industry 4.0 technologies like IIoT, Artificial Intelligence (AI), Robotics, Data and Image Analytics, and Big Data were the most preferred technology choices across the four SMEs. The case companies excellently leveraged several BMC related 'building blocks' such as 'Key Partners', 'Key Activities', 'Key Resources', 'Cost Structure' and 'Customer Relationships'. 'Channels' and 'Revenue Streams' turned out to be low impact areas (with 'Value Proposition' and 'Customer Segments' as medium impact areas). A lean manufacturing approach with datadriven decision-making combined with a High-Mix-Low-Volume (HMLV) strategy was the preferred Smart Manufacturing route adopted by the four SMEs. The case companies (which have benefitted significantly from the ongoing Smart Factory ecosystem expansion led by A*STAR) were moderately advanced on their I4.0 transformation journey. They pursued a (smart) incremental I4.0 adoption strategy via a phased and bite-sized module implementation approach. Challenges ahead include heavy focus on bottom-line metrics, precision engineering skills upgrading and wider use of business models and supply chain innovations such as Product-Service Systems (PSS), Device-as-a- Service (DaaS) or prescriptive AI.

Industry 4.0 encompasses adopting opportunities from end-to-end digitalisation with connected computers and increasingly autonomous automation systems equipped with intelligent machine learning algorithms that control the robotics without much input from humans. Smart manufacturing technologies (a part of the smart industry, an extension of Industry 4.0 initiatives and implementation) involve automating various processes. It aims to connect various units in real-time while enhancing profitability by reducing costs, increasing labour productivity, and improving overall efficiency. The impetus for our paper is the assumption that the adoption of smart manufacturing technologies such as advanced robotics, cyber-physical systems, or the Internet of Things (IoT) continues to be a challenge for Small and Medium Enterprise (SME) manufacturing firms in Singapore. Industry 4.0 adoption issues include significant capital investment, developing technical skills, recruiting new talent, re-skilling of the workforce, training needs, top management support, etc. But the traditional manufacturing model is fast evolving. Ultimately, adopting the right enabling technologies in a phased manner with proper planning remains a critical factor for the success of Industry 4.0 initiatives. This paper contributes to the minimal Asian management literature about Industry 4.0 matters in SMEs by interviewing eight key experts who are very knowledgeable about the subject matter. The qualitative study sheds light on the drivers and barriers to better understand current business dynamics, potential issues, focus areas, and initiatives to smoothen this implementation, to help catapult local manufacturers to the next level. A structured questionnaire was designed based on the current body of knowledge to conduct qualitative semi-structured interviews with key specialists and decision-makers across Government agencies, Institutes of Higher Learnings (IHL's), suppliers/providers of Industry 4.0 technology, business associations, and the SME sector. The analysis of the interviews suggests that factors or drivers such as technology push by the Government with robust funding and training support, skilled labour shortages including imported labour dependence, productivity and efficiency issues, the pressure to innovate business models due to increased competition, and the impact of Covid-19 are propelling SMEs to adopt Industry 4.0. Some of the barriers include high investment costs and return on investment concerns, capability concerns, mindset issues, and lack of ecosystem concerns. The extent to which local SMEs are ready for Industry 4.0 will be further examined during the 2nd phase of the research project.