Exploring the impact of Industry 4.0 on the business models of small and medium-sized manufacturing enterprises in Singapore

• Main Author: SURIANARAYANAN GOPALAKRISHNAN
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Industry 4.0; Smart Manufacturing; SME; Business Model Innovation; Qualitative; Asian Studies; Entrepreneurial and Small Business Operations; Organizational Behavior and Theory; Technology and Innovation
• Online Access: https://ink.library.smu.edu.sg/etd_coll/375; https://ink.library.smu.edu.sg/context/etd_coll/article/1373/viewcontent/GPBA_AY2017_DBA_Surianarayanan_Gopalakrishnan.pdf
• Institution: Singapore Management University

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.