IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i17p9793-d626304.html
   My bibliography  Save this article

Conceptualizing Smart Manufacturing Readiness-Maturity Model for Small and Medium Enterprise (SME) in Malaysia

Author

Listed:
  • Syed Radzi Bin Rahamaddulla

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
    Fakulti Teknologi Kejuruteraan Pembuatan Dan Mekatronik, Universiti Malaysia Pahang, Pekan 26600, Malaysia)

  • Zulkiflle Leman

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • B. T. Hang Tuah Bin Baharudin

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

  • Siti Azfanizam Ahmad

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia)

Abstract

Manufacturing enterprises today are forced to face radical challenges in the disruptive concepts of Smart Manufacturing (SM) and Industry 4.0 to stay competitive. Most Multinational Enterprise (MNEs) have initiated their journey towards adopting SM. As a mainspring of many manufacturing economies, Small and Medium-Enterprise (SMEs) are still struggling to understand the complexity offered in SM, and many of them are not ready to embrace the concept of SM. To overcome this, SMEs first need to assess their readiness and maturity before embarking on an SM journey. The existing available readiness assessment model seems to be suitable for MNEs, and there is still a lack of tailored models that suit SMEs. This paper sought to pinpoint the conceptual framework from the review of the existing readiness-maturity assessment and identify the gap of existing model as well as proposed a tailored model framework that are suitable for SMEs. Ultimately, this model will be used to pursue a comprehensive scholarly study across Malaysia. The proposed model is enhanced with 4M attributes as the dimension and embedded with the characteristic of Industry 4.0 build component to help the SME’s overcome the possible uncertainties in adopting SM concept.

Suggested Citation

  • Syed Radzi Bin Rahamaddulla & Zulkiflle Leman & B. T. Hang Tuah Bin Baharudin & Siti Azfanizam Ahmad, 2021. "Conceptualizing Smart Manufacturing Readiness-Maturity Model for Small and Medium Enterprise (SME) in Malaysia," Sustainability, MDPI, vol. 13(17), pages 1-18, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9793-:d:626304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/17/9793/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/17/9793/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leonardo Sastoque Pinilla & Raúl Llorente Rodríguez & Nerea Toledo Gandarias & Luis Norberto López de Lacalle & Mahboobeh Ramezani Farokhad, 2019. "TRLs 5–7 Advanced Manufacturing Centres, Practical Model to Boost Technology Transfer in Manufacturing," Sustainability, MDPI, vol. 11(18), pages 1-14, September.
    2. Ray Y. Zhong & Chen Xu & Chao Chen & George Q. Huang, 2017. "Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2610-2621, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cihan Ünal & Cemil Sungur & Hakan Yildirim, 2022. "Application of the Maturity Model in Industrial Corporations," Sustainability, MDPI, vol. 14(15), pages 1-25, August.
    2. Xiurui Xu & Guangming Hou & Junpeng Wang, 2022. "Research on Digital Transformation Based on Complex Systems: Visualization of Knowledge Maps and Construction of a Theoretical Framework," Sustainability, MDPI, vol. 14(5), pages 1-19, February.
    3. Bhatia, Purvee & Diaz-Elsayed, Nancy, 2023. "Facilitating decision-making for the adoption of smart manufacturing technologies by SMEs via fuzzy TOPSIS," International Journal of Production Economics, Elsevier, vol. 257(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    2. Sumera Ahmad & Suraya Miskon & Rana Alabdan & Iskander Tlili, 2020. "Towards Sustainable Textile and Apparel Industry: Exploring the Role of Business Intelligence Systems in the Era of Industry 4.0," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    3. Vaibhav S. Narwane & Rakesh D. Raut & Sachin Kumar Mangla & Manoj Dora & Balkrishna E. Narkhede, 2023. "Risks to Big Data Analytics and Blockchain Technology Adoption in Supply Chains," Annals of Operations Research, Springer, vol. 327(1), pages 339-374, August.
    4. Imran Bashir Dar & Muhammad Bashir Khan & Abdul Zahid Khan & Bahaudin G. Mujtaba, 2021. "A qualitative analysis of the marketing analytics literature: where would ethical issues and legality rank?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 242-261, September.
    5. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    6. J. Piet Hausberg & Kirsten Liere-Netheler & Sven Packmohr & Stefanie Pakura & Kristin Vogelsang, 2019. "Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis," Journal of Business Economics, Springer, vol. 89(8), pages 931-963, December.
    7. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    8. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    9. Monica Shukla & Ravi Shankar, 2022. "Modeling of critical success factors for adoption of smart manufacturing system in Indian SMEs: an integrated approach," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1271-1303, December.
    10. Colombari, Ruggero & Geuna, Aldo & Helper, Susan & Martins, Raphael & Paolucci, Emilio & Ricci, Riccardo & Seamans, Robert, 2023. "The interplay between data-driven decision-making and digitalization: A firm-level survey of the Italian and U.S. automotive industries," International Journal of Production Economics, Elsevier, vol. 255(C).
    11. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    12. Qin, Wei & Sun, Yan-Ning & Zhuang, Zi-Long & Lu, Zhi-Yao & Zhou, Yao-Ming, 2021. "Multi-agent reinforcement learning-based dynamic task assignment for vehicles in urban transportation system," International Journal of Production Economics, Elsevier, vol. 240(C).
    13. Liu, Weihua & Long, Shangsong & Wei, Shuang, 2022. "Correlation mechanism between smart technology and smart supply chain innovation performance: A multi-case study from China's companies with Physical Internet," International Journal of Production Economics, Elsevier, vol. 245(C).
    14. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    15. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    16. Shenle Pan, 2019. "Opportunities of Product-Service System in Physical Internet," Post-Print hal-02155622, HAL.
    17. Tan, Bing Qing & Xu, Su Xiu & Kang, Kai & Xu, Gangyan & Qin, Wei, 2021. "A reverse Vickrey auction for physical internet (PI) enabled parking management systems," International Journal of Production Economics, Elsevier, vol. 235(C).
    18. Marikyan, Davit & Papagiannidis, Savvas & Rana, Omer F. & Ranjan, Rajiv & Morgan, Graham, 2022. "“Alexa, let’s talk about my productivity”: The impact of digital assistants on work productivity," Journal of Business Research, Elsevier, vol. 142(C), pages 572-584.
    19. Guoqing Zhang & Yiqin Yang & Guoqing Yang, 2023. "Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America," Annals of Operations Research, Springer, vol. 322(2), pages 1075-1117, March.
    20. Pulin Li & Kai Cheng & Pingyu Jiang & Kanet Katchasuwanmanee, 2022. "Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 103-119, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9793-:d:626304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.