IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i18p8096-d1745341.html

Assessment of Smart Manufacturing Readiness for Small and Medium Enterprises in the Indian Automotive Sector

Author

Listed:
  • Maheshwar Dwivedy

    (School of Engineering & Technology, BML Munjal University, Gurgaon 122413, India)

  • Deepak Pandit

    (School of Management and i2E, BML Munjal University, Gurgaon 122413, India)

  • Kiran Khatter

    (School of Engineering & Technology, BML Munjal University, Gurgaon 122413, India)

Abstract

This study evaluates the degree to which small and medium sized enterprises (SMEs) are prepared to adopt smart manufacturing in contrast to large enterprises, a transition that depends on the effective use of the Internet of Things, artificial intelligence (AI), and advanced analytics. While many large multinational companies have already integrated such technologies, smaller firms still struggle because of tight budgets, limited technical expertise, and difficulties in scaling new systems. To capture these realities, the investigation refines the Initiative Mittelstand-Digital für Produktionsunternehmen und Logistik-Systeme (IMPULS) Industry 4.0 readiness model, which was initially developed to help German SMEs, so that it aligns with the circumstances faced by smaller manufacturers. A thorough review of published work first surveys existing readiness and maturity frameworks, highlights their limitations, and guides the selection of new, SME-specific indicators. The framework gauges readiness across six dimensions: strategic planning and organizational design, smart factory infrastructure, lean operations, digital products, data-driven services, and workforce capability. Each dimension is operationalized through a questionnaire that offers clear benchmarks and actionable targets suited to the current resources of each enterprise. Weaving strategic vision, skill growth, and cooperative support, the approach offers managers a direct path to sharper competitiveness and lasting innovation within a changing industrial landscape. Additionally, a separate Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is provided for each dimension based on survey data offering decision-makers concise guidance for future investment. The proposed adaptation of the IMPULS framework, validated through empirical data from 31 SMEs, introduces a novel readiness index, diagnostic gap metrics, and actionable cluster profiles tailored to developing-country industrial ecosystems.

Suggested Citation

  • Maheshwar Dwivedy & Deepak Pandit & Kiran Khatter, 2025. "Assessment of Smart Manufacturing Readiness for Small and Medium Enterprises in the Indian Automotive Sector," Sustainability, MDPI, vol. 17(18), pages 1-46, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8096-:d:1745341
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/18/8096/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/18/8096/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    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. Kokil Talan & Narain Gupta, 2026. "Comparative analysis of industry 4.0 and blockchain adoption readiness dimensions in manufacturing sector: a systematic literature review and research agenda," Future Business Journal, Springer, vol. 12(1), pages 1-18, December.
    2. Jagjeevan Kanoujiya & Bhakti Agarwal & Shailesh Rastogi & Sanchal Tarode & Smita Bodne, 2026. "Does competitive pressures affect the Indian SMEs industry 4.O adoption: using quantile regression analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 15(1), pages 1-21, December.

    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. Maximilian Klöckner & Christoph G. Schmidt & Stephan M. Wagner, 2022. "When Blockchain Creates Shareholder Value: Empirical Evidence from International Firm Announcements," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 46-64, January.
    2. Liangfei Qiu & Yili (Kevin) Hong & Andrew Whinston, 2022. "Special Issue of Production and Operations Management “Social Technologies in Operations”," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 868-869, February.
    3. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    4. Mikko Ketokivi & Joseph T. Mahoney, 2020. "Transaction Cost Economics As a Theory of Supply Chain Efficiency," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 1011-1031, April.
    5. Jun Pei & Ping Yan & Subodha Kumar, 2023. "No Permanent Friend or Enemy: Impacts of the IIoT-Based Platform in the Maintenance Service Market," Management Science, INFORMS, vol. 69(11), pages 6800-6817, November.
    6. Subodha Kumar & Rakesh R. Mallipeddi, 2022. "Impact of cybersecurity on operations and supply chain management: Emerging trends and future research directions," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4488-4500, December.
    7. Jingying Ding & Woonghee Tim Huh & Ying Rong, 2024. "Feature-Based Inventory Control with Censored Demand," Manufacturing & Service Operations Management, INFORMS, vol. 26(3), pages 1157-1172, May.
    8. Yining Wang & Quanquan Liu, 2025. "Estimation of High-Dimensional Contextual Pricing Models with Nonparametric Price Confounders," Operations Research, INFORMS, vol. 73(6), pages 3065-3084, November.
    9. Yi‐Jen (Ian) Ho & Siyuan Liu & Jingchuan Pu & Dian Zhang, 2022. "Is it all about you or your driving? Designing IoT‐enabled risk assessments," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4205-4222, November.
    10. Shao, Xiao-Feng, 2020. "What is the right production strategy for horizontally differentiated product: Standardization or mass customization?," International Journal of Production Economics, Elsevier, vol. 223(C).
    11. Zhe Yuan & Yitong Wang & Tianshu Sun & Huilan Xu, 2025. "Democratizing Data Analytics Products to Drive SME Growth: A Natural Experiment," Manufacturing & Service Operations Management, INFORMS, vol. 27(4), pages 1053-1067, July.
    12. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    13. Zhao-Zhen Zhu & Yue Chen & Jiang Zhao & Zhu-Ying Yu, 2023. "Intelligent Manufacturing and Value Creation in Enterprises: Lessons from a Quasi-Natural Experiment in a Chinese Demonstration Project," Sustainability, MDPI, vol. 15(15), pages 1-17, July.
    14. Lv, Xiaolei & Shi, Liangxing & He, Yingdong & He, Zhen, 2025. "Condition-based production: Maximizing manufacturing revenue considering failure risk and reject rates," European Journal of Operational Research, Elsevier, vol. 327(1), pages 218-231.
    15. Delgosha, Mohammad Soltani & Hajiheydari, Nastaran & Talafidaryani, Mojtaba, 2022. "Discovering IoT implications in business and management: A computational thematic analysis," Technovation, Elsevier, vol. 118(C).
    16. Marcel André Hoffmann & Rainer Lasch, 2025. "Unlocking the Potential of Predictive Maintenance for Intelligent Manufacturing: a Case Study On Potentials, Barriers, and Critical Success Factors," Schmalenbach Journal of Business Research, Springer, vol. 77(1), pages 27-55, March.
    17. Boxiao Chen & Xiuli Chao, 2020. "Dynamic Inventory Control with Stockout Substitution and Demand Learning," Management Science, INFORMS, vol. 66(11), pages 5108-5127, November.
    18. Rizwan Abbas & Gehad Abdullah Amran & Irshad Hussain & Shengjun Ma, 2022. "RETRACTED: A Soft Computing View for the Scientific Categorization of Vegetable Supply Chain Issues," Logistics, MDPI, vol. 6(3), pages 1-17, June.
    19. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2023. "Machine Learning Methods for Data-Driven Demand Estimation and Assortment Planning Considering Cross-Selling and Substitutions," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 158-177, January.
    20. 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).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2025:i:18:p:8096-:d:1745341. 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.