Skip to main content
SpringerLink
Log in

Prioritisation of Challenges Towards Development of Smart Manufacturing Using BWM Method

  • Chapter
  • First Online:
Internet of Things (IoT)

Abstract

In the era of digitalisation, daily life is equipped with digital products and services. These smart products now become the necessity of everyday life. To fulfil the huge amount of demands of such product in a sustainable way, smart manufacturing is evolving. However, the development of smart manufacturing is facing many challenges from various perspective. These challenges need to overcome for the development of smart manufacturing. Therefore, the aim of this chapter is to identify and prioritise these challenges, which can be helpful to overcome these challenges. In this study, 16 challenges are identified towards the development of smart manufacturing from the literature review and experts’ input. Additionally, these challenges are categorised in four dimensions. After that, these dimensions and their associated challenges are prioritised based on their importance using the best worst method (BWM). The result clearly shows that infrastructure-related challenges are the most significant while consumer related challenges are least significant. The identified challenges are helpful for the development of smart manufacturing. The prioritisation of these challenges assists the management and policymakers to formulate the strategies for the mitigation of these challenges. This study provides 16 challenges that can be evaluated by manufacturers/companies to realize the readiness for smart manufacturing transformation. This chapter provides an understanding of the smart manufacturing and associated challenges towards its development.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ahmad, S., & Alam, M. (2014). Balanced- ternary logic for improved and advanced computing. International Journal of Computer Science and Information Technologies, 5(4), 5157–5160.

    Google Scholar 

  • Ahmadi, H. B., Kusi Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using best worst method. Resources, Conservation and Recycling, 126, 99.

    Article  Google Scholar 

  • Alam, M., & Alam, B. (2013). Cloud query language for cloud database. In: Proceedings of the International conference on Recent Trends in Computing and Communication Engineering – RTCCE 2013, Hamirpur, HP, pp. 108–112, ISBN: 978-981-07-6184-4 https://doi.org/10.3850/978-981-07-6184-4_24.

  • Alam, M., Sethi, S., & Shakil, K. A. (2015). Distributed machine learning based biocloud prototype. International Journal of Applied Engineering Research, 10(17), 37578–37583.

    Google Scholar 

  • Ali, S., Affan, M., & Alam, M.. (2019). A study of efficient energy management techniques for cloud computing environment. 2019 9th international conference on cloud computing, Data Science & Engineering (Confluence).

    Google Scholar 

  • Ben Sta, H. (2017). Quality and the efficiency of data in “smart cities”. Future Generation Computer Systems, 74, 409–416.

    Article  Google Scholar 

  • Bibri, S. (2018). A foundational framework for smart sustainable city development: Theo-retical, disciplinary, and discursive dimensions and their synergies. Sustainable Cities and Society, 38, 758–794. https://doi.org/10.1016/j.scs.2017.12.032.

    Article  Google Scholar 

  • Chen, T., & Lin, Y. C. (2017). Feasibility evaluation and optimization of a smart manufacturing system based on 3D printing: A review. International Journal of Intelligence Systems, 32(4), 394–413.

    Article  Google Scholar 

  • Cheng, K., Niu, Z. C., Wang, R. C., Rakowski, R., & Bateman, R. (2017). Smart cutting tools and smart machining: Development approaches, and their implementation and application perspectives. Chinese Journal of Mechanical Engineering, 30(5), 1162–1176.

    Article  Google Scholar 

  • Cheraghalipour, A., & Farsad, S. (2018). A bi-objective sustainable supplier selection and order allocation considering quantity discounts under disruption risks: A case study in plastic industry. Computers & Industrial Engineering, 118, 237–250.

    Article  Google Scholar 

  • Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012). Understanding smart cities: An integrative framework. 45th Hawaii international conference on system. Science, 2289–2297.

    Google Scholar 

  • d’Aquin, M., Davies, J., & Motta, E. (2015). Smart cities’ data: Challenges and opportuni-ties for semantic technologies. IEEE Internet Computing, 19(6), 66–70. https://doi.org/10.1109/mic.2015.130.

    Article  Google Scholar 

  • Elkhodr, M., Shahrestani, S., & Cheung, H. (2016). The internet of things: New interoperability, management and security challenges. International Journal of Network Security & Its Applications, 8(2), 85–102.

    Article  Google Scholar 

  • Hecklau, F., Galeitzke, M., Flachs, S., & Kohl, H. (2016). Holistic approach for human resource management in industry 4.0. Procedia CIRP, 1–6. https://doi.org/10.1016/j.procir.2016.05.102.

    Article  Google Scholar 

  • Hermann, M., Pentek, T., & Otto, B. (2016, January). Design principles for industrie 4.0 scenarios. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 3928–3937). IEEE.

    Google Scholar 

  • Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34.

    Article  Google Scholar 

  • Javaid, M., Haleem, A., Khan, S., & Luthra, S. (2020). Different flexibilities of 3D scanners and their impact on distinctive applications. International Journal of Business Analytics, 7(1), 37–53. https://doi.org/10.4018/ijban.2020010103

    Article  Google Scholar 

  • Kadera, P., & Novák, P. (2017). Performance modeling extension of directory facilitator forenhancing communication in FIPA-compliant multiagent systems. IEEE Transactions on Industrial Informatics, 13(2), 688–695.

    Article  Google Scholar 

  • Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111–128.

    Article  Google Scholar 

  • Khan, S., Asjad, M., & Ahmad, A. (2015a). Review of modern optimization techniques. International Journal of Engineering Research And, V4(04). https://doi.org/10.17577/ijertv4is041129

  • Khan, I., Naqvi, S. K., & Alam, M. (2015b). Data model for big data in cloud environment. computing for sustainable global development (INDIACom), 2015 2nd International conference on, 11–13 March 2015, New Delhi, India, IEEE (pp. 582–585).

    Google Scholar 

  • Khan, S., Shakil, K., & Alam, M. (2017a). Cloud-based big data analytics—a survey of current research and future directions. Advances in Intelligent Systems and Computing, 654, 595–604.

    Article  Google Scholar 

  • Khan, S., Liu, X., Shakil, K., & Alam, M. (2017b). A survey on scholarly data: From big data perspective. Information Processing & Management, 53(4), 923–944.

    Article  Google Scholar 

  • Khan, S., Shakil, K., Arshad Ali, S., & Alam, M. (2018). On designing a generic framework for big data-as-a-service. In 2018 1st International conference on advanced research in engineering sciences (ARES). https://doi.org/10.1109/ARESX.2018.8723269.

  • Khan, S., Khan, M., Haleem, A., & Jami, A. (2019a). Prioritising the risks in halal food supply chain: An MCDM approach. Journal of Islamic Marketing, ahead-of-print(ahead-of-print).

    Google Scholar 

  • Khan, M., Khan, S., & Haleem, A. (2019b). Analysing barriers towards management of Halal supply chain: A BWM approach. Journal of Islamic Marketing.

    Google Scholar 

  • Khan, S., Khan, M. I., & Haleem. A. (2020). Blockchain enabled supply chain: An implementation perspective. Our Heritage, 67(5), 318–334.

    Google Scholar 

  • Kumar, V., Kumar, R., Pandey, S. K., & Alam, M. (2018). Fully homomorphic encryption scheme with probabilistic encryption based on Euler’s theorem and application in cloud computing. Advances in Intelligent Systems and Computing, 654, 605–611.

    Article  Google Scholar 

  • Kumari, A., Kumar, V., YahyaAbbasi, M., & Alam, M. (2018). The cryptanalysis of a secure authentication scheme based on elliptic curve cryptography for IOT and cloud servers. In 2018 international conference on advances in computing, Communication Control and Networking (ICACCCN)

    Google Scholar 

  • Kusiak, A. (2017). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517.

    Google Scholar 

  • Kymäläinen, T., Kaasinen, E., Hakulinen, J., Heimonen, T., Mannonen, P., Aikala, M., & Lehtikunnas, L. (2017). A creative prototype illustrating the ambient user experience of an intelligent future factory. Journal of Ambient Intelligence and Smart Environments, 9(1), 41–57.

    Article  Google Scholar 

  • Lee, S. G., Chae, S. H., & Cho, K. M. (2013). Drivers and inhibitors of SaaS adoption in Korea. International Journal of Information Management, 33(3), 429–440.

    Article  Google Scholar 

  • Li, D., Tang, H., Wang, S., & Liu, C. (2017). A big data enabled load-balancing control for smart manufacturing of industry 4.0. Cluster Computing, 20, 1–10.

    Article  Google Scholar 

  • Luthra, S., & Mangla, S. (2018). Evaluating challenges to industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179.

    Article  Google Scholar 

  • Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering, 127, 925–953.

    Article  Google Scholar 

  • Masood, T., & Egger, J. (2019). Augmented reality in support of industry 4.0—Implementation challenges and success factors. Robotics and Computer-Integrated Manufacturing, 58, 181–195.

    Article  Google Scholar 

  • Moktadir, M., Ali, S., Kusi-Sarpong, S., & Shaikh, M. (2018). Assessing challenges for implementing industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection, 117, 730–741.

    Article  Google Scholar 

  • Pacaux-Lemoine, M., Trentesaux, D., Zambrano Rey, G., & Millot, P. (2019). Designing intelligent manufacturing systems through human-machine cooperation principles: A human-centered approach. Available at: accessed 13 July 2019.

    Google Scholar 

  • Pamučar, D., Petrović, I., & Ćirović, G. (2018). Modification of the best-worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert Systems with Applications, 98, 89–106.

    Article  Google Scholar 

  • Peraković, D., Periša, M., & Zorić, P. (2019). Challenges and issues of ICT in industry 4.0. Lecture Notes in Mechanical Engineering, 259–269.

    Google Scholar 

  • Perales, D. P., Valero, F. A., & García, A. B. (2018). Industry 4.0: A classification scheme. In Closing the gap between practice and research in industrial engineering (pp. 343–350). Cham: Springer.

    Chapter  Google Scholar 

  • Pereira, T., Barreto, L., & Amaral, A. (2017). Network and information security challenges within industry 4.0 paradigm. Procedia Manufacturing, 13, 1253–1260.

    Article  Google Scholar 

  • Qaiser, F. H., Ahmed, K., Sykora, M., Choudhary, A., & Simpson, M. (2017). Decision support systems for sustainable logistics: A review and bibliometric analysis. Industrial Management & Data Systems, 117, 1376–1388.

    Article  Google Scholar 

  • Rajput, S., & Singh, S. (2018). Identifying industry 4.0 IoT enablers by integrated PCA-ISM-DEMATEL approach. Management Decision. https://doi.org/10.1108/md-04-2018-0378.

    Article  Google Scholar 

  • Rajput, S., & Singh, S. (2019). Industry 4.0 − challenges to implement circular economy. Benchmarking: An International Journal.

    Google Scholar 

  • Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems, 88, 173–190.

    Article  Google Scholar 

  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

    Article  Google Scholar 

  • Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130.

    Article  Google Scholar 

  • Rezaei, J., Hemmes, A., & Tavasszy, L. (2017). Multi-criteria decision-making for complex bundling configurations in surface transportation of air freight. Journal of Air Transport Management, 61, 95–105.

    Article  Google Scholar 

  • Schuh, G., Anderl, R., Gausemeier, J., ten Hompel, M., & Wahlster, W. (2017). Industrie 4.0 maturity index. Managing the digital transformation of companies. Munich: Herbert Utz.

    Google Scholar 

  • Shakil, K. A., & Alam, M. (2016). Recent developments in cloud based systems: State of art. International Journal of Computer Science and Information Security (IJCSIS), 14(12).

    Google Scholar 

  • Siddiqui, M. S., Legarrea, A., Escalona, E., Parker, M. C., Koczian, G., Walker, S. D., & Ulbricht, M. (2016). Hierarchical, virtualised and distributed intelligence 5G architecture for low-latency and secure applications. Transactions on Emerging Telecommunications Technologies, 27(9), 1233–1241.

    Article  Google Scholar 

  • Sufiyan, M., Haleem, A., Khan, S., & Khan, M. (2019). Evaluating food supply chain performance using hybrid fuzzy MCDM technique. Sustainable Production And Consumption, 20, 40–57. https://doi.org/10.1016/j.spc.2019.03.004

    Article  Google Scholar 

  • Sun, S., Cegielski, C. G., Jia, L., et al. (2016). Understanding the factors affecting the organizational adoption of big data. The Journal of Computer Information Systems, 58, 193–203.

    Article  Google Scholar 

  • Tuptuk, N., & Hailes, S. (2018). Security of smart manufacturing systems. Journal of Manufacturing Systems, 47, 93–106. https://doi.org/10.1016/j.jmsy.2018.04.007.

    Article  Google Scholar 

  • Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13, 2039–2047.

    Article  Google Scholar 

  • Wang, J., Ma, Y., Zhang, L., Gao, R., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156.

    Article  Google Scholar 

  • Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962.

    Article  Google Scholar 

  • Yeh, C.-C., & Chen, Y.-F. (2018). Critical success factors for adoption of 3D printing, Technol. Forecast.Soc. Change, 132, 209–216.

    Google Scholar 

  • Zhong, R. Y., Xu, C., Chen, C., & Huang, G. Q. (2017). Big data analytics for physical internet-based intelligent manufacturing shop floors. International Journal of Production Research, 55(9), 2610–2621.

    Article  Google Scholar 

  • Zhou, K., Liu, T., & Zhou, L. (2016). Industry 4.0: Towards future industrial opportunitiesand challenges in: 2015 12th international conference on fuzzy systems andknowledge discovery. FSKD, 2015, 2147–2152. https://doi.org/10.1109/FSKD.2015.7382284.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, India

    Shahbaz Khan, Mohd Imran Khan & Abid Haleem

Authors
  1. Shahbaz Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohd Imran Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abid Haleem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abid Haleem .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Mansaf Alam

  2. College of Computer & Information Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

    Kashish Ara Shakil

  3. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Samiya Khan

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Khan, S., Khan, M.I., Haleem, A. (2020). Prioritisation of Challenges Towards Development of Smart Manufacturing Using BWM Method. In: Alam, M., Shakil, K., Khan, S. (eds) Internet of Things (IoT). Springer, Cham. https://doi.org/10.1007/978-3-030-37468-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37468-6_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37467-9

  • Online ISBN: 978-3-030-37468-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation