SpringerLink
Log in

A Framework for the Application of Industry 4.0 in Logistics and Supply Chains

  • Chapter
  • First Online:
Sustainable Supply Chains: Strategies, Issues, and Models

  • 1295 Accesses

Abstract

This research aims to identify and understand the contemporary practice of using Business Analytics (BA) in improving the performance of logistics companies by conducting exploratory case studies. We present seven case studies using a within-case and cross-case analysis of the practice of BA use in UK logistics firms. We position our analysis under major BA application areas identified in previous third-party logistics surveys. Based on an in-depth analysis, we present a Value-Adding Input-Output (VAIO) framework to support an understanding of the use of Business Analytics in logistics companies. One of the main findings is the recognition of four antecedents (skills, systems, technology, and trust issues) before deriving value from business analytics investments. When the antecedents are in place, it is possible for logistics companies to derive value by engaging in BA application areas. The value dimensions ultimately help logistics firms to be competitive in the market place. The framework supports the applicability of the Resource-Based View of a firm for BA use in logistics. The framework developed in this chapter provides a practical basis for logistics companies to derive value from their investments in Business Analytics. The Value-Adding Process Framework is a new framework suggested in this chapter.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • 3PL study. (2014). 2014 THIRD-PARTY LOGISTICS STUDY: The state of logistics outsourcing—Results and findings of the 18th Annual Study, C. John Langley, Jr., Ph.D., and Capgemini.

    Google Scholar 

  • 3PL study. (2017). 2017 THIRD-PARTY LOGISTICS STUDY: The state of logistics outsourcing—Results and findings of the 21st Annual Study, C. John Langley, Jr., Ph.D., and Capgemini.

    Google Scholar 

  • Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 1–32.

    Google Scholar 

  • Agarwal, R., & Dhar, V. (2014). Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443–448.

    Google Scholar 

  • Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131.

    Google Scholar 

  • Alharthi, A., Krotov, V., & Bowman, M. (2017). Addressing barriers to big data. Business Horizons, 60(3), 285–292.

    Google Scholar 

  • Arunachalam, D., Kumar, N., & Kawalek, J. P. (2017). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review (In Press).

    Google Scholar 

  • Ayed, A. B., Halima, M. B., & Alimi, A. M. (2015, May). Big data analytics for logistics and transportation. In 2015 4th International Conference on Advanced Logistics and Transport (ICALT) (pp. 311–316), IEEE.

    Google Scholar 

  • Ben-Daya, M., Hassini, E., & Bahroun, Z. (2017). Internet of things and supply chain management: A literature review. International Journal of Production Research, 1–24.

    Google Scholar 

  • Buer, S. V., Strandhagen, J. O., & Chan, F. T. (2018). The link between Industry 4.0 and lean manufacturing: Map** current research and establishing a research agenda. International Journal of Production Research, 56(8), 2924–2940.

    Google Scholar 

  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

    Google Scholar 

  • Barney, J., Wright, M., & Ketchen, D. J. (2001). The resource-based view of the firm: Ten years after 1991. Journal of Management, 27, 625–641.

    Google Scholar 

  • Barton, D., & Court, D. (2012). Making advanced analytics work for you: A practical guide to capitalizing on big data. Harvard Business Review, 90(10), 78–83.

    Google Scholar 

  • Bersimis, S., Koutras, M. V., & Maravelakis, P. E. (2014). A compound control chart for monitoring and controlling high quality processes. European Journal of Operational Research, 233(3), 595–603.

    MathSciNet  MATH  Google Scholar 

  • Bertsekas, D. P. (1998). Network optimization: Continuous and discrete models. Belmont: Athena Scientific.

    MATH  Google Scholar 

  • Bose, R. (2009). Advanced analytics: Opportunities and challenges. Industrial Management & Data Systems, 109(2), 155–172.

    Google Scholar 

  • Caicedo, F., Blazquez, C., & Miranda, P. (2012). Prediction of parking space availability in real time. Expert Systems with Applications, 39(8), 7281–7290.

    Google Scholar 

  • Caridi, M., Moretto, A., Perego, A., & Tumino, A. (2014). The benefits of supply chain visibility: A value assessment model. International Journal of Production Economics, 151, 1–19.

    Google Scholar 

  • Cecere, L. (2012). Big data: Go big or go home, Supply Chain Insights LLC. Retrieved March 21, 13, from http://supplychaininsights.com.

  • Chen, J. C., Cheng, C. H., Huang, P. B., Wang, K. J., Huang, C. J., & Ting, T. C. (2013). Warehouse management with lean and RFID application: A case study. The International Journal of Advanced Manufacturing Technology, 69(1–4), 531–542.

    Google Scholar 

  • Chen, M. C., Hsu, C. L., Hsu, C. M., & Lee, Y. Y. (2014). Ensuring the quality of e-shop** specialty foods through efficient logistics service. Trends in Food Science & Technology, 35(1), 69–82.

    Google Scholar 

  • Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of big data analytics in European firms. Journal of Business Research, 70, 379–390.

    Google Scholar 

  • Cullinane, K., & Toy, N. (2000). Identifying influential attributes in freight route/mode choice decisions: A content analysis. Transportation Research Part E: Logistics and Transportation Review, 36(1), 41–53.

    Google Scholar 

  • Davenport, T. (2006). Competing on analytics. Harvard Business Review, 84(5), 150–151.

    Google Scholar 

  • DHL. (2010). Delivering tomorrow: Towards sustainable logistics, Deutsche Post AG, Headquarters, Bonn, Germany. Retrieved October 23, 2017, from http://www.dhl.co.uk/content/dam/downloads/g0/logistics/green_logistics_sustainable_logistics_study_en.pdf.

  • DHL. (2013). Big data in logistics: A DHL perspective on how to move beyond the hype. Troisdorf, Germany: DHL Customer Solutions & Innovation.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Luo, Z., Wamba, S. F., & Roubaud, D. (2017). Can big data and predictive analytics improve social and environmental sustainability? Technological Forecasting and Social Change (in press).

    Google Scholar 

  • Dullaert, W., & Zamparini, L. (2013). The impact of lead time reliability in freight transport: A logistics assessment of transport economics findings. Transportation Research Part E: Logistics and Transportation Review, 49(1), 190–200.

    Google Scholar 

  • Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904.

    Google Scholar 

  • Ferguson, D. M., Hill, N. C., & Hansen, J. V. (1990). Electronic data interchange: Foundations and survey evidence on current use. Journal of Information Systems, 4(2), 81–91.

    Google Scholar 

  • Fuhr, J. P., & Pociask, S. B. (2007). Broadband services: Economic and environmental benefits. American Consumer Institute.

    Google Scholar 

  • Goes, P. B. (2014). Editor’s comments: Big data and IS research. MIS Quarterly, 38(3), iii–viii.

    Google Scholar 

  • Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., et al. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.

    Google Scholar 

  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

    Google Scholar 

  • Gupta, A., & Maranas, C. D. (2003). Managing demand uncertainty in supply chain planning. Computers & Chemical Engineering, 27(8), 1219–1227.

    Google Scholar 

  • Hamdan, A., & Rogers, K. J. (2008). Evaluating the efficiency of 3PL logistics operations. International Journal of Production Economics, 113(1), 235–244.

    Google Scholar 

  • Handfield, R., Straube, F., Pfohl, H. C., & Wieland, A. (2013). Embracing global logistics complexity to drive market advantage. BVL International: DVV Media Group GmbH.

    Google Scholar 

  • Hashem, I. A. T., Yaqoob, L., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.

    Google Scholar 

  • Hazen, B. T., Skipper, J. B., Ezell, J. D., & Boone, C. A. (2016). Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers & Industrial Engineering, 101, 592–598.

    Google Scholar 

  • Heijden, H. V., & Garn, W. (2013). Profitability in the car industry: New measures for estimating targets and target directions. European Journal of Operational Research, 225(3), 420–428.

    Google Scholar 

  • Hitt, M. A., Xu, K., & Carnes, C. M. (2015). Resource based theory in operations management research. Journal of Operations Management, 41, 77–94.

    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.

    Google Scholar 

  • Holzapfel, A., Kuhn, H., & Sternbeck, M. G. (2016). Product allocation to different types of distribution center in retail logistics networks. European Journal of Operational Research, 264(3), 948–966.

    MathSciNet  MATH  Google Scholar 

  • Jiang, S., Fiore, G. A., Yang, Y., Ferreira, Jr, J., Frazzoli, E., & González, M. C. (2013). A review of urban computing for mobile phone traces: Current methods, challenges and opportunities. In Proceedings of the 2nd ACM SIGKDD international workshop on Urban Computing (p. 2). ACM.

    Google Scholar 

  • Koshak, N., Nour, A., & Center, K. G. I. (2013). Integrating RFID and GIS to support urban transportation management and planning of Hajj. In The 13th International Conference on Computers in Urban Planning and Urban Management.

    Google Scholar 

  • LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.

    Google Scholar 

  • Malomo, F., & Sena, V. (2017). Data intelligence for local government? Assessing the benefits and barriers to use of big data in the public sector. Policy & Internet, 9(1), 7–27.

    Google Scholar 

  • Marchet, G., Melacini, M., Perotti, S., Sassi, C., & Tappia, E. (2017). Value creation models in the 3PL industry: What 3PL providers do to cope with shipper requirements. International Journal of Physical Distribution & Logistics Management, 47(6), 472–494.

    Google Scholar 

  • Matusiak, M., de Koster, R., Kroon, L., & Saarinen, J. (2014). A fast simulated annealing method for batching precedence-constrained customer orders in a warehouse. European Journal of Operational Research, 236(3), 968–977.

    MathSciNet  MATH  Google Scholar 

  • Meyr, H. (2004). Supply chain planning in the German automotive industry. OR Spectrum, 26(4), 447–470.

    MATH  Google Scholar 

  • Opresnik, D., & Taisch, M. (2015). The value of big data in servitization. International Journal of Production Economics, 165, 174–184.

    Google Scholar 

  • Otto, A. (2003). Supply chain event management: Three perspectives. International Journal of Logistics Management, 14(2), 1–13.

    Google Scholar 

  • Ramanathan, R. (2017). Energy and environmental issues in international business. Journal of Contemporary Management and Development Studies, 4(1), 44–60.

    Google Scholar 

  • Ramanathan, R., Bentley, Y., & Ko, L. W. L. (2012). Investigation of the status of RFID applications in the UK logistics sector. Logistics & Transport Focus, the official magazine of the Chartered Institute of Logistics and Transport (UK), 14(11), pp. 45–49.

    Google Scholar 

  • Ramanathan, R., Karpuzcu, T., & Ramanathan, U. (2016). Impact of e-commerce in B2B physical distribution: Diffusion of innovations perspective. In I. N. Lee (Ed.), Encyclopedia of E-Commerce Development, Implementation, and Management (pp. 2256–2270), IGI Global, Hershey, PA 17033, USA, Chapter 162.

    Google Scholar 

  • Ramanathan, R., Philpott, E., Duan, Y., & Cao, G. (2017). Adoption of business analytics and impact on performance: A qualitative study in retail. Production Planning and Control, 28(11–12), 985–998.

    Google Scholar 

  • Ramanathan, U., & Gunasekaran, A. (2014). Supply chain collaboration: Impact of success in long-term partnerships. International Journal of Production Economics, 147, 252–257.

    Google Scholar 

  • Reinmoeller, P., & Ansari, S. (2016). The persistence of a stigmatized practice: A study of competitive intelligence. British Journal of Management, 27(1), 116–142.

    Google Scholar 

  • Rinehart, L. M., Cooper, M. B., & Wagenheim, G. D. (1989). Furthering the integration of marketing and logistics through customer service in the channel. Journal of the Academy of Marketing Science, 17(1), 63–71.

    Google Scholar 

  • Stadtler, H., & Kilger, C. (2002). Supply chain management and advanced planning: Concepts, models. Springer, Berlin: Software and Case Studies.

    Google Scholar 

  • Tian, X., Han, R., Wang, L., Lu, G., & Zhan, J. (2015). Latency finance. The Journal of Finance and Data Science, 1(1), 33–41.

    Google Scholar 

  • Tortorella, G. L., & Fettermann, D. (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975–2987.

    Google Scholar 

  • Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.

    Google Scholar 

  • Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.

    Google Scholar 

  • Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110.

    Google Scholar 

  • Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.

    Google Scholar 

  • Wang, Y., Kung, L., & Byrd, T. A. (2017). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3–13.

    Google Scholar 

  • Whitmore, A., Agarwal, A., & Da Xu, L. (2015). The internet of things—A survey of topics and trends. Information Systems Frontiers, 17(2), 261–274.

    Google Scholar 

  • Wu, Y. C. J., Huang, S. K., Goh, M., & Hsieh, Y. J. (2013). Global logistics management curriculum: Perspective from practitioners in Taiwan. Supply Chain Management: An International Journal, 18(4), 376–388.

    Google Scholar 

  • Xu, L. D., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243.

    Google Scholar 

  • Yin, R. K. (2012). Applications of case study research (3rd ed.). Thousand Oaks, USA: Sage Publications.

    Google Scholar 

  • Yu, Q., & Wang, K. (2016). Applications of IoT in production logistics: Opportunities and challenges. WIT Transactions on Engineering Sciences, 113, 233–240.

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank British Academy for funding to carry out this research.

Author information

Authors and Affiliations

  1. University of Bedfordshire, Luton, LU1 3JU, UK

    Ramakrishnan Ramanathan, Elly Philpott & Yanqing Duan

  2. Nottingham Trent University, Nottingham, NG1 4FQ, UK

    Usha Ramanathan

Authors
  1. Ramakrishnan Ramanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elly Philpott
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Usha Ramanathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanqing Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Usha Ramanathan .

Editor information

Editors and Affiliations

  1. Nottingham Business School, Nottingham Trent University, Nottingham, UK

    Usha Ramanathan

  2. University of Bedfordshire Business School, Luton, UK

    Ramakrishnan Ramanathan

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

Ramanathan, R., Philpott, E., Ramanathan, U., Duan, Y. (2020). A Framework for the Application of Industry 4.0 in Logistics and Supply Chains. In: Ramanathan, U., Ramanathan, R. (eds) Sustainable Supply Chains: Strategies, Issues, and Models. Springer, Cham. https://doi.org/10.1007/978-3-030-48876-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-48876-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48875-8

  • Online ISBN: 978-3-030-48876-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation