IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2019i1p86-d300470.html
   My bibliography  Save this article

A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research

Author

Listed:
  • Francesco Facchini

    (Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, 70126 Bari, Italy)

  • Joanna Oleśków-Szłapka

    (Department of Management Engineering, Poznan University of Technology, 60-965 Poznań, Poland)

  • Luigi Ranieri

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy)

  • Andrea Urbinati

    (School of Industrial Engineering, LIUC Università Cattaneo, 21053 Castellanza, Italy)

Abstract

The adoption of Industry 4.0 technologies has become particularly important nowadays for companies in order to optimize their production processes and organizational structures. However, companies sometimes find it difficult to develop a strategic plan that innovates their current business model and develops an Industry 4.0 vision. To overcome the growing uncertainty and dissatisfaction in implementing Industry 4.0, new methods and tools that specifically address dedicated companies’ areas, such as logistics, supply chain management, and manufacturing processes, were developed to provide guidance and support to align companies’ business strategies and operations. In particular, this paper develops and presents the application of a maturity model for Logistics 4.0, focusing on the specific applications of Industry 4.0 in the area of logistics. To do so, extant maturity models, linked to the context of Industry 4.0 implementation in logistics processes, were examined in the main scientific research. Afterward, two companies have been investigated through a survey, built around three fundamental macro-aspects, named (i) the propensity of the company towards Industry 4.0 and Logistics 4.0, (ii) the current use of technologies in the logistics process, and (iii) the investments’ level towards Industry 4.0 technologies for a Logistics 4.0 transition. By doing so, a maturity model for Logistics 4.0 emerged as the main result of our research, able to identify the level of maturity of companies in implementing the Industry 4.0 technologies in their logistics processes. Moreover, the model highlighted the strengths and weaknesses of the two investigated companies with respect to the transition towards Logistics 4.0. On the basis of the obtained results, a roadmap for enhancing the digitalization of logistics processes, according to the principles of the fourth industrial revolution, was finally proposed.

Suggested Citation

  • Francesco Facchini & Joanna Oleśków-Szłapka & Luigi Ranieri & Andrea Urbinati, 2019. "A Maturity Model for Logistics 4.0: An Empirical Analysis and a Roadmap for Future Research," Sustainability, MDPI, vol. 12(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:86-:d:300470
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/1/86/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/1/86/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Rohrbeck, René & Schwarz, Jan Oliver, 2013. "The value contribution of strategic foresight: Insights from an empirical study of large European companies," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1593-1606.
    3. Bernd Carsten Stahl & Michael Obach & Emad Yaghmaei & Veikko Ikonen & Kate Chatfield & Alexander Brem, 2017. "The Responsible Research and Innovation (RRI) Maturity Model: Linking Theory and Practice," Sustainability, MDPI, vol. 9(6), pages 1-19, June.
    4. Fernando E. Garcia-Muiña & Rocío González-Sánchez & Anna Maria Ferrari & Lucrezia Volpi & Martina Pini & Cristina Siligardi & Davide Settembre-Blundo, 2019. "Identifying the Equilibrium Point between Sustainability Goals and Circular Economy Practices in an Industry 4.0 Manufacturing Context Using Eco-Design," Social Sciences, MDPI, vol. 8(8), pages 1-22, August.
    5. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.
    6. Jingxiao Zhang & Hui Li & Steve Hsueh-Ming Wang, 2017. "Analysis and Potential Application of the Maturity of Growth Management in the Developing Construction Industry of a Province of China: A Case Study," Sustainability, MDPI, vol. 9(1), pages 1-36, January.
    7. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    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. Nikolay Dragomirov, 2022. "Digital Transformation Perspectives in Warehousing – Initial Steps and Projections," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 6, pages 133-153.
    2. Danlian Li & Qian Cao & Min Zuo & Fei Xu, 2020. "Optimization of Green Fresh Food Logistics with Heterogeneous Fleet Vehicle Route Problem by Improved Genetic Algorithm," Sustainability, MDPI, vol. 12(5), pages 1-17, March.
    3. Maja Trstenjak & Tihomir Opetuk & Hrvoje Cajner & Natasa Tosanovic, 2020. "Process Planning in Industry 4.0—Current State, Potential and Management of Transformation," Sustainability, MDPI, vol. 12(15), pages 1-25, July.
    4. Jesus Gonzalez-Feliu & Mario Chong & Jorge Vargas-Florez & Irineu de Brito & Carlos Osorio-Ramirez & Eric Piatyszek & Renato Quiliche Altamirano, 2020. "The Maturity of Humanitarian Logistics against Recurrent Crises," Social Sciences, MDPI, vol. 9(6), pages 1-22, May.
    5. Daniel Teso-Fz-Betoño & Ekaitz Zulueta & Ander Sánchez-Chica & Unai Fernandez-Gamiz & Aitor Saenz-Aguirre, 2020. "Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot," Mathematics, MDPI, vol. 8(5), pages 1-19, May.
    6. Koopo Kwon & Jaeryong So, 2023. "Future Smart Logistics Technology Based on Patent Analysis Using Temporal Network," Sustainability, MDPI, vol. 15(10), pages 1-17, May.

    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. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    2. Pan, Qiaohong & Luo, Wenping & Fu, Yi, 2022. "A csQCA study of value creation in logistics collaboration by big data: A perspective from companies in China," Technology in Society, Elsevier, vol. 71(C).
    3. Ostadzad, Ali Hossein, 2022. "Innovation and carbon emissions: Fixed-effects panel threshold model estimation for renewable energy," Renewable Energy, Elsevier, vol. 198(C), pages 602-617.
    4. Hans-Joachim Schramm & Carolin Nicole Czaja & Michael Dittrich & Matthias Mentschel, 2019. "Current Advancements of and Future Developments for Fourth Party Logistics in a Digital Future," Logistics, MDPI, vol. 3(1), pages 1-17, February.
    5. Leonardo de Assis Santos & Leonardo Marques, 2022. "Big data analytics for supply chain risk management: research opportunities at process crossroads," Post-Print hal-03766121, HAL.
    6. Michele Gorgoglione & Achille Claudio Garavelli & Umberto Panniello & Angelo Natalicchio, 2023. "Information Retrieval Technologies and Big Data Analytics to Analyze Product Innovation in the Music Industry," Sustainability, MDPI, vol. 15(1), pages 1-16, January.
    7. Papanagnou, Christos & Seiler, Andreas & Spanaki, Konstantina & Papadopoulos, Thanos & Bourlakis, Michael, 2022. "Data-driven digital transformation for emergency situations: The case of the UK retail sector," International Journal of Production Economics, Elsevier, vol. 250(C).
    8. Zhucui Jing & Ying Zheng & Hongli Guo, 2023. "A Study of the Impact of Digital Competence and Organizational Agility on Green Innovation Performance of Manufacturing Firms—The Moderating Effect Based on Knowledge Inertia," Administrative Sciences, MDPI, vol. 13(12), pages 1-17, December.
    9. Fu, Shuke & Ge, Yingchen & Hao, Yu & Peng, Jiachao & Tian, Jiali, 2024. "Energy supply chain efficiency in the digital era: Evidence from China's listed companies," Energy Economics, Elsevier, vol. 134(C).
    10. Maniyassouwe Amana & Pingfeng Liu & Mona Alariqi, 2022. "Value Creation and Capture with Big Data in Smart Phones Companies," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    11. Vendrell-Herrero, Ferran & Bustinza, Oscar F. & Opazo-Basaez, Marco, 2021. "Information technologies and product-service innovation: The moderating role of service R&D team structure," Journal of Business Research, Elsevier, vol. 128(C), pages 673-687.
    12. Anhang Chen & Huiqin Zhang & Yuxiang Zhang & Junwei Zhao, 2024. "Manufacturers’ digital transformation under carbon cap-and-trade policy: investment strategy and environmental impact," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    13. Yu, Wantao & Zhao, Gen & Liu, Qi & Song, Yongtao, 2021. "Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    14. Videsh Desingh & Baskaran R, 2022. "Internet of Things adoption barriers in the Indian healthcare supply chain: An ISM‐fuzzy MICMAC approach," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(1), pages 318-351, January.
    15. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    16. Elizabeth Gibson & Tugrul Daim & Edwin Garces & Marina Dabic, 2018. "Technology Foresight: A Bibliometric Analysis to Identify Leading and Emerging Methods," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 12(1), pages 6-24.
    17. Adem Emre & Seher Somuncu & Meltem Korkmaz & Ebru Demirci, 2024. "Conceptual awareness levels of digital logistics among Turkish university students," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    18. Oier Imaz & Andoni Eizagirre, 2020. "Responsible Innovation for Sustainable Development Goals in Business: An Agenda for Cooperative Firms," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    19. Christoph Markmann & Alexander Spickermann & Heiko A. von der Gracht & Alexander Brem, 2021. "Improving the question formulation in Delphi‐like surveys: Analysis of the effects of abstract language and amount of information on response behavior," Futures & Foresight Science, John Wiley & Sons, vol. 3(1), March.
    20. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.

    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:12:y:2019:i:1:p:86-:d:300470. 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.