IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i8p1252-d791167.html
   My bibliography  Save this article

Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach

Author

Listed:
  • Sadia Samar Ali

    (Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Rajbir Kaur

    (Government Girls College, Panchkula 134113, India)

Abstract

Developing a promising technology that copes with the industrial warehousing environment requires special preparation. It includes infrastructure, equipment, resources, knowledge, efficiencies, and strategies for dealing with failures. This study examines Technology 4.0 driven warehouse practices and performance based on a thorough literature review. The study presents a unique proposition as it considers a two-fold fuzzy Delphi analysis to rank the Technology 4.0 driven practices using best-worst method (BWM) based on experts’ responses. Warehouse performance measures are evaluated by the Combined Compromise Solution (CoCoSo) method. The results indicate the contributions of a ‘Man-machines or robots for facilitating human’; ‘Planning system for management’; ‘Storage systems’ as as leading practices contributing to ‘improved inventory management’, ‘effective storage and distribution’, and ‘improved distribution and shipping or delivery process’. Using this study, researchers and managers will better understand how to adopt technology in warehouse management system.

Suggested Citation

  • Sadia Samar Ali & Rajbir Kaur, 2022. "Exploring the Impact of Technology 4.0 Driven Practice on Warehousing Performance: A Hybrid Approach," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1252-:d:791167
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/8/1252/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/8/1252/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    2. de Sousa Jabbour, Ana Beatriz Lopes & Jabbour, Charbel Jose Chiappetta & Foropon, Cyril & Godinho Filho, Moacir, 2018. "When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 18-25.
    3. Li, Xiang, 2020. "Reducing channel costs by investing in smart supply chain technologies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    4. Qiu, Xuan & Luo, Hao & Xu, Gangyan & Zhong, Runyang & Huang, George Q., 2015. "Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP)," International Journal of Production Economics, Elsevier, vol. 159(C), pages 4-15.
    5. Mahroof, Kamran, 2019. "A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse," International Journal of Information Management, Elsevier, vol. 45(C), pages 176-190.
    6. K. L. Choy & G. T. S. Ho & C. K. H. Lee, 2017. "A RFID-based storage assignment system for enhancing the efficiency of order picking," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 111-129, January.
    7. Jingjing Hao & Haoming Shi & Victor Shi & Chenchen Yang, 2020. "Adoption of Automatic Warehousing Systems in Logistics Firms: A Technology–Organization–Environment Framework," Sustainability, MDPI, vol. 12(12), pages 1-14, June.
    8. Nynke Faber & René B.M. De Koster & Ale Smidts, 2018. "Survival of the fittest: the impact of fit between warehouse management structure and warehouse context on warehouse performance," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 120-139, January.
    9. Shashank Kumar & Balkrishna E. Narkhede & Karuna Jain, 2021. "Revisiting the warehouse research through an evolutionary lens: a review from 1990 to 2019," International Journal of Production Research, Taylor & Francis Journals, vol. 59(11), pages 3470-3492, June.
    10. Yavas, Volkan & Ozkan-Ozen, Yesim Deniz, 2020. "Logistics centers in the new industrial era: A proposed framework for logistics center 4.0," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    11. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    12. Paul J. Reaidy & A. Gunasekaran & A. Spalanzani, 2015. "Bottom-Up Approach based on Internet of things for Order Fulfillment in a Collaborative Warehousing Environment," Post-Print halshs-01374073, HAL.
    13. Tseng, Ming-Lang & Wu, Kuo-Jui & Chiu, Anthony SF. & Lim, Ming K. & Tan, Kimhua, 2018. "Service innovation in sustainable product service systems: Improving performance under linguistic preferences," International Journal of Production Economics, Elsevier, vol. 203(C), pages 414-425.
    14. Wang, Jianxin & Lim, Ming K. & Zhan, Yuanzhu & Wang, XiaoFeng, 2020. "An intelligent logistics service system for enhancing dispatching operations in an IoT environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    15. Birger Wernerfelt, 1984. "A resource‐based view of the firm," Strategic Management Journal, Wiley Blackwell, vol. 5(2), pages 171-180, April.
    16. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    17. Reaidy, Paul J. & Gunasekaran, Angappa & Spalanzani, Alain, 2015. "Bottom-up approach based on Internet of Things for order fulfillment in a collaborative warehousing environment," International Journal of Production Economics, Elsevier, vol. 159(C), pages 29-40.
    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. Li Zhou & Huwei Liu & Junhui Zhao & Fan Wang & Jianglong Yang, 2022. "Performance Analysis of Picking Routing Strategies in the Leaf Layout Warehouse," Mathematics, MDPI, vol. 10(17), pages 1-28, September.
    2. Serkan Karakas & Mehmet Kirmizi & Huseyin Gencer & Kevin Cullinane, 2024. "A resilience assessment model for dry bulk shipping supply chains: the case of the Ukraine grain corridor," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(3), pages 391-413, September.

    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. Abirami Raja Santhi & Padmakumar Muthuswamy, 2022. "Pandemic, War, Natural Calamities, and Sustainability: Industry 4.0 Technologies to Overcome Traditional and Contemporary Supply Chain Challenges," Logistics, MDPI, vol. 6(4), pages 1-32, November.
    2. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    3. Yen Sheng Tsai & Wei-Hsi Hung, 2023. "A low-cost intelligent tracking system for clothing manufacturers," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 473-491, February.
    4. Akhtar, Pervaiz & Khan, Zaheer & Tarba, Shlomo & Jayawickrama, Uchitha, 2018. "The Internet of Things, dynamic data and information processing capabilities, and operational agility," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 307-316.
    5. 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).
    6. Olumide Emmanuel Oluyisola & Fabio Sgarbossa & Jan Ola Strandhagen, 2020. "Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications," Sustainability, MDPI, vol. 12(9), pages 1-29, May.
    7. Abderahman Rejeb & Karim Rejeb & Imen Zrelli, 2024. "Analyzing Barriers to Internet of Things (IoT) Adoption in Humanitarian Logistics: An ISM–DEMATEL Approach," Logistics, MDPI, vol. 8(2), pages 1-27, April.
    8. Asterios Stroumpoulis & Evangelia Kopanaki & George Karaganis, 2021. "Examining the Relationship between Information Systems, Sustainable SCM, and Competitive Advantage," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
    9. Popkova, Elena G. & Sergi, Bruno S., 2020. "A Digital Economy to Develop Policy Related to Transport and Logistics. Predictive Lessons from Russia," Land Use Policy, Elsevier, vol. 99(C).
    10. Carmen Talavera & Joan R. Sanchis, 2020. "Alliances between For-Profit and Non-Profit Organizations as an Instrument to Implement the Economy for the Common Good," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    11. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    12. Chen, Li-Ming & Chang, Wei-Lun, 2021. "Supply- and cyber-related disruptions in cloud supply chain firms: Determining the best recovery speeds," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    13. Delgosha, Mohammad Soltani & Hajiheydari, Nastaran & Talafidaryani, Mojtaba, 2022. "Discovering IoT implications in business and management: A computational thematic analysis," Technovation, Elsevier, vol. 118(C).
    14. Natalia Khan & Wei Deng Solvang & Hao Yu, 2024. "Industrial Internet of Things (IIoT) and Other Industry 4.0 Technologies in Spare Parts Warehousing in the Oil and Gas Industry: A Systematic Literature Review," Logistics, MDPI, vol. 8(1), pages 1-23, February.
    15. Rodríguez-Espíndola, Oscar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel & Emrouznejad, Ali, 2022. "Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    16. 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.
    17. Mourad Makaci & Paul J. Reaidy & Karine Evrard Samuel & Valérie Botta-Genoulaz & Thibaud Monteiro, 2017. "Pooled warehouse management : An empirical study," Post-Print hal-01531304, HAL.
    18. Noha Mostafa & Walaa Hamdy & Hisham Alawady, 2019. "Impacts of Internet of Things on Supply Chains: A Framework for Warehousing," Social Sciences, MDPI, vol. 8(3), pages 1-10, March.
    19. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    20. Frank, Alejandro Germán & Dalenogare, Lucas Santos & Ayala, Néstor Fabián, 2019. "Industry 4.0 technologies: Implementation patterns in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 15-26.

    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:jmathe:v:10:y:2022:i:8:p:1252-:d:791167. 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.