IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v270y2018i1d10.1007_s10479-016-2263-8.html
   My bibliography  Save this article

Distribution network design with big data: model and analysis

Author

Listed:
  • Gang Wang

    (University of Massachusetts Dartmouth)

  • Angappa Gunasekaran

    (University of Massachusetts Dartmouth)

  • Eric W. T. Ngai

    (The Hong Kong Polytechnic University)

Abstract

This study addresses the problem of locating distribution centers in a single-echelon, capacitated distribution network. Such network consists of several potential distribution centers and various demand points dispersed in different regional markets. The distribution operations of this network generate massive amounts of data. The problem is how to utilize big data generated to identify the right number of distribution centers to open and the right assignment of customers to opened distribution centers while minimizing the total handling and operation costs of distribution centers, transportation, and penalty. Restrictions on both network capacity and single sourcing strategy are also considered. This study formulates this problem as mixed-integer nonlinear program. The effects of different scenarios on distribution-center locations as demand, the operation costs of distribution centers and outbound transportation, and the number of customers are analyzed through simulation on randomly generated big datasets. Empirical results indicate that the model presented is appropriate and robust. The operational value of big data in the distribution network design is revealed through a case study in which several design alternatives are evaluated.

Suggested Citation

  • Gang Wang & Angappa Gunasekaran & Eric W. T. Ngai, 2018. "Distribution network design with big data: model and analysis," Annals of Operations Research, Springer, vol. 270(1), pages 539-551, November.
    • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2263-8
    DOI: 10.1007/s10479-016-2263-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-016-2263-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-016-2263-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Lidong Wang & Cheryl Ann Alexander, 2015. "Big Data Driven Supply Chain Management and Business Administration," American Journal of Economics and Business Administration, Science Publications, vol. 7(2), pages 60-67, June.
    3. Jia Shu & Qiang Ma & Sijie Li, 2010. "Integrated location and two-echelon inventory network design under uncertainty," Annals of Operations Research, Springer, vol. 181(1), pages 233-247, December.
    4. J Shu & G Wang & K Zhang, 2013. "Logistics distribution network design with two commodity categories," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1400-1408, September.
    5. Shu-Hsien Liao & Chia-Lin Hsieh & Yu-Siang Lin, 2011. "A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems," Annals of Operations Research, Springer, vol. 186(1), pages 213-229, June.
    6. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    7. Yu-Chung Tsao & Qinhong Zhang & Tsung-Hui Chen, 2016. "Multi-item distribution network design problems under volume discount on transportation cost," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 426-443, January.
    8. Chae, Bongsug (Kevin), 2015. "Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research," International Journal of Production Economics, Elsevier, vol. 165(C), pages 247-259.
    9. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    10. Wang, Gang & Lei, Lei, 2012. "Polynomial-time solvable cases of the capacitated multi-echelon shipping network scheduling problem with delivery deadlines," International Journal of Production Economics, Elsevier, vol. 137(2), pages 263-271.
    11. Souza, Gilvan C., 2014. "Supply chain analytics," Business Horizons, Elsevier, vol. 57(5), pages 595-605.
    12. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
    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. Ahmad Ibrahim Aljumah & Mohammed T. Nuseir & Md. Mahmudul Alam, 2021. "Traditional marketing analytics, big data analytics and big data system quality and the success of new product development," Post-Print hal-03538161, HAL.
    2. Surajit Bag & Shivam Gupta & Lincoln Wood, 2022. "Big data analytics in sustainable humanitarian supply chain: barriers and their interactions," Annals of Operations Research, Springer, vol. 319(1), pages 721-760, December.
    3. Niu, Yi-Feng & Zhao, Xia & Xu, Xiu-Zhen & Zhang, Shi-Yun, 2023. "Reliability assessment of a stochastic-flow distribution network with carbon emission constraint," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Aljumah, Ahmad Ibrahim & Nuseir, Mohammed T. & Alam, Md. Mahmudul, 2021. "Organizational Performance and Capabilities to Analyze Big Data: Do the Ambidexterity and Business Value of Big Data Analytics Matter?," OSF Preprints an8er, Center for Open Science.
    5. Seddigh, Mohammad Reza & Targholizadeh, Aida & Shokouhyar, Sajjad & Shokoohyar, Sina, 2023. "Social media and expert analysis cast light on the mechanisms of underlying problems in pharmaceutical supply chain: An exploratory approach," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. Ruozhen Qiu & Shunpeng Shi & Yue Sun, 2019. "A p -Robust Green Supply Chain Network Design Model under Uncertain Carbon Price and Demand," Sustainability, MDPI, vol. 11(21), pages 1-22, October.
    7. Vicky Ching Gu & Bin Zhou & Qing Cao & Jeffery Adams, 2021. "Exploring the relationship between supplier development, big data analytics capability, and firm performance," Annals of Operations Research, Springer, vol. 302(1), pages 151-172, July.

    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. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    2. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "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, Elsevier, vol. 114(C), pages 416-436.
    3. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    4. Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    5. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    6. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    7. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    8. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    9. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    10. Purva Grover & Arpan Kumar Kar, 2017. "Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 203-229, September.
    11. Yaping Zhao & Zelong Yi, 2021. "Pricing of a Three-Stage Supply Chain with a Big Data Company," SN Operations Research Forum, Springer, vol. 2(4), pages 1-19, December.
    12. Shuihua Han & Yufang Fu & Bin Cao & Zongwei Luo, 2018. "Pricing and bargaining strategy of e-retail under hybrid operational patterns," Annals of Operations Research, Springer, vol. 270(1), pages 179-200, November.
    13. Ionica Oncioiu & Ovidiu Constantin Bunget & Mirela Cătălina Türkeș & Sorinel Căpușneanu & Dan Ioan Topor & Attila Szora Tamaș & Ileana-Sorina Rakoș & Mihaela Ștefan Hint, 2019. "The Impact of Big Data Analytics on Company Performance in Supply Chain Management," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    14. Dong-Hui Jin & Hyun-Jung Kim, 2018. "Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    15. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    16. Nishikant Mishra & Akshit Singh, 2018. "Use of twitter data for waste minimisation in beef supply chain," Annals of Operations Research, Springer, vol. 270(1), pages 337-359, November.
    17. Lei Xu & Runpeng Gao & Yu Xie & Peng Du, 2019. "To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain," Sustainability, MDPI, vol. 11(8), pages 1-14, April.
    18. Tino T. Herden & Benjamin Nitsche & Benno Gerlach, 2020. "Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations," Logistics, MDPI, vol. 4(1), pages 1-27, February.
    19. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    20. Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).

    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:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2263-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.