IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v36y2024i2p616-634.html
   My bibliography  Save this article

A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations

Author

Listed:
  • Alireza Aghasi

    (Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331)

  • Arun Rai

    (Center for Digital Innovation and Computer Information Systems Department, J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia 30303)

  • Yusen Xia

    (Institute for Insight, J. Mack Robinson College of Business, Georgia State University, Atlanta, Georgia 30303)

Abstract

Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site.

Suggested Citation

  • Alireza Aghasi & Arun Rai & Yusen Xia, 2024. "A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 616-634, March.
    • Handle: RePEc:inm:orijoc:v:36:y:2024:i:2:p:616-634
    DOI: 10.1287/ijoc.2022.0260
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2022.0260
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijoc.2022.0260?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
    ---><---

    References listed on IDEAS

    as
    1. Shunyuan Zhang & Dokyun Lee & Param Vir Singh & Kannan Srinivasan, 2022. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features," Management Science, INFORMS, vol. 68(8), pages 5644-5666, August.
    2. Li Xiao & Min Ding, 2014. "Just the Faces: Exploring the Effects of Facial Features in Print Advertising," Marketing Science, INFORMS, vol. 33(3), pages 338-352, May.
    3. Ryan Dew & Asim Ansari & Olivier Toubia, 2022. "Letting Logos Speak: Leveraging Multiview Representation Learning for Data-Driven Branding and Logo Design," Marketing Science, INFORMS, vol. 41(2), pages 401-425, March.
    4. Carliss Y. Baldwin & Kim B. Clark, 2000. "Design Rules, Volume 1: The Power of Modularity," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262024667, April.
    5. Mengxia Zhang & Lan Luo, 2023. "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp," Management Science, INFORMS, vol. 69(1), pages 25-50, January.
    6. Prithwiraj Choudhury & Dan Wang & Natalie A. Carlson & Tarun Khanna, 2019. "Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles," Strategic Management Journal, Wiley Blackwell, vol. 40(11), pages 1705-1732, November.
    7. Paul P. Momtaz, 2021. "CEO emotions and firm valuation in initial coin offerings: An artificial emotional intelligence approach," Strategic Management Journal, Wiley Blackwell, vol. 42(3), pages 558-578, March.
    8. Philp, Matthew & Jacobson, Jenna & Pancer, Ethan, 2022. "Predicting social media engagement with computer vision: An examination of food marketing on Instagram," Journal of Business Research, Elsevier, vol. 149(C), pages 736-747.
    9. Sendil K. Ethiraj & Daniel Levinthal & Rishi R. Roy, 2008. "The Dual Role of Modularity: Innovation and Imitation," Management Science, INFORMS, vol. 54(5), pages 939-955, May.
    10. Rosanna K. Smith & Elham Yazdani & Pengyuan Wang & Saber Soleymani & Lan Anh N. Ton, 2022. "The cost of looking natural: Why the no-makeup movement may fail to discourage cosmetic use," Journal of the Academy of Marketing Science, Springer, vol. 50(2), pages 324-337, March.
    11. Liu Liu & Daria Dzyabura & Natalie Mizik, 2020. "Visual Listening In: Extracting Brand Image Portrayed on Social Media," Marketing Science, INFORMS, vol. 39(4), pages 669-686, July.
    Full references (including those not matched with items on IDEAS)

    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. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
    2. Hui Li & Jian Ni & Fangzhu Yang, 2024. "Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data," Papers 2405.15929, arXiv.org, revised Jun 2024.
    3. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
    4. Xin (Shane) Wang & Neil Bendle & Yinjie Pan, 2024. "Beyond text: Marketing strategy in a world turned upside down," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 939-954, July.
    5. Sanjith Gopalakrishnan & Moksh Matta & Hasan Cavusoglu, 2022. "The Dark Side of Technological Modularity: Opportunistic Information Hiding During Interorganizational System Adoption," Information Systems Research, INFORMS, vol. 33(3), pages 1072-1092, September.
    6. Talke, Katrin & Müller, Sebastian & Wieringa, Jaap E., 2024. "Technical newness: Putting a spotlight on its dynamic nature and effects," EconStor Preprints 300544, ZBW - Leibniz Information Centre for Economics.
    7. Daniel A. Levinthal & Maciej Workiewicz, 2018. "When Two Bosses Are Better Than One: Nearly Decomposable Systems and Organizational Adaptation," Organization Science, INFORMS, vol. 29(2), pages 207-224, April.
    8. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.
    9. Alex Burnap & John R. Hauser & Artem Timoshenko, 2023. "Product Aesthetic Design: A Machine Learning Augmentation," Marketing Science, INFORMS, vol. 42(6), pages 1029-1056, November.
    10. Mohsen Jafari Songhori & Madjid Tavana & Takao Terano, 2020. "Product development team formation: effects of organizational- and product-related factors," Computational and Mathematical Organization Theory, Springer, vol. 26(1), pages 88-122, March.
    11. Songcui Hu & Richard A. Bettis, 2018. "Multiple Organization Goals with Feedback from Shared Technological Task Environments," Organization Science, INFORMS, vol. 29(5), pages 873-889, October.
    12. Rahul Kapoor, 2013. "Persistence of Integration in the Face of Specialization: How Firms Navigated the Winds of Disintegration and Shaped the Architecture of the Semiconductor Industry," Organization Science, INFORMS, vol. 24(4), pages 1195-1213, August.
    13. Sungyong Chang, 2023. "Two faces of decomposability in organizational search: Evidence from singles versus albums in the music industry 1995–2015," Strategic Management Journal, Wiley Blackwell, vol. 44(7), pages 1616-1652, July.
    14. Nicholas Burton & Peter Galvin, 2020. "Component complementarity and transaction costs: the evolution of product design," Review of Managerial Science, Springer, vol. 14(4), pages 845-867, August.
    15. Dewen Yao, 2013. "Understanding Industrial Innovation and Upgrade from Modularization’s Perspective," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 3(6), pages 1-11, December.
    16. Kannan Srikanth & Anand Nandkumar & Deepa Mani & Prashant Kale, 2020. "How Firms Build Isolating Mechanisms for Knowledge: A Study in Offshore Research and Development Captives," Strategy Science, INFORMS, vol. 5(2), pages 98-116, June.
    17. Vincent Frigant & J.F Lampon & P. Cabanelas, 2017. "The new automobile modular platforms: from the product architecture to the manufacturing network approach?," Post-Print hal-02150377, HAL.
    18. Jörg Claussen & Tobias Kretschmer & Nils Stieglitz, 2015. "Vertical Scope, Turbulence, and the Benefits of Commitment and Flexibility," Management Science, INFORMS, vol. 61(4), pages 915-929, April.
    19. Schwenzow, Jasper & Hartmann, Jochen & Schikowsky, Amos & Heitmann, Mark, 2021. "Understanding videos at scale: How to extract insights for business research," Journal of Business Research, Elsevier, vol. 123(C), pages 367-379.
    20. Gang Zhang & Ruoyang Gao, 2010. "Modularity and incremental innovation: the roles of design rules and organizational communication," Computational and Mathematical Organization Theory, Springer, vol. 16(2), pages 171-200, June.

    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:inm:orijoc:v:36:y:2024:i:2:p:616-634. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.