Analytics Applications in Fashion Supply Chain Management-A Review of Literature and Practice

被引:7
作者
Stahl, Christina [1 ]
Stein, Nikolai [1 ]
Flath, Christoph M. [1 ]
机构
[1] Julius Maximilians Univ Wurzburg, Inst Informat Management, D-97070 Wurzburg, Germany
基金
美国国家卫生研究院;
关键词
Supply chains; Industries; Supply chain management; Bibliographies; Companies; Prediction algorithms; Optimization; Advanced analytics; big data; content analysis; fashion industry; supply chain management; EXTREME LEARNING-MACHINE; HYBRID INTELLIGENT MODEL; BIG DATA ANALYTICS; BUSINESS ANALYTICS; DATA SCIENCE; INFORMATION; OPTIMIZATION; INTEGRATION; TECHNOLOGY; REVOLUTION;
D O I
10.1109/TEM.2021.3075936
中图分类号
F [经济];
学科分类号
02 ;
摘要
Fashion companies' chance to survive the current pandemic is much dependent on their analytics skills. Despite this urge and the arising possibilities in the "data era," analytics activities are still underestimated and scattered across different fashion supply chain functions. Therefore, this article positions itself at the important intersection of analytics and fashion supply chain management. This article analyzed analytics applications across all relevant supply chain functions within the fashion industry. We conducted our literature review with a focus on different forms of data-driven decision making applied within fashion supply chain functions. We systematically compared the findings from a structured literature review and a content analysis of corporate annual reports and detailed state-of-the-art analytics examples. We highlight deviations in the analytics level: Research papers have a strong focus on advanced analytics methods while most companies are struggling to establish descriptive analytics capabilities. Based on this, we derive and detail managerial and research implications. Having created a holistic overview, this article presents itself as a cornerstone for further analytics-focused research within the fashion industry. Also, it provides managers with insights into the current landscape of analytics applications and develops the vision of a future analytics-driven fashion supply chain.
引用
收藏
页码:1258 / 1282
页数:25
相关论文
共 130 条
  • [1] Abd Jelil R, 2018, SPR SER FASH BUS, P97, DOI 10.1007/978-981-13-0080-6_6
  • [2] Al-Kassab Jasser, 2013, J. theor. appl. electron. commer. res., V8, P112, DOI 10.4067/S0718-18762013000200010
  • [3] Primal-Dual Algorithms for Order Fulfillment at Urban Outfitters, Inc.
    Andrews, John M.
    Ferias, Vivek F.
    Khojandi, Aryan, I
    Yan, Chad M.
    [J]. INFORMS JOURNAL ON APPLIED ANALYTICS, 2019, 49 (05): : 355 - 370
  • [4] AnnualReport, 2018, FAST RET
  • [5] [Anonymous], 2018, STAT FASH 2018
  • [6] Distribution network deployment for omnichannel retailing
    Arslan, Ayse N.
    Klibi, Walid
    Montreuil, Benoit
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2021, 294 (03) : 1042 - 1058
  • [7] Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice
    Arunachalam, Deepak
    Kumar, Niraj
    Kawalek, John Paul
    [J]. TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2018, 114 : 416 - 436
  • [8] Ashmawi S., 2019, Procedia Comput. Sci, V163, P209, DOI DOI 10.1016/J.PROCS.2019.12.102
  • [9] Dynamic Procurement of New Products with Covariate Information: The Residual Tree Method
    Ban, Gah-Yi
    Gallien, Jeremie
    Mersereau, Adam J.
    [J]. M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT, 2019, 21 (04) : 798 - 815
  • [10] The Big Data Newsvendor: Practical Insights from Machine Learning
    Ban, Gah-Yi
    Rudin, Cynthia
    [J]. OPERATIONS RESEARCH, 2019, 67 (01) : 90 - 108