Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry
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DOI: 10.1111/poms.13426
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- Lai, Kee-hung & Feng, Yunting & Zhu, Qinghua, 2023. "Digital transformation for green supply chain innovation in manufacturing operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
- Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
- Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
- Kumar Detwal, Pankaj & Soni, Gunjan & Kumar Jakhar, Suresh & Kumar Srivastava, Deepak & Madaan, Jitender & Kayikci, Yasanur, 2023. "Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain," Journal of Business Research, Elsevier, vol. 158(C).
- Feng, Yunting & Lai, Kee-hung & Zhu, Qinghua, 2022. "Green supply chain innovation: Emergence, adoption, and challenges," International Journal of Production Economics, Elsevier, vol. 248(C).
- Jackson, Ilya & Ivanov, Dmitry, 2023. "A beautiful shock? Exploring the impact of pandemic shocks on the accuracy of AI forecasting in the beauty care industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
- Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
- Zlatana Nenova & Jennifer Shang, 2022. "Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 583-606, February.
- Zhitao Xu & Adel Elomri & Roberto Baldacci & Laoucine Kerbache & Zhenyong Wu, 2024. "Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective," Annals of Operations Research, Springer, vol. 338(2), pages 1359-1401, July.
- Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
- Zizhuo Wang & Chaolin Yang & Hongsong Yuan & Yaowu Zhang, 2021. "Aggregation Bias in Estimating Log‐Log Demand Function," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 3906-3922, November.
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