Academic Journal
Strategic Data Analytics Under Supplier Encroachment.
| Τίτλος: | Strategic Data Analytics Under Supplier Encroachment. |
|---|---|
| Συγγραφείς: | Chen, Yonghui1 (AUTHOR) yonghui.chen@liverpool.ac.uk, Ma, Guangrui2 (AUTHOR) magr@connect.ust.hk, He, Qiao-Chu3 (AUTHOR) heqc@sustech.edu.cn, Chen, Ying-Ju4 (AUTHOR) imchen@ust.hk, Shen, Zuo-Jun Max5 (AUTHOR) maxshen@hku.hk |
| Πηγή: | Manufacturing & Service Operations Management (M&SOM) (INFORMS). Jul/Aug2026, Vol. 28 Issue 4, p1324-1338. 15p. |
| Θεματικοί όροι: | *Data analytics, *Supply chain management, *Data protection, Data collection platforms, Demand forecasting |
| Περίληψη: | Problem definition: To enhance supply chain collaboration, major retailers increasingly grant suppliers access to their data platforms, leveraging data analytics to deliver valuable demand insights. However, such access raises the risk of channel encroachment by suppliers. This paper examines the retailer's strategy for deploying data analytics in the presence of supplier encroachment. Methodology/results: Under the Bayesian persuasion framework, the retailer's data analytics strategy is formulated as a signaling rule that maps market states into predictions with partial informational granularity. We further compare two organizational structures of data governance: a shared data platform, which delivers identical demand predictions to both the retailer and the supplier, and a separate data platform, which provides accurate predictions for the retailer, offering less precise insights to the supplier. On a shared data platform, the retailer adopts perfectly accurate analytics when the supplier's encroachment cost is either very high or very low. At moderate encroachment costs, the shared platform generates informative yet biased forecasts, which surprisingly benefit both firms because of the ease of double marginalization. By contrast, under a separate platform, no informative predictions are generated for the supplier at moderate encroachment costs. Overall, comparing the two organizational structures, the retailer prefers a shared platform when the supplier's encroachment cost is relatively high. Managerial implications: Our findings reveal a nonmonotonic relationship between the accuracy of data analytics and suppliers' encroachment efficiency, offering practical guidance for retailers in implementing effective learning algorithms. Moreover, we highlight the benefits of transparent and integrated data analytics within vertical distribution channels, providing managerial insights into the organizational choices underpinning retailers' data governance. Funding: This work was partially supported by the National Natural Science Foundation of China [Grant 72571122] and the Research Grants Council of Hong Kong [Grants C6020-21GF, GRF 16501722]. The authors also acknowledge support from the Southern Key Laboratory of Technology Finance (Guangdong) at the Southern University of Science and Technology (Shenzhen, China). Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0412. [ABSTRACT FROM AUTHOR] |
| Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: bsx DbLabel: Business Source Index An: 195252282 RelevancyScore: 1492 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1491.61413574219 |
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| Items | – Name: Title Label: Title Group: Ti Data: Strategic Data Analytics Under Supplier Encroachment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Yonghui%22">Chen, Yonghui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yonghui.chen@liverpool.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Guangrui%22">Ma, Guangrui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> magr@connect.ust.hk</i><br /><searchLink fieldCode="AR" term="%22He%2C+Qiao-Chu%22">He, Qiao-Chu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> heqc@sustech.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Ying-Ju%22">Chen, Ying-Ju</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> imchen@ust.hk</i><br /><searchLink fieldCode="AR" term="%22Shen%2C+Zuo-Jun+Max%22">Shen, Zuo-Jun Max</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> maxshen@hku.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Manufacturing+%26+Service+Operations+Management+%28M%26SOM%29+%28INFORMS%29%22">Manufacturing & Service Operations Management (M&SOM) (INFORMS)</searchLink>. Jul/Aug2026, Vol. 28 Issue 4, p1324-1338. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br />*<searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+protection%22">Data protection</searchLink><br /><searchLink fieldCode="DE" term="%22Data+collection+platforms%22">Data collection platforms</searchLink><br /><searchLink fieldCode="DE" term="%22Demand+forecasting%22">Demand forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Problem definition: To enhance supply chain collaboration, major retailers increasingly grant suppliers access to their data platforms, leveraging data analytics to deliver valuable demand insights. However, such access raises the risk of channel encroachment by suppliers. This paper examines the retailer's strategy for deploying data analytics in the presence of supplier encroachment. Methodology/results: Under the Bayesian persuasion framework, the retailer's data analytics strategy is formulated as a signaling rule that maps market states into predictions with partial informational granularity. We further compare two organizational structures of data governance: a shared data platform, which delivers identical demand predictions to both the retailer and the supplier, and a separate data platform, which provides accurate predictions for the retailer, offering less precise insights to the supplier. On a shared data platform, the retailer adopts perfectly accurate analytics when the supplier's encroachment cost is either very high or very low. At moderate encroachment costs, the shared platform generates informative yet biased forecasts, which surprisingly benefit both firms because of the ease of double marginalization. By contrast, under a separate platform, no informative predictions are generated for the supplier at moderate encroachment costs. Overall, comparing the two organizational structures, the retailer prefers a shared platform when the supplier's encroachment cost is relatively high. Managerial implications: Our findings reveal a nonmonotonic relationship between the accuracy of data analytics and suppliers' encroachment efficiency, offering practical guidance for retailers in implementing effective learning algorithms. Moreover, we highlight the benefits of transparent and integrated data analytics within vertical distribution channels, providing managerial insights into the organizational choices underpinning retailers' data governance. Funding: This work was partially supported by the National Natural Science Foundation of China [Grant 72571122] and the Research Grants Council of Hong Kong [Grants C6020-21GF, GRF 16501722]. The authors also acknowledge support from the Southern Key Laboratory of Technology Finance (Guangdong) at the Southern University of Science and Technology (Shenzhen, China). Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0412. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1287/msom.2023.0412 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1324 Subjects: – SubjectFull: Data analytics Type: general – SubjectFull: Supply chain management Type: general – SubjectFull: Data protection Type: general – SubjectFull: Data collection platforms Type: general – SubjectFull: Demand forecasting Type: general Titles: – TitleFull: Strategic Data Analytics Under Supplier Encroachment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Yonghui – PersonEntity: Name: NameFull: Ma, Guangrui – PersonEntity: Name: NameFull: He, Qiao-Chu – PersonEntity: Name: NameFull: Chen, Ying-Ju – PersonEntity: Name: NameFull: Shen, Zuo-Jun Max IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul/Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15234614 Numbering: – Type: volume Value: 28 – Type: issue Value: 4 Titles: – TitleFull: Manufacturing & Service Operations Management (M&SOM) (INFORMS) Type: main |
| ResultId | 1 |