A Prescriptive Data Pipeline Framework for Modeling Cost-to-Serve Variability and Enhancing Operational Transparency in CPG Ecosystems

Samuel Oladapo Taiwo1 & Oluwafijimi Mayowa Ayodele2
1,2 Rawls College of Business, Texas Tech University, United States
DOI – http://doi.org/10.37502/IJSMR.2024.71212

Abstract

Cost-to-serve (CTS) variability remains a persistent challenge in Consumer-Packaged Goods (CPG) ecosystems due to fragmented data architectures, complex supply-chain dynamics, and increasing regulatory and transparency requirements. Existing CTS approaches are predominantly descriptive or predictive, offering limited guidance for optimal decision-making under operational and regulatory constraints. This study proposes a prescriptive data pipeline framework that reconceptualizes CTS variability as an optimization-driven decision problem embedded within a governance-aware analytical architecture. Adopting a constructive research approach, the framework integrates driver-based financial modeling, machine learning, and prescriptive optimization with embedded mechanisms for data provenance, traceability, and explainable artificial intelligence. The proposed architecture enables the transformation of heterogeneous operational data into auditable, privacy-preserving, and actionable intelligence, supporting cost optimization while addressing ethical and regulatory accountability demands. By embedding transparency and governance directly within the analytics pipeline, this research advances prescriptive analytics literature and provides a foundational model for scalable, responsible decision intelligence in complex CPG supply chains.

References

  • Anifowose, “The Business Analytics Value Chain: Aligning Data Strategy with Corporate Performance Metrics,” Journal of Computational Analysis and Applications, vol. 33, no. 1A, pp. 751–766, 2024, https://eudoxuspress.com/index.php/pub/article/view/4166/3043 .
  • O. Taiwo, O. O. Aramide, and O. R. Tiamiyu, “Blockchain and Federated Analytics for Ethical and Secure CPG Supply Chains,” Journal of Computational Analysis and Applications, vol. 31, no. 3, pp. 732–749, 2023, https://eudoxuspress.com/index.php/pub/article/view/4024/2920.
  • O. T. Samuel Oladapo Taiwo, “PFAITM: A Predictive Financial Planning and Analysis Intelligence Framework for Transforming Enterprise Decision-Making,” International Journal of Scientific Research in Science Engineering and Technology. Technoscience Academy, p. 472, Oct. 17, 2022. doi: 10.32628/ijsrset25122272.
  • Hiniduma, S. Byna, J. L. Bez, and R. Madduri, “AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI,” Proceedings of the 36th International Conference on Scientific and Statistical Database Management. ACM, pp. 1–12, Jul. 10, 2024. doi: 10.1145/3676288.3676296.
  • Kumar, S. K. Shrivastav, and S. S. Oberoi, “Application of Analytics in Supply Chain Management from Industry and Academic Perspective,” FIIB Business Review, vol. 13, no. 5. SAGE Publications, pp. 503–516, Sep. 06, 2021. doi: 10.1177/23197145211028041.
  • Hirvonen, K. Kauppi, and J. Liesiö, “Identifying enablers for the successful deployment of prescriptive analytics – a multiple case study,” European Business Review, vol. 37, no. 2. Emerald, pp. 245–277, Aug. 29, 2024. doi: 10.1108/ebr-08-2023-0253.
  • Matschewsky, M. L. Kambanou, and T. Sakao, “Designing and providing integrated product-service systems – challenges, opportunities and solutions resulting from prescriptive approaches in two industrial companies,” International Journal of Production Research, vol. 56, no. 6. Informa UK Limited, pp. 2150–2168, May 31, 2017. doi: 10.1080/00207543.2017.1332792.
  • O’Sullivan et al., “Improving transparency and quality assurance: Operationalising semi-automated data provenance tracking in a Trusted Research Environment,” International Journal of Population Data Science, vol. 9, no. 5. Swansea University, Sep. 10, 2024. doi: 10.23889/ijpds.v9i5.2539.
  • Rakibul Hasan Chowdhury, “The evolution of business operations: unleashing the potential of Artificial Intelligence, Machine Learning, and Blockchain.,” World Journal of Advanced Research and Reviews, vol. 22, no. 3. GSC Online Press, pp. 2135–2147, Jun. 30, 2024. doi: 10.30574/wjarr.2024.22.3.1992.
  • Yang, M. Z. Abedin, Y. Qiao, and L. Ye, “Toward Trustworthy Governance of AI-Generated Content (AIGC): A Blockchain-Driven Regulatory Framework for Secure Digital Ecosystems,” IEEE Transactions on Engineering Management, vol. 71. Institute of Electrical and Electronics Engineers (IEEE), pp. 14945–14962, 2024. doi: 10.1109/tem.2024.3472292.
  • O. T. Samuel Oladapo Taiwo, “PFAITM: A Predictive Financial Planning and Analysis Intelligence Framework for Transforming Enterprise Decision-Making,” International Journal of Scientific Research in Science Engineering and Technology. Technoscience Academy, p. 472, Oct. 17, 2022. doi: 10.32628/ijsrset25122272.
  • Anifowose, “Augmented Decision Intelligence: Leveraging AI and Predictive Analytics for Executive Strategy Formulation,” Journal of Computational Analysis and Applications, vol. 31, no. 3, pp. 750–777, 2023, https://eudoxuspress.com/index.php/pub/article/view/4136 .
  • Anifowose, “The Business Analytics Value Chain: Aligning Data Strategy with Corporate Performance Metrics,” Journal of Computational Analysis and Applications, vol. 33, no. 1A, pp. 751–766, 2024. https://eudoxuspress.com/index.php/pub/article/view/4166
  • O. Lawal, “Next-Generation GRC Framework: Integrating ESG and Cyber Risk Metrics,” Journal of Computational Analysis and Applications, vol. 31, no. 3, pp. 778–795, 2023. https://eudoxuspress.com/index.php/pub/article/view/4141
  • O. Lawal, “Human-AI Collaboration in Security Operations Centers (SOC 2.0): Opportunities, Challenges, and Pathways Forward,” Journal of Computational Analysis and Applications, vol. 33, no. 8, pp. 7156–7175, 2024. https://eudoxuspress.com/index.php/pub/article/view/4138
  • U. Uke, “Lean Six Sigma-Driven Maintenance Process Optimization in African Manufacturing Industries: A Systematic Literature Review,” Journal of Computational Analysis and Applications, vol. 29, no. 6, pp. 1346–1366, 2021. https://eudoxuspress.com/index.php/pub/article/view/4028
  • U. Uke, “Circular Economy and Asset Life Extension: Engineering Approaches for Industrial Sustainability,” Journal of Computational Analysis and Applications, vol. 25, no. 8, pp. 134–152, 2018. https://eudoxuspress.com/index.php/pub/article/view/4137
  • Areghan and O. S. Ndibe, “Explainable AI for Autonomous Threat Detection in Critical Infrastructure Systems,” Journal of Computational Analysis and Applications, vol. 33, no. 8, pp. 6841–6857, 2024. https://eudoxuspress.com/index.php/pub/article/view/4026
  • Oluwabukola Racheal Tiamiyu and Ogochukwu Susan Ndibe, “From Compliance Burden to Enforcement Precision : AI Strategies for Reducing False Positives in Anti-Money Laundering Systems,” International Journal of Scientific Research in Science, Engineering and Technology, vol. 11, no. 5. Technoscience Academy, pp. 421–433, Sep. 30, 2024. doi: 10.32628/ijsrset2513837.
  • O. Salami, “Leveraging Natural Language Processing to Detect Non-Compliance in Clinical Documentation: Current Advances, Challenges, and Future Directions,” International Journal of Scientific Research in Science, Engineering and Technology. Technoscience Academy, pp. 459–473, Oct. 17, 2023. doi: 10.32628/ijsrset2513822.
  • O. Taiwo, O. O. Aramide, and O. R. Tiamiyu, “Explainable AI Models for Ensuring Transparency in CPG Markets Pricing and Promotions,” Journal of Computational Analysis and Applications, vol. 33, no. 8, pp. 6858–6873, 2024. https://eudoxuspress.com/index.php/pub/article/view/4027