Open AccessArticle
A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks
Managing Solution Architect, Fujitsu America, Inc. 2801 Telecom Parkway, Richardson, TX 75082, USA
*whom correspondence should be addressed. E-mail: prahlad.chowdhury@fujitsu.com
Received: 27 November 2025, Revised: 21 December 2025, Accepted: 23 December 2025, Published Online: 15 January 2026
(This article belongs to the Special Issue on SP8 (Special Issue on Digital and Engineering Transformations in Science and Technology (SI-DETST-26)) and the Section Information Systems – Computer Science (ISC))
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APA Style
Chowdhury, P. (2026). A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks. Journal of Engineering Research and Sciences, 5(1), 35–45. https://doi.org/10.55708/js0501004
Chowdhury, P. (2026). A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks. Journal of Engineering Research and Sciences, 5(1), 35–45. https://doi.org/10.55708/js0501004
Chicago/Turabian Style
Prahlad Chowdhury. "A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks." Journal of Engineering Research and Sciences 5, no. 1 (January 2026): 35–45. https://doi.org/10.55708/js0501004
Prahlad Chowdhury. "A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks." Journal of Engineering Research and Sciences 5, no. 1 (January 2026): 35–45. https://doi.org/10.55708/js0501004
IEEE Style
P. Chowdhury, "A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks," Journal of Engineering Research and Sciences, vol. 5, no. 1, pp. 35–45, Jan. 2026, doi: 10.55708/js0501004.
P. Chowdhury, "A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks," Journal of Engineering Research and Sciences, vol. 5, no. 1, pp. 35–45, Jan. 2026, doi: 10.55708/js0501004.
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Abstract
Many retail and consumer packaged goods (CPG) companies use disconnected data pipelines, which can slow down decisions and increase costs. This paper introduces a cloud-native data architecture that brings together sell-in, sell-out, marketing, e-commerce, and financial data into one managed source of truth. This setup helps teams make timely and reliable decisions. Built on Snowflake, the pipeline uses contract-based ingestion, standardized dimensions, and automated testing. It also sets clear goals for data freshness (media within 6 hours, POS within 48 hours), reliability (at least 99% successful runs), and performance (95% of runs finish within 60 minutes). When tested in three markets and eight product categories, this approach cut the median decision cycle by 25% (from 8.0 to 6.0 hours) and lowered compute costs by 40%. Using standardized KPIs, incremental modeling, and smart retries, the system achieved 95% alignment between planned and actual campaign ROI across over 200 campaigns. FinOps features like auto-suspension, workload isolation, and detailed credit-per-row tracking reduced idle compute spending by at least 30% without slowing performance. The design also supports GreenOps goals by reducing scanned data through pruning and right-sizing, which led to measurable drops in CO₂ emissions without sacrificing analytical accuracy. Overall, these results show a proven, ESG-friendly model for fast and auditable decision-making. The design can be expanded to include streaming data, geo-based experiments, and carbon-aware scheduling, with expected efficiency gains of 10 to 20%. This approach also offers better data governance, stronger privacy controls, and easy scaling to new markets.
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