Edge Computing for Smart Warehousing: A Case Study at GlobalLogistics Corp.

Thesis Abstract

Edge computing-enabled smart warehousing promises reduced latency, improved real-time analytics, and resilient operations in logistics networks. As GlobalLogistics Corp. scales its warehousing footprint to integrate autonomous robots, IoT sensors, and dynamic slotting, persistent challenges arise in data processing bottlenecks, fault tolerance, and decision latency that undermine throughput and accuracy. The study aims to evaluate how edge computing architectures influence operational performance, data governance, and workforce adaptations within a large-scale warehousing context. Specific objectives are to (1) quantify the impact of edge computing on order picking throughput and cure time-to-fill rates, (2) examine latency, reliability, and energy consumption of edge versus cloud-centric processing, (3) identify governance, security, and privacy considerations associated with distributed data processing, (4) assess user acceptance and operational readiness among warehouse staff, and (5) develop a practical framework for deploying edge-enabled smart warehousing in a multi-site setting. A mixed-methods research design is employed, combining quantitative performance measurement with qualitative insights. The population comprises three GlobalLogistics Corp. distribution centers operating under high-throughput e-commerce fulfillment, with a total workforce of 1,200 operational staff and 180 autonomous devices. A stratified random sample of 120 data-logged shipment cycles and 60 hours of on-floor observation are collected over a six-month period, complemented by 25 semi-structured interviews with warehouse managers, technicians, and line operators. Data collection instruments include edge-enabled telemetry logs, system performance dashboards, standardized time-and-motion observation templates, and interview guides grounded in technology acceptance theory (TAM) and the diffusion of innovations (DOI) framework. Validity and reliability are ensured through triangulation across telemetry metrics, process benchmarks, and cross-verify interviews, with instrument piloting in a preliminary three-week period. Data analysis employs regression analysis and ANOVA to compare key performance indicators (throughput, order accuracy, latency, and energy consumption) between edge-enabled and traditional cloud-centric processing, supplemented by time-series analysis to detect trend stability. Thematic analysis is applied to interview transcripts to extract themes related to perceived usefulness, ease of use, security concerns, and readiness. Key expected findings include (i) statistically significant improvements in order picking throughput and reduction in latency when edge computing processes data at the local node versus centralized cloud processing, with effect sizes indicating practical relevance for high-demand periods; (ii) measurable reductions in data transmission costs and energy consumption per device due to localized processing and edge caching strategies; (iii) identifiable governance gaps and security risk profiles in distributed architectures, including potential exposure to firmware tampering and data sovereignty issues; (iv) positive correlations between staff perceived usefulness/ease of use and actual adoption rates, moderated by training quality and change management practices; and (v) a validated deployment blueprint outlining hardware, software, and organizational controls tailored to GlobalLogistics’ multi-site operations. The study contributes to knowledge by providing empirical evidence on the operational and organizational implications of edge computing in large-scale warehousing, bridging gaps between theory and practice in intelligent logistics. It advances a contextualized framework that integrates performance metrics with human factors and governance considerations, drawing on the Technology Acceptance Model, the Diffusion of Innovations, and the Resource-Based View of the firm to interpret findings. The anticipated conclusion highlights edge computing as a critical enabler of real-time decision-making in smart warehousing, contingent on robust governance, interoperability standards, and workforce readiness. Recommendations include (a) implementing a phased edge deployment with standardized telemetry interfaces across sites, (b) adopting a security-by-design approach with continuous monitoring and anomaly detection, (c) investing in operator training programs anchored in TAM constructs, and (d) developing a centralized but federated data governance model to balance local processing benefits with enterprise-level analytics. The study notes limitations related to the generalizability beyond GlobalLogistics’ operational context and suggests avenues for future research, including cross-industry replication and longitudinal assessment of total-cost-of-ownership impacts.