Resolutis Contributes to the 2026 LF Edge State of Edge Report
Chapter 5: Retail Supply Chain: Compute Meets Reality, authored by John Ward, Fractional CTO at Resolutis
The Linux Foundation’s State of Edge report is one of the most referenced publications in the edge computing industry. It brings together practitioners, researchers, and engineers from across the global ecosystem to document where edge technology actually stands, not where vendors would like it to be.
Resolutis is proud to have contributed Chapter 5 to the 2026 edition: Retail Supply Chain: Compute Meets Reality.
What the chapter covers
The title is deliberate. There is a significant gap between how edge AI in retail supply chain is discussed, in analyst decks, at trade shows, in press releases, and what the engineering reality looks like when you try to deploy compute at scale in a live retail environment.
The chapter addresses that gap directly, working through the practical implementation of Linux-based edge infrastructure in fast-moving consumer goods (FMCG) warehouse and retail environments, where real-time decision-making, continuous uptime, and integration with decades-old legacy systems are not optional features, they are survival requirements.
The core argument is straightforward: the cloud-centric model, which dominated the first wave of retail AI, is reaching its limits. Processing images of supermarket shelves via a round-trip to a cloud API is costly, slow, and brittle. Moving that inference to the edge, to a camera, a handheld scanner, a local Linux server, changes the economics and the performance characteristics entirely.
The specific problems edge compute solves
The chapter works through several concrete problem areas:
On-shelf availability (OSA) is one of the most important operational metrics in retail. The industry average of 93–95% OSA translates to an estimated $100 billion in lost US retail sales annually. An OSA increase of just 1% lifts sales by 2–4%. The challenge is that traditional OSA measurement, store associates walking the floor every morning, captures a single point-in-time snapshot that bears little relationship to conditions throughout a trading day. Edge vision systems change this: continuous shelf monitoring, processing images locally, detecting stockouts in real time rather than through periodic manual audits.
Legacy WMS integration is the unglamorous reality that most edge AI deployments have to navigate. The majority of warehouse management systems were built over a decade ago in C and C++, are largely proprietary, and create substantial architectural constraints. The chapter addresses how containerised, modular architectures built on open-source Linux foundations, including LF CIP for long-term kernel support, allow edge AI to be layered onto existing infrastructure without wholesale replacement.
The economics of edge vs cloud inference is quantified directly. Edge AI at the shelf can reduce overall cloud costs by 40% to 90%, driven primarily by eliminating data transfer fees and reducing cloud compute time. For a retailer running vision inference across thousands of stores, the difference is not marginal.
The last-mile task generation problem. Turning raw shelf intelligence into prioritised, actionable work for store associates, is addressed through integration with handheld devices (Zebra, Honeywell), planogram compliance systems, and computer-generated ordering workflows. The chapter covers how edge processing on the devices themselves enables offline operation, which is critical in stores where connectivity is inconsistent across large footprints.
Why this matters beyond retail
The patterns in this chapter are not unique to retail. The same architecture, distributed edge compute, local inference, intermittent connectivity, integration with legacy enterprise systems, applies across manufacturing, logistics, healthcare, and any environment where data needs to be processed at the point of origin rather than shipped to a cloud and back.
Greater Manchester sits at the centre of significant retail and logistics infrastructure. The businesses operating that infrastructure, distribution centres, retail estates, last-mile operations, are the exact businesses that stand to benefit most from well-deployed edge AI, and the exact businesses that have historically had the least access to the engineering depth required to build it correctly.
Contributing to the State of Edge report is part of how Manchester Edge AI Lab puts Northern engineering capability into the global conversation about where this technology is heading.
Read the report
The 2026 LF Edge State of Edge Report is available now. Chapter 5, Retail Supply Chain: Compute Meets Reality, begins on page 80.
Download the 2026 State of Edge Report