Forward-looking retailers are increasingly relying on an in-store combination of data collection through IoT devices with various types of sensors, AI for decisions and transactions on live data, and digital signage to communicate results and allow for interaction with customers and store associates.
The applications built on this data- and AI-centric foundation range from more traditional “stores that know what’s missing from inventory” to more forward-looking smart physical shopping carts that use on-cart cameras, weight sensors, and deep learning models to track items going in and out of the cart and ensure accurate pricing.
This combination of multi-model sensors (e.g., video cameras, scales, RFID scanners), requirements around hyper-local access to data sources for rapid response times, and the need to be always available drives the need for hosting the critical software component of these systems in the store. The latency profile and brittle infrastructure of cloud-only hosting solutions are non-starters for many of these business-critical applications.
By hosting applications on their in-store edge computing infrastructure, retailers avoid the need to send data to the cloud and back and instead transact data faster and more reliably.
This shift to hosting applications at the in-store edge is key to the future of retail stores because it provides intrinsic benefits above and beyond what can be done on traditional centralized IT-infrastructure.
Why Traditional Retail IT Is No Longer Enough
The current generation of in-store digital services relies heavily on centralized cloud systems. Many point-of-sale (PoS) systems, inventory management suites, and other types of store analytics services are hosted outside of the store and are accessed by customers and store associates through browser-based solutions.
With the introduction of more data-rich services, built on multiple high-bandwidth data sources, and acted on by inference-based AI applications, this cloud-only architecture starts to come up short in a couple of ways:
- The network and processing latency profile between the in-store data sources and the nearest cloud footprint, including the time to act on the data becomes prohibitive for customer-facing interactive features
- The bandwidth load from a store with high resolution cameras, continuous RFID scans and beacon-based mobile location analytics can be substantial. Moving raw data from all these sources to the cloud instead of acting on it locally becomes expensive and inefficient.
- With more of the in-store digital services becoming an integral part of the fundamental customer expectations, any downtime becomes a critical challenge. Relying on complex upstream infrastructure for basic services like self-checkout is simply a non-starter as stores can’t slow down just because the internet slows down.
Modern retailers need instant and robust decisions and transactions in the store, something cloud-only solutions cannot provide, but is best provided by a combination of in-store edge computing for the fast path, and cloud computing for the slower and more long-term application profile.
What Is Edge Computing in Retail?
Edge computing in retail can be defined as placing general-purpose computers within store premises to host a wide variety of applications. This is in contrast to computers in a remote data center, or IoT devices, which are generally single-purpose and tied to a specific type of sensor.
By placing these computers physically close to sensors and IoT devices in the store, the time between capturing data in the physical environment through sensors and the time it is available to local applications is kept to an absolute minimum. It removes the need for the data to travel to a distant cloud data center before being made available to applications.
Above and beyond keeping delays to a minimum, allowing applications to run on computers that are physically located in the store eliminates the need to rely on internet connectivity for business-critical services. Applications in the store have access to all locally created data, as well as customer- and associate-facing interfaces, and do not strictly require upstream connectivity for their core functionality, making the operational model more resilient to network outages and better suited to the realities of in-store operations.
Why Edge Computing Needed in Retail
Traditional in-store IT consists mainly of vertically integrated and vendor-specific solutions where each application or feature is hosted on its own hardware, and feature upgrades are done manually by local IT technicians. This is a slow and costly exercise that requires carefully scheduled on-site visits by traveling IT teams. It holds back the ability to rapidly iterate on forward-looking features and initiatives, and accumulates significant technical debt over time.
The diverse set of technology choices (hardware, operating systems, application frameworks) across proprietary vendor solutions makes monitoring and observability hard. In these environments, each vendor solution provides its own upgrade paths and tools, and its own ways of monitoring the health and performance of the applications. Teams in charge of in-store operations have no choice but to keep many separate but parallel operational stovepipes of tools, lacking a coherent overview of their infrastructure and applications.
Retailers now deploy edge computing to provide the foundation for a fully automated infrastructure and application lifecycle, and to provide a single platform that can host a wide variety of vendor solutions using standard building blocks for health and performance monitoring. This approach also creates a path to integrate the in-store edge infrastructure with the tools used for their cloud footprint, further reducing the operational and organizational overhead and increasing the speed with which they can trial and deploy new software solutions in the store environments.
Core Pillars of Edge Computing in Retail
Breaking down the core elements of a successful introduction of edge computing in retail stores, we find three themes: operational resilience, data security and compliance, and real-time responsiveness.
In-store Operational Resilience
Stores must be able to keep operating even under adverse conditions like internet outages or other infrastructure-related problems. Retailers may be willing to lose ephemeral services like access to loyalty programs during such outages, but the fundamental features that allow customers to complete purchases and leave the store must be kept alive.
This means that all key services required by, e.g., the checkout process, must be hosted locally to remain available under adverse conditions. This needs a deeper understanding of the runtime requirements of the key components of the checkout process (e.g., the PoS system, the software operating the checkout lane equipment, etc). For example, it is common for such software to require specific configuration in terms of licensing keys, as well as access to logging endpoints for audit trail purposes.
Any in-store edge computing architecture must include analysis and local implementation of application services necessary to keep the store open.
Retail Data Security and Compliance
The environment for computers located in store environments is vastly different from that of computers located in data centers. Data centers provide physical security in terms of locked doors and security guards, while it is not uncommon for edge computers in stores to be physically in reach of customers.
This fact must be taken into account when designing the security posture of the infrastructure. Data and applications must be protected in-flight and at rest, and there must be ways of protecting data on stolen computers. This includes a variety of approaches across the distributed domain, including (but not limited to) Zero Trust access models for the call home process, automating vulnerability patching routines and cryptographic key rotations, as well as distributed firewalls with site-specific policies reflecting the unique risk profile and operational context of each store layout and location.
Protecting the in-store edge infrastructure requires a layered defense strategy tailored for securing the local environments without sacrificing agility or uptime.
Real-Time Responsiveness of In-store Workloads
Applications hosted locally in stores have access to locally created data with very low latency due to the physical proximity to the sensors. This provides a uniquely valuable location in the infrastructure for applications that need to rapidly act and transact on data from multiple sources.
The local runtime environment must be able to enable fast data paths for both networking and access to accelerators (e.g., GPUs and NPUs). This means that resource management must be a central part of the lifecycle management of the local applications. Applications explicitly requiring access to hardware-backed resources must be scheduled only on hosts that have such resources available.
The mapping between resource requirements from the application layer to the resources available on the local hosts must be an integral part of the design of the management platform.
Conclusion: The Future is Decentralized
Edge computing is at the core of the next generation of in-store retail experiences. It provides the necessary agility, security and robustness required by the current generation applications and in preparation for next generation AI-centric applications. With the right design, it matches the capabilities of the compute investments done by the infrastructure teams, with the requirements from the application teams aligned with the business vision.
Keep reading: H&M Group Pioneers Edge Computing in Retail with Avassa
Carl Moberg, CTO and Co-founder, Avassa
