How IoT Computing and AI Are Making Retail Smarter

The retail environment is changing. It's getting smarter, thanks to IoT and AI computing platforms, supported by high performance computing systems.

Read on to learn more about this retail revolution.

Internet of Things (IoT) computing is steadily changing the modern retail environment. It wasn't so long ago that retail stores, restaurants, cafes, and other commercial locations, were essentially inert. They may have been governed by a high performance computing system, but the space itself had remained largely unchanged for decades.

IoT computing shattered this status quo. With an IoT system in place, the retail environment becomes part of the computing network itself. Components and items within this environment become smart, responding to system changes and feeding data back into the network.

But how does this work in practice? Well, the possibilities are almost endless, but let's take a look at a few applications:

• Smart doorways and gateways measure foot traffic into and out of the retail space.
• Integrated location tracking tags – supported by solutions like RENITY ARTEMIS – make it easy to locate items on the shop floor or in a warehouse.
• Inventory management systems leverage the data from location tracking tags for even easier identification and location.
• Intelligent point-of-sale (POS) solution support personnel and customers by offering more streamlined transactions.
• Self-service KIOSK terminals make the retail experience even more straightforward.
• Beacons and tags communicate with one another, enhancing safety by providing alerts and reducing the danger of collision.
• Service buttons support the customer experience by offering quick connections to staff.
• Monitoring chips keep track of environmental conditions within stores and warehouses, triggering automated changes to thermostatic temperature controls and other inputs.
• All of the different components of the IoT computing system become individual data points and sources, feeding information back to a centralized system.
  

To get the best out of IoT technology, we need to do more than simply deploy smart devices around the retail space. We need to enhance the capability of these devices, creating an artificially intelligent network that optimizes business operations and management duties.

This is where a strong AI computing platform comes into play. A platform like this gives IoT devices the decision-making power needed to become truly smart – graduating from simple collectors and deliverers of information to intelligent nodes that can perform actions based on the information they receive.

In this sense, we can see an obvious progression in the evolution of the smart retail environment.

1. Traditional retail – The first phase of the smart environment, characterized by an inert space overseen by a high performance computing network.
2. IoT-enabled retail – The second phase, with IoT-enabled data collection and processing nodes deployed around the space.
3. Artificially intelligent retail – The third phase, where the IoT-enabled devices do not only send their data to a central system but can use this data in their own decision making. They can also draw data from other IoT-enabled devices within the network, further enhancing their capability.

Here are a few examples of how AI and IoT computing can be deployed side by side:

• Scanners – supported by the RENITY AIR solution – recognize objects based on empirical and experiential data and execute actions based on this recognition.
• Systems identify anomalies within the space – such as an item in the wrong place, or missing inventory – and issue instructions based upon this.
• Forecasting systems analyze product-buying patterns and make predictions and recommendations on future inventory orders.
• Wearable tech like the QSer Service Button helps to optimize staff response times and improve service levels.
• Business intelligence systems draw and manage data from an ever-growing selection of sources, achieving unprecedented levels of operational insight.
• Systems achieve greater autonomy, offering a smooth and streamlined experience to customers.

While IoT and AI computing platforms certainly go hand in hand, we need to do more than simply deploy these network components together and hope for the best.

Fortunately, modern high performance computing components and systems are highly capable, delivering the support needed to run effective IoT and AI-based networks. High capacity server boards deliver the processing power necessary to run an expanding IoT network, while rackmount and tower workstations provide the flexibility needed to build an effective modular server room.

Machine Learning Support

AI computing platforms are not static entities. They are designed to learn and to gain an improved understanding of their surroundings. This requires training and complex data models, which in turn depend on high levels of computing power and data storage.

A typical computing system is not capable of providing this. Instead, retail businesses rely on high performance computing systems to support efficient and effective machine learning processes.

Enhanced Data from Edge Computing

There is a significant cross-over between edge computing components and IoT devices. IoT devices are data sources themselves, collecting information and feeding this back to the central system. Edge computing components can include IoT devices, but they may also include other components that are deployed close to the source of the data.

By using edge computing, retail operations managers can improve the quality of the data they collect. Edge devices can both add extra datapoints, increasing the richness and complexity of the dataset, and they can also refine the data received from IoT devices at the source.

As datasets grow, the computing power required to process data increases along with it. High processing capacity computer systems are becoming more and more important in the retail environment.

Effective Feedback Loops

Artificial intelligence develops through a process of trial and error, gathering empirical evidence on the outcome of different actions and making decisions accordingly. This necessitates feedback loops and an ongoing process of assessment and analysis.

Again, this requires high levels of computing power and large data stores. Both of these are features of an effective high performance computing system.

Balanced Capability and Resource Usage

You may have already noticed a pattern emerging here – effective AI deployment requires enormous computing resources. In the past, this requirement put AI and IoT beyond the reach of most retail operations. However, high performance computing systems are becoming increasingly accessible, enabling more companies to benefit from AI and IoT.

A balance is needed here. Companies need to be able to leverage high levels of computing power. However, at the same time, they need to keep power and resource usage low, managing the carbon footprint of the enterprise. The best high performance computing systems will strike this balance with ease, optimizing resource and power usage without compromising on CPU capability.

The Avalue product range offers a variety of options for retail organizations seeking to get the best out of smart technology. Browse our product pages, or reach out directly to our team to learn more.