Digital transformation as a business priority has been the theme of the past decade. But in the early 2020s, in response to the global COVID-19 pandemic, digital transformation was boosted into overdrive. Businesses that were on a five or even a ten-year transformation roadmap were suddenly attempting to make radical changes in five to ten weeks.
Here at ZDNET, we’ve taken you through many deep dives into the technologies driving digital transformation. Most of our coverage has been technology-focused, ranging from AI to cloud to mobile to the edge, and more.
In this article, we’re going to take a slightly different approach. Rather than start with the technology and what you can do with it, we’re going to visit a prototypical business and look at all the technologies it might need to integrate in order to meet its growth and profitability goals.
Because many of these initiatives tend to be confidential inside the real-world companies performing them, in this article we’re going to be talking about a fictional distributed home and building goods chain retailer: Home-by-Home. That way, we can dive into some of the areas of business operations that a real enterprise might not be comfortable revealing publicly.
Case study: Home-by-Home
At a base level, Home-by-Home stores need to be able to handle normal checkout and customer transactions. While this is an operation common to nearly all retailers, it’s also one that’s deeply infused with technology and innovation.
Each checkout transaction triggers a treasure trove of data updates. The stock level for any purchased product needs to be reduced, possibly triggering a reorder or a warehouse-to-retail shipping transfer. That decision might be sent to a human purchasing agent or might be managed by AI, which would factor in a wide range of worldwide pricing and availability issues in order to make the optimal determination.
Data on individual customers, stores, and regions is passed into an analytics engine to give product managers insights into purchasing trends, and possibly surface new trends that might not be obvious without access to live data.
And because most of Home-by-Home’s stores have wireless shelf-talker tags (tiny displays that act as the labels that show customers the price of an item), another AI process factors in sales rates, demand, and available inventory, which will then reduce or increase prices in the store aisles dynamically, or initiate an on-the-spot discount sale offering.
On a global level, the retailer needs to track supply chain issues worldwide, and factor in weather, political, and shipping analytics to ensure goods are where they need to be when needed. AI plays a role here, too. In fact, we’ll see that AI is playing a bigger and bigger role throughout Home-by-Home’s entire extended network as well as its supply chain.
By combining API access and microservices with big data and real-time analytics, Home-by-Home and its suppliers can account for the constantly shifting terrain of international supply and demand, and change vendors, orders, and promotions to suit as-it’s-happening availability and logistics.
The company has thousands of stores ranging from about 105,000 square feet up to about 170,000 square feet, stocking 30,000 to 60,000 individual products depending on the market it operates in. To keep track of all this inventory on each store’s floor, each store uses a ton of IoT, especially in RFID and theft prevention. The RFID items also help speed checkout for some of the lines where consumers check themselves out.
Additionally, the company uses a range of sensors to manage environmental control (humidity control is critical in some departments) and energy expenses. While Home-by-Home has long had security cameras in-store and in parking lots, it recently started pumping video feeds through a series of intelligent image processing systems that help immediately flag security incidents and accidents.
Because so much processing has to be done in real-time and in the individual stores, Home-by-Home has invested heavily in the edge-to-cloud concept. Each store has its own secured and temperature-controlled computing bay that functions like a mini data center and operates out of a box the size of a small shed. On-the-spot real-time work is handled at the edge (each store), and data is constantly fed from the store to Home-by-Home central data systems and integrated cloud operations.
The company has a comprehensive e-commerce offering through desktop browsers and a mobile app, which helps manage product availability, ordering, and the fulfillment/shipping process. Since more than 70% of online customers order through the mobile app and even use the mobile app while actually in the store, the company has made a huge investment not only in the quality of the app, but in the integration between the app and the business information and real-time data flowing back from the stores to the cloud.
Since 2000, Home-by-Home has been converting larger stores into dual-purpose facilities, using them for customer visits during the day and as e-commerce fulfillment warehouses after closing hours. The company has added autonomous pick-and-pack robots for the overnight shift, leading to even more reliance on real-time inventory management, cameras, and AI. All of these improvements have allowed the company to deliver heavier and more commonly ordered goods directly to local-to-store consumers while cutting down the wait time and shipping costs considerably. Central warehouses responding to e-commerce orders still stock another few hundred thousand more obscure SKUs that are shipped via the package delivery services.
Earlier this year, Home-by-Home acquired a competitor with 450 stores and has begun a considerable migration effort to move them from old point-of-sale systems and central siloed databases to the edge-to-cloud digital transformation that’s actively in practice throughout Home-by-Home’s operations.
End-to-end integration across all stores and vendors
There is one general operating principle by which Home-by-Home measures all of its IT decisions: everything must integrate, and do so smartly. It’s not enough just to have constant streams of data coming from the stores to organization-wide databases.
That data has to go to the right places at the right time, and trigger the right operations. Data flow also can’t just be one way. Data has to move from vendors and suppliers to various corporate departments to stores and back again.
Home-by-Home defines operations at the edge as everything that happens at the store level, but also everything that happens during shipping, at docks, and even at vendor warehouses. Home-by-Home has been systematically refining its vendor choices, factoring in whether its IT operations can share API data and microservices in order to have an up-to-the-minute global view of operations.
Home-by-Home does still operate its own data centers. It has two facilities that manage confidential information including personal employee data, financial data, data that needs to be localized for various tax benefits, and information that might affect public stock performance.
But the company also invests heavily in cloud infrastructure as well as SaaS implementations. As a general rule, any application that can be provided by signing up on-demand is chosen over the time it would take to build in-house.
All of this end-to-end integration from edge to cloud, across all stores and into vendors, factoring in weather and logistics forecasting, and tracking shippers can be enormously complex. The sheer number of IT systems, accounts, dashboards, and management consoles is staggering. But when Home-by-Home decided to make uncompromising digital transformation a core value, it set out to find vendors that could also provide the integration it needed to make it manageable.
Dynamic provisioning and on-demand infrastructure from edge to cloud is key to its solution. That way, as it adds new resources – like when it had to spin up support for the 450-store chain it acquired earlier this year – it’s not relying solely on forklifting infrastructure. Much of the back-end functionality can simply be scaled up as needed and dynamically provisioned.
Seasonal surges are also accommodated, allowing the company to add about 30% additional IT infrastructure resources for the critical home improvement seasons, but then scale back down and reduce spending during the months when consumers are focused on other interests.
HPE GreenLake is an example of one of the companies that offers edge-to-cloud services that bring the centralized dashboard, on-demand provisioning, and pay-as-you-go benefits of public cloud infrastructure to on-premises computing and edge computing installations. This is what a company like Home-by-Home needs to be able to begin provisioning the services for its new acquisition immediately. There is no order-and-wait period for new configurations.
Other edge-to-cloud providers like AWS Outpost, Azure Stack, Google Anthos, IBM Cloud Satellite, and Red Hat’s Edge Validated Patterns offer their own take on the edge-to-cloud stack. The key takeaway is that IT professionals no longer need to silo their solutions to solve problems at different points in their operational infrastructure.
Edge-to-cloud platforms help aggregate entire solutions, providing the benefits of individual vendor offerings, but without the chaos of many different control consoles and billing requirements. Instead, it’s possible to have the benefits of the best available solutions, but operate an entire hybrid, multi-cloud, multi-vendor, multi-constituent network as a coherent whole. This results not only in productivity and cost-savings, but reduces errors and improves overall security.