The “endless aisle” concept isn’t new, but it’s definitely the future for many supply chain operators. This retail strategy enables customers at a physical store to virtually browse and order any products that are either out of stock or not sold in-store and have them shipped to the store or their home. A fulfillment center or another nearby retail location that has the item in stock fills their order.
Increasingly, consumers expect an endless aisle experience. The pandemic has accelerated the transition to digital shopping and fundamentally changed consumers’ purchasing mindset. Today’s consumers regularly buy everything from daily groceries to new cars online or through an app, and they expect fast delivery — even within an hour, in many cases. If the retailer they go to first can’t meet that expectation, the consumer can open any number of apps and purchase the same product from another retailer, either brick-and-mortar or online, and pick it up or have it delivered when they want it.
So, the pressure is on to create the endless aisle. However, supporting this strategy will require most supply chain operators to significantly modernize their operations, including implementing solutions powered by artificial intelligence (AI) and machine learning (ML). It requires a mindset shift for operators — from thinking about technology not only as a tool to help lower supply chain costs, but also as the key to preventing missed sales opportunities, filling more orders faster, and increasing profitability.
Top challenges to building the endless aisle
1. Legacy limitations and lack of insight
Many companies, especially in the retail space, have already focused a lot of attention on creating the front-end experience for the endless aisle, giving their customers various digital options for ordering products from both in-store and online inventories. But it’s on the back end where most businesses fall short on delivering this experience: They can’t get the right products from here to there fast enough.
Several issues can hinder an organization’s ability to achieve a true endless aisle experience:
Outdated facilities, order management systems, and supply chain processesInflexible systems that prevent order fulfillment from multiple warehouse or retail locationsThe lack of true, real-time visibility into inventory status (i.e., what is available, where it is located now and where it needs to be)The inability to project where the next order will most likely originate so that inventory can be staged at the closest location to fill that order at the lowest cost
AI and ML play a leading role in helping supply chain operators overcome these limitations and build a next-generation supply chain. Following is a closer look at how these advanced technologies can enable the endless aisle by helping organizations to develop intelligent warehousing and engage confidently in more predictive decision-making.
2. Creating smarter, more flexible warehouses
Historically, supply chain operators have had dedicated warehouses and distribution centers that serve specific customers or regions. That strategy creates complexities for companies in forecasting the type and amount of inventory needed at those facilities. The result is that companies can’t flex much or at all.
No organization can create smarter warehouses or a more agile, flexible supply chain without updating their back-end technology first. Most will also need to rethink their entire order management process — including whether there’s a different way to handle it rather than with their inflexible, traditional enterprise resource planning (ERP) system, which lets them map specific products only to specific locations and offers very little visibility.
If these organizations have intelligent warehousing systems within their supply chain, they could request and supply any inventory they have to any customer or geography at any time. They could also confidently enable the endless aisle concept while at the same time reducing shipping costs and delays.
To create intelligent warehousing and deliver the endless aisle, many organizations will need to wrap new technologies like AI and ML around their legacy ERP system to improve and extend its capabilities or even completely replace certain functions. Integrating their ERP system and warehouse management system will also be a critical measure to ensure efficiency and timeliness when the business eventually starts shipping inventory from more places to serve customers in any location.
3. Enabling more predictive, proactive decision-making
Predictive modeling, using both AI and ML, lets an organization know how much inventory to stock, and where to place the goods based on historical and current patterns and behaviors. This insight is a must for any supply chain operator that wants to stay ahead of trends, prepare for future sales, and accelerate order-to-fulfillment time.
ML is also an excellent tool for minimizing costs and lost revenue due to obsolescence, excess inventory, and stockouts. And AI tells the organization where future demand is likely to originate and suggests where future inventory should be placed as it arrives. AI also helps supply chain operators avoid costs from excess shipping charges, long transit times, and stockouts and obsolescence.
These advanced technologies are also essential to providing real-time data insights that inform supply chain “digital twins” — logical views of the physical supply chain used for simulation modeling — that allow the business to understand, well in advance, what options it has to fulfill customer requirements when supply chain disruptions inevitably occur.
Many companies that have made significant progress on their journey toward building a next-generation supply chain are also using AI and ML to enhance their forecasting so they can address their “SKU problem.” They are better able to determine what inventory they need to have on hand instead of keeping two of everything on the shelf “just in case.” More organizations are also embracing AI and ML as force multipliers for their supply chain workforce; intelligent automation is helping them overcome current labor shortages while allowing their existing workers to be more productive.
There is no one-size-fits-all approach to modernizing the supply chain, creating intelligent warehousing, and laying the groundwork for the endless aisle. Each company’s journey will vary in scope and duration. Some organizations will choose to augment their existing infrastructure with more intelligent solutions, while others will go so far as to set up entirely new and separate supply chain operations. But the need for change is urgent, and those businesses that act now regardless of any further disruption or uncertainty that may be on the horizon are those that will emerge as tomorrow’s supply chain leaders.
Connect with the authors:
Director – Supply Chain, Protiviti
Director – IoT and Emerging Technology, Protiviti
Artificial Intelligence, Machine Learning