Retail is facing battles on all fronts. Up against Amazon’s streamlined operations on one side, and a higher demand for flawless customer service and experience on the other, it’s either think or sink for many. However, AI has thrown some early adopters a lifeline.
As Amazon has proven with its stranglehold on the retail industry, online shopping is unquestionably the future. Shopping malls, the traditional epicenters of retail, currently are being converted into warehouses to secure greater geographical accessibility for online shopping.
For traditional retail companies, it’s a vastly different world out there, but there still may be room for the traditional brick-and-mortar retail experience.
In this online-driven universe, retailers need to arm themselves with tools and capabilities that will keep business ticking; transform their turnaround times; and stock the right products at the right time. They need to turn to automated end-to-end inventory management.
AI-assisted technology running on big data can help optimize inventory at all levels in the demand chain. It even can predict future buying behavior, and detect and act on supply chain anomalies in a timely fashion. With the implementation of “smart” warehouses, retailers are beginning to reach new levels of efficiency.
In this current climate, it’s time retailers start looking at these AI-assisted, analytically rich business processes as a priority and strategic investment, rather than an optional helping hand. Those that don’t will break.
Semi-Manual Top-Down Management Just Won’t Cut It
Meeting consumer demand and identifying short-term trends in a timely manner are near impossible to do today without heavily relying on advanced analytics. Almost all retailers now have stores, branches and warehouses scattered all over the country, each holding a slightly different assortment of products. Therefore, the old, manual ways to manage retail inventory and plan-o-grams just won’t cut it.
These new approaches allow you to predict consumer demand more accurately and allocate inventory to your locations with very high placement accuracy. This results in less reliance on replenishment and can help reduce out of stocks. Managing inventory like this needs to be conducted through a big data-enabled automated retail inventory management system. Doing this semi-manually simply isn’t feasible.
Being able to optimize inventory management can provide efficiencies in inventory flow, while also reducing overall costs, which sometimes result from excessive inventory in unproductive locations, or slow-moving and obsolete items.
Automated inventory management can detect anomalies in supply chain, allowing retailers to be proactive and deflect potential issues, such as by transferring more stock to the right location ahead of time.
Lack of adaptability to AI-automated systems and inefficient operations are among the common factors that lead to the fall of major retailers. Ex-employees at Sears, which closed its doors last year, have named “selling products consumers don’t want, not maintaining stores and inventory” as problems contributing to its years of financial struggle.
Keeping Up With Our On-Demand World
When customers opt to head into a store rather than order online, they expect to find what they are looking for immediately. Not having the right stock in-store can ruin the customer experience. A smart system driven by analytics can ensure that shelves are properly stocked and with the right products. Predictive analytics even can determine when extra supplies will be needed at certain times.
Although Amazon has spoiled us with same-day deliveries, automated inventory management insights may prove to be the leg-up that retailers need to bolster customer experience and compete head-to-head with the e-commerce giants.
In fact, while almost all retailers now have e-commerce offerings of their own to complement their store operations, brick and mortar arguably is still the best way for retailers to engage customers in compelling ways. In a recent poll, 53 percent of customers said that their most positive retail experience was in an actual store. Clearly there are still things that online retail can learn from the traditional stores.
Automated inventory management insights also allow inventory to be personalized. With department stores dropping like flies, it is clear that shoppers do not want to spend hours trawling through stores in search of a product. Retailers should be using data to decide on the most relevant inventory for each store. If you have knowledge of the stock that customers actually want, then you can shrink stores drastically.
The same goes for e-commerce. To meet the customer’s demand for immediate shipping, stores can use automated inventory management to better store their products, even moving merchandise closer to those who will be ordering it.
For example, rather than bulk ordering all inventory to one warehouse, data and predictive analytics can determine which sections of supplies should be stored in smaller warehouses in different locations. Customers therefore will receive their orders in a shorter period of time.
Adopting New Strategies
A huge benefit of an AI-assisted inventory management system lies in the fact that it can provide deeper insight into demand, and help develop new strategies. Machine learning, in particular, can highlight where certain pockets of demand are hiding.
With trends constantly changing, and certain products evolving into a niche, (like vinyl, for example) managing inventory manually, let alone finding the right customer base, can be tricky. Without the data of where and when people are demanding the “hot” products, things quickly can turn sour for retailers.
AI is particularly helpful for pinpointing short-lived demands for products. This could be a movie that just won an Oscar, or a specific product a celebrity was papped using. Smart inventory tools combine ML-based anomaly detection and AI to spot these sporadic changes in product interest and find who most likely will buy them, and where they are situated. This knowledge then allows stores to stay on top of consumer trends and react quickly to specific pockets of demand that pop up every now and then.
Demand forecasting through machine learning could be a valuable tool for predicting which specific products will be in demand. By using AI-integrated supply chain tools, forecasting errors could be decreased by 20 – 50 percent, a recent study claimed.
It’s easy to see why this is important: Full implementation of machine learning AI would allow supply chains to be more efficient by giving accurate prediction of future trends. This would expand customer satisfaction and allow supply chains to be more reactive and flexible.
Making an Entire Warehouse ‘Smart’
Automated vehicles, sophisticated drones flying overhead, and robots gliding between rows of products seems like a scene out of a science-fiction novel, but some retail companies today are constructing smart warehouses that implement the latest in AI technology. Fully utilizing all the capabilities of ML, AI, and other automated analytic techniques, these warehouses have the potential to completely upend the traditional retail system.
AI has allowed retailers to automate almost the entire inventory process, greatly shortening the time it takes for a products to be sent to the designated stores. Automated picking tools combined with automatic guided vehicles (AGVs) allow for a quick retrieval of products. With the assistance of AI programs and self-learning algorithms, the entire process can be optimized for efficiency based on additional factors or experiences.
These smart warehouses already have proven to be successful in reducing cost and increasing efficiency for the retailers that use them. Ryder, a supply chain solution provider with warehouses in Chicago, Miami and Dallas, has claimed 20 percent better efficiency, 100 percent product visibility, and a 20 percent decrease in operational costs after the implementation of smart warehouses. With advantages such as these, retailers could be forced to adapt to the technology or risk being left behind.
Reaction to customer demand remains a crucial element to the success of the smart warehouse. The better a store is stocked, the happier the customer will be. The focus of smart warehouses currently is on creating and expanding technologies that permit more mobility and flexibility, in order to better fulfill the ever-changing demands from customers.
By removing unstable variables such as human error and replacing them with standardized machine automation, smart warehouses can react fluidly to customer demand, with AI systems constantly learning and improving. Through smart warehouses, AI innovation will continue to advance alongside feedback from customer expectations and fulfillment demands to create the best possible technology to suit the needs of the customer.
With AI, ML and robust optimization-based prescriptive analytics techniques, inventory management is no longer a matter of guesswork. Automated inventory management insights can help retailers cut down on costs, maximize sell-through and ensure a store has the correct amount of inventory on hand for each stock keeping unit (SKU).
This intelligence can ensure that retailers make the best decisions — down to the level of a unique SKU-location, which is now more important than ever in this hypercompetitive landscape. Amazon has firmly taken the reins from traditional retailers appears to have no inclination to return them.
By continuing to integrate inventory management with the latest AI, retailers can be assured of sustained success now and into the future.
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