Retail supply-chain executives are under intense pressure to support the seamless omnichannel experiences that consumers are demanding in the digital age.
Consumers want more product variety, faster delivery, affordability, and pickup and return over any channel. Meeting those expectations depends on retailers running a cost-efficient supply chain that can react quickly and accurately. When it comes to delivering a superior customer experience, that’s every bit as important as personalized front-end marketing.
Times are tough for retail supply-chain executives. The stakes are sky-high, as every day seems to bring news of another high-profile setback or failure.
The retail supply chain is a complex beast that’s difficult to tame. A recent study by Sapio Research found that:
Those results are, unfortunately, not surprising. Nearly two-thirds of retailers and manufacturers still use Excel for supply-chain planning, according to a study by eyefortransport. And 46% rely on manual, time-consuming supply-chain processes.
“Now more than ever, an efficient supply chain will be the critical growth enabler for both retailers and manufacturers,” the Reuters report says. "Legacy planning tools like Excel spreadsheets and limited-capability, traditional planning solutions are no longer suitable for solving today’s complex supply chain challenges."
When a Band-Aid Isn’t Enough
Retail supply chains are innovating to better meet customer demands. One example is the pop-up warehouse, situated in high-demand areas to accelerate last-mile delivery. These entities can also double as a location for consumer pickups, or offer a limited retail selection. In addition, retailers are utilizing ship-from-store models, turning physical stores into ad hoc fulfillment centers.
Both models are getting goods to consumers faster, but they can introduce additional costs that trim margins. They’re Band-Aid solutions that don't address the root cause of supply-chain inefficiency: siloed applications, fast-growing data volumes, and inherent human limitations.
Excel-based planning and conventional demand-forecasting tools that use data from systems for enterprise resource planning (ERP), warehousing, inventory, sales operations and logistics can’t keep up. Data volumes and application complexity are rising just as fast as consumer expectations.
As a result, retailers can’t react quickly to changes in demand. With lead times set months in advance, they’re slow to adapt if demand soars in one area yet falls in another, or if e-commerce sales exceed projections. Often they’ll resort to overstocking inventory, risking high carrying costs and unsold product if sales fall short.
Managing the many dynamics of today’s real-time supply chain is proving virtually impossible for human planners. There’s simply too much data, too many applications and too many variables to account for. Meanwhile, old-school retailers are losing ground to digital natives like Amazon that use next-gen technology such as artificial intelligence to help optimize the supply chain and consumer experience.
“The need to digitize to cope with the increasing velocity of the retail supply chain is only going to grow,” the Reuters report says. “For forward-thinking retailers, it’s essential to start thinking about how to use new analytics technology not just to analyze and understand the past, but to make better decisions for the future.”
AI-Powered Cognitive Automation
AI has made its way into our consumer lives, helping us to select products, avoid traffic jams and even choose healthcare treatments. Now it’s doing the same on an enterprise scale, by giving companies groundbreaking capabilities to run faster, more visible and less-costly supply chains.
AI is a foundational technology in what’s called cognitive automation, which brings deep machine learning (ML) analytics into supply-chain operations. An AI-powered cognitive automation platform will do data crawls thousands of times a day across all relevant applications, aggregating that information into a single cognitive data layer.
It’s called a cognitive layer because that’s where AI and ML algorithms are applied to analyze situations, predict outcomes and make recommendations for optimal actions based on objectives — reallocating inventory, reducing costs or speeding up delivery time, for example.
Unlike conventional methods, these analytic insights aren't drawing on data that's weeks or months old; they’re based on near-real-time information. Cognitive automation spots trends and issues as they unfold to enable swift intervention. And because cognitive automation is connected to transactional systems, corrective measures can execute automatically, without humans having to log into systems to modify processes.
Cognitive automation is already delivering multimillion-dollar improvements in supply-chain operations at CPG, pharmaceutical and manufacturing companies. Retailers that start small and scale up stand to reap multiple rewards, including:
Better forecasting. Cognitive automation consolidates highly granular SKU-level data across multiple applications, channels and geographies. AI analysis of sales trends, demographics, SKU varieties and other variables improves accuracy in having the right product in the right place at the right time.
Agile inventory reallocation. Cognitive automation constantly analyzes real-time stock and sales data with a speed and scope not possible with traditional tools. It makes recommendations to reallocate inventory as conditions change, or may suggest promotions for undersold goods.
Integrated planning. By drawing on a broad range of data, cognitive automation helps retailers coordinate across production, inventory, marketing, merchandising, promotions, logistics and other areas that are typically siloed. Merchants can make data-driven decisions across full lifecycles, rather than relying on educated guesses.
Improvements in transportation and logistics. Cognitive automation equips retailers to accelerate fulfillment and minimize costs by analyzing real-time variables such as on-hand inventory, demand fluctuations, carrier availability, freight costs, lead times and more. If disruption occurs, AI will recommend alternatives to meet objectives.
Disruption across the retail landscape will continue in 2020 and beyond. It’s becoming crystal clear that technological innovation is what separates winning retailers from the laggards. Retailers that invest smartly in technology to make data-driven decisions and orchestrate processes will be equipped to survive and thrive in a fast-changing industry.
Arnaud Morvan is senior director of customer engagement with Aera Technology.
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