The omnichannel retail environment has changed dramatically over the past year, with consumers quickly embracing online ordering, both for pickup and delivery.
The trend continued in 2021, with first-quarter e-commerce sales up 39.1% from last year’s levels and accounting for around 13.6% of total retail sales, according to the US Department of Commerce data.
The increase in omnichannel volume, combined with continued pressures in the labor market and challenges in the macroeconomic environment, is forcing retailers to rethink their supply chain operations and highlights the need to invest in both in deep learning and automation. Retailers are seeing faster inventory turns and inventory misalignments due to changes in customer habits, while poor inventory visibility leads to delays and higher costs, resulting in lost sales and an impact on margins. Retailers are now focusing on aligning their supply chain with the aspirations of their omnichannel customers.
Major retailers have taken advantage of advanced data analytics to react quickly to market changes and reduce their logistics costs. Increasingly, retailers are turning to artificial intelligence (AI), machine learning (ML) and deep learning (DL) solutions to improve the efficiency of their supply chains and automate processes. supply chain decisions.
For example, a recent report from McKinsey & Co.¹ found that the successful implementation of AI-powered supply chain management has enabled early adopters to improve logistics costs by 15%, stock levels 35% and service levels 65%, compared to slower competitors.
“Retail supply chain operations seek to use AI-powered models to more accurately forecast demand, improve throughput at distribution centers and optimize last mile delivery,” said Azita Martin, Managing Director of AI in Retail, Consumer Staples, and Restaurants at NVIDIA.
“AI is becoming an essential element in creating the operational agility necessary to respond to market opportunities and challenges,” she said.
Supply chain solutions leveraging AI, DL and ML help retailers, consumer staples manufacturers and restaurants to optimize demand forecasting, reduce omnichannel fulfillment costs and to streamline warehouse, shipping and other logistics operations, promoting both efficiency and customer satisfaction.
Demand forecast, which has often relied on batch processing that can take weeks using incomplete data, is one of the key areas where AI is driving a return on investment in retail, CPG and CPG operations. catering.
It allows retailers to adjust sales forecasts faster based on trends, consumer behavior, and other variables to ensure inventory levels are optimized by store location and products are in stock to boost the sales.
“With faster models, retailers are able to make frequent decisions aligned with their sourcing and on-site delivery policies,” said Nick Underwood, CTO Retail, Dell Technologies.
Amid increasing demands for efficiency in the supply chain, retailers can use hyper-tagged real-time demand data and the ML powered by graphics processing units (GPUs), to improve sales forecasting with instant learning, he said.
“It can transform the performance of customers’ supply chain, from purchasing to warehouse transfers, production planning and shipping,” said Underwood.
Likewise, demand forecasting also plays an important role in the fast food industry by helping to regulate production levels to avoid wastage and thus improve margins. Operators in this segment use advanced analytics to predict customer flow and more accurately define production levels, as well as optimize ingredient inventory.
Adapting to omnichannel retail
In retail, the sharp increase in consumer demand for curbside pickup and online shopping, in-store pickup (BOPIS) services, as well as rising expectations for day-to-day fulfillment Likewise, have put pressure on retailer distribution models and increased interest in automation.
Target, for example, reported that sales through its drive-up service, in which it processes same-day orders by bringing them directly to consumers’ cars, increased 600% last year.
Retailers are responding to omnichannel demands by exploring automated solutions in the form of micro-distribution centers using automated order picking, warehouses with robotic pickers and autonomous vehicles, and even autonomous vehicles that deliver directly to customers’ homes. AI supports vision-based systems for these solutions with advanced product recognition and navigation technologies that learn on the job and can even be trained in the lab using digital twinning.
To meet the demands of picking and shipping and packaging operations that require rapid visual inspections of items moving on conveyor belts, companies are also using computer vision-based visual inspection for quality control. and precision.
Retailers are also looking to minimize shipping costs by using advanced “sorting” technologies that minimize carrier handling costs. The automation of the sorting process to deliver products to people’s homes, as well as the optimization of delivery routes for carriers, helps speed up delivery times, increase successful deliveries and reduce spending on last mile. For example, businesses are using AI to estimate delivery times and monitor the impact of potential disruptions, allowing retailers to react instantly and select alternate delivery routes.
With the challenges of today’s work environment, the need for increased supply chain efficiency, and the growing demand for omnichannel shopping, there has never been a better time for retailers to make their supply chains ‘smarter’ and more agile with technologies that leverage AI.
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