Deep learning, meet bricks and mortar! Interesting video showing person detection, entity tracking, item detection, item classification, ownership resolution, action analysis, and shopper inventory analysis, all working together to visualise which person has what item in real time (courtesy of Standard Cognition).
Even though Walmart was founded in 1962, it’s on the cutting edge when it comes to transforming retail operations and customer experience by using machine learning, the Internet of Things (IoT) and Big Data. In recent years, its patent applications, position as the second largest online retailer and investment in retail tech and innovation are just a few reasons they are among the retail leaders evolving to take advantage of tech to build their business and provide better service to their customers.
Network of physical locations and online capabilities lead to innovation
Lauren Desegur, VP of customer experience engineering at WalmartLabs said, “We’re essentially creating a bridge where we are enhancing the shopping experience through machine learning. We want to make sure there is a seamless experience between what customers do online and what they do in our stores.”
While its arch nemesis in business may be Amazon.com, Walmart has the advantage of using the best of both worlds—with over 11,000 brick-and-mortar stores and its online experience—in its laboratory to develop retail tech that catapults sales and customer satisfaction. Walmart was an early adopter of RFID to track inventory and has a tech incubator called Store No. 8 in Silicon Valley to“incubate, invest in, and work with other startups, venture capitalists and academics to develop its own proprietary robotics, virtual and augmented reality, machine learning and artificial intelligence technology.”
Walmart is bullish on big data — especially when it comes to finding ways to better serve its shoppers.
Big data volume continues to grow, but Walmart is using it to the company’s — and its customers’ — advantage. By analyzing the robust information flowing throughout its operations, the discounter has gained a real-time view of workflow across its pharmacy, distribution centers, stores and e-commerce, according to a company blog.
Here are five ways that Walmart is using big data to enhance, optimize and customize the shopping experience:
1. To make Walmart pharmacies more efficient. By analyzing simulations, the discount giant can understand how many prescriptions are filled in a day, and determine the busiest times during each day or month. Big data also helps Walmart schedule associates more efficiently, and reduce the time and labor needed to fill perceptions.
2. To improve store checkout. While it is still only testing the process, Walmart is using predictive analytics to anticipate store demand and determine how many associates are needed to man registers. The data also reveals the best form of checkout at each store: traditional stations or self-checkout.
3. To manage the supply chain. The company uses simulations to track the number of steps from the dock to the store. The result: more optimized routes to the shipping dock. The strategy also pinpoints the number of times a product gets touched along the way to the customer. Big data also reveals transportation lanes and routes for the company’s fleet of trucks. This insight helps Walmart keep transportation costs down and more accurately schedule driver times, according to the blog.
4. To optimize product assortment. By analyzing customer preferences and shopping patterns, Walmart can optimize how to stock shelves and display merchandise. Big data also provides insight into new items, discontinued products and which brands to carry, the blog said.
5. To personalize the shopping experience. By analyzing shopper’s preferences, Walmart can develop a more consistent, tailored shopping experience. If a customer is shopping for baby products for example, Walmart can use data analytics to anticipate their needs then create personalized mobile rollback deals for these shoppers.
For manufacturers, distributors, and retailers, managing the supply chain for best results is the lifeblood of the company. For at least six decades, companies have used systems to track and control the flow of goods from point of manufacture, through transport to distributors, then to retailers—and finally, into the hands of the end customer.
Meanwhile, marketing is often detached from the supply chain and often doesn't have access to data on supply chain issues such as manufacturing schedules, or returns to distribution centers, or buying trends in specific stores. Unfortunately, this can lead to lost opportunities for revenue via targeted marketing campaigns.
Rick Chavie, CEO of Codifyd, which provides data navigation solutions for B2B and B2C distributors, describes the need for a "supply chain of content."
"Everyone working within the supply chain has one objective," said Chavie, "And that's to get product pushed out and into the stores or online sales sites as quickly as possible. However, now that retailers are operating in a more customer-centric world, they are beginning to realize that they can improve sales performance by pushing the right content to customers at the same time that they are pushing their product. They can do this if they have a better understanding of what their customers want in different retail channels."
Before the doors open daily at each of the 2,213 Zara outlets around the world, floor staff and store managers huddle to have an all-important meeting.
During this time, they share details of the previous day's best-selling items, pieces returned by customers, shopper feedback as well as trends that staff have been noticing on the ground.
Has that new khaki jacket been flying off the shelves? A request has already been made for a restock. Are more customers returning that flouncy pink skirt? The design has been flagged.
No detail is too small for the flagship brand of Spanish clothing company Inditex, the biggest multinational fashion retailer in the world.
Even casual chats with Zara staff are not off-limits. The prized information you share about quality and new trends you would like to see on the racks is likely scribbled down.
"We speak to our product teams in Spain daily to discuss what is or isn't selling well, as well as customer comments on what they do and don't like," says a store manager from Zara Singapore when asked about the brand's daily pow-wows.
"We're always keeping a close eye on how our customers are responding to the current collections and the trends they may be asking for."
Ford will pour $1 billion over the next five years into an artificial intelligence company tasked with developing the technology that will one day drive its autonomous vehicles.
The technology could also be licensed to other automakers in the future, executives said.
Pittsburgh-based Argo AI was founded late last year by Bryan Salesky and Peter Rander, who previously worked on self-driving car initiatives at Google and Uber, respectively. The company will include the staff inside Ford that has been developing its virtual driver system for the past several years.
In a phone call last week, CEO Mark Fields said the investment will help Ford bring its self-driving cars to market by its previously stated goal of 2021. It will also open a new revenue stream if Ford licenses the technology to carmakers who have not developed their own autonomous driving systems.
That licensing model will put Ford in direct competition with Waymo, Google's self-driving car company, which announced plans this year to develop both hardware and software for self-driving cars. Previously, Waymo focused solely on software, but executives decided that it was necessary to also build the sensors and cameras on the vehicle if its system was to be sophisticated enough to handle fully autonomous driving.
Alibaba Group has announced a Big Data Anti-Counterfeiting Alliance that will use big data in a bid to end the sale of counterfeit goods on the ecommerce giant's online platform.
The company already has local heavyweights Huawei, as well as Samsung on board, with the 20 member-strong alliance receiving strong support from brands, trade associations, intellectual property experts, and regulators, in addition to support from government bodies and law enforcement agencies in China.According to Jessie Zheng, Alibaba Group chief platform governance officer, the alliance aims to pool resources and increase collaboration to promote a safe and healthy global ecommerce ecosystem where brands are protected from "IP pirates".
In return, brands and other members of the alliance have committed to share their expertise on IP authentication and anti-counterfeiting data with Alibaba.
"The most powerful weapon against counterfeiting today is data and analytics, and the only way we can win this war is to unite," Zheng said.
"Alibaba welcomes brands and other organisations in the creation of the world's first Big Data Anti-Counterfeiting Alliance. With our robust data capabilities, we are confident the alliance will accelerate the digital transformation in our global fight against counterfeits."
Alibaba said it will provide alliance members with big data and advanced technological support in their IP enforcement work, including helping to block, screen, and take down infringing listings.
With more than 1 billion product listings across its platforms at any given time, Alibaba said its monitoring system scans more than 10 million product listings per day. In the 12 months ending August 2016, Alibaba said it removed more than 380 million product listings and closed down 180,000 third-party seller stores.
Earlier this month, Alibaba said it had sued two vendors for selling fake Swarovski watches on Taobao, marking the first time the company has taken legal action over counterfeit goods.
A lawsuit was filed in the Shenzend Longgang People's district court against sellers Liu Huajun and Wang Shenyi, asking for 1.4 million yuan for "violation of contract and goodwill
When I hear the term Big Data, I think of data pouring in from different sources in an unstructured, structured or a combination of both formats in the sense that it cannot be easily managed by traditional database management systems that are already in play, example a relational database management system like SQL Server or Oracle. “Big Data” is not necessarily about how big data is but can be seen as a concept which provides an opportunity to manage and mine the various formats of data sets to help to uncover new insights into an organization’s existing data. To formally define what “Big Data” is, I would like to refer to Gartner’s definition: “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation” (Gartner Group).
Bottom line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.
Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.
Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.
The ten ways big data is revolutionizing supply chain management include:
Chocolate, coffee and other popular commodities face supply chain scares, but big data can help secure and improve farming yields around the world
As shoppers, we’ve become used to the reliable presence of brands in supermarkets. The idea of food scarcity and disruption to supplies doesn’t come into plans for our weekly food shop.
But the reality for many global food manufacturers is uncertainty. Chocolate production is one example. Some 40% of the world’s cocoa comes from the Ivory Coast, grown on farms with only a few hectares of cocoa trees. In China alone, US firm The Hershey Company estimates that sales of chocolate will grow 60% between 2014 and 2019 to a value of $4.3bn. This is partly thanks to a new-found love of chocolate among China’s growing middle classes.
But it’s not just chocolate. The problem is widespread, particularly with ingredients that only grow in specific climates, such as vanilla, tea, coffee and palm oil. Last year, the global coffee market saw shortages partly due to a drought in Brazil. This led to steep price rises and prompted Starbucks to acquire a new 600-acre Arabica farm in Costa Rica to study sustainability issues first hand.
A shortage of palm oil has been a recurring political issue in Thailand, where it’s a staple in household cooking. The government resorted to importing palm oil from neighbours in 2011 to ease what was becoming a national crisis.
The supply of vanilla has also been unstable. Supplied by a small number of farms where it is a very minor crop, there have previously been occasions of oversupply and low prices, so farmers naturally shifted their land to grow more lucrative crops. Bad weather in India and Mexico in 2012 led to a scarcity of vanilla pods and steep price rises in 2013. The shortfall in supplies was made up for by relying on farmers in Madagascar.
Big data and small farms
Food production in these supply chains is often based on large numbers of small farmers, working on different types of land using different techniques.
In other industries, such as automotive, there are global networks of information-sharing among players in supply chains which can result in significant cost reductions, reliability and sustainability benefits. Aston Martin, Jaguar Land Rover and Toyota Motor Europe, for example, share a system for identifying and managing potential risks around health and safety, financial stability and ethics in their supply chains. The global car manufacturers also use an online portal with updated information, as well as data from a standard financial health-check on suppliers in the chain.
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