How are distribution centers in the US utilizing innovative AI and machine learning techniques?

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How are distribution centers in the US utilizing innovative AI and machine learning techniques?

Hello! Thank you for your question about finding the case studies showing how distribution centers in the US are utilizing innovative AI and machine learning techniques. The most useful source were Forbes and Business Insider.
The short version is that as requested, I found five case studies including Walmart, Amazon, Kohl’s, C.H. Robinson, and North Face showing how they utilize innovative AI and machine learning techniques. Below you will find a deep dive of my research and findings.

METHODOLOGY
I have done an extensive search through corporate websites, trusted media sites and, common google search to find required case studies. Based on my research, I have realized that there are not many case studies highlighted, relating to the use of AI and machine learning, concerning distribution centers specifically. However, I found some relevant case studies including Walmart and Amazon. First, I have attempted to find the case study on Amazon relating to the use of AI and machine learning, concerning distribution centers. I found a detailed case study on Amazon and Walmart. Then, I looked for other related case studies but I could not find much information available. After searching extensively through online resources, fortunately, I found three other case studies on Kohl’s, North Face, and C.H. Robinson. Below you will find a deep dive of my findings.

Case Studies

1. Walmart

According to Lauren Desegur, VP of customer experience engineering at WalmartLabs, “we are 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.”
According to an article published in Forbes, 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 has recently launched Pick-up Towers in some of its stores. These are 16 x 8-foot self-service kiosks conveniently located at the entrance to the store. The kiosk retrieves online orders for customers and the customers can just scan a barcode on their online receipt. Within 45 seconds after the scan, the products they purchased will appear on a conveyor belt. Customers have given positive reviews to these Pick-up Towers and found as an improvement over the store’s traditional pickup process.
Apart from that Walmart has also adopted few other steps to improve the customer experience. Now, customers in the pharmacy and money services areas will be able to use the Walmart app for some aspects of the checkout process. Customer, instead of waiting until they reach the counter would be able to bypass the main queue to get in and out of the store more quickly. This step is to help customers bypass the checkout process entirely with the use of computer vision, sensors and machine learning. Moreover, Walmart already uses ML (machine learning) to optimize the delivery routes of their associate home deliveries.

2. Amazon

According to a recent article published in Business Insider, "Amazon’s product recommendations are largely driven by machine learning, as the company utilizes its huge database of consumer purchases to predict what specific items customers would be interested in". Also, with the extension of its business into other areas, it will need machine learning algorithms that can parse increasingly diverse data sets.
Machine learning and natural language processing (NLP) are at the core of Amazon’s digital assistant Alexa. Apart from that, Amazon's logistics business is heavily reliant on machine learning and there are thousands of factors in each individual order fulfillment, therefore, employing artificial intelligence (AI) that can reroute, change delivery arrival times, and make other adjustments accurately and efficiently is extremely valuable. Amazon is also interested in drone delivery and machine learning can enable drones to fly autonomously.
Recently, Amazon has filed a number of machine learning and AI focused patents. Additionally, the company is looking towards a new revenue stream by making its machine learning and AI available to other companies through Amazon Web Services (AWS). For retailers, the use of AI to personalize the customer journey could be a huge value-add. This may help in sales gain of 6-10%. It is estimated that the use of AI by the retailers will boost profitability rates by 59% in the wholesale and retail industries by 2035.

3. Kohl’s

According to Ratnakar Lavu, Chief Technology Officer for Kohl, retailers should use the machine learning, artificial intelligence, and chatbots for both customer and internal communication. Kohl’s development team is currently working on the same.
Kohl’s is testing chatbots for communicating with customers who have the questions like “where’s my order”, as well as password resets. These are one of the most common customer service calls. To compete with the big retailers such as Amazon, Kohl’s is continually transforming the store experience. Kohl’s associates have an app with built-in intelligence that helps them determine the optimal method of fulfilling an online order in store.
To create the differentiation from Amazon Go, the company is launching Kohl’s Pay which is a mobile app that lets Kohl’s cardholders combine all their coupons, discounts and points with one scan at checkout. This is a way to increase the lifetime value and loyalty of the company’s customers by reducing friction and increasing engagement.
Kohl’s is using a lot more analytics and machine learning to make it a more compelling experience. Kohl’s Pay is being piloted at 25 U.S. stores. The company has decided to solve a problem that’s highly relevant to its customers from a payment standpoint. Also, it is the fastest way to check out in their environment.
Additionally, Kohl’s is also working on mobile technology that will help customers navigate the store more easily and find an item faster. The re-launch of a recent platform at Kohl’s enables things like machine learning and pattern recognition to serve up search results based on what product pages someone visited.

4. C.H. Robinson

The company has introduced an innovation with the launch of Navisphere Vision. This is a supply chain technology that provides real-time global visibility across all modes and regions in one platform. It is said to be the next generation supply chain visibility tool. Navisphere Vision is not only global but it goes far beyond visibility and helps customers predict supply chain disruptions before they even occur.
The article also mentioned that Navisphere Vision utilizes API technology to aggregate all other supply chain and information sources into one single location. It provides customers the most streamlined, real-time solution available. It also brings a new level of machine-learning and data science which has not been experiencing earlier by the supply chain industry." Microsoft is a customer of C.H. Robinson; using Navisphere since 2016. According to Alaina Hawkins, Senior Manager of Global Logistics at Microsoft, Navisphere Vision is tremendously powerful. It helps them make decisions on a more precise, real-time level so that they can address any challenges and address them. It helps create predictability throughout the supply chain and increase collaboration to deliver products to consumers on time.

5. North Face

North Face, an outdoor sportswear brand has developed an online shopping tool using IBM’s Watson to replicate the conversations occurred in the store while shopping online. The online shopping has revolved around a grid of products on a web page, on a white background, with tools to search and filter. The company wanted to create something new and different, something that may be personal and intuitive and something that really allowed customers to speak in their natural voices, in their natural language and tell what they want and what they needed.
North Face's team has built a tool which is called as Fluid XPS powered by IBM Watson. This tool delivers dialogue-driven product recommendations. This starts with a question, “Where and when will you be using this jacket?” Customers are prompted to write a full sentence and the platform begins by narrowing down products by conditions and gender. Then a series of questions have been designed to let consumers see the product selection change as they provide more information.
CONCLUSION
To wrap it up, after searching through online resources I have determined that there are not much detailed case studies available related to how distribution centers in the US are utilizing innovative AI and machine learning techniques. However, based on my research I was able to find five case studies. These case studies include Amazon, Walmart, Kohl’s, C.H. Robinson, and North Face.
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Sources
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