Case studies of Digital Transformation in Supply Chain Management and Finance

Published by Ngoc Tran on

Walmart data analysis

John Bogle once said “Learn every day, but especially from the experiences of others. It’s cheaper!”. Learning from others’ experience eventually save you a lot of efforts, time and money. So, why not looking at how several companies around the world have been making use of digital transformation, especially in logistics and finance industry?

#1: Walmart – Data mining

The American multinational retailer Walmart uses data mining to track its customers (see the image above). Mining customer data helps Walmart recognize hidden sales patterns of an individual customer, resulting in recommending products that customer may be interested in, or purchased before. Besides, it analyzes every clickable action on its own website, trending on Twitter, or local events. One of the most noticeable examples is that Walmart learned to analyze how local weather forecast can affect the local demand. In 2004, when Hurricane Charley was expected to hit Florida’s Atlantic coast, Walmart’s chief information officer asked her staff for forecast based on what had happened ahead of the hurricane. Strawberry Pop-Tarts was figured out to be sold seven times their normal sales rate. Such result certainly prevents a company from lost sales and thus increases safety stocks as well as revenues.

#2: DHL – Cold chain technology

The German logistics service provider DHL has always been famous for being enthusiastic about keeping pace with digital transformation, especially on the cold chain where a high demand of vaccine shipments need to be met globally. Medical products and vaccines require strict care in a temperature-controlled storage during the delivery, so any issues on the way can cost considerably. What DHL has approached is to use sensors and smart tracking devices attached to shipping containers. It therefore can record environmental conditions outside the box and precise location to identify bumps and shocks. In addition, an alarm function by sensors will be raised when the packages are vulnerable to external environmental conditions. More importantly, data gathered from different transport routes helps build up a detailed picture of several environmental conditions at different times of the year and by different carriers, which eventually sport patterns (e.g. frequency of delays) to improve the logistics chain. DHL has launched an upgraded risk management tool called DHL’s Resillience360 platform which incorporates data from other sources into their analysis, increasing SC visibility.

#3: ING – Big Data and Internet of Things (IoTs)

Big Data and Internet of Things (IoTs)

Source: Elusive studio

Under the pressure of technology giants and data-driven trends ING, a Dutch bank providing multinational banking and financial services, established a BI team which closely collaborates with its customer department. Firstly, it collects the disparate data sources generated by its customers and processes these data by a Lambda architecture, which is designed to handle huge quantities of data through batch and stream processing. The result is analyzed to deliver more customized messages to each customer. Besides, the company has introduced real-time forecasting on future expenses of individual customers, which can be done via ING’s mobile app. Secondly, ING has applied machine-learning backends for batch processing and fraud detection in order to enhance marketing and security. In details, outlier algorithms from the technique can predict irregular transactions. Regarding IoTs, a simple example is that ING banks are connected to mobile phones, payment terminals and ATMs, and soon household equipment. The main challenges that ING and other banks are facing are how to handle a significant amount of data effectively, and a focus on privacy of individual customers and staff.

Thank you for reading and I see you in the next blog!


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