Data Mining for Logistics and Transportation

By Danielle N.

Big Data has made an immense impact on logistics and transportation sector by changing how businesses operate and has become highly critical and beneficial for companies to utilize. From personally customers’ experiences on the web to managing supply chains more efficiently, Big Data has changed the way businesses keep up as technology advances. Data mining is a concept used to analyze data from different sources and is utilize to summarize meaningful information. This information is an important factor that can be used to increase revenue, cuts costs, or both. It is a useful analytical tool for analyzing data and businesses are able to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified (Wu 2012).

For logistics and transportation, companies such as Amazon uses data mining to find the exact customers’ needs, and UPS uses data mining to balance both efficiency and cost effectiveness when transporting thousands of packages each day. Companies such as Amazon and Google, have incorporated a procedure to figure out how to present their products that is uniquely shaped to the customers’ wants and needs without changing the customer experience. For the first time they can actually know what the customer wants, instead of making guesses, because they have access to so much more information about the customers than they used to. Big data is a loose term for the collection and organization of large amounts of data that is relevant to companies and their business goals. For example, Amazon uses big data resources such as customers browsing habits and previous purchases to create a custom email of products they are likely to buy. With the recent advances in database technology the ability for companies to gather this information and process it has become much quicker. Amazon has created their own web services application called Amazon Web Services (AWS). Through this middle man type service, Amazon is able to cut the costs of managing and processing online service data and help companies personalize their customers’ shopping experience as Amazon has already mastered (Amazon.com). The company phrases it “rapidly scale virtually any big data application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, server-less computing, and internet-of-things processing” (Madden 2012). We are seeing companies utilize this tactic in almost every industry capitalizing on the growth of the online shopping industry. Ultimately, this gives the customers a sense of control. Smart shoppers will be more knowledgeable about their data-gathering and use the results more effectively and appropriately (Meadows 2013).

Similarly, The United Parcel Service (UPS) is working to personalize their customers’ experience but in a different manner. They use big data to implement vehicle sensors on all 46,000 of its delivery trucks and in various parts of its warehouses and even in the air flights. WorldShip Integration is a program that UPS uses to give the customers options to personalize the process of how their packages are shipped and delivered (UPS.com). If they are connected to a data file, they can see the complete shipment information for each order or as a batch. With WorldShip, it helps both the customers and the warehouse on where each package is at and the information about the package. WorldShip helps customers’ streamline order entry, contact customer service, accounting, procurement, and show the billing processes. Business is more efficient when each system communicates well with each other and create a more user-friendly site for both the consumers and the company (Terdiman 2010). Furthermore, UPS also has sensors on all its delivery trucks that keep track of speed, direction, braking, and overall performance and they use this information to create better for efficient routes for delivering packages.

A few challenges and limitations to data mining for these two companies are both keeping and maintaining the valuable information they gain when working with customers. Customers are usually only comfortable giving their personal information to companies they trust, so keeping this data safe and secure is very important to continuing to maintain that reputation for Amazon or UPS. They also have the limitation of trying their best to keep human error from entering the picture. Having employees follow policy and procedure when going through routes on deliveries, helps keep them on track and on time and any deviation from that can cause a domino effect of issues through the rest of the day. This same practice of procedure can help these companies also keep track of their packages and products making sure when, where, and how each customer receives their delivery adhering to the highest level of efficiency they strive to achieve (Rijmenam 2014). The final issues these companies face is the randomness of whether and how it effects travel throughout larger distances. While small local commutes may be easy to manage, having trucks that traverse large cross country or even cross continent distances leaves for the inevitability of issues with weather forcing companies to hope that the weather predictions are reliable and accurate. Any inaccuracy in the weather may lead to slowing or even stopping of packages from continuing to move in transit until the conditions better and allow for proper and safe transport.

Overall, Big Data and Data Mining have and will be the key for the success of these companies but also there will be challenges that will come by as technology is developing day by day. There will be more diverse customers and competitors which will motivate these companies to create new models and logistics to adjust to these changes. They must create a way to learn and collect the right data from each customer, employees, and warehouse so that there is an exact and correct data to analyze. As long as they use the data correctly, the companies will be able to utilize their sources efficiently and effectively.

Works Cited
Madden, S. (2012, May 02). How Companies Like Amazon Use Big Data To Make You Love Them. Retrieved February 05, 2017, from https://www.fastcodesign.com/1669551/how-
companies-like-amazon-use-big-data-to-make-you-love-them
Meadows, C. (2013, November 03). Data Mining | Amazon and Google Use It to Sell Us What We Want. Retrieved February 5, 2017, from http://teleread.com/amazon-mines-data-to- find-customer-tastes/
Rijmenam, M. V. (2014, May 27). Why UPS Spends More Than $ 1 Billion on Big Data Annually [Scholarly project]. In SmartDataCollective.
Terdiman, D. (2010, April 02). UPS turns data analysis into big savings. Retrieved February 4, from https://www.cnet.com/news/ups-turns-data-analysis-into-big-savings/
Wu, X. (2012). Data Mining with Big Data [Scholarly project].

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