Big Data has profoundly influenced numerous industries, and logistics and supply chain management are no exceptions. In fact, many CEOs in the logistics sector have stated that the “information explosion” is one of the driving forces behind transformation and a key opportunity for innovation and improvement within their companies.
Many experts are fascinated by Big Data’s potential to address challenges in cost management, operational speed, and data diversity. Moreover, companies can leverage insights from Big Data to enhance their supply chain operations and overall performance.
1. What is Big Data?
Big Data refers to datasets so large and complex that traditional data processing tools and applications are unable to handle them efficiently. However, within these massive datasets lie valuable insights that, if extracted successfully, can significantly benefit businesses, scientific research, epidemic forecasting, and even real-time traffic condition monitoring.
Therefore, these datasets must be collected, organized, stored, searched, and shared differently from conventional data management methods.
According to Intel (September 2013), the world was generating one petabyte of data every 11 seconds — equivalent to 13 years of HD video. Companies themselves are generating and managing their own Big Data. For example, eBay uses two data centers with a total capacity of 40 petabytes to store search queries, customer recommendations, and product information.
Similarly, Amazon.com handles millions of transactions daily, along with requests from roughly half a million marketplace partners. As of 2005, Amazon operated three of the world’s largest Linux databases, with capacities of 7.8TB, 18.5TB, and 24.7TB respectively.
Facebook manages over 50 billion photos uploaded by users, while YouTube and Google store every single search query and video, along with associated metadata.
According to SAS Group, here are some remarkable statistics about Big Data:
RFID systems generate data volumes over 1,000 times greater than traditional barcodes. During the first 4 hours of Black Friday 2012, Walmart processed over 10 million transactions — approximately 5,000 per second.
UPS receives about 39.5 million customer requests per day.
VISA processes over 172,800,000 card transactions per day.
Twitter users post 500 million new tweets daily, while Facebook’s 1.15 billion members generate massive amounts of textual, visual, and video data.
2. The Impact of Big Data on Logistics and Supply Chain Management
As more systems and devices become interconnected, exponentially more data is generated and collected every single day. A study by IDC found that the volume of data doubles every two years and was expected to reach 44 zettabytes (44 trillion gigabytes) by 2020.
This rapid data growth is nothing new for the logistics and supply chain industry. Historically, supply chain professionals have generated vast amounts of diverse information — including routes, carriers, delivery times, transportation modes, pricing, revenue, and profit margins — all stored in company databases.
So, how exactly can Big Data transform the logistics industry? Let’s look at a few examples.
A company may want to determine which transportation modes or shipping lines can maximize profit for a specific destination while maintaining on-time delivery. Big Data can make that possible.
A shipping line might wish to analyze how seasonal changes, specific weather conditions, or time periods affect delivery performance for particular routes. Big Data can also make that possible.
Applying Big Data to supply chain management (SCM) enables companies to forecast demand more accurately, better understand customer purchasing cycles, and estimate future warehouse capacity based on historical data.
Given the vast amount of structured and unstructured data available across enterprises, it’s only a matter of time before companies begin to fully exploit this “gold mine” of information.
Big Data has already played a crucial role in Amazon’s success. The company leverages data from over 152 million customers (and counting) to understand purchasing behavior and recommend products based on buying history and related preferences.
Data should not be viewed merely as intangible information “in the cloud,” but as a strategic asset — a gold mine for any organization.
3. Case Study: UPS
Every day, UPS processes an average of 16.9 million packages, totaling over 4 billion shipments per year using more than 100,000 delivery vehicles.
Given these massive numbers, there are countless ways UPS utilizes Big Data — one of the most impactful being transportation network optimization.
Between any two points (A to B), there are multiple possible routes — but which one has the least traffic, the fewest stoplights, the highest speed limits, or the shortest distance? For a parcel delivery company like UPS, understanding such data is critical.
Through telecommunication technology and advanced algorithms, UPS can determine optimal delivery routes, reduce idle time, and implement predictive maintenance.
The algorithm used by UPS can analyze up to 200,000 potential routing options in real time. By leveraging this massive dataset, UPS can predict how each vehicle will perform on specific routes and identify stages in the delivery process where efficiency can be improved.
Since implementing this Big Data-driven optimization system, UPS has saved over 39 million gallons of fuel and reduced its delivery routes by more than 364 million miles. The company is now planning to apply the same technology to its air freight operations.
