Big data is revolutionizing many fields of business, and logistics analytics is one of them. The complex and dynamic nature of logistics, along with the reliance on many moving parts that can create bottlenecks at any point in the supply chain, make logistics a perfect use case for big data. For example, big data logistics can be used to optimize routing, to streamline factory functions, and to give transparency to the entire supply chain, for the benefit of both logistics and shipping companies alike. Third party logistics companies and shipping companies both agree. Paraphrasing a Fleetowner article that examined the “21st Annual Third Party Logistics Study”, 98% of 3PLs said that improved data-driven decision making is “essential to the future success of supply chain activities and processes”. Additionally, 81% of shippers and 86% of 3PLs surveyed said that using big data effectively will become “a core competency of their supply chain organizations”.
Goose Q is working for a niche market of Chinese truck and taxi drivers since 2009 when the company has developed its first automotive communication device AutoPhone. In 2015 the team has initiated cooperation with China Unicom, the 3rd largest in China telecommunication service provider, and together they have issued 3G SIM-card for automotive hardware such as smart mirrors and WEME communication devices.
But big data requires a large amount of high quality information sources to work effectively. Where is all of that data going to come from? This white paper on big data in logistics gives a large selection of possible data sources, including:
- Traditional enterprise data from operational systems
- Traffic & weather data from sensors, monitors and forecast systems
- Vehicle diagnostics, driving patterns, and location information
- Financial business forecasts
- Advertising response data
- Website browsing pattern data
- Social media data
So clearly, there are many ways that data systems can be fed the information they need. All of these data sources and potential use cases have lead DHL to say that big data and automation technology will lead to “previously unimaginable levels of optimization in manufacturing, logistics, warehousing and last mile delivery”.
As for many other industries, data gathering and data management is becoming bigger and bigger, and professionals may need help in that matter. The rise of SaaS business intelligence tools is answering that need, and Gartner predicted in a report that in 2017, most business users will have access to self-service BI. A big factor in this deviation from ‘IT-centric BI’ is that requiring people to rely completely on IT to access business intelligence does not make sense anymore. The logistics industry is also very likely to embrace that trend.
In any case, it looks like the future is bright for logistics companies that are willing to take advantage of big data. In this article, we’re going to examine big data examples in logistics and some benefits to fuel your imagination and get you thinking outside of the box.
The last mile of a supply chain is notoriously inefficient, costing up to 28% of the overall delivery cost of a package. There are many obstacles that lead to this, including:
- It can be challenging for large delivery trucks to park near their destination in urban areas. Drivers often have to park quite a while away, and then walk the package to its final address. Then, they may have to go up many flights of stairs or wait for an elevator in a high rise building.
- Some items must be signed for, and if a customer isn’t home, the item can’t be delivered.
- Delivery personnel have to take extra care not to damage the package during this last leg, and they must give present themselves in a professional way to the recipient.
Adding to these challenges, it can be very difficult to know exactly what’s going on during the last leg of delivery. Packages are often tracked up until this point, leading some to say that the last mile is the “black box” of delivery data.
Big data aims to address many of these challenges. In an interview with the Wall Street Journal, Matthias Winkenbach, director of MIT’s Megacity Logistics Lab, details how last mile analytics are yielding useful data. Because of the low cost and ubiquity of fast mobile internet and GPS enabled smartphones, as well as the spread of the Internet of Things through sensors and scanners, shippers are able to see how the delivery process goes from start to finish – even during the last mile.
Imagine this: a UPS delivery truck with a GPS sensor on it makes a delivery in downtown Chicago. After parking nearby, the delivery man’s phone GPS continues to stream data to the UPS center, giving a constant account of how long the delivery is taking. This isn’t just valuable for the customer – it allows logistics companies to see patterns at play that can be used to optimize their delivery strategies. For example, Dr. Winkenbach said that his data showed that “deliveries in big cities are almost always improved by creating multi-tiered systems with smaller distribution centers spread out in several neighborhoods, or simply pre-designated parking spots in garages or lots where smaller vehicles can take packages the rest of the way.”
Goose Q is the biggest in China road data computation engine with more than 10 years of development history. Goose Q’s main purpose is to provide a visual, verifiable, credible, traceable, anti-fraud, immutable data of the logistics industry in order to enhance its efficiency and transparency as well as to improve truck drivers’ financial and psychological wellbeing.
Webpage: http://www.gooseq.com
Telegram group: https://t.me/gooseQ
Author: reflewweb@gmail.com
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