December 4, 2014


Big data in Logistics

Big data: What type of value does it drive in the Logistics Industry?


Across industries, Big Data has generated big buzz – and the logistics sector is no exception. In fact, logistics CEOs rank “information explosion” amongst the top drivers of transformation for their organizations. Many IT professionals are attracted by the promise of Big Data’s ability to mitigate the costs of managing a growing volume, velocity and variety of data. However, despite plenty of talk about Big Data capabilities, many wonder about the reality beyond the hype. Here we reveal ways in which Big Data solutions can drive business results, allowing logistics companies to rise above the hype and reap big benefits.

Logistics and Big Data are perfectly matched

The logistics sector is ideally placed to benefit from the technological and methodological advancements of Big Data. A strong hint that data mastery has always been a key to the discipline is that, in its ancient Greek roots, logistics means “practical arithmetic”. Today logistics providers manage a massive flow of goods and at the same time create vast data sets. For millions of shipments every day, origin and destination, size, weight, content, and location are all tracked across global delivery networks. But does this data tracking fully exploit value? Probably not.

Most likely there is huge untapped potential for improving operational efficiency and customer experience, and creating useful new business models. Consider, for example, the benefits of integrating supply chain data streams from multiple logistics providers; this could eliminate current market fragmentation, enabling powerful new collaboration and services. Many providers realize that Big Data is a game changing trend for the logistics industry. In a recent study on supply chain trends, sixty percent of the respondents stated that they are planning to invest in Big Data analytics within the next five years.

However, the quest for competitive advantage starts with the identification of strong Big Data use cases. We present a number of use cases specific to the logistics sector.

When companies adopt Big Data as part of their business strategy, the first question to surface is usually what type of value Big Data will drive? Will it contribute to the top or bottom line, or will there be a non-financial driver? From a value point of view, the application of Big Data analytics falls into one of three dimensions.

The first and most obvious is operational efficiency. In this case, data is used to make better decisions, to optimize resource consumption, and to improve process quality and performance. It’s what automated data processing has always provided, but with an enhanced set of capabilities.

The second dimension is customer experience; typical aims are to increase customer loyalty, perform precise customer   segmentation, and optimize customer service. Including the vast data resources of the public Internet, Big Data propels CRM techniques to the next evolutionary stage. It also enables new business models to complement revenue streams from existing products, and to create additional revenue from entirely new (data) products.

Analytic tools can be especially useful in helping transportation companies mine and refine data to determine which information is valuable for optimizing business outcomes. Today, big data capabilities enable the integration of existing and new sources of data without the higher costs associated with the traditional data warehouse environment. Big Data and analytics also allow for more rapid capture and integration of time-sensitive data from numerous and varied sources, such as instrumented equipment.

Looking ahead, there are admittedly numerous obstacles to overcome (data quality, privacy, and technical feasibility, to name just a few) before Big Data has pervasive influence in the logistics industry. But in the long run, these obstacles are of secondary importance because, first and foremost, Big Data is driven by entrepreneurial spirit. Several organizations have led the way for us – Google, Amazon, Face Book and eBay, for example, have already succeeded in turning extensive information into business. But apart from the leading logistics providers that implement specific Big Data opportunities, how will the entire logistics sector transform into a data-driven industry?

What evolution can we anticipate in a world where virtually every single shipped item is connected to the Internet? We may not know all of the answers right now, but this trend report has shown there is plenty of headroom for valuable Big Data innovation. Joining resources, labor, and capital, it is clear that information has become the fourth production factor and essential to competitive differentiation.

It is time to tap the potential of Big Data to improve operational efficiency and customer experience, and create useful new business models. It is time for a shift in mindset and a clear strategy and application of the right drilling techniques.

Use Cases in Logistics

Let’s look into some of the sample use cases in logistics under each dimension.

Operational Efficiency Use Cases

  • Real-time route optimization:

This solution plans the vehicle route for delivery trucks based on real time shipment data. Based on the shipment data received, and by considering the other factors like the geographical, environmental and recipient status, the route for the delivery truck is planned on the go. This benefits 3PL companies and minimizes cost, improves carbon monoxide emissions by reduced mileage, and meets the commitment given to customers.

  • Crowd based pick-up and delivery:

Due to urbanization, a majority of the population lives in cities that are highly populated, making it nearly impossible for a 3PL to deliver goods at the promised time. Crowd based pick-up and delivery is a new strategic initiative that identifies commuters, taxi drivers and students to take over the last mile of delivery on the route as they are, in any case, traveling/passing these delivery locations. This eliminates delivery vehicles and saves cost while also delivering the goods to customers on time.

  • Predictive network planning:

This use case is around network planning for shipment. As soon as the order is received, 3PLs plan the network end-to-end. While planning, past shipment delivery data to the same destination is used for a foolproof delivery of the new shipments. This eliminates the dwell time in the entire shipment lifecycle, saves cost, and ensures delivery as committed.

Customer Experience Use Cases

  • Customer loyalty management:

As the cost of winning a new customer is far more than retaining an existing customer, customer loyalty solutions help in identifying valuable customers by using data smartly. This solution helps 3PLs serve customers just as they expect to be served, thereby improving customer satisfaction and decreasing customer churn.

New Business Models Use Cases

  • Environmental intelligence:

The accelerated growth of urban areas increases the importance of city planning and environmental monitoring. By using a variety of sensors attached to the delivery vehicles, logistics providers can provide rich environmental data statistics. By collecting data like the measurement of ozone, dust pollution, temperature, humidity, traffic density, noise, and parking slot utilization along city roads, it would be easier for logistics providers to provide valuable data to authorities, environment agencies and real estate developers, and get additional revenue out of it.

In Conclusion

Travel and transportation providers can embrace Big Data and analytics to more accurately model and optimize demand, capacity, schedules, pricing, customer sentiment, revenue, cost, and more. As Big Data is increasingly being utilized to improve operational efficiency, customer experience, and new business models, it is evident that Big Data and logistics are indeed perfectly matched.Read more about Logistics Solutions and Systems offered by HCL Technologies.