We’ve all heard the old adage that knowledge is power. Vast amounts of information from diverse sources – aka “big data” – hold a huge treasure trove of hidden potential. But like oil, you have to drill deep to get to it.
One of the central challenges businesses face today is getting the most out of our information. What’s the key message in all that data? How can we use it to optimize our business? And how can we use it make our customers happier – to make their lives easier?
To get good answers to these questions, we’re going to drill deep and invest a lot of time and energy into developing big data models that inform and add value. And we’ve already started drilling. DHL’s new trend report – Big Data in Logistics – charts the course for smart big data concepts.
Big data presents opportunities and challenges for logistics companies
According to an IDC study, digital data bits already outnumbered the stars in the universe as early as 2008 – and the volume of data now doubles roughly every two years. Smartphones, RFID systems and webcams – which continually send and receive data, mostly without human involvement – are the main drivers behind this explosion. But the enormous amount of data alone is not the challenge, instead it’s the diverse nature of the data. For example, how should we process photo or video material? How can we relate it to “hard facts” such as time, costs or materials requirements? And how can we measure customer behavior? These are precisely the issues that all businesses will have to deal with to stay competitive – including logistics companies.
DHL’s parcel business provides a good example for just how much data a modern company could potentially be dealing with. In Germany alone we deliver about 3.4 million parcels every day. We record up to 150 parameters for each one, such as size, weight, destination or contents, which amounts to over 300 million pieces of new information every day.
As you can see, big data and logistics were made for each other. With the right tools, logistics companies can become search engines for users from every industry. The main challenge here is how to analyze the data so it can be used as a basis for everyday business decisions. Without the right analytics tools, that’s like searching for the proverbial needle in a haystack.
It won’t be easy, but one thing’s for sure: professional big-data analysis is no longer an option, it’s an obligation.
Big data use today – a very promising start
Even though big data is in its infancy, some companies are already successfully using highly advanced tools with complex algorithms to enhance their operations. For example, by analyzing various parameters such as business hours, deliveries, vacation periods and weather forecasts, the German drugstore chain “dm” optimized its shift assignments to match the number of employees per shift with the daily business requirements. That’s not just extremely efficient, it also bolsters customer satisfaction and improves employee work-life balance – a true win-win situation.
Tesco, a British supermarket chain, used a detailed data analysis to optimize its point-of-sale promotions and product supply. For example, an analysis of delivery and inventory data for the last five years showed that on at least one weekend per year, the demand for barbecue products increases sharply. So Tesco developed a plan that allowed the company to rapidly respond to the increased demand, ensuring maximum sales and high customer satisfaction.
These examples show that every company has a data treasure trove. The key is to drill deep and tap into it!
Big data research at DHL
Big data is also key to our daily parcel delivery business. Our innovation team uses the Logistics Trend Radar to identify issues that will become important for logistics in the future. My team from DHL Trend Research used it to prepare Big Data in Logistics, which includes our research of methods for collecting and analyzing big data sets and establishing relationships among them to gain new insights. For example, our research project “DHL Parcel Volume Prediction” is investigating the correlation between Google search terms, weather conditions or flu outbreaks and the online shopping patterns of consumers. That has a direct effect on the number of parcels underway in our network – valuable information for ensuring smooth delivery.
Predicting potential supply chain disruptions is another application that could enhance our customers’ experience and improve their satisfaction. We collect and analyze data about a region’s political and economic developments with “Resilience 360” – our new supply chain risk management tool. This can help small and medium-sized businesses maintain their operations and ultimately maximize customer satisfaction. And it can mean that we deliver goods faster. It’s all about transparent data collection and analysis at the global level aimed to boost cross-sector planning and significantly optimize global supply chain monitoring – in real time.
The future belongs to those who use data precisely
The examples above show that big data can play an important role in reaching three goals: increasing business efficiency, improving customer satisfaction and developing new business models – in short, to a modern and successful business. To get the most out of big data, we need to find ways to focus on and only use the data that offers real added value. Simply collecting big data sets for their own sake isn’t productive. DHL’s R&D teams will continue to drill deep into big data models in order to find the fuel to further boost efficiency and ultimately satisfy more customers.
If you want to drill deeper, find the complete trend report, Big Data in Logistics, here.