Analytics in the Operation & Logistics Industry
Delivery of parcels? DHL does more with Advanced Analytics.
This article is made possible with DHL. Special thanks to Zoe and Zexel from SMU BIA.
At the heart of DHL’s services is an intense focus on understanding their Customers and their businesses. Whether it is the final delivery of packages or the management of logistics operations in various industries, the company successfully coordinates the many moving pieces of complex supply chains to achieve smooth operations and ultimately customer satisfaction.
In this article, BIA joins Mr Prerit, Senior Data Scientist from DHL Advanced Analytics in a friendly interview to debunk some common myths about data scientists and to find out how analytics plays a part in DHL.
Delivery in Progress…
DHL’s Express division is probably what comes to mind immediately when you think of DHL, with their many couriers on the roads performing daily deliveries. With the boom of e-commerce, the company finds itself delivering more packages — in fact, they deliver over 1.3 billion packages worldwide every year.
In the logistics industry, this is referred to as last-mile delivery, which refers to the final step of the delivery process to the end recipient. DHL Express and sister division DHL eCommerce Solutions offer last-mile delivery to their customers. For last-mile logistics, solutions such as the prediction and optimization of delivery timings, order fulfilment and punctuality become especially important. Route optimization through analysing traffic data to find the fastest routes that couriers can take to their destinations help speed up deliveries.
But is DHL really all about just delivering packages?
Ding-Dong, your parcel has arrived! What’s next?
DHL has, in fact, a total of 5 business units, each specializing in different logistics solutions. Aside from DHL Express and eCommerce Solutions, there is DHL Global Forwarding that offers global air, ocean and ground freight transport, and DHL Supply Chain which specializes in end-to-end supply chain management. DHL happens to be the world’s largest logistics company, providing customers with complete supply chain solutions and strategic logistics management.
Mr Prerit’s work at DHL Advanced Analytics spans across all business units and their unique requirements. For a start, we asked Mr Prerit what his day is like.
“A lot of time is spent analysing data,” Mr Prerit revealed. “We take the time to communicate sufficiently with our customers and comprehend the businesses that they run. Sometimes, the submitted problem statements are not explicit enough, in which case we will carefully run through their proposals and identify problems that can be treated with industry-tailored solutions from DHL.”
From what it seems, the process of understanding a customer demands personalized attention. Mr Prerit shares that as DHL prides itself to be a customer-centric company, it is necessary to put himself in the shoes of the customer and view the data from a different angle. He then rearranges the data such that business insights can be drawn and converted into business decisions which aid the customer. “The biggest challenge here is to milk out the actionable insights that users can understand,” says Mr Prerit.
So, DHL is not just a company that delivers packages; they deliver results for their clients too.
“Yes, we’re not just about moving things from point A to point B. We help clients solve operational issues — and for those who have already adopted a lean manufacturing approach, DHL finds a way for them to gain a strategic edge.”
Data Scientists VS Data Anybody — same same but different?
“Ah! Interesting question,” Mr Prerit exclaims and ponders a while. “Well, there are essentially data engineers, data analysts and people like myself, data scientists.
Data engineers work on how to extract, upload and clean data. Data engineers will pass their finished products to data analysts.
Data analysts analyse the data obtained from engineers. They try to make sense of the data, exploring how the data can tell us more about clients’ operations and organization. They then pass this information on to data scientists like me.
Data scientists are the ones coming up with solutions based on the analysis of the data. And in order to do so, we spend time understanding the clients’ businesses, what they want to achieve and how they want to be helped by us.
“So yes, we are quite different in terms of responsibilities. But we work together very closely, not in silos,” Mr Prerit clarified.
From this, we finally got a very clear explanation of how each role takes on the differing tasks.
Analytics = Lots of excel sheets?
Another common myth is that analytics is all about playing with excel sheets. True to a small extent, “but Excel always has a limit of 1.04 million rows. In DHL, we work with much more than that,” says Mr Prerit with a laugh. “It is still a very useful tool, competent in helping us analyse data, but only if the data is less voluminous, more structured.”
Typically, there are four “genres” of analysis that Mr Prerit’s work revolves more around.
- Descriptive Analytics: This simply refers to data that reveals what has happened. The basic approach is through Excel, Marketing Reports, Dashboards and KPIs.
- Diagnostic Analytics: This refers to the data that reveals why something has happened. Excel is good for this too, Mr Prerit mentioned.
- Predictive Analytics: It is data that predicts future events, detecting problems before they occur.
- Prescriptive Analytics: This is the data that proffers indications or steps on how to move on, or what actions to take. Things like complex statistical models, machine learning are all needed at this stage — Excel simply doesn’t have the capability nor scalability to work through these models, Mr Prerit explains.
So what are the expected skills a data scientist should possess?
“Learn Python. Notably, being familiar with python libraries like pandas, numpy, sklearn. We can use existing models in these libraries to quickly analyse data. Some companies use R too.
“A degree in data science is not compulsory. On the flip side, a lot of self-study is demanded. Learning online from others by talking through forums or communities is a good start. You don’t need to be an expert in all aspects; what you do need is a strong foundation in aspects like programming, statistics.”
Hard skills are not just all — Mr Prerit explains that a data scientist should have good clarity in his thoughts, possess problem-solving skills, and good communication skills. After all, there is a need to truly understand the issues that a customer faces, and present solutions that directly address these issues.
The Future is Automated
Warehousing is no longer just about men driving loader trucks. DHL employs state-of-the-art technology at the warehouse stage of parcel deliveries.
“At certain warehouses, we have smart glasses that function as a navigator. Someone wears the glasses, scans a QR code to find out what they need to pick up, and the glasses can show you the way around the warehouse,” says Mr Prerit.
At this point, we were awed at the vastness of digital applications DHL implemented in their warehouse — it felt as though we were peeking into the future.
But that’s not all: “We also have a bot called the EffiBOT that is able to remove any need for humans to carry packages from the depths of the warehouse all the way out. It automatically follows our people to the exact location and they only need to transfer the package from shelf to bot, and it’s able to find its way out of the warehouse.”
Check out more of DHL’s cool & futuristic innovations here.
Being curious, we questioned Mr Prerit about the future: how far is Singapore from employing drones as a delivery vehicle?
“ DHL has extensively tested the concept of drone delivery to deliver medical supplies. Closer to home, DHL Express has just launched a regular delivery service in China. However drone regulations are different everywhere, and I think they will be used in areas where it makes sense”
But this has not stopped DHL from introducing other delivery methods. Mr Prerit tells us that DHL’s sustainability goals declare the company to be carbon-free by 2050. A key driver of this goal is the use of electric vehicles. DHL, as part of the DPDHL Group, uses the Streetscooter (https://www.dpdhl.com/en/media-relations/specials/electro-mobility.html), an electric van specially customised for deliveries.
“We are looking into electric vehicles as modes of delivery, as they have zero-emission. 9000 StreetScooters are already dispatched across Germany, and the results look extremely promising”
Before we ended the hearty conversation, we asked Mr Prerit what he might say to aspiring data scientists.
“Keep learning from communities or forums. You don’t have to be a master at every technical aspect, but as long as you have a foundation, that’s already a good start.”
