Impacts of Big Data in Logistics

January 23, 2017

When the Google search bar pops up popular searches similar to the one you are typing in, or when Facebook suggests some pages that you might like according to your account’s history, it is all based on the information these companies have collected about you after monitoring your online activities. The web pages you visit and online messages you send every day continually generate data about you. This is of course not restricted to online activities. When you go to a supermarket to buy groceries and pay with your credit card, data is also being obtained about your shopping tendencies and financial activities. Due to our increasing use of smart devices, it has never been easier for businesses to collect our information. When all this data is amassed and linked to each other, we have Big Data – the new holy grail to help businesses succeed in this modern age.

 

 

Big Data has traditionally been most useful in sales and marketing, where it is used to anticipate sales volumes, customer product preferences, and optimize work schedules [1]. However, the rapidly growing logistics sector can also benefit greatly from this wealth of information in some major areas as follows:

 

1. Inventory demand forecast

 

For a retail store, having to tell a customer that the product they want is out of stock is considered a very disappointing failure. It happens very often in apparel stores where certain sizes of an item of clothing might run out due to the product’s popularity. On the other hand, having too many of a certain item in stock is also bad for business as it wastes storage space while contributing nothing to sales revenue. Therefore, “the ability to correctly predict demand has become a key factor for profitable business” [2]. These days, companies which have been operating for several years can accumulate historical data from all of their transactions and input them into statistical models aimed at analyzing trends and make predictions. The more data is input the more precise these predictions are, which means managers should take into account as many contributing factors as possible, such as promotional events and weather. “Advanced machine learning and optimization algorithms can look for and exploit observed patterns, correlations, and relationships among data elements and supply chain decisions – e.g., when to order, how many to order, where to put them, and so on” [3]. Big Data can definitely play a major role in improving business efficiency by helping logistics managers to accurately forecast demand for each product and plan inventory accordingly.

 

 

2. Last-mile delivery optimization

 

Another area where Big Data can certainly become a game-changer is in route optimization. Last-mile delivery is a crucial and especially expensive part of distribution, where most managers focus on finding cost-cutting solutions for their business. The most important feature here is route optimization. Even when the items have been fully loaded on the truck and ready to go, the driver still has to face more challenges on the road. With constraints such as the store’s opening hours or the client’s preferred delivery time windows, these drivers will have to find ways to navigate themselves through unpredictable traffic and potential road blocks to make it in time. Route optimization software will help them do this job much more quickly and effectively. It will take in all available data on local traffic characteristics, time windows, types of vehicle and so on, and generate the best delivery route for each individual shipment. Practical application of this technology has been shown to help cut distribution costs by 40%. A highly effective software like vRoute can also use historical data on all past trips to make accurate predictions about service time. All these features will make planning much more straightforward for logistics managers.

 

3. Customer relationship management

 

“For every business, it is vitally important to learn about customer demand and satisfaction” [4]. This area is where Big Data makes the greatest contribution. Suppose reports show that a regular customer has suddenly reduced the number of delivery orders, the manager will want to check out this customer’s detailed transactions. If it turns out that he or she has recently experienced an unsatisfactory delivery (a lengthy delay, for example), the logistics manager can then take note and start implementing customer service activities accordingly in order to retain this customer. A compiled list of a customer’s activities can also show the company their preferences which will help make future transactions more satisfactory. Last-mile delivery is the last leg of distribution and also a crucial consumer touch point. Delivery people have the opportunity to directly interact with customers and collect valuable data about them in order to make the company’s database even bigger.

 

The local picture

 

While all logistics managers are already well aware of the benefit of data, it is still a challenge for businesses in developing countries such as Vietnam to fully exploit this game-changer. A first step is of course data collection. Small businesses might adopt traditional methods and store data manually and separately, while only a systematic and interconnected collection of data across all business functions can truly boost efficiency. In order to create and utilize Big Data for your own business, it is essential that you first start with building an effective system of data collection and storage. This is relatively difficult for Vietnamese enterprises as very few parts of supply chain management here are truly digitized. Big Data is so big that it can only be handled digitally. Therefore, companies should seriously consider digitizing their processes and turning themselves into more data-driven businesses.

 

Sign up for a free demo of vRoute - Route Optimization Software

 

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Reference

 

[1] Big Data in Logistics – A DHL perspective on how to move beyond the hype, December 2013, page 3

[2] Big Data in Logistics – A DHL perspective on how to move beyond the hype, December 2013, page 11

[3] https://www.linkedin.com/pulse/impact-big-data-analysis-supply-chain-drivers-hrishikesh-kurhekar

[4] Big Data in Logistics – A DHL perspective on how to move beyond the hype, December 2013, page 22

 

 

 

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