Effects of Big Data in Logistics

Big data is revolutionizing many fields of business, and logistics industry 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, this makes logistics a perfect case for big data. In logistics industry big data can be used to optimize routing, streamline factory functions, and to give transparency to the entire supply chain, for the benefit of both logistics and shipping companies. Third party logistics companies and shipping companies both agree that improved data-driven decision making is much needed to the future success of supply chain activities and processes. The global logistics industry involves an series of recipients like shippers (manufacturers, retailers, distributors) who make/sell the products that need to be stored or moved; logistics service providers (third-party logistics providers (3PLs), 4PLs, freight forwarders, ocean shipping, trucking, rail, air cargo) who store and move products, logistics hubs (airports, sea ports, rail terminals) and regulatory authorities (customs) are looking at using the immense data available at their disposal to make profitable decisions based purely on data and not on past trends or any other factor which does not have the same effect that data can bring about.

Big data on the other hand requires a large amount of high quality information sources to work effectively. Where is all of that data going to come from? Big data in logistics gives a large selection of possible data sources which include:

• Traditional enterprise data from operational systems

• Forecasting systems and data points

• 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 feed 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 logistics industry is slowly embracing that trend. It looks like the future is bright for logistics companies that are willing to take advantage of big data.

The next big question is, how will big data impact the logistics industry and how can we measure the success?

The immediate transformation would take place in the operations area of logistics, when past data will show how things can be done differently. For example, route planning can be optimized. Logistics use dynamic routing systems which calculate the routes based on incoming shipment data, traffic situations, holidays, delivery sequence, weather conditions and recipient status to name a few.

Route optimization also plays a crucial part in the case of determining which vehicles (truck, ship, airplane or train) to choose over possible routes and junction points in order to optimize the flow throughout the supply chain in terms of cost and time.This routing intelligence enables companies to save time, manage staff costs, reduce mileage and minimize unsuccessful deliveries. With the help of predictive analytics, logistic companies have started saving unwanted waste of their revenue by anticipating risks and changing how operations are handled when it comes to shipping, routing, holiday rush, weather/ road conditions etc..

Data analytics have also enhanced customer experience radically by understanding what a customer wants, when he wants, where he wants it delivered. Logistics companies have taken all of this information which is one of their primary priorities to change a customer’s experience on their first contact with the logistic company or during their constant interaction with them. Amazon is a classic example of “anticipatory shipping” technique which is aimed to help the online retailer to anticipate customer demand in specific locations and adjust their inventory accordingly. The demand prediction is based on previous purchases, previous searches and the time spent looking at specific items in order to meet the customers’ needs precisely. Real time tracking of vehicles through sensors or RF devices provide reporting data that is real time which can help logistics companies make timely decisions preventing unwanted route changes or driver taking longer routes. These analysis improves performance and process quality output while optimizing on resource. Data on traffic conditions, weather, end user availability and more helps to dynamically revise routes and provide instant driving direction updates to drivers. This helps to optimize delivery which drives down the product cost.

Big Data and data analytics can actually bring about tangible changes to the logistics industry in the following areas:

Warehouse Management – Manage inventory more efficiently and in real time, with proper allocation of warehouse capacity, thereby providing uninterrupted shipping.

Customer Experience – reduce service costs, better resource utilization which in turn results in providing accurate and timely shipments enhancing overall customer experience.

Maintenance – asset purchases and maintenance workcan be plannedaccurately, risks can be reduced, service quality can be improved andunwanted or additional costs can be avoided.

The “last mile” phenomenon is well-known in logistics: as a matter of fact during the transportation process, the last short section usually represents the biggest challenge or demands the greatest effort. It can even be up to 28% of the total shipping cost; finding the exact address, problematic parking, remote location, and handling fragile packages play a major role in it.Previously, this last stage was a road block, hence logistic companies did not see how it is being done, but technology now allows this: data collected through sensors can optimize transport solutions, create new guidelines that reduce these costs.

Big data in logistics can be used to reduce inefficiencies in last mile delivery, provide transparency to the supply chain, optimize deliveries, protect perishable goods, and automate the entire supply chain. The logistics industry is making every effort to run of the mill operations to data analytics; complicated reports to data visualization tools and gut-feelings to predictive analysis to optimize the final output. Big data is helping the logistics industry with accurate data-driven insights to achieve effective business decisions, improved investment decisions, derive new strategies and develop more powerful projects and innovations.

By,

Mr. Alwin John

Academic Director

Laurus Institute