How To Utilise Big Data in Logistics
The importance of utilising big data in the Logistics industry has accelerated, since vast amounts of data is being generated from telematics, barcode scanners, RFID readers, software systems managing operations and positioning system devices on vehicles and in mobile phones
The importance of utilising big data in the Logistics industry has accelerated, since vast amounts of data is being generated from telematics, barcode scanners, RFID readers, software systems managing operations and positioning system devices on vehicles and in mobile phones. And let’s admit it: as customers; we want to track where our package/order is (simultaneously!), expect it to be delivered promptly and we don’t hesitate to share any comments or complaints. So the data we generate – product reviews, social media comments, likes, blogs and online comments- are also re-shaping this traditionally fragmented industry.
Because of these reasons, logistics started to position itself to put big data to better and efficient use. Let’s take a look at in which cases logistics is leveraging big data:
We can say the benefits might not be crystal clear in every field of logistics; however the most direct results can be seen in the core business of Logistics: Operations.
a. (Real-Time) Route Optimization: Logistics commonly 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 chain in terms of cost and time.
This routing intelligence enables companies to save time and cost on staff manual sequencing, reduce mileage and minimize unsuccessful deliveries. As a matter of fact, UPS has saved over 39 million gallons of fuel and avoided 364 million miles since they started route optimization in 2001. In addition to this, they have managed to reduce engine idle time by 10 million minutes- impressive!
b. Address verification: The verification of a delivery address is a fundamental requirement for logistics. Address verification tools ensure the data entered into a database is accurate upon entry with real-time auto-complete and offers faster deliveries and optimized routes via accurate GeoCoordinates.
c. Shift Planning: In logistics, calculating and managing shifts are not only subjected to staff availability. It involves many other parameters such as network planning (long term production and demand forecasts, daily sales for each individual store), supply chain planning, transport capacity planning and periodic busy times. With predictive analysis, companies foresee these complex fluctuations and support strategic investments into the whole network. Calculating the routes in advance enables logistics to easily identify available skilled staff to critical shifts and also contributes to a better employee satisfaction as employees gain a more consistent work-personal life balance.
d. Real-time analysis: Sensors in vehicles are capable of reporting data ın real-time. These reports and analysis improve performance and process quality as well as optimizing resource consumption. Now companies are able to query locating materials or vehicles without disrupting the system’s operation. They are also able to manage crowd-based delivery where a last minute schedule change is announced to the drivers, and an occasionally available driver delivers shipments along routes they would take anyway.
By the help of predictive analysis, it is possible to anticipate internal risks such as shipping styles, maintenance requirements, shipping routes and external risks like weather conditions, road conditions, busy periods etc. In fact logistic companies started saving millions in preventing inefficient and unplanned maintenance by applying big data analytics. Their algorithms predict maintenance requirements of their delivery vehicles so that they won’t face the consequences of any unplanned breakdown of a vehicle resulting in late deliveries and unhappy customers.
Big data analytics are also used to predict churn by mapping customer information, behaviour and feedback (social media, call centers etc.) against business parameters.
Since customer service expectations have changed radically, determining and acting on what customers want, when and where they’ll want is one of the priorities in logistics. Big data is being used to perform precise customer segmentation and targeting, to optimize customer interaction and to understand the customer requirements. This comprehensive view on customer information enables companies to enhance service/product quality and portfolio.
An example is Amazon “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.
UPS also created a service for its customers in order to enrich the customer experience. My Choice enables customers to manage delivery location and timing via mobile devices real-time solving one of the biggest customer problems – staying home for a package
Recently, the logistics industry is making more efforts to trade manual calculations to data analytics; complicated reports to data visualization tools and gut-feelings to predictive analysis to optimize such a network, which has various components. Big data is helping the industry with accurate data-driven insights to achieve effective business decision-making, improved investment decisions, derive new strategies and develop more powerful projects and innovations.