Part of our mobility, transport & logistics thought leadership series
Data is undoubtedly the foundation of innovative digital solutions and executions. Often, it comes from rich sources of data that most companies may already have access to. Over nine out of ten players in the transportation & logistics industry believe that data analytics is crucial to making perceptive decisions . However, based on our wide experience with clients in the industry, the most major obstacles in data analytics are the consequences of not knowing where to start and what value can be generated from data.
This article is written to give you a glimpse of what data analytics can do for your business. These are essentially thought starters and hopefully, give you initial ideas and find inspiration.
We have divided the vertical into separate transportation and cargo/logistics sections to allow you to go directly to the section that applies to you.
Demand Prediction for a Taxi Company
Transportation service businesses such as taxi companies have to constantly manage their fleet of vehicles to efficiently service commuting customers. Even with the increasing adoption of mobile apps as a means to book ride services on demand, matching the right vehicle supply to seemingly erratic demand surges in a timely manner is still a challenge.
But with the aid of rich data and analytics, service providers can take a look at historical customer data accumulated over years of operations, dissect and process it, and eventually be able to predict the exact time frames for commuter surges even including the most frequented passenger hotspots. Thus, enabling the deployment of enough vehicles on the ground to fully satisfy demand without sacrificing serviceability in other areas of coverage.
Feedback Data Driving Product Roadmap for Public Transport
Technology is always a great addition to any transport service. It aims to add efficiency and convenience. However, where most companies fall short is sticking to pre-conceived technology structured and past experience throughout solution development. This leaves valuable customer feedback overlooked and untapped for an otherwise more customer-relevant tech architecture.
Denmark’s public transport provider, Din Offentlige Transport or DOT, integrated a user feedback functionality into their mobile app experience. The backend system collects close to 30,000 feedback messages generated by the system over several periods of time. This treasury of data supplemented with Natural Language Processing enabled them to cluster massive feedback messages into related themes and issues. This led to the management team to efficiently and swiftly identify bugs, usability issues, and prevalent user behaviours leading to the improvement of the overall app user experience.
Predictive Maintenance for Rail Management
The success of a transportation service hinges on continuous and reliable operations. At the heart of this are the vehicles that do the hard work in hauling the physical weight and will inevitably sustain gradual wear and tear.
Unlike a few decades ago, modern transport companies are now able to monitor and manage the maintenance of their fleet through rich data. For example, rail transport service providers can utilize predictive maintenance to plan and oversee periodic unit upkeep as well as keeping close monitoring of rail track conditions. Rich data enables failure prediction, failure diagnosis, failure type classification, and can even equip mechanics with information through recommended maintenance actions after failure diagnosis and identification. This helps avoid unexpected service disruptions and, most importantly, prolong the serviceability of the trains or any vehicle in the fleet.
Cargo & Logistics
Agile Inland Shipping
The synergy between shipping and inland logistics is critical in the supply chain. Delays and mishaps can cost the logistic companies large sums of money and may even actually have a direct impact on the cost of the goods or products that will be purchased by end-consumers. Given the many factors to consider such as the ship’s time of arrival at the port, availability of trucks for long-haul runs, among others, systems have to be in place to keep track of critical information for timely decision making.
Data can help optimize the dispatching of vehicles to ensure availability and timeliness. Data can allow the prediction of estimated arrival times of shipment and can automate the schedule and dispatch of land vehicles. Enriching data with information on route traffic situations and weather can further improve the efficiency and accuracy of scheduling.
Real-Time Route Optimization
One of the biggest challenges of logistics companies is ensuring that their operations are highly optimized to maximize gains and keep costs at a minimum. Route planning for last-mile delivery is especially challenging as it also takes into consideration drop-off times to make sure all packages are delivered within the allotted schedule.
Equipping the drivers with real-time data on traffic situations and receiver availability allows them the flexibility to re-route as needed. Data made accessible through mobile technology gives them real-time visibility of the best routes to take, which customers would make the most sense to serve first and provide the estimated times of delivery for customer visibility.
Operational Capacity Planning
Allocating resources is an integral part of the business. This means managers have to ensure that there are enough vehicles of the right type and load capacity while keeping tabs on personnel shifts to ensure there is enough manpower to keep operations running smoothly. Traditionally, managers would rely on available historical data or worse – gut feel from past personal experiences. Given the modern era’s highly demanding customers, past practices not rooted in data and technology can only take businesses so far.
Integrated data from functional areas such as personnel, transport dispatch, and distribution networks can provide a holistic view of what a given period of time entails in terms of resources needed. Visibility on incoming shipments, customer delivery schedules, vehicle availability up to personnel shift schedules will allow efficient allocation of resources to match expected workload in near-real time.
These are just some of the amazing executions that some leading transportation & logistics companies have made by maximizing data and pairing it with the right technologies. Most companies are sitting on rich data resources but are often still left wondering what can be done with it to provide business value. The possibilities are indeed endless and it makes complete sense why taking that step towards data analytics can be unnerving. But given these few ideas, we are hoping to spark inspiration to finally drive you into your data journey.
Learn more about technology in the mobility, transport & logistics vertical here
 Global Trade, “Big Data = Big Changes in Logistics, Transportation”, 2016
 Leveraging Big Data to Manage Transport Operations (LeMO), “Case Studies”, 2021
 DHL, “Big Data in Logistics a DHL Perspective”, 2014