How Big Data And Logistics are Working Together

Logistics companies have initiated many project prototypes for the exploitation of big data analysis and several amazing projects that will soon be part of our everyday lives. This includes using Spark on Hadoop for real-time analysis to assess large data volumes stored on registers’ logs, database, excel, or HDFS that has completely changed the business dynamics. Here are some of the big data projects related to the logistics sector:

Volume Analysis

Logistic companies seeking to optimize budgets and resource allocation have always grappled with the problem of predicting parcel volume on a given day of the year, month, or week. Logistic companies are currently investing in the area to determine patterns that help to predict peak volumes. It is an ideal use case since data scientists are able to generate recommendations by running batch analysis.

Parcel Health Data

It is important for the transportation of medicines and other commodities in general to be done in a controlled environment. For instance, some medications should be stored between 2 and 8 degrees Celsius. Some type of equipment are fragile and require extra care while handling. It is quite costly for logistics companies and even the end-consumer to manage the whole process. This is why companies are working to find alternative routes that guarantee safe delivery within the set parameters.

Data scientists and researchers are deploying IoT sensors on parcels for monitoring shock level, temperature, and other factors. Analyzing the data in offline mode is used for defining the most economic and safest passage for sensitive/fragile commodities.

Routine Economy

Should we be leveraging the existing infrastructure or should our company organize our own plane for transporting parcels? Which is the provider with better transparency, costs, routing paths, and facilities? After all, you don’t want to send a larger, heavy trucks where smaller more efficient ones would go as it will cost more money. Data scientists are working on some prototypes that can answer such types of questions. They simply use big data analysis for studying massive volumes of parcel date for predicting the routes that are most cost effective, reliable, and viable for growth in the future. For management, the outcome is accurate data, which is necessary for making decisions about things such as the airlines to use or the warehouse services that are the perfect match for their needs.

Transparency for Management

High on the list of wants for product owners are the current situation risk analysis and resolutions. It could be a major union strike or political unrest in a given region or simply a case of vehicle breakdowns but management always wants a clear view of the problem and possible solutions.

In some logistics firms, one of the strategic projects currently in the prototype stage is enablement of management to search for problems and system-generated suggestions on strategy options or alternative routes. The challenge lies in dealing with the variety and massive volumes of unstructured data, which comes from everything from IoT sensors mounted on transportation vehicles to social media for risk assessment and defining resolutions.

For this type of data processing that allows management to anticipate and handle issues before they arise, Apache Spark on Hadoop is the perfect choice. The ability of Spark to analyze different streams of data is the best way to predict events and provide alternatives

Transparency for the End Client

Parcel delivery time is something that customers are very conscious about. Predicting the exact time when a parcel will arrive is a nightmare for logistics companies. The timing will depend of several variables such as driver health, stability of vehicle, traffic status, and number of packages. Packages are delivered in the millions every day. It is only by analyzing volumes of data such as driver efficiency info, vehicle health, and current traffic conditions that an approximate time-window can be provided for when a package will be delivered.

The pilot project currently in the prototype stage requires real-time analysis of data. Tools for real-time data analysis allow logistics companies to provide accurate information about the status of packages with total transparency to their clients.

Campaigns & Web Analytics

One area where logistics companies are using big data is in web traffic analysis. For instance, a German-based logistics company is currently using big data to provide web visitors with more personalized services. Different services and campaigns can be automatically offered based on the interests of the visitor.