Big Data Applied to Logistics
Software that performs Big Data analysis of company data and, where possible, the supply chain
Big Data is an expression created to designate the enormous amount of data that the world generates every second. There are millions of terabytes that invade the Internet in a mere blink of an eye. By the way, we are already in times of yottabytes (the equivalent of 1024 zettabytes, but more on that later). Analyzing so much stuff like that requires considerable effort in Information Technology. Today this is possible and is being carried out in Logistics 4.0.
The software that performs Big Data analysis of the company’s data and, when possible, the supply chain to which it is linked, represents a considerable investment in financial terms. However, they make life much easier for managers, saving time and reducing expenses.
We’re talking about software capable of analyzing structured data (duly organized and ready for interpretation) and unstructured data (dispersed records from different sources, which need to go through a classification process). This software makes managing the supply chain much more rational.
Big Data is used in Logistics to control inventories, adjust the distribution flow and transport routes, avoid possible fraud both in product inventory and in the movement of goods, carry out preventive maintenance of systems and machines and also to facilitate personalized service to customers, among other things.
The optimization of the tasks mentioned in the previous paragraph is only possible thanks to data mining in the companies’ traditional operations systems, activities of transport fleets, meteorological and traffic information, economic forecasts, online behavior of consumers and warning of shortages of sales points.
Now see some advantages of applying Big Data to your company’s logistics:
- Increased operational efficiency, thanks to the dynamic analysis of different data;
- Greater predictability of seasonal demands;
- Increased supply chain information when partners understand the importance of data integration;
- Modeling of distribution networks, creating more economical alternatives;
- Improved Last Mile management, developing more efficient routes and maximizing fleet performance;
- Cost reduction, cutting waste and finding more appropriate solutions for each identified problem;
- And, finally, increased customer satisfaction, which has a more pleasant shopping experience.
Sounds simple. But it is not. The tools that analyze these floods of terabytes are based on five fundamental principles of Big Data , which define its structural nature :
- VOLUME : that is, the abundance of data generated by companies during their daily operations;
- VARIETY : such data come from the most different sources and sometimes need different analyzes based on their origin, so the chosen digital tool must be able to carry out this interpretation;
- SPEED : Imagine several avalanches coming from several mountains simultaneously, towards you. Well then, the chosen software must be able to handle all this volume of data from different sources in the shortest possible time in the specifics of your business;
- VERACITY : here we are talking about the quality of the data obtained, as well as its reliability and precision;
- VALUE : after due analysis, it is necessary to understand the usefulness of such information and the positive results it can generate. This necessarily depends on human evaluation.
After assimilating these five principles, you need to take some extra care when buying a tool of this size for your company. Go after software that, among other things, complies with industry regulations (in particular the General Data Protection Law), that allows you to set up action plans in case of cyberattacks, that defines who will have access to the data, that uses the Machine Learning to analyze several possibilities simultaneously, and that, finally, identify problems, creating an alert whenever they are identified.
The advice above is important. However, before purchasing a Big Data software , we must be humble enough to do some prior planning, in order not to make a mistake in choosing the product:
- Create an action plan with deadlines for the execution of each stage, the elaboration of contingencies and the definition of the responsible personnel. Also determine what is the expectation of the program functioning at the end of the implementation. Thus, it is possible to trace who the main users will be, when the updates will be necessary and even what the different levels of access will be;
- Simultaneously, think about developing a data analysis or data driven culture . Simply put, your team must interact with this new tool;
- Also try to train your employees and listen to their opinion. Investing in technology is not enough. It is also necessary to think about Human Resources;
- After this first review, your planning team will likely suggest a product that fits your operational size. Not always the most robust software is the best for your company. The team you assembled must estimate the volume of data to be used and then look for the most appropriate digital version for your reality;
- To make the choice easier, you can research what big data tools other companies in the industry are using. In Logistics, the practice of benchmarking is very common;
- With this data in hand, set a budget to purchase and deploy the product;
- Choose a company as agile in post-sales as Big Data promises to be in data analysis (or nearly so).
READING BONUS: CURIOSITIES!
The first report on the processing of an abundance of data dates back to 1663. In that year, John Graunt used a considerable volume of information, from different sources, to study the bubonic plague epidemic in Europe, the first recorded pandemic in history. After that, the first report on the use of equipment to tabulate data occurred in 1890, in the United States, due to the North American demographic census.
The first digital machine designed to process an abundance of data was built in England in 1943. The computer, called the Colossus, could intercept 5000 characters per minute and was vital in the Allied effort to defeat the Nazis during World War II. Germany had an encrypted system that was considered unbreakable, until then.
In 1989, British scientist Tim Berners-Lee created the World Wide Web, aiming to facilitate the exchange of information between people. What Berners-Lee didn’t know was that his invention would end up revolutionizing how data was generated and the amount of it.
The term Big Data was used for the first time only in 1997, but the expression only started to be applied in fact from 2005, when Roger Magoulas, from O’Reilly Media, published an article talking about the subject.
And so much information has never been produced as it is today. Therefore, it was necessary to create expressions that indicated a virtual measure of data. Out of curiosity, follow the table below, taken from the website infowester.com :
- “ 1 byte = 8 bits
- 1 Kilobyte (KB) = 1024 bytes
- 1 Megabyte (MB) = 1024 kilobytes
- 1 Gigabyte (GB) = 1024 megabytes
- 1 Terabyte (TB) = 1024 gigabytes
- 1 Petabyte (PB) = 1024 terabytes
- 1 Exabyte (EB) = 1024 petabytes
- 1 Zettabyte (ZB) = 1024 exabytes
- 1 Yottabyte (YB) = 1024 zettabytes ”
To get an idea of such dimensions, ONE exabyte equals ONE BILLION gigabytes !
We’ll stop here! A hug and see you next!