21 Nov 2022
Data Science In Shipping And Logistic Industry
Ekeeda Moderator
Works at Ekeeda
Investment of resources and time in data science is one of the critical advances in the last couple of years. While there are many sectors where Data science has spread its wings, there are still many areas that need to be tapped upon.
Data science is known as an interdisciplinary field that makes use of scientific methods, processes, algorithms, and systems to extract knowledge and valuable insights from noisy, structured, or unstructured data forms. Data scientists then apply the knowledge & actionable insights from data across a wide range of application domains.
Everyone is so curious to know about the invention of Data Science and how did it originate? In 2001, William S Cleveland combined computer science with data mining to build a more technical approach to statistical analysis. Thus, as a result of this combination, data science was born. Data Science plays a key role in knowledge extraction and derives valuable insights into any business form and its operations.
Today, businesses emphasize making data-driven decisions to achieve organizational goals and beat the competition. As we move ahead in this digital age, we generate tons & tons of data about where we are, what we do, where we travel, etc. Data science and data analytics have already made a deep impact on varied industries such as Sales & Marketing, E-commerce, Telecomm, Education, etc. and the Maritime time industry is also not left out in this pursuit of data collection, data analytics, and data interpretation.
Data Analysis done in the shipping industry is known as ‘Maritime Data Analytics'. There are several benefits of data analytics in maritime such as tracking vessels, supply chain management, improving human & environmental safety, and increasing efficiency across the sector. Today, in this internet-driven world the demand-supply chain has increased exponentially and thus 90% of the goods & items are transported by sea route.
You order your Versace Bag or Louis Vuitton shoes from your smartphones, but ever wondered how the product reaches intact at your doorstep? Logistics and shipping play a critical role in it. Thus, it’s important to make use of data analytics to increase the safety of your product. The shipping industry uses marine assurance systems and applications to mitigate risks and enhanced productivity. Also, Data science is used to manage ship sensors & build predictive analytics, that will help prevent delays and improve the overall operational efficiency of the shipping industry.
There are several key uses of data science in logistics and shipping industry. All thanks, to this technological upgradation, the shipping industry has grown in manifolds and even stronger over the past few years. Wonder what data scientists do? Data scientists will extract useful information from these big data sets through data mining. This mined data can then be analyzed using a variety of data science techniques, such as machine learning, Data visualization, Deep learning, Natural Processing Language (NLP), and more where historical data is analyzed to find rising trends and patterns. It will then be applied to new data to predict & mitigate future behaviours, outcomes and risks.
When you apply data science in Logistics, shipping, and supply chain industries it will help improve efficiency and competition. The Council of Supply Chain Management Professionals predicts Data science has been significant for the shipping and logistics industry’.
Companies need to take a deeper look and determine where better knowledge and understanding could improve their business. For some, it is to optimize shift times, others would-be tracking consignments or aligning goods based on geographical locations. Every company will have its own set of needs to improve efficiency, deliver superior customer experience and book higher profit margins to beat the competition. Just make sure the starting point should create value and come up with a potential consensus that can change the dynamics of the company’s business goals.
In today’s time, with the help of skilled data scientists, it has become quite easy to apply data science techniques and tools to achieve organizational goals. To bring value, systems need to gather & categorize data effectively and simplify the end process. It is important to access real-time insights to come up with structured, unstructured, or semi-structured forms of data. Some companies build out in-house data science teams to handle their data collection, analysis, and insight as per their needs & requirements. For many, the decision becomes just so integral part of the business to analyze data through data science tools and advanced techniques.
The end goal is to empower companies with critical market observations & insights required to make better and data-driven decisions.
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Sources: Addepto
Where will data science & analysis help the maritime business? Will quick data analysis will lead to an efficient marketing plan or better sales? Is there a competitive advantage? Will there be a better market position? Review these thoughts in mind will help you understand what needs to be done:
Logistics
In logistics, the application of data science will help companies optimize their operation efficiently. It includes things like package-to-delivery routes, ways to manage fuel, and more accurate forecasting of supply & demand. Applying data science techniques to logistics will help companies use quickly delivered insights to adjust as required along the way. Many logistics companies like – Bluedart, FedEx, DHL, eKart, Shadowfax, etc. use data science and analytics to calculate the best routes to gain process efficiency, reduce cost, and time savings.
Supply Chain Management
The supply chain has become a more strategic element in a company’s business. Companies need to start to analyze how to automate the demand forecast, optimize the renewals, lead times, and make inventories more accurately reflect market demand, and improve on-time production & delivery. The aim is to make the supply chain more efficient and easily predictable through data science techniques. Improved insights will lead to more alertness so that adjustments will be made in real-time – and any form of crisis is successfully gauged. Many companies use data analytics and machine learning to predict stock shortages and alert retailers to reorder.
Shipping Management
Till now the industry had a little piece of information regarding shipping, leaving companies in the dark about many elements. Without the analysis, it was quite impossible for shippers to know the impact that shipping costs have on the profit margins. However, by analyzing the shipping process – from carrier negotiation to packages, businesses could optimize their operations and find ways to cut costs without cutting down the speed. Ecommerce companies like Amazon, Myntra, Flipkart or TataCliq use data analytics to identify their shipping was costing a fortune to the company, and allow it to make necessary adjustments. Data Science and analytics will enable it to model future expansion costs.
Manufacturing
When you apply data science techniques to the manufacturing process, companies will get closer to the industry goals. They will deliver the right products and services in the right quantity at the right time. To achieve this can lower the cost of goods & make items less expensive. There are many ways data science will be applied to manufacturing systems such as – monitoring facility processes, modelling maintenance, recognise patterns in downtime, review safety practices, etc. Later, data scientists will build out and improve operations to reflect what they’ve learned. Data science will minimize the risk, lower costs and improve productivity. Many manufacturing companies will use data science to analyze the wear & tear of equipment & identify the potential machinery breakdowns right before the event has occurred.
When your business becomes data-driven it will benefit your organization. Elements could be uncovered & optimize the efficiency, effectiveness, and cost. Data science could be applied across any critical area of your maritime business where better insights & improvements are required. Data science is not once for a time process; your company and the industry are constantly evolving and we will have to meet the rising technological advances. It means your data and analytics systems will need constant updates. When you do it correctly, you are able to attain your business goals and maintain a competitive advantage.
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At present, in the shipping industry, data science is most commonly applied to marine assurance processes and AIS (automatic identification system) data. The shipping industry will immensely benefit from the wider application of marine data analytics providing the humongous amount of data generated every day. The shipping industry contains data ranging from the smallest detail of a vessel to various interactions, sensor dedicated, etc. The shipping industry has to further digitalize, automate and streamline the existing processes to unlock the full potential of maritime data analytics.
With the digitalization of life each day, each minute, and each second, more and more people have realized the true potential of data collection. Thanks to the digital information age, it has never been easier to gather & process data. Businesses, researchers, and governing bodies have started using data mining and data analytics tools to extract potential trends and insights from data. The use of relationships within data sets to produce useful information and reporting techniques. The wider the application of data science across the shipping and logistics industry, the better would be outcomes in terms of customer experience, business goals alignments, and higher profit margins.
• Programming languages – Programming skills can help you extract data, clean data, and even visualize it. Some of the most common use and compatible languages with data science are Python, R, and SQL
• Visualization Tools – Such tools are used to make data digestible. Some data visualization tools include Tableau, Google charts, and data wrapper.
• Database Management Systems (DBMS) – It is used to store and manage vast amounts of data; some examples of DBMS include Clipper, FoxPro, and Microsoft Access.
Data science is one of the most in-demand fields and techniques that has gained pace in the last couple of years. It provides valuable insights into raw data that is important to enhance business operations and boost profit margins. Today, Data is everywhere and it’s estimated that every day, a staggering 2.5 quintillion bytes of data is generated.
With this mind-boggling number, it’s easy to see how much of an impact data can have. With data taking over the world, you might be anxious to learn a bit about data science.
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