Until not so long ago, the logistics industry was dependent on outdated manual processes with inflexible equipment and machinery, which meant that productivity, profit opportunities and customer satisfaction fell by the wayside. But that is now changing. Advances in digital technologies, ever-changing customer preferences and the success story of e-commerce make logistics a perfect case study for Data Science. Combining analytics, relevant statistics, artificial intelligence (AI) and machine learning (ML) to explore trends and identify patterns will give a huge boost to revolutionising LSP businesses.
A study by the Council of Supply Chain Management Professionals shows that 93% of shippers and 98% of 3PL companies believe that data analytics is critical to making smart decisions. Nearly 81% of shippers and 86% of 3PL companies surveyed said that effective use of Big Data and Data Science will become a “core competency of their supply chain organisations”. Furthermore, 71% of them believe that Big Data will improve quality and performance.
All these points only underline the importance of data in logistics. Why don’t we delve a little deeper into how it can be helpful?
Scope of Data Science in Logistics
Increasing operational efficiency: ensuring operational standards and eliminating operational inefficiencies are two important goals. Data is a means by which you can track changes in operations. With operational data and data science skills in hand, you can track and measure KPIs such as cost, value, services and waste at regular intervals to prevent disasters and take corrective action. This will increase efficiency and provide transparency to take these actions.
Improving forecasting: With current forecasting methods such as single or multiple regression, time series analysis, etc., where the mean absolute error is usually over 20%, obtaining more reliable results from forecasting models requires a larger number of variables and analogies to deal with. Data science can contribute to better forecasting by capturing data in real time and analysing data from multiple sources more quickly and accurately.
Route Optimisation: Route optimisation is the process of finding the shortest possible route to a specific location. It helps avoid problems such as the vehicle routing problem (VRP), which involves finding the optimal route for a vehicle to deliver the item to the customer. The route optimisation algorithm takes into account data such as the quantity of goods ordered, the geographical distance between the pick-up and delivery locations, the frequency of the order, etc. Data science can be used to find the closest vehicle and the information can be shared without delay. Trends can also be identified based on the number of orders, climate, average speed on the route, fuel consumption and time. Big Data also helps in determining travel behaviour accurately and more comprehensively. The collection of environmental data through the sensors attached to the vehicles helps in identifying pollution, noise levels, traffic details, etc. According to the data, route optimisation has the potential to reduce CO2 emissions by 5% to 25%, increase mileage by 5% to 15%, reduce labour costs and reduce planning and management time by 25% to 75%.
Customer satisfaction: In fact, studies by Bain & Company and Earl Sasser of Harvard Business School have shown that as little as a 5 per cent increase in customer retention can lead to an increase in profits of between 25 and 95 per cent. Information about customers’ preferences, likes and dislikes is essential for customer retention, which is often fragmented and riddled with unwanted data. The application of data science here can increase customer loyalty, provide clear customer segmentation and optimise customer service. It will also trigger the evolution of CRM techniques. Big Data will provide a comprehensive overview of customer requirements and service quality, which can be used to improve product quality.
Risk Assessment: There is a need to track and predict events and processes that may lead to supply chain disruptions. Data Science helps build a resilient transport model by using data and intelligently predicting disruptions and then alerting the appropriate stakeholders.
End-to-end visibility: Data Science combined with analytics, information from sensors, real-time monitoring and 5G technology will make it easier to ensure end-to-end visibility across the supply chain.
Conclusion
There is no doubt that Data Science and Data Analytics will play a bigger role in the logistics industry. From tracking compliance to reducing supply chain bottlenecks to streamlining the supply chain and reducing errors, data will play a big role in the future. Analytics will optimise operations, routes and customer satisfaction by providing hassle-free solutions and improving visibility. Logistics is indeed on the cusp of Big Data transformation. As a popular buzzword says these days, data is the electricity of the 21st century. All you need to do is choose a technology partner that can help you reap the benefits by providing the means to harness the power of data.
