Logistics work processes generate massive amounts of data throughout the supply chain. Big data analytics, or the targeted processing and use of data, provides logistics service providers with enormous potential for maintaining operational standards and increasing efficiency. We show how to use data in logistics to promote consistent and customer-focused process optimization.
What is Big Data?
Big data refers to data sets that are larger or have more properties than traditional databases can handle. Big Data is distinguished by one or more characteristics: large data volumes, high velocity, or a wide variety of data. Data complexity is increasing as a result of artificial intelligence, social media, and the Internet of Things.
Data, like rough diamonds, is initially just a raw material with inherent value, but without systematic processing, it is worthless, just as a rough diamond is unsuitable for a diadem. The data information must be combined with experience and expertise to generate exploitable knowledge through big data analytics.
It is possible to identify important historical trends, identify changes in the status quo in real time, and produce projections for the future by analysing large amounts of data. Recognizing structured data from unstructured data is essential.
Structured data comprises specific figures from traditional operating systems that are saved in a searchable and predefined format. They provide immediate information on internal company key figures, are well-organized, and can be used directly.
Unstructured data is derived from disparate and dispersed data sources, such as vehicle motion data, other traffic data, etc. Unstructured data must be cleaned and prepared for analysis.
Why and how does big data help the logistics industry?
Every process in logistics generates empirical values that help to access data. Every movement, every transported item, and every system contain data that can be collected and stored for analysis.
Logistics analysts utilize the insights gained from the data to generate forecasts and automate processes, ultimately enhancing operational efficiency.
As a result, big data analyses improve supply chain transparency and decision-making. This enables strategic optimization of individual supply chain steps and significantly improves service levels. The spectrum here ranges from more efficient pallet storage in a facility to improved customer service.
Giving logistics participants a filtered, well-organized, and comprehensible real-time picture of the state of affairs in transit or logistics centres is a major potential presented by big data analytics. Sensor data processing yields accurate location data and status information on assets, including vehicles: when should the next maintenance be done, if the vehicle is in working order, or if it is just broken.
In general, big data analytics can provide logistics companies with the transparency they need to improve shipment storage and transportation, as well as the utility and lifespan of equipment and facilities.
Use Cases for Big Data Logistics
Big data analytics’ overarching goal is to protect operational standards while increasing efficiency. Transparent operational data and its specific analysis lower costs, preserve value, improve services, and avoid waste and risks.
Stock and Storage Management
Keeping track of and monitoring all inventory data entails knowing where to find the necessary supplies in the warehouse at any given time. Inventory data from systems and sensors can be analyzed to determine if stocks are running low or if pallet racks are empty. Forecasting inventory can help predict the expected pattern of incoming orders and deliveries during the upcoming peak and off-peak seasons.
Optimization of Routes
It’s not just about finding the shortest path from point A to point B. Vehicle information, product data, GPS data, weather data, and even shift schedules are all used to calculate the best possible route for a specific vehicle carrying a specific load.
The best route is not always the quickest. Security concerns are also significant.
For shock-sensitive goods, logistics planners choose routes that avoid cobblestones and employ shock sensors to monitor the safety of the cargo during transportation. Furthermore, data on fuel consumption and driving behavior, as well as environmental data collected via vehicle sensors, can help to calculate the eco-balance of deliveries and improve routing in terms of environmental impact.
Capacity Planning
Vehicles, storage space, and personnel are all important factors in determining the achievable handling capacity. To identify the most efficient logistics process steps, big data analytics compares the utilization and availability of various capacity factors for each step. Additionally, this facilitates capacity forecasting. Big data helps in matching machine and vehicle capacities with staff capacities or available vehicles during peak demand periods.Undercapacity lowers service performance and, consequently, customer satisfaction, while overcapacity incurs costs. Preventing over- and under-capabilities is possible with the use of big data.
Conclusion
Data is far too valuable to waste. It serves as the foundation for future automated logistics process control within a transparent value chain. In a highly competitive market, successful players must optimize processes to ensure their long-term viability. As a result, improved analytical processes are becoming a driver of business success, particularly in light of increased cross-company data exchange in the supply chain. It’s not just in logistics.
