Is Logistics Data Reliable?
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Data and analytics are transforming many industries and businesses, and logistics is not an exception. The complex and dynamic nature of this sector, as well as the intricate structure of the supply chain, make logistics a perfect use case for data. Valuable insights obtained through data leveraging enable the industry players to optimize routing, to streamline factory functions, and to give transparency to the entire supply chain, for the benefit of both logistics and shipping companies alike.
Although the data that needs to be processed and managed becomes highly complex, it’s worth the effort to adopt the data culture as advanced data analytics helps consolidate an industry that has been traditionally fragmented.
The arrival and spread of big data usage dramatically changed the way businesses use to work with their analytics. Companies can now anticipate slow and busy periods, potential future supply shortage, and act accordingly.
According to the research, as much as 93% of shippers and 98% of third-party logistics companies believe that logistics data analytics is critical to making intelligent decisions. 71% of them believe that big data improves quality and performance.
Big data is revolutionizing many fields of business, and logistics data analytics is one of them. The complex and dynamic nature of logistics, along with the reliance on many moving parts that can create bottlenecks at any point in the supply chain, make logistics a perfect use case for big data.
For example, Logistics big data can be used to optimize routing, to streamline factory functions, and to give transparency to the entire supply chain, for the benefit of both logistics and shipping companies alike. Third party logistics companies and shipping companies both agree
Logistics data is a very important source of information used for evaluating all major Key Performance Indicators (KPIs). It may also play significant role in assessing the commercial aspects of the logistics. A significant competitive advantage can be gained by maintaining a consistent data set as it will show, over time, targeted areas for cost reduction as well as identify savings opportunities.
Impact of Big Data on Logistics and Supply Chain Management Operations
For logistics companies, capitalizing data is a crucial strategy for implementing an effective data-driven business model. And there are three major areas where the implementation of big data in logistics can be the most impactful. These are:
- Enhanced operational efficiency by using maximum resources, improving transparency, and making process quality and performance better
- Improved customer experience to maintain customer loyalty and retain them
- Increased revenue by making the effective data-driven business model
Logistics data should be used for analyzing logistics costs, mode benchmarking, landed cost analyses and simple breakdown of mode, carrier, etc. by region. The examination of this data first requires a look at historical data. The ability to obtain the data from a client is critical in order to determine current supply chain situation as well as future needs and savings opportunities.
Logistics data and analytics to optimize the operations for the following purposes:
Solving inefficiencies and ensuring operational standards are respected by tracking any metric along the supply chain. Performance managers transform data insights into actionable results, such as the optimization of resource consumption or delivery routes. For instance, shippers expect the drivers to arrive on time, maintain docking schedules, and avoid wasted time. Logistics Data can help us to understand and monitor the performance of the workforce.
It can also highlight when and which machines are performing poorly, allowing managers to intervene quickly. When the data is shared among partners, it can be used to improve the efficiency and transparency of the whole supply chain or network of partners.
Order Processing Capabilities
Using data in logistics to improve your processes does not necessarily require existing orders and information from KPIs and metrics. Having accurate, efficient data integration into your processes frees up additional space for the entry of new orders. Ultimately, this leads to more orders shipped, which further drives demand for your services within the supply chain. As a result, your business improves, and the amount of data in logistics grows to reveal problems within your existing processes.
Since modern commerce demands exceptional scrutiny and visibility, data in logistics can be used to pass information along to customers. This includes online shipment status options and notifications for customers when shipments become distressed. Furthermore, the same data can be used to identify other routes and solutions to avoid causing an additional distressed shipment.
If your existing shipping processes involve international trade, increased visibility may also be a concern for avoiding penalties, fees, and delays for violations of compliance regulations. Having this data in logistics readily available can help clear up any potential problems at the point of entry or port, which helps promote timely and less expensive shipments across international borders.
Generating Accurate Forecasts
Using data in logistics to generate an accurate forecast of shipping processes sounds complex. However, it’s only the application of historical data about given time frames to determine your needs for a given event. For example, holiday forecasts in shipping are often used to add more workers to loading docks, shipping centers, and warehouses. Having more employees in place will alleviate the congestion from high-volume times, which is the overall goal of data applications.
Thanks to real-time GPS data, weather data, road maintenance data, and fleet, and personnel schedules integrated into a system looking at historic trends, the most optimized routes and time are selected for delivery. Improvement of visibility and order-to-cycle processes: logistics sector is using data to boost the speed of delivery and provide customers with real-time visibility over orders.
Consumers expect shipments to be fast and easily trackable. Analyzing data to improve these operational aspects continuously leads to better quality partnerships and increased customer loyalty.
Digitalization of crucial operations
Advanced analytics is applied to optimize operations in pricing, routing, and partial load shipment consolidation. As for the customer experience, the digital front end provides customers with a problem-free experience and improves internal operational visibility, automating previously manual processes. Automation of core internal business processes will surely simplify labor-intensive logistics operations.
Towards a data-driven logistics
Like many other industries, the logistics industry is going through several changes and adapting to the new digital environment. The leverage of data through innovative technologies that allow companies to have better and new data, and use it in more robust applications, is speeding up the path to a more efficient and sustainable supply chain.
Blog 61 - Is Logistics Data Reliable?
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