How is data analytics changing logistics business?
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 data analytics is critical to making intelligent decisions. 71% of them believe that big data improves quality and performance.
Logistics companies are using data and analytics to optimize their 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. 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.
Real-time data sharing with all partners is essential. The insights that a company gathers are useful both for a logistics company and for its partners. This type of data sharing in logistics can help improve operational efficiency by capturing fluctuating customer demand, external factors, and the operations of the partners. It will enhance transparency and help all stakeholders to streamline their processes, ultimately improving the quality of operational processes, and the overall performance of the logistics business.
Order processing capabilities
Having accurate, efficient data integration into the processes frees up additional space for the entry of new orders. Ultimately, this leads to more orders shipped, which further drives demand for the services within the supply chain. As a result, a business improves, and the amount of data in logistics grows to reveal problems within the existing processes.
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.
Metrics, KPIs and forecasting
These elements allow to monitor performance on an ongoing basis and identify inefficiencies based on real-time information. As soon as data indicates errors in picking rates, delays in picking procedures or that shipping lacks some items; managers have the information they need to intervene immediately. Predictive analytics can also be used to anticipate demand peaks for any product, at any time, at the right place, and the right price. Moreover, having insights on how customer demand will evolve helps plan and anticipate shifts, inventory shortages, and reduces costs.
Development of new business models and projects
New digital platforms will help remove supply chain inefficiencies, solve problems associated with asset underutilization, improve demand-supply matching, and increase visibility and connectivity across systems. The use of the solutions that will enhance operational clarity and connectivity between previously siloed systems enables stakeholders to connect throughout the supply chain.
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.