Why to Have Predictive Analytics in Your Transportation Management

1. Why to Have Predictive Analytics in Your Transportation Management In the transportation sector, numerous technologies are available to the professionals in the logistics and supply chain to deal with huge data that they receive every day. Much of such data is of the past and couldn’t help professionals to avoid service failure or the issue that has already taken place. As a result, freight forwarding companies have unhappy clients and have to pay fines. Predictive analysis, amid so many technologies, is the one, which uses machine learning (ML) and artificial intelligence (AI), allows shipping firms to know what may happen in the future and what can be done to avoid the same. Here are three advantages that can help you know why Predictive Analytics is important for your business: No Service Failure Service failures can lead you to shut down your product production when the product is on the way to the manufacturing facility with a JIT (just-in-time) environment. This shutdown can cause a huge loss. Further, late loads with service failures on the way to a client’s end or a distribution centre may make your customers to pay substantial fines.

2. The implementation of predictive analytics into the process of your transportation management can assist you to forecast the failure of services before they take place. Predictive analytics receive your operational data – weather, historical transportation times, and the patterns in service failures – to give you real time information on the status of late shipments. With this, your supply chain professionals will have an idea on how much delay will be in a shipment and estimate the time for delivery. This technology sends alerts to you earlier so that you can look for a better alternate to minimize the impact and prevent failures. Insights on market trends In the logistics industry, shipping charges change from time-to-time. As a result, businesses face challenges to avail transportation services at a competitive and fair price. This technology goes beyond the analysis of general market trends and historical pricing and unlocks real-time trends and current price ranges. With this, companies find having a balance between service quality level and reduction in the total costs of supply chain more comfortable. Flexibility in volume and labour planning The labourer market is a continuous pain point for freight forwarding companies. They face a scarcity of labourers when the demand is higher. Distribution centres and warehouses need to be able to predict the swings in their customers’ product volume and workforce demands. Most of logistics firms are stuck in reacting to high product demands without knowing the required workforce capacity. With predictive analytics, accurate prediction of demand volume becomes more comfortable by using real-time internal as well as external data signals. Consequently, freight forwarders easily optimize their customer requirements and workforce levels. Conclusion Predictive analytics, which utilizes ML and AI, is a beneficial tool to streamline the management of transportation process in product storage and transit. Its implementation helps businesses to prevent service failures, have flexible planning for product volume & workforce and avail insights on market trends.