Within the ever-changing shipping and freight management market, there is an underlying need to know and understand shipping data trends running throughout the industry. Transportation analytics is rapidly evolving into the next age of discipline for the supply chain. TechTarget further explained that point:
“The discipline of supply chain analytics has existed for over 100 years, but the mathematical models, data infrastructure and applications underpinning these analytics have evolved significantly. Mathematical models have improved with better statistical techniques, predictive modeling and machine learning.
Data infrastructure has changed with cloud infrastructure, complex event processing (CEP) and the Internet of Things. Applications have grown to provide insight across traditional application silos, such as ERP, warehouse management, logistics and enterprise asset management.” From peaks and valleys in sales to highs and lows in income channels, transportation analytics can help managers, 3PLs and partners stay on the same page, focused and unified.
Four types of transportation data analytics are usually implemented in stages throughout the freight supply chain network. Companies must note that no one type of analytical process will be better than the other when used alone. They are interrelated and interconnected, each one offering different insights and data. With data being necessary for every freight logistics and shipping chain, transportation analytics are becoming common among transportation and freight forwarding providers.
Descriptive analytics for transportation logistics
Descriptive transportation analytics focus on describing or summarizing the existing data collected throughout the various channels. It can be defined by using existing business intelligence tools to get a clearer picture of going on or what has happened with shipments, payments, delays, damage reports, customer feedback, and more. It can be combined with any other aspect of data related to transportation logistics. These analytics literally describe the history of what happened within the freight network, such as the ins and outs of carrier sourcing from the past.
Diagnostic analytics and data interpretations
Understanding what happened is a great starting point, but it begs the question, “why?” Diagnostic analytics seeks to answer that question. With a strong focus on past performance, this analytical process commonly gets used to determine the causes leading to the supply chain’s current state. The result of transportation analytics often helps to interpret data into something easier to share and apply throughout the freight network. Accessing and sharing diagnostic data with team members and third parties make it easier to respond to, correct and prevent future issues. This form of analytics is where the meaningful gains of process improvement begin.
Predictive analytics that impacts shipping chains
Feeding on diagnostic data, predictive transportation analytics emphasizes predicting the possible outcomes, a core benefit of their use in volatile freight markets. That is an immeasurable benefit for an industry rife with both risk and volatility. The traditional theory that transportation management marches to a bi-annual beat is on the way out the door. And teams need a better way to how what happened becomes what is on track to happen. Transportation predictive analytics prepare managers and frontline workers with insight into the future. By using statistical models and implementing automation and digital techniques, freight managers can control the end-to-end shipping chain. Such data access allows for more control and adaptability in the long run.
Prescriptive analytics for preventative planning
The final type of transportation analytics commonly used in freight logistics goes by the name of prescriptive analytics. Data collected with earlier methods gets used to recommend one or more courses of action on a point being analyzed. Using such an approach helps managers develop several responses to a single issue arising by predicting the outcomes of a certain choice. These are the active form of analytics, showing what a company needs to do differently to achieve an optimum result. The data then highlights and prescribes the best option.
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Successful freight management depends on using data-driven decision making. And analytics forms an integral part of a successful transportation management strategy. Start getting the full, end-to-end view with the top four types of analytics by subscribing to a freight data engine like SONAR. And you can further simplify logistics by leveraging the analytics values within SONAR, too.
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The 4 Types of Analytics
