How predictive analytics are reshaping transportation management

Feb 19, 2021
Innovation

Success in business tends to require a good understanding of numbers and this has only become more important in the age of big data. New technologies have made it possible for us to collect vast amounts of information but the real benefit comes from what it’s used for. Predictive analytics is described as “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data” where the end goal is not only to understand what has happened but to provide a best assessment of what is going to happen in the future. It does this by leveraging science and analytical trends to create algorithms and formulas and then feeds these a combination of market insights and economic data with trends found in other relevant data sources to arrive at a forecasted output useful for decision making and planning.

Why the use of predictive analytics is growing

Organizations are using predictive analytics more and more to help overcome difficult challenges and make new opportunities possible. Common uses include detecting fraud, optimizing marketing campaigns, reducing risk and improving operations. By identifying patterns and preparing for the likelihood of events to come, companies can run their day-to-day operations much more smoothly. Predictive analytics are becoming more frequently used across all business functions, in different ways, to help anticipate events, avoid risks and create new solutions. Given each of these benefits can have significant impacts on bottom lines, it’s not surprising that the Global Predictive Analytics Market size was valued at 6 billion euros in 2019 and is projected to reach 29 billion euros by 2027.

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For manufacturing and automotive sectors, predictive analytics are used to identify factors leading to reduced quality and production failures, as well as to optimize parts, service resources and distribution. In the energy industry, they help to predict equipment failures and future resource needs, mitigating safety and reliability risks, or improving overall performance. Retailers everywhere are using predictive analytics for merchandise planning and price optimization, to analyze the effectiveness of promotional events and to determine which offers are most appropriate for consumers.

Predictive analytics in the supply chain

When it comes to the supply chain sector, predictive analytics benefit organizations in both day-to-day execution of operations and in building tactical and strategic programs to achieve growth and revenue targets. By forecasting future supply chain and logistical events, organizations can gain a competitive advantage and reduce expenses associated with stocking inaccuracies and poor management of goods, deliveries and time.

In transportation, predictive analytics aid in decision making and bring automation to supply chain processes. Supply chain’s are dynamic, constantly evolving in response to market demand, competitive pressures and supply constraints. Sometimes growth opportunities are missed or costs increased in response to unforeseen challenges or disruptions. The ability to predict these could help many organizations save money and be more efficient and productive by being able to ‘skate to where the puck is going to be rather than to where it is’.

Observing historical patterns and applying instincts are no longer enough when competing in our increasingly digitalized world. The ability to forecast accurately is what separates organizations. For this reason, the use of accurate and timely data and analytics is now deeply incorporated into the planning and execution phases of any supply chain. And as supply chains become increasingly filled with connected sensors and IoT technologies, predictive analytics are becoming more powerful than ever before.

Moving from reactive to predictive supply chain transportation

An end-to-end transportation visibility platform’s primary role is to predict an ETA (Estimated Time of Arrival). Obtaining an ETA for a shipment in transit has a wide range of benefits. Sharing ETAs provide customers with visibility of their shipment and helps shippers to manage expectations, helping to ensure deliveries are on-time, avoiding production line halts or stocks outs for example.

Giving advance notice of delays allows for reactive measures to be taken, mitigating negative customer impacts, in turn producing higher levels of customer satisfaction. These benefits summarize the way in which visibility of ETAs unlocks greater value from a supply chain, shifting its capabilities from ‘proactive’ to ‘predictive’.

The benefits of predictive analytics for transportation management

Predictive ETAs can reduce administration costs for organizations by automating processes and allowing teams to focus on exceptions. This allows shippers to optimize resources required to do things like sending delivery notifications, calling carriers to follow up on the whereabouts of deliveries, scheduling of docks and processing of payments through workflows.

In turn, this can increase productivity of customer service teams, as well as operations teams at warehouses and distribution centers. It allows these teams to make changes to dock scheduling on the fly, optimizing the labour at cross-docks by receiving proactive ETAs.

Some of the main benefits predictive analytics bring include:

  • Avoiding production line halts and stock outs
  • Proactively managing potential disruptions in advance to mitigate negative impacts
  • Reducing administration costs and free up resources to focus on value-adding tasks by automating processes and notifications
  • Improving agility of operations teams to better utilize resources and adapt schedules on the fly to maximize capacity and overall cost efficiency
  • Enabling carriers to optimize their operations to reduce overall transport costs

Harnessing this technology demands strong expertise. At Shippeo, an in-house team of data scientists and data engineers have implemented a proprietary algorithm that allows for market-leading accuracy and reliability of ETAs. The team has worked on the architecture of their machine learning model over the past 2 years, using cutting edge techniques and over 200 data parameters. This development takes a vast amount of manpower and time.


Discover more about the business value of accurate and reliable predictive ETAs in our white paper or get in touch with our expert team to learn more about how to take advantage of predictive analytics capabilities for transportation within your supply chain.

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