February 26th, 2019 | Written by



  • In a relatively short time, technology has drastically changed the supply-chain ecosystem.
  • Trucks are an indispensable part of the U.S. economy.
  • Predictive analytics uses historical service data and machine learning to identify and predict outcomes.

Without a crystal ball to predict disasters and variables beyond our control, freight companies need strategies to help them avoid as many service disruptions as possible. These aspirations are actually possible by using data from technologies such as artificial intelligence (AI), machine learning and predictive analytics. Technological advancements such as these can help reduce downtime and improve efficiency, productivity, service-level agreement compliance and customer satisfaction.

When dealing with regions prone to hurricanes, earthquakes and other natural disasters, the safety of those who live and work there is the priority. In addition, to those who work in the supply-chain industry, the significant impact and disruptions caused by weather-related events is high on their list—particularly in regards to trucking.

Trucks are an indispensable part of the U.S. economy. Tractor trailers carry more than 70 percent of the freight tonnage transported throughout the country, which means interruptions (natural disasters, weather related or driver shortages) are more than just an inconvenience. As trucking companies enhance their preparedness plans, supply-chain solutions that embrace new technologies can help mitigate longer-term logistical and financial nightmares.

In a relatively short time, technology has drastically changed the supply-chain ecosystem. The most immediate and noticeable benefit has been the introduction of automation—a human-centric endeavor to manage many manual processes, interactions, touchpoints, handoffs and even the physical assets inherent in the supply chain. Longer-term benefits will be driven by the data created with every process and interaction across the supply chain, however. The future of supply-chain optimization harnesses the power of these technologies and their massive amounts of data and applies it to the real-time decision-making process.

The Data-Driven Supply Chain

When applied to AI and machine learning, data is the driver for predictive capabilities. With it, future performance can be optimized based on past results. With powerful potential to positively impact every aspect of the supply chain, environmental data offers insight into external factors such as historical traffic and weather patterns that can inform crisis plans, or be used to help reduce fuel costs, maximize productivity and meet increasing demands.

The real value is created when this external data is combined with enterprise data—identifying patterns and areas for optimization within each company, to fuel better planning and resource utilization during emergencies, and every other day of the year. Predictive analytics uses historical service data and machine learning to identify and predict outcomes—which becomes increasingly valuable as companies collect more and more information.

Predictive Analytics, Predicting Weather

Each year, storms put an incredible strain on the supply chain as flooding and power outages close ports and prevent trucks from entering affected areas. Predictive models can provide an early look at upcoming weather systems, while historical data can speak to what those models have led to in the past. This helps companies to make data-informed decisions like whether their trucks should hit the road or not.

While predicting the path and impact of hurricanes is not a perfect science, leveraging analysis from previous storms arms companies with important information such as which roads to approach and avoid, where utilities are historically weakest, and the most efficient (and safest) path to the destination.

For the freight sector, predictive analytics are highly useful for managing capacity problems by enabling a more accurate assessment of contributing factors to future performance, such as weather, job type, driver availability and day of the week. This information powers schedule optimization, the tracking of shipments, routing and job prioritization.

The end result is greater efficiency which can even have a positive impact on the massive shortage of truck drivers in the U.S., a problem that is escalated by hurricane season as many drivers are left to sit on the sidelines while fleet from elsewhere are redirected to crisis regions, taking them off their regular routes. The impact can also be felt by industries reliant on freight and on time-sensitive logistics, particularly the retail industry which shares peak pre-holiday shipping and preparation season with the most prevalent time of year for hurricanes.

As supply chain operators continue to embrace advanced technologies, organizations are poised to be in prime position to take more control over their shipping process—regardless of external factors at play. Hurricanes are an unavoidable catastrophe, but data used in the right way can help mitigate the duration and severity of any disruption.

Pervinder Johar is CEO of Blume Global, a leader in global logistics and digital supply chain solutions, which is headquartered in Pleasanton, California, and has offices in Chicago, Hong Kong and Wellesley, Massachusetts.