Opinion: Using Predictive Analytics for More Efficient Supply Chains

Logistics companies depend greatly on accuracy, timeliness and efficiency to meet distribution and customer demands. The use of predictive analytics, however, is changing the game for this industry because of its ability to determine real-time patterns, track data and anticipate these demands to streamline operations.

Predictive analytics can be used to improve services for small- and large-scale supply chain operations. Companies such as Amazon.com, Apple, and Whole Foods already are reaping the benefits with highly efficient supply chains, especially when it comes to agility, collaboration and overall execution.

According to a study conducted in 2017 by the Hackett Group, 66% of supply chain leaders say advanced analytics is critical for their logistics operations in the next two to three years.

But what exactly is predictive analytics? And how can it positively affect the logistics industry?


Inbal Axelrod

Theory of Probability

When it comes to supply chains, understanding what occurred in the past is not nearly as important as knowing what should happen in the future.

As its name states, predictive analytics is an advanced technique that uses algorithms to predict future outcomes based on data that a company already has — historical data. Technically, it doesn’t actually predict anything; rather, it applies the theory of probability to derive what may happen in the future based on past experiences. Ultimately, it’s used to anticipate future behavior by tracking historical data to establish patterns and make predictions accordingly.

Businesses use predictive analytics for many reasons, such as planning, inventory management and sales forecasting. By using it, professionals working in the logistics field could create solutions that, in time, improve reliability, customer satisfaction and reduce costs. They can do so connecting the dots between trends and patterns in data that could help their companies proactively respond to future developments.

For example, managers can leverage predictive analytics when looking to find the shortest and fastest route that takes into consideration conditions such as weather, traffic, mileage, dwell time and so on. This complex task can be made simple with the use of predictive analytics so that logistics managers can rest assured that drivers will be able to reach their planned destinations at the right time.

In addition, with the driver shortage, companies are looking to retain their drivers, and predictive analytics can greatly help. Through the use of embedded sensors on vehicles, logistics managers can receive real-time updates on possible driver situations and behaviors that indicate a driver is getting ready to leave, and make subtle changes that accommodate and satisfy drivers accordingly.

Further, companies can use the data to prevent unnecessary expenses and errors in their supply chain processes.

Supply and Demand

Supply and demand are at the core of every supply chain. With today’s tough business landscape, distributors and retailers must ensure that their supply-demand cycle is balanced. Moving toward predictive analytics and away from cost strategies is likely to be the answer.

Predictive analytics opens the door for greater supply chain cost controls. By using it, companies are always in the know about precisely what is needed, and if it’s feasible in their current situation.

For example, mileage, tire pressure, temperature, unsafe driving and fuel consumption. These are but a few of the types of data that predictive analytics can account for. Through the use of smart sensors, data can be aggregated, accessed by logistics managers and used to make smarter decisions.

In the case of vehicle wear and tear, sensors can detect when a vehicle needs to be serviced and so the logistics manager is able to better prepare by having the right parts ready on time or by having the vehicle booked in for service to avoid delays.

As predictive analytics continues to grow, so will its capabilities to drive strategic planning. Logistics companies are able to reach greater efficiencies and prevent financial losses.

From delivery management to managing warehousing costs, everything in the supply chain can be forecast through predictive analytics to offer logistics providers with solutions to help them stay on top of customer demands, trends — and the competition.

Inbal Axelrod is the co-founder and chief marketing officer at MyRouteOnline, a multiple stop route optimizer that helps make businesses more efficient.