How to Use Predictive and Prescriptive Analytics

Modern logistics demand modern analytics. Prescriptive and predictive analytics help companies see the proverbial iceberg coming and learn how best to avoid it.

What’s in this article:

• What are Predictive and Prescriptive Analytics?

• What Do Predictive and Prescriptive Analytics Do Best in Logistics?

• How to Implement High-Level Analytics in Logistics

Predictive and prescriptive analytics can help companies stay ahead of the curve and solve challenges that are holding them back in today’s complex supply chain world.

What are Predictive and Prescriptive Analytics?

Predictive analytics focuses on forecasting likely outcomes for the future. Using historical data from inside the company and sources beyond the company, this type of analysis can help determine what will likely happen under certain circumstances.

Prescriptive analytics is used to find the best solution to a problem, helping companies overcome obstacles that have long stood in the way of higher earnings, growth, and success. They use historical data to determine which strategies and decisions are most likely to move the company closer to their goals.

Predictive and prescriptive analytics are not only used in the supply chain. They’re used across almost every industry, allowing them to harness the power of anticipating what’s next and knowing what to do when it arrives.

Most Companies Focus on the ‘What Happened?’ Instead of the ‘How to Do Better in the Future’

Most companies get stuck on the ‘What happened and why did it happen?’ level of analytics. Referred to as descriptive and diagnostic analytics, these types of analytics are all that many companies have room for. They simply don’t have the tools needed to collect and share better data or analyze it, and they don’t have the resources to pull in useful data from outside of the organization.

What Do Predictive and Prescriptive Analytics Do Best in Logistics?

There are specific areas of a logistics operation where these types of analytics can make a big impact and provide the information needed to improve decision-making velocity and performance in the long run.

Pricing Loads

Predicting exactly the right price for a load in the future is essentially the holy grail in logistics. The market is affected by so many different factors and changing so rapidly, that it’s beyond human computing power to do so effectively.

Even large companies with access to mass amounts of market data can’t predict load prices perfectly.

Even large companies with access to mass amounts of market data can’t predict load prices perfectly. However, by using as many variables as possible and gaining an understanding of the drivers of transportation costs, plus high-tech solutions like AI, 3PLs can provide more consistent and accurate pricing data as their systems collect more data.

Finding the Right Carrier for the Job

In an incredibly segmented carrier market, it can be difficult to find the right carrier for the job. Many shippers result to using the first reasonable quote they get.

High-level analytics can help companies go beyond finding a truck.

High-level analytics can help companies go beyond finding a truck. They can help explore and evaluate the risks associated with using each carrier at various time intervals and price points.

Improving Execution

A business that doesn’t provide its customers with high levels of service won’t stay in business for long. Predictive and prescriptive analytics can help gain visibility to any gaps in service before they happen and plan to improve execution.

You’ve likely heard some variation of the saying, “Everybody wants good, cheap, and fast service, but you only get to pick two.” By keying in on what’s most important for a company, analytics can help them see the pit before they fall into it and make a plan to steer clear of it by aligning the right resources at the right time.

Creating Better End-to-End Experiences

At the end of the day, improving performance almost always involves improving both shipper and carrier experiences, helping to build better relationships on both sides.

Plus, when companies spend less time digging through data and using it to try to make the right decision, they have more time to focus on the other aspects of their business.

3PLs are Often the Best Solution for Implementing High-Level Analytics

One 2018 survey showed that 67.4% of respondents were still using spreadsheets to run their supply chains in some capacity.

Though this number has likely gone down in the past few years, this reliance on outdated technology is very common in supply chain. It’s an industry of late adopters despite the tech boom we’ve seen in the past couple of years.

It’s not that Excel isn’t a handy tool. It’s simple to stick new data in there and disseminate it across departments. The problem? To err is human. When human error affects the data, that data no longer provides the appropriate insights.

As such, technology or the lack thereof, is often a barrier to improved processes and smarter business decisions in the logistics industry. Having the staff to operate technology or to conduct analysis using less tech-savvy data collection methods presents yet another barrier.

Choosing a 3PL to handle analytics can send a company leaping over all barriers that previously stood in their way.

Choosing a 3PL to handle analytics can send a company leaping over all barriers that previously stood in their way. They have the technology, staff, time, neutrality, and expertise to streamline and augment data collection and turn it into actionable insights.

Listen to BlueGrace VP of Managed Logistics, Randy Ofiara’s, Freightwaves Fireside Chat on predictive and prescriptive analytic.