How Using BI Can Transform Your Business

Many industries, such as transportation and logistics, have deployed enterprise software to help operate and manage work processes for decades now. And, as a result, companies and the solutions providers have collected a treasure trove of information, not only about an individual company, but about the industry as a whole. Until recently, with the advent of business intelligence (BI), few have put this information to work.

Now, we are in a transformational stage of the industry where general insights and “what if” scenarios can have a profound impact on transportation and logistics thanks to BI.

BI is not all that new of a term. In fact, it has become a common best practice across several different industries. We’ve seen just how powerful it can be within the transportation and logistics industry from using data to create ELD restrictions to using the analytics to improve operations in our own businesses. That’s why it’s not a secret that the proliferation and accessibility of data has fueled the growth of BI in transportation and logistics.

Within our industry, we have collected enormous amounts of data that can be put to work to answer questions about operations, routing, maintenance, condition monitoring, and service, among others. BI tools help business executives streamline decision-making processes by combing search outcomes, merged data, and queries that run against the data in one, easily obtainable location.

Today there are numerous vendors that sell BI tools, and customers often choose one over the other based on either ease-of-use or because they already have the necessary technology in place – IBM, Oracle, and SAS, are prime examples.

But those in the transportation and logistics space would benefit the most from solutions specifically geared towards their industry not only because of functionality based on experience, but they can mine the data collected specifically from the industry that they operate in to gain real insight beyond their own situation.

So, we are new well into the stage of “transformation” being driven by the next level of BI – predictive or proactive analytics. The information that software systems collect across all areas of businesses in this space and throughout every level of the supply chain enables you to make choices to continually improve best practices.

Beyond Predictive Analytics

However, it’s the ability of BI to take those insights to the next level to predict an occurrence and prepare for it or to be proactive in addressing an issue before it occurs. You can analyze data from repair orders to predict when parts might break, which will help reduce costly emergency maintenance. You can look at weather forecasts or past traffic patterns to predict when there will be road delays, which enables you to reroute trucks to avoid possibly missing a delivery deadline. Or, you can review past customer orders to see the frequency at which they order certain products and predict when the next order should be placed, which allows you to plan for when your fleet will be in use.

What’s beyond predictive analytics? Prescriptive analytics is the next stage in the development of BI. It effectively tells you what the best decision is to make for your business based on years of collected data involving similar scenarios.

The new “augmented” technology will combine the reporting of BI with the predictions of predictive analytics to evaluate “what if” scenarios to tell you which the best choice is to make – the next big thing in development at technology suppliers today.

So, while many transportation companies know the value of BI within their businesses, applying the deep learning of predictive analytics can help them improve efficiencies across multiple areas of business operations, which ultimately will save money.

Also, by deploying predictive analytics for routing, they can better plan for delays and even optimize their fleet’s usage, which may help them take on more business. Once transportation and logistics providers can effectively apply the knowledge of predictive analytics, then they will be ready to leverage that knowledge to jump to the next tier of prescriptive analytics. But, before that happens, companies can adopt best practices and apply the use of effective BI to raise their businesses to the next level.

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