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USING BIG DATA TO IDENTIFY, PREDICT AND PREVENT CRITICAL ISSUES

INTRODUCTION

LEVERAGING BIG DATA TO SOLVE YOUR BUSINESS CHALLENGES Big Data has become a big challenge for many businesses and industries – and fleet management is no exception. Fleets now have the capability to track hundreds of data points on every vehicle in their fleet. While fundamentally good, the amount of data that suddenly becomes available can be startling. Modern fleet management requires the ability to refine this incredible wave of data into actionable information that helps guide the strategic decisionmaking process. A company needs to be able to develop reports that provide insight into a fleet’s day-to-day operations. But they also need more than that. Companies need to be able to develop reporting that can help C-level business professionals see the broader impact that the fleet has on a business and understand with precision what decisions need to be made to ensure that the investment made into this area of the business delivers meaningful returns. Instead of simply trying to sort through mountains of fleet management data, the future is focused on intuitive analytics coupled with management through outliers. By using statistical analytics combined with Big Data, actionable insights can be leveraged pinpoint the exact vehicles that create the costliest inefficiencies that result in the increased spend to a fleet.

Big Data

BY THE NUMBERS BIG DATA BY THE NUMBERS

37%

FACTORS DRIVING INVESTMENT IN BIG DATA? When asked to name the most important factors driving investment in Big Data, 37% of C-level respondents to a recent survey cited the need for greater insights into their business and customers. Other drivers included faster time-to-answer / time-to-decision capability, increasing the speed-to-market, and the need for greater analytics capabilities.

40%

VARIETY, VOLUME OR VELOCITY? Among the businesses surveyed, 40% cited variety as the primary technical driver behind their investments in Big Data. Companies are seeking to integrate more sources of data - both new and legacy sources.

$50M

INVESTMENT IN BIG DATA. The percentage of Fortune 1000 companies expecting to invest $50 million or more into Big Data is expected to grow to 26.8% by 2017. Spending on Big Data solutions is on the rise overall as well.

69.9%

BIG DATA IS MISSION CRITICAL. The number of companies reporting that investment in Big Data is mission critical or very important is nearly 70% of all firms surveyed. Only 1.8% reported that Big Data was not important.

BIG DATA CHURNS OUT BIG PROBLEMS Organizations are operating in the age of analytics, and Big Data is the method used by many to take the existing state of things to identify patterns, trends, and associations. According to a December 2016 report by McKinsey & Company, while Big Data’s potential keeps growing, taking advantage of it means corporations must incorporate analytics into their strategic vision to make better and faster decisions. Many organizations, however, have failed to make a big impact with Big Data. Instead, the report found only a small amount of the potential value from data and analytics were being captured. A disconnect remained between turning these insights into actual changes in operations to positively impact the bottom line. The trends found in the McKinsey report echo in the fleet industry. Over the past five years, fleet management companies have partnered with software giants to develop sophisticated solutions for managing Big Data so that it can yield valuable insights. Telematics, driver safety, fuel transactions, maintenance POs – every data point tied to a vehicle can be tracked. In a given year, a fleet of 300 vehicles will generate an average of 1,254 data points associated with maintenance; if you add fuel data this number jumps to 30,000 and once telematics is added, the number of data points rises to just over 15 million. Without a tactical plan to harness the power of that data, many companies will simply end up with a lot of noise and very little insight.

Big Data

BIG DATA GENERATES BIG DATA POINTS BIG DATA POINTS GENERATES

1,254 Maintenance Data Points

In one year, a 300 vehicle fleet generates on average:

15M Telematics Data Points

30,000 Fuel Data Points

Generic Reports Produce Generic Outcomes For many years, when a fleet manager has had a detailed question about their fleet’s performance, he or she has had to reach out to analysts who then query and run reports to get a base understanding of the fleet and how it is operating. Multiple reports might be needed in certain instances so a broad information base can be established to provide a foundation for detailed analysis. Analysts would often spend hours pulling the necessary fields and then wait for the results to become available – frequently the process would take a day or even two. Once these reports finally became available, existing technology produced search results that were often times overwhelming, generic and impossible to provide actionable outcomes. It took analysts an additional week or two to complete the analysis that offered general ideas as to what could be causing higher costs to a fleet. While any information is helpful, the cumbersome process that was once the norm was not keeping up with the speed of modern business.

DROWNING IN DATA

LOTS OF INFORMATION WITHOUT THE WHY While initially seen as a breakthrough within the industry, Big Data became overwhelming and not the empowering advancement it was once thought to be. The first generation of data analysis tools created a general idea surrounding the fleet but no concrete evidence to allow companies to make strategic decisions. The data produced extensive detail about the fleet’s operations but couldn’t tell fleet managers why something was happening, and more important – how to prevent it. Why was the fuel spend increasing? Why were maintenance costs in one segment higher than the other? Why should drivers stick to the assigned preventive maintenance schedule? The first generation of fleet management technology ended up giving companies with more to do, not less. Time was wasted while wading through mountains of data to get to the reports that impacted their fleet the most. Every system comes with its own learning curve, but at the end of the day administrative time was wasted due to the amount of time it took to complete daily tasks. Ultimately, how good is having access to data if there isn’t anything worthwhile that can be done with it?

The Shift from Generic Ideals to Pinpoint Accuracy

Pareto principal meets Fleet Management

The future of fleet management data will shift from an ocean of Big Data to pinpoint accuracy that is vehicle focused. The next generation of fleet management software will successfully deploy specially designed statistical analyses that coincide with Big Data reports to generate specific reasons as to why certain items are contributing to higher costs to a fleet. Every vehicle will be able to be ranked along a bell curve from low to high to spotlight high-spend vehicles quickly, allowing companies to analyze the data quickly with less time focused on searching for the highest cost drivers to a fleet. Instead of concentrating on an entire fleet’s data to try to determine cost outliers, the tool easily identifies the vehicles that have the biggest impact on cost and reliability allowing fleet personnel to focus only on the vehicles that require their immediate attention.

Statistical analysis coupled with Big Data puts Pareto’s 80-20 rule into effect by focusing on the 20% of vehicles that may be driving 80% of the spend, which results in a quicker move to more prescriptive action.

Companies will be able to drill down quickly with precision and accuracy into the exact reasons a fleet is experiencing increased costs, including whether drivers are using premium gas or frequenting costlier gas stations; driving patterns; the vehicle’s age, make or model; or the application of the vehicle. Advanced reporting tools will bundle a series of reports and critical key performance indicators (KPIs) and share them directly with the customer or analyst who has immediate access to benchmarking information. Automatically, the outliers and anomalies will become evident and the vehicles or drivers contributing to the bottom line will become clear.

Instead of providing general ideals as to why fleet spending is trending in certain directions, statistical analysis coupled with Big Data will identify the reasons a fleet is experiencing increased costs. The company will have access to specific issues that can be addressed to realize significant outcomes. Statistical analysis coupled with Big Data essentially puts Pareto’s 80-20 rule into effect by focusing on the 20 percent of vehicles that likely to be driving 80 percent of the spend, which results in a quicker move to more prescriptive action.

MOVING THE NEEDLE FURTHER

PREDICTIVE ANALYSIS Predictive Analytics in Action IN ACTION Example:

This evolution – which is happening today and will continue to intensify – will take data from merely insightful to genuinely impactful. Partnering with a fleet management company that offers management through outliers can produce results that allow companies to make significant changes to their operations with almost instantaneous results. New solutions will empower companies to do deep dives into their data easily, analyzing current maintenance data together with the vehicle’s previous history in order to establish failure ratios. Additionally, the opportunity to take the analysis a step further to identify units or components that have not yet had repairs will allow companies to more accurately predict and manage future costs and repairs. Intuitive intelligence will give companies the ability to focus easily with laser-like precision on the units within the fleet that impact the highest cost ratio. The presentation of exactly the right data at the right time will allow leadership to make the right decisions quickly and take remedial action which will result in cost savings and a positive impact to the company’s overall bottom line. Further, companies can also use this refined data to improve vehicle selection, specification, replacement cycling, fuel efficiency and maintenance criteria which shifts fleet management as a whole from a break-fix model to a predict-and-prevent model.

EXAMPLE: PREDICTIVE ANALYTICS IN ACTION

MEASURES FREQUENCY Predictive analytics measures the frequency of brake failures and repairs and can tell fleet managers that a vehicle will require a brake service after so many months in service and after so many miles on the road.

FORECASTS PATTERNS This results in budgeting and planning for the work that will need to be done as well as minimizing catastrophic failures through preventative measures.

PREDICTS TRENDS Fleet managers can now predict major and minor component failures, which enhances vehicle replacement strategy as well as year-over-year budgeting.

Taking this example one step further, once the worst case offender vehicles have been identified and action has been taken to reduce the cost to a fleet, intuitive intelligence through statistical analysis will allow companies to shift the median to identify the next most costly vehicles to a fleet, striving for additional ways to decrease spending even further.

CONCLUSION

PREDICTIVE ANALYTICS CREATES CERTAINTY With most fleet management companies providing data solutions to their clients, the ability to decipher the data into something that not only makes sense but also gives direction has become hard to find. Big Data produces numerous reports that generalize what is going on in a fleet, which only results in more questions and more headaches.

The future of data analytics will change this, however. Organizations will be able to take quick action based on solid, real-time data and management by outliers. New solutions will provide insight on a fleet’s operations quickly, allowing for improved time-to-answer / time-to-decision speed. Efficiencies will increase and costs will naturally decrease as a result. Smart companies should seek out partnerships that offer advanced data management with powerful statistical analytics in order to create actionable insights that definitively identify the actions that must be taken to see positive results to fleet operations and the overall bottom line.

About ARI ARI, a Holman Enterprises company, has revolutionized fleet management with technology that enables organizations around the world to realize new levels of efficiency and value by leveraging the power of data through the ARI insights® portal and other customized solutions. Founded in 1948, ARI, now the largest family-owned company in the industry, has continuously uncovered new ways for fleet managers to translate their fleets’ data into decreased costs and improved driver safety. ARI manages more than 1.3 million vehicles in North America, the UK and Europe, and together with its strategic partners, more than 2.3 million vehicles worldwide. Headquartered in Mount Laurel, New Jersey, ARI has been recognized as one of the “100 Best Companies to Work For” by Fortune magazine for five years in a row. Learn more at arifleet.com and on LinkedIn, Facebook and Twitter.

Global Headquarters 4001 Leadenhall Road Mount Laurel, NJ 08054 United States of America (856) 778-1500 arilfeet.com

06/20/2017 USING BIG DATA TO IDENTIFY ... - Automotive Digest

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