Today’s fleets have a large flow of data at their disposal, and fleet managers must determine what they would like to measure.
Your Key Performance Indicators (KPIs) will certainly lead you in the right direction, so look at areas that drive key business decisions. That can be on-time preventive maintenance (PM) completion, downtime, cost per mile (CPM), repair code analysis, en route failures, etc.
Once you’ve established the topic for analysis, it’s best to look at what the leading indicator is for improving performance in that area. For example, we all know that a quality PM results in a reduction of costs, e.g., labor and parts costs versus emergency road service. Emergency road service will cost you 30% more than had you fixed a problem on a scheduled basis.
We all have schedules for our PM services, but do we know what intervals we are actually running? Do we know how many times we are bringing a unit in for unscheduled repairs between service? Are we optimizing our touches while running oil out to a safe extended drain interval? These are just several questions that get the ball rolling toward your data aggregation on a single topic.
In this example, we should be using the data to help us understand what our actual results were versus the schedule. Next it will tell us the quality of the PMs by showing us the touches between service. We have now identified what we are going to research, but there are more steps to follow on the data aggregation and analysis journey.
To achieve best in class data aggregation and analysis, you should consider the following steps.
Step 1
Determine the scope of your data aggregation and analysis by identifying the questions you want to answer, the type of data you need, and the methods you will use to analyze the data.Step 2
Collect the data you need from reliable and relevant sources, such as databases, surveys or other sources.Step 3
Clean and prepare the data. Before analyzing the data you should clean and prepare it to ensure its accuracy and completeness. This involves removing duplicates, filling in missing data and checking for errors.Step 4
Analyze the data. Use statistical and analytical methods such as clustering or machine learning algorithms to analyze the data. Choose the method that best fits your data as well as the questions you want to answer.Step 5
Visualize the data. Use visualization techniques to help you identify patterns and trends in the data. This can help you concisely communicate your findings to others.Step 6
Draw insights and make data driven decisions. This may involve creating new strategies, improving processes or identifying opportunities for improvement.Step 7
Continuously monitor your data and refine your analysis as needed. This can help you stay up to date on changes and trends in your data and allow you to adjust your approach as needed.It’s important to use a combination of technical and analytical skills, as well as critical thinking and problem-solving abilities, to drive best in class data aggregation and analysis. Additionally, using the right tools and technologies, such as data management systems and analytic software, can also help streamline and improve your data aggregation and analysis process.
