Predictive analytics: the ability to use well documented historical and accurate data to improve future logistics or fleet maintenance operations.
Data — especially quality data — is the key to predictive analytics. The good news (also the bad news) is that we have gone from data cups, to data pools, to data lakes, to today, where we are dealing with data oceans. In short, most fleets now have access to data — lots and lots and lots of data.
However, it is important to remember that not all data is created equal. The primary focus to ensuring the usefulness of collected data is to filter out the noise by identifying and eliminating anomalies while searching for repeatable patterns. These patterns are ultimately what your predictive analytics will be based on — detecting and understanding historical data resulting in an actionable decision.
Consider this example: You are reviewing your available data and detect that you are consistently having to replace the alternator on a subset of vehicles. Historic data is telling you that 60% of the time this alternator type, on this specific vehicle vintage, fails in the 65,000 to 85,000 mile range. The question you have to answer is: "Would it be economical to campaign all alternators in that subset of vehicles, before that mileage target band is reached, in an attempt to prevent a road call?" While there might still be life left in some of the alternators you replaced, would the proactive investment be offset by the potential savings of avoiding road calls for a driver, vehicle and product that was disabled on the side of the road?
From the logistics side, another repeatable pattern target would be traffic conditions. You can evaluate historical traffic patterns at specific times of the day (or seasons) to determine if there will be a high probability of traffic-related delays on specific routes that could impact your driver’s ability to get their loads delivered on time. On-time delivery is one of the most critical key performance indicators for most clients, and you can use data to help you gain an understanding of what may be contributing to any delays.
Is the delay caused by road conditions or weather? Does the driver get to the location on time, but can’t get into the loading dock? Can the driver not get to the next stop on time due to a pattern of delays at the previous stop?
You can use your history to understand what has happened in the past and predict what is likely to happen in the future. Armed with data, you can work with your customers and operations teams to reduce delays and adjust delivery expectations. Having data makes these kinds of conversations easier (and more constructive) because you can demonstrate what is occurring.
