ITV Predictions Need to be Based on More than Current Data
Just like our friend Ebenezer Scrooge in A Christmas Carol needed three ghosts to convince him to change his ways, three types of data are needed to provide an accurate prediction in your supply chain. For the purposes of this blog post, we shall call them the Data of ITV Past, Data of ITV Current, and Data of ITV Real Time, and we’ll talk about how they all play together to give Logisticians the most accurate insight into when their assets will be arriving.
While none of the data sources by themselves provide enough information, each of the different types of data play an important part.
- Data of ITV Past provides insight into assets that have traveled similar routes during similar time periods.
- Data of ITV Current shows where an asset is currently in its delivery cycle.
- Data of ITV Real Time provides insight into other factors that could affect its transit.
But the real value comes in by combining these three sources to create an accurate projection of when an asset can be reasonably expected to reach its final destination. Data of ITV Past and Data of ITV Current can both tell you where a shipment was or is at any point in time; however, the real power of ITV is knowing when a shipment will get to you.
To help express how the three data sources work together, let’s take an example. Let’s say that Package A is manufactured in Los Angeles, and is needed in New York. And, let’s imagine that this package is afraid to fly, so it’ll be making the journey by land. According to Google Maps, this journey will roughly take 40 hours.
Travel Time from LA to NY
Why you need Data of ITV Past
Data of ITV Past can provide a good base projection for the length an asset will spend in transit. For instance, the 40-hour estimate above is based on roads taken and speed limits of those roads. However, imagine the delivery is taking place in February when the windy and mountainous roads of Ohio/Pennsylvania may prevent the delivery truck from sustaining the max speed limit. Having historical data to query by time-frame would alert you to this potential problem, and allow for a more accurate projection.
When looking at Data of ITV Past, more relevance needs to be placed on more recent information as it is likely to be more accurate. If on our journey across the United States, we have collected 10 years of data – but three years ago a new road was constructed to save time crossing the Rocky Mountains, then we want our predictions to recognize that. Not that the other 7 years of data would be ignored completely, but more emphasis would be placed on the newer 3.
Why you need Data of ITV Current
Data of ITV Current allows for update projections as the asset is out for delivery. For instance, the Google Maps image above identifies three potential routes above. What if road construction at the Missouri/Illinois border forces the truck to take the detour route. Data of ITV Current will recognize that a route change has occurred, and update the scheduled delivery date accordingly.
Why you need Data of ITV Real Time
So now we are on a new path, and a combination of Data of ITV Past and Data of ITV Current can update the projected delivery time based on historical context. However, with it being February, imagine that snow is being called for in the West Virginia mountains. This is the value add of Data of ITV Real Time, being able to identify the current conditions, and use this information to refine the search through historical data to increase the accuracy of projected timelines. For instance, maybe 8 historical deliveries have traveled the path we are on in February, but only 2 did it in snowy conditions. With an understanding of current weather conditions, the model can lean more heavily on these two instances instead of the other six. Again, increasing the accuracy of the prediction and the final delivery time.
While there is no silver bullet when it comes to data related to delivery projections, combining Data of ITV Past, with Data of ITV Current and Data of ITV Real Time can provide a more accurate arrival date and allow your Logisticians to more accurately view their assets currently being transported within their supply chain.