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More insight with completed journey data

Thu | 19 Dec 2019 | News

More insight with completed journey data

This week, we have delivered a feature within Simacan with which our users have all the data of completed trips at their disposal. This provides users with access to an enormous amount of data that has not yet been made available. Through this feature, by applying data analysis, insight can be obtained into planning versus actuality and realisation. This is done, among other things, by making data available that provides insight into driving times, waiting times, number of affected stops and different dwell times compared to transport planning. Martin de Jonge, product owner at Simacan, tells more about this.

It all starts with trip schedules that are integrated with planning systems in our Simacan Control Tower. Rides in the planning are linked via a Fleet Management System (FMS system). The stops of a trip then come as orders in a board computer or an app on the phone. We retrieve data from the linked systems. When the driver starts a journey, the system continuously collects data: when the vehicle stops or stands still for a moment, or the engine is running, where the vehicle is located, or the route is deviated from, the vehicle is too late or too early and so on. We process this data again to provide users with up-to-date overviews of affected stops and expected times of arrival and delays. A lot of interesting information can be obtained from these results, both before, during and after the journey.

When the ride is complete, there is a mountain of interesting information available. Until now, users of our platform did not have access to this data, but the new situation changes that: we have launched a service with which we push the data to the customer as soon as a ride has been completed. This gives them access to the raw data of all journeys. This is very useful to use in their business intelligence operations.

What can you, as a customer, do with these data?
It really is a wealth of information with which the daily operation can be analyzed. On all sorts of levels, from individual rides to the overall operation. For example by doing timeliness analysis: how many stops were on time, how many were too late or too early. And then trying to make adjustments. Example: transporter A is 80% of the planned stops on time, transporter B is 60% of the stops on time. Through data analysis, insights are gained and a possible cause can be found or a connection can be made and transporter B's trip performance can be improved.

"Whether it's a small or large company, you can always take your own operation to the next level and set new goals."

Another example is to investigate why vehicles arrive too late, take too long journeys or stop too early. Is this, for example, due to the driving time or departure time and at what times? Or is it due to the dwell time we measure at a stop location or the location where a hub is located? Or perhaps the fact that journeys are started too late? With that insight you can make adjustments. Analyses can reveal that the stopping time needs to be extended, that a distribution centre is not planned efficiently in terms of capacity, etc.

With the data you create insight into your own operations and those of your partners in the chain. Only when you know how your operation is going, you know where you can adjust to improve. Whether it's a small or large company, you can always take your own operation to a higher level and set new goals.

What do you advise clients of Simacan to do with the data now?
We already provide insight into the operation with the Control Tower. This enables you to make real-time adjustments. But if you want to make it more efficient; smarter in planning and higher quality in execution, you need to analyse the current operation on all sorts of levels and cross sections. That's why we now offer the possibility to supplement your own data with all the trip data we have. This allows you to combine different data sources and build your own dashboards and intelligence layers on top of them. Being able to look back at data provides new insights: in November 2019, for example, 90% of planned stops were monitored and on time. Based on this, an operation can set new targets: next year 94% will be efficient and for that purpose we will plan long journeys on weekdays a little wider and short journeys a little tighter.

Learning together with new insights
At Simacan we find it important to share insights. We have therefore set ourselves the goal of allowing transporters to learn from each other. For example, we want to do this by organising a workshop where the lessons learned are shared with each other. All measures taken can be measured on the basis of the results: how do the measures taken affect efficiency and do we also see effect?

In this way we will continue to work together on an ecosystem where everyone on the loading and transport side looks at the same information. Together, we can plan and carry out the operation better and better and make adjustments based on facts and knowledge.

Profit for all
With this important application, we are taking another step towards making transport operations more transparent, more efficient, more economical and, above all, more sustainable. And that's profit for everyone.