The Planner is an improvement to the existing Material Order (MO) Consolidation features. It consists of two main components: automation and AI-powered load & route optimisation.
Note: This feature is currently under active development and improvement, and the UI is subject to change.
Automation
This component allows existing setups to become fully automated, removing the need for manual intervention in the process of creating consolidated MOs. To manage how this automation operates, a new tab was added to the settings page, as seen below:
In this new tab, you will now find several new fields that govern how this consolidation rule is automated.
The first, "Automated?" is a flag that enables the whole functionality. Even if the setting previously had other automation-related properties configured, if the field is not ticked, then the consolidation can only be run manually as before.
The values of the other fields will not be cleared if you choose to temporarily disable the automation, allowing you to toggle it on and off as needed without the need to reconfigure it from scratch.
The "Automation Window" represents the number of days in the future (as specified in the "Collect Schedule From" column) that the automation will consider MOs as valid candidates for automation. A value of 0 means that only MOs scheduled to be collected "today" will be automated.
To accommodate the complex needs of the logistics world, the setting is equipped with advanced scheduling capabilities. You can define any number of hours in a given day that the consolidation should be executed ("Execution Time"), as well as varied filters that govern which days of the month this setting will be executed on - in the example, as no filters were defined, it will run every day at 9 a.m. and 9 p.m.
As this can become complex quickly, you can see at the bottom two fields that inform you of the subsequent scheduled runs, helping you visualise if the configuration you intended is the same one the system will follow.
Examples
Every weekday at midnight
First day of the month at EOD
Every 8 hours, every day of the holiday season
Load & Route Optimization
Manually combining LTL MOs is a rather cumbersome and time-consuming process. The more MOs being combined, the longer this process takes, and the less likely it is to approach the optimal solution, potentially missing out on savings by planning fewer trucks or less optimal routes.
To achieve this, we developed an AI-powered algorithm that identifies optimal solutions from all eligible MOs. The larger the number of orders, the more optimised the final configuration will be, as the algorithm has access to a larger selection of loads, which will, therefore, make it more likely to find perfect fitting combinations.
To enable this setting for a given leg, choose it from the dropdown:
Any orders fed into this leg will be sent to the algorithm, which will calculate the optimal load and afterwards create the corresponding Transport Orders in Control Tower. This means that it may take a few moments for the optimised orders to appear in CT after a setting is run.
You can customise the algorithm's behaviour to your needs; to do so, you can tweak the relative weight values for the algorithm fitness functions in the new "Load and Route Optimizer" tab:
UI subject to change
These weight values (0-100) denote a relative importance between the different functions; if all have the same value, they are of equal importance. If volume has a value of 20 and weight has a value of 1, then you consider approaching 100% volume usage as being 20 times more important than minimising the weight on the trucks.
Fitness functions
Function | Find configurations that... |
Volume | ... occupy as much volume in the trucks as possible |
Weight | ... weigh as little as possible |
LDM | ... have as many loading meters occupied as possible |
Visualisation
Different areas of expertise will always have communication gaps. While someone planning a load manually may find the load reasonable, the warehouse team may find the plan is not realistic for a multitude of reasons. Any situation like this will lead to delays (and therefore added costs). To avoid this, one would need employees who are familiar with both domains, and even then, human error would still be a factor.
AI can be used to bridge this gap, as it can be continuously improved to the point where most (if not all) of the concerns of all domains involved in the process can be addressed. However, this also means that the AI will not initially be in such a state and will likely miss certain factors it has not yet been taught about.
To alleviate both issues, we have included a visualisation component that allows you to see the solution devised by the AI and share it with warehouse staff. This is available as a new column for transport orders, "Visualizer", which, for TOs created by the algorithm, will contain a shareable URL to a 3D representation of the load generated by the AI.
New column, with an external URL for AI planned loads
This URL will lead to a standalone page featuring an interactive 3D representation of the truck:
