Historically, it has proven difficult to routinely load a trailer or container to maximum utilization given the quick turnaround time. Even unloading within an allotted amount of time can be a challenge if you don’t know how pallets or packages were loaded – and how many items are actually in there – until you open the door.
Staff are often given a clipboard (or, if they’re lucky, a mobile computer) with instructions on what to do and when and then left to figure out how best to either make all the freight fit in the space or clear it all out within the allotted amount of time.
Loading operations, in particular, are kind of like playing a game of Tetris. Except, in this case, “points” are earned by stacking as many pallets and packages as possible into a trailer or container and ensuring there’s minimal space remaining when the timer buzzes. (No one wants to leave space underutilized.) And though supervisors may be nearby (also with a clipboard) to help coach the team along, even they are limited in their ability to influence the strategy employed come game time. Their understanding of what is happening inside the trailer or container is limited to what they can see or the information conveyed by the load teams. As a result, it can be difficult to influence the fill rate and wall height to meet fullness goals before the door closes.
When it comes to unloading a trailer, the supervisor might feel as though one person can handle an incoming shipment. But, once the truck pulls up, it could become clear that it is a full load and more resources are needed to turn that unit around on schedule. The problem is that, unless the supervisor actually sees that variance between planned and actual needs, that unloader might have to fly solo – and that could start a chain reaction of delays or force the next loading team to move even faster than planned.
That’s why Zebra first developed SmartPack™ a few years back. We wanted to eliminate those blind spots and give multiple stakeholders within transportation and logistics (T&L) organizations a way to gain complete, real-time situational awareness. You’ll be surprised at how much you can learn about your load operations, and how much you can improve them, when you have a clear view of every movement taken, every second and in every corner.
However, expanding one’s operational visibility is just the first step to improving workflow execution.
As I mentioned in my last blog post, you need empirical data to make informed decisions about your load operations, and you need it in real time. That’s why SmartPack has evolved into much more than just a video feed aggregator since its inception.
The Core Value of SmartPack: Driving T&L Beyond “Digitalization”
It’s important to create a digital “system of record” that can log loading actions in real time and serve as a single source of truth about what is or isn’t occurring across your operations. But it’s even more important to stand up a “system of engagement”.
Although knowledge is power, you won’t be able to drive meaningful improvements unless you understand why something is happening and exactly what they must do to address it. Moreover, you must be able to close the loop in order to benefit from these insights and maximize your return on investment (ROI). If you recognize that something is going awry during load workflows, or you see an opportunity to improve fill rate efficiency, utilization, or adherence to critical times, then you must be able to tell someone “on the inside” the next best step to take to either mitigate that issue or maximize the opportunity.
That’s why the second generation (and current state) of SmartPack is providing so much more value for action-driven supply chain organizations right now. In addition to capturing load operations from every vantage point, this system of engagement is also providing the metrics needed to optimize future operations.
Even though supervisors are still monitoring load operations from the sidelines, they’re finally in a position to act on what they’re seeing thanks to a host of machine and computer vision technologies that are now aggregating data about work in motion every 15 seconds and conducting some additional post-processing telemetry. They just have to login to the SmartPack dashboard to see “what happened” and determine “why it happened”. So, instead of simply choreographing workflows, supervisors and logistics managers can better assess how trends are unfolding without having to conduct a deep – or manual – analysis. This gives them an opportunity to revise strategies to achieve better outcomes in future workflows, which is the core value of SmartPack.
Incredible, right?! With demands on capacity continuing to grow, and showing no signs of slowing, any technology tool that can help supply chain organizations optimize their fill strategies, improve resource utilization and ship more goods with fewer trips are going to pay off.
However, even those who employ SmartPack as a system of engagement in the way I just described may find that they eventually hit a wall in value gains (no pun intended). That’s because a system of engagement is still heavily reliant on a human’s ability to spot an issue or opportunity and then make the right decision about how and when to address it. The problem is that no one person can keep an eye on all trailer loading stations or air cargo container unit load devices (ULD) within his or her purview, much less across an entire facility or region – even with the help of machine and computer vision systems and mobile devices. Could you simultaneously watch 12 video streams and not miss something?
That’s why we spent most of 2020 further developing SmartPack’s capabilities and refining new “premium value” for the SmartPack solution as a “system of intelligence”.
How We’re Leveraging Machine Learning and Prescriptive Analytics to Lighten the Load on Supervisors and Logistics Managers
We recognized that for T&L organizations to meet all of their goals, they would need a full-scale “system of intelligence” that automates much of the “coaching” role. So, we incorporated machine learning algorithms that automatically sense, analyze and act on both sudden and subtle issues in the load process that can impact fulfillment and compliance, such as fill rate inefficiencies or underutilization of trailers and containers. These algorithms are governed by a set of dynamic rules that use prescriptive analytics software to propose and support the execution actions to enhance outcomes.
Think of the loading and unloading workflows as a timeline of work activity that can be segmented into four contiguous durations, each with a critical time and/or work rate goal assigned to it.
If SmartPack recognizes that goals aren’t being met, then it will prompt immediate action by sending a real-time alert to the supervisor and or team via their mobile computers. The message will share what is exactly wrong – maybe the work activity is moving too slowly or walls of parcels are not being erected in a way that will allow target fullness by a specific time deadline to be achieved. Corrective action will also be communicated to get the work activities back on track. If the corrective action repeatedly gets ignored by the team and/or supervisor, then the supervisor’s manager will be notified in an escalation alert to get the work activity back on track. The images show some of the visual alerts that loaders and supervisors may receive to help them actively coach their teams.