How Data Collection and Artificial Intelligence Are Building and Digitizing the Next-Generation Warehouse

Tags: Warehousing, Logistics, Technology , Supply Chain

Supply chain operations are largely planned around how events are expected to happen in an ideal world, with little or no ability to respond in real time to the millions of micro disruptions that may occur. These daily disturbances, even as simple as delayed arrival of a shipment due to traffic, create billions of dollars of lost productivity every year in the supply chain.

As supply chains increasingly transition from being viewed as a cost center to a competitive differentiator, optimization and efficiency has become critical to success in today’s world focused on next day delivery. To support this transition, the importance of real-time data and analysis is rising rapidly.

Garbage in. Garbage Out.

While we all look toward the future and the fully autonomous warehouse, we are simply not there yet (with some very high-profile prototypes as exceptions). In the meantime, many operators are taking a step-by-step approach to increasing efficiency and improving operational insight through better data collection and analysis to solve specific problems. It’s no surprise then that Forrester estimates the broadly titled “IoT Solutions” market will reach over $400 billion by 2023, with Inventory and Supply Chain Management making up over a quarter of that.

Both man and machines’ decision-making capabilities are limited primarily by the quality of data available. Leveraging the advances in sensor technology to enable quicker, lower-cost deployments, operators can collect better types of data – like visual and spatial – at a fraction of the cost of previous technologies. The ability to collect data at an incredible scale, with higher quality and increased context, has the power to unleash artificial intelligence to handle real-time operations decisions in logistics.

Where to Start?

Loading Dock. Loading docks are an obvious place to start when looking to increase operational efficiency, given that all goods enter and exit through them. Inefficiency and lost productivity at the dock have been estimated to cost in the billions of dollars per year in the US alone.

To increase visibility into truck dock operations, there are a wide range of solutions focusing on sensors at the dock and other devices to monitor dwell time and feed this information back in real time to dock scheduling systems. Some of the more interesting solutions go one step further by taking a visual approach to monitoring both inside and outside the truck dock, then using artificial intelligence and visual analytics to analyze the events and efficiency of every party while the truck is at the dock.

While these solutions provide significant value as standalone deployments, when the output is fed back into Transportation and Warehouse Management Systems, there is now an opportunity for dynamic adjustments and task prioritization based on real-time data from the truck or distribution center.

Activity and Productivity. Another high-value application in the logistics center is worker and equipment monitoring. Workers can suffer from periods of inactivity due to the unavailability of key equipment to complete a given task, creating an analog to the “dwell time” issue mentioned above. Today’s technologies are finally enabling highly accurate GPS-like information indoors. Though the ability to determine the locations of both people and assets, workers can be paired with the optimal equipment to accomplish their task that is available for them in the least amount of time.

Further, worker and equipment paths can be analyzed over time to see broader patterns and density. Machine learning can identify “hot spots” and “cold spots”, providing insight into better resource distribution and inventory location.

What’s Next?

These are just a couple of ways that real-time data can begin to transform warehouse operations, but this is only the beginning. As more data is collected and analyzed about the actual real-time physical environment, the data will enable instant adjustments to priorities, tasks, and assignments in response to “on the ground” conditions, generating significant productivity and efficiency improvements.

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