It has been said that data in the twenty-first century is like oil in the eighteenth century. I don't agree with this 100%. Like oil, data has to be refined before it is used, and it's only as valuable as the insights that are drawn from it. Further, oil is a limited commodity —and we all know that the deluge of data is getting bigger every day. And with the proliferation of sensors everywhere, especially in mobile devices, it is much easier to gather data from business operations and also get much better insights from it.
One time, during a mobile device deployment in a distribution center, one operator was describing the distances involved when the system helped execute certain tasks. These were complex and varied maneuvers. Even with all the flexibility, configurability, and flag features in today's warehouse management systems, configuring something like that completely accurately can be difficult. The supervisor and I walked with this operator to pick an order—and what he said was true: the system directed him to aisle 70, then 71, then back to 35, then to 73, and so on. We were stymied, since this was not how the system was supposed to work. (Of course, in the end, it turned out the problem was a misconfiguration.) What was missing early on was a report that included the number of steps r distance travelled by the operator each day. That would have made spotting the problem quicker—and the road to eliminating inefficiency much smoother.
In terms of distribution center operations, picking is a sensitive operation. It is bound by time, and it contributes directly to the bottom line. Picking faster means shipping orders faster. The faster orders can be picked and shipped, the more orders that can be invoiced, and that brings money in more quickly. Optimizing picking then is critically important—and by reducing steps, you reduce picking time.
Typically, pickers are measured by picks per hours—and so their perceived value is related to their speed as well. That's not really fair, though, because a variety of elements, including walking distance, order profile, and what orders arrive each day, can impact efficiency. Distribution center managers classify products as Velocity A SKUs (fast moving or fast selling SKUs), Velocity B SKUs (moderately selling SKUs), and Velocity C SKUs (slow moving SKUs).
Fast moving SKUs go into fast moving locations, while moderately moving SKUs get slotted into moderately moving locations and slow moving SKUs into slow velocity locations. This ensures that picking tasks are sorted by location and densely located to maximize picking and minimize walking. Unfortunately, if slotting is not done, or there's a shift in SKU velocity with a seasonal change, the system becomes much less accurate. Insights around the amount of walking operators need to do would be incredibly useful to the supervisor.
The latest Apple devices we deploy have inbuilt sensors that track many things:
Steps
Walking distance
Height
Weight
Heart rate
Respiratory rate
Body temperature
In a distribution center, steps and walking distance would be most useful. It would make sense to take this data at the department level and draw a bell curve to see the outliers. This could be a powerful tool, especially after a go live where the warehouse management system (WMS) needs tweaking to support the expected throughput.
The same data could also prove useful during performance analysis reviews to analyze operations in an end-to-end process flow of the distribution center. This analysis typically is done before doing a WMS process design to deploy a new WMS. Any process that has an unusual walking time needs to be analyzed thoroughly and alternates need to be explored to optimize it.
Do you see other ways mobile phone sensors could help with supply chain operation? Let us know your thoughts in the comments section below.