The four lessons in Part 1, explained in more detail, were as follows:
Lesson number 1: The ideal automation solution should be quick and easy to deploy and upgrade.
Lesson number 2: The ideal automation solution is equipped with vision technology that allows the robot to see any obstacles in its path so that the robot can stop or bypass the obstacle to avoid injury to personnel.
Lesson number 3: The ideal automation solution is designed to communicate in real time with a warehouse management system to eliminate the need for a separate “black box” warehouse control subsystem.
Lesson number 4: The ideal automation solution is designed to enable fast, easy and flexible scaling to enable the distribution center to respond to changing demand profiles and significant increases in throughput requirements.
In the second part of this week, I go over lessons 5 to 8:
Lesson number 5: The ideal automation solution should be small and light enough to operate on a free-standing or rack-supported mezzanine so that vertical space can be fully utilized in the warehouse.
To navigate its designated area within the warehouse, Kiva relies on two-dimensional barcodes that are positioned on the floor and act as guides. Thus, the Kiva system must be precisely defined during its implementation. So this is not the type of automation that can be deployed in neighboring warehouse aisles without major effort. In other words, expanding the Kiva system involves a major project with a long lead time.
Lesson number 6: The ideal automation solution should be flexible enough to allow a new robot to be easily and quickly introduced into new and existing warehouse aisles without requiring significant delays or investment costs.
The underside of the Kiva robot is open and exposed to the ground, so dirt can build up inside its body. This requires frequent vacuuming to remove dust, plastic bags, and other types of debris that collects in the robot’s body.
Lesson number 7: The ideal automation solution should be designed so that it can operate in an industrial environment without the need for frequent cleaning.
Perhaps one of the most interesting lessons from the Kiva system is that inbound labor productivity is reduced compared to conventional labor productivity. This now seems counterintuitive given that the put-away process no longer involves travel time. After all, Kiva robots move pods from storage stations to storage, eliminating operator travel time. How could this automated approach be slower than a person manually storing the goods?
To answer this question, using an example works best. Let’s say an item with 50 units needs to be stored in the Kiva system. You don’t just put all 50 units on a single incoming pod. Instead, the units should be spread across many pods, so it may be necessary to put 5 units across 10 pods for example.
The reason for this is that the inventory should be spread across multiple pods to minimize the wait time for order picking. During outbound picking operations, if multiple picking workstations require the same item at approximately the same time and the item is only stored on one pod, then there will be a dwell time of the operator incurred since the pod must travel from the picking station to the next. Suppose, for example, that all inventory for an item is hosted on a single pod and three order pickers need the same item at the same time, then 2 of the 3 pickers may experience dwell time while waiting for the arrival of the pod. Residence time should be minimized to ensure maximum productivity. The way to ensure this is therefore to distribute the inventory of each item over several pods.
To quantify this claim, let’s say the standard conventional put-away process involves an operator moving goods into the warehouse and RF scanning confirming the entry transaction into a shelving bin. While this process can be accomplished with a productivity rate of 220 units per hour, the equivalent process of storing the same product on multiple Kiva robots results in a comparable productivity rate of 158 units per hour, a loss of efficiency of about 28%. Of course, the picking rates are better with the automated solution, but by extension the entry into stock suffers a corresponding loss of efficiency.
Lesson number 8: The ideal automation solution should be designed to enable productivity improvements for inbound and outbound operations for maximum efficiency.
It should be pretty clear.
So how did Welty take these lessons to design a similar but different solution to the Kiva System? You can find our full white paper here: Locus Robotics – Independent Consultant Review of Autonomous Robots in Distribution Centers
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