AGV Archives

Moving to the Fully Flexible Assembly Line

Automotive Industry | By Steve Banker • 06/11/2018

The traditional assembly line moves products from station to station on conveyor lines or overhead cranes. As the work in process moves to a new station, workers, and increasingly robots and cobots – robots that can work safely in proximity to humans – do their work before the product is moved to the next station.

Traditional Assembly Line Using an Overhead Crane

But Rob Keij, the Commercial Director at Kivnon, says that is the old way of doing business. Kivnon is a Spanish provider of Automated Guided Vehicles (AGVs). “AGVs have traditionally been used to move products from point “A” to “B”. They are still used that way.” But AGVs can also be used as “flexible fabrication platforms” to replace conveyors, overhead cranes, and other forms of bolted down conveyance.

Using AGVs to Transition to a Flexible Assembly Line

For example, one of Kivnon’s customers manufactures automotive engines. They replaced a monorail with 35 AGVs. This makes it easier to produce products flexibly. If there is a model change, the manufacturer can add two more steps and four more meters to the production line. Or if maintenance is being done machinery in one section of a factory, AGVs can be routed around workers in that area of the factory. This is something a traditional bolted down line can’t accommodate without costly and time-consuming modifications to the material handling equipment.

Kivnon offers a couple different types of AGVs. One of their AGVs follows a path that senses magnetic tape that has been laid down on the floor. Another type of AGV sold by the company uses simultaneous location and mapping (SLAM); basically, the AGV has an internal map of the facility embedded in its controls and sensors triangulate on “targets” such as mapped walls and racks so that the AGV can stay on its path.

Operators can click on the AGV application and quickly design a new path or make modifications to an existing path. According to Bob Trebilcock, Editorial Director at Supply Chain Management Review, and the go to guy on all things related to material handling, “years ago these kinds of path changes required custom coding. Today, many AGV suppliers have software where these kinds of changes can be made by users fairly easily.”

In comparing AGVs that run on magnetic tape versus SLAM, Mr. Keij said that magnetic tape is cheaper, but that it takes longer to put down or pull up tape when a line change is needed. With SLAM, the facility needs to be mapped initially, and then is a more time-consuming process than putting down tape, so startups takes longer. “But if you want full flexibility, you need mapping (SLAM).”

Mr. Keij asserts that their are other advantages that AGVs have over bolted down material handling systems: they can be implemented more quickly, are less expensive to maintain, and they free up more space in a factory.

In doing research on flexible automation, I’ve noted some acrimony between suppliers of autonomous mobile robots (AMRs) and suppliers of automatic guided vehicles. AMRs can autonomously redefine their route to avoid an obstacle.

But from Mr. Keij’s perspective, much of this is just marketing hype. The Kivnon AGVs can be programmed to connect with manufacturing execution systems, robotic cells, and automatic doors to allow for more efficient product handoffs, production, and travel. Having one of their AGVs order an automatic door to open is, after all, also a form of autonomy.

Further, having a mobile robot autonomously redefine its path “does not work in an automotive factory.” Mr. Keij explained, “manufacturers have a specific time frame they are looking for material to be delivered to the line.” If a robot picks a new path to avoid an obstacle, the desired sequence of material delivery to the line can be disrupted. Kivnon takes a lean approach to line deliveries. If obstacles arise, and it necessary to deliver material to the line within tight time windows, Kivnon has monitoring software that detects that an AGV has stopped. This software sends proactive alerts on phones or via email or even causes a light or sound alarm to switch on. For example, if a person has put a box in the path of an AGV, the alert might be programmed to go off after two minutes if that AGV has stopped on a location on the loop where it would not normally stop. “We think it is smarter to move the box and maintain the sequence, then it is to navigate a different path.”

In conclusion, as I’ve talked to suppliers in the AGV/AMR industry, I’ve come to realize that this is not just a situation where AMRs are “smart” and AGVs are “dumb.” There are a range of different types of intelligence that these machines can possess; it can be counterproductive to pay extra for a form of intelligence or autonomy not needed to complete a particular kind of task.

Kiva Systems Acquired at a Very Steep Price!

Material Handling | By Steve Banker • 04/09/2012

Amazon.com’s acquisition of Kiva Systems keeps coming up in my conversations with industry peers and colleagues. Kiva is a provider of advanced robotic material handling systems — or looked at in an alternative way, the company brought to market much more intelligent Automatic Guided Vehicles (AGVs) than we had seen before. Adrian wrote about the acquisition shortly after it occurred. Now I’d like to add my two cents.

Back in 2009, I wrote a Strategic Report titled “Warehouse 2025” where I talked about how a new generation of more intelligent AGVs and goods-to-man robots would change warehousing. Here is an excerpt:

Robotic solutions offer the prospect of achieving a highly automated warehouse with less risk, based on a more scalable investment path and more process flexibility. These solutions change the way we think about optimization in the warehouse, the way various warehouse processes will be conducted, what types of companies will be willing to invest in highly automated warehouses, and even what a warehouse will look like in the future.

What made me particularly bullish on this new technology was its ROI and risk profile compared to existing forms of high-end automation. I talked to a material handling consultant who had done a warehouse analysis for one of his customers. The analysis looked at using Kiva versus using conveyors with pick-to-light zones and A-Frames for a customer.

The consultant found that the warehouse productivity between the two scenarios would have been almost the same. The cost was also similar.

However, in order to simulate the conveyor scenario, the consultant made five-year assumptions about volume growth and order profiles. He knew that these assumptions could prove to be invalid in just a few years. Further, even if the estimates proved roughly right, for a few years the conveyor automation would have been underutilized. Then for several years the system would have been maximally cost efficient. Then, as volumes grew past the design parameters, there would be a period where labor-intensive workarounds would be needed to handle parameters beyond what the system was designed to do.

In contrast, Kiva scaled. When volumes were lower, you bought just the number of “bots” needed. As volumes grew, you added bots. Kiva was the obvious choice because the risks were so much lower.

Further, the only reason productivity was roughly the same between the two scenarios was because the conveyors were to be used in conjunction with A-Frames. In a conveyor warehouse without automatic dispensers, which do not make sense in many warehouses, the productivity of bots is clearly higher. Automatic dispensers also need to be frequently loaded and require careful planning on an ongoing basis.

Despite being a real fan of this new technology, I was stunned by the acquisition price. Kiva, as a private company, did not report revenues and Amazon did not disclose the company’s revenues either. Nevertheless, my feeling, based on staying periodically in touch with one of the company’s executives, is that Kiva generated about $100 million in revenues. One of my industry peers had a conversation with someone in the investment community who also felt Kiva was about $100 million in revenues. If true, Amazon paid 7 to 8 times revenues for Kiva, which is an extraordinarily high multiple.

Because Amazon is a public company and $775 million will almost certainly be considered “material,” we should eventually know what Kiva’s revenues were at the time of the acquisition. But we may not know until Amazon publishes its 10-K/annual report next January.

Other companies that have come to market with intelligent AGVs, such as Egemin and Seegrid, have to be thrilled. However, they are not quite apple-to-apple comparisons with Kiva. Kiva’s sweet spot is “each picking,” while the other solutions are a better fit for mixed-case pallet building and unmanned full pallet moves.

I concluded my 2009 Strategic Report by saying that “the adoption of new business technologies is often surprisingly slow and it will be many years, if ever, before these emerging robotic solutions obsolete (traditional) warehouse technologies. That is why this Strategy Report is titled, ‘Warehouse 2025.’” That is how long I thought it might take this form of technology to “cross the chasm.” It could happen faster, I felt, if there was some big event that generated lots of publicity and buzz. Amazon’s purchase of Kiva could be that event.

Kellogg’s Use of Automated Guided Vehicles

Material Handling | By Steve Banker • 06/14/2010

The ongoing developments in intelligent and flexible material handling systems, which are basically robots, continue to fascinate me. For example, automatic guided vehicles (AGVs) used to be fairly dumb machines that moved from point to point by following a preset trail of magnets in the warehouse floor. Humans loaded them at one end of the warehouse and unloaded them at the other. But AGVs have gotten a whole lot smarter.

At the RedPrairie user conference a few weeks ago, Tom Grohalski, a project manager at the Kellogg Company, talked about how Kellogg’s is using AGVs with forks on them from Egemin. AGVs take pallets from the end of the production line and stack them in a bulk warehouse. Other AGVs then take product from the warehouse and stage them on the dock. As a result, this warehouse needs very few people. However, “chasers” and “super users” are still needed. Some AGVs stop due to small obstructions, such as shrink wrap dangling down from a pallet, or they go off course and chasers needed to put them back on track. The “super users” handle IT glitches and issues. Kellogg’s also has forklift drivers on the dock to load the truck, but these drivers are now almost three times more productive.

Here are some takeaways from Grohalski’s presentation:

Kellogg’s conducted simulations to figure out how many AGVs it would need to handle peak production in conjunction with various fulfillment scenarios.
The RedPrairie WMS tells the system where to put the product. The AGVs have a control system that contains a vision system (i.e., the AGVs could run without reading magnets in the floor), sensors, a digital map of the warehouse, and the move logic. The RedPrairie put-away configuration logic needs to make sure too much product isn’t being unloaded in the same zones; otherwise, the slow-moving AGVs (350 feet per minute) would have congestion problems. “AGVs are polite,” Grohalski commented, “they will patiently wait their turn.”
The dimensional pallet and warehouse layout master data needs to be very, very accurate.
Kellogg’s ran two pallets high from the production line and would stack pallets up to four high. Pulling from the warehouse to the staging area, AGVs might take one or two pallets. Consequently, there could be bays that would only have three high pallets. Thus, there was a tradeoff in using AGVs. Kellogg gained labor efficiencies but lost some ability to fully utilize all of its warehouse space.
The pallets were scanned off the production line. After that the WMS knew their license plate and where they were to be stored and the pallets did not need to be rescanned in the warehouse.
Kellogg’s experienced big reductions in damage and improved safety.
Kellogg’s expects a three year payback period and it is on schedule to achieve that target.

In conclusion, AGVs are becoming more like forklifts, and some forklifts are becoming more like AGVs. If you add a vision control system and control software to forklifts, they begin to act a lot like AGVs (see our posting on “New in Flexible Warehouse Automation: Real-Time Location Forklift Automation”). Meanwhile, AGVs, which used to just tug pallets or move carts, are beginning to act more like forklifts because the latest generation of AGVs have forks on them that move up and down and can stack pallets.

Goldilocks and Warehouse Technologies for Food Distribution

Food & Beverage Industry | By Steve Banker • 11/04/2009

We all remember the story of Goldilocks and the 3 Bears, where one bed was too soft, one was too hard, and third was just right. When it comes to warehousing technologies for food distribution, I see the same thing.

In grocery, most store shipments are composed of mixed-SKU pallets. The layout of a grocery distribution center (DC) generally mirrors a store’s layout. So, if fruits and vegetables are found in the first aisle of a store, they would also be in the first few aisles of a DC. Food distribution centers are typically very large (half a million square feet at the low end, with most facilities at over 1 million square feet). Pallets at the DC are stacked in reserve storage above a pick location in vertical racks, and cases are picked from pallets on the floor.

In a warehouse that is “too soft,” worker tasks are directed by a warehouse management system (if a warehouse operation does not use a WMS and RF terminals, the company is not soft—it is in the Stone Age!). Selectors use RF guns to confirm picks, and they typically walk up an aisle, putting cases on a pallet jack, then walk down the next aisle (aisle traffic is generally one way). After snaking up and down a few aisles to build a pallet, they make the long journey to the shipping dock.

A warehouse that is “too hard” is almost totally automated. For example, Kroger implemented an automation system that can receive and put-away full pallets, as well as break down and rebuild pallets into store-ready mixed pallets according to how the products will be put away on store shelves. This all happens with almost no human intervention. Very cool, but also very, very risky. My guess is that the payback for this type of setup can’t be less than 10 years. Each DC replenishes a number of stores. However, stores are always closing and opening, mergers are common, and it is very conceivable, perhaps even likely, that the warehouse cost savings across the network are eaten up by high transportation costs. Further, this type of material handling is almost totally unsalvageable, as opposed to forklifts and other mobile solutions that can be used in new facilities when older ones are closed.

So, what is “just right?”

Currently, at the very least, it is WMS plus RF terminals plus Voice. Voice recognition from vendors like Vocollect for picking has good payback, often less than a year. “Just right” may also include Automatic Storage and Retrieval Systems (AS/RS) for full pallet put-away and pallet let down to the floor location for picking. I’ve come across unionized (high wage) food warehouses where the combination of voice and AS/RS had a payback of less than three years, while substantially lowering the costs-per-case picked. From my perspective, if you can get a payback of less than three years, the risk profile is acceptable.

But even in these warehouses, because they are so large, there is a lot of wasted labor that occurs when a selector travels with the pallet jack to and from the shipping docks. In existing food DCs, I believe “just right” will eventually also include the use of a new generation of intelligent (or robotic, if you prefer) automatic guided vehicles (AGVs). These AGVs will solve the empty travel issue and allow the selector to keep working. Seegrid and Kollmorgen offer these types of AGVs.

But what is “just right” today might be “too soft” tomorrow. Kiva Systems, for example, has developed robots that can retrieve a pallet and bring it to a worker who then builds mixed-SKU pallets at a station. The assembled pallets are then brought to the shipping dock by another robot.

However, I’m only half sold on this. These robots pick from pallets positioned on the floor that are not, and cannot be, stored vertically in racks. Because of this, the warehouse footprint required might be too large, unless it is built with mezzanine levels which include “bot lifts” (robot elevators). So, while the Kiva Robots have a good payback period (about two years) for small and medium-sized warehouses, I’m not convinced that it would be as good a solution when you add in bot lifts and mezzanine storage. But we should have some good proof points on this, either pro or con, within the next few years.

Bottom line: In warehousing, “just right” can have a very short half life.

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