请教关于WAVE 的问题

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Warehouse Management: To Wave or not to Wave?
   
      
  It’s been a long time since I got this granular with distribution issues (see Task Interleaving in the DC – Reality of Myth?), but some recent discussions with a couple of companies and some smart distribution experts got me on the track of a basic but very important distribution question: when does it make sense to use “waves” to process order batches and release picks to the floor, and when not?

To get everyone on the same page, a “wave” is basically an automated grouping of orders by some criteria that is released to the floor for processing as a set. Grouping attributes might be a set of carriers, a group of stores in retail, high priority orders, orders requiring a specific type of value-added services, or even a specific customer or three if they order in large enough quantities. In general, a wave should consist of something like 45 minutes to 2 hours worth of work.

Let’s dismiss for a second distribution models like flow through or cross dock where waving just doesn’t apply. What’s the alternative to wave processing? Some form of straight order release, where orders flow right to a work queue, with high priority orders and/or those deemed important by a supervisor (e.g., the truck is here!) bubbled to the top. And are their maybe some alternatives in between?

At a high level, wave processing does allow creating of a manageable block of work, can allow the picks to be sequenced in logical groups that meet any number of operational and shipping requirements, and (depending on the scope of the supporting WMS technology) enable order batches to be released that balance work across areas.

In these recent conversations, however, I’ve heard some shippers wonder whether some of the expected efficiency of wave processing is lost in terms of labor workforce downtime at the end of waves. It also seems that in today’s increasingly complex DCs and distribution channels, what is often really needed is an intelligent release of different batches to different processing areas, in effect a series of waves that is different than the traditional model.

I asked SCDigest friend Noah Dixon, VP of Product Management for Catalyst, where he thought the use of wave processing made the most sense.

“One thing that is different today than a few years ago is how orders come down to the DC,” Dixon noted. “Before, almost everyone got a batch download of orders in the morning, and that big batch lends itself to wave thinking. Now, while big one time downloads are still frequently found, many more companies get orders in near real-time all day, which changes your operating model.”

Wave processing is the “moment of truth” when the WMS looks for consolidation opportunities to reduce travel time and transportation costs, Dixon added.  “In addition, when the work force moves through the facility with the work or where the material handling control systems need it, wave processing is really the only way to go.”  However, he added that “most warehouses run better if the work is released when needed rather than in bigger groups.  Exceptions will be fewer and less expensive.” He also added that in facilities that are mostly pallet picks for truckload and multi-stop TL shipments, such as food and consumer packaged goods companies, it is generally easier and faster to release orders based upon appointment times rather than waves.  

Brian Hudock, a principle at Tompkins Associates, offered some similar thoughts. He’s seen waves used to facilitate a group of workers first completing picks in one area for the wave of orders, then moving to another area for the next wave, etc., in addition to the traditional retail model where a wave is released for a group of store replenishment picks, followed by the next, etc. But he noted with the growing complexity of DCs, the order release process is one where there is still often a lot of customization in WMS implementations to meet the unique needs of each company.

“I’m not sure you could or would want to build those specific rules into a base WMS product,” Hudock added, noting that the end effect may use some wave principles, but does not involve a “master wave” across areas, but rather an interleaved series of smaller ones.

Finally, Jim Barnes of enVista thinks there may be a hybrid model, even in retail. “Too often waves are used trying to optimize picking efficiency, when for highly automated environments we should be trying to optimize equipment utilization,” Barnes told me. For many environments, this would better be done by “waveless” picking that still gains consolidation efficiencies through local area batching, using “dynamic” inventory allocation.

By the way, if you are at all interested in Warehouse Management Systems, you really benefit from our How to Select a WMS 2006 Videocast (where I am joined by Jim Barnes and SCDigest’s Mark Fralick). We have a similar one for Transportation Management Systems as well, both in two weeks, though you can watch on-demand later if you can’t make the original dates.

We’re out of space. I am still pondering. More in a few weeks. We’d love your thoughts.

Where does use of pick waves makes sense and where not? Is this changing based on changing DC and order dynamics? Are there alternatives between a full wave orientation and straight order release to the floor? Let us know your thoughts.

smallwood

最初由 fc_ge 发布
[B]Wave and Batch Picking: Time scheduling method for picking by SKUs. Wave picking is releasing a defined amount of work to pickers for a set time. Batch picking involves picking a particular item for many orders simultaneously and then sorting the items later by order. [/B]

Batch picking is a process where multiple orders are filled simultaneously, and it is used to reduce transit time. With a “man to goods” system where an order selector travels to the product to fill orders, batch picking can drastically reduce the travel time. In a “goods to man” system where product is delivered to the selector, batch picking can reduce delivery traffic.
This paper addresses batch picking in a “man to goods” system. With modern technology, the transition to a batch pick system can be very inexpensive and un-complex both in implementation and operation. This paper provides the basis for determining the benefit of transitioning a “pick ticket” based order fulfillment system into a batch picking system.
To analyze the benefits of a proposed transition to a batch fulfillment system, the order fulfillment process is divided into three time categories.
􀂉 Pick Time – (PT) the time to retrieve an item from its storage location and place the item in the “order” container.
􀂉 Transit Time – (TT) the time to travel to an item
􀂉 Setup and Close Time – (CT) the time to prepare or setup the “order container” prior to putting any items into it and the time to complete the order container once the required items have been collected.
To analyze the potential benefits of a system, the values for each of the above items must be known. Obtaining these numbers is a very easy process; do not believe those that would tell you that it is complex. Just follow the following five steps:
1. Obtain the normal or average overall picking productivity for a worker Base Fulfillment Rate (BFR) in units per hour. For an existing system, this is easily obtained by dividing the total number of units picked, packed and shipped over some period of time by the number of workers that preformed that work. Normalize the value into the number of units per hour. Insure that the time used does not include “non-productive time”. The result is an average BFR units per hour that a worker can pick, pack and make ready for shipping. . If there are no existing metrics, this number will need to be estimated. There are many existing installations that should be similar enough to get an estimate. Additionally, if necessary, there are several simple techniques to refine such estimates.
2. Obtain the average units per order container (i.e. carton) (UC) by either reported metrics or estimation.
3. Obtain the order container (carton) Setup and Close Time (CT) in seconds through direct measurement. This time does NOT include any pick time or travel time. It only includes preparation time prior to picking and completion time following picking. This measurement is always done through observation with a stop watch. If there is no existing system to measure, set up and measure thetime of a simulated operation with real goods, cartons, simulated labels, tapers, staplers, etc. Take many measurements and calculate an average.
4. Determine the Pick Time (PT) in seconds also through direct measurement using a stopwatch. The pick time should not include any walk time but should include any required location or SKU verification, the picking of the product and the placement or packing in the order container. Make many measurements and take an average.
5. Once the above values are obtained the average Transit Time (TT) in seconds is calculated. This calculation yields a TRUE representation of the REAL AVERAGE TRAVEL TIME, for there are no other “productive time” operations that the worker may be doing other than prepare, travel, pick, pack and close. The formula is:
TT = (3600/BFR) – ( PT + (CT / UC))
For a system that has a base fulfillment rate (BFR) of 120 units per hour, 10 units per carton (UC), a pick time of 6 seconds and a carton setup and close time of 60 seconds, the transit time is:
TT = (3600/120) – ( 6 + (60/10))
TT = (30) – (6 + 6)
TT = 18 seconds
To batch-pick with a pick cart is one of the most popular ways of reducing transit time per transaction. Picking several orders at the same time will reduce the transit time by nearly the number of orders picked simultaneously – the size of the batch (SB). There is a small increase in handling time of each item due to the need to select which order container to put the item into – the selection time (ST). There are means to nearly eliminate the additional selection time (ST) through lights and automatic pushers. ST is almost never greater than 2 seconds and in many cases can be less than .5 seconds.
The calculated transit time for the new batch fulfillment system (TTN) is based on the calculation of transit time (TT) for filling single orders (see above). The formula for calculation is:
TTN = (TT / SB) + ST
Converting from a paper based single order fulfillment system as described above to a batch picking system with carts holding nine orders (SB) would yield a travel time of
TTN = (TT / SB) + ST
TTN = (18 / 9 ) + 2
TTN = 4 ; or a travel time reduction of TT – TTN = 18 – 4 = 14 seconds
The fulfillment rate of the new batch system (NFR) is calculated as follows:
NFR = 3600 / ( TTN + PT + ( CT / UC ) )
NRF = 3600 / ( 4 + 6 + ( 60 / 10 ) )
NFR = 3600 / 16
NFR = 225
The single order fulfillment rate (FR) in the example above was 120. With the new fulfillment rate of 225, the productivity increase is a whopping 187% (225 / 120)