On receipt of a customer order the warehouse must perform checks such as verifying that inventory is available to ship. Then the warehouse must produce pick lists to guide the order-picking. Finally, it must produce any necessary shipping documentation and schedule the order-picking and shipping. These activities are typically accomplished by a warehouse management system, a large software system that co¨ordinates the activities of the warehouse. This is all part of the support to expedite the sending of the product to the customer.
Order-picking typically accounts for about 55% of warehouse operating costs; and order-picking itself may be further broken like this [21]:
Activity | % Order-picking time
Traveling | 55%
Searching | 15%
Extracting | 10%
Paperwork and other activities | 20%
Notice that traveling comprises the greatest part of the expense of order-picking, which is itself the most expensive part of warehouse operating expenses. Much of the design of the order-picking process is directed to reducing this unproductive time.
The outbound processes of the warehouse are initiated by receipt of a customer order, which may be thought of as a shopping list. Each entry on the list is referred to as an order-line and typically consists of the item and quantity requested. The warehouse management system (WMS) then checks the order against available inventory and identifies any shortages. In addition, the WMS may re¨organize the list to match the layout and operations of the warehouse for greater efficiency. For example, if a customer has ordered 15 of a particular item, the warehouse management system (WMS) may check to see how the item is packaged. If 12 of the item comprise a carton, the WMS may convert the order-line for 15 eaches to two pick-lines, one for 1 carton and the other for 3 eaches. In many warehouses, each-picking and carton-picking are separate processes, and the pick-lines are diverted appropriately.
Pick-lines are instructions to the order-pickers, telling them where and what to pick and in what quantity and units of measure. Each pick-line (or, more briefly, pick or line) represents a location to be visited, and since travel is the largest labor cost in a typical warehouse, the number of pick-lines is an indication of the labor required.
Note that a pick (line) may require more than one grab if, for example, several items of a sku are to be retrieved for an order. Generally, this represents a much smaller proportion of the labor, because it is controllable by appropriate packaging (for example, pick one carton rather than 12 eaches).
The WMS organizes pick-lines into pick-lists to achieve still more efficiencies, so that an order-picker may be able to concentrate on one area of the warehouse and so reduce travel. In addition, the WMS may sequence the pick-lines so that the locations to be visited appear in the sequence in which they will normally be encountered as the picker moves through the warehouse. (This will be explored in more detail in Chapter 10).
The pick-list may be a physical sheet of paper, or merely a sequence of requests communicated by a stream of printed shipping labels, or by light, RF, or voice transmission.
The most labor-intensive order-picking is the picking of less-than-carton quantities, referred to typically as broken-case or split-case picking. Broken-case picking is labor-intensive because it requires handling the smallest units of measure in the warehouse and this is generally resistant to automation because of the size and variety of skus to be handled. In contrast, carton-picking (picking full cartons) can sometimes be automated because of the relative uniformity of cartons, which are almost always rectangular and packed to resist damage.
The pick face is that 2-dimensional surface, the front of storage, from which skus are extracted. This is how the skus are presented to the order picker. In general, the more different skus presented per area of the pick face, the less travel required per pick. An informal measure of this is sku density, which counts the number of skus available per unit of area on the pick-face. If a warehouse has a suitably high sku density then it will likely achieve a high value of pick density, or number of picks achieved per unit of area on the pick face, and so require less travel per pick.
Sometimes it is useful to interpret the informal measures sku density and pick density as measuring skus or picks per unit of distance along the aisle traveled by an order-picker. One can then talk about, for example, the pick density of an order. An order that is of high pick density does not require much travel per pick and so is expected to be relatively economical to retrieve: we are paying only for the actual cost of retrieval and not for travel. On the other hand, small orders that require visits to widely dispersed locations may be expensive to retrieve because there is more travel per pick.
Pick density depends on the orders and so we cannot know it precisely in advance of order receipt. However, it is generally true that pick density can be improved by ensuring high sku density, which is number of skus per foot of travel.
Pick density can be increased, at least locally, by storing the most popular skus together. Then order-pickers can make more picks in a small area, which means less walking.
Another way to increase the pick density is to batch orders; that is, have each worker retrieve many orders in one trip. However, this requires that the items be sorted into orders either while picking or else downstream. In the first case, the pickers are slowed down because they must carry a container for each order and they must sort the items as they pick, which is time-consuming and can lead to errors. If the items are sorted downstream, space and labor must be devoted to this additional process. In both cases even more work and space may be required if, in addition, the orders themselves must be sorted to arrive at the trailer in reverse sequence of delivery.
It is generally economic to batch single-line orders. These orders are easy to manage since there is no need to sort while picking and they can frequently be picked directly into a shipping container.
Very large orders can offer similar economies, at least if the skus are small enough so that a single picker can accumulate everything requested. A single worker can pick that order with little walking per pick and with no sortation.
The challenge is to economically pick the orders of intermediate size; that is, more than two pick-lines but too few to sufficiently amortize the cost of walking. Roughly speaking, it is better to batch orders when the costs of work to separate the orders and the costs of additional space are less than the extra walking incurred if orders are not batched. It is almost always better to batch single-line orders because no sortation is required. Very large orders do not need to be batched because they will have sufficient pick density on their own. The problem then is with orders of medium-size.
To sustain order-picking product must also be replenished. Restockers move skus in larger units of measure (cartons, pallets) and so a few restockers can keep many pickers supplied. A rule of thumb is one restocker to every five pickers; but this will depend on the particular patterns of flow.
A restock is more expensive than a pick because the restocker must generally retrieve product from bulk storage and then prepare each pallet or case for picking. For example, he may remove shrink-wrap from a pallet so individual cases can be retrieved; or he may cut individual cases open so individual pieces can be retrieved.
1 answer
仓库在收到了客户订单后,就必须进行检查,例如验证否有库存可供发货。如果有货 ,则生成拣货单去指引拣货。然后,准备相关必要的发货文件,安排拣货和发货。这些作业,通常都是由仓储管理系统来完成,该系统是一套用于协调仓库内活动的大型系统。以上动作,都是为尽快给客户交付订单。
拣货通常要占仓库运营中的55%的人力成本支出,拣货它又可按如下分解:
活动 拣货时间百分比
行走 55%
定位货品 15%
取货 10%
纸单或其它动作 20%
请注意,行走是拣货中最大的开销部分,同时它也是仓库运营支出的大头。许多关于优化拣货设计的目标,都是为了减少无价值的时间开销。
出货流程是由接收到客户订单后触发的,也可以将客户订单视为购物清单。清单中每一行都代表着一个品项,通常含有SKU信息和需求数量。WMS用订单需求去查核可用库存,以确定是否有短缺。此外,WMS可能会分组品项行以匹配仓库的布局和作业,以便提高发货效率。例如,如果客户订购了15个某个品项,那么WMS可能会看看这个品项在库内的包装是什么样的,如果它是每箱12个,WMS会将这条品项行转换为两条拣货行,一行为1箱,另一行为3个。另外,件拣和箱拣的作业在许多仓库里是分开的,因此拣货任务行也会被拆分开。 请注意,因订单中的SKU需求量不同,同时一个品项行需要几次提取才能完成。但一般来说,这代表了是为了减少人力支出,因为它是按包装作业(例如,拣一箱而不是12个)。
WMS为了使作业更加有效率,会将订单品项清单分解了多个拣货任务清单,这样可以让拣货工人可以专注地完成本区域的作业,以减少行走路线。并且,WMS还会将拣货任务行进排序,以便让任务行按照工人行走路线顺序出现(更多细节将会在第10章讨论)。
拣货任务清单可以是一张纸,又或是按顺序打印的拣货标签,又或是通过光、射频或语音等方式传输过来的一系列指令。
如果不能按箱拣货,那将会是最为劳动密集型的活动,通常也叫做拆箱拣货或分箱拣货。拆箱拣货之所以是劳动密集型,因为需要处理到库内存储的最小单元,而这个最小单元通常是多种多样且尺寸不一,无法采用自动设备来处理。而货箱通常是长方型且不易损,按箱拣货则能用自动化来实现。 拣货工作面通常是指需要被取的SKU货位前的二维平面,在那里SKU将呈现到工人面前。通常来说,在拣货工作面出现的SKU品项越多,则每次拣货的行程将会越短。一种非正式的度量方法是SKU密度,它是指计算拣货区上每单位面积上可用的SKU数量。如果一个仓库有合适的SKU密度,那么将会有一个较高拣货密度或较高的每单位面积中拣货次数,这样会有效地减少行走时间。
有时,将非正式的SKU存储密度和拣货密度,也被定义成为沿着通道行进时的每单位距离中的拣货次数,它也是同样的。由此可以引申出,订单拣货密度。一个高拣货密度的订单自然就不需要走太远的路,因此可以期待其拣货作业是相对经济的:短时间内拣货越多越有价值,走得越远越是浪费。另外,如果拣一个品项分散在全仓的小订单,那可就需要全仓几近跑上一圈,走上这么远,那么其成本也将会高昂的。
拣货密度是订单需求来决定的,在没有收到订单之前基本无法准确知道。但是,可以通过确保较高的sku密度(即每英尺行程的sku数量)来提高拣货密度。 比如,至少可以将所有热销的SKU存放在一起来提升拣货密度。那样子,工人就可以在一个区域完成更多拣货,这就意味更少的行走。
另一个增加拣货密度的方法,是合单拣货,让工人在一次行走中完多个订单的拣货任务。要么边拣边播,要么先批拣后分播。边拣边播时,既要每个订单准备容器,还要在拣出货品时将其分播入对应订单的容器中,拣货速度将会变慢了,且有可能导致差错率上升。如果先批拣后分播,需要为分播准备额外的场地和人力。但两种流程都会需要增加操作环节和场地需求,并且订单还需要按照到货的逆序送达装车区。
通常,单品订单处理起来比较有经济性,这些订单在拣货不用分播,也可以直接拣货进入发货容器里去。
非常大的订单经济性也不错,如果SKU们尺寸不大的话,一个工人可以在一次性行走中完成该订单的需求,既无需走得太远,也不需要二次分播。 挑战将来自于中等规模的订单们,它们有超过两行以上订单行,但是又因行数太少,单次摘果经济性太差。简单地说,如果批拣的分播和额外空间的成本要小于因为单订单摘果而产生额外行走付出的成本的话,还是采用批拣货比较合适。对单品订单进行批拣总是比较合适的,因为它不需要二次分拣。而有很多品项行的大订单也不需要批拣,因为它能保证足够拣货密度。而位于大订单和单行订单之间的中等规模订单,始终是会让人头痛。
为了让拣货能持续不继地进行,补货是必须的。以大单元(托/箱)将库存从存储区移动到拣货区,只需要少量的补货工就能支撑多个拣货工的需求。通常每5个拣货工需配1个补货工,具体比例还需要视业态而定。
补货工人费用通常会比较拣货工更高,因为他需要将货品从存储区成批量取出,然后拆托或拆箱补入拣货区,供拣货工拣货。
This guy is lazy,Introduction has not been set