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This paper is an introduction to the features and concepts of an Adaptive Warehouse Management System or AWMS™. The features of a non-adaptive system are reviewed and then contrasted with the features of an adaptive system. These concepts are then used in an application example to further demonstrate an AWMS.

Non-adaptive systems more often take the position that reality is represented by the systems internal data and that non-conformant events (or exceptions) should be forced to conform to that internal representation. If conformity cannot be forced, the non-conformant event should be treated as a “logged anomaly” which essentially ignores the event. An inherent design flaw in a non-adaptive WMS is that the “data is reality”, and in order to change reality, the system is first changed and it is assumed the real world will follow. This limited view often results in data inconsistencies due to incomplete or non-existent information updates after one of those exceptions.

On the other hand, an Adaptive Warehouse Management System (AWMS) uses the basic design principle that “internal data models reality”. An adaptive system recognizes inconsistencies in information, and resolves those inconsistencies. Further, an AWMS may often have features that evaluate events as they occur and determine the individual action to take based on the current conditions. “Real-time dynamic optimization” is the term used by VAS to describe this evaluation and setting of a new course of action.

The following example for the Mandate® AWMS starts with a discussion of warehouse locations and location identifiers. An AWMS will typically have unique location identifiers for any place that may contain items to be managed within the AWMS. Examples include fixed locations such as racks of all types, floor positions, bins, rooms, areas, yards, buildings, facility, workstations, “lost location”, stackers, carousels, conveyors, and moveable locations such as trailers, pallets, cartons and totes. Locations in the Mandate® AWMS have an attribute called the “parent location”. The “parent location” of a location indicates the “current location” of the item. The parent location of a fixed location is not normally modified. An example of location parentage is a carton has a parent of a pallet, which has a parent of a workstation, which has a parent of an area, which has a parent of a building or facility.

An AWMS example includes a concept of what VAS describes as “believing the last liar”. This concept is different from “believing the last lie”. A lie is something that is not true, while a liar is a source of information, which is known upon occasion, to provide false information. The notion of believing the last liar implies that new information has intrinsically greater validity than old information. This notion is inherently true because, when evaluating the source of the old information, it too will be discovered to have originated from a liar. Of course, this concept or philosophy cannot be the only basis for making decisions that will establish a current view of reality but it is the basis for adaptiveness. To “adapt” is to “change based on the current environment”.

When put into these terms, no WMS provider in his or her right mind would wish to claim to be non-adaptive. Many will claim that the adaptive (or non-adaptive) features are just part of the specification of the system. In certain cases, this may well be true.

The true distinction of an AWMS is that it has an inherent inclination to accept new information to establish its current image of reality.

Now, for the example: Carton 1234, an assumed unique carton license plate number, is believed to have been placed on pallet 1. Pallet 1 is reported to have been placed in rack location XYZ. For this example, assume the carton and pallet are in their respective desired locations so that the AWMS has no inclination to move them.

The wording of these statements of conditions and events in an AWMS is important. All statements need to be understood as the “most likely truth” and in an AWMS are only believed to be true. This is a recognition that any and all statements of fact have, under certain circumstances, been known to be false. In the statement above, cartons have been known to have been mis-labeled as to make their license plate numbers non-unique. Cartons have also been known to be placed on the wrong pallet or even lost. Additionally pallets themselves have been mis-labeled and placed in the wrong location. Locations have even been known to be mis-labeled. For our example however, since this is the only information known to the AWMS, it will report the actual location of carton 1234 as on pallet 1 in rack location XYZ. This is the perception of reality in the AWMS.

Now, a new piece of information is received by the AWMS. A conveyor barcode reader reports an unexpected carton – carton 1234 at a particular zone. The AWMS will inherently adapt to this situation unless specific conditions are set forth to prevent its adaptation. To adapt, an AWMS “knows” that a single carton cannot be in two places at the same time so an AWMS will remove the carton from pallet 1 and automatically update the inventory for both pallet 1 and location XYZ to reflect the loss of the carton and its contents. The AWMS will then automatically update the location of the carton to reflect that it is now in the reported conveyor zone.

With the newly given input, the AWMS will report the actual location of carton 1234 to be on the conveyor. The AWMS will also report of pallet 1 and rack location XYZ as not including carton 1234. This is the newly adapted perception of reality in the AWMS.

As stated before, an AWMS often has features that support real-time dynamic optimization. If it has these features the AWMS will evaluate events as they occur and determine the individual action that should be taken based on the current condition. This is not as complex as it sounds. Recognize that most operational events are not unexpected – most events are exactly what are expected. Additionally, if the planning process includes only decisions that have to be made at that particular time, exceptions will not result in massive re-plans.

In our example, if real-time dynamic optimization is not supported, the AWMS would normally look at the desired location of the carton, and determine it should be on Pallet 1 in rack location XYZ. The AWMS will attempt to route the carton to that location. Typically, this would cause the carton to be routed to a no-read area where the carton is manually rejoined to the pallet.

If real-time dynamic optimization is supported, the AWMS, detecting the unexpected condition would re-evaluate the desired location of carton 1234. The rules for re-evaluation are defined to minimize operational impacts. Examples of existing Mandate AWMS rules for re-evaluation include 1) check if the carton could be used to efficiently fill a current order and if so use the unexpected carton and cancel any less efficient pending actions to fill that same order. 2) If there is no order for use, evaluate the stock levels for various stock areas and determine where it is best to re-stock the carton. Update the desired location of the carton to reflect the results of the re-evaluation. Then route the carton “toward” this newly optimized desired destination.
To conclude, an adaptive warehouse management system with real-time dynamic optimization is constantly resolving inconsistencies in information and optimizing the best course of action to take based on the current conditions and new information.

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Batch picking or batch order fulfillment is an operation where multiple orders are filled simultaneously rather than filling a single order at a time. Batch picking fulfillment systems can normally improve productivity. Productivity or efficiency of batch picking operations can be further extended through both dynamic and virtual batching. Dynamic batching allows new orders to be incorporated into the order “pick pool” on the fly. With a larger pick pool, efficiency is improved because better, or more efficient orders, may be selected for a batch. Virtual batching is where orders are added to a single batch as existing orders in the batch are completed. In static batching, a batch is created by selecting orders from the order pool, and the batch is complete only when all orders in the batch are complete. Static batching systems are the least efficient batch fulfillment systems. Using real time dynamic optimization, as described later in this document, can further optimize dynamic and virtual batching.

To demonstrate the differences in each of these batch fulfillment systems the following application examples are provided.

Static Batch

Incoming orders are received in two daily order downloads. Each of the daily downloads are processed into two processing (delivery) waves making a total of four daily delivery waves. Once a daily download is processed, new orders cannot be added, modified or deleted from either of the two created waves. As waves are created, product (inventory) is allocated to fill the orders. The inventory allocation defines the pick locations for each of the orders in a wave. Orders within the wave are grouped together in pick batches. The number of orders in a pick batch (batch size) for this example is six, which in this case is limited by the pick cart. In this example the average number of line items (different SKUs) in an order is five. Orders are shipped in a single carton. Each pick batch will require the picker (order selector) to make a “loop” in the fulfillment zone, starting at fixed location and ending back in that location when all orders and thus the pick batch is complete. The software sequences the orders for the order selector to make the shortest trip around the loop. During pick batch creation (download processing) the specific orders selected for each batch may be optimized based on some criteria (i.e. reduction in the number of locations to visit) to help improve efficiency.

Dynamic Batching

This application example allows new orders to be received continuously throughout the day. The received orders include an individual priority code specifying either a delivery wave or just a delivery priority. All received orders are kept in an “order pool”. Pick batches are not created until needed (a pick cart needs a new pick batch). The software creates a pick batch by selecting the orders with the “highest” order priority from the “order pool”. Pick batch optimization may occur just as in static batching. Orders in the “order pool” may be deleted or modified as necessary. Dynamic batching maximizes operational flexibility to place last minute orders and to make modifications to existing orders. There may be productivity improvements due to the larger order pool size that could benefit order selection optimization in batch creation.

Virtual Batching

This application provides for a single “virtual batch” where new orders may be added to the batch as individual orders complete. In virtual batching, the notion of a “batch completing” does not exist. In this example, the batch size is still limited at six by the cart. The picking loop no longer has a beginning or an end, as it is just an endless loop. As orders (or cartons) complete, they are removed as soon as possible from the cart “put” locations (cells) to provide room for a new order. Completed carton removal may be accomplished by several operational means including using ergonomically unusable pick locations as temporary holding places for completed cartons or by providing multiple unloading locations in a loop. Virtual batching is most useful when one or more of the following conditions exist: 1) the picking loop is very long, 2) average lines per container is small or 3) when dealing with multi-case orders. The estimated walking time reduction factor when using virtual batching compared with static batching can be calculated with the following formula:

Static Batching To Virtual Batching Transit Time Reduction Factor =

1 – ( ( ( L – 1 ) / L ) * ( 1 / C ) )

where:

L is average line items per container

C is average containers per order

In this example there are five line items per order and one carton per order. The transit (walk) time reduction factor is 1–(((5-1)/5)*(1/1)) = 1–((4/5)*(1)) = 1–(.8) = .2. Transit time is reduced by 20% if virtual batching is used instead of ordinary static batching.

Additional Optimizations in Dynamic and Virtual Batching

Real Time Optimization of Next Order to Release: Transit time reduction factor can be increased further in a virtual batching application with real time optimization of new order selection. New orders added to the virtual batch can be selected from the available orders based upon which order will complete in the shortest distance from the current location. This optimization is more efficient with the greater number of available orders as provided by dynamic batching.

Dynamic Order Inventory Allocation

Using real-time order inventory allocation can reduce further the transit (walking) time. In many situations an item can be picked from more than one location. Instead of having an order stock allocation process that pre-defines the location from where an item needs to be picked, the software can decide, in real-time, the most convenient place from where to pick the item. Dynamic order allocation not only increases the picking productivity of the operation, but also simplifies the handling of shortages.

Summary

The features described in this document are examples of real time dynamic optimization. Batch picking with carts or modules is an in-expensive approach to increased productivity. Some of the described features may not apply to a specific batch picking application; likewise, other dynamic features may be advisable for that specific application. Carts, modules and systems based on Mandate® based SOFT™ technology, provide ideal solutions for Dynamic and Virtual Batching applications. Real time optimization and Dynamic Order Inventory Allocation are built in features of the Mandate® AWMS™.

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Order fulfillment operations can be greatly benefited through the application of Adaptive WMS principles and associated features. An Adaptive WMS or AWMS™ has an inherent inclination to accept new information to establish its current image of reality and to use that information in determining subsequent action. This principle, when applied to order fulfillment operations yields immense operational flexibility allowing work to be organized as the current situation dictates. This paper describes VAS’ Smart Order Fulfillment Technology (SOFT™) and Mandate® AWMS features and principles as they apply to order fulfillment and provides examples of how they provide nearly unlimited operational flexibility.

At VAS we are passionate about some seemingly trivial concepts and the words used to describe them. One word for which we have a passion is “Plan” or “Planning”. To us, a “Plan” is something that is prone to doom or failure—our mothers taught us this while we were young (i.e. the best laid plans of mice and men…) and Murphy continues to demonstrate that our mothers were absolutely right! At VAS you will notice that when we use the word “plan” or “planning” we preface it with an adjective such as “adaptive”, “incremental” or “dynamic”. The definition of a plan is “HOW to get somewhere”.

The driving or motivating force of an AWMS is not in “planning”, it is in “objectives”. An objective specifies “where we want to go” or “what we wish to achieve” and does not include the notion of “how to get there”.

We also have a passion for the terms “warehouse” and “distribution or fulfillment centers”. We view these facilities as “production facilities”, where the word production denotes action or work not storage or product flow. Likewise we feel the use of the “bucket brigade” metaphor is a poor choice because that metaphor focuses only on movement along a single path and does not address the actions that must be executed.

With that said, does SOFT and the Mandate AWMS plan? It absolutely does, and it plans all over the place and all the time. VAS Postulate #1: the most efficient method of planning is to create a “plan” only to the point at which a failure to complete an action specified in the plan could impact future actions. We call this “incremental planning”. VAS Postulate #2: the most efficient means of reaching an objective is to create incremental plans based solely on current conditions and determination of the single, most efficient next action to reach the final objective. We call this “dynamic optimization”. Within SOFT, many times a single subsequent action is pre-planned. This action is planned based on the assumption that the current action will complete successfully. SOFT does this to mask or hide the planning calculation time so that an order selector or worker has no delay in the presentation of the next action prior to completion of the currently assigned task.

The creation of an adaptive order fulfillment with dynamic optimization begins with defining the order fulfillment objective(s). The most basic fulfillment objective is to “deliver completed orders for shipment as quickly and efficiently as possible”. Other objectives are stated as rules that limit how the operation may be accomplished. Some examples of rules are definitions of order fulfillment (sequence), how cartonization is preformed, SKU mixing in cartons, shortage handling, fulfillment zone sequencing, carton flooding etc. VAS does not use “canned rules” from which one must choose.

Consideration should be given regarding of the establishment of order fulfillment rules. It should be recognized that rules might be established through both operational procedures and internally by the software. The fewer the internal rules the more flexibility the AWMS has in determining how to complete orders. The more flexibility available, the greater the productivity that can be achieved due the freedom available in making incremental plans. Rules defined by operational procedures are easier to change providing greater flexibility.

A few examples of how an AWMS provides operational flexibility will now be discussed. An AWMS normally does not plan any work other than that which may be executed at the current time. Because future work is not pre-planned, great operational flexibility is provided by the AWMS order fulfillment process to add, delete and reprioritize orders.

Another example of flexibility is demonstrated in how AWMS order fulfillment systems sequence orders through completion zones. Normally an order (or an order carton) is moved from one pick zone to the next only when the current pick zone has completed all the items (work) within that zone. However, with an AWMS based order fulfillment system, if there are no rules prohibiting it, a partially completed order carton in one zone may be moved to another zone for work. Once in the new zone subsequent work will be completed in that zone. The AWMS has not forgotten the primary objective of completely filling the order so at some later time, the carton would be re-routed back to the zone where there was work remaining for completion. This feature provides great operational flexibility for a number of circumstances such as a temporary out of stock situation where workflow must be maintained, and workload balancing.

There are other examples of flexibility of the AWMS based order fulfillment system in the completion of work within a single zone. Normally the sequence of item completion is in the sequence of stock locations within the zone. Workers are directed to the next nearest location. However, if a worker goes on break or moves unexpectedly to another position in the zone, the suggested work location could require additional travel time. The AWMS based system allows a worker to identify their current location and a new most efficient action will be planned for the worker. Among other more obvious benefits, this feature provides a means of avoiding aisle congestion by allowing workers to move past congested areas.

Another example of the benefits of an AWMS based order fulfillment system is the way an AWMS handles the completion of an item and avoids shortages. An AWMS order fulfillment system does not normally have rules that prevent it from filling an order with an item regardless of where the item is currently located. This feature provides an operational means to complete orders with stock normally considered un-accessible such as in transit stock. In the Mandate AWMS, this includes filling orders from stock “falling from heaven” that had been lost and is newly found.

There are many other benefits of using an AWMS based order fulfillment system that become evident as one comes to understand the basic nature of an AWMS. Find out how the principles of SOFT and AWMS systems can improve your operations.

AWMS™ is a trademark of Vargo Adaptive Software. The term AWMS™ may be freely used by any party to describe a WMS that has an inherent inclination to adapt to and accept new information to establish current conditions.

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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:

• 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.
• Obtain the average units per order container (i.e. carton) (UC) by either reported metrics or estimation.
• 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 stopwatch. If there is no existing system to measure, set up and measure the time of a simulated operation with real goods, cartons, simulated labels, tapers, staplers, etc. Take many measurements and calculate an average.
• 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.
• 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)

Why batch pick? Because you are not running a gym!