Category: Tech Notes

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Most systems using article sorters to process orders also use automated equipment for product delivery. The automated equipment includes devices such as carousels, stackers, conveyance, sortation, queues, buffers, induction stations etc. Article sorters themselves are a batch-picking device that allows delivered product to fill multiple orders. VAS has a great deal of experience in the management of both the sorter and the equipment that is responsible for the delivering of product to the sorter. This paper identifies and discusses some of the interesting non-intuitive factors and concepts that should be addressed in article sortation systems.

When designing a delivery system for an article sorter there is one design factor that needs initial definition and consideration that will influence nearly all future decisions. That factor is the ability of the delivery system to control the sequence of the product arrival. The degree of sequence control will impact future decisions. This does not imply that either sequenced delivery systems or non-sequenced delivery systems are better or worse, it just identifies that many other design implications will be based on that factor.

Another factor is the determination if the sorter is to operate using a virtual wave and a related issue of the expected order completion rate. Sorters that are to operate using a virtual wave are more dependent upon product delivery sequence while sorters operating using multiple waves have some interesting order completion rate issues that need to be addressed.

Another factor is the determination if the sorter has a “drop station” and a separate “completed order queue”. This factor has significant sorter (and system) throughput implications.

A factor that many designs address is to “organize orders”—to group orders together that have similar SKU requirements to improve efficiency.

A final factor discussed is the consideration of sort rate and its relationship to the number of induction zones, product routing to the induction zones and tray rate.

Product Arrival Sequencing

This factor is primarily determined by the type of equipment that is used for product retrieval i.e. a manual selection process, a carousel, or a stacker. If a manual product selection process is used, the ability to sequence product arrival is inversely proportional to the retrieval efficiency. The same is true when using a carousel, however the inefficiency is normally expressed in a reduction of delivery throughput. Using a stacker, the throughput is not as adversely affected by product delivery sequencing and VAS Engineers have designed and installed systems using stackers that exceeded stacker manufacturers throughput rates by 30% or more while maintaining the same travel speed. The sequencing of delivery of product to a sorter allows a sorter to easily operate in a virtual wave mode. This mode increases sorter productivity and evens out the flow of completed orders.

Operation of a Sorter using a Virtual Wave

To operate a sorter in a virtual wave, if the product delivery system is capable of delivering product in sequence, the management of product delivery should be controlled by only one rule—the fastest completion of orders. This rule dynamically selects product for delivery that will complete the most orders. Of course, when the product is delivered, it is used for any and all orders that have a requirement filling the most complete first. If the delivery system is not capable or has limited product delivery sequencing ability, more complex rules are applied. The result of the application of these rules will lead to orders completing in bunches, with an initial period of time with no orders completing and then a group complete together. Picking without virtual waves and with limited product delivery sequencing the bunching of order completion is most significant.

Operating using Separate Drop Stations and Completed Order Queues

Sorters operating with a separate “Completed Order Queue” will have a significant throughput advantage over a sorter without such a queue. This is because the sorter is not able to start filling a new order until the previous order is removed from the drop station. The order completion bunching further impacts this situation further reducing the system capacity. To understand this, consider a sorter that picks static waves. Imagine that each delivered product (SKU) will normally be used for 3 orders. As the last SKU in the order arrives, the last 3 orders would be completed according to the example. The next to the last SKU arrival would also be used for 3 orders, however if the delivery sequence were not precisely controlled those three orders were not the same 3 as were completed by the previous SKU. That would mean that the next to the last SKU delivered also completed 3 orders that were most likely not the subsequent orders. The thought process goes on to the final 10 SKUs delivered. If the sorter had 300 drop stations (orders), the chances would be that nearly 30 orders would be completed with those 10 SKUs. Order completion is “bunched” at the end of the wave, resources for packing or completion of orders are thus backlogged due to the bunching and the sorter drop stations are not available for new orders, induction must stop and sorter efficiency limited.

Organization of Orders

Many sortation designs consider or attempt to “organize” orders grouping orders together that have similar SKU requirements to improve efficiency. VAS engineers have successfully implemented designs where groups or “categories” of product have been delivered to a sorter filling stock category orders. However, we have had no success in devising a means of grouping a particular group of orders together for picking. This type of a problem requires a technique called “linear programming”. In addition to requiring great computing resources, our experience is that the efficiency gained by “cherry picking” good orders to group together is short lived. You wind up with a bunch of bad orders and their pick efficiency is even worse together. The resulting net overall gain is absolutely nothing.

Sort Rate

The sort rate of a sortation system is determined by many factors, however realistic estimates can be made if the proper consideration is given. Probably the single most elusive factor to consider is the non-productive time. The sorter tray rate, the number of induction and corresponding drop zones, and the ability to select the induction zone for each SKU arrival will determine the resulting sort rate. VAS engineers are experts in the calculation of the sort rate.

Distinction between a WMS and a WCS

A note should be made on the distinction between warehouse control, and warehouse management. In the MHE industry, there has been somewhat of an inclination to create a layer between equipment control and warehouse management systems. Some call this layer a “Warehouse Control System” or a WCS. VAS feels that such a layer is not a good idea. Today’s systems are driven by data. This is particularly true with automatic article sorters and their associated delivery system. Good equipment control needs good data in order to make real-time decisions. The availability of the data necessary to make these decisions is embodied in the WMS. We feel that the WCS concept has arisen out inadequacies in software provider’s offerings and “software product packing”. Packaging of a WMS does not require that the WMS have all the features of any or all other WMS but only that it have all the features necessary for it’s intended application current requirements and a means to address future improvements. The Mandate® AWMS™ with its adaptive real-time nature is a WMS that is ideally suited for a facility that intends to use article sorters for order fulfillment.

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We often are asked questions such as: “If mobile workstations such as smart carts are such a good idea and they are so simple, why haven’t they dominated order fulfillment operations?” A good question. There is really only one answer. The concept of mobile workstations such as smart carts and mobile order fulfillment modules have always made sense, however their implementation lacked just one ingredient—a dependable, inexpensive standardized means of communication. Sounds too simple? Ideally Smart Carts and mobile order fulfillment systems provide an order selector (picker) with a mobile workstation that contains all the tools and data necessary for the efficient completion of orders. They can be moved to the product rather than requiring product to be moved to a stationary workstation. If the efficiency of operation of both workstations is identical, then mobile stations will make sense where mobile system equipment costs less than alternatives requiring product movement to workstations.

Our staff’s experience with mobile order fulfillment workstations started in the mid 1970’s with a system called WICS installed at Robbins Air Force Base in Georgia. What we believe was the first such system used mobile workstations on modified Crown stock picking vehicles and consisted of a computer, display screen, keyboard, printer, 80 column card reader (the old IBM punch cards), and a badge reader. At that time, there was no off-the-shelf communication or computing hardware available and literally the entire system, electronics as well as software (both operating and application), had to be constructed from scratch. Workers (order selectors) were taken to the product. This system included fully automated computer controlled routing of the vehicles to the product without worker intervention. Once at the location, the worker was instructed as to the action they were to take. This system operated up until the early 1990s. Of course, the design and construction of the communication and computing hardware was expensive and thus limited the application of the mobile order fulfillment technology to a small customer base.

Technology advancements in computing hardware began to blossom during the late 70’s and early 80’s. Less expensive, commercially available computing platforms that could be used for mobile order fulfillment operations emerged (single board computers, Apple, Multi-buss, etc. and later IBM PC’s). The availability of off–the-shelf computing hardware provided one of the ingredients that were necessary to make mobile order fulfillment systems both cost effective and widely applicable.

Following the availability of suitable computing platforms, operating system software began to emerge that could be applied to projects. The operating system software further reduced the implementation costs of mobile fulfillment systems.

However, even with the lower costs of both computing hardware and operating software the cost of development was still too high for mobile order fulfillment systems to make sense when compared to fixed workstations. The major factors that limited their appeal were the rapid changes in technology and the lack of standardization of both hardware and operating systems. These factors created a very short life for the developed system sometimes to as little as 24 months. Enter the 1990’s and the years of standardization. In the late 80’s and the early 90’s both inexpensive computing hardware and operating system standards emerged. These standards made it possible for development work for mobile order fulfillment operations to have a much longer usable live.

Finally mobile order fulfillment stations began to make sense if it were not for one missing ingredient—a communication system. As mentioned early in this paper, the earliest solutions required that communication hardware be designed and constructed from scratch. As the years progressed, off-the-shelf communication solutions emerged, however until the mid to late 1990’s these solutions were all proprietary, each vendor insuring that their solution was NOT compatible or operable with any other vendor. Not only were the communication systems proprietary, the vendors embedded it into their own proprietary mobile computing hardware thus negating much of the progress that had been made in hardware and software standardization.

Although order fulfillment solutions could be built upon any and all these technologies, the cost of the unique development effort increased and the application became less universal. Of course, there were means for development to allow configuration for the use of competing technologies, but these raised costs. It was equivalent to designing an automobile engine to run on gasoline, diesel, propane, and hydrogen.

Enter 802.11—In the mid 1990s a specification was created that standardized wireless communications. This standard provided the last ingredient necessary to create low cost efficient mobile workstations. In the few ensuing years, equipment conforming to that standard emerged and in the last two years, this standard has gained nearly universal acceptance. This standard allows engineers to construct systems with low cost commercially available hardware and utilize standard interfaces that would not require modification as equipment vendors modified their own offerings. VAS recognized this benefit and started using 802.11 before the specification was ratified.

Mobile order fulfillment applications can now be constructed with features that are no longer subject to nearly immediate obsolescence. The application of such features too many installations reduces the cost of the development of the features and thus reducing the cost of each individual installation.

Mobile order fulfillment systems make sense when their productivity benefits, compared to their associated cost, provides the best return on investment. Reliable, low cost, standardized wireless communication now makes this possible!

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One of the best techniques to improve productivity in a piece picking operation is the clustering of orders to be picked together. Of the several options to cluster orders, picking carts are frequently the first selected, as they are the least expensive and most flexible option. Regrettably picking carts often neither fully-achieve the originally calculated productivity improvements nor are well accepted by the pickers.

Following are the thirteen principal conditions that prevent picking carts from reaching their full potential and recommendations about how to address them.

1. Bad mechanical or ergonomic design

Often, the physical cart design is not given proper consideration. Each facility or operation has unique requirements that dictate the cart design. Small details can make a big difference. In the end, one of the largest factors in achieving productivity objectives is user acceptance. The adding of a shelf, or a step can be the key. Maneuverability is of extreme importance.

2. Congestion

Carts will increase congestion just because they occupy more space. If congestion is an issue, the carts mechanical design needs to insure that carts may easily pass one another in the work areas. Fast moving SKUs may need to be replicated (multi-locations for the same SKU) in different areas of the rack (contiguous locations do not help with congestion). Software needs to support this feature. The good thing is that congestion is easily predicted, and a well-designed system should be able to avoid this pitfall.

3. Mental sorting of orders

Requiring workers to mentally sort their orders reduces efficiency and adds unpredictability in the productivity of the system. Since the primary productivity improvement in the use of picking carts is travel reduction, elimination of any potential backtracking as a result of mental mis-sorting is essential.

4. Inefficient procedures

When picking carts are introduced, operating procedures are normally changed. There is always the temptation to make “other” process improvements when a change is incorporated. Many times these “other” improvements reduce the efficiency making it impossible to determine productivity gains resulting from the use of the carts. Streamlining the process through the elimination of unnecessary steps should be the prime objective. Where possible, additional process improvements should be delayed in order to measure the actual productivity improvements from the cart. Streamlined processes are the second most important factor in gaining worker acceptance.

5. Inefficient use of cart order fulfillment space

Order fulfillment carts reach their highest productivity when the number of orders being processed on the cart is maximized. As individual orders are completed yet continue to occupy an order fulfillment slot, the cart productivity decreases. Cart mechanical designs, operating procedures and order fulfillment software should allow dynamic re-assignment of order fulfillment locations (virtual batching). This feature will allow carts to continuously operate at maximum productivity by maintaining full utilization of all the accessible fulfillment locations.

6. Order starvation.

Carts operate most efficiently with maximum order and pick density. If there are insufficient orders to fill a cart, the supporting software should make provisions by allowing the cart to efficiently move to another work zone where there is available work or possibly wait for additional orders. A real problem with order starvation is that workers seem to be very busy (they are never idle) but what they are doing is walking too much and accomplishing very little. A system that recognizes this condition and compensates for it will maintain higher average efficiency.

7. Handling of shortages

Having pickers replenishing locations depleted of a requested SKU can be highly inefficient and unnecessary. For instance, if this is the only location for the SKU, the only possibility is to short the order. On the other hand, if there are other locations with the same SKU the system could re-allocate the picking location. An “adaptive” system that can automatically handle the exception will also drive up the average efficiency.

8. Restraining picker’s hand

Unfortunately, most pickers have only two hands, and they cannot spare either one of them. Hand held terminals, scanners, or clipboards reduce this key resource by 50%. Picking a device up, setting it down, pulling it from a holster, replacing it – all these operations can severely reduce productivity. Look for systems that keep your workers key resources as free as possible.

9. Directing pickers to locations already determined as out of stock

Make sure that the system directs pickers based on the latest current data; adapt the execution in real-time to the latest known system conditions. Reliance on printed lists produced much earlier for allocation works just fine as long as there are no exceptions. However, once an exception occurs, productivity can be decreased dramatically. Although these events may be rare, the recovery is very expensive. Real time systems, if properly designed, avoid these situations.

10. Selecting containers too small for the order (cartonization error)

If cartonization is system-directed, the picker needs an easy way to split orders when they do not fit in their cartons. In order to help the picker to make the best decision, provide him/her with information about pending picks for the order.

11. Providing incomplete information to the picker

In order for pickers to perform their job they need adequate and complete information. Displays that cannot provide complete information (i.e.: hand held terminals, pick to light displays) slow down the picker.

12. Forcing the wrong technology for a function

Today we have new technology everywhere. Just because it is new or intriguing does not mean that it will improve your productivity. For example, voice technology may seem perfect to free workers hands, however in providing information to a worker or operating in a noisy environment voice may fall short. Simulate the proposed system to determine if the technology would help. Do not be shy about using new technologies, several if needed, to simplify the picker’s job. For some functions a scanner is the best tool, for others voice is best, for others a full display is required. The added productivity should quickly pay back for such technologies.

13. Inflexibility in allowing workers to better complete their jobs

No matter how optimized the process is, often the workers (at least the experienced ones) will find an alternate way to accomplish a task. Allowing workers to reverse their picking path, skip a job, modify the picking path, carry more orders, etc. will pay off in increased productivity.

Picking carts can significantly increase productivity with a very low initial investment. This is particularly true when comparing them to more automated devices such as tilt tray sorters, carousels, pick to light systems, etc. They are among the most flexible solutions and one of the few automation options that allow incremental growth to meet future requirements. Like all solutions, in order for pick carts to meet expectations they require a good design. The potential productivity increase through use of pick carts is something that is easy both to simulate and to emulate. Simulation is done mathematically, while emulation is done empirically using actual working conditions including workers. A good system provider is capable of providing this assistance prior to any investment.

For over 30 years greedy algorithms have been used to solve optimization problems. This paper describes their application to logistic problems.

Regardless of effort, inconsistencies between data and “what is” will occur. Error recovery must be considered as important as error prevention in the operation of a distribution center.

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Voice directed Picking is receiving a lot of notoriety these days. This paper attempts to present a fair comparison of benefits and shortcomings of VDP (Voice Directed Picking), RFP (RF Directed Picking), and PTL (Pick to Light). The compared technologies all allow real-time pick decisions. Paper based picking is not included in the comparison because it does not allow significant real time optimization of the picking process.

Separation of a “Human (Worker) Interface” and Functionality

Many times when comparing voice directed picking to other pick technologies the distinction is blurred between the human interface and system functionality. In order to make a fair comparison, functionality must be separated from the human interface. With any of the compared technologies, it is possible to dynamically dispatch work and retrieve completion information from the worker. Vendors of any of the technologies may not actually provide dynamic work optimization, which may lead to a false conclusion when making any comparison. This paper is focused only on the human interface.

Human interface Overview

VDP—A portable terminal that primarily uses voice (aural) commands to direct the worker and primarily relies on speech recognition of the worker to obtain completion information

RFP—A portable terminal that primarily uses visual commands to direct the worker and primarily uses a scanner to obtain completion information from the worker

PTL—Fixed hardware that use visual commands to direct the worker and the worker uses buttons to report completion information

Although the primary interfaces are as described above, each of the technologies may use other means to communicate with the worker. For example, some VDP terminals may be equipped with a bar code reader, a PTL or RFP system may use aural communication through the use of a sound transducer, and a PTL system may also use RF terminals. For the purpose of the comparison, only the primary interface is considered for the given technology.

Worker (Human) Characteristics and Limitations

In conjunction with the picking device, the resources or characteristics of workers must also be considered. A crucial resource of a picker (or selector) is the worker’s hands. Pick-to-light systems and RF systems rely on the worker’s eyes. Voice systems rely on the worker’s ears and memory. Visual information is captured as needed by a worker—the worker may select relevant information as needed. Aural information is “lost” if not captured by a worker and must be re-requested. Another characteristic of humans is that their individual speech is subject to change. These factors need consideration in evaluation of a human interface.

The following table compares the three systems based on several features of high relevance in fulfillment systems (1 is not so good, 2 is good, 3 is excellent):

	VDP	RFP	PTL
Pick Productivity in High Density Areas	2	2	3
Pick Productivity in Low Density Areas	2	3	1
Freedom to use both hands	3	1	3
Ability to get directions	2	2	3
Providing completion information	2	3	3
Initial system “training”	1	3	3
Simultaneous work at same location	3	3	1
Dependence on selector’s memory	1	3	3
Far distance identification of next location	2	3	1
Last-steps identification of next location	1	2	3
Reduction of pick errors	2	3	2
Correction of errors	2	3	1
Battery replacement	1	1	3

None of the compared systems directly reduce walking. The factors that affect productivity are related to the picking tasks once the worker is at the next pick location. However, in identifying pick locations, PTL becomes the best choice in high-density areas while RFP is the best choice for long travel distances. Walk reduction strategies (i.e.: batching or clustering) are not within the scope of this paper.

Some “trained” voice systems have problems dealing with workers not speaking normally (i.e.: workers with colds). Often these systems require re-training of the worker’s terminal.

In pick-to-light systems, selectors validate the pick by pushing confirm buttons. In voice systems, selectors read back check-strings. Long check strings negatively impact productivity. Short strings could create accuracy problems.

It is becoming common practice for voice systems dealing with long check strings and/or noisy backgrounds to provide workers with hand held scanners for product validation. Such a system is a hybrid of RFP and VDP and should be named “Voice Assisted Picking”. Regretfully, the primary VDP benefit of total availability of the worker’s hands is negated in these systems.

Low lighting environment pick areas can be problematic for RFP systems while noisy environments are normally not suitable for VDP systems.

The price of pick-to-light systems increases with the number of pick locations and it is independent of the number of workers. On the other hand, the price of VDP and RFP systems increases with the number of workers and it is independent of the number of locations.

A Final Note on Dynamic Optimization

Each of the considered technologies has the inherent capability of allowing real time decisions to direct the workflow. It is only through dynamic optimization that any of the systems can reach their full potential. Although not part of the consideration for selecting a human interface for the picking operation, dynamic optimization is the icing on the cake of the picking process.