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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.

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As specific business needs dictate, the following requirements should be considered when evaluating a batch pick system. The batch pick system should:

• Either increase picking productivity and reduce the overall operational labor OR increase operational capacity and possibly eliminate facility expansion
• Provide user (selector) interfaces that are simple, easy to read, visible for all work positions and free the use of selectors hands as much as possible
• Satisfy order accuracy requirements
• Provide physical compatibility of carts, modules and stations within aisles and the overall facility
• Provide seamless integration of carts, modules and stations of multiple types
• Allow highly efficiently handling of normal job processing as well as exceptions
• Provide a simple means of allowing customized pick exceptions
• Support multiple picking strategies (pick and pass, bucket brigades, gather and pack, etc.
• Minimize release and setup time for complete & new orders
• Organize work to minimize transit time
• Allow selectors to re-locate and change direction of their assigned work
• Allow efficient transfer of containers between locations
• Adapt, in real time, to operational variances and user modified conditions
• Allow simple inclusion of new storage locations and deletion of existing locations
• Allow simple organization of the pick path sequence suggested by the system
• Minimize disruption of existing operations during installation
• Minimize modifications required to existing software system

In addition to the above stated requirements, the following system features are also worth considering:

• Number of and ergonomic suitability of batch pick cells
• “Next start” order optimization
• Efficiency in utilization of pick cells, allow staging to avoid wasting
• Battery life
• Multiple validation options for the picked item:
  • No validation
  • Scan storage location
  • Scan item UPC
  • Scan storage location or item UPC
  • Scan storage location and item UPC
  • Full visual validation
• Multiple validation options the correct order to pick:
  • No validation
  • Scan cell
  • Scan container
  • Scan cell or container
  • Scan cell and container
  • Voice assisted picking
• Put lights options
  • Single light per cell
  • Put lights—numerical display per cell
  • Put lights with one confirmation button per vehicle
  • Put lights with one confirmation button per cell
• Automatic drawers with push/pull mechanisms
• Zone inventory managed by:
  • Host computer
  • Batch pick system
• Cartonization:
  • Host cartonized (system has to allow selectors to split cartons in case of cartonization error)
  • Selector cartonized
• Vehicles
  • Push cart
  • Self-propelled cart
  • Cherry picker vehicle (flat rack and carousel rack)
• Storage locations
• Fixed and non-fixed SKUs
• Single and multiple SKUs per location
• Single and multiple locations per SKU
• Multi-zone operations
• Video camera to help selector “see” what is in front of the cart
• Internal phone system/PBX connection to workstations
• Shortest path information to reach the next location from any place
• Pick-Through-Zero for “opportunistic” cycle count
• Weight validation

Smart Cart Vendors

SOFT™ supports all the listed features for batch pick systems and can implement any additional features that the customer may require for their specific application.

Vendors:

• Vargo Adaptive Software
• Able Storage Systems
• Innovative Picking Technologies
• Knapp
• Lightning Pick