Confidently Committing to a Distribution Center Design - Part 2: Developing a Throughput Design Tool

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Supply ChainAdvisors LLC
Confidently Committing to a Distribution
Center Design When Demand is Unpredictable
Part II
Developing a Throughput Design Tool and Determining Pick Strategy

Commonwealth Supply Chain Advisors • 20 Park Plaza, Suite 400 | Boston, Massachusetts 02116
(O) 617.948.2153 | (F) 617.507.6112 | www.commonwealth-sca.com
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Table of Contents
Introduction ...................................................................................................................................................................... 3
Why the Distribution Center World Revolves Around Picking ............................................................................................ 4
Summary of the Throughput Design Process.................................................................................................................... 5
Define Order Types ..................................................................................................................................................................... 5
Determine Return on Investment Parameters ........................................................................................................................... 6
Define Peak Season and Surge Times ...................................................................................................................................... 6
Compile Order Data .................................................................................................................................................................... 6
Create a Labor Profile ................................................................................................................................................................. 6
Create a Throughput Profile Chart ............................................................................................................................................. 7
Calculate Labor Performance ..................................................................................................................................................... 7
Apply Growth Rates .................................................................................................................................................................... 7
Project Future Costs.................................................................................................................................................................... 8
Benchmark Performance ............................................................................................................................................................ 8
Create a “Long List” of Potential Solutions ................................................................................................................................ 8
Determine Pick Strategy ..................................................................................................................................................10
Test Alternate Visions of the Future .................................................................................................................................11
Evaluate Other Factors ...................................................................................................................................................11
Appendix A: Overall DC Design Process .........................................................................................................................12
Start Planning Your Distribution Center Today .................................................................................................................13
Additional Resources: ............................................................................................................................................................... 13
About Commonwealth Supply Chain Advisors ........................................................................................................................ 13
About the Authors ...........................................................................................................................................................14

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Confidently Committing to a Distribution Center Design
When Demand is Unpredictable - Part II
Developing a Throughput Design Tool and Determining a Pick Strategy
Introduction
Part I of this series focused on how companies can develop a storage design tool, a data model to represent the various types of storage
required in a distribution center (DC) with varying levels of growth and SKU proliferation. This is a vital first step in designing a DC –
determining how large the forward pick areas and overstock areas must be, and what storage mediums are required. Armed with this
information, major strategic decisions can be made such as whether to expand a DC, whether a new DC will be large enough to handle
growth, and whether or not to consolidate multiple facilities under one roof. A major goal of DC design can be satisfied by using this
tool: efficient space utilization.
But space is only part of the DC design equation. To have a meaningful impact on the corporate bottom line, the DC must continuously
improve labor efficiency and enable improved customer service. To meet these goals, important decisions must be made as to the
processes which will be used within the operation, and what forms of warehouse automation are required to support those processes.
This installment of the series on DC Design (Part II) will focus on building a Throughput Design Tool with an eye to determining an
overall Pick Strategy and specifying appropriate material handling equipment.
Supply ChainAdvisors LLC
Space
Utilization
Customer
Service
Labor
Efficiency
Part I of the DC Design Series can be accessed by visiting http://www.commonwealth-sca.com/resource-library/

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Why the Distribution Center World Revolves Around Picking
When determining optimal processes within the DC, it is usually advisable to begin with evaluating pick methodology, and then developing inbound and replenishment processes to support the picking methods. There are several reasons why picking should usually be “the straw that stirs the drink” in the DC:
 Product is generally put-away or replenished in a small number of large transactions, and gets picked in a large number of small transactions. Thus, outbound labor per unit is higher than inbound labor per unit, and greater returns will be achieved by optimizing outbound labor than inbound. (Example: 1,000 units of a SKU are put away all at once when a full pallet is deposited in the forward pick area. Those 1,000 units are picked and shipped in a series of 500 orders, each of which contains two (2) units each. Pick labor is exponentially higher than put-away labor).
 Outbound processes usually have very short cycle time requirements. There is generally a narrow window between the time an order is received and when it must be picked and shipped. Processes must be designed to facilitate picking which is not just labor efficient, but also fast.
It is for these reasons that it usually makes sense to begin the design process with an evaluation of picking requirements, since this is the biggest opportunity to reduce labor and improve customer service. This “bias” towards pick processes over inbound and replenishment will be manifested in the following ways:
 A short replenishment interval for the forward pick area is advisable in order to increase SKU density and reduce walking, even if this means that additional labor must be added to replenish pick bins. Generally speaking, pick labor savings will still offset additional replenishment labor.
 The bins in the forward pick area will be slotted in the optimal pick sequence, rather than the optimal replenishment sequence.
Exception to the Rule: Multi-Step Replenishment
There are times when optimizing bins in forward pick can add excess labor to the replenishment process, and a more complex replenishment methodology may be worth considering. For example, the overstock area may contain very large pallet level quantities of a SKU, and be slotted in very random order. Since travel distances are so great as product is picked from overstock, it usually makes sense to group these picks in overstock bin sequence. However, if the replenishment consists of full cases picked to a pallet, when that mixed-SKU pallet is brought to the forward pick area to be put-away, it may be impossible to follow a logical travel path as the cases required can be buried at the bottom of the pallet. While the pick sequence was optimized, the put sequence is not. Given the greater travel distances in the overstock area, this may be an acceptable trade off. However, there are two other processes companies have employed in an effort to make the trade-off more equitable:
1. Pick to a sub-divided movable unit: Special fixtures can be designed which essentially allow a multi-shelf cart to be affixed to the fork of a multi-level order-picker. Product is picked and placed in a designated shelf on the picking unit. When it is time to put product away, few if any, cases are buried under others, and the put-away can be done in bin sequence. There may be some trade -offs, however, in that less product can usually be picked to a sub-divided fixture than to a pallet alone.
2. Multi-step replenishment: This process is used when there is a very frequent replenishment interval to forward pick (such as multiple times in a day or week). It involves setting up a “ready reserve” or staging area between overstock and forward pick. The ready-reserve area is slotted in the same sequence as the forward pick area, but contains a somewhat larger supply of each SKU than forward pick. The idea is that the time consuming move from random, palletized overstock is made into this ready reserve area. The time-sensitive replenishment to forward pick is made from this ready reserve area – picked and put in the same sequence. While the overall amount of labor required for replenishment may actually increase, the timing of the critical forward-pick area re-stock is shortened. In certain operations, this trade-off can be worthwhile.

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Summary of the Throughput Design Process
The following sections outline a recommended methodology for designing a throughout design tool. A general summary of the steps involved is shown below.
Define Order Types
When modelling outbound product flow, it is first important to define the various types of orders which can be picked. Create a few distinct order profiles based upon how an order must be picked.
Sometimes it is easiest to do this by sales channel, as each channel sometimes has a distinct profile. For instance, e-commerce orders may consist of one or two lines, and be drawn from a large SKU set. Wholesale or retail orders may have dozens of lines but may be drawn from a smaller SKU set.
Key order attributes to look for when defining order types are:
 Lines per order
 Pieces per line
Define order types
Determine ROI parameters
Define peak season and surge times
Compile order data
Create a labor profile
Create a throughput profile chart
Calculate labor performance
Apply growth rates
Project future costs
Benchmark performance
Create a “long list” of potential solutions
Determine pick strategy
Test alternate visions of the future
Evaluate other factors
Determine pick methodology (next installment)

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 SKU set (number of potential SKUs which the order can be drawn from)
 Multi-delivery orders (i.e. ship the same SKUs to multiple stores)
 Pick type: piece pick, case pick, pallet pick
Determine Return on Investment Parameters
There are usually numerous varieties of material handling solutions that might improve the operation of a given DC. However, only a fraction of these often prove to be cost-justifiable.
One of the goals of a throughput analysis is to quickly eliminate solutions which will not provide an attractive return-on-investment (ROI) to the company, and focus energy around solutions that will. In order to do this, a thorough understanding of the operational goals and ROI parameters is vital. This will usually involve executive-level participation.
At the outset of the analysis, it is important to answer questions like:
 What about the operation are we trying to improve?
 How do the possible operational improvements translate to value?
 Are we trying to reduce labor costs?
 Are we trying to reduce order fulfillment cycle times?
 What is the longest acceptable period of time for the solution to pay for itself? (the ROI period)
Define Peak Season and Surge Times
It is important to arrive at both an average throughput model as well as to calculate the worst-case scenario in terms of throughput. For many companies, this means looking at two key factors:
 Seasonal peaks
 Daily surges
Many companies see significantly increased volumes of business during certain times of the year (for example the 4th quarter retail holiday season). Calculating seasonal peaks generally involves comparing outbound order data from a normal period with that of the peak season to establish a peak factor (ex.: 22%).
Companies often have daily surges in volume as well. For example, many customers may wait and place orders right before the distributor’s published order cutoff time, causing a surge of orders during this period. This daily surge (the busiest hour of the day) should also be calculated.
When determining a worst-case scenario for throughput, both of these factors must be combined (i.e. one must look at the busiest hour of the day during the busiest season of the year).
Compile Order Data
The data required to analyze throughput is generally a detailed history of sales orders for a period of time, ideally 12 months.
This data set should include line-level detail for each sales order. Key fields can include:
 Sales order #
 Sales order type (see above section)
 Line #
 SKU #
 Quantity to be picked
 Cartonization data
 Date Processed
Create a Labor Profile
In order to rapidly evaluate the ROI for various forms of material handling equipment, it’s important to understand the makeup and costs of the current workforce in place within the warehouse. It must be defined, at as granular a level as possible. At a minimum, functions like picking, packing, and shipping must be broken-out as categories, but it can also be immensely helpful if the labor force can be further categorized by what order types the worker is picking, packing, or shipping. This is not always an easy task, especially if workers

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are cross-trained and play multiple roles on any given day. In these instances, it may be necessary to examine an hourly breakdown of labor by worker, if this is known.
The output of this exercise is a table which lists each role, and the number of full-time equivalents (FTEs) or Labor Hours spent performing that role on both an average and peak period.
At this stage of the analysis, it’s important to remember that the information does not have to be perfect. The goal is to create a general ROI model to begin narrowing down the list of potential processes and technology that can improve the operation.
Once the number of FTEs performing each task has been established, the cost of each FTE must be factored in. It is usually advisable to look at “fully loaded” rates which include salary, payroll taxes, and benefits. Calculate the total annual payroll by job function.
Create a Throughput Profile Chart
Once these parameters have been established, then a Throughput Profile Chart can be created. This chart looks at several characteristics of each of the order types:
 Total orders per day
 Total lines per day
 Total pieces per day
 Total outbound cartons per day
These factors must be calculated for all order types, and for both average and peak/surge periods.
When the total volumes are calculated, then averages can be compiled including:
 Average lines per order
 Average pieces per line
 Average cartons per order
It is generally useful to further break these numbers down to the hourly level, and in some cases to the minute. Labor standards are often based on hourly work levels, and material handling capacities are often based on volumes at the minute level. Additionally, it is often useful to perform some basic statistical analysis on the calculated averages to ensure they are representative of “typical” values (i.e. one extremely large order skewing the average lines per order).
Calculate Labor Performance
The volume of outbound product flowing from the DC has now been established. It is next necessary to merge this information with the labor profile which was compiled earlier. The goal of this step is to turn the throughput information into a set of standardized key performance indicators (KPIs) which can be used to benchmark the performance of the DC.
The most important KPI to calculate when analyzing a piece-pick distribution operation is usually hourly lines picked per worker. As long as the individual items picked are relatively small, then the unit count picked is generally not as important as lines picked. For example, the difference in time between picking three pencils and ten pencils is negligible, making the line count a more meaningful measure in this instance. Also, many inventory systems track piece counts of bundled units, making handled pieces impossible to track.
However, for case-pick operations, or other picking where items are bulky, the unit count can make a more significant difference in evaluating pick rates.
Apply Growth Rates
Once the current throughput of the facility is calculated, then growth rates can be applied. Just as with defining the ROI parameters, executive participation at this step can be very useful. Some of the questions to be answered include:
 What annual rate of volume growth is expected in the operation?
 Do growth rates vary based on the type of order being shipped?
 What is the time horizon for which the analysis will be performed?

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A cautionary note: sales forecasts are often based on a dollar value of growth which is predicted. This does not often translate one-to- one into DC volume growth. Growth may be expected to be greater for certain high-dollar-value products which can skew the results somewhat. It is important to get a forecast for outbound distribution volume.
As has been noted previously, determining a growth forecast can often be a challenging task when the future is uncertain. Just as when the Storage Design Tool was created, the Throughput Design Tool must be architected to allow key variables like growth rate to be manipulated, with the overall results displayed in real-time.
When growth rates are applied, a separate table should be created listing the projected future throughput of the building in pieces, lines, and cartons, for both average and peak periods.
Project Future Costs
Once the growth of throughout has been projected, it is next necessary to calculate the increased annual labor cost required to process this throughput. The baseline assumption which should be made is that the rates at which orders are picked, packed, and shipped by workers will not change.
Project what the annual labor cost increase will be if the volume growth projections are realized but there is no change to distribution processes or technology.
Benchmark Performance
Once KPIs have been established, it is now possible to benchmark the performance of the DC and to begin to narrow down the list of potential solutions.
Published benchmark data on DC performance rates can be found from numerous sources. It is wise to take a realistic view of this data. Since handling characteristics can vary so widely from one operation to another, only a few benchmarks are really of broad value. These include pick rates (broken out by piece picking and case picking), pick accuracy, and order turn-around times. There are many other published benchmarks which Commonwealth has found to be of questionable value. For instance, “lines received per hour” can vary tremendously due to a number of factors. The receipt could be a small parcel that contains six lines and can be processed very quickly. Another receipt could also contain six lines, but could consist of an entire container of product with thousands of cases to count and label. A broad KPI in this area is of little value.
Commonwealth Supply Chain Advisors maintains a detailed table of dozens of different types of pick methodologies and generally accepted rates at which the picking can take place. Even with this detailed data, it is vital to have a seasoned expert review the specific requirements of the operation to determine what pick rates might be attainable with process changes. There are a number of very subjective factors which influence pick rates which cannot be listed in a matrix, including: relative size of the product, type of data confirmation which occurs, method of inserting product into an order container such as a box or envelope, etc. An experienced set of eyes can often weigh some of these subjective factors and help set realistic expectations for pick levels.
Create a “Long List” of Potential Solutions
To ensure that all viable processes and technologies are considered, it is important to start with a complete list of available solutions. Commonwealth uses a list similar to the one in the box below as a starting point for evaluation. The list is divided first into Pick Strategies and Pick Methodologies.
Pick Strategies consist of three basic ways to pick product (vehicle-based, conveyor-based, and goods-to-picker), along with some variations of each. Pick Methodologies refer to the process used to pick; the same methodology can often be used for all three pick strategies (example: zone picking). See the checklist of potential solutions to consider below:

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Packing
Pick-to-shipper
Wave management
“Wave-less” picking
Re-sequencing
Buffering
Sorting
Pre-sort
Recirculation sequencing
Vehicle-based picking
Low level
oCart-based
oPallet truck-based
Multi-level
oMulti-level order pickers
Conveyor-based picking
Pure conveyor system
Carts with conveyor
Powered vehicles with conveyor
Goods-to-picker systems
Carousels
AS/RS
oMini-load
oShuttle
oOther
Robotic
Cluster picking
Types:
oConveyor-based cluster picking
oCart-based cluster picking
oPallet-truck-based cluster picking
oOrder-picker-based cluster picking
Features:
oEarly out
oDynamic re-batching
Zone picking
Sequential zone pick (pick and pass)
Simultaneous zone pick (pick and consolidate)
oVNA pick for slow movers
oAS/RS pick for slow movers
oDiverse size items
Conveyor-based vs. cart-based
2. Pick Methodology (Covered in Part III)
Batch picking
Primary pick
oOne SKU to one MU
oMultiple SKUs to one MU
Secondary pick/put
oSingle line orders
oSingle unit-single line orders
oMulti-line orders from homogenous MUs
oMulti-line orders from mixed MUs
oVarieties
Pick-to-tote
Pick-to-pallet
Pick-to-tow-vehicle
oSort methods
Manual sortation
Unit sortation
Single-sort
Multi-sort
3. Post-Pick Strategy (Covered in Part III)
1.Pick Strategy
Checklist of Potential Solutions
Pick Methodology (Continued)

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Determine Pick Strategy
The next step is narrowing the list down from the universe of potential solutions to a “medium list” for closer consideration. The easiest way to make this “first cut” is usually to perform a general cost-based ROI analysis of the three major pick strategies:
 Vehicle-based picking
 Conveyor-based picking
 Goods-to-picker systems
Some of the solutions on the list may be able to be crossed off quickly and intuitively, without extensive analysis or benchmarking. For instance, if a company employs ten workers in the DC, and is seeking a two year ROI for their solution, it is possible to immediately eliminate solutions like goods-to-picker systems, since the known cost of these solutions is generally hundreds of thousands or even millions of dollars. Even if two-thirds of the labor costs could be reduced with this technology, two-year savings would be about $400,000, not nearly enough to pay for an Automated Storage and Retrieval System (AS/RS) (note: such a system may still be warranted if space savings are a key component to ROI). So, a range of solutions may be able to be eliminated intuitively before the analysis even begins. Even if the proposed solution is a “simple” process change, a certain base cost for IT modifications, testing, and roll-out should be assumed.
Once some solutions have been intuitively crossed off of the list, a benchmark-based analysis can further refine the range of options and establish the general pick strategy.
The first step in this process is to go back to the Throughput Profile Chart and revisit the potential labor costs which will be seen if throughout increases but pick rates remain the same. This is the baseline future state cost. Next, utilize some “generous” benchmarks of potential pick rates with various forms of technology and test to see if an attractive ROI can be obtained.
For example, some horizontal carousel implementations have produced pick rates in excess of 400 lines per hour per worker. While these rates depend heavily on a number of subjective factors, they can be a good starting point for this medium-level ROI test. If an attractive ROI cannot be established at a rate of 400 lines per hour, then it will certainly not be seen at a more realistic rate of 200 – 300 lines. It may be possible to cross some solutions off the list using this method. Apply the potential pick rate of 400 lines per hour to the projected throughput rate in the DC and establish the number of FTEs required if this automation were employed. Calculate the labor cost savings over the two-year ROI period in our example. Then, create a very general estimate of what the cost of a properly-sized carousel system might be, and determine if it can be paid for within the two-year ROI period based on the projected labor savings.
Obviously this exercise will involve some working knowledge of the costs associated with the various forms of technology which are available in the DC. For the purposes of this step, a general order of magnitude cost estimate will often be sufficient. It is usually not cost- efficient this early in the process to begin working with equipment vendors to engineer and price solutions at a detailed level. Again, published benchmark costs have been produced which can be of use, but as always, it is helpful to have an experienced eye review the potential concepts as well. One key tool which can assist in this process is the Storage Design Tool which was discussed in a previous installment of this series. For technology such as carousels, AS/RS, and conveyor-based pick modules, the equipment cost scales based upon the amount of storage which is required, so having a good figure for this using the Storage Design Tool is vital. Often this data coupled with some benchmarks on cost per unit of storage can help work up a good high-level cost estimate for the equipment.
Vehicle-Based Picking Examples
Pick Cart (Image Source: Creform Corporation)
Pallet truck (Image Source: The Raymond Corporation)
Goods-to Picker System Examples
Carousel (Image Source: Abel Womack)
AS/RS (Image Source: Abel Womack)
Conveyor-Based Picking Example
Conveyor-Based Pick Module

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Following these steps can be an effective way to determine the general strategies which are, at least, feasible in the context of the specific project at hand.
Test Alternate Visions of the Future
In keeping with our theme of designing a DC when the future is uncertain, the next step is to use the Throughput Design Tool to experiment with the range of potential visions for the future which could play out.
By manipulating variables such as volume growth in various order types, variations in labor costs, and other factors, it is possible to establish the various break points at which certain picking strategies make financial sense. Experiment with both conservative and aggressive growth projections, and over various time periods. Weigh the value of keeping the current technology against the efficiency gains of new technology. A properly architected Throughput Design Tool will allow the user to make statements like:
“If we continue our current rate of growth, we’ll be able to cost justify a goods-to-picker system by year six.”
Evaluate Other Factors
Labor savings, while a key factor in the choice of a Pick Strategy, should not be the only criteria to consider. Additional areas which influence the decision should be:
 Space Savings
 Flexibility
 Scalability
 Complexity
For example, a vertical lift module system may not be cost justifiable based on labor savings, but the space savings it offers may be enough to make the technology worth considering. The Storage Design Tool discussed in the first installment of this series can be an excellent way to evaluate space savings.
In another example, a mini-load AS/RS may offer significant labor savings, but the business requirements over time may be so uncertain that an AS/RS would be too inflexible of a choice.
All of these factors should be carefully weighed when choosing a pick strategy.
In Our Next Installment (Part III): Determine Pick Methodology
Once a Pick Strategy has been selected, additional order analysis is required to determine the optimal Pick Methodology. Our next installment in the series will focus on this step.

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Appendix A: Overall DC Design Process
The steps contained in this guide are part of a larger process which has been developed by Commonwealth Supply Chain Advisors to design DCs when demand is uncertain. The overall steps in this process are:
Determine Space Requirements
(Covered in Part I of the series)
Determine Pick Strategy
(Covered in this installent of the series -Part II)
Determine Pick Methodology
(To be covered in Part III of the series)
Determine Inbound Processes (To be covered in Part IV of the series)

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Start Planning Your Distribution Center Today
Additional Resources:
Paper: Confidently Committing to a Distribution Center Design When Demand is Unpredictable, Part I – Creating a Storage Design Tool
Ebook: 6 Ways to Postpone Distribution Center Expansion
Paper: E-Commerce in the Distribution Center – Making a Graceful Transition
Presentation: Improving Warehouse Productivity Without Tier 1 Technology
About Commonwealth Supply Chain Advisors
Commonwealth is a leading supply chain consulting firm that helps companies of all sizes structure their supply chain networks, design distribution centers, and select and implement warehouse management systems (WMS). Commonwealth is based in Boston and works with clients across the globe. For more information, visit www.commonwealth-sca.com or contact Jennifer Thomas at (617) 948-2153.

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About the Authors
Ian Hobkirk
Mr. Hobkirk is the founder and Managing Director of Commonwealth Supply Chain Advisors. Over his 20-year career, he has helped hundreds of companies reduce their distribution labor costs, improve space utilization, and meet their customer service objectives. He has formed supply chain consulting organizations for two different systems integration firms, and managed the supply chain execution practice at The AberdeenGroup, a leading technology analyst firm. His career has provided him with a broad perspective on how to solve supply chain problems without automatically resorting to expensive technology. Mr. Hobkirk has authored dozens of white papers on supply chain topics, and his opinions have been featured in publications such as DC Velocity, Modern Materials Handling, and The Journal of Commerce.
John Diebold
Mr. Diebold is Director of Consulting for Commonwealth Supply Chain Advisors. Over his career, he has designed over 100 distribution centers and led dozens of pure distribution center optimization initiatives. He has worked for many of the top companies in distribution automation: FKI Logistex, White Systems, Kardex Remstar, and Sapient Automation which gives him a truly unique perspective on when and how to apply technology in the distribution center. Mr. Diebold holds a Master of Industrial Engineering with focus on Systems Engineering from New Jersey Institute of Technology.

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