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

http://www.commonwealth-sca.com/


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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/


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

o Cart-based

o Pallet truck-based

Multi-level

o Multi-level order pickers

Conveyor-based picking

Pure conveyor system

Carts with conveyor

Powered vehicles with conveyor

Goods-to-picker systems

Carousels

AS/RS

o Mini-load

o Shuttle

o Other

Robotic

Cluster picking

Types:

o Conveyor-based cluster picking

o Cart-based cluster picking

o Pallet-truck-based cluster picking

o Order-picker-based cluster picking

Features:

o Early out

o Dynamic re-batching

Zone picking

Sequential zone pick (pick and pass)

Simultaneous zone pick (pick and consolidate)

o VNA pick for slow movers

o AS/RS pick for slow movers

o Diverse size items

Conveyor-based vs. cart-based

2. Pick Methodology (Covered in Part III)

Batch picking

Primary pick

o One SKU to one MU

o Multiple SKUs to one MU

Secondary pick/put

o Single line orders

o Single unit-single line orders

o Multi-line orders from

homogenous MUs

o Multi-line orders from mixed MUs

o Varieties

Pick-to-tote

Pick-to-pallet

Pick-to-tow-vehicle

o Sort 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.


http://www.commonwealth-sca.com/resources/committing-to-distribution-center-design/

http://www.commonwealth-sca.com/resources/committing-to-distribution-center-design/

http://www.commonwealth-sca.com/resources/6-ways-to-postpone-your-distribution-center-expansion/

http://www.commonwealth-sca.com/resources/e-commerce-in-the-distribution-center-making-a-graceful-transition%e2%80%a8/

http://www.commonwealth-sca.com/resources/improving-warehouse-productivity-without-resorting-to-tier-1-technology/

http://www.commonwealth-sca.com/services/



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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.


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

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