Kong Chenying

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DESIGN AND MANAGEMENT OF A LEAN ORDER PICKING SYSTEM

A thesis presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Chenying Kong

November 2007


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This thesis titled

Design and Management of a Lean Order Picking System

by

CHENYING KONG

has been approved for

the Department of Industrial and Systems Engineering

and the Russ College of Engineering and Technology by

Dale T. Masel

Associate Professor of Industrial and Systems Engineering

Dennis Irwin

Dean, Russ College of Engineering and Technology


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ABSTRACT

KONG, CHENYING, M.S., November 2007, Industrial and Systems Engineering

Design And Management Of A Lean Order Picking System (107 pp.)

Director of Thesis: Dale T. Masel

Order picking is the process of retrieving items from storage according to

customer orders. It is significant to warehouse management because it typically accounts

for over half of the operation costs.

In this research, an order picking system that is designed by applying lean

principles is discussed. The discussion is focused on three important decisions: layout

design, storage location assignment, and workload scheduling. Combination of existing

strategies has been applied to layout design in order to achieve the goal of lean. Buffer

trays are applied in the fast pick area to reduce the impact of imbalance between zones.

Heuristics are developed to assign storage locations and schedule workload, to optimize

the performance of the system.

The testing results show that the proposed warehouse with assignment and

scheduling results obtained from the heuristics is more efficient than a traditional

warehouse in terms of requested labor time of completing orders in one wave.

Approved: _____________________________________________________________

Dale T. Masel

Associate Professor of Industrial and Systems Engineering


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ACKNOWLEDGMENTS

This research could not be completed without the help and support of many

people who are gratefully acknowledged here.

I would like to express my deepest love and gratitude to my parents, Minggui

Kong and Fahui Chen. With their love and continuous support, I could have the

opportunity and courage to study oversea at Ohio University, and hence own such a

wonderful and precious experience.

I sincerely appreciate my advisor Dr. Masel, for his valuable ideas, advice, and

guidance during my whole research and writing of the thesis. When I was doing

internship in Chicago, his encouragement and support gave me a lot of confidence to

finish the thesis. During my short time in Athens in writing the final parts of the thesis, he

put high priority on it. His patience, kindness and thoughtfulness are highly appreciated.

Im also grateful to my committee members, for their suggestions and comments

that help me better complete this research, and for their time spent in helping me finish

the thesis.

I owe special thanks to the tutors in the Writing Center, who helped me revise the

thesis with a lot of patience and encouragement.


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TABLE OF CONTENTS

Page

Abstract............................................................................................................................... 3Acknowledgments............................................................................................................... 4List of Tables ...................................................................................................................... 8List of Figures................................................................................................................... 10CHAPTER 1: Introduction ............................................................................................... 11

1.1

Importance ................................................................................................ 11

1.2 Background............................................................................................... 121.2.1 Warehouse Decision Making ........................................................ 121.2.2 Lean Principles and Application ................................................... 14

1.3 Objective................................................................................................... 14CHAPTER 2: Literature review........................................................................................ 16

2.1 Warehouse Operations.............................................................................. 162.2 Warehouse Decision Making.................................................................... 18

2.2.1 Layout Design ............................................................................... 182.2.2 Storage Location Assignment ....................................................... 222.2.3 Scheduling & Sequencing ............................................................. 262.2.4 Picking........................................................................................... 28

2.3 Lean Principles and Application............................................................... 31CHAPTER 3: Methodology.............................................................................................. 36

3.1 Situation.................................................................................................... 36


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3.1.1 System Description........................................................................ 363.1.2 Assumptions .................................................................................. 403.1.3 Explanation of the Proposed System Based on Lean Principles... 40

3.2 System Model ........................................................................................... 433.2.1 Labor Time in the Fast Pick Area ................................................. 453.2.2 Labor Time in the Traditional Area .............................................. 463.2.3 Labor Time of the Proposed System............................................. 48

3.2.4

Labor Time of the Traditional Warehouse.................................... 49

3.3 Storage Assignment Method..................................................................... 513.3.1 SKU Selection............................................................................... 523.3.2 Space Assignment ......................................................................... 543.3.3 Mathematical Model of Space Assignment................................... 57

3.4 Scheduling Method................................................................................... 583.4.1 Order Grouping ............................................................................. 593.4.2 Workload Scheduling .................................................................... 65

3.5 Summary of Lean Applications in the New System................................. 68CHAPTER 4: Testing and Results.................................................................................... 70

4.1 Simulation................................................................................................. 704.1.1 Description of the simulation model ............................................. 704.1.2 Experiment Set Up ........................................................................ 714.1.3 Simulation Results vs. Mathematical Model Results.................... 73

4.2 Effectiveness of the Heuristics.................................................................. 75


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4.2.1 Experiment Set Up ........................................................................ 754.2.1.1Traditional Area Size Determination ............................................ 774.2.2 Heuristics Results vs. Random Results ......................................... 80

4.3 Performance of the Proposed System....................................................... 824.3.1 Experiment Set Up ........................................................................ 824.3.2 Proposed System Results vs. Traditional Warehouse Results ...... 83

CHAPTER 5: Conclusion................................................................................................. 88

5.1

Summary of Results.................................................................................. 88

5.2 Applications .............................................................................................. 895.3 Future Work.............................................................................................. 89

References......................................................................................................................... 91Appendix: STORAGE LOCATION ASSIGNMENTS AND WORKLOAD

SCHEDULES FROM THE HEURISTICS ...................................................................... 94


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LIST OF TABLES

Page

Table 3.1: One-Month Demand Forecast of 20 SKUs............................................... 53Table 3.2 (a): APZs and Zone Assignment for Step 1 .................................................... 54Table 3.2 (b): APZs and Zone Assignment for Step 2 .................................................... 55Table 3.2 (c): APZs and Zone Assignment for Step 3 .................................................... 55

Table 3.2 (d): Final Result for APZs and Zone Assignment ........................................... 55Table 3.3: Space Assignment Result.......................................................................... 57Table 3.4: Demands of Four SKUs for Twenty Orders ............................................. 62Table 3.5: Sequence for Order Grouping................................................................... 63Table 3.6: Grouping Result for First Eleven Orders.................................................. 64Table 3.7: AWS for Grouping Result in Table 3.6: (a) first attempt; (b) corrected

assignment................................................................................................. 64Table 3.8: Order Grouping Result.............................................................................. 65Table 3.9: Demand and AW for Five Groups............................................................ 67Table 3.10: Workload Schedule for Group 1............................................................... 68Table 3.11: Workload Schedule for Five Groups ........................................................ 68Table 3.12: Summary of Lean Principle Application .................................................. 69Table 4.1: Parameters and Values used in the Experiment........................................ 71


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Table 4.2(a): Demand Distribution for Test A ............................................................... 72Table 4.2(b): Demand Distribution for Test B ............................................................... 72Table 4.3: Total Processing Time of Test A and Test B............................................ 73Table 4.4: Demand Distribution for Experiment of Heuristics.................................. 76Table 4.5: Parameters and Values used in the Experiment........................................ 76Table 4.6: Parameters and Values used for Two Tests.............................................. 80Table 4.7: Parameters and Values used in Four Tests ............................................... 82Table 4.8: Warehouse Size for Four Tests................................................................. 83Table 4.9: Labor Time Results for Four Tests........................................................... 84Table 4.10 t-Test Results ............................................................................................ 85


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LIST OF FIGURES

Page

Figure 2.1 Layout of a traditional warehouse adapted from [14]............................ 19Figure 2.2 The warehouse layout of simultaneous picking and replenishment

adapted from [20]...................................................................................... 25Figure 3.1 Layout of the fast pick area....................................................................... 37Figure 3.2 Layout of the presented warehouse .......................................................... 39Figure 3.3: Arrangement of three processes................................................................ 49Figure 3.4: Matrix of scheduling problem................................................................... 59Figure 4.1: Simulation model layout........................................................................... 71Figure 4.2: Comparison on the results: (a) Test A; (b) Test B.................................... 74Figure 4.3: Traditional area illustration....................................................................... 79Figure 4.4: Labor time comparison: random vs. heuristics: (a) Small warehouse; (b)

Large warehouse....................................................................................... 81Figure 4.5: Labor time comparison: proposed vs. traditional warehouses: (a) 50 orders,

small capacity; (b) 50 orders, large capacity; (c) 100 orders, small capacity;

(d) 100 orders, large capacity ................................................................... 86


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warehouse will decrease significantly. That is the reason why order picking is an

important topic in warehousing management, and why a lot of research has been done on

it.

1.2 Background

1.2.1 Warehouse Decision Making

There are numerous issues to consider as far as warehouse decision making is

concerned. The issues can be classified into three levels: (1) warehouse design, (2)

warehouse management, and (3) warehouse control [2].

Warehouse design is a process of making strategic decisions, which means the

decisions have long-term (more than one year) influence and are not easy to change.

Warehouses need to be designed to meet basic demands (sufficient throughput for

customer orders) and any special requirements (short response time, low investment or

operational cost, high customization, etc.).

Warehouse design should be based on the demand, and consider size and layout

of the warehouse, basic storage equipment, and material handling devices. All of those

affect order picking throughput and cost, directly or indirectly. Size and layout decide the

material flow in the warehouse and travel distance for order picking; and storage

equipment and material handling devices decide the convenience and speed of putting

away and retrieving.

Warehouse management is at the tactical level, and concerns the medium-term

(from one month to a year) planning issues. Two main issues are inventory management


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and storage location assignment. In this research, the latter is one of the issues to be

discussed.

Inventory management is the process of deciding inventory composition (which

product should be kept in stock) and inventory level (how many of each product should

be in stock), as well as keeping stocks in good condition.

Storage location assignment is the process of deciding where to store the products.

It may be based on analyzing historical orders or projected inventory levels. It has direct

influence on order picking because storage location determines the picking distance for

workers.

Warehouse control refers to the operational decisions which have short-term

(hours, days) effects on the warehouse system. General control issues are route of putting

away and retrieving goods, wave generation, schedule of pick location, and sequence of

orders to be fulfilled.

The aspects of warehouse control that will be covered in the following sections

are scheduling the locations to pick from and sequencing orders. Pick location scheduling

is the process of assigning the pick location for each item in each order. If all products are

just assigned to storage locations with one location for each item, the scheduling will be

very simple. However, if some products are assigned to multiple locations, the scheduling

becomes complicated and should be completed based on rules. Sequencing orders is to

put customer orders in a sequence for picking to meet the goals of the warehouse.


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1.2.2 Lean Principles and Application

Lean manufacturing is a method developed from JIT manufacturing in Toyota [3].

The principles that lean promotes include waste reduction, customer-orientation, flow,

line balancing, and lower batch size [32]. Today, lean principles are not only for

manufacturing, but are also used in many other areas, such as sales, purchasing, medical

service, warehousing, and distribution [3], [7], [9]. Lean principles are widely applied

because they help organizations achieve higher throughput, lower operation cost, faster

response, and better quality. Consequently, customers are more satisfied with the service.

In this research, lean principles will be discussed systematically and applied to the

design and management of an order picking system.

1.3 Objective

This research discusses an order picking system that considers the lean principles.

The discussion is based on the three levels of warehousing: design, management and

control. Consequently, layout, storage location assignment, scheduling, and sequencing

are studied in the following sections.

The main goal of the research is to develop heuristics which can help optimize the

performance of the presented order picking system. The heuristics are developed for

storage location assignment and workload scheduling.

In order to compare the efficiency of using the heuristics to the presented

warehouse without using them, and to compare the efficiency of the presented warehouse

to a traditional warehouse, testing is conducted based on two experiments: (1) the

proposed warehouse with random storage assignments and workload schedules vs. with


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the assignment and scheduling results from the developed heuristics; (2) a traditional

warehouse using a batch picking strategy vs. the proposed warehouse with storage

location assignments and workload schedules from the heuristics. Analytical models are

developed to calculate the performance measurements for the testing.

The performance measurement is the labor time per wave of a warehouse, which

is the total labor time requested to fulfill a certain number of orders. In order to make the

systems comparable, the number of orders in a wave is fixed.


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CHAPTER 2: LITERATURE REVIEW

This section introduces the operations that occur in a warehouse and summarizes

the research and accomplishments in warehouse design, management and control. The

lean principles to be applied in this research are then described.

2.1 Warehouse Operations

Warehouses can be run by different parties in the supply chain and also can be

used for different purposes. However, the main reasons for having a warehouse are

storage and consolidation. The time difference between supply and demand is solved by

storing finished products when there is no demand. Manufacturers can achieve

production economies without worrying about insufficient demand at the time of

production. Customers do not have to wait for a production cycle to get the product.

Instead, they can have it shipped directly from the warehouse.

Also, a warehouse collects products from different manufacturers and

consolidates the shipment to one consumer. The customer need not receive ten separate

boxes if he orders ten different products, and the manufacturer does not have to send five

shipments if products are ordered by five customers. Therefore, transportation economies

are achieved. As far as consolidation is concerned, a warehouse is the place where

manufacturers send full truckload shipments, pallet loads are split into smaller quantities,

and different products are consolidated according to customer orders.

De Koster and Roodbergen [12] describe the general process used in most

warehouses as follows. When incoming products arrive, the warehouse will receive them

and unload them at the receiving dock. Their quantity and quality will be checked; and at


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the same time, labels or tags might be attached to them to enable tracking. After

recording their profiles, they will be put in the storage area. The product will stay there

until it is requested by the customer.

Upon receiving a customer order, the warehouse will process it and generate a

picking list that indicates the product, quantity, and location in the warehouse. Therefore,

the worker will know where to retrieve products. The process of collecting requested

products according to a customer order is order picking. The order picker can process one

order at a time, which is called Single Order Picking (SOP), or multiple orders at once,

which is called batch picking. Batch picking allows the picker to retrieve more items in

one trip, so picking density is increased and average travel distance for each order is

reduced.

For SOP, items can be packed for shipping right after order picking. However, for

batch order picking, items must be sent to another location in the warehouse for sorting

before packing.

Lin and Lu [26] describe and compare SOP and batch picking in their research,

and analyze what situations the two strategies are suitable for in a traditional order

picking system, where travel distance is significant to operation cost. They conclude that

orders with many items and many quantities are suitable for SOP, while orders with few

items and few quantities are suitable for batch picking. It is helpful for warehouse

managers to decide which strategy they are going to use in order picking.


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2.2 Warehouse Decision Making

In order to improve the efficiency or throughput of the warehouse, considerable

effort has been made to study the three levels of warehouse decision making, which, in

this research, are layout design, storage location assignment, and scheduling and

sequencing. In this section, the research and accomplishments related to those three

aspects will be discussed.

2.2.1 Layout Design

The basic functional departments in a warehouse include a receiving dock, a

shipping dock, a storage area, a packing station, and an office area. The layout in this

research focuses on the area where order picking occurs, which is part of the storage area.

In the storage area, a common scene is that products are stored on parallel racks or

shelves, with aisles in between (See Figure 2.1). Typically, storage equipment includes

pallet racks, gravity flow racks, bin shelves, drawers, automated storage/retrieval systems

(AS/RS), and carousels [6].

According to the automation level in the order picking process, warehouse

systems can be divided into three categories: (1) manual, (2) automated, and (3)

automatic [34].

A manual warehouse system is also known as a picker-to-product system [34],

where the order picker goes to storage locations to put away or/and pick up items. The

picker could either walk or drive a vehicle, and a conveyor might be used to transport

picked items.


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Figure 2.1 Layout of a traditional warehouse adapted from [14]

An automated warehouse system is called a product-to-picker system [34],

because in the system the computer-controlled equipment sends/brings items to/from the

storage location for the picker who stands in a fixed position. Two common automated

systems are carousels and Automated Storage and Retrieval Systems (AS/RS). A carousel

is the material handling equipment that holds products in bins and drawers rotating

horizontally or vertically in a closed loop. Compared with a carousel, the AS/RS is

typically a much larger system. It stores products on parallel racks or shelves, and an S/R


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machine travels in a fixed route along the aisle to put away and/or pick up products and

bring them to/from the picker.

An automatic warehouse system uses robots or dispenser machines which take the

place of order pickers. This system is usually used because of high-speed requirements or

dangerous environments. Such a system requires a very large investment and also has

product limitations, such as only small to medium sizes and standardized shapes.

Generally speaking, there are many issues to consider when designing a

warehouse. However, a commonly used strategy in designing a warehouse is to divide the

storage into two areas: a forward area (also called a fast pick area) and a reserve area.

In the forward area, a limited quantity of each Stock Keeping Unit (SKU) is

stored and it is where most of the picks occur. The material handling unit can be either a

case or a unit piece. In the reserve area, storage density is high, so this is where most of

the inventory is stored. Usually, the material handling unit is a pallet.

This configuration is used because two storage areas are designed to focus on

their own functions: the forward area is for order picking with the purpose of a short

walking distance, while the reserve area is for storing with an expectation for a high stock

load.

Bartholdi and Hackman [6] discuss the forward-reserve problem. They describe

some methods of selecting the SKU to the fast pick area, determining the quantity of each

SKU, and locating the SKUs. Other researchers have also considered design or layout of

the forward-reserve area, such as [14] [13] and [30].


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Besides the forward-reserve design, an additional common strategy is zoning.

Zoning divides the storage area into multiple zones with order pickers working

simultaneously and separately in their specific zones. Typically, one zone is assigned

with one order picker who is responsible for only the fixed area. This strategy is used to

limit travel distance and reduce congestion.

At the design level of the layout, the workload should be balanced among zones

to avoid the situation where some zones are always busy while others are idle. When

using zoning, picked items from zones still need to be sorted into orders before packing.

Both SOP and batch picking can be combined with zoning. If batch picking and

zoning are combined, it is referred to as picking in waves. When picking in waves, the

workload of the picked items in one wave should be equally distributed among zones.

After all order pickers finish their work, another wave can begin.

In the research described by Lin and Lu [26], batch picking is combined with

zoning, and through simulation they conclude that the strategy of batch picking and

zoning is suitable for the orders with few quantities and/or few items in a traditional order

picking system where travel distance is significant to affect operation cost.

In recent years, new ideas have been addressed into warehouse design. Koh et al.

[23] design a system, where horseshoe buffer is applied at the front end of each aisle in

the AS/RS. Most of the previous research on the end-of-aisle system assumes that each

aisle requests one order picker to work. However, with the horseshoe buffer, an order

picker can work on two or more aisles. Therefore, such a configuration helps save the

labor cost, and results in a more flexible system.


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De Koster [11] develops an order picking system where packing is combined and

no separate packing station is needed. In the system, picking is done directly into the

shipping cartons. Shipping cartons are sealed after picking, and invoices are

automatically printed and attached to cartons. The storage area is divided into zones and

uses conveyors to transport bins through all zones. At the same time, an effort is made to

balance the workload among picking zones, such as equal size and even distribution of

fast moving items, in order to avoid bottlenecks.

Some warehouse systems are used for special purposes resulting in changed

layouts. A common example is the crossdock, which is popularly used in a distribution

center. It is designed for fast-moving items and mainly plays a role in consolidation. In a

crossdock, the function of storing is low because most of the items are housed less than

24 hours. The significant feature of the layout of a crossdock is that its receiving and

shipping docks are usually bigger than those in a common warehouse, as increased

staging is necessary to implement the transportation consolidation [5].

2.2.2 Storage Location Assignment

Before putting away a product in the storage area, a location has to be chosen.

The process of choosing storage locations for products is called storage location

assignment. There are several approaches widely used to decide the storage location:

demand-based storage, class-based storage, and family grouping [12]. Usually, the

dedicated storage policy or/and random storage policy is/are combined with above

approaches.


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For dedicated storage, every SKU is assigned a fixed location where only that

SKU can be stored. The advantage of this method is that workers are familiar with the

locations since they do not change. Also, the manager can control where high demand

SKUs are stored, so popular SKUs can be assigned to the convenient locations. The

disadvantage is that the space utilization is low under this rule. Because a location is

reserved for only a single SKU, it cannot be taken by another SKU even if the original

SKU is out of stock. However, the shortcoming of this rule can be minimized and the

benefit can be retained when the rule is applied to the fast pick area, which is just a small

area compared with the reserve area, and out-of-stock situations are quite rare in the fast

pick area. Dedicated storage policy is applied to the storage assignment in many research

projects, including [24], [25], and [33].

Under a random storage policy, the storage location is selected randomly. When a

location becomes empty, it is open to any SKU, which results in a high space utilization.

The problem of this policy is that locations of products change over time, so workers

have to be directed to locations. Also, since a SKU can be assigned to more than one

location, it takes more time to put it away, and the system becomes more complicated to

manage. Usually this policy is used in bulk storage areas. Random storage policy is also

widely used, and its related research can be found in [18], [28], and [29].

Demand-based storage means that items are assigned according to customer

demand. There are several measures for demand, such as popularity, turnover, volume,

pick density, and cube-per-order index (CPO). Among those measures, CPO Index is

quite widely used. The CPO Index of an item is the required total volume divided by the


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turnover [16]. For example, an item that requires 1000 cubic feet of space with a turnover

of 200 orders per day, has a CPO index of 1000/200=5. The items with the smallest CPO

should be located at the place that is easiest to reach.

The class-based storage approach divides products into several classes, usually

three. The dedicated storage policy is used among classes, so each class is restricted to a

fixed area. The random storage rule is applied within a class, so a product can be assigned

to any location in the area assigned to its class.

Family grouping is a method considering relations between items. The products

with strong relationships, like similarity in product function, are assigned next to each

other. Since the items with strong relationships are more likely to appear in the same

order, they can be picked in one trip. The precondition of this method is that the

similarities between pairs or groups of products are known or can be calculated.

Family grouping can be combined with other storage assignment methods. Liu

[27] presents a clustering technique based on similarity and is combined with the

demand-based storage assignment rule. Liu demonstrates the potential benefit of the

clustering technique for broken case order picking. In the research, the similarity of any

pair of items is calculated, which is the probability that they appear in the same order.

After a similarity matrix is generated, storage locations are assigned according to the

clusters of items and the required quantity. The item with the largest quantity is assigned

to the nearest place to the In/Out point. The remaining items in the same cluster are

assigned to the nearby locations. The simulation results show that the average distance


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per item using this clustering technique is smaller compared with the random storage

assignment.

The traditional way of replenishment is based on a medium length cycle, which

means from one month to a year. However, some recent studies [20], [30] indicate

interest in short-term replenishment and reassignment in order to be more responsive to

the customer demand.

Kim et al. [20] describe a warehouse (refer to Figure 2.2), where picking and

replenishment are implemented at the same time, so while pick zones are working on

todays orders, replenish zones are restocking for tomorrows orders.

Figure 2.2 The warehouse layout of simultaneous picking and replenishment

adapted from [20]


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The cycle time for both areas is 20 hours plus 4 hours for pan swapping, which is

switching the empty tote in picking area with a full tote from replenish area. In order to

make the system more flexible, they decrease the pan swapping time through reducing

the total number of totes to be replenished. They also try to keep products in the same

location, if possible, in order to reduce the movement of replenishment locations. Their

new logic results in a 22.3-hour order cycle time, which is 1.7 hours less than the original

operation.

Peters and Smith [30] describe a design for the fast pick area of a distribution

center. The fast pick area is divided into two halves: one is for picking; the other is for

replenishment. Picking and replenishment are conducted at the same time, and the two

halves in the fast pick area change their function alternately. The replenishment is

specifically for the next shifts picking, so they reconfigure the fast-pick area after every

shift, which is called dynamic slotting in their research. Their strategy aims at reducing

the size of the fast pick area, so the travel distance can be decreased, which would reduce

order picking time and lower picking cost. Their experimental results show that the

purpose can be achieved through optimal slot assignment.

2.2.3 Scheduling & Sequencing

A warehouse will often process thousands of orders per day. Before picking, the

sequence of orders should be set. If an item is assigned to multiple locations, its pick

location for a certain order must be selected. Also, if zones are applied, the sequence of

picks in a zone has to be determined.


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Liu [27] develops a clustering technique for the family-based storage location

assignment that was discussed in section 2.2.2. In the paper there is a clustering technique

for sequencing customer orders based on similarity. First, the similarity of any pair of

customers, which is the probability of ordering the same item, is calculated. Second, the

sequence of orders is based on clusters of customers. The group with the largest average

order quantity will be picked first. Then, the second largest order quantity group goes

next, and so on. The simulation results show that the average distance per item using the

clustering technique is smaller than that in first-come-first-served rule.

Kim et al. [22] develop a rule for pick location selection. In their research, one

SKU can be assigned to multiple picking zones, so a decision has to be made on zone

selection for such a SKU when it needs to be picked. According to the rule, the location

for the most inflexible items, each of which has only one storage location, should be

chosen first. The most inflexible of remaining items goes second, and so forth. After

assigning the inflexible items, there are few zones with no or low workload. Since the

rule attempts to evenly balance the workload among picking zones, few possible

locations are left for flexible items to be assigned, which makes the selection process

much easier. The experimental results demonstrate that the sequencing rule can help

reduce the misses of picking work, and make the system run at a higher conveyor speed,

which helps reduce the cycle time.

In another paper [21], Kim et al. present two cluster-based sequences for picking

in a zone in an automated warehouse: the x-coordinate based heuristic and the clustering-

based sequencing algorithm. Since the length of the warehouse is much longer than the


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width, the gantry robots movement is mainly in the horizontal direction. So the authors

use a simple x-coordinate based heuristic when the pick locations are sparse (horizontal

distance is dominant) under the assumption that the picked item drops vertically below its

location to the buffer. In this case, the gantry robots just need to move from left to right

(assuming the starting point is on the left) while picking. Experimental results show that

the x-coordinate based heuristic can generate good sequences.

In order to achieve optimal solutions, Kim et al. propose a clustering-based

sequencing algorithm. They put the locations into one cluster where horizontal movement

does not dominate, which means the horizontal distance between any of the locations in a

cluster is less than the corresponding vertical distance. To route the clusters, they follow

the x-coordinate based heuristic. In each cluster, other optimization methods can be

considered. The use of the clustering-based algorithm divides the big routing problem

into multiple small ones, and it is much easier to find optimal or near optimal solutions

for the small routing problems. From the experimental results, they demonstrate that this

algorithm generates optimal sequences for all the tests.

2.2.4 Picking

Four routing policies are simply introduced below, because one of them will be

applied in the labor time estimation of the traditional area in the proposed warehouse in

the Methodology section. Petersen [31] introduces several kinds of routing policies that

are normally used, and the ones mentioned below are traversal, return, midpoint, and

composite. The first one is traversal strategy, which arranges the walking route from one

end of an aisle to the other end. The picker needs to walk through a whole aisle for all the


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aisles in his picking zone. The second policy is called return strategy. The picker starts

picking from one end of an aisle, turns back when he finishes all picking work in that

aisle, and exits from the same end. The third policy is midpoint strategy. A picking area

is divided into two equal halves when using this strategy. The picker needs to turn back

when he reaches the midpoint, no matter whether the requested picks are finished or not.

The other half of the storage area will be picked in the same way. The fourth policy is

called composite strategy, which combines the advantages of both traversal and return

strategies and makes the routing flexible. If there are many requested items in one aisle,

the picker can do a traversal route; and if the requested items are few and concentrated on

one end of the aisle, the picker can do a return route.

After designing a warehouse layout and developing the methods to manage the

warehouse, an analytical model is needed to evaluate the performance of the warehouse.

Gray et al. [15] promote an analytical approach to calculate the pick cycle time of a

traditional warehouse doing batch picking. The pick cycle time is considered as the total

time of finishing all orders in one cycle (batch). In their approach, they consider walking

time, picking time generated by all kinds of technologies, and a constant unloading time.

The traversal strategy is applied in pickers routes, and pickers are working in separate

zones, so they calculate the walking time as below.

2 ALWT V NZ

= (1)

where,

WT Walking time in one cycle

AL Aisle length


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V Walking speed of the picker

NZ Number of zones in the picking area

The technology here means the storage equipment, which could be shelves,

pallets, and flow racks.

( )1

T

t t

t

BSPT ST GT N

NZ =

= +

(2)

where,

PT Picking time per in one cycle

BS Batch Size, the average number of items requested per batch

ST Stop Time, the constant time of each stop

GTt Unit grab time when using technology t

Nt Number of items requested to use technology t

t The type of the storage equipment

A constant unloading time is considered in their research, and it is the time that a

picker unloads a carton onto the conveyor, and receives a new carton and picking list. In

their approach, the pick cycle time is calculated as below.

PCT WT PT UT = + + (3)

where,

PCT Pick Cycle Time

UT Unload Time

Besides picking time, packing time should also be considered to estimate the

performance of a warehouse. Badurdeen [4] estimates the packing time based on the

number of items and the number of orders, because he considers two packing activities in


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the study: putting items into cartons and seal the cartons. The study assumes that the time

of putting an item into a shipping carton and the time of sealing a carton keep constant.

Therefore, the time of putting picked items into shipping cartons is determined by the

number of items; and the time of sealing cartons depends on the number of orders.

The above two studies can be the references to calculate the performance

measurement of a traditional warehouse, which will be included in the testing of this

thesis. Also, they are valuable in developing a mathematical model to evaluate the

performance of the proposed system in this research.

2.3 Lean Principles and Application

All lean is about is creating more value with less of everything, says Jim

Womack, the president of the Lean Enterprise Institute [9]. The definition is simple, but it

describes the essence of lean, and it is pursued by all for-profit organizations. More

value means higher output and better quality, while less of everything means lower

cost and less throughput time. Output, quality, cost, and throughput time are four

performance measurements used by profit organizations. Better performances on all of

these metrics can be achieved through applying lean principles.

Lean manufacturing is a methodology developed from JIT production in Toyota

[3]. Lean helps organizations become competitive through waste elimination and product

(or service) quality improvement in a flow environment which is pulled by customer

demands. The lean principles explained in this part will be applied in this research to

warehouse design, management, and control.


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First, waste identification and reduction are very important steps in lean

manufacturing. All the activities that do not add value should be eliminated. The wastes

include materials, time and effort spent on the production process. They are divided into

seven categories [10]:

(1) Waste from over-production, which is the inventory and investmentbeyond customer demands.

(2) Waste of motion. Those motions are the handling and movement causedby over-production.

(3) Transportation wastes. They result from inefficient plant or cell layout.(4) Processing wastes, which are processing of some parts unnecessary for

production completion or do not add value to products.

(5) Waste of waiting or queuing time, which might be caused by inefficientwork flow and bottlenecks. This idle time causes increase of production

cycle time.

(6) Product defect, which are the products that do not match qualityqualifications. It will decrease when inspection work for quality is

improved and strengthened.

(7) Inventory cost. Holding inventory costs money through space occupation,extra handling, damage, and obsolescence.

Second, lean manufacturing calls for a pull system, where customers demand

direct production flow and quantity. Based on the required production quantity, plans can


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be made for the manufacturing process. Therefore, no waste from over-production will

happen.

Third, the manufacturing process should be a continuous flow. If the products or

materials continue to flow, the number of their stops is reduced. Since they do not stay

somewhere waiting for a machine or worker, the wasted time due to waiting can be

eliminated. In order to achieve a continuous flow, the utilization of scarce resources must

be maximized, and the workload among all processes should be evenly balanced.

Lot size reduction is another requirement for lean manufacturing. Several years

ago, large lot size production was promoted by many manufacturers because

manufacturing economies could be achieved. However, lean encourages no batches, so

the lot size should be just one. The reason for doing this is that the large lot size leads to

high inventory of both finished and in-process products, which increases waste of

inventory cost. Also, producing in batches results in queuing and waiting, and makes the

system less flexible.

Visibility is also important in lean. It is requested that both activities and parts in

the workplace are visible for everyone, in order to ensure the safety and instant control of

the work.

Besides the lean principles discussed above, there are other ones, such as

workplace organization and preventive maintenance. However, since they are not related

to the proposed warehouse system in the following part, no detailed explanation is given

here.


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Today, lean principles are not only for manufacturing, but also can be applied to

warehousing management. Lean warehousing would be defined as achieving higher

throughput, lower cost, and better customer service through waste elimination of time and

space.

Ackerman [1] gives suggestions on lean warehousing based on his experience in

warehouse management. The space utilization of storage locations should be maximized

by increasing stack height and reducing aisle width and the number of aisles. In order to

make the material handling processes lean, the fast moving products should be separated

from the slow moving ones. Appropriate information technology should be applied to the

warehouse to assist management in achieving higher efficiency and accuracy.

Baudin [7] introduces five ways to improve visibility in warehousing. First, labels

should be attached to the grid of columns supporting the ceiling, so workers can easily

know their own positions. Second, the dock numbers should be visible when dock doors

are open, so it is better to write numbers on the side of the dock instead of printing

numbers on roll-up doors. Third, three-sided overhead signs should be used to indicate

zones, so they can be seen from all directions. Fourth, separators should be used for

shelves and bins in order to avoid mixture. Finally, rack aisles should be oriented in

parallel with inbound/outbound direction, so the view will not be blocked.

Lean warehousing is a new topic in this research area; for this reason, the research

is limited. In brief, most former research is not based on a systematical method of lean,

but from the viewpoint of warehousing management. Most of the authors give

suggestions based on their experience ([1], [7]) rather than research results. Similar


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systems have been described for picking books [19] and vitamin supplements [8].

However, there has been little investigation to develop methods to optimize the

performance of this kind of system.

In this research, an order picking system will be presented based on the principles

of lean manufacturing and further study will be conducted on the optimization of the

system performance. The equipment and strategies used in designing the order picking

system are not all new, but the concept of putting them together is new, as well as the

heuristics proposed to optimize the system.


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CHAPTER 3: METHODOLOGY

In this section, the methodology used in the study is explained. An order picking

system is described with basic assumptions and explanations according to lean principles.

Then, the system model, storage assignment method, and scheduling method, are

presented and explained separately.

3.1 Situation

3.1.1 System Description

The candidates for the warehouse are small to medium size products with regular

shapes, such as books, stationery, cosmetics and medicine. The warehouse uses the

forward-reserve configuration, so the storage area is divided into two districts: a fast pick

area and a reserve area. The fast pick area is divided into separate zones, where all

products are stored on flow racks (See Figure 3.1).In each zone, there is one order picker

who processes one customer order at a time. The storage location will be reassigned

periodically to adapt the system to demand changes. Beside the fast pick area, there is

another area used for order picking. It has the layout of a traditional warehouse and stores

the SKUs that are not assigned in the fast pick area. This area will be called the

traditional area in this research.

The pick-to-light system is applied to help pickers finish their work correctly

and quickly. The lights on the rack indicate to pickers where, what, and how many to

pick. There are also lights on the buffer trays that tell the pickers where to place, which is

called a put-to-light system. Each buffer tray temporarily holds items from one order.

When the right shipping carton comes to the right position, the tray will transfer items


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into it automatically. The buffer trays are used as a time buffer against the time difference

between the pickers availability and the containers arrival. More explanation of using

buffer trays will be given in section 3.1.3.

Figure 3.1 Layout of the fast pick area

Scanners are located at the beginning of all zones, so they can determine carton

position by scanning the bar code or RFID tag on the carton. The information obtained

from scanners is the input into the computer, which controls the transfer of items from

buffer trays to shipping cartons.


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There is no separate packing station in the facility because shipping cartons are

used directly to hold the picked items, and a machine is used to seal the cartons at the end

of the conveyor.

In the traditional area, items are stored on shelves like a traditional warehouse.

The area is separated evenly into zones by aisles, and there is one picker who is

responsible for picking all the items in each zone. The requested items in the traditional

area are picked in waves. One wave of orders in the traditional area just means a certain

number of orders for the fast pick area. There is no batch picking in the fast pick area.

The route strategy used in this area is traversal strategy, which means the picker

goes through the entire aisle and ends at the other side of the aisle. The traversal strategy

is used in the traditional area, because the walking time of pickers is easy to calculate.

Also this routing strategy fits well in the traditional picking area, because both ends of the

aisles have conveyors to transport picked items to sorting and packing area, and pickers

can place items at either end.

The sorting lanes are connections between the fast pick area and the traditional

area (see Figure 3.2). The shipping cartons are transported out of the fast pick area by the

main conveyor, and there are two branches for two kinds of cartons.


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Figure 3.2 Layout of the presented warehouse

The branch Conveyor I is for the shipping cartons that do not need items from the

traditional area. The items requested for those orders are all located in the fast pick area,

and orders can be fully completed there. Those cartons go directly for sealing and then

are shipped out.

The branch Conveyor II is for the shipping cartons that need items from the

traditional area, which are transported out from the traditional area by sorting conveyor

and then are sorted to individual lanes. The workers at the end of the lanes put the items

into the right trays, which are controlled by the computer system. The trays transfer items

into the right shipping cartons coming from the fast pick area.


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3.1.2 Assumptions

In this research, it is assumed that an order picker in the fast pick area can take all

the items listed on an order for a picking zone in one trip, since all items are small. This

assumption is made in order to simplify calculation of travel distance. The system can be

adapted to the situation where some picked items are too big to be all picked at one time.

The only change is the formulas of calculating travel distance based on the percentage of

orders that require multiple trips.

When estimating the picking time in the fast pick area, the number of units of the

SKU to be picked is considered. Routing will not be considered in the fast pick area,

since it is an insignificant factor there compared with picking in the traditional area. But

the walking time will still be included in order to make the time estimation model

realistic. The walking time in a zone is assumed to be a constant since traveling and

routing are insignificant.

The forecast of customer demand is assumed to be reliable, because the storage

assignment is going to be based on the forecast. If the forecast is unreliable, the proposed

heuristics for storage assignment method and scheduling can still be used, but there might

be more trips to the traditional area and/or some out-of-stocks. The warehouse manager

could reduce the time between reassigning storage locations to avoid the impact of poor

forecasts.

3.1.3 Explanation of the Proposed System Based on Lean Principles

To develop the proposed system, lean principles are applied to order picking,

especially the fast pick area. First, lean tells us to be customer-oriented, i.e. to do only


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activities that provide value to the customers. The non-value-added work is a waste for

both customers and the warehouse. Customers just want to get their products at the right

time in the right place. So movement and handling activities should be avoided when

they are unnecessary. Batching orders into waves is a strategy that warehouses use to

increase picking density. It is helpful for warehouses because waves increase the number

of picks per trip for order pickers. However, the extra work and handling of sorting and

packing after picking does not bring any benefit for customers. Therefore, the fast pick

area in this research has no wave for picking, which also matches the lean principle of

reducing the batch size. As the batch size is reduced to one, the items in one order need

not wait for pickers while the pickers are working on other orders in the wave, which

means less waiting time.

Second, the process should flow. Usually, in a warehouse there is a separate

packing station, which means picking products and putting them into intermediate

containers and moving them into shipping cartons in the packing station. Then, there will

be extra put-down and pick-up work; also products spend more time in queue for packing.

Since it is not a rule to keep an area for each operation, it is possible to combine two

operations and remove one function area. Sometimes, picking and sorting are combined

in one working process, called sort while picking [6]. In the fast pick area, the packing

station is eliminated and products will be placed directly into shipping cartons after

picking, and a machine will seal them at the end of the conveyor. As a result, the workers

are no longer necessary in the packing station. Meanwhile, the process continuously

flows and products can spend less time waiting.


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Third, waste is further reduced by using a fast pick area and picking zones, both

of which can help reduce travel. In a fast pick area, SKUs are concentrated into a smaller

area, which means higher pick density. Thus, pickers are more likely to finish one order

in a smaller area with less travel. A fast pick area is called a warehouse within the

warehouse [6]; a picking zone can also be thought of as a fast-pick area in a fast-pick

area. Each picking zone has an order picker, who is responsible for all the picks in that

zone and works on only one order at a time. The pickers work area is limited into a

smaller area, and consequently, there will be even less travel and faster response. A

conveyor passes from the first picking zone to the last one. Shipping cartons are put on

the conveyor, and each carton will pass all zones to finish one order.

Fourth, an effort is made for higher utilization of scarce resources. Since order

picking is labor-intensive, the order picker is the scarce resource in this situation. There

are two ways to maximize the use of workers. One is to apply advanced technology to

help them work more efficiently. Here a pick-to-light system is used, which tells

pickers what to pick, where to get the items, and how many are needed. Also a put-to-

light system is applied on the buffer trays, indicating pickers where to place the pick

items. Thus, pickers can finish their work faster with fewer errors.

The other way is to keep pickers working all the time. It is difficult to achieve that

in a common picking zone, because the workload assigned to all zones is not equal all the

time in practice, even if it is equal on average. Maybe a picker wants to work in advance

of the next order when he is idle; however, if he has no place to hold the picked SKUs, he

still cannot move on. As a result, pickers would alternate between busyness and idleness,


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and they spend time waiting for containers. In this research, the buffer tray is used as a

time buffer against the time difference between the pickers availability and the

containers arrival. Each picking zone has several buffer trays, the number of which

depends on required throughput and number of picking zones. Each buffer tray

temporarily holds picked items from one order. Pickers shuttle between flow racks and

buffer trays. When the right shipping carton comes to the right position, the buffer tray

will transfer the items it is holding into the carton automatically. There are also lights on

buffer trays, which indicate that the picker can place the product into the tray where the

corresponding light is on. This is known as a put-to-light system.

However, the application of buffer tray is insufficient to balance the workload

among all picking zones without proper management. If items with high frequencies are

concentrated in one or several zones, workers in those zones will be very busy and

workers in other zones will have a lot of idle time. An appropriate method should be

applied in storage location assignment in order to balance the workload across picking

zones. If large orders, which require a large amount of picking work, are sequenced

together, order pickers will not have enough time to finish all orders and so will miss

some orders. Thus, customer orders should be scheduled in a proper sequence based on

the assignment. Section 3.3 and 3.4 describe the heuristics for achieving this balance in

assigning storage locations, grouping orders, and scheduling workloads.

3.2 System Model

The system model is used to estimate the labor time spent in finishing all

requested orders of a wave in the whole lean system, which contains a fast pick area and


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a traditional area. The size of a wave is decided by the number of orders, and all waves in

one day should be the same size. The wave here is from the traditional area, because the

orders are processed in waves there. In the fast pick area, orders are processed one by one,

but those from the wave 1 are processed earlier than those from the wave 2 in order to

match the working pace in the traditional area.

The labor time per wave is the sum of all the laborers working time in one wave,

including picking time and traveling time in both areas, as well as packing time in the

traditional area. The labor time per wave is taken as the performance measurement of the

system, because labor is the main operation cost of a warehouse, especially in the order

picking area. If labor time per wave is reduced, the total labor time per day is reduced as

well, and the operation cost will decrease accordingly. This performance measurement

will be compared with the labor time per wave happened in a traditional warehouse in the

Section 4 of the thesis.

Labor time is the working time generated by workers, so the work completed by

machines will not be included. In the proposed warehouse, sealing cartons is operated by

a sealing machine at the end of the main conveyor, thus there is no sealing time in

packing time calculation. In the traditional warehouse, there are packers at the end of

sorting lanes. They seal the carton after they finish putting all items for one order into one

carton, so there is sealing time for packing time calculation in the traditional warehouse.

The sorting time is not included in labor time, because sorting items and transporting

them from picking area to packing area are completed by automatic sorting machine in

both proposed and traditional warehouses. The shipping carton dispatch time in the


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proposed system is the same as that in a traditional warehouse, because they both depend

on the number of orders. For this reason, the dispatch time is not included in calculating

labor time for both systems in order to simplify the comparison.

3.2.1 Labor Time in the Fast Pick Area

The processing time in the fast pick area is the time spent for one waves orders in

finishing all the work in the fast pick area, which includes picking time and traveling time

in a zone.

PT NG PCT=

(4)

PCT GT WT= + (5)

0GT GT TW = (6)

where,

PT Pick Time, time of finishing picks of a wave in the fast pick area

PCT Pick Cycle Time, time of finishing all picks in a group

n Number of zones in the fast pick area.

NG Number of groups in one wave

GT Grab Time, time of grabbing all items of in a zone in a group

GT0 Unit Grab Time, time of grabbing an item

TW Takt Workload, the maximal number of items per zone per group

WT Walk Time, time of traveling in a zone to pick items in a group

The orders in one wave are put into groups for scheduling purpose. Section 3.4

explains how to group orders of one wave. The pick cycle time should be smaller than the

takt time, so the assigned workload in one group can be finished in available time.


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The labor time per wave in the fast pick area is the sum of each pickers

processing time. The number of pickers is equal to the number of zones.

f LT PT n= (7)

where,

LTf: Labor time per wave in the fast pick area

3.2.2 Labor Time in the Traditional Area

In the traditional area, items are stored on shelves like a traditional warehouse.

The area is separated into zones by aisles, and in each aisle there is one picker who is

responsible for all the picks in that aisle. The route strategy used in this section is

traversal strategy, which means the picker goes through the entire aisle and ends at the

other side of the aisle. Both ends of the aisles have conveyors to transport picked items,

so the travel distance of each picker in one wave is calculated as the aisle length. Assume

that the picker can finish all picks for a wave in one route.

0trALWT

V= (8)

where,

WT0 Walk Time, time of traveling of a picker in a wave

ALtr Aisle Length in the traditional area

V Walking speed of a picker

Besides walking, pickers stop for grabbing requested items, so the grabbing time

should be included. Assume the time of grabbing an item remains the same, which means

it depends on the number of requested items.

0tr tr GT GT N = (9)


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where,

GTtr Grab Time in the traditional area


Ntr Number of items requested to pick in the traditional area

After finishing picking items for one wave, pickers place them on the sorting

conveyor which takes them to the specified sorting lanes. There are workers at the end of

sorting lanes, who take items from the lane and put them into the trays. This is considered

as packing time for the labor time calculation of the traditional area, and the workers are

called packers although they do not really pack shipping cartons. Assume the time for

putting an item into a carton remains the same, which means the total packing time in the

traditional area depends on the number of requested items.

0tr tr PaT PaT N = (10)

where,

PaTtr Pack Time in the traditional area

PaT0 Unit Pack Time, time of putting an item into a carton

The labor time per wave in traditional area is the sum of each workers time spent

on traveling, grabbing, and packing.

( )0tr tr tr tr LT WT NP GT PaT = + + (11)

where,

LTtr Labor time per wave in the traditional area

NPtr Number of pickers in the traditional area


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3.2.3 Labor Time of the Proposed System

There are mainly three operations in the proposed warehouse: picking in the fast

pick area, picking in the traditional area, and packing in the traditional area. In the

traditional area of the proposed warehouse, pickers work one wave earlier than packers,

so packers do not need to wait for the picked items. When packers start working, the

items picked from the first wave just arrive at the sorting lane. The pickers in the fast pick

area start working one wave later than the packers. When shipping cartons coming from

the fast pick area arrive at the traditional area, the items picked from the traditional area

are in the buffer trays and ready to be transferred into the shipping cartons.

To realize the operating process described above, the processing time of those

three operations should be roughly equal to each other (see Figure 3.3); otherwise the

operation with longest processing time becomes a bottleneck. If a bottleneck occurs,

other operations need to wait for it, which means the process cannot continuously flow

and there will be waiting time and idle time. The concurrency of the three operations can

be achieved by adjusting the number of pickers and packers in the traditional area as well

as the number of orders per wave.

When operating the system, the traveling of items from drop point to the sorting

lane will starts a little bit later than the picking in the traditional area. The traveling of

shipping cartons from the fast pick area to the packing area will also start some time later

than the picking in the fast pick area.


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wave 1 wave 2 wave 3

`

wave 1

wave 1 wave 2 wave 3

Picking time per wave in the traditional area

Travel time from drop point to sorting lane

Packing time per wave

Picking time per wave in the fast pick area

Travel time from the fast pick area to packing area

Figure 3.3: Arrangement of three processes

By doing this, there is no waiting time for packers, and the labor time of the

proposed system is simply the sum of all the laborers working time, including grabbing

time and walking time in both areas, as well as packing time in the traditional area.

f tr LT LT LT = + (12)

where,

LT Labor time per wave in the proposed warehouse

3.2.4 Labor Time of the Traditional Warehouse

The formula used to estimate the labor time of the traditional warehouse is similar

with time estimation in the traditional area of the proposed warehouse. But the traditional


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warehouse has one more part of time included in the packing time, i.e. sealing time. That

is because the shipping cartons are sealed by packers instead of sealing machines.

0

ALWT

V= (13)

where,

WT0 Walk Time, time of traveling of a picker in a wave

AL Aisle Length in the traditional warehouse

V Walking speed of a picker

0GT GT N = (14)

where,

GT Grab Time per wave in the traditional warehouse


N Number of items requested per wave to pick in the traditional

warehouse

After finishing picking items for one wave, pickers place them on the sorting

conveyor which takes them to the specified sorting lanes. There are packers at the end of

sorting lanes who take responsibility for putting items into cartons and sealing the cartons

before shipping. The time of those two processes are considered as two parts of the

packing time in the labor time estimation of the traditional warehouse. Assume that the

time of sealing a carton remains the same, which means the sealing time depends on the

number of orders. The total packing time is the sum of putting time and sealing time [4].

( ) ( )0 0PaT PuT N ST NO= + (15)


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where,

PaT Pack Time in the traditional warehouse

PuT0 Unit Put Time, time of putting an item into a carton

ST0 Unit Seal Time, time of sealing a carton

NO Number of orders per wave

The labor time per wave in the traditional warehouse is the sum of each workers

time spent on traveling, grabbing and packing.

( ) ( )0t LT WT NP GT PaT = + + (16)

where,

LTt Labor Time per wave in the traditional warehouse

NP Number of pickers in the traditional warehouse

3.3 Storage Assignment Method

A storage assignment method is a group of rules that is used to assign the SKUs to

a storage location. Since routing in the fast pick area is not considered in this research,

the storage locations within a picking zone make no difference. Consequently, the storage

assignment method in this part is about SKU selection (which SKU should be stored in

the fast pick area) and space assignment (how many zones a SKU is located in and which

zone it is placed).

The basic rule of the method is to evenly balance the demand among picking

zones. If the demands across zones are not balanced, there must be some zones that are

always busy and some are idle. If the demands are evenly distributed, the idle time of

zones can be reduced. So this rule matches the lean principle of eliminating idle time.


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The second rule is to reduce rush and travel. If a rush happens to one zone, the

zone would be a bottle neck which causes waiting of other zones. According to the lean

principle of reducing waiting time, rushes should be avoided.

3.3.1 SKU Selection

The SKUs to be stored in the fast pick area are based on the demand and ordering

frequency of the SKU. The demand of a SKU is the number of items requested by all the

orders during a time period. In this research, SKU selection is based on monthly demand

forecasts.

The ordering frequency of a SKU is the number of times that it is requested by

different orders. According to the rule of reducing travel, if two SKUs have the same

demand, the SKUs with higher ordering frequency will be put to the fast pick area. The

SKU with higher frequency is more likely to be ordered and picked, so more travel is

required if the SKU is put in the traditional area.

The SKUs that are not chosen to be put in the fast-pick area will be stored in the

traditional area. If those SKUs are ordered, they will be retrieved from the traditional area,

and they will be combined with the ones picked in the fast-pick area before the shipping

carton is sealed. Since those are low demand items, the picking work in the traditional

area does not significantly add to the operating cost.

Consider a warehouse having twenty SKUs that allows 20% of the SKUs to be

stored in the fast pick area. Table 3.1 shows an example with one months demand

forecast for twenty SKUs.


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Table 3.1: One-Month Demand Forecast of 20 SKUs

SKU Demand Percentage Accumulated Percentage

S1 1238 34.83% 34.83%S2 791 22.26% 57.09%

S3 469 13.20% 70.29%

S4 320 9.00% 79.29%

S5 114 3.21% 82.50%

S6 107 3.01% 85.51%

S7 107 3.01% 88.52%

S8 101 2.84% 91.36%

S9 64 1.80% 93.16%

S10 56 1.58% 94.74%

S11 53 1.49% 96.23%

S12 28 0.79% 97.02%S13 27 0.76% 97.78%

S14 21 0.59% 98.37%

S15 15 0.42% 98.79%

S16 14 0.39% 99.18%

S17 12 0.34% 99.52%

S18 10 0.28% 99.80%

S19 5 0.14% 99.94%

S20 2 0.06% 100.00%

The Demand column shows the estimated demand for each SKU for one month.

Percentage is the ratio of a SKUs demand to total demand. In Table 3.1, SKUs are

sorted in a descending sequence according to demand; and the Accumulated

Percentage tells that the first four SKUs stand for 79.3% of the total demand. Based on

the total demand and pick frequency, the first 20% of SKUs are S1, S2, S3, and S4.

According to the heuristic of SKU selection mentioned in this section, S1, S2, S3, and S4

will be assigned in the fast pick area, and other SKUs will be stored in the traditional area.


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3.3.2 Space Assignment

Space assignment is based on the forecasted demand for the coming month, and

the storage locations will be reassigned monthly. The SKUs chosen to be stored in the

fast pick area will be assigned to one or more zones. Two decisions are needed to be

made for space assignment. One is how many locations for each SKU; and then to which

zone each SKU should go.

The space assignment approach begins by allocating each SKU with one zone.

There is an index to be calculated, i.e. average number of picks per zone (APZ). The APZ

for SKU Si is the ratio of the total demand ofSi to the number of zones it is assigned to.

After allocating each SKU with one zone, the second step is computing APZ for every

SKU and finding out the highest APZ. Then the SKU with highest APZ should be

allocated with one more zone. The next step is recalculating the APZ for each SKU, and

repeating the second step. The assignment will stop when the total number of assigned

locations reaches the capacity in the forward area.

Continuing the example in last section, if there are five zones in the fast pick area,

and each zone has two storage locations, in total ten locations are available to store the

four SKUs. According to the heuristic, each SKU is assigned with one zone first. Their

current APZs are shown in Table 3.2 (a).

Table 3.2 (a): APZs and Zone Assignment for Step 1

SKU S1 S2 S3 S4 # Location Taken

Demand 1238 791 469 320

# Zones 1 1 1 1 4

APZ 1238 791 469 320 (6 available)


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The highest APZ is that of SKU S1, so S1 is assigned with one more zone, as

shown in Table 3.2 (b).

Table 3.2 (b): APZs and Zone Assignment for Step 2


Demand 1238 791 469 320

# Zones 2 1 1 1 5

APZ 619 791 469 320 (5 available)

Now, SKU S2 has the highest APZ, which allows for one more zone for S2. Then,

new results for APZs are calculated as Table 3.2 (c) shows.

Table 3.2 (c): APZs and Zone Assignment for Step 3


Demand 1238 791 469 320

# Zones 2 2 1 1 6APZ 619 395.5 469 320 (4 available)

The above assignment is repeated until all ten locations are taken. The final

results of space assignment are shown in Table 3.2 (d).

Table 3.2 (d): Final Result for APZs and Zone Assignment


Demand 1238 791 469 320

# Zones 4 3 2 1 10

APZ 309.5 263.7 243.5 320 (0 available)


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After determining the number of zones for each SKU, the next decision to make is

where to assign each SKU. Inflexible SKUs are those items with low demands and

assigned with one or two locations, meaning that they either do not have high ordering

frequencies or their ordering quantities are small. Those SKUs should be assigned first

and spread among zones to balance the average picking frequencies and quantities.

Flexible SKUs are those items that have high demands, and they should be split into

multiple zones to make workload balancing possible and easier.

A single SKU should not be assigned to the same zone twice, because it is not

helpful for splitting the SKU into zones. Also, several zones should not have the same

combination of SKUs. If, by chance, two of the SKUs both have very high demand for an

order, its hard or even impossible to balance the workload. The flexible SKUs should be

located together with inflexible SKUs in order to reduce rush. As a result of following

above three rules, the final space assignment will combine popular SKUs with less

popular SKUs, and the combination for each zone will most likely be different.

In the case mentioned before, the most inflexible SKU is S4 because it has the

lowest demand and frequency. So S4 should be assigned first, and it is stored in Zone 1.

The next SKU to be assigned is S3. In order to spread inflexible SKUs among zones, S3

is placed into zones without S4, which are Zones 2 and 3. For the same reason, S2 is

assigned to Zone 4, 5, and 3. The reason of placing S2 together with S3 instead of S4 is

that S4 is the most inflexible SKU and it should be stored together with the most popular

SKU S1. Based on the number of locations assigned, the four SKUs should be placed into

five zones as shown in Table 3.3.


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Table 3.3: Space Assignment Result

Zone 1 Zone 2 Zone 3 Zone 4 Zone 5S4 S1 S3 S1 S3 S2 S2 S1 S2 S1

3.3.3 Mathematical Model of Space Assignment

This mathematical model is developed to better understand the space assignment

method and help users to apply the heuristics into their warehouse storage location

assignment.

Indices:

i index of SKU, 1, 2, 3, ,I.

j index of picking zone, 1, 2, 3, ,J.

Variables:

APZi Average number of picks per zone for SKU Si.

TDi Total assignment demand of SKU Si for all zones.

Ni Number of zones that have SKU Si.

n Number of zones in the fast pick area.

Y Number of locations in a zone.

wij =1, if there is SKU Si located in Zonej,

=0, if no SKU Si located in Zonej.

Objective Function:

{ }( )i Min Max APZ i , (17)

Subject to:


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each order will be scheduled into a bin in the matrix. So there are two decisions to make

and two constraints to consider. It has to be decided which group and which zone (when a

SKU is assigned to multiple zones) each item should be assigned to. However, the item is

not free to go anywhere, because it has to go to the group which includes its order and it

can only be assigned in the zone which has the SKU.

Zone 1 Zone 2 Zone 3 Zone 4 Zone 5

Group 1

Group 2

Group 3

Group 4

Group 5

Figure 3.4: Matrix of scheduling problem

When scheduling the items, the two decisions and two constraints should be

considered at the same time to get a global solution. However, its very hard to do the

scheduling by hand. Therefore, in this research, the scheduling problem will be solved

through two steps: order grouping and workload scheduling.

3.4.1 Order Grouping

It is difficult and usually impossible to evenly distribute the workload for a single

order among all zones due to fewer requested items than number of zones. Imagine an

order with a demand of nine items no matter how many SKUs, and a fast pick area with

ten zones. There is no way to balance the demand among zones for this order. If several


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orders are grouped together, the workload of the large order is more likely to be balanced.

Therefore, rather than attempting to balance an individual order, we group orders for

scheduling purposes. The goal will be to evenly distribute the workload of a group, and

by using buffer trays the orders will still be picked one by one. Normally, the group size

(number of orders per group) is equal to the number of buffer trays in a zone. The former

can be smaller than the latter, but it cannot be bigger; otherwise there are not enough

places to hold the picked items.

The takt time is the available time for the whole picking line to finish the demand

of each group. The takt time is equal to the total time available for one day divided by the

number of groups. It is important to keep the takt time constant for one days work

because it decides the conveyor speed. No warehouse wants to adjust the conveyor speed

all the time. Since takt time is a constant, the demand of a group should be the same. For

this reason, orders cannot be randomly put together, and they need to be scheduled

following the heuristic below.

The heuristic begins with sorting all orders by demand in descending sequence.

The largest order goes to the first group, and the second largest order goes to the second

group, and so forth. After finishing a round, the next order should be assigned to the

group currently with smallest demand instead of the first group again. All the remaining

orders follow this rule. The purpose of doing this is to balance the workload among

groups; therefore, it is possible that the number of orders per group is not equal to each

other.


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During order grouping, there is one index to be calculated, which is the current

average workload per zone for each SKU in each group (AWS). AWSij is the ratio of SKU

Sis demand assigned to the Groupj, to the number of zones having SKU Si.

i

ij

ijN

DAWS = (21)

where,

Dij Current demand of SKU Si assigned to Groupj

Ni Number of zones that have SKU Si

The AWS values are used as a standard to see whether current grouping is fine or

not. They should always be smaller or equal to the takt workload, which is the maximum

workload

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Kong Chenying