Improvement of the Storage and Picking Processes in ...

Improvement of the Storage and Picking Processes inWarehouse Logistic Systems

João Pedro Capinha da Silva

Thesis to obtain the Master of Science Degree in

Mechanical Engineering

Supervisors: Professor João Miguel da Costa SousaFH - UAS Thomas Kaps

Examination CommitteeChairperson: Professor João Rogério Caldas PintoSupervisor: Professor João Miguel da Costa Sousa

Members of the Committee: Professor Carlos Baptista CardeiraProfessor Susana Margarida da Silva Vieira

June 2015

I dedicate my thesis to my parents and sister who, as always, supported me in my studies.

I also dedicate my thesis to Mr. Horst Lieberwirth who gave me the opportunity to write this thesis at

his company in Germany and to my supervisors João Miguel da Costa Sousa and Thomas Kaps who

always showed me the right direction to take.

iii

Acknowledgments

I would like to take this opportunity to thank the people that supported me during the thesis elaboration.

Starting from the university that provided me years of learning and challenging situations that helped

me to think by myself and question everything with a critical thinking. In this final phase, special thanks

to my thesis supervisor, Prof. João Miguel da Costa Sousa, who was always very effective understanding

my problems and quickly providing me a direction to find a correct solution.

Next, a special thank to Diesel Technic AG, who provided me a very interesting real scenario to

apply my knowledge as well as many process experts, like Eng. Thomas Kaps, who dedicated some of

their working time to provide me all the information I needed to build this work and accepted from

the beginning a new way of thinking about their processes, even if at the first time it was not clear for

them in what would it result and how could the company benefit from it. Special thanks to Mr. Horst

Lieberwirth who gave me the opportunity to produce my thesis in his company, providing me very good

conditions to do that.

Lastly, thanks to my family and friends who were always a good motivation when I needed.

All those people contributed to provide and help me finishing one of the most challenging and de-

manding projects I’ve done during my studies.

v

Resumo

Hoje em dia, a empresa que disponibiliza o produto mais rapidamente ao cliente tem uma grande van-

tagem no mercado. Alguns clientes estão dispostos a pagar mais por um produto caso este chegue mais

rápido às suas mãos comparando com o serviço de outras empresas.

Pensando nisso, as empresas de logística têm sempre em mente a melhoria contínua dos seus processos

de forma a reduzir os tempos de entrega dos produtos e satisfazer os seus clientes. Devido à complexidade

destes sistemas, análises utilizando métodos tradicionais podem ser de difícil implementação e soluções

como simulação de eventos discretos podem ser úteis.

Nesta dissertação, modelam-se e simulam-se processos de uma empresa de logística que se encontra

no mercado de reposição independente de peças para veículos comerciais.

São apresentados dois modelos dos processos internos da empresa considerados de interesse para a

dissertação de mestrado e são discutidos os resultados, comentando os efeitos práticos de uma futura

aplicação na empresa.

O primeiro modelo tem como objectivo avaliar o tempo total de processamento das encomendas e o

segundo modelo avalia o desempenho da área de embalagem de um dos tipos de encomendas mais comuns

nesta empresa.

Este trabalho pode apenas ser realizado com um profundo conhecimento do sistema em estudo. Nesse

sentido, esta dissertação foi realizada em parte ao longo de seis meses nos escritórios e armazém da em-

presa em questão. Desta maneira foi possível uma total envolvência nos processos e pessoas que neles

trabalham.

A natureza iterativa das simulações tirou também vantagem deste proximidade aos processos logísti-

cos.

Palavras-chave: Logística, Simulação de eventos discretos, Melhoria, Modelação

vii

Abstract

Nowadays, the fastest company delivering the product to the customers has a big advantage in the market.

Some costumers are willing to pay even a higher price if it will result in a product on their hands first

then with the service of other companies.

Thinking about that, logistics companies have always in mind the improvement of their processes in a

way that the delivery times can be shortened and the costumers more satisfied. Due to the complexity of

such systems, the traditional analysis methods can be of hard implementation and solutions like discrete-

event simulation appear to be very useful.

In this dissertation are modeled and simulated logistics processes of a company whose business is

located in the independent after market of commercial vehicles parts.

Two models are presented. The first one evaluates the total lead time of all the orders and the second

one analyses the performance of the packing area of the most common type of orders in this company. A

comment about the implementation in the real processes is done.

This work can only be done by someone who has a deep knowledge of the system in study. For this

reason, this dissertation was done, in part, during a six month stay in the company offices and warehouses.

This way, a total involvement in the logistic processes was possible.

The iterative nature of simulation also took advantage of this proximity to the logistic processes.

Keywords: Logistics, Discrete-Event Simulation, Improvement, Modeling

ix

Contents

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Resumo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

Contents xi

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi

1 Introduction 1

1.1 Introduction to System Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Discrete-Event Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.3 Steps in a Simulation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.4 Logistics and Warehouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.5 Introduction to the Company in Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.6 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.7 Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Logistic Warehouse System Description 7

2.1 Company’s Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Warehouse Working Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3 Replenishment task in ‘Big shelf 1’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4 Replenishment task in ‘Big shelf 2’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.5 Order Picking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.5.1 Picking in the Small Shelf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5.2 Picking in big shelves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.5.3 Split of Picking Orders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.6 Packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3 Modeling 20

3.1 Modeling with SIMIO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2 Time Distributions from Real Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.2.1 Distribution Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

xi

3.2.1.1 Picking Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.1.2 Packing Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2.1.3 Number of Picks per Order . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3 Lead Time Simulation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.3.1 Modeling the Lead Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.4 Parcel Service Orders Packing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.4.1 Modeling the Packing for Parcel Service Orders . . . . . . . . . . . . . . . . . . . . 36

3.5 General Conditions of Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4 Results 41

4.1 Validation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.2 Lead Time Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

4.3 Parcel Service Orders Packing Model Results . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.4 Results Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.4.1 Lead Time Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.4.2 Parcel Service Orders Packing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5 Sensitivity analysis in the Packing of Parcel Service Orders 49

5.1 What would happen if there was an increase of 20% of total parcel service orders while

maintaining the same configuration? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.2 What would happen if there was an increase of 10% more orders 1 hour (Private Attach-

ment A.1 g)) after rush time while maintaining the rest of the day? . . . . . . . . . . . . . 52

5.3 What would happen if 10% of the morning orders were done 0.5 hours (Private Attach-

ment A.1 g)) after rush time? It means less orders in the morning and more in the end of

the day, while maintaining the total number of daily orders. . . . . . . . . . . . . . . . . . 54

5.4 What would happen if 1 (Private Attachment A.1 g)) temporary operators (2.5 hours

shift (Private Attachment A.1 g)) were removed and 3 (Private Attachment A.1 d)) extra

full-time afternoon were hired, while maintaining the same orders quantity? . . . . . . . . 56

6 Conclusions 58

Bibliography 61

A Confidential Appendix 65

xii

List of Tables

2.1 Warehouse shelves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.1 Real Data - Number of Orders (Confidential attachment table A.2) . . . . . . . . . . . . . 30

3.2 Real Data - Number of Standard Orders (Confidential attachment table A.3) . . . . . . . 30

3.3 Real Data - Percentage of internal orders picks in each picking area . . . . . . . . . . . . . 31

3.4 Real Data - Percentage of parcel service order picks in each picking area . . . . . . . . . . 31

3.5 Real Data - Percentage of standard order picks in each picking area . . . . . . . . . . . . 31

3.6 Picking operators by picking area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.7 Standard Orders Packing Working Shifts (Confidential attachment table A.4) . . . . . . . 33

3.8 Parcel Service Orders Packing Working Shifts (Confidential attachment table A.5) . . . . 34

3.9 Parcel Service Order Rate Table - Packing (Confidential attachment table A.14) . . . . . 38

3.10 Time Not Effectively Picking (Confidential attachment table A.6) . . . . . . . . . . . . . . 39

4.1 Number of Created Orders (Confidential attachment table A.7) . . . . . . . . . . . . . . . 42

4.2 Total Processing Time (s) (Confidential attachment table A.8) . . . . . . . . . . . . . . . 42

4.3 Total Processing Time by Order (s) (Confidential attachment table A.9) . . . . . . . . . . 42

4.4 Total Number of Picks (Confidential attachment table A.10) . . . . . . . . . . . . . . . . . 43

4.5 Total Number of Picks by Area (Confidential attachment table A.11) . . . . . . . . . . . . 43

4.6 Total Packing Time (s) (Confidential attachment table A.12) . . . . . . . . . . . . . . . . 43

4.7 Lead Time (h) (Confidential attachment table A.13) . . . . . . . . . . . . . . . . . . . . . 44

4.8 Results Actual Situation - Table (Confidential attachment table A.15) . . . . . . . . . . . 45

5.1 Results Scenario 1 (Confidential attachment table A.16) . . . . . . . . . . . . . . . . . . . 50

5.2 Results Scenario 1 - Proposed solution (Confidential attachment table A.17) . . . . . . . . 50


5.4 Results Scenario 2 - Proposed Solution (Confidential attachment table A.19) . . . . . . . 52


5.6 Results Scenario 3 - Proposed Solution (Confidential attachment table A.21) . . . . . . . 54


A.1 Confidential Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

A.2 Real Data - Number of Orders - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . 69

xiii

A.3 Real Data - Number of Standard Orders - Confidential . . . . . . . . . . . . . . . . . . . . 70

A.4 Standard Orders Packing Working Shifts - Confidential . . . . . . . . . . . . . . . . . . . . 70

A.5 Parcel Service Orders Packing Working Shifts - Confidential . . . . . . . . . . . . . . . . . 71

A.6 Time Not Effectively Picking - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . 71

A.7 Number of Created Orders - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

A.8 Total Processing Time (s) - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

A.9 Total Processing Time by Order (s) - Confidential . . . . . . . . . . . . . . . . . . . . . . 72

A.10 Total Number of Picks - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

A.11 Total Number of Picks by Area - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . 73

A.12 Total Packing Time (s) - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

A.13 Lead Time (h) - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

A.14 Parcel Service Order Rate Table - Packing - Confidential . . . . . . . . . . . . . . . . . . . 74

A.15 Results Actual Situation - Table - Confidential . . . . . . . . . . . . . . . . . . . . . . . . 74

A.16 Results Scenario 1 - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

A.17 Results Scenario 1 - Proposed solution - Confidential . . . . . . . . . . . . . . . . . . . . . 76

A.18 Results ’What-if’ Scenario 2 - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

A.19 Results Scenario 2 - Proposed Solution - Confidential . . . . . . . . . . . . . . . . . . . . . 77


A.21 Results Scenario 3 - Proposed Solution - Confidential . . . . . . . . . . . . . . . . . . . . . 79


xiv

List of Figures

1.1 Steps in a Simulation Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 Diesel Technic Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Big Shelf 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Big Shelf 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Unhandy Items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.5 Small Shelves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.6 Convoyer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.7 Pallets lift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.8 Fluxogram - Put-away process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.9 Fluxogram - Replenishment of big shelf 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.10 Fluxogram - Replenishment of big shelf 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.11 Picking in the Big Shelf 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.12 Fluxogram - Picking Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.13 Fluxogram - Packing process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.14 Packing - Pallet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1 Log-Logistic distribution, α = 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2 Parcel service orders picking time per item - Time distributions (Confidential attachment A.1) 23

3.3 Standard Orders Picking Time per Item - Time Distributions (Confidential attachment A.2) 24

3.4 Packing time per item - Time distributions (Confidential attachment A.3) . . . . . . . . . 25

3.5 Exponential distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.6 Number of Picks - Parcel Service Orders (Confidential attachment A.4) . . . . . . . . . . 26

3.7 Number of Picks - Standard orders (Confidential attachment A.5) . . . . . . . . . . . . . . 27

3.8 Lead Time model flowchart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.9 Simio Model Schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.10 Lead time model - 3D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.11 Packing Parcel Service Orders - Fluxogram . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.12 Packing Parcel Service Orders - Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.13 Packing Parcel Service Orders - Model 3D . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.1 Actual Situation - Plots (Confidential attachment figure A.6) . . . . . . . . . . . . . . . . 46

xv

5.1 Results Scenario 1 - Plots (Confidential attachment figure A.7) . . . . . . . . . . . . . . . 51



5.4 Results Scenario 4 - Plots (Confidential attachment figure A.10) . . . . . . . . . . . . . . 57

A.1 Parcel Service Orders Picking Time per Item - Time Distributions (s) - Confidential . . . 66

A.2 Standard Orders Picking Time per Item - Time Distributions - Confidential . . . . . . . . 67

A.3 Packing Times - Time Distributions (s) - Confidential . . . . . . . . . . . . . . . . . . . . 68

A.4 Number of Picks - Parcel Service Orders - Confidential . . . . . . . . . . . . . . . . . . . . 68

A.5 Number of Picks - Standard Orders - Confidential . . . . . . . . . . . . . . . . . . . . . . 69

A.6 Results Actual Situation - Plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

A.7 Results Scenario 1 - Plots - Confidential . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76




xvi

Chapter 1

Introduction

All values presented in this dissertation with a reference to the confidential attachment are not real values

and can only be considered after multiply/divide by the respective constant present in the confidential

attachment.

1.1 Introduction to System Simulation

Simulation is a technique that allows to have a representation of a real world system. Having the main

structure of the system and the needed constraints, one is able to represent the real problem and analyze

it faster and in the virtual reality world [1].

The model is a group of mathematical and logical rules that simulate the relationships between the

system entities. Those entities are usually the part of the system that we want to conclude something

about. Because of that, it is important to define well what the entities of our model are [2].

With the simulation model, it is possible to try different scenarios that change some of the model

constraints which represent real world situations. The model is a good representation of the reality

that allows us to make conclusions about the real world system, always considering its constraints and

limitations.

Simulation is very useful to study systems before they are built up allowing companies to avoid

wasting money without knowing how the system will behave in the future. This extremely important in

companies that have a very high demand and can’t stop their work to try new possibilities before they

are successfully proved in theory.

1.2 Discrete-Event Simulation

Discrete-event systems simulation (DES) is the modeling of systems in which the state variable changes

only at a discrete set of points in time. The simulation models are analyzed by numerical methods

rather than by analytical methods. Analytical methods employ the deductive reasoning of mathematics

to “solve” the model. For example, differential calculus can be used to compute the minimum-cost policy

for some inventory [1]. Numerical methods employ computational procedures to “solve” mathematical

1

models. In the case of simulation models, which employ numerical methods, models are “run” rather

than solved. That is, an artificial history of the system is generated from the model assumptions, and

observations are collected to be analyzed and to estimate the true system performance measures [1]. Real

world simulation models are rather large, and the amount of data stored and manipulated is vast, so

such runs are usually conducted with the aid of a computer. In contrast to optimization, DES allows

incorporation of stochastic processes [3]. Also, it is possible to study “what-if” scenarios and test multiple

situations. With a wide range of applications, it is often used to analyze manufacturing and logistics

(airports, transports, warehouses, supply chains...). It allows to study complex systems that were really

hard to solve mathematically, in a simple and robust way. For all these reasons, DES is used in this

thesis.

1.3 Steps in a Simulation Study

According to [1], fig. 1.1 shows the step guides for a robust simulation study. The steps are explained

bellow:

1. Problem Formulation : Every study begin with a statement of the problem. This problem can

range from simply testing a set of assumptions about the system to testing a totally new system

configuration.

2. Setting Objectives: The objectives should indicate what questions are going to be answered by

the simulation. Deciding on this desired outcome will aid in designing the structure of the model.

This is a critical step in the process because it will inevitably dictate the model’s need for detail

and complexity. This complexity should not exceed the requirements as posed by the objective.

3. Setting Scope and Overall Project Plan : Given that a list of objectives has been set, the scope

of the project can be defined. The scope should only include details that are required to achieve

the project’s objectives. Given that this can be a difficult task because the primary variables that

are driving the system are not always apparent, it is best to start with a simple model and build

towards greater complexity. In addition, all assumptions going into the model should be clearly

documented. This will serve as a formal record and better support the implementation phase at

project completion.

Sketch the Layout : This is a good way to visualize all of the necessary inputs that are required

to achieve the goals that have been set. It will serve as a guide throughout the project and aid to

understand how the elements to be modeled interact with one another.

4. Data Collection : Once all of the necessary inputs have been identified, collections of the required

data can begin. There will probably be a constant interplay between the construction of the model

and the collection of necessary data. As the complexity of the model increases, the required data

elements may also change.

5. From Layout to Simulation Software : Re-create layout with a software package. Carefully

label all entities to avoid confusion later on.

6. Validation : Validation is used to determine that the model is an accurate representation of the

2

real system. Validation is usually achieved through calibrating the model, an interactive process of

comparing the model to the actual system and using discrepancies between the two to improve the

model. Depending on the results, validation may prompt a step backwards to include a component

in the model that may have previously been considered unnecessary. Two of the key factors in

having success with validation are choosing the appropriate metrics and knowing when the results

are within an acceptable range. Choosing the appropriate metric may be simple if there is an

abundant amount of data available from the real system, however if it is not the case, it may be

considerably more difficult to determine what are the appropriate measures for comparison. Once

the metrics have been chosen, it must be decided how accurate the simulation output needs to be

in order to declare success. Should the simulation output be within 10% or 30% of the real system?

The answer is that it depends on the given system and whether or not the discrepancy between

the simulation and the real system is acceptable and can be explained. Even of the model does not

exactly match reality, it may excellent for relative comparisons.

7. Experimental Design : Now that the model has been validated, the objectives that were set in

step 2 can be tested. An initialization period must also be determined. This allows the system to

stabilize before any results are taken.

8. Documentation : There are basically two steps of documentation: background data and program

data. Background data includes any historical data that may have been used to drive the simulation.

Program data includes a listing of the coding required to create the simulation program. This

type of documentation will ensure that the project can be passed on to another programmer with

confidence.

9. Implementation : The success of implementation depends on how well the previous steps have

been performed. If the model and underlying assumptions cannot be properly communicated, im-

plementation will probably suffer. A modeler must be prepared to properly defend the assumptions

and data behind the model, otherwise it will be extremely difficult to justify any capital expenditures

for implementation.

3

Figure 1.1: Steps in a simulation study

1.4 Logistics and Warehouses

It is increasingly more common that customers need the finished products in a shorter period after they

ordered it. Because of that, logistics systems are continuously searching new ways to improve their

performance resulting in shorter delivery times and ideally, at the same time, smaller costs [4].

A successful performance of a warehouse depends on the appropriate design, layout and operation

of the warehouse and material handling systems. When designing the warehouses, a balance between

edibility, layout configuration, storage density and throughput capacity in order to achieve an effective

design at a minimum cost has to be achieved. Estimates indicate that, depending on the type of industry,

at least 25% of the cost of a product is represented by physical movement. Therefore, every decision

4

related to warehousing can reduce the logistics cost. Order picking has long been identified as the most

labor-intensive and costly activity for almost every warehouse. The cost of order picking is estimated

to be as much as 55% of the total warehouse operating expense [5]. Any under performance in order

picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently

for the whole supply chain. In order to operate efficiently, the order-picking process needs to be robustly

designed and well managed.

1.5 Introduction to the Company in Study

Diesel Technic keeps commercial vehicles moving around the world as one of the largest suppliers of

commercial vehicle spare parts in the Independent Aftermarket (IAM).

The full- range is continually being developed and provides the wholesale trade with the opportunity to

meet market demands at all times. Distribution partners in more than 140 countries value the experience

and expertise of Diesel Technic as a reliable full-service partner for the wholesale trade [6].

The department Logistics Planning in the headquarters of the Diesel Technic Group in Kirchdorf -

Germany is responsible for the planning and realization of national and international logistic projects

as well as for the optimization of the internal logistics. During the last 8 years, the logistic facilities in

the headquarters of the Diesel Technic Group were extended most intensive in 2 steps. On base of a

continuously growth of the business and also of the inventory, the existing big aisle racking system with

a capacity of approx. 5.500 pallet places, the VNA-Racking system with approx. 6.800 pallet places and

the multi-tier shelving system with roughly 17.000 running meters of shelving are reaching their limits

[7].

1.6 Problem Statement

Diesel Technic supplying requirements are growing fast and to keep or to improve the service quality and

speed to the costumers there is the need of thinking about new strategies. Part of those strategies are

directly related to the internal logistics of the company, regarding the improvement of all the warehouse

internal tasks. In the end, the sum of all processes improvement will lead to a big amount of time/money

saved, which represents a better and more economic final product.

Diesel Technic develops every year a number of improvement projects. One of the projects was to

reduce the total lead time of clients orders. Other is to test a new strategy regarding one type of order

that has a very fast processing time, parcel service orders). By fast, means that the orders arrive and

must be finished in the same day to be delivered by the transportation companies to the final costumer

in the next day. The company wants to accept this type of orders until a later hour so it can provide a

better service to the customers.

Those complex changes in the warehouse working processes imply physical and people changes and

are not easy to do while the company is in activity. It is a good task for a simulation model that

represents the real system and allows to make non-intrusive changes. After the model being implemented

5

and validated, scenarios can be tested and the future real system changes have now an extra support

before being implemented.

1.7 Dissertation Outline

This dissertation aims to analyze how can a real logistics warehouse problem be simulated and conclusions

being drawn.

Everything is referent to a real company and all data presented in this report comes from the company’s

databases.

The report is divided in 6 chapters. First, a theoretical introduction is done, explaining the simulation

techniques used in the dissertation.

Second, the general behavior of the logistic system is explained and all the data needed, like the

company’s layout, to create a conceptual model is presented.

Third, the simulation model, as well as the used data, are presented and all the considerations are

explained.

Fourth, results from both models are presented.

Fifth, a more detailed analysis is done to the second model and different scenarios are tested.

By last, conclusions are drawn about all the models in the dissertation and suggestions are given to

the future of the company activity.

6

Chapter 2

Logistic Warehouse System Description

2.1 Company’s Layout

Diesel Technic’s main warehouse has seven main areas as shown in fig. 2.1:

1. Incoming Warehouse

2. Deployment Area

3. Wide Aisle Shelves (Big Shelf 1 – BS1)

4. Narrow Aisle Shelves (Big Shelf 2 – BS2)

5. Small parts Shelves (Small shelves - SS)

6. External Warehouses (There are two more external warehouses in a city near this location. Company

trucks are used to transport the pallets to them)

7. Packing Area

Figure 2.1: Diesel Technic Germany plant

7

Incoming warehouse (fig. 2.1 - 1) is where the process of the whole system begins because it is where

the new products arrive and are packed into Diesel Technic brand boxes, after doing an extensive list of

tasks like rigorous quality checks and laser brand recording. From here, the products go to the outgoing

warehouse (fig. 2.1 - 3, 4, 5 and 6) by being deposited in the deployment area (fig. 2.1 - 2). From this

area, depending on the product characteristics, outgoing warehouse workers will store the products in

some of the storage areas (fig. 2.1 - 3, 4, 5 or 6). Up to this point, where the products are stored in the

shelves, the picking process is done.

Client orders arrive from the office to the warehouse management software and operators pick the

parts from the shelves. Once the order is finished (i.e. all individual items from the order are picked),

depending on its type, it goes to a packing area where the order is rechecked and prepared to ship to the

customer.

From now on in this dissertation, “Pick” is defined as the process which comprises the physical dis-

placement of the operator from where he is until the destination shelf, summed to the time he needs to

get the item from that shelf, time to count the number of samples to pick from the pallet and store it

safely in his forklift platform. Just after this procedure, the operator informs the warehouse management

software that the pick is done and it shows him the information to the next pick of the current order.

In the case that the pick is the last of the order, the operator can choose a new order and start picking

again.

The focus in this thesis are the processes of picking from the shelves until all picks are done. After

that, the packing must be done and the order is considered finished. The time between the input of the

order in the warehouse management system and the output after packing is the so called ‘Lead Time’.

2.2 Warehouse Working Process

After all quality checks and packing individual items into pallets processes in the Incoming warehouse,

the products go to the deployment area and wait to be transported to the next step, which consists on

the storage of the products in 3 main areas:

• Big shelf 1

• Big Shelf 2

• Small shelf

The difference between ‘Big Shelf’ and ‘Small Shelf’ is the volume of the products to store. It means

that the ‘Small shelf’ warehouse will store products that have a small overall volume (usually less than

one full pallet) and which can be stored in the small shelves (although they can be also big in size, there

is still the physical limit of shelves dimensions) and the bigger volume products (usually full pallets) go

directly to the big shelves in complete pallet or not. For example, if it is a full pallet of small components,

it goes to the big shelf. If it is just a small portion of the same small components, it goes to the small

shelf. In the case of having an isolated product whose destination is a pallet in the big shelves, the

operator can place manually inside an existing pallet on shelves if the production serial number from the

8

products matches with the ones existing in the destination pallet. If the production number is different,

the operator creates a new position in the shelves for the new items.

For those reasons, the deployment area is subdivided in two areas. One to temporarily store the “big

shelf” and another one for “small shelves” products. Temporarily storing because the pallets stay here

some minutes until some warehouse operator is free to transport them to the final destination in the

shelves.

Some workers from the warehouse are responsible for the transportation of the deployment area pallets

to the warehouse shelves. This task is called ‘Put Away’. The employees identify the boxes by scanning

the bar-code with the mobile data equipment (MDE). The system automatically shows the zone and the

location code where the items should be transported by forklift.

In the “big shelves”, five different situations can occur:

1. The new pallet is transported to a picking position (HRK1 fig. 2.2 - 1) or to a replenishment position

(HRR1) in “Big Shelves 1”, fig. 2.2 - 2. The idea is to have always products available for picking

in the lower levels. When they are already available, the pallet goes to the replenishment position

in the shelf and waits to go to the picking position (which happens when the picking position is

empty).

Figure 2.2: Big Shelf 1

2. Same as in the previous point, but in the Big shelf 2, fig. 2.3.

9

Figure 2.3: Big Shelf 2

3. The pallet is transported to the external warehouses (EXT1 - Fig. 2.1 - 6) because the main

warehouses are full of that kind of products and there is no need to have extra pallets with this

same product in the warehouse. Usually those products are sold very often and because of that the

warehouse should always have it available.

4. When the items are too big to fit the normal shelves, like truck bumpers or steering arms, they

are called big sized items and have a special destination, which is still considered in the warehouse

management system “big shelf 1”, but it is on a different area inside the warehouse (BOL, fig. 2.4)

which is divided in different shelf areas organized by flexible shelves for unhandy items.

Figure 2.4: Unhandy Items

5. If there is a client order including a full pallet while to the put away of this same pallet is in process,

it is transported directly from the deployment area for the packing area for delivery.

In the “small shelves” area, the procedure is different. It is a four floors building (KA, KB, KC and

KD) with small shelves covering all area, fig. 2.5.

10

Figure 2.5: Small Shelves

Connecting the floors there are two elevator systems which are able to transport pallets or plastic

boxes from the ground level until all next levels. The forklift operators deliver the product pallets into

the base of the elevator and the bar code is automatically scanned. Once it is done, the machine knows

which is the destination and transports it to the correct destination floor.

Once on the correct destination floor, one operator, who is responsible for the put away on all four

floors, finds an empty place on the shelves where the product can fit and inform the management system

the final destination as well as the number of products that fitted on that specific location. If he/she is

not able to fit all products from the same type together, he/she will find another empty shelf and does

the same procedure again. Up to this moment, the warehouse management system knows exactly where

and how many products are on the shelves and knows that they are available for picking.

Figure 2.6: Convoyer System

11

Figure 2.7: Pallets Elevator

In all three main areas (“big shelf 1”, “big shelf 2” and “small shelves”), the put away process is

considered chaotic. In this dissertation, by chaotic it means that there is not a special place in the

shelves for a product to be stored. Warehouse operators can place the new products anywhere where it is

empty or simple put inside an existing pallet if the production number of the product is the same. Then,

they inform the warehouse management system about the new location and it saves the location and

quantity information. Usually the heaviest products like truck brake drums or brake disks are stored in

the lower levels and the lightest ones are stored on top levels of the shelves because of physical equipment

restrictions and prevention of operators physical bad conditions. At the same time, despite there is no

special storing locations for each type of product, the workers try to keep the products organized by type

and, in the small shelves floors, the items are divided by brand between the floors. The flowchart from

fig. 2.8 resumes the ‘put-away’ process.

12

Figure 2.8: Fluxogram - Put-away process

2.3 Replenishment task in ‘Big shelf 1’

The replenishment task in this warehouse starts on replenishment levels in ‘Big Shelf 1’ or in ‘Big shelf

2’. There are two operators all time working on this task. The operators drive forklifts and transport the

pallets between the replenishment and the picking positions. The reservation places in this warehouse

are all places in the last level of the complete warehouse (level 50).

13

Figure 2.9: Fluxogram - Replenishment of big shelf 1

2.4 Replenishment task in ‘Big shelf 2’

The replenishment task in this part of the warehouse starts in the deployment area and from the replen-

ishment level in this warehouse. The workers get the pallets from the deployment area and place it on a

special shelf. After that, the ‘Big shelf 2’ forklift driver, will store the pallets on its final destination using

a special forklift which only works inside this warehouse guided by a wire system. This special forklift

and laser technology is used because here the aisles are very narrow and there is no margin at the sides

to move the forklift and a small driving mistake could cause an accident.

Figure 2.10: Fluxogram - Replenishment of big shelf 2

14

As a resume, table2.1 shows the reserve and picking places on shelves from both warehouses:

Area Zone Aisle Pillar LevelBig Shelf 1

Big Parts Storage BOL1 0001-0004Number: 2 - -

BOL2 0001-0005Number: 5 - -

BOL3 0001-0002Number: 2 - -

Big Shelf 1 HRK1 HA00-HA21Number: 21

000-047Number: 47

000-040Number: 5

HRR1 HA00-HA21Number: 21

000-047Number: 47

050Number: 1

Big Shelf 2 HRK2 HB00-HB11Number: 11

000-051Number: 47

000-050Number: 6

HRR2Part 1

HB00-HB11Number: 11

000-051Number: 47

060-099Number: 5

HRR2Part 2

HB12-HB14Number: 3

000-051Number: 47

000-099Number: 11

Table 2.1: Warehouse shelves

2.5 Order Picking

Any incoming sales order is subdivided in up to 6 picking orders (KA, Kommissionierauftrag) for the 4

floors of small shelves and 2 big shelf areas. Those picking orders are fulfilled in the 6 different storage

area and consolidated in deployment areas (Auftragszusammenführungsbereiche, AZF) for an interim

period.

Picking orders (KA) are classified into 3 types to mirror the receiver and the lead time.

1. Internal orders: Orders for internal customers, i.e. picking items or pallets for internal tasks like

quality checks or marketing material like catalogs and merchandising.

2. Standard orders: Orders for external customers with normal lead time.

3. Parcel service orders: Orders for external customers with shortened lead time (express delivery via

UPS, TNT, etc).

Due to the different lead time of the orders, they have different picking priorities. It means that the

highest priority orders should be picked first, even if they arrive later to the warehouse. The original

priority of an order can be raised by the shift supervisor in case the completion date of an individual

orders needs to be brought forward. In extreme cases, it is even possible that an employee interrupts a

current order where some positions are already picked to fulfill the high priority order first and finish the

lower priority order afterwards. Those decisions are made by the warehouse chief leaders and also by the

picking operators and there are no deterministic rules to model this behavior.

Depending on volume and quantity of items, different transport devices are used for the pick:

1. KA less than 20 kg - Transport in box

2. KA between 20 kg and 250 kg - Transport on pallet

15

3. KA more than 250 kg - Transport in cardboard box on pallet

Cardboard boxes are available in different sizes and with or without oiled paper as protection against

humidity for e.g. sea transport. Beside wooden pallets (EURO or sea freight pallet) there is a choice of

special pallets like Sirex, IPPC and plastic pallets.

The pick process allows a permanent stock control. If a location should be empty after the pick,

the warehouse management system prompts the employee to confirm or deny the information. In case

the location is not empty (against the warehouse location information), the excess quantity has to be

reported. The stock correction is done in the warehouse management system and transferred to the office.

In case shortage quantities are discovered while picking, the employee has to inform the shift supervisor

to clarify if a shortage transaction is done or additional inspections are done first. As long as the missing

item is still available at a different location (than the given one), a zero transaction for the given location

just creates a new pick order from an alternative location. As soon as all lines of a certain picking order

are picked, the operator confirms with his signature on the final inspection report that he/she takes the

full responsibility for the pick quality.

2.5.1 Picking in the Small Shelf

Mainly directed by a pick-by-voice system, the small parts are picked out of the shelves, collected in

boxes or on pallets and consigned at the conveyor belt. The conveyor system identifies the box/pallet

automatically and carries it to the distribution center for the deployment destinations.

Trolleys are used to allow that several boxes are transported from/to the belt at the same time. The

picking path is pre-defined by the warehouse management system and considers the disposition of shelves

to make sure that the operator picks the different items in the chronological order and walks as less as

possible.

2.5.2 Picking in big shelves

In the big shelf area, items are picked from either floor storage, shelves or replenishment and commissioned

mainly on pallets (and some boxes) for transfer to the packing areas. Special forklift for picking allow

picking from both sides of the aisles. To optimize the driveway in the aisles and avoid turns, criteria are

defined that make sure that items are picked in an order that follows the arrangement of shelves. The

given pick order guides the picking operator from pick to pick, but he/she still needs to consider weight

and volume of the items to decide if the started picking pallet can still accommodate the next item.

In the narrow aisle high shelf area (Big Shelf 2) the forklift is led by a wire guidance system. In

the wide aisle high shelf area the trucks drive without guidance. The small aisle trucks have a personal

protection equipment (Personenschutzanlage, PSA), a laser at the front and end of the lift identifies

barriers and stops movement automatically in case of danger.

In the daily pick process, the employee gets a pick order (KA) with several lines (items), where the

driveway is pre-given in consecutive order of locations. The picker though can override the order if

weight or volume limits of the picking pallet are exceeded and pick the position at a later point in time.

16

In addition to the pick area certain aisle areas are used for replenishment stock. If the quantity at the

pick area is not sufficient for an order, replenishment is ordered in the background and transferred to the

pick area with separate transport order, while the open pick position is placed back to the end of the

pick order list until replenishment is in place.

As soon as a pick pallet is full or complete, the pallet is reported “full” and the warehouse management

system proposes a location in the deployment area for interim storage. The targeted location can be

overridden if necessary. In case not all positions of a pick order fit onto one pallet, one or more additional

empty pallets are used.

Figure 2.11: Picking in the Big Shelf 1

2.5.3 Split of Picking Orders

An order with several order lines (picks) can be split by the shift supervisor into multiple orders with

lower volume to reduce the lead time. In this way, several operators can work at one order at the same

time for a quicker fulfillment. The sub-order show the same tracking number (Lieferverfolgungsnummer,

SSP) as the original one. An order can only be split as long as no employee is registered for the original

order. Once registered, the employee needs to unsubscribe to still allow the partition. The fluxogram on

fig. 2.12 explains the general Split of Picking Orders behavior.

17

Figure 2.12: Fluxogram - Picking process

2.6 Packing

After picking the products from both big shelf warehouses and the small shelf warehouse, the pallets and

boxes with products go to the packing area. There, they wait for the packing process, fig. 2.13. The

packing process can be divided into 2 main processes:

1. Packing for KEP (UPS, TNT. . . )

2. Packing for normal orders

The internal orders are not packed. The procedure of both points is similar but the size of the order

and the due date differs. Operators find the products to pack and join them if it is more than one box or

pallet for a costumer, fig 2.14. They decide the package considering the weight, dimensions and number

of the products. After closing the package, the product is ready to ship. If it’s going by UPS or TNT, it

is shipped in the same day. If it is going for a container or truck, it goes to a waiting area and waits for

the truck arrival, which can be some days later.

18

Figure 2.13: Fluxogram - Packing process

Figure 2.14: Packing - Pallet

19

Chapter 3

Modeling

3.1 Modeling with SIMIO

SIMIO [8] is a discreet-event simulation software and it was used in this dissertation. It has important

components for the modelling already implemented in the SIMIO standard library and it is also possible

to create new ones or to change the internal behavior of the existing ones in order to get the desired

characteristics for more custom models.

Having the system studied and understood, the next step is to implement the system processes in the

computer. The implementation consists in placing the SIMIO objects (servers, workers, conveyors, etc)

as well as the entities (it is something with distinct and independent existence like an order or a pick in

this case) in the model and create the structure (connections between the objects) that will represent the

behaviour of all those parts.

Next, the model should be tested. If the results are not the expected, changes must be done and the

model checked again. This is an iterative process.

Having the model structure built, distributions for the different steps must be found.

3.2 Time Distributions from Real Data

The computational model should represent all the process important parts and having information about

the processing time on each part. With this information, one can characterize every single part of the

process and, in the end, have results that are important for the analysis and future decisions that may

be made based on the model.

One important step of the modelling is the data acquisition. It is of crucial importance that the input

data of the system represent the real data the closest possible. When analyzing the input data, usually

there are three different scenarios that can occur [9]:

1. No data exists

2. There is no data in the desired format

3. Lots of good data exists

20

In the case that no data exists, we should make some assumptions based on real life experience that we

believe that approach well the reality. After that, sensitivity tests must be done to test the parameters.

When the data exists, but not in the desired format, one needs to use the available data to make

intelligent guesses for the required data and be aware that the data can be from an atypical moment of

the process and it may mean that what we are considering real data, is not relevant data and does not

characterize the real world system.

In the last case, we have access to lots of good data. The company in study has a very good warehouse

data base system and lots of data are collected every day to keep the internal processes under control and

to enable studies like this one. Unfortunately, not all data needed is available and some of the available

data has a different format, which makes this study a mix between the three enumerated points. On

cases like this, it is very important to have a good understanding of the processes to select the useful

data for the model.

The model must be the simplest possible, while ensuring the model validation and relevant results

to analyse. From a complex database, queries were done to obtain the relevant data that generally

characterizes:

• Number of orders

• Number of picks

• Picking time

• Packing time

With available data in a proper format, the next step is to create probability distributions that

represent all processes and that represent the stochastic behavior of the system.

To create the random distributions, histograms from the real data must be plotted to identify the

closest random distribution to fit the data. After that, the distributions must be tested to check if it is

still needed to estimate better the distribution parameters.

On alternative, fitting data software can be used. Sometimes it is even possible that none of the

typical distributions fits the data and personalized functions must be created. This last case has the

disadvantage that makes the simulation run slower, which in big simulation models can represent a

performance problem.

After having the real data important for the system selected and well formatted, the next step should

be plotting it and trying to find some statistical distribution that fits the data. The statistical distributions

are used in simulation because they can reflect the stochastic behavior of the real life in the simulation

and they are easy to compute.

Despite that, sometimes it is not easy to associate some real data sample with a parametric statistical

distribution, although that it is very common to find a good one.

To fit the real data, distribution fitting toolbox from Matlab was used. Having the data series fitted

to some distribution, it is easy to implement it in the simulation software and start running the tests.

This thesis is based on the data from one full month in activity, July 2014, corresponding to more

than 400.000 (Private Attachment A.1 a)) lines which are equivalent to picks (the act of moving to a

21

picking location, get the item from the shelf and put it in the picking pallet/box). This month was chose

because it represents month with the highest number of orders for this company.

3.2.1 Distribution Fitting

3.2.1.1 Picking Times

It was observed that the time distributions for the picking times depend on the picking area and on the

order.

Despite there is available data for all picking areas, the internal orders data regarding the picking

times does not exist. The dimension of the internal orders is similar to the Parcel Service Orders and it

is assumed in this dissertation that the picking time for the Internal Orders is the same as the picking

time for the Parcel Service Orders.

The best approximation found for the picking times was a log-logistic distribution [10] as can be

observed in fig. 3.1. This function is described by:

Figure 3.1: Log-Logistic distribution, α = 1

There are several different parameterizations of the distribution. The one shown gives reasonably

interpretable parameters and a simple form for the cumulative distribution function. The parameter

α > 0 is a scale parameter and is also the median of the distribution. The parameter β > 0 is a shape

parameter. The distribution is unimodal when β > 1 and its dispersion decreases as β increases.

And the probability distribution is given by equation (3.1).

f(x, α, β) =(βα )(

xα )β−1

(1 + ( xα )β)2

(3.1)

Where

x > 0, α > 0, β > 0 (3.2)

Despite the picking is done in the same physical place, the difference between the picking times for a

standard order when comparing to the other orders can be explained by the fact that the parcel service

orders have a much bigger priority and usually arrive late to the system and must be delivered in the

22

same day. This situation increases the pressure over the operators and they pick it faster.

Next, the fitted distributions from the real data are presented.

Figure 3.2: Parcel service orders picking time per item - Time distributions (Confidential attachment A.1)

The distributions from fig. 3.2 represent the picking time per item in each picking area for the Parcel

Service Orders.

23

Figure 3.3: Standard Orders Picking Time per Item - Time Distributions (Confidential attachment A.2)

The distributions from fig. 3.3 represent the picking time per item in each picking area for the Standard

Orders.

24

3.2.1.2 Packing Times

Figure 3.4: Packing time per item - Time distributions (Confidential attachment A.3)

The distributions from fig. 3.4 represent the packing time per item in each picking area.

3.2.1.3 Number of Picks per Order

The number of picks was found as being fitted by an exponential distribution [11] that is approximated

by:

Figure 3.5: Exponential distribution

And the probability function is given by:

f(x;λ) =

λe−λx, x ≥ 0

0, x < 0(3.3)

25

Figure 3.6: Number of picks - Parcel service orders (Confidential attachment A.4)

26

Figure 3.7: Number of Picks - Standard orders (Confidential attachment A.5)

The distributions of fig. 3.6 and 3.7 represent, respectively, the number of picks that parcel service

orders and standard orders have (Real designation of Standard Orders in Private Attachment A.1 b)).

27

3.3 Lead Time Simulation Model

In this section, the lead time model is presented and its general behavior is explained. This model is

intended to simulate the total lead time that all the order types have inside the logistic system.

3.3.1 Modeling the Lead Time

The objective of the model is to analyze the route of the orders since they enter the warehouse, are

processed and leave it finished. During the simulation period, the general behavior of the model order

entities follows the fluxogram in fig. 3.8:

Figure 3.8: Lead Time model flowchart

28

Figure 3.9: Simio Model Schematic

Figure 3.10: Lead time model - 3D

29

The model has 9 entities that represent the orders and the picks (the act of going to a shelf and get

the item):

• Internal Order

• Parcel Service Order

• Standard Order – Type 1






• Pick

The orders arrive to the system following three rate tables (Internal Order, Parcel Service Order and

Standard Order) with the average number of arriving orders per hour.

During the whole month in study, the percentage of created orders and respective number of picks

are shown in the next table:

Type of Order Percentage of Orders Nr. of orders Nr. of picksInternal Order 14% 4.016 15.236

Parcel Service Order 78% 20.904 63.024Standard Order - All Types 8% 1.820 248.304

Table 3.1: Real Data - Number of Orders (Confidential attachment table A.2)

For the standard orders, a new sub-table was created because a subdivision exists that has a big

importance inside the warehouse due to different priorities according to standard order types (6 is the

biggest and 1 the smaller). The same does not happen with internal orders and parcel service orders

because even if the orders have a different priority, all must be finished in the same day and so, in the

end, they have the same priority.

Type of Standard Order Priority Percentage of Orders QuantityType 1 6 7% 116Type 2 5 6% 100Type 3 4 29% 520Type 4 3 20% 368Type 5 2 29% 536Type 6 1 9% 164

Table 3.2: Real Data - Number of Standard Orders (Confidential attachment table A.3)

Once created, each order will generate an amount of picks (as presented in section 3.2.1.3). Then,

the picks are routed for the picking area and are processed during the time obtained in the probability

distribution for the picking times.

The picking areas are:

• Big Shelf 1

30

• Big Shelf 2

• Small Shelf 1

• Small Shelf 2

• Small Shelf 3

• Small Shelf 4

For each type of order, the percentage of picks are done in the following areas:

Picking Area Percentage of PicksBig Shelf 1 26%Big Shelf 2 6%Small Shelf 1 17%Small Shelf 2 13%Small Shelf 3 14%Small Shelf 4 24%

Table 3.3: Real Data - Percentage of internal orders picks in each picking area


Table 3.4: Real Data - Percentage of parcel service order picks in each picking area


Table 3.5: Real Data - Percentage of standard order picks in each picking area

When there are picking lines from an order waiting to be picked in a picking area, i.e., the picking for

this order has already began but is not finished yet, it is very common to pause the picking for a standard

order, that usually are orders with big quantity of picks, to pick for a higher priority parcel service order

or to an internal order, that are orders with smaller quantity of picks. In this way, the high priority

orders will not wait for the lower priority orders to be finished and their time in system is reduced when

comparing with the situation where all orders have the same priority and there is no overtake of picks.

After all picks from an order are done, they are grouped together and follow to the packing area, where

the packing time (PT ) is calculated based on the average packing time per pick (APT ) and the number

of picks (NrP ) the order contains:

PT = (APT ) ∗ (NrP ) (3.4)

31

After packing, the order is ready to ship and the time between the order input in the warehouse

management system and the finished order is the lead time, which is a relevant metric in this dissertation.

Every working station requires the manual work of the company operators. For the picking there is a team

of 40 operators (Private Attachment A.1 c)) in ‘big shelf 1’ and ‘big shelf 2’, but exists a limit capacity

of 4 (Private Attachment A.1 c)) picking operators in the ‘Big Shelf 2’. In each ‘Small shelf’ floor work

4 (Private Attachment A.1 c)) picking operators per shift. A resume is presented in the table 3.6

Picking area Number of operatorsBig shelf 1 and 2 (shared team) 40Small shelves (each floor) 4

Table 3.6: Picking operators by picking area

In the packing step, there are two situations:

1. Packing standard orders

2. Packing parcel service orders

The internal orders are not packed because they are for internal use in the company like quality control

or merchandising. In the packing standard orders, there are 24 (Private Attachment A.1 c)) workers per

shift working full-time (Table 3.7).

In the parcel service orders packing, there are 8 (Private Attachment A.1 c)) workers per shift full-

time and 32 (Private Attachment A.1 c)) part-time every day to reinforce the period of the day when the

parcel service orders are more common. Table 3.8 resumes the existing shifts in the parcel service orders

packing.

32

Morning Shift Afternoon Shift5:00 - 5:305:30 - 6:006:00 - 6:306:30 - 7:007:00 - 7:307:30 - 8:008:00 - 8:308:30 - 9:009:00 - 9:309:30 - 10:0010:00 - 10:3010:30 - 11:0011:00 - 11:3011:30 - 12:0012:00 - 12:3012:30 - 13:0013:00 - 13:30

24 Workers

13:30 - 14:0014:00 - 14:3014:30 - 15:0015:00 - 15:3015:30 - 16:0016:00 - 16:3016:30 - 17:0017:00 - 17:3017:30 - 18:0018:00 - 18:3018:30 - 19:0019:00 - 19:3019:30 - 20:0020:00 - 20:3020:30 - 21:0021:00 - 21:3021:30 - 22:0022:00 - 22:3022:30 - 23:00

24 Workers

Table 3.7: Standard Orders Packing Working Shifts (Confidential attachment table A.4)

33

Morning Shift Afternoon Shift 5h Reinforcement 1h Reinforcement5:00 - 5:305:30 - 6:006:00 - 6:306:30 - 7:007:00 - 7:307:30 - 8:008:00 - 8:308:30 - 9:009:00 - 9:309:30 - 10:0010:00 - 10:3010:30 - 11:0011:00 - 11:3011:30 - 12:0012:00 - 12:3012:30 - 13:0013:00 - 13:3013:30 - 14:0014:00 - 14:3014:30 - 15:0015:00 - 15:3015:30 - 16:0016:00 - 16:30

8 Workers

16:30 - 17:00 16 Workers

17:00 - 17:3017:30 - 18:0018:00 - 18:3018:30 - 19:00

16 Workers

19:00 - 19:30

8 Workers

19:30 - 20:0020:00 - 20:3020:30 - 21:0021:00 - 21:3021:30 - 22:0022:00 - 22:3022:30 - 23:00

Table 3.8: Parcel Service Orders Packing Working Shifts (Confidential attachment table A.5)

Out of the operators working time, if some new order arrives to the warehouse management software,

it will not be processed and it waits until the next working day for processing.

To better understand the model work-flow let us follow an example order in fig. 3.9:

Supposing that a ‘Standard Order – Type 1’ was created, it will cause, in the next instant of the simulation,

the creation of a number of picks based on the respective probability distribution. In this case, standard

order type one, it follows an exponential distribution with an average of 162 (Confidential attachment A.1

d)) picks per order. The picks travel to the picking areas according to the probability of going to each

picking area and are processed (57% travel to BS1, 7% to BS2, 10% to SS1, 12% to SS2, 8% to SS3 and

6% to SS4). In this instant of the simulation, the entity order (represented in the model by a pallet) is

waiting for all the picks corresponding to this order to be finished. When all picks of this order are ready,

they are batched to the entity order (pallet) and are allowed to travel to the packing station where, after

waiting for packing workers availability, they are packed. After the packing process, the entity order with

34

all its picks is removed from the simulation and statistics are collected about the ‘Standard Order – Type

1’ order as well as the pick entity. The simultaneous creation of order and respective picks is used because

it is impossible to sub-divide entities in a simulation program, i.e., one can’t send one order to different

locations in the model at the same time, although it happens in the real life system where exists orders

sub-division. Consequently, when creating picks that are associated to an order, one can send individual

picks to each picking area and simulate the orders sub-division.

Recall that the model simulates the picking and packing processes, the following simplifications were

assumed:

1. Because the objective is to study the picking and packing performance, it is assumed that the

products are always available in the warehouse. This simplification is valid, because the orders

are only created and sent from the sales office to the warehouse when the product is available

in the system (whether in the main warehouse or in the external ones), i.e., if some costumer

needs 100 pieces of a specific part, he/she first calls to the company and decides if he will buy the

amount available (for example 60 pieces) in the company or if he waits until the company has the

product available to order the complete amount. This means that there are no picking orders when

the number of parts exceeds the available parts inside the warehouse (Except in the case that an

employee has done a picking mistake, which is not significant for the simulation).

2. Some areas that are not used very often were included in the big shelf 1 for picking time considera-

tions. Also, the warehouse management software joins those areas. This simplification makes sense

because those areas are inside the physical space of the big shelf 1 and the same picking workers

are shared by those areas.

3. As it is impossible to model the human decisions regarding the priority changes of the orders, a

priority number was associated to each type of order and this number is not changed during the

simulation run. When an order is splitted, all suborders will share the same priority. In the reality,

it can happen that the priority of an order is changed because, for example, the client needs it in

a shorter time even if he/she is not a high priority client. Also, the sequence in which the orders

are picked is a decision made by the picking operators. It is guided by the orders priority, but very

often that order is not chosen for several reasons like size, weight, workers will, etc. The simulation

can only simulate precise rules and so it is guided by the initial order priority that does not change

during the simulation run.

4. The company does not keep track of the internal orders data, so it was assumed that the picking

times of those orders is the same as the picking times of the parcel service orders. This assumption

is valid because the dimension and priority of those orders is approximately the same, which is very

different from the standard orders.

35

3.4 Parcel Service Orders Packing Model

The packing of parcel service orders is also a subject of interest for the future strategy of the company

because there is the plan of changing the packing configuration in order to accept orders until a later hour

in the afternoon and to know how the workers shifts can be re-arranged in a more efficient way. This can

be explained by analyzing the company clients. As a big number of the clients are wholesalers, they tend

to wait until the last minute to place the order (waiting to see if additional orders of their clients will

arrive in the same day), avoiding making multiple orders over the day and paying more transportation

fees. This causes a very hard time in the packing area for parcel service orders at the end of the day.

Accepting orders until a later hour will bring to the company some advantage and get more market share

because the clients prefer a company that lets them order until a later hour. Some strategies are already

defined but needed to be tested. In this section the packing for Parcel Service Orders is studied with

more detail.

3.4.1 Modeling the Packing for Parcel Service Orders

When the orders are picked, they are transported to the packing area where specialized operators con-

solidate the different boxes of the same costumer, protect and pack the items into delivery boxes. From

this point on, the simulation starts. This process is of simple modelling and can be compared with a

machine (with a capacity equal to the number of available operators at the time) that receives items and

processes them for a certain period of time depending on the quantity of items in each order.

To model this situation, one entity order is created. Every order has a determined number of items

that is created according to a probability distribution with an average of 12 (Private Attachment A.1

c)) items per order. For every item, the operators need an average of 20 (Private Attachment A.1 c))

minutes to pack (this time includes choosing the order, getting it from the “ready to pack shelf” and

pack). Whenever some operator is free from his/her task, he/she starts to deal with the next order and

starts packing.

36

Figure 3.11: Packing Parcel Service Orders - Fluxogram

Figure 3.12: Packing Parcel Service Orders - Model

37

Figure 3.13: Packing Parcel Service Orders (representative illustration) - Model 3D

There are 24 packing stations (Private Attachment A.1 e)) that are operated by workers whose working

period was presented in the section 3, Table 3.8. The orders arrive according to rate tables (number of

new orders by hour) shown next.

Table 3.9: Parcel Service Order Rate Table - Packing (Confidential attachment table A.14)

The simulation runs for a regular/typical packing day.

In this model, it is considered that all operators have the same characteristics. In reality, they are not

all equal because some heavy parts cannot be handled by women. But for the simulation, the numbers

are an average of the real data and we can still consider that all operators have the same characteristics.

It is also irrelevant the case when some order is partially finished but the packing operators have to wait

until the last item arrives from the picking areas. This situation brings lateness to the packing step, but

as it happens around one or two times a day, it is ignored for simulation effects. Considering the number

38

of available operators and each operator day schedule, the total available packing time every day can be

calculated by:

Tp =

W∑i=1

Hi (3.5)

Where Tp - Total available packing timeW - Number of packing operatorsHi - Working hours per operator

In this way, considering the total available operators on a typical day, there are 14400 (Private

Attachment A.1 c)) minutes of packing time every day. In a hypothetical situation where the operators

are 100% of the time busy, the sum of all operators busy time should be 14400 (Private Attachment A.1

c)) minutes. However, from the real data, we know that it is around 13576 (Private Attachment A.1 c))

minutes, which shows that the operators in this area are around 94% busy on a normal situation. This is

the first result that supports the fact that the packing for the parcel service orders is reaching its limits.

3.5 General Conditions of Models

The models were simulated for one month period that had 23 business days. Only business days are

considered, because the company does not work on weekends, and the time per shift in this company

is 8h12m. The company works with two shifts per day, every day (from 5 in the morning to 23 in the

evening). In reality, the workers work less on Friday but compensate that during the week, so the weekly

hours (41 hours) are correct.

At a first approach, running the simulation for such a period resulted in unexpected values when consid-

ering the resources utilization, i.e., the operators seemed to have an under expected busy time (effectively

picking time), although the other metrics (processing times per picking area, number of picks) were as

expected. Facing this result, some field work had to be done again to find possible periods of time when

the workers are not picking, but doing other tasks. When talking with the warehouse group leaders, it

was found out that, from the 8h12min shift, the following times, on average, are not used for effectively

picking every day for every worker:

Task Time (min)Task 1 15Task 2 20Task 3 15Task 4 30Task 5 15Task 6 10Total 105

Table 3.10: Time Not Effectively Picking (Confidential attachment table A.6)

Considering all this time, the actual picking time for every worker comes down to approximately:

Pt = S −Wt (3.6)

39

Where Pt - Time effectivelly pickingS - Total shift timeWt - Wasted time

This way, the actually used time for picking are 6 hours and 37 minutes. The simulation workers busy

time percentage is around this time, which confirms that the assumptions of the “not effectively picking

times” are correct.

40

Chapter 4

Results

4.1 Validation Metrics

For validation, model results are compared with the real data. Once the simulation metrics (averages

and standard deviations when applicable) are closer to the real values, it is possible to guarantee that the

model represents the real situation and that it is not subject to lots of variability. After this verification,

it is possible to trust in the model results when testing different scenarios. According to [1], as validation

metrics, we should use some total values that represent the total amount of entities that circulate in the

system like, for example, the total number of picks. In this way we can verify if the model is resulting in

the expected number of entities. As a complement, the averages of the important metrics should also be

done. In this case, the lead time is an important average. The metrics used for model validation are:

1. Pi - Total processing times of each picking area

2. Pij - Processing times in each picking area by order type

3. S - Total number of picks

4. Sj - Number of picks in each picking area

5. Tj - Total packing time per order type

6. Lk -Lead time per order type

Points 1 and 2 check the quality of the processing times modelling (picking time distributions). The

points 3 and 4 check the quality of the number of picks and pick destination areas. Point 5 checks the

packing performance and point 6 checks the overall performance of the orders including the waiting times

that are dependent on the order priorities and availability of operators.

4.2 Lead Time Results

In the results, a negative percentage of error means that the model value is smaller than the real value.

A positive value means the opposite.

41

Model Real Data ErrorInternal Orders 3864 4016 -3.75%

Parcel Service Orders 20788 20904 -0.55%Standard Orders 1896 1820 4.26%

Total 26548 26740 -0.70%

Table 4.1: Number of Created Orders (Confidential attachment table A.7)

Table 4.1 shows that the number of created orders in the model is very close to the real data.

Model Real Data ErrorHRK1 27.994.876 26.995.692 3,70%HRK2 3.499.260 3.322.544 5,32%KT01 2.329.980 2.239.076 4,06%KT02 2.353.120 2.111.792 11,43%KT03 1.832.588 1.763.444 3,92%KT04 1.223.224 1.111.592 10,04%All Areas 39.233.048 37.544.140 4,50%

Table 4.2: Total Processing Time (s) (Confidential attachment table A.8)

Internal OrderModel Real Data Error

HRK1 351.836 396.556 -11,28%HRK2 84.668 93.600 -9,54%KT01 124.420 138.612 -10,24%KT02 94.600 104.708 -9,65%KT03 100.076 106.560 -6,09%KT04 154.472 163.388 -5,46%Total 910.072 1.003.424 -9,30%

Parcel Service OrdersModel Real Data Error

HRK1 2.940.136 2.909.048 1,07%HRK2 523.216 511.348 2,32%KT01 265.196 253.000 4,82%KT02 432.008 403.568 4,82%KT03 229.872 217.932 5,57%KT04 179.436 164.160 9,31%Total 4.560.860 4.458.876 2,29%

Standard OrdersModel Real Data Error

HRK1 24.702.908 23.690.088 4,28%HRK2 2.891.372 2.717.596 6,39%KT01 1.940.368 1.847.464 5,03%KT02 1.835.512 1.603.516 14,47%KT03 1.502.644 1.439.132 4,41%KT04 889.316 784.044 13,43%Total 33.762.116 32.081.840 5,24%

Table 4.3: Total Processing Time by Order (s) (Confidential attachment table A.9)

Tables 4.2 and 4.3 show that the total processing time for each picking area, i.e., the sum of all the

individual picking times over one month of work, is also very close to the real data. This means that the

probability distributions that provide to the model the picking times per item are well estimated. Some

differences can be the result of some seconds difference due to possible out-liners in KT02 and KT04.

42

It is also concluded that the assumption that the internal orders processing times are the same as the

parcel service orders is not completely accurate because it resulted in faster processing times for internal

orders than reality. This is not important because internal orders are not a target of this dissertation.

Model Real Data ErrorHRK1 180.524 174.640 3,37%HRK2 24.572 23.444 4,81%KT01 32.636 31.636 3,16%KT02 38.408 36.760 4,48%KT03 29.484 28.476 3,54%KT04 20.940 20.028 4,56%

All Areas 326.564 314.984 3,68%

Table 4.4: Total Number of Picks (Confidential attachment table A.10)

Internal OrderModel Real Data Error

HRK1 4.052 3.916 3,42%HRK2 988 948 4,30%KT01 2.440 2.464 -0,91%KT02 2.024 1.980 2,24%KT03 2.140 2.080 2,83%KT04 3.592 3.500 2,58%Total 15.236 14.888 2,32%

Parcel Service OrdersModel Real Data Error

HRK1 33.652 33.036 1,87%HRK2 6.104 5.956 2,47%KT01 5.184 5.172 0,24%KT02 9.024 8.776 2,83%KT03 4.892 4.888 0,05%KT04 4.168 4.044 3,10%Total 63.024 61.872 1,86%

Standard OrdersModel Real Data Error

HRK1 142.824 137.688 3,73%HRK2 17.480 16.540 5,68%KT01 35.008 24.000 4,21%KT02 27.360 26.004 5,21%KT03 22.456 21.508 4,40%KT04 13.180 12.484 5,58%Total 248.304 238.224 4,23%

Table 4.5: Total Number of Picks by Area (Confidential attachment table A.11)

Tables 4.4 and 4.5 show that the number of picks done in every picking area are very well estimated.

It means that the picks are travelling to the correct picking areas and in correct quantity.

Model Real Data ErrorParcel Service Order 18.521.280 18.737.312 -1,15%

Standard Order 32.173.232 34.738.256 -7,38%

Table 4.6: Total Packing Time (s) (Confidential attachment table A.12)

43

Table 4.6 shows that the packing times for the parcel service orders packing area are very close to the

real data. By the other side, in the standard orders packing area there is a considerable difference. It can

result from the fact that some orders have a double check and the average times for this step were found

by talking with the process operators. When guessing data, it is possible to have some resultant error.

Model Real data Errorµ σ µ σ µ σ

Internal Order 0,68 0,24 - - - -Parcel Service Order 6,04 2,44 6,16 2,68 -1,95% -8,96%Standard Order - Type 1 217,36 54,36 240 81,36 -9,43% -33,19%Standard Order - Type 2 366,32 65,92 380 120,84 -3,60% -45,45%Standard Order - Type 3 211,92 84,76 320 139,52 -33,78% -39,25%Standard Order - Type 4 489,72 141,72 636 161,72 -23,00% -12,37%Standard Order - Type 5 219,76 82,36 676 119,48 -67,49% -31,07%Standard Order - Type 6 251,72 63,28 964 201,24 -73,89% -68,55%

Table 4.7: Lead Time (h) (Confidential attachment table A.13)

Table 4.7 shows the final result of all the data estimations and model rules. It is observed that highest

priority orders (Parcel service orders, standard orders type 1 and 2) result in a lead time close to the real

data. The same does not happen to the remaining orders. It is an indication that the prioritization rules

in the model are not the same applied in the reality, although they are the theoretical ones that should

be applied in the real life.

44

4.3 Parcel Service Orders Packing Model Results

In this model, the results are on a daily basis.

The plot from fig. 4.1 a) shows the sum of orders created (ready to pack) and the sum of orders

destroyed (packing finished), as well as the difference between both (number in system/waiting to be

packed). This plot goes in agreement with the real data plot and it means that our system is well rep-

resented in the model when concerning to the orders input. Whenever the difference between the lines

increases (Orders Ready to Pack – Orders Packed), it means that the input buffer of ready to pack orders

is getting bigger and the available workers cannot deal with it. If this line tends to increase some of the

orders will not be finished on time.

The second plot fig. 4.1 b), represents the number of workers available on every hour. This plot is useful

to know the workforce available on every part of the day and relate its direct effect on the number of

orders in system.

The third plot, fig. 4.1 c), represents the number of orders that are currently in system (waiting for

packing or being packed). Ideally, in the end of the day, those lines should be zero. It does not happen

in the simulation because while in the real life the workers finish the current order even if they need to

make some extra time, the model considers the shift times precisely and stops packing when the workers

are off-shift. It is considered that if, in the end of the simulation, there is one order per packing table,

it is the same as considering that all orders were actually packed. If this number exceeds the number of

tables, it means that there are effectively orders waiting to be packed.

The fourth plot fig. 4.1 d), shows us the difference between the total number of workers in shift against

the real number of busy workers. This plot is useful because it allows us to, instead of only knowing when

the system is too busy, knowing when it is over dimensioned and there are workers free in some parts of

the day. In this case we verify that most of the day all the workers are busy.

Model Real Data Error Std. Dev. ErrorModel Real DataNumber of Orders 908,00 908,00 +0,00% - - -Number of Picks 2.640,00 2.692,00 -1,93% - - -Total Packing Time (min) 13.199,20 13.577,60 -2,79% - - -Average Packing Time (min) 58,00 59,60 -2,68% 11,8 12,3 -4,42%Maximum Packing Time (min) 576,80 570,00 -1,19% - - -Minimum Packing Time (min) 10,40 4,80 -126,09% - - -Average Total Time in System (min) 189,20 - - - - -

Table 4.8: Results Actual Situation - Table (Confidential attachment table A.15)

45

(a) Ready to pack (red) vs Packed (blue)Difference (yellow)

(b) Available workers

(c) Number in System

(d) Busy workers

Figure 4.1: Actual Situation - Plots (Confidential attachment figure A.6)

46

4.4 Results Discussion

4.4.1 Lead Time Model

The results show that the number of picks, location and packing times are close to the reality. By the

other side, except for the higher priority orders, the results about lead time are very different from the

reality. This means that the processing times and picking locations are well modelled, but the waiting

times are not. This can result in bigger or smaller total time in system (lead time) for each order that has

to wait for other order to be processed. Obtaining those results, more inquiry was done to the warehouse

workers to try to find extra rules that decide the picking order priority. Unfortunately, there are a lots

of rules that change all the time depending on the chief leader, number of orders, category of the orders,

weight, shape and many others. This is a characteristic of this real system that can compromise the

quality or the applicability of such a simulation.

As stated in [12] – Rule 10, “The system to be simulated must be thoroughly understood before

simulating or the analyst will be forced to guess or be creative. Some systems are so complex that building

an accurate model (within an acceptable schedule and budget) is not possible. This is often the case

where (complex) human behavior is a critical part of the simulated system. For example, because modern

automated distribution centers are complex, they are frequently simulated prior to implementation or

modification. Most are driven by computerized warehouse management system (WMS) software, which

selects and combines orders to process. Almost all of the actual order processing (picking) is performed

manually, and people run the facility, even in automated facilities. Typically, the scenario simulated is

an average day, and the model results can be quite accurate. But in a real facility when an unusual event

occurs and the orders start falling behind schedule, people will change their normal behavior or activities

to find a way around the system constraints in an attempt to meet the schedule. This behavior can be

quite varied and virtually impossible to completely describe and simulate for all possible scenarios. Model

results for these crash-case scenarios almost never match what occurs in the real system, and simply are

unreliable.”

This is the case of Diesel Technic’s warehouse. The complexity of the processes and the human factor

being so relevant makes it a very hard or even impossible task to build a computational representation of

the reality. It can actually be possible to make such a reliable simulation, but there is the need of collecting

new data regarding every special rule and, even after that extensive work that could take months, the

results can be quite different from the real data when talking about lead times. This is in agreement with

the previous transcription when says that some systems are too complex to model within an acceptable

time and budget. Because this approach goes out of this thesis objective and schedule, it was decided

that it is not realistic to make a simulation model of the picking in this company. Facing this conclusion,

it was decided to study more specific parts of the processes as the Packing of the Parcel Service Orders.

Those parts are not so dynamically affected by human decisions and so, simulation can be a useful tool

again. In the next section, a new model of a more specific part of the company is presented. This model

helps to get an insight of the second objective of this thesis: ‘How can the packing performance of the

parcel service orders be improved?’

47

4.4.2 Parcel Service Orders Packing

The result of this model shows that, contrary to what happened in the Lead Time model, in this situation,

the modelling and simulation when applied results in a close representation of the reality in the company.

As can be observed in the results plots, the day starts with zero orders in system and around the

beginning of the packing shift new orders start to arrive. As they arrive, they start being processed

by the operators and the line of destroyed orders also starts to raise, although it has a slower growth.

This situation causes the “Orders Ready to Pack – Orders Packed” line to grow as well, showing that

there are more orders being created than being destroyed. The number of orders in system is directly

influenced by the availability of operators and the rate per hour of new orders. During rush time (Private

Attachment A.1 f)) is when the Parcel Service Orders are more frequent but, at the same time, the

maximum number of packing operators are at their working station and, consequently, the gap between

“Orders Ready to Pack” and “Orders Packed” lines is shortened until the moment when it disappears and

there are again no orders waiting for processing in system. This result was expected because there is no

record of late orders during this month in the real data. Despite that, it is expected that the end of the

day period become the bottleneck if the number of orders increase.

Looking for the results from table 4.8, it is verified that this simulation is very close to the real data

and we can, from now on, assume that it represents well the current activity of the company. Having

the model validated, one can think about the sensitivity analysis. The next section discusses important

scenarios that have interest for the company.

48

Chapter 5

Sensitivity analysis in the Packing of

Parcel Service Orders

Having the validated model, the sensitivity analysis can be done changing important variables and eval-

uating the effect. After talking with the company responsible people for the logistical processes, some

questions without a current answer appeared.

1. What would happen if there was an increase of 20% of total parcel service orders while maintaining

the same configuration?

2. What would happen if there was an increase of 10% more orders 1 hour (Private Attachment A.1

g)) after rush time while maintaining the rest of the day?

3. What would happen if 10% of the morning orders were done 0.5 hours (Private Attachment A.1

g)) after rush time? It means less orders in the morning and more in the end of the day, while

maintaining the total number of daily orders.

4. What would happen if 1 (Private Attachment A.1 g) temporary operators (2.5 hours shift (Private

Attachment A.1 g)) were removed and 3 (Private Attachment A.1 d)) extra full-time afternoon

were hired, while maintaining the same orders quantity?

The scenarios are tested and a possible solution is suggested for each case.

It is important to consider that the proposal solutions are not necessarily optimal and have the only

objective of showing examples of information that can be obtained using modelling and simulation.

49

5.1 What would happen if there was an increase of 20% of total

parcel service orders while maintaining the same configura-

tion?

In this scenario, it is expected to have around 20% more orders than the real data. By the other side, the

total packing time should remain similar because in the real data situation the workers are already almost

100% ("Time used for packing" divided by "Total available packing time") busy and, even with a bigger

number of orders, they will always have the upper limit of their available time for packing. Observing the

results from the table 5.1 and figures 5.1, we can observe that it was not possible to pack all the orders on

time. The difference between created and destroyed orders is around 19,5% of the created orders when

the day ends.

So, this is a good indicator that the actual configuration and working force will not keep providing

an efficient service in the case of an increase in the Parcel Service Orders and that a new strategy must

be planned.

Analyzing the “Orders in system” plot (fig. 5.1 (a)), it is verified that the rush time (Private Attach-

ment A.1 f)) has a bigger demand and the orders start to accumulate. In this way, this period of time

should be reinforced. It can be done extending the “3 hour reinforcement operators” (Private Attach-

ment A.1 d)) in 6 (Private Attachment A.1 d)) extra hours.

The results from this change are presented on table 5.2 and graphs 5.1. With this reinforcement, all

orders could be packed and the operators are busy around 92% of the time, which is in the same order

as the actual situation.

Model Real Data Error Std. Dev. ErrorModel Real DataNumber of Orders 1.076,00 908,00 +18,50% - - -Number of Picks 3.220,00 2.692,00 +19,61% - - -Total Packing Time (min) 13.356,40 13.577,60 -1,63% - - -Average Packing Time (min) 58,00 59,60 -2,68% 12,1 12,3 -1,60%Maximum Packing Time (min) 567,60 570,00 -0,42% - - -Minimum Packing Time (min) 10,40 4,80 +126,09% - - -Average Total Time in System (min) 261,20 - - - - -

Table 5.1: Results Scenario 1 (Confidential attachment table A.16)

Model Real Data Error Std. Dev. ErrorModel Real DataNumber of Orders 1.080,00 908,00 +18,94% - - -Number of Picks 3.200,00 2.692,00 +18,87% - - -Total Packing Time (min) 15.039,20 13.577,60 +10,76% - - -Average Packing Time (min) 55,60 59,60 -6,71% 11,5 12,30 -6,50%Maximum Packing Time (min) 501,60 570,00 -12,00% - - -Minimum Packing Time (min) 10,40 4,80 -126,09% - - -Average Total Time in System (min) 237,60 - - - - -

Table 5.2: Results Scenario 1 - Proposed solution (Confidential attachment table A.17)

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(a) Ready to pack (red) vs Packed (blue) (b) Ready to pack (red) vs Packed (blue) - SolutionDifference (yellow) Difference (yellow)

(c) Available workers (d) Available workers - Solution

(e) Number in System (f) Number in System - Solution

(g) Busy workers (h) Busy workers - Solution

Figure 5.1: Results Scenario 1 - Plots (Confidential attachment figure A.7)

51

5.2 What would happen if there was an increase of 10% more

orders 1 hour (Private Attachment A.1 g)) after rush time

while maintaining the rest of the day?

In this scenario, it is considered that the arrival rate of the orders is the same, but in the end of the

day, there is an increase of 10% more orders while maintaining the packing workers configuration. This

situation should result in an overload of the end of the day active workers.

That situation can be observed in the plots where after rush time (Private Attachment A.1 f)) the

number of orders in system starts to decrease, but not at a sufficient level. This will result also in late

orders and this situation is not favorable for the company.

To solve this, 4 (Private Attachment A.1 c)) hours extension on all the “2.5 hours reinforcement

operators” (Private Attachment A.1 g)) can be done.

Model Real Data Error Std. Dev. ErrorModel Real Data -Number of Orders 940,00 908,00 +3,52% - - -Number of Picks 2.736,00 2.692,00 +1,63% - - -Total Packing Time (min) 13.219,20 13.577,60 -2,64% - - -Average Packing Time (min) 57,60 59,60 -5,37% 11,3 12,3 -8,13%Maximum Packing Time (min) 576,80 570,00 +1,19% - - -Minimum Packing Time (min) 10,40 4,80 +116,67% - - -Average Total Time in System (min) 190,00 - - - - -


Model Real Data Error Std. Dev. ErrorModel Real DataNumber of Orders 940,00 908,00 +3,52% - - -Number of Picks 2.720,00 2.692,00 +1,04% - - -Total Packing Time (min) 13.336,80 13.577,60 -1,74% - - -Average Packing Time (min) 56,80 59,60 -4,70% 11,5 12,3 -6,50%Maximum Packing Time (min) 581,20 570,00 +1,96% - - -Minimum Packing Time (min) 8,40 4,80 +75,00% - - -Average Total Time in System (min) 198,80 - - - - -

Table 5.4: Results Scenario 2 - Proposed Solution (Confidential attachment table A.19)

52






53

5.3 What would happen if 10% of the morning orders were done

0.5 hours (Private Attachment A.1 g)) after rush time? It

means less orders in the morning and more in the end of the

day, while maintaining the total number of daily orders.

In this situation, the morning period is less busy than the end of the day, as it was expected. This

situation leads to a 89% of busy time and, as a consequence, more late orders because the morning

operators were free and at the end of the day, the available workforce was not enough to recover all the

late orders. The end of the day is overcharged and needs some workforce reinforcement. As a suggestion,

4 “morning workers” (Private Attachment A.1 c)) can be removed and 16 (Private Attachment A.1 c))

“20 hours workers” (Private Attachment A.1 c)) must be replaced by 24 Attachment A.1 c)) “3 hours

workers” (Private Attachment A.1 g)).

This solution had shown to be good resulting in no late orders.

Model Real Data Error Std. Dev. ErrorModel Real DataNumber of Orders 904,00 908,00 -0,44% - - -Number of Picks 2.792,00 2.692,00 +3,71% - - -Total Packing Time (min) 12.760,40 13.577,60 -6,02% - - -Average Packing Time (min) 60,80 59,60 -5,37% 12,0 12,3 -2,44%Maximum Packing Time (min) 469,20 570,00 -17,68% - - -Minimum Packing Time (min) 10,40 4,80 -116,67% - - -Average Total Time in System (min) 151,20 - - - - -


Model Real Data Error Std. Dev. ErrorModel Real DataNumber of Orders 908,00 908,00 +0,00% - - -Number of Picks 2.808,00 2.692,00 +4,31% - - -Total Packing Time (min) 13.285,60 13.577,60 -2,15% - - -Average Packing Time (min) 58,40 59,60 -1,81% 12,5 12,3 +1,63%Maximum Packing Time (min) 537,60 570,00 -5,68% - - -Minimum Packing Time (min) 7,60 4,80 +58,33% - - -Average Total Time in System (min) 266,40 - - - - -

Table 5.6: Results Scenario 3 - Proposed Solution (Confidential attachment table A.21)

54






55

5.4 What would happen if 1 (Private Attachment A.1 g)) tem-

porary operators (2.5 hours shift (Private Attachment A.1

g)) were removed and 3 (Private Attachment A.1 d)) extra

full-time afternoon were hired, while maintaining the same

orders quantity?

In this scenario, 6 (Private Attachment A.1 d)) temporary workers were replaced by 2 (Private Attach-

ment A.1 e)) full-time afternoon operators. This situation leads to a bigger number of available operators

during the day except from the period from 14h to 19h, where there are less 3 (Private Attachment A.1

d)) operators than in the normal situation. This is not desired due to this period of the day being the

most active and it would result in more late orders, as confirmed by the simulation. This way, this change

should not be done.

Model Real Data Error Std. Dev. ErrorModel Real DataNumber of Orders 920,00 908,00 +1,32% - - -Number of Picks 2.720,00 2.692,00 +1,04% - - -Total Packing Time (min) 10.502,40 13.577,60 -22,65% - - -Average Packing Time (min) 45,60 59,60 -16,69% 11,4 12,3 -7,32%Maximum Packing Time (min) 539,60 570,00 -0,42% - - -Minimum Packing Time (min) 10,40 4,80 -129,57% - - -Average Total Time in System (min) 157,20 - - - - -


56

(a) Ready to pack (red) vs Packed (blue)Difference (yellow)

(b) Available workers

(c) Number in System

(d) Busy workers


57

Chapter 6

Conclusions

In this chapter, conclusions about all parts of the dissertation are presented.

To study the system, two main situations were studied:

1. How can the company reduce the total lead time of the orders?

2. What would be the effect of determined changes in the packing of parcel service orders?

To be able to answer to those questions, a very good knowledge of the system must exist. For that,

data from the company was analyzed and lots of experts in the processes (office and warehouse workers)

were consulted. With all that information collected, it was possible to start modeling data and producing

the simulation.

The first model resulted results that indicate a correct modeling of the real data. However, some

internal rules regarding different priorities of the orders were considered impossible to program in the

computer due to its unpredictable behavior. It was found out that there is not a specific rule being

applied in the warehouse processes. In reality, the warehouse workers take many decisions depending on

several factors and, during the day and depending on the worker on shift, those rules change. This way,

it is not possible to produce a simulation on the computer that will “mimic” what is happening in the

warehouse, when concerning the waiting times of the orders (very influenced by the priority given to the

orders that changes because of many factors). By the other side, it is possible to calculate the theoretical

performance in the case where the decisions of the warehouse workers were more controlled and followed

a unique pattern as the model does and see the impact that it can have in the real situation in the case

it is applied. As stated before, this dissertation model ended up with a faster system than the real one

showing that there is space to improve the processes.

Those results are good for the company and it is a good way to question the actual processes the way

they are done and to start reflecting about why there is not a rule for the prioritization of the orders

working the way it was expected. Maybe the actual process is more advantageous. But at the same time,

it is really hard to evaluate new strategies if there is not a clear picture of the actual one. Or maybe it

exists in theory, but the bridge to the practical part is not being done. This conclusion opens a series of

new questions that can be very useful for the future of the company.

For the dissertation, it is also an important result which strengthens the idea that not all the real

58

scenarios are of easy and direct simulation and that the way the data is interpreted can be decisive.

In the second part of the dissertation, it was presented a model that approached better the real life

system. Having on hands a part of the system where the most events are identified and known well as not

having big variations and different priorities, the simulation provides results that can support a future

decision in the real life system.

Concluding, simulation is a very important technique to help improving the processes of the companies

when it is hard to study them by traditional methods as mathematical theories or in field changes to the

processes. Despite that it must not be used in situations where:

• The problem can be solved using “common sense analysis”

• The problem can be solved analytically

• It is easier to perform direct experiments on the real system

• The cost of the simulation exceeds possible savings

• There are not proper resources available for the project

• There is not enough time for the model results to be useful

• There is no data – not even estimates

• The model can’t be verified or validated

• Project expectations can’t be met

• The system is too complex or can’t be defined

Fortunately, both situations of easy application of simulation and impossibility to determine some

rules were found in this dissertation. This situation opens space to discuss several possible simulation

results and is very interesting in terms of a dissertation. Even the situation where the simulation could

not mimic the real system ended up being a good initiation of discussion about some of the processes

of the company and it will end up on new future projects that will contribute to improve the internal

logistics and keep providing a better service for the costumers, as it was seen that exists space to improve.

As a suggestion of future work to be implemented in the company, it can be interesting to improve

the way the data is registered in the databases. Sometimes it was not clear what the database data

was really about because it is recorded automatically when the workers click in the buttons but there

is not a warranty that they are clicking it at the expected theoretical instant. This can result in not

so correct “real data” and will, for sure, contribute to the error of a simulation that considers the “real

data” as absolutely correct. This way it will be easier and faster to get knowledge from the data and the

internal tasks of every process will be more outlined making it easier to evaluate performances and check

the effect of changes. It would be also interesting to analyze if it is possible to re-think the theoretical

prioritization of orders model and to implement it on a period of some days to collect data that should

be more close to the result of a simulation. This way it would be possible to validate better the model

that includes the waiting times caused by different orders prioritization. It is the random nature of the

complex logistic systems that makes them so interesting and target of so many studies. Studies that

generally are directed to small parts of the systems and applied individually expecting a better overall

result. That is the opposite of other types of systems like mathematical (physical, mechanical, chemical)

59

systems that have a set of known equations that can be connected resulting in a final known picture of

the system. The same does not happen in logistic systems like the one studied in this document where

some parts are difficult to model and specially to connect to the other parts. Furthermore, the unknown

behavior of the human elements of the system ends up on a system that can be hard to model the whole

picture.

Concluding, simulation is a powerful tool possible to apply to very complex logistic systems but, in

some cases, it can be too difficult and time consuming to apply to obtain the expected results.

60

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