Assembly Line Balancing for Variable Production

JOEL NUORA
ASSEMBLY LINE BALANCING FOR HIGH-MIX, LOW-VOLUME
PRODUCTION
Master’s Thesis
Examiners: Professor Miia Martinsuo
and lecturer Ilkka Kouri
Examiners and topic approved by the
Faculty Council of the Faculty of
Business and Built Environment on
6th March 2013

ii
ABSTRACT
TAMPERE UNIVERSITY OF TECHNOLOGY
Master’s Degree Programme in Industrial Engineering and Management
NUORA, JOEL: Assembly line balancing for high- mix, low-volume production
Master of Science Thesis, 93 pages, 1 Appendix page
November 2013
Major: Industrial Management
Examiners: Professor Miia Martinsuo, lecturer Ilkka Kouri
Keywords: Assembly line balancing, production scheduling, high-mix, low-
volume, takt time, production levelling
This master’s thesis presents assembly line balancing methods, which aim to
improve continuous material flow in a variable environment. The most important
purpose of assembly line balancing is to continuously equalize the workload
between employees. Moreover, aspects related to production scheduling and
control methods for high-mix, low volume assembly lines are also discussed in
this work. The thesis is made as an action research so that available infor-
mation from literature is used and evaluated with the viewpoint of the needs and
problems of the case company. The goal of this thesis is to improve the produc-
tivity of the assembly line of power series hooklifts, with balancing methods and
a more organised production scheduling system.
Nine different assembly line balancing methods are presented, which are all
applied for the operation in the case company to improve the flow of materials.
The most significant method is the conventional way to first divide the total
workload to workstations as equally as possible and then allocate employees
based on average standard times. The first balancing action provides a good
starting point for the application of the methods which focus more on variable
standard times. The other balancing methods include multi-skilled workforce,
pre-assembly stations, different routings, production levelling, in-process inven-
tory, work time arrangements, task assignment variations and waste elimination
from bottleneck stations.
As a result of this thesis standard times of work tasks are used systemically for
assembly line balancing and production scheduling. Applying assembly line
balancing methods has equalized the workloads between employees, de-
creased waiting times and provided a good potential for productivity improve-
ment. For production scheduling the thesis presents a plan based on production
rate oriented system that aims at a more precise target setting. Related to the
scheduling system, a new visual assembly control system has been taken in
use, which has significantly improved target setting practices and real time pro-
duction control.

iii
TIIVISTELMÄ
TAMPEREEN TEKNILLINEN YLIOPISTO
Tuotantotalouden koulutusohjelma
NUORA, JOEL: Kokoonpanolinjan tasapainottaminen varioivassa ja matalan
volyymin tuotannossa
Diplomityö, 93 sivua, 1 liitesivu
Marraskuu 2013
Pääaine: Teollisuustalous
Tarkastajat: professori Miia Martinsuo, lehtori Ilkka Kouri
Avainsanat: Tuotannon tasapainottaminen, tahtiaika, hienokuormitus, varioiva
kokoonpano
Diplomityö esittää kokoonpanolinjan tasapainottamismenetelmiä, joiden pää-
määränä on tarjota paremmat edellytykset materiaalien tasaiselle virtaukselle
varioivassa tuotannossa. Kokoonpanolinjan tasapainottamisen tärkeimpänä
tarkoituksena on jakaa työkuorma jatkuvasti tasaisesti työntekijöiden kesken.
Työssä käsitellään myös hienokuormitukseen liittyviä käsitteitä sekä kontrolloin-
timenetelmiä varioivalle ja matalan volyymin kokoonpanolinjalle. Työ tehdään
toimintatutkimuksena, jossa kirjallisuudesta löytyvää tietoa pyritään hyödyntä-
mään sekä arvioimaan kohdeyrityksen tarpeiden ja ongelmien kautta. Työn ta-
voitteena on parantaa vaihtolavalaitteiden kokoonpanolinjan tuottavuutta tasa-
painotusmenetelmien ja järjestelmällisemmän hienokuormituksen avulla.
Työssä esitetään yhdeksän erilaista tuotannon tasapainotusmenetelmää, joita
kaikkia on sovellettu kohdeyrityksen toimintaan asennuslinjan tasaisen virtauk-
sen edistämiseksi. Merkittävimpänä tasapainotusmenetelmänä voidaan pitää
tavanomaista tapaa jakaa ensin työmäärät keskiarvojen mukaan mahdollisim-
man tasaisesti työpisteille, minkä jälkeen työntekijät sijoitetaan eri työpisteisiin
standardiaikojen keskiarvojen mukaan. Tämä ensimmäinen toimenpide antaa
hyvän lähtökohdan muiden enemmän varioivan tuotannon huomioon ottavien
menetelmien soveltamiselle. Muut esitetyt menetelmät ovat monitaitoiset työn-
tekijät, esiasennus, vaihtoehtoiset reititykset, työjonon tasapainotus, välivaras-
tot, liikkuvat työtehtävät, työaikajärjestelyt sekä pullonkaulatyöpisteiden kehitys.
Työn tuloksena kohdeyrityksen kokoonpanolinjan työvaiheiden standardiaikoja
käytetään järjestelmällisesti kokoonpanolinjan tasapainottamisessa ja hieno-
kuormituksessa. Tasapainotusmenetelmien soveltaminen on tasoittanut kuormi-
tuksia työntekijöiden välillä, vähentänyt odotusaikoja ja tarjonnut edellytykset
tuottavuuden parantamiselle. Hienokuormituksen osalta tuloksena on suunni-
telma laitemääriin perustuvasta tavoitteenasettelusta, jolla pyritään tarkempaan
tuotannon ajoitukseen. Tähän liittyen kohdeyritykselle on myös laadittu uusi vi-
suaalinen asennuksenohjausjärjestelmä, jonka avulla tavoitteenasettelua ja re-
aaliaikaista tuotannonohjausta on pystytty parantamaan huomattavasti.

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ACKNOWLEDGEMENTS
This master’s thesis is made for Cargotec Finland Oy, Multilift Raisio factory, in
collaboration with Tampere University of Technology, Department of Industrial
Engineering. I would like to express my gratitude to my thesis supervisors Ilkka
Kouri and Miia Martinsuo for the guidance with the project.
I would also like to thank the company for this very motivating and interesting
project. I am deeply grateful for the assistance given to me by the whole Raisio
factory personnel and it has been a privilege working with you. Special thanks
for Esko Kleemola, Asko Nevalainen and Seppo Kantola for the support and the
discussions related to the development actions of this thesis work.
Finally I would like to thank my family and friends for their encouragement and
support through the whole studentship.
Turku, 25.10.2013
Joel Nuora

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CONTENTS
1 Introduction..................................................................................................1
1.1 Foreword ..............................................................................................1
1.2 Objectives and scope ...........................................................................2
1.3 Methodologies ......................................................................................3
1.4 Company presentation..........................................................................5
2 Assembly line balancing and control............................................................7
2.1 Definition and purpose of assembly line balancing...............................7
2.2 Assembly line balancing key terminology .............................................9
2.2.1 Time standards........................................................................10
2.2.2 Production scheduling .............................................................11
2.2.3 Takt time and production rate..................................................12
2.3 Assembly line balancing for variable environment..............................16
2.4 Assembly line balancing methods ......................................................19
2.4.1 Assembly line balancing based on average station times .......19
2.4.2 Flexible multi-skilled workforce................................................24
2.4.3 Pre-assembly for optional modules .........................................27
2.4.4 Different routings for variable products....................................28
2.4.5 Sequence planning to level out the workload ..........................30
2.4.6 In-process inventory to avoid idle time ....................................33
2.4.7 Assignment of identical tasks to different stations ...................34
2.4.8 Work time arrangements .........................................................35
2.4.9 Continuous improvement of current bottleneck station............36
2.5 Synthesis of assembly line balancing for high-mix, low-volume
production..................................................................................................38
3 Analysis of demountables assembly line ...................................................41
3.1 Production system in the case company ............................................41
3.2 Work analysis .....................................................................................46
3.3 Interview analysis ...............................................................................48
3.4 Assembly line time study ....................................................................49
4 Demountables assembly line development................................................53
4.1 Assembly line balancing .....................................................................53
4.1.1 Assembly line balancing based on average station times .......53
4.1.2 Flexible multi-skilled workforce................................................55
4.1.3 Pre-assembly for optional modules .........................................58
4.1.4 Different routings for variable products....................................59
4.1.5 Work queue levelling ...............................................................61
4.1.6 In-process inventory to avoid idle time ....................................63
4.1.7 Assignment of identical tasks to different stations ...................64
4.1.8 Work time arrangements .........................................................65
4.1.9 Continuous improvement of current bottleneck station............66

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4.2 Production control...............................................................................67
4.2.1 Production scheduling and target setting.................................67
4.2.2 Visual management tools for production control......................68
4.2.3 Restrictions and problem solving.............................................71
5 Testing and implementation.......................................................................74
5.1 Implementation of assembly line balancing methods..........................74
5.1.1 Average load percentage towards ideal situation ....................75
5.1.2 Increased use of multi-skilled employees ................................76
5.1.3 New pre-assembly station .......................................................77
5.1.4 Different routings for complex products ...................................78
5.1.5 Sequence planning to support production flow........................79
5.1.6 More detailed in-process inventory planning ...........................79
5.1.7 Flexible tasks between workstations .......................................80
5.1.8 Change to one shift system .....................................................80
5.1.9 Problem solving and 5S for bottleneck stations.......................81
5.2 Implementation of new production scheduling system .......................82
6 Discussion .................................................................................................85
6.1 Result analysis ...................................................................................85
6.2 Subjects for further studies.................................................................87
6.3 Conclusions........................................................................................88
7 References ................................................................................................90
Appendix 1: Example of time and period dependent variances in a high-mix
production
Appendix 2: Product and workstation dependent variances in the power series
demountable assembly line
Appendix 3: Power series demountables assembly line balancing actions
Appendix 4: Power series demountables assembly line balancing calculations
with average target times
Appendix 5: Productivity of the demountable assembly line during 2013
Appendix 6: MAU Raisio value stream map
Appendix 7: Interview
Appendix 8: Temporary production scheduling system

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TERMS AND DEFINITIONS
Cycle time The operation time required to complete one process
in the value stream.
Station time The cycle time of one workstation, which is a sum of
task times based on product specifications.
Total cycle time The sum of all cycle times in a process from the
scope’s first station to the completion of the scope’s
last station of the scope.
Lead time The total amount of time elapsed from the start of the
first phase to the completion of last station.
Takt time The amount of time between two consecutive unit
completions in order to exactly meet the demand.
Formula for calculating the takt time: available produc-
tion time divided by demand.
Planned cycle time The amount of time between two consecutive unit
completions in order to meet the demand, taking into
account unplanned downtime or problems with allow-
ance time.
Production rate The number of completed units or throughput of an
assembly line, which is an inverse ratio for takt time
for same or longer period.
Takt-driven system Aims to synchronous movement of units using takt
time based scheduling.
Production rate The number of completed units during a predeter-
oriented system mined period used as a primary scheduling criteria.

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1 INTRODUCTION
In this thesis assembly line balancing methods are studied and evaluated for
variable environments. The objective is to find solutions for creating a smooth
and organised production flow. This is a big challenge for high-mix assembly
lines. Assembly line balancing is directly connected to productivity and efficien-
cy of the operation by reducing work overloads and idle time. The aim of this
introductory chapter is to presents the purpose of this thesis, the research
method and the case company of the study
1.1 Foreword
Since the times of Henry Ford’s conveyor-based mass production to today’s
more flexible assembly systems, assembly lines have been an active field of
research. The first assembly line balancing related studies were made in the
1950s and the core idea was only to assign tasks equally to workstations. For
several decades the research concentrated on these simple assembly line bal-
ancing problems, which have many restricting assumptions making them appli-
cable only for single model assembly lines. Today’s more complex product re-
quirements and more variable assembly systems require also more extensions
for assembly line balancing. More research has been recently conducted to
solve more realistic and variable balancing problems. However, there is still a
clear gap between theories and practice, because studies often take into ac-
count only a single or just a few extensions for assembly line balancing prob-
lems. Real-world variable assembly systems require a lot of these extensions in
a combined manner. Thus, there is a need for more flexible assembly line bal-
ancing practices that are applicable for various kinds of flexible assembly lines.
(Boysen et al, 2008; Becker & Scholl 2006)
This thesis will concentrate on the assembly line balancing problems of a real-
world high-mix, low-volume environment. The work is made for Cargotec Fin-
land Oy Multilift factory in Raisio, by focusing on a demountables mixed-model
assembly line. The idea is to study many different balancing methods simulta-
neously first as an alternative development ideas and then in practice. The sec-
ond chapter will concentrate on production balancing and scheduling theories,
which are related to variable low-volume type production needs. Chapters 3, 4
and 5 concentrate on the practical side of this work by introducing the current
situation, the implementation plan as well as implemented development actions.

2
The final chapter focuses on the theoretical and practical views from a com-
parative angle and also presents the conclusions of the thesis.
1.2 Objectives and scope
The main objective of this master’s thesis is to plan alternative solution ideas for
assembly line balancing in high-mix, low-volume production at the Raisio facto-
ry. Based on the balancing related study also production scheduling for the de-
mountable assembly line is analysed. The theory part is mainly focused on
build-to-order type of production in a variable environment. It also examines the
production of complete equipment rather than individual parts. These kinds of
production environments are normally very customer oriented and can be found
in business such as industrial machines, trucks or airplanes.
The research question is: How can the Hiab Raisio factory create a balanced
and organised material flow in a low-volume and high-mix type of demountable
machine assembly line? For Raisio demountable factory the main goals are to
improve productivity and shorten the lead time in assembly line. The objective is
to create a smooth, well planned and organized production flow. There are no
ready-made solutions or proposals for line balancing or takt time, so a master’s
thesis study on this topic is needed. The sub-objectives for balancing are to
minimize waiting times in demountable assembly line and to create a clear tar-
get system which would be based on standard times. The target system will
require visual management improvements and some clarification for production
planning. Other fundamental aims are to increase the overall Lean manufactur-
ing awareness among the employees and to emphasize the importance of elim-
ination of non-value added activities from demountables production.
The development work is reconfiguration of the already existing production sys-
tem rather than developing totally new assembly line. The scope of this thesis is
assembly line of power series demountables from the output area of the paint
shop to the final workstation before testing. Subassemblies are also covered,
because they work with the same pace with the main assembly line. The scope
of the thesis is also presented in the assembly line flowchart in figure 3.2 with
bolded workstations. Development of the outsourced paint shop is left out of the
scope because it does not follow the same production system with the assem-
bly line, and because work time arrangements are not the same. Pipe bending,
which is made as a pre-assembly, is also not covered due to its batch type of
production and different scheduling periods. Final testing is not in the scope,
because its scheduling is based more on quality problems, delivery times and
current product mix of all demountables, rather than standard times of power
series hooklifts. The main focus is on material flow within the assembly line,

3
while inbound and outbound material logistics are studied only in case of re-
strictions, problem solving and production scheduling. Employee engagement
and change management are closely related to the development of assembly
line, but they are not deeply discussed in this thesis. For example in balance
calculations all employees are perceived to have the same competence, moti-
vation and capacity regarding to workloads, which does not reflect to real world
assembly work. In case of standard times the scope is to only use available ma-
terial from ERP system and not to make any detailed stop-watch time studies.
There are no complex mathematical formulations or algorithms in the thesis that
exist in many assembly line balancing theories. It was acknowledged that the
source data is not reliable enough for that kind of statistical research and the
production system is too flexible for very accurate calculations. The idea was to
get a rough balance situation by recognizing the assembly line bottleneck and
other production flow restrictions.
1.3 Methodologies
The methodology of this thesis is an action research, which is aimed at to use
appropriate knowledge to improve practices in an organisation’s context.
Throughout the project, theories from assembly line planning related literature
were used to support decisions in balancing and controlling activities for de-
mountable assembly line. Figure 1.1 illustrates the methodology of the project,
which is also used as a structure of the thesis. The first phase is a development
of different alternative ideas to solve the research question. Ideas are generated
through the literature review and an analysis of the current situation. In the next
phase, these ideas are evaluated with empirical data and logical thinking, which
will result in an implementation plan with selected alternative ideas. In testing
and piloting the plan is implemented in practice for demountable assembly line
and the consequences of different changes are analysed. Finally the results and
empirical work are compared to the literature review in the framework of the
discussion chapter.
Figure 1.1. Methodology of the thesis.

4
The analysis of the current situation was made with empirical participative ob-
servation, interviews, data collection and daily discussions with personnel. The
analysis of the production line was made at the beginning of the year 2013.
The idea behind the observation period was to learn the assembly line better, to
get to know the employees and to find development ideas for production. The
observation was conducted through a two-day hands-on line-work and daily
visits in the assembly area. The findings were first listed as notes, which were
used as checklist for comprehensive report of current situation analysis. Waiting
and idle time were detected to be the most significant inefficiencies of the as-
sembly work and it emphasizes the need of this assembly line balancing work.
Data collection was the biggest part of the empirical work done for this thesis. In
the analysis of the current situation the most important task was to determine
the workstation balance situation by dividing target times to workstations and
calculating of capacity requirements. The data source was the ERP -system
and the work was mainly done through Spreadsheet software calculations. The
source data included total current order book of highly variable power series
demountables. The main analysed factors where cycle times of each work-
station and the differences of standard times between products.
The development project for balancing and production scheduling was made
during spring 2013. The most critical issues were recognized based on the
analysis of current situation. Action plans were planned through meetings, a few
trainings and various tests within the assembly line. There were meetings held
for definition of target times, sequence planning, visual management and gen-
eral development meetings of factory’s lean team. The actual changes in the
assembly were made together with employees, supervisors and managers.
Small changes were usually based on statistical data and discussions with dif-
ferent responsible persons. The test weeks were based on the changes in the
assembly line balancing, but concentrated more on new production scheduling
and a target setting system. The development work was documented mainly to
weekly report made by the author of this thesis. The report included information
of the results previous week, completed hours, productivity, differences com-
pared to targets, report of different changes and author’s opinion of next short-
term development objects. The overall idea of the development project is to
create a plan for future ways of working and it is not aimed implementing all the
changes presented during the thesis project. The most significant assembly line
balancing and controlling actions are made in the long term after having been
well planned, tested and all consequences are recognized. The thesis will pro-
vide an analysis of current situation, a study of the subject, balancing methods,
as well as the first steps in implementing changes. The purpose is also to create
an environment for continuous improvement for assembly line balancing.

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1.4 Company presentation
This work has been done for Hiab’s Raisio factory, where Hiab Multilift de-
mountables are assembled, designed and managed. The roots of Multilift are
already in the year 1947, when Terho brothers patented demountable working
with cables. This cable lift enabled the founding of the Raisio Multilift factory in
1961 and it is currently the only production facility of Cargotec in Finland. The
Multilift brand name has gone through many acquisitions and owners. It was
first bought by Sponsor Oy in 1968, followed by Partek in 1977. In year 2002
Kone Oyj acquired Partek and made Cargotec as one of its business area for
load handling solutions. Cargotec Corporation demerged from Kone and be-
came an independent stock listed company in 2005. (Teräväinen 2005)
Cargotec improves the efficiency of cargo flows around the world in over 120
countries with an extensive product portfolio. Cargotec’s turnover was 3.3 billion
euros and the average number of personnel was 10 500 in 2012. Cargotec is
composed of three well-known brands MacGregor, Kalmar and Hiab which are
now working as individual business areas. This work is done for Hiab business
area of which sales was 840 million euro with 3038 people in 2012. Hiab pro-
vides different on-road load handling solutions for various transport and delivery
sectors. Its offering contains loader cranes, forestry and recycling cranes, truck
mounted forklifts, tail lifts and demountables. Hiab products are used, for in-
stance, on construction sites, forestry, warehousing, waste handling as well as
by the Defence forces. (Cargotec Oyj, 2013a 3, p.73)
Figure 1.2. Hiab Multilift S-model.

6
Demountables are now sold as Hiab products and Multilift is regarded as a well-
known product name for global market leader demountable solutions. The core
idea of Hiab Multilift demountables is that the truck can be driving all the time
and carry out multiple tasks because containers can be loaded and unloaded
separately. Demountables are used, for example, in waste handling and recy-
cling businesses as well as by fire brigades and defence applications.
(Teräväinen 2005, p.19)
There are three different product families of demountable products: hooklifts
(figure 1.2), cablelifts, and skip loaders. Hooklift is the most important Multilift
product family and it is divided into power series, small hooks and special prod-
ucts. The scope in this thesis is assembly line of power series hooklifts. All the
products are designed modular and assembled from options chosen by cus-
tomers so that there can be thousands of different kinds of variations of hook-
lifts. There is an assembly line for power series hooklifts and assembly cells for
small hooklifts, cablelifts and defence products. Today all welding and part
manufacturing is made by suppliers and Raisio factory only assembles the de-
mountables. More detailed presentation of the demountables production system
is in chapter 3.1.

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2 ASSEMBLY LINE BALANCING AND CON-
TROL
Assembly line balancing and production balancing are not totally unequivocal
terms, because they are presented in at least in three different kinds of con-
cepts. The most common viewpoint is to balance the speed and volumes of the
production to meet customer demand as closely as possible. Another very
common perspective for balancing is the workload balance based on a certain
time period, which is also called production levelling or known through the Jap-
anese term heijunka. However, in this study production or assembly line balanc-
ing means process design for workloads between assembly line workstations
and employees. The core purpose is to equalize the amount of work between
employees and to improve material flow in the assembly line. In this chapter
there is first an introduction to assembly line balancing and its purposes. After
that the assembly line key terminology and concepts related to production
scheduling are presented. The final sections focus on assembly line balancing
methods and solution ideas for high-mix low-volume environment.
2.1 Definition and purpose of assembly line balancing
An assembly line is a flow-oriented production system where the productive
units performing the operations, referred to as stations, are aligned in a serial
manner. The workpieces visit stations successively as they are moved along
the line. Assembly line balancing was first introduced by Salveson in 1955 in his
pioneer work where production design problems were analysed with prece-
dence graphs and a planned cycle time together with a mathematical formula-
tion. The assembly line balancing problem consists in determining a set of tasks
for every workstation so that precedence relation requirements between single
tasks are not violated and operation time does not exceed the planned cycle
time. In a classical time-oriented assembly line balancing the objective is to min-
imise the manpower needed to assemble one product and the number of sta-
tions which also leads to minimal idle time. (Salveson 1955; Baybards 1986)
Assembly line balancing consists of scheduling and controlling the production in
order to meet the required production rate and to achieve a minimum amount of
idle time. In assembly line balancing all tasks are assigned to workstations so

8
that each station has approximately same amount of work at all times. An un-
balanced line may lead to overburden in some stations, high variation in output,
waiting times and poor efficiency. Instead, well balanced assembly line has to-
tally opposite effects and it promotes a one piece flow for the assembly line.
(Konnully 2013)
The purposes of assembly line balancing are to:
 Equalize the workload among the assemblers
 Establish the speed of the assembly line
 Identify the bottleneck operation
 Assist in plant layout
 Determine the number of workstations
 Determine the labour cost of assembly
 Establish the percentage workload of each operator
 Reduce production cost. (Stephens & Mayers 2010, p.111)
The most important objective of assembly line balancing is to give each opera-
tor as close to the same amount of work as possible. The workstation with the
largest time requirement is designated to be 100% workstation and is the limit of
output of assembly line. The station is a bottleneck station and it should be the
first priority for development actions. Through a well-balanced assembly line
idle time is minimized and a continuous production is enabled. This leads to a
better productivity of the assembly line. Also speed of the assembly line is a
consequence of balancing calculations, because the amount of workstations
and workers influence on cycle time, which determines the speed of production.
(Stephens & Mayers. 2010, p.111)
Production balancing requires a lot of calculations of production related indica-
tors like cycle times, lead times, standard times and resources. The inputs for
assembly line balancing problems are precedence constraints based on product
and time requirements. These elements can be visualized with precedence
graphs, which contain a node for each task of the assembly system. Figure 2.1
shows a precedence diagram for 10 tasks having task times between 1 and 10
time units. Nodes weight for task times and lines for the sequence constraints.
In this example the precedence constraints require tasks 1 and 4 to be com-
pleted before processing task 5. The tasks are assigned to different stations as
equally as possible so that precedence and capacity constraints are fulfilled at
all times. (Becker & Scholl 2006, p.695)

9
Figure 2.1. Precedence graph. (Becker & Scholl 2006, p.695)
Production balancing may often influence to the number of workstations and
layout changes. This is more common in mass-production type of assembly
where operations are planned in seconds and where there is only one worker
per station, whereas in low-volume production issues related to space and prob-
lem solving, among others, can lead to changes. The assembly line balance
situation is normally visualized through column charts. These charts represent
the differences between workloads between workstations and they are used as
a main visualization tool in this thesis. Examples of column charts can be found
in figures 2.3, 2.4, and 2.6.
The final listed purpose of assembly line balancing is to reduce production
costs. The main improvement comes from the equalized workload, because the
non-productive idle and waiting times are used for assembly work instead. This
leads to a better productivity because the available time is used more effectively
to standard times instead of waiting. The cost savings gained through a better
productivity thus come from more standard hours sold or reduced number of
employees. Another perspective, alternative for the usual time-oriented balanc-
ing, is called cost-oriented assembly line balancing. The objective of this ap-
proach is to minimise the unit costs by giving a value for each task and then
minimise labour and capital costs by reducing idle time by prioritizing most ex-
pensive tasks (Amen 2006, p.749).
2.2 Assembly line balancing key terminology
In this chapter different assembly line control methods are analysed briefly for
high-mix, low-volume assembly line. The definitions for standard time, cycle
time, lead time and other main production scheduling terms are explained brief-
ly to avoid misunderstandings. The terms are presented because they are nec-
essary for production balancing which is the main subject of this thesis. This
chapter also assesses the possibilities and readiness to implement a takt time –
based production system for demountable assembly line which was the initial
vision in the beginning of thesis project. The focus will then be on comparison of

10
takt-driven and production rate oriented system which will be defined and ana-
lysed focusing on high-mix, low-volume assembly lines.
2.2.1 Time standards
Time standards have many informational purposes in an organisation. They are
the most basic yet very important sources for production planning, cost alloca-
tion and control, inventory management, performance evaluation, incentive
pays and decisions for alternative methods of operation. The main idea of time
standards is to determine how much time it takes to conduct one operation. For
a facilities planner, the standard time is the primary input for determining the
required resources and capacities to meet the production schedule. Time
standards are also the main source for assembly line balancing. (Stephens &
Mayers 2010, p.51)
Cycle time is the time required to complete one process in a value stream or the
time between two discrete units of production. Cycle time alone describes the
time in one workstation and this time can also be called station time. In the de-
mountable production the definition for station time is also station’s target time.
In this thesis, the total cycle time refers in this thesis to the operation time of all
stations from the first assembly station to the last phase including all pre- and
subassemblies. Planned cycle time shown also in figure 2.4 is the desired sta-
tion time, which is usually higher than real cycle time and lower than demand
rate. The difference between the planned cycle time and the station time can be
perceived to be idle time, waiting or slowed pace of work (Rother 2013).
Productivity is a measure of output divided by input and the sources can be ei-
ther number of units or earned hours. Number of units produced per period can
be good indicators for plant or whole industries but not for smaller divisions.
Therefore, without time standards it is impossible to calculate productivity for
individuals in a reliable way, especially in variable environments. Already in the
1980’s it was discovered in a 400 plant study that an operation that is not work-
ing towards time standards typically works only 60% of time. Those operations
working with time standards work at 85% of time. In a plant of 100 people this
improvement equals to 41 extra people, or about million dollars per year in sav-
ings. (Stephens & Mayers. 2010, p.62) More recent outlook from Greg Lane
(2007) suggests a productivity increase from 10 to 15 per cent if time is associ-
ated with all work and if it is visually compared to actual time.

11
2.2.2 Production scheduling
The production planning and control function of an organisation is responsible
for ensuring that production activities are as efficient as possible. Its purpose is
to find the best and the cheapest methods to produce the required quantity and
quality at the right time. Production planning is the choice from several alterna-
tives how to utilise the resources available to achieve the desired objectives.
Control is monitoring performance by comparing the results achieved with the
planned targets so that operations can be improved through proper corrective
actions. (Aswathappa & Shridharabhat 2009, p.208)
The purpose of production scheduling is to make a detailed plan for the produc-
tion processes. The basis for production scheduling is the longer term rough cut
planning. Planning the schedule for different tasks requires the knowledge of
standard times and of the current situation in production. The timeframe for pro-
duction scheduling is normally kept as short as possible which typically means
from one week to one day. With a short timeframe it is possible to get more
specific information and reliable plan. Good delivery accuracy and high produc-
tivity are common goals of production scheduling. (Haverila et al. 2009, p.417)
In a lean environment, the production control department plays an absolutely
vital role and it is responsible for very detailed planning. It includes capacity
planning down to a process level. Getting all the right parts to the right point on
time is probably the biggest issue. Production planning department should
make a daily or an hourly plan for each process and compare them with pro-
cess capabilities and realization. (Lane 2007, p.46)
All workstations should have a schedule of what will be occurring during the
day. In high-mix, low-volume environment, where cycle times are normally cal-
culated in several minutes, standard times may not be particularly precise. Cy-
cle times must be close but not necessarily exact. For example 410 minutes can
be counted as seven hours. A continuous updating of standard times is neces-
sary in order to ensure reliability of assembly line balance calculation and prod-
uct costing. (Larco et al. 2008, 74, p.106)
The production planning for different phases in assembly can be done with
backwards or forwards scheduling. Frontwards scheduling starts from the start-
ing time of production and when resources become available to determine due
date. The starting time of the second phase is calculated by adding the time
required to complete the first phase. The next phases are scheduled with the
same system until all phases and the finishing time is calculated. Backwards
scheduling starts from the planned due date so that the starting time of the final

12
phase is calculated backwards in time. The same system is used to calculate
the beginning time of the second last phase and then finally continued to the
first phase. This is the most common system in production planning programs.
(Haverila et al. 2009, p.419)
There are various different charts and tables to visually manage production
schedules. The most popular tool to display schedules is the Gantt chart, which
is used to graphically display the workloads of each work centre. There are two
types of Gantt charts: the workload chart as well as the scheduling chart. In
both charts time elapses on vertical axis. In the Gantt workload chart the hori-
zontal axis shows the amount of work while the vertical bars depict workloads
for different periods. In Gantt scheduling chart different workload groups are on
the vertical axel and tasks are shown with different colours with horizontal bars,
which length depicts time required to complete the phase. (Aswathappa &
Shridharabhat 2009, p.312)
Computer systems are the best for monitoring production control, because as
the data is available as soon as it is entered to the system. Old fashion cards
are slow in comparison and they are subjects to even more errors (Larco 2004,
p.108). The programs that are used for production scheduling are based on dif-
ferent kinds of algorithms that will solve optimisation problems and generate
alternative plans, which are used to support the final decisions made by the
planner. (Haverila et al. 2009, p.419)
2.2.3 Takt time and production rate
Takt is a German word meaning a musical beat, stroke of an engine or a regular
rhythm. These are natural extensions to think of takt time as the time between
beats of the pace of production. Takt time is the average amount of time that
must be elapsed between the completions of two units in order to meet the de-
mand. A takt time based system is transferred also as paced production in
many references which mean that the all stations have common cycle time. This
time matches to the rate of how customers require finished units. This pace is
calculated with demand and net available production time, which means the
working time without breaks. (Baudin 2002, p.42)
Takt time can be likened to conductor’s baton keeps the orchestra in synchro-
nized order (Rother & Harris 2001, p.13). Liker (2004, p.94) compares takt time
to the heart beat of one-piece flow or the person in key position of coxswain

13
coordinating the pace for rowing so that any rower would not under or underper-
form. Analogy of takt time for high-mix products can be compared to chairlift
system presented in figure 2.2 where the workload can be different but the time
between chairs is constant. If there is a heavy load the lift just needs more pow-
er but the frequency will not be affected (Baudin 2002, p.43).
Figure 2.2. The chairlift analogy for takt time in mixed-flow line (Baudin 2002,
p.43)
Takt time provides a good picture of customer demand over a period of time.
The customer takt should be reviewed for example every two weeks because of
demand changes. Effective operation time is calculated by subtracting breaks
and planned downtime from the total available time. When net available time is
divided by the demand for the same period the result is takt time. Takt time itself
is not enough for production scheduling and to be used for cycle time because
there are always problems occurring in production. That is why production is
scheduled for planned cycle time, which is the desired pace of the production.
Planned cycle time is faster than takt time because it accommodates changeo-
vers, downtime and possibly some other non-value added activities. (Rother
2013, p.18)
Lane (2007) calls takt time as pure takt time and planned cycle time as actual
takt time. In actual takt time the basis for calculations is the overall equipment
effectiveness rate. It is more preferred in part manufacturing rather than assem-
bly, but the system is the same. The actual takt time should be compared to
standard times and cycle times for each task. The result is usually showed with
assembly line balancing graphs which are discussed in the next chapter. Takt
time, planned cycle time and standard times are used for production scheduling
to plan activities as efficiently as possible. However, takt time based production
scheduling cannot be applied to all assembly line environments. In low-volume

14
build to order environment, where processes are managed rather with day-by-
hour boards or Gantt’s scheduling charts, takt time is not used. (Lane 2007,
p.36)
The takt time allows defining an ideal state for production one-piece flow with
exactly matching station times. This ideal state can be called as takt-driven pro-
duction, where all deviations are translated to different inefficiencies or wastes.
In takt-driven production takt time gives the direction for operation, but in real-
world assembly lines it is never perfectly realized. Time per demand calculation
is the way to calculate takt time, but it does not tell the rules of how to use the
number or how it maps to shop floor. Takt-driven operation is not relevant for
example in business with non-repetitive operations, where it becomes more dif-
ficult to balance the work among stations with broaden mix of products. In many
production plants the inverse ratio is used which will give the same information
with production rate over a period. Demand per time calculation gives mathe-
matically the equivalent result, but the shop floor operation may be totally differ-
ent. Working at a takt time of 1 minute and making 60 units per hour gives the
same throughput during an hour, but the scheduling system may differentiate
significantly. In terms of units per hour it does not matter if nothing comes out
for the first 59 minutes of an hour as long as all 60 units are completed in the
end. In takt-driven operation unit will come out every minute according to
planned cycle time. (Baudin 2012)
As introduced, the alternative approach for takt-driven operation is to concen-
trate on completed units over a predetermined period. This system does not
have well-established definition and it is called with many different terms like
production rate -oriented system, takt rate -system or throughput -oriented pro-
duction planning. In this thesis the approach is called with production rate ori-
ented system. It is not paced production because the time between two prod-
ucts are completed can fluctuate. Production rate for certain predetermined pe-
riod is much more flexible in variable assembly compared to takt time, because
different products take different time to be completed. Production rate -oriented
system will smooth difficulties in capacity allocation because the requirements
can be divided for longer timeframes than in takt-driven operation.
Production rate or using day-by-hour boards is good especially in shared pro-
cesses where work is done without a solid forecast. The rate and schedule will
serve as clear targets for assembly for a certain period when all different pro-
jects should be completed. Standard times and available capacity are used in
target setting for the rates. The system will help in capacity planning because it
is easier to see where production is late when compared to the targets. The cur-
rent status can be visualized versus plans and ability to prioritize different tasks

15
will increase. With good plans, targets and visualization the current imbalance is
indicated clearly and it is easier to make corrective actions faster. A clear
schedule will also encourage operators to list problems that cause delays.
(Lane 2007, p.36)
In production rate oriented system cycle time is not always the same for all sta-
tions so the control system is normally unpaced. The system can be either un-
paced asynchronous or unpaced synchronous. In asynchronous movement the
products are transferred forward to other works station as soon as they are
completed. In order to balance workloads buffers are needed to avoid waiting
times. Under synchronous system all stations would wait for the slowest station
to finish before the work pieces are transferred. This will cause waiting times but
buffers are not necessary (Boysen et al. 2008, p.8).
The target production rate is calculated based on demand for certain time peri-
od. Takt time calculations may support the scheduling decisions but are not di-
rectly used because of variable product cycle times. The period for the rate is
decided based on product specifications and the required accuracy of plans.
The minimum for the period is planned cycle time of one product which is then
practically the same than takt time based production. The period can also be
the average cycle time to assemble two products. The normal system is to plan
the rate for a longer period such as half a day, day or even a week.
Figure 2.3. Comparison of takt time and production rate based systems with
variable cycle times.
Comparison of takt- driven and production rate -oriented systems
20 units, 8h production , one station, cycle times vary from 12min to 28min, 15% of allowance time
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
min
units
0
50
100
150
200
250
Period 1 (1. half day) Period 2 (2. half day)
min
10 units
TAKT-DRIVEN
Takt time = 480min / 20 = 24min
Planned cycle time =
(1-0,15) x 24min = 20,4min
PRODUCTION RATEORIENTED
Production rate for half a day periods =>
10 units per 240min period
Actual period time =
(1-0,15) x 240=204 min
cycle times for
each product

16
Figure 2.3 is an example of a situation of one station’s work for 8 hours. The
target is to complete 20 products, which is the demand of the period for the sta-
tion. There are both takt-driven and production rate oriented systems illustrated
in a high-mix, low-volume production where cycle times vary significantly from
12 minutes to 29 minutes. In both cases the amount of work is the same 380
minutes which is 100 minutes less than 8 hours. In both cases this 15% allow-
ance percentage can be subtracted from the available time to get the planned
cycle time or actual scheduled period time.
In takt-driven production all the cycle times should be as equal as possible but
in variable production it is not necessarily possible. In the example many prod-
ucts cross the planned cycle time and also takt time. In these products more
resources or better productivity is needed to reach takt time. Additionally in-
process inventory can also be used to even out the workload so that the next
stations do not need to wait for products. There is much more unevenness cre-
ated if variable cycle times are tried to fit to the takt-driven system without very
detailed scheduling. In the production rate oriented system these variable fac-
tors are divided into longer periods when the workload seems to be much more
even and short term balancing problems are avoided. The operator only needs
to complete all the required parts during predetermined period while the sched-
uler is responsible that the total cycle time fits to the demand and allowance
rates. In the product rate oriented system product variances fade because of
longer time periods and because it is much easier to reach the targets. In both
systems it is important to aim at to decrease variances in station times and
there are different methods presented in chapter 2.4 for this purpose.
2.3 Assembly line balancing for variable environment
Originally assembly lines were developed for a cost effective mass production
of standardized products and it was also the focus on production planning relat-
ed literature. Since the first mathematical formulation of assembly line balancing
by Salveson 1955 the research focused for many decades on the core problem
to assign tasks to different stations evenly. This was usually done with numer-
ous simplifying assumptions which can only be generalized to mass-production
environment of homogeneous products. When the results were tried to apply in
real world production systems it was understood that product requirements do
not often reflect with the assembly line balancing calculations. These simplified
formulations are today labeled as simple assembly line balancing problems
(SALB) and they have only two constraints considered. In SALB cycle time con-
straint means that station time of any station cannot exceed the planned cycle
time and precedence constraint means that the requirements of assembly order
must be carefully observed. SALB characteristics are applicable for a single

17
model assembly line, which is paced with a fixed cycle time and has no assign-
ment restrictions. In the simple assembly line all the stations are equally
equipped and the idea is to maximize the line efficiency with station times that
are as near to the planned common cycle time as possible. (Baybards 1986,
p.150; Scholl & Becker 2006, p.667)
As mentioned in the first chapter the scope is in high-mix, low-volume assembly
line for complete equipment. The standard assumption for assembly line bal-
ancing is the traditional single model production and many publications study
this perspective. Today’s assembly lines have changed dramatically since the
early versions due to more complex product requirements and diversified cus-
tomer needs. Companies have to be able to individualize their products with
modularisation or mass-customization. For example car manufacturer BMW
offers various optional features that in theory would allow 1032
different models
which are produced in one assembly line. Better production techniques and
production planning enable efficient flow-line systems also for varying low-
volume assembly-to-order production. The main principles are the same in sim-
ple assembly line balancing and mixed-model assembly line balancing but in
the latter all the calculations, problem solving and restrictions are more com-
plex. (Boysen et al. 2008, p.1-3)
More flexible assembly line requirements have also attracted the attention of
researchers and a great amount of different extensions of basic assembly line
balancing studies have been made. Assembly line balancing research evolved
towards formulating and solving generalized problems (GALBP) with different
additional characteristics such as cost functions, equipment selection, U-shaped
line layout and mixed-model production (Scholl & Becker 2006, p.667). The last
one of these characteristics, the mixed-model assembly line balancing, is the
most important extension for this study that concentrates on high-mix produc-
tion of built-to-order products.
In mixed-model assembly line (MALB) the models may differ from each other
with respect to size, color, tasks, task times, precedence relations and many
other variables. Consequently it is almost impossible to find a line balance when
workloads of different stations have the same station time and equipment re-
quirements for all models. In these kinds of environments the conventional con-
straints are no longer relevant, because there can be flexibility in local cycle
time violations and also employees need to be flexible. Cycle time is no longer
the implicit maximum station time because the primary station time must be de-
fined from the average cycle time. Employees must be flexible enough to qualify
several tasks in order to balance the line. The analogy of MALB consists of find-
ing the optimized number of station, cycle times and line balance such as in

18
SALB. However, the work is a lot more complex because of the large amount of
variable factors while the station time must be smoothened for each station
separately. (Becker & Scholl 2006, p.706)
In simple assembly line balancing problems the capacity of the line is defined
from the amount of workstations, because workplaces and operators can be
perceived as the same attribute. In a more variable environment this definition is
not necessarily applicable because many products manufactured on assembly
line are large enough to be worked at several workers simultaneously on one
workstation. Moreover, the stations are often designed merely based on product
structures than on common cycle time and workload may also differ between
stations. In these kinds of variable environments the productive capacity is not
defined by the number of workplaces but by the number of employees required.
Because station times may significantly vary between workstations significantly,
the stations are balanced with the amount of employees. However, it is often
proposed to distribute the total work content as evenly as possible among the
stations because it promises better product quality due to a more standardised
work system. (Becker & Scholl 2009, p.359-361)
In a variable environment it can be challenging to allocate and calculate accu-
rate real workloads of workstations because the cycle times are not the same
for every product or model. For high-mix, low-volume line where standard times
fluctuate, determining average standard time per process is more accurate for
determining resources. The balance of the assembly line is then calculated by
dividing the resources equally based on average standard times. The resource
calculation is straight forward but the resource allocation may not be as simple
and accurate because there is so much variance in times. (Hobbs 2011, p.236)
There are two aspects in assembly line balancing for mixed-model assembly
lines. The first aspect is the equal allocation of the total workload to all employ-
ees based on average station times. This is called vertical balancing and it is
described more in detail in chapter 4.3.1. The other one is more horizontal bal-
ancing, which aims to decrease the variability of station times in order to avoid
occasional work overload or idle time. This method is described more in detail in
chapter 4.3.3 where the method used is to decrease variability by assigning op-
tional modules to pre-assembly. Vertical balancing is important for all kinds of
assembly lines but horizontal balancing is a characteristic only for mixed-model
assembly with variable station times. (Merengo et al. 1999, p.2839)
One of the objectives of assembly line balancing is to determine bottleneck sta-
tion, which is the slowest operation or the most loaded station that is constrain-
ing the assembly line throughput. In high volume plants, a bottleneck can be

19
determined also visually from predetermined buffers before and after work-
station. For example if the buffer before is full and the one after is empty, the
workstation is likely to be a bottleneck or at least a local constraint, and no
deeper analysis is needed. For low-volume production the bottleneck can be
less obvious because the bottleneck can change place depending on certain
condition. (Lane 2007, p.71)
Assembly line balancing for more complex mixed-model lines has regarded as a
tactical level problem. It can be solved by dividing tasks equally to different sta-
tions, assigning unlimited buffers and determination of production sequence of
all models for each station separately. However, competitive markets require
more flexible production systems that respond rapidly to changes in the market
conditions. Then unlimited buffers are not a solution in assembly line systems
and workloads must be planned more in detail in order to avoid unbalance. In
flexible systems with limited buffers mixed-model assembly line balance prob-
lem becomes an operational problem, because task assignment and operations
scheduling must be considered simultaneously with a shorter timeframe.
(Öztürk et al. 2013, p.436)
Larco et al. (2008, p.56) has come to a conclusion that assembly line balancing
and designing layout in a variable environment is more like an art than basic
production planning, because there are so many different factors to be consid-
ered simultaneously. Multi-skilled employees, different routings, scheduling
problems and determination of bottlenecks are just a few extensions compared
to a mass-production environment. These kinds of environments require skilled
planners and self-management from employees in order to operate the facility in
an efficient manner. (Larco et al. 2008, 56; p.91)
2.4 Assembly line balancing methods
Various optimization methods have been introduced and discussed in literature
for assembly line balancing. The methods aim to support decision makers to
configure the assembly systems as efficiently as possible (Boysen et al. 2008).
In this chapter nine different methods presented. They are also perceived as
alternative solution ideas to be implemented in practice for the case company’s
needs. All these alternatives can be used in parallel. However in mixed-model
line at least two methods must be used because both vertical and horizontal
balancing aspects need to be considered.
2.4.1 Assembly line balancing based on average station times
The purpose of this first method is simply to equalize the workload for all em-
ployees based on the workstation planning, capacities and average station

20
times. This is the most common and almost compulsory method to balance as-
sembly lines. It is also presented in all sources that present how assembly line
should be designed and it fits to all kinds of productions. In a high-mix produc-
tion line some other methods must also be used but balancing according to av-
erage workloads is the basis and starting point for actions and for the use of
other methods. This will define the normal situation, which balances the work-
loads on a very long term period, but also considers short-term variations in
production.
There are many factors that affect the production balancing based on workloads
and a lot of calculation is needed. Values that need to be considered in assem-
bly line balancing are, for instance, all standard times, the available working
time, number of workstations, number of workers, routings and demand. The
current production set-up normally defines the most important factors to be
evaluated for reconstructive assembly line balancing. For example the product
structure, the employees’ skills as well as available space can be restrictions
that define the perspective for the plans and actions.
In assembly line balancing the first thing is to evaluate and compare the total
cycle time with the theoretical takt time. In simple assembly line balancing prob-
lems it will give a rough estimation for the number of employees needed and
speed of the line. There are big differences in allocation of these values in dif-
ferent production systems. In simple assembly line balancing problems for mass
production requirements the station times are always the same. The calculated
cycle time is divided equally between workstations, which are usually defined to
match the takt time as presented in chapter 2.2.3. Furthermore, an early study
for mixed-model assembly line by Thomopoulos (1970) attains for equality of
workloads across all workstations and models to enable synchronous move-
ment in assembly line.
The first step in transformation from simple assembly line balancing problem to
mixed model balancing is to compute average task times for workstations.
Becker & Scholl (2006, p.707) call this process as a reduction to single-model
problem. The next step is the minimization of cycle time differences from aver-
age station time and to aim for synchronous takt-driven production. For high
volume assembly line Baudin (2001, p.54) proposes that the cycle time of the
bottleneck station should be equal or multiple of other stations. Then resource
allocation would be pretty simple too because the resources are divided with the
same share than the multiples of station times. To achieve such accurate and
detailed station times, a very comprehensive production planning and schedul-
ing for assembly line must be conducted.

21
These traditional viewpoints presented above indicating that all stations must be
equally equipped with respect to machines and workers is not often applicable
in real-world variable assembly lines. The average task time ensures that the
cycle time is sufficient to perform all tasks on average but even in an optimal
solution considerable inefficiencies such as work overload or idle time may oc-
cur. There are also many restrictions and constraints related, such as flexibility
requirements, problem solving, technological capabilities or position in assem-
bly work. (Becker & Scholl 2006, p.697)
Table 2.1 shows an example of assembly line balancing problem and a tech-
nique for capacity calculation for every workstation. First average standard
times for all existing workstations must exist and takt time needs to be calculat-
ed based on demand. Additionally, allowance percentage or desired productivity
is needed in order to get the planned cycle time for a certain available time pe-
riod. In the example, daily demand is 20 for the day’s production. The allowance
percentage compared to the takt time is set to 80% so that 20% of time is re-
served for problem solving, training or other inefficiencies which are not taken
into account in standard times. In comparison Toyota usually balances their
highly efficient high-volume facility to 95% of allowance time but there process-
es are stabilized and leaders are taught to solve problems efficiently (Lane
2007, p.144).
Table 2.1. Assembly line balance calculation (modified from Stephens & May-
ers. 2010, p.111)
The system above is modified for low-volume environment and manual assem-
bly work. In this example the system is very inflexible because only average
workloads are used and other balancing options are not handled. The times are
presented in minutes and hours instead of seconds which are usually used in a
Daily demand 20 18,5
Time available (min) 480 4
Desired allowance/productivity percent 80 % 74
Takt time (min) 24,0 1
Planned takt time (min) 19,2 100,0 %
0,308
Operation
No.
Average time
standard for
one product
Number of
workers,
stations or
machines
Rounded
up
Cycle time
per station
or machine
Load
Hours per
unit per
worker
Max
units
per
day
Total
productivity
(compared to
100% time)
A1 102 5,31 6 17,0 91,9 % 1,850 22 71 %
A2 99 5,16 6 16,5 89,2 % 1,850 23 69 %
A3 74 3,85 4 18,5 100,0 % 1,233 20 77 %
SA1 80 4,17 5 16,0 86,5 % 1,542 24 67 %
SA3 77 4,01 5 15,4 83,2 % 1,542 24 64 %
SA4 50 2,60 3 16,7 90,1 % 0,925 23 69 %
A7 118 6,15 7 16,9 91,1 % 2,158 22 70 %
T otal 600 36 11,100 69 %

22
conveyer based production. The number of stations is presented also in number
of employees on one station which is more common in industrial low-volume
assembly work. With these values it is possible to calculate the number of sta-
tions, machines or employees in workstation. (Stephens & Mayers 2010, p.111)
The assembly line set up in this example is the same as in case company, but
the values are made to demonstrate assembly line balance problem. Sub-
assemblies are presented with SA and main assembly line stations with A, and
the sequence of the assembly is from top to down. The average time standard
is presented in the second column for all stations and in variable imbalanced
production those can vary significantly, because normally the layout is planned
more according to product structure than equal amount of work for every sta-
tion. In this example the total cycle time is 600 minutes, which means that it
takes 10 active hours to assemble an average product. Number of workers is
calculated by dividing the average time standard by planned cycle time for each
station. In the next column the computed amount of workers is rounded up to
the next whole number because the idea is to seek for the right head count and
if rounded down the demand or rate targets would not be reached. The assem-
bly line cycle time is presented in the fifth column by dividing the time standard
by the number of workers.
Workstation A3 has the highest cycle time and it is the bottleneck station of the
example. Bottleneck stations are marked as 100% station in balance calcula-
tions which present the place of the current maximum workload of the assembly
line. However, it does not mean 100% productivity because it would be calcu-
lated from the total time available and actualized working hours and here actual-
ized work hours are not concerned. The balance load percentages of the other
stations are calculated based on the workload of the 100% station and the
numbers tell how busy each workstation is compared to the bottleneck station
(Stephens & Mayers. 2010, p.116). The idea of this table is to determine the
amount of employees needed for workstations with given starting values and
the balance situation of the assembly line. The result seeks the minimum num-
ber of employees in order to balance the assembly line with current process
setup by using only average standard times. The numbers can be compared to
actual current situation for indicative action plans for changes. In the last col-
umn we can see that if the demand target is reached with given values the
productivity of the bottleneck station is 77% which is 3% lower than desired.
The total productivity of the assembly line would be only 69% (11% below de-
sired) when actualized standard times are divided by total day’s hours of the
employees. Even theoretical calculations cannot reach to better maximum val-
ues and it underlines the complexity of assembly line balancing for variable en-
vironments.

23
In the example we can see that assembly station SA3 employees work only
83% of time compared to the bottleneck station and the difference represents in
most cases waiting time or slowed pace of work. According to Stephens & May-
ers (2010, p.112) the cost of balancing is calculated from the difference of the
most loaded station compared to the least loaded or slowest activity. In the ex-
ample table the lowest load percentage is 83,2 % and the hours per unit is
1,542. The cost of balancing calculation is presented in table 2.2 with starting
values of volume for one year 10000 and the hourly rate 20€.
Table 2.2. Cost of balancing (modified from Stephens & Mayers. 2010, p.112).
There are many ways to develop the balance situation and productivity of the
presented situation in the example and as discussed before the first priority
should concern on actions for bottleneck station. If there are more employees
added to 100% station when A1 with the second highest load of 92% will turn to
100% station. This improvement will affect all stations with an approximately 8%
increase in load percentage (except A3), and the assembly line will be more
balanced and faster. By adding that one extra person to the 100% station would
save approximately 8% for 32 workers, which is equal to the workload of 2.6
employees. The best balance with these kinds of calculations is the lowest total
number of hours per unit and not the productivity because it is related to com-
pleted standard hours. Another method is to make bottleneck operations more
effective by decreasing the amount of inefficient non-value added activities.
(Stephens & Mayers. 2010, p.113)
The traditional form of presenting assembly line balance situation is histogram
graphs. Figure 2.4 presents the balance state of the previous example based on
the cycle times. The pillars can easily be compared to each other as well as
both to the customer takt and the planned cycle time. (Rother 2013, p.18)
Balanced cost (hours per unit
for the lowest loaded station) 1,54 hours
Individual cost (83,2% x 1,54) - 1,33 hours
Hour per unit savings 0,21 hours
Units per year x 10000 pieces
Hours per year 2083 hours
Cost of an hour x 20 euros
Savings per year (euros) = 41667 euros

24
Figure 2.4. Balance state based on cycle times. (Rother 2013, p.18)
Quite many restrictions exist in the traditional assembly line balancing based on
average cycle times, because it is almost impossible to analyse all influential
attributes related to the real-world work. Issues such as problem solving, devia-
tions in employee skills, lacking parts and demand fluctuations are not normally
analysed together. In many assembly line methods the purpose is also to find
the exact and most suited number of workers for assembly line without any dis-
cussions of excess of capacity or instant hiring of people. It must also be re-
membered that in Lean manufacturing environment, employees should not be
laid-off for cost savings or based on short term economic logic, because it
would make more harm for productivity actions than advantage (Liker 2004,
p.77). In this thesis the number of employees on the assembly line is perceived
to be fixed even though the traditional assembly line balancing calculations
would support other decisions.
Another restriction is that in unpaced an asynchronous lines throughput can
often be improved if less workload is assigned to central stations compared to
those located at the beginning or the end of the line. This concept which partial-
ly challenges traditional assembly line balancing is known as “bowl phenome-
non” and the effect seems to be stronger when the deviations in processing
times are higher (Hillier et all. 1993, p.1-2). Usually studies and publications
analyse only some isolated parts of the assembly line balancing problems. Ac-
cording to literature covering made by Boysen et al. (2008, p.15) only 15 out of
312 assembly line balancing articles deal with real-world assembly line prob-
lems.
2.4.2 Flexible multi-skilled workforce
In a high-mix production environment multi-skilled workforce is an extremely
valuable resource and as for the employees it is one of the key requirements in
Lean manufacturing. Multi-skilled workforce gives flexibility for production plan-
ning and capacity calculations. It is also a way to balance uneven workload in

25
different workstations when employees change places according to the needs
from fluctuations in workloads. In chapter 3.1 assembly line balancing was pre-
sented based on average workloads of workstations but this method does not
recognize the need of flexibility of high-mix production.
There is an example figure 2.5 which represents workloads of four different
workstations during four periods in a paced assembly line. The example is pre-
sented in a paced and synchronised system without buffers so that the figure
highlights differences of variable station times. All the stations have as much
work but it is unevenly divided during the 4 periods. The assembly line is bal-
anced between average workloads as presented in the previous chapter, but
here time and period dependent variances are presented as well. The workload
can fluctuate at least in four different ways which are a) product mix, b) work-
stations differences, c) variances in workstation cycle times and d) differences
in period total times. Of course changes in demand, problems and many other
factors can also influence on balance situation. A more detailed table with
source numbers for this example is presented in appendix 1.
Figure 2.5. Time depended variances in high-mix production.
a) Variances in product mix mean the differences between total cycle times.
Some models with many customized options are simply much more labour-
intensive than basic products. However, in the example figure every product
has the same total cycle time of 12 but only unit 4 is demonstrated from the be-
ginning to the end. b) Workstation differences mean the variances between cy-
cle times compared to other stations. As mentioned in this example this factor is
also simplified so that the sum of all stations is 12 hours for these 4 periods. c)
Variance in workstation cycle times mean the range from the minimum time to
maximum time of the workstation, which normally depends on the optional at-

26
tributes. In the example presented, both the difference compared to other sta-
tions and the workstation variance range is from 2 to 4. d) Fluctuation in work-
load between periods is also a very important factor which will be analysed
more in-depth in chapter 2.4.5 production levelling. In the example figure it is
clearly visual that different time periods have high cycle time variances from 9 to
15 time units.
When workstation workloads considerably fluctuate between products it makes
little sense to daily rearrange or remove physical workstations. Instead, a more
logical solution is to adjust the number of flexible labour resources so that no
idle time is generated and productivity increases (Hobbs 2011, p.233). In these
kinds of cases presented in figure 2.5 multi-skilled employees are an excellent
way to balance the workload between all employees. For example during period
1 all stations should have an equal amount of employees which is 25% per sta-
tion. When products move forward to period 2 it must be ensured that work-
station 2 has more employees than average because of the higher workload.
During period 3 the total workload is very high but workstation 3 has only 3 time
units and its multi-skilled employees can be allocated to other stations with
more labour-intensive products. During period 4 workstation 3 should instead
have double the amount of resources compared to workstation 2. These balanc-
ing actions described above in highly variable environment would not be possi-
ble without multi-skilled employees who change place based on the standard
time requirements.
Planning a system in order to manage a flexible workforce can be difficult and it
has a lot of restrictions. There must be right standard times and enough time for
each job so that tasks can be performed. The employee must also have the
right skills for the specific job while materials also need to be available when
labour resources are changed between workstations. The most challenging part
is creating a culture of self-management so that people know what tasks are to
be fulfilled and that they are aware of the boundaries. Employees need to be
able to move from one workstation to another without missing a beat. (Lane
2007, p.74)
In order to promote self-management among workers, leaders must work to-
ward becoming leaders, coaches, mentors or advisors rather than remaining in
the role of authoritarian bosses. They need to be ready to step in and help. That
is the way how operators and line leaders learn how to balance their own work
as different products ought to flow through production. The workers in the work-
stations where the complex or variable tasks are completed must be multi-
skilled so that they can perform whatever special or unusual tasks are called
for. One option for managing resources is to create a team of “floaters” who are

27
always ready to help the currently highest loaded station. (Larco et al. 2008,
p.48, p.60, p.86)
There are many restrictions in using multi-skilled workforce as a balancing
method. First of all management must have a proper competence matrix so that
they know who have abilities and willingness to do different tasks (Lane 2004,
p.146). When there is more than one worker in a workplace performing tasks
related to the same workpiece simultaneously, the workers obstructing each
other should be avoided. This can be achieved by a detailed production plan-
ning or subdividing the workpiece to responsibility areas. (Becker & Scholl
2009, p.361)
There are also differences in skills and all employees are not able to perform
tasks in the required standard time. When using multi-skilled employees the
worker should always be able to meet the standard time. Coromias et al. (2008)
suggests that if a task is done by a skilled worker the normal standard time
should be used but if it is assigned to unskilled worker the standard time should
be multiplied by a factor greater than 1. Another solution to this problem is that
the employee’s capacity is calculated by a factor under 1 person in the case of
an unskilled or a temporary worker.
Another restriction consists also of the employees’ change resistance and of
motivational factors. For these issues an adequate awarding system should be
in place so that multi-skilled workers would truly be motivated to change places
and improve their skills. If multi-skilled employees are used as one of the bal-
ancing methods a good controlling system is necessary to support the deci-
sions. However, establishing such awarding system is a true challenge for a
high-mix environment where the production situation is quite unstable and hard
to measure reliably. Problems related to instant “hiring or firing” also prevail
when it comes to capacity requirements as was already discussed in the previ-
ous chapter.
2.4.3 Pre-assembly for optional modules
In order to forward products near to the same pace on mixed-model assembly
line all the station times must be matching at each station. This can be done by
changing more work to subassembly lines from the products which standard
times are over takt time (Baudin 2002, p.113). Pre-assembly is a balancing al-
ternative to level out the peaks in the workload so that optional modules are
assembled already beforehand and the workload in the main assembly line
would be as smooth as possible at all times. Pre-, and subassemblies create
more flexibility in production scheduling because the assembly does not have to
be performed at the same time with the main assembly line. Of course just-in-

28
time principles with minimum inventory and work-in-process must be planned,
but it is not that exact if the parts are only available on time for the main assem-
bly line. When workload is more even it is much easier to create a flow for the
main assembly line.
Assigning tasks to preassemblies is known also as horizontal balancing for
mixed-model assembly lines. The idea is to minimize variances in station times
over all models. This will reduce difficulties in sequence planning and reduce
overloads or idle time in the assembly line. (Merengo et al. 1999, p.2839) There
are three different methods to perform and measure horizontal balancing in the
mixed-model assembly line. The first alternative objective is to minimize the
sum of absolute differences compared to the average station time (Thomopou-
los 1970). A second alternative is to minimize the maximal deviation of station
time of any model compared to the average station time. A third option is to
minimize the sum of cycle time violations of all models in all stations. (Becker &
Scholl 2008, p.708)
The assembly line needs to be loaded so that all stations are always full and
subassembly stations make no exception. These must be scheduled by calcu-
lating backward from the time each subassembly will be needed in final assem-
bly. This creates a cascading linkage backward time from main line to sub-
assembly stations and their possible subassemblies. On the other hand it may
be possible to plan subassemblies without affecting the final assembly as long
as they are completed before the time they are needed. In order to secure that
subassemblies are available when needed the work must begin far enough in
advance. Software used in production scheduling must be capable of making
the calculations for subassemblies too. The controlling of preassemblies can be
compared to making a menu by a chef who needs to start cooking servings at
different times so that they are all served at the same time when needed. (Larco
et al. 2008, p.79, p.100)
When assembly is moved to be preassembled from the assembly line it is im-
portant to also think about make-or-buy decisions. For example outsourcing can
be the best alternative for the subassembly work. Pre-assembly can also be
used by totally opposite way by returning some tasks from pre-assembly to
main assembly line. If there is a low workload at any assembly line main station
it is possible to enrich he workload with additional work from pre-assembly or
from suppliers to the main line. (Baudin 2002, p.113)
2.4.4 Different routings for variable products
Routing is referred to be the sequence of steps required to assemble a single
product. The product is routed from the first assembly station to the second sta-

29
tion and further until the product is finished. Assembly charts are used to show
the sequence of these steps. The sequence of assembly may have several dif-
ferent routing alternatives and time standards are required in order to decide
which assembly sequence is the best. (Stephens & Mayers 2010, p.107)
One balancing method is to change the layout so that it enables assembly line
to adapt to variable standard times of products. This can be done by arranging
different routings or even a totally separate part of the assembly line for more
special product modifications. The main line would do all the standard work and
the alternative routing would operate on more customized versions. Alternative
routings will mitigate the fluctuation of standard times and help in production
planning if takt times of all workstations would constantly be more even regard-
ing workloads. Products that require extra steps are sent to alternative paths
and then they rejoin the main line later when customized options are assem-
bled. This could be compared to scheduling local trains that stop every station
and express trains that stop only in large cities (Larco et al. 2008, p.51).
The different routings can be arranged for example by duplication of work-
stations so that they work parallel side by side in an assembly line. In duplicate
stations the work does not usually start at the same time because of varieties in
processing times, random problems and repair times. In this case buffers are
needed in front of and behind of duplicated stations because it is extremely hard
to schedule the production so that all assembly areas would be full. With paral-
lel stations it is possible to decrease the unproductive portion of the planned
cycle time. (Becker & Scholl 2006, p.701)
The method is pretty much the same than in the pre-assembly alternative pre-
sented before but different routings come to in case when the assembly must
be done directly to the product when pre-assembly is not possible. Different
routings or new secondary assembly line may require lot of planning for layout
and scheduling. It can also be done with very simple decisions on how the work
is arranged in workstations. For example it is sometimes possible to arrange
more space inside a workstation so that more products are assembled at the
same time and enables a situation that a standard product can overtake more
complex one when the waiting time is minimized. If there is already a parallel
assembly in a workstation it can be decided that the other workplace only con-
centrates on more complex products and the other one on standard versions.
The risks related lacking parts or quality problems do not affect the assembly
line so much when there is more than one unit in-process in production.

30
2.4.5 Sequence planning to level out the workload
This chapter discusses production levelling, production smoothing, mixed-model
sequencing and other terms referring to production queue’s sequence planning
as one assembly line balancing method. Planning production order is in many
assembly line balancing theories presented as the only method to balance pro-
duction for the variable production or mixed-model assembly line, but the topics
often are then more often concentrated on high volume production. Assembly
line balancing and sequencing problems are closely interrelated. However,
probably due to the computational complexities involved, these two problems
are usually addressed independently of each other. (Fernandes & Groover
1995)
There are two basic objectives in model sequence planning when studied from
different perspectives. The first objective is to minimize work overloads or idle
time, which occur when there are fluctuations in station times. For this objective
the approach is mixed-model sequencing, which aims to avoid sequence de-
pended work overloads based on detailed scheduling. This approach studies
operation times, worker movements, station requirements and other operational
characteristics. The second objective is to level part usages in order to support
just-in-time objectives because of deviations in material requirements. The ap-
proach for this problem is level scheduling according to demands and material
needs following lean and just-in-time principles. Model sequences are planned
with such a manner that material usages are as smooth as possible. In this
study the focus is on the first objective, mixed-model sequencing, but some
techniques from just-in-time principles do also apply for smoothen capacity utili-
zation. (Boysen et al. 2009, p.350)
The first assembly line balancing method presented demonstrates that cycle
times need to be determined by observing average station times over all mod-
els. This is also labelled as a reduction to single-model problem. As a conse-
quence, the station times of some models are longer than the planned cycle
time, whereas those of others are shorter. Whenever multiple labour-intensive
models, follow each other in direct succession at a specific station, a work over-
load situation occurs. In such situations, workers are not able to finish the prior
products in time and the cycle time or the planned cycle time might be exceed-
ed. Line stoppage, utility workers, off-line repair or higher local production
speed at the station are examples of reactions to compensate the overload. A
more proactive way to avoid overload is to find a sequence of models which
balance the workload by altering high station times to less work intensive ones
at each station. Planning the production sequence for a short term product mix
is a way to minimize the overloads in workstations and better assembly line bal-

31
ance can be achieved. The amount of overloads by itself is also one measure of
efficiency of the assembly line balance. (Boysen et al. 2008, p.4; Boysen et al.
2011, p.4736)
As discussed in chapter 2.4.3 related to pre-assembly, horizontal balancing has
a great influence on sequence planning. The better the horizontal planning,
meaning less variance in mixed-model line station times, works the better re-
sults are possible from short term sequence planning. The objectives of mixed-
model sequence problems arise per shift, day or week with particular demand
and volume of different models. (Becker & Scholl 2006, p.707)
The next four different types of methods with different timeframes to level out
the workload are discussed for a built-to-order environment. The methods do
not exclude each other but are just used in different occasions in queue plan-
ning. The first queue planning method starts already before the orders and the
last is used already when the product is on assembly line. The sequence plan-
ning systems for different timeframes are:
1. The product is allocated with predetermined slot-based levelled sequence
2. The production sequence is levelled according to time of delivery
3. Short term daily production and sequence planning
4. Self-management of the next chosen product from the buffer
In the first timeframe the sequence decisions are already made before the actu-
al order is received. Products are scheduled according to predetermined pro-
duction sequence and received orders are allocated with a slot-based system to
the next available free slot. At the same time the predetermined slot-based sys-
tem defines the capacity and the resource constraints. The slots can be based
on a specific product model or the total cycle time of the product.
Production levelling is planning with the aim to get a balanced total workload,
volume and product mix for production. In lean manufacturing production level-
ling is known also with Japanese term “heijunka”. In levelled production prod-
ucts are not built according to the actual flow of the customer orders but it takes
the total volume of orders of a certain period and levels them out so that the
same mix is made each period. Achieving heijunka is fundamental to eliminating
unevenness (mura), which is, in turn, fundamental for eliminating overburden
(muri) and non-value adding activities (muda). When production levelling is
planned and executed effectively the assembly line will theoretically balance
itself after the planned period and resource calculations will thus become sim-
pler. Through this method, flexibility also increases for customers and demand
is smoothened for upstream processes for suppliers creating less inventory.
(Liker 2004, p.114-116)

32
The next timeframe for the sequence planning is set to after the orders are re-
ceived. The queue should be planned according to received orders for a given
time period. In variable production environment this system is called mixed-
model sequencing. The purpose is to find a sequence where work overload and
idle time is minimized. The basic idea is to allocate labour-intensive and more
simple products consecutively. In a mixed-model line this can simply be done
with a total cycle time or by taking into account all variations in all workstations.
This sequence planning is done for a certain time period and it aims to balance
the sequencing periods compared to each other. There is a vast number of pub-
lications for calculating the most effective way to sequence mixed-model as-
sembly lines but in some fields of business the product variety is simply too
large to allow reliable calculations. The only reliable estimation in this field is a
prognosis of single customized options which influences the most for assembly.
Following this prognosis or the determination of the option occurrences, a joint
precedence graph must be made to imply how it really affects the assembly line
workstations. This mixed-model sequencing method based on estimations of
option occurrences is not necessarily the most efficient but it is the most reliable
for very large varieties of products. (Boysen et al. 2008, p.5)
The third timeframe for queue planning is just before the production starts. The
input for this timeframe is the planned sequence, but in real-world assembly
systems there are always exceptions and restrictions compared to ideal se-
quence. This short term production planning takes available material, quality
problems and current production situation into account. The idea is to re-
schedule the sequence according restrictions with best possible way. For this
purpose a flexible scheduling programs are very advantageous.
The last chance to arrange the sequence is when the products are already in
production. The idea is that supervisors and employees would have self-
management to choose correct products from the buffer so that the workstation
is not overloaded with labour-intensive products for long time. The system is
applicable only if there are more than one product in buffer. This system re-
quires that standard times are visually available and employees would have
basic production planning knowledge.
There are many restrictions in using production levelling for the high-mix –low-
volume production system because it is so vulnerable for problems. The chang-
es in work queue because of lacking parts or quality problems must be easily
recalculated. Assembly should not be started if all parts are not been received
from suppliers and then the work queue must be changed. That will mix up the
well planned sequence and then queue levelling will not work as a balancing
method anymore.

33
In production levelling for mixed model assembly line it is not only the total cycle
time which needs to be concentrated but the whole mix. As presented in 2.2 the
total cycle time is not normally divided evenly to workstations but the custom-
ized options define the real situation workstation specifically. For example in
some cases the station time can be very low even though the total assembly
cycle time would indicate very work-intensive product. In next section in-process
inventory is discussed and that can be used together with production levelling
effectively because it reduces the need of accurate calculations. Together
mixed-model sequencing and buffers are an effective way to balance assembly
line and in creating flow in high-mix environment.
2.4.6 In-process inventory to avoid idle time
In unpaced and asynchronous assembly lines workpieces are always moved as
soon as the operations are completed at a station. After transference the station
starts to work with the next unit, unless the preceding station is unable to deliver
it. To minimize waiting times in asynchronous lines, buffers needs to be in-
stalled in-between stations, which can temporally store workpieces for in-
process inventory. Synchronous assembly line works with the same beat and
in-process inventories are needed only for exceptions and flexibility. (Boysen et
al. 2008, p.9)
Using in-process inventory in the assembly line is more like traditional mass
production thinking than lean, but in mixed-model assembly line balancing it is a
good way to smooth peaks in cycle times and it gives flexibility in case of prob-
lems. Buffers can also be used as a visual production controlling method. Buffer
places helps to visualize work-in-process workloads and identify where too
much capacity or manpower is available (Lane 2007, p.92). The inventory can
be used to maintain the targeted takt time when a process is incapable of
achieving the takt time rate (Hobbs 2011, p.232). There is also a trade- off be-
tween installation costs (productivity) and achievable throughput when installing
buffers, because the latter usually increases when more buffers are installed
(Boysen et al. 2008, p.9) Buffers naturally increase work in process level but at
the same time it ensures that all workstations have work to do and decreases
waiting times. In a highly variable production environment buffers can together
with production levelling reduce overloads and improve smooth material move-
ment. Naturally, the most important thing is to create a flow for production.
There are two restrictions related to buffers between workstations when they
are used as a balancing alternative for an unpaced mixed-model assembly line.
The first one is blocking, which occurs when the downstream buffer is full and
the station cannot move completed units forward. Another problem is starving,

34
which occurs when idle time is generated because upstream buffer is empty.
These problems can be solved by assigning more buffer places or by concen-
trating on more detailed production scheduling. (Merengo et al. 1999, p.2843)
In the lean environment it is important to define rules for the buffer places so
that no excess inventory and overproduction is generated. In lean manufactur-
ing this is normally controlled with kanban -systems which indicate the material
needs for products. In a mixed model assembly line another way to indicate the
needs is a constant work in process –system (CONWIP) which is based on
more queue sequence than the amount of certain parts or materials. In a
CONWIP -system the in-process inventory is controlled by the consumption by
a demand. The production of the next unit in queue is triggered only when the
next station has finished its work. The CONWIP systems have been found to
have superior performance especially in variable environments compared to
other systems with respect to the average work-in-process level, variability of
processing times. CONWIP has also been identified to be easier to control and
have a shorter lead time than kanban systems due to the better management of
customized work in-process products. (Pettersen & Segerstedt 2009, p.206)
2.4.7 Assignment of identical tasks to different stations
On multi-product or mixed-model assembly line the normal system to assign
tasks to different workstations is to examine precedence diagrams and product
structures. Normally there are common tasks between products that are always
performed in the same stations. However in case of optional features it is possi-
ble to seek the shortest-route formulation and assign tasks to different stations
in order to optimize current production balance. In this method identical tasks
are performed in different stations so that the assembly line balancing would be
done in a product specific way. The objective is to decrease the station time
variances in a high-mix assembly line. The method for this system is to use
combined precedence diagrams and the optional modules would be assigned to
the lowest loaded station based on production sequence. Another option is to
find the best possible task assignment solutions separately for all the different
models by using computational minimization of variances between station
times. (Erel & Gokcen 1999, p.195)
One suggestion is also to plan standard times based on time-slots by dividing
and combining different task times to fixed standard time for modules. For ex-
ample with fixed 10 minutes module times it is easier to assign tasks to different
stations and balance the line simultaneously. This would require lots of standard
time planning, possible layout changes and strict modularity from the products.
However, it is reminded (Boysen et al. 2008) that investments made for assign-
ing similar tasks to different stations can be considered an improved balance,

Assembly Line Balancing for Variable Production

Assembly Line Balancing for Variable Production

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