Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
PhD Thesis
1. C378etukansi.kesken.fm Page 1 Tuesday, December 21, 2010 3:43 PM
OULU 2011
C 378
C 378
U N I V E R S I T Y O F O U L U P. O. B . 7 5 0 0 F I - 9 0 0 1 4 U N I V E R S I T Y O F O U L U F I N L A N D
ACTA U N IIV E R S IIT AT IIS O U L U E N S IIS
U N V E R S T AT S O U L U E N S S
ACTA
A C TA U N I V E R S I TAT I S O U L U E N S I S
S E R I E S E D I T O R S
Dayou Yang
C
TECHNICA
TECHNICA
ASCIENTIAE RERUM NATURALIUM
OPTIMISATION OF PRODUCT
Dayou Yang
Professor Mikko Siponen
BHUMANIORA
University Lecturer Elise Kärkkäinen
CHANGE PROCESS AND
CTECHNICA
DEMAND-SUPPLY CHAIN IN
Professor Hannu Heusala HIGH TECH ENVIRONMENT
DMEDICA
Professor Olli Vuolteenaho
ESCIENTIAE RERUM SOCIALIUM
Senior Researcher Eila Estola
FSCRIPTA ACADEMICA
Information officer Tiina Pistokoski
GOECONOMICA
University Lecturer Seppo Eriksson
EDITOR IN CHIEF
Professor Olli Vuolteenaho
PUBLICATIONS EDITOR
Publications Editor Kirsti Nurkkala UNIVERSITY OF OULU,
DEPARTMENT OF MECHANICAL ENGINEERING;
DEPARTMENT OF INDUSTRIAL ENGINEERING AND MANAGEMENT
ISBN 978-951-42-9354-2 (Paperback)
ISBN 978-951-42-9355-9 (PDF)
ISSN 0355-3213 (Print)
ISSN 1796-2226 (Online)
2.
3. ACTA UNIVERSITATIS OULUENSIS
C Te c h n i c a 3 7 8
DAYOU YANG
OPTIMISATION OF PRODUCT
CHANGE PROCESS AND DEMAND-
SUPPLY CHAIN IN HIGH TECH
ENVIRONMENT
Academic dissertation to be presented, with the assent of
the Faculty of Technology of the University of Oulu, for
public defence in Auditorium IT115, Linnanmaa, on 28
January 2011, at 12 noon
U N I VE R S I T Y O F O U L U , O U L U 2 0 1 1
5. Yang, Dayou, Optimisation of product change process and demand-supply chain in
high tech environment
University of Oulu, Faculty of Technology, Department of Mechanical Engineering, P.O.Box
4200, FI-90014 University of Oulu, Finland; University of Oulu, Faculty of Technology,
Department of Industrial Engineering and Management, P.O.Box 4610, FI-90014 University of
Oulu, Finland
Acta Univ. Oul. C 378, 2011
Oulu, Finland
Abstract
Information and communications technology (ICT) companies face challenges in an unpredictable
business environment, where demand-supply forecasting is not accurate enough. How to
optimally manage product change process and demand-supply chain in this type of environment?
Companies face pressures to simultaneously be efficient, responsive and innovative, i.e. to
minimise costs, and shorten order delivery and product change periods.
This thesis included three action research cycles within a real demand-supply chain of a
significant international actor. Each action research cycle sought answers by going into one
extreme of minimising costs, diminishing order delivery period, or shortening product change
periods. In practice, these research cycles included the case company changing their business
accordingly for each of these cases. Conducting required changes in the case company were
economically significant trials.
The results of this doctoral dissertation provide tips for global high tech companies. Large
international companies typically have manufacturing sites in different parts of the world.
According to the results, mental shift from local optimisation to a global one is required for
efficient manufacturing operations.
Companies have traditionally considered their strategy as a choice between minimising costs,
quick delivery, and rapid product change. Also, companies have believed that one single strategy
is adequate and applicable to all of their products. According to this thesis, different products may
have a different strategy. This would allow companies to flexibly react to the needs of different
customer groups, business environments, and different competitors. In addition, strategy can be
changed relatively often, monthly, weekly, or even daily.
Based on the results of this doctoral thesis, companies must harmonise their product portfolio
globally, including all their sites. Once the same product version is at all sites, they can help each
other from components supply viewpoint. Consequently, product changes can be taken through
quicker.
Keywords: action research, agile, demand supply, innovativeness, lean, optimisation,
synchronization
6.
7. Yang, Dayou, Tuotemuutosprosessin optimointi ja kysyntä-tarjontaketju korkean
teknologian yrityksissä
Oulun yliopisto, Teknillinen tiedekunta, Konetekniikan osasto, PL 4200, 90014 Oulun yliopisto;
Oulun yliopisto, Teknillinen tiedekunta, Tuotantotalouden osasto, PL 4610, 90014 Oulun
yliopisto
Acta Univ. Oul. C 378, 2011
Oulu
Tiivistelmä
Informaatio- ja kommunikaatioalan yritykset kohtaavat haasteita toimiessaan vaikeasti ennustet-
tavassa liiketoimintaympäristössä, jossa tilaus-toimitusennusteet ovat epätarkkoja. Miten tällai-
sessa ympäristössä hallitaan optimaalisesti tuotemuutosprosessi ja tilaustoimitusketju? Yrityksil-
lä on paineita olla samanaikaisesti tehokkaita ja innovatiivisia: miten minimoida sekä kustan-
nuksia että lyhentää toimitus- ja tuotemuutosaikoja.
Tämä väitöskirja tehtiin toimintatutkimuksena merkittävän kansainvälisen yrityksen todelli-
sessa tilaus-toimitusketjussa. Toimintatutkimus eteni vaiheittain kokeilemalla kolmea eri ääri-
päätä minimoimalla 1) kustannuksia, 2) toimitusaikoja ja 3) tuotemuutosaikoja. Käytännössä
nämä ääripäät sisälsivät case-yrityksen liiketoiminnan muuttamista vastaavasti sisältäen talou-
dellisesti merkittäviä kokeiluja.
Tämän väitöskirjan tulokset tarjoavat käytännön esimerkkejä globaaleille korkeanteknologi-
an yrityksille. Suurilla kansainvälisillä yrityksillä on tyypillisesti valmistusyksiköitä eripuolilla
maailmaa. Tämän tutkimuksen tulosten mukaan yritykset tarvitsevat asennemuutoksen paikalli-
sesta optimoinnista globaaliin, jotta tuotanto toimisi tehokkaasti.
Perinteisesti yritykset ovat ymmärtäneet strategian tarkoittavan valinnan tekemistä kustan-
nusten minimoinnin, nopeiden toimitusaikojen tai nopeiden tuotemuutosten välillä. Yritykset
ovat myös uskoneet, että yksi yrityskohtainen strategia kattaa kaikki yrityksen tuotteet. Tämän
väitöskirjan tulosten mukaan yrityksen eri tuotteilla voi olla erilainen strategia. Tällainen ratkai-
su mahdollistaa nopean reagoinnin muutoksiin asiakasryhmien tarpeissa, liiketoimintaympäris-
tössä ja kilpailutilanteissa. Strategiaa voidaan myös muuttaa usein, kuukausittain, viikoittain tai
jopa päivittäin.
Tämän väitöskirjatutkimuksen tulosten mukaan, yritysten tulisi harmonisoida tuoteportfo-
lionsa globaalisti kattaen kaikki tuotantolaitokset. Silloin kun yrityksen kaikissa valmistusyksi-
köissä valmistetaan samaa tuoteversiota, yksiköt voivat auttaa toisiaan komponenttien hankin-
nassa. Tuotemuutokset voidaan tällöin toteuttaa nopeammin.
Asiasanat: innovatiivisuus, ketteryys, kysyntä, optimointi, synkronointi, tarjonta,
toimintatutkimus
8.
9. Acknowledgements
This dissertation is about a research initiated in a tough situation of high-tech
manufacturing back in 2002. It was economic hard time with lean strategy as a
must in the company where the researcher was employed. However, the company
had to struggle with inaccurate forecasts in their daily work making product
change management more challenging. Earlier planning, or even comprehensive
knowledge over JIT (Just-In-Time), was not enough due to big lead-time gaps in
demand-supply. Thus this learning journey was initiated to develop new solutions.
The research was conducted cycle-by-cycle, and the outcomes were gradually
implemented to IT over the years. During this process, many people provided
their valuable assistance.
I am very grateful to my supervising professors - Harri Haapasalo and Kauko
Lappalainen for their professional guidance through the whole research process.
Their strong commitment always inspired me to overcome any difficulties.
Constructive advice from Dr. Janne Härkönen, Dr. Pekka Belt and Dr. Matti
Möttönen of the University of Oulu were especially helpful. They helped to
broaden my way of thinking about my research and the dissertation and helped
me to see things from multiple viewpoints. Also I wish to thank Professor Juha-
Matti Lehtonen being so supportive and patient when I was struggling while
aiming to a breakthrough. Deep in my heart, great thanks belong to Mr. Ari
Kurikka who has remarkably coached me from the very beginning until all the
action research cycles were finished. The insight of focusing on the whole
demand-supply network kept the research aiming for a win-win solution to all
network parties. Special acknowledgement goes to Mr. Arto Tolonen for many of
his valuable advices. Especially with his “Design for Excellence” contribution
implemented in the company, it made it easier for this research to operate with
less product variants. I want to present my sincere thanks to Mr. Jukka Kukkonen,
Mr. Ville Jokelainen, Mr. Kaj Sundberg and Mr. Jussi Parviainen for supporting
me when conducting this research besides my daily work. I very much appreciate
the help and interest of my other colleagues for their insightful inputs. My warm
thanks belong to AAC Global Oyj and other native English-speaking friends for
their language assistance. I also need to acknowledge the financial aid from
Finnish Foundation for Economic Education.
In addition, I would like to thank the pre-examiners of this study - Professor
Petri Helo and Dr. Lasse Pesonen for their valuable comments and
recommendations.
7
10. Finally, my deepest gratitude belongs to my wife Weilin and my children
Yuchen & Tina. I value their support and care to tolerate my mental absence due
to this work. Their patience makes my learning journey possible and rewardable.
Oulu, December 2010 Dayou Yang
8
11. Abbreviations and key terminology
3C Capacity, Commonality, Consumption (management system)
3D CE Three Dimensional Concurrent Engineering
ABM Agent-Based Manufacturing
AR Action Research
ATO Assemble-To-Order
BAM Business Activity Monitoring
BOM Bill Of Material
BPR Business Process Re-engineering
BTO Build-To-Order
CAD Computer Aided Design
CIB Change Implementation Board
CIM Computer Integrated Manufacturing
CLM Council of Logistics Management
CLCP Closed Loop Change Process
CM Configuration Management
CMMI Capability Maturity Model Integration
CPM Corporate Performance Management
CRB Change Review Board
CRM Customer Relationship Management
DA Delivery Accuracy
DNA Deoxyribonucleic Acid
ECN Enterprise Change Notice
ECR Enterprise Change Request
EMS Electronics Manufacturing Services
ESP Equalised and Synchronised Production
ERP Enterprise Resource Planning
EVDB Events and Venues Database
FAT Focus, Architecture, and Technology
FMS Flexible Manufacturing System
GIT Goods In Transit
i2 A management application supplier
ICH Inventory Collaboration Hub
IQ Intelligence Quotient
IT Information Technology
JIT Just-In-Time
9
12. MAS Multi-Agent System
MICE Multimedia, information, communications, and electronics
MRP Material Requirements Planning
MTO Make-To-Order
MTS Make-To-Stock
NMS Network Managed Supply
NPI New Product Introduction
OEM Original Equipment Manufacturer
OPP Order Penetration Point
OPT Optimised Production Technology
OSS Operation and Support Subsystem
PASSI Process for Agent Societies Specification and Implementation
PC Personal Computer
PMBOK Project Management Body of Knowledge
PS Physical Stock
PTK PASSI Tool Kit
PTO Pack-To-Order
PWB Printed Wiring Board
R&D Research and Development
RIA Rich Internet Application
RDBMS Relational Database Management System
ROI Return on Investment
SAP A management application supplier
SCM Supply Chain Management
SCOR Supply-Chain Operations Reference
SOA Service Oriented Architecture
STO Ship-To-Order
TOC Theory of Constraints
TPI Trading Partner Integration
TQM Total Quality Management
TTC Time to Customer
TTM Time to Market
UML Unified Modelling Language
VMI Vender Managed Inventory
VOP Value Offering Point
WIP Work In Process
10
13. Please note that following list describes the terminology for the purpose of this
dissertation rather than giving official definitions.
Minimise costs (Lean) = Creating value with as little work and waste as possible.
Quick delivery (Agility) = Responsiveness in demand fulfilment
Fast product change (Innovativeness) = Making product changes as quick as
possible
Zero-series = series after proto type in product development, before actual
volume production
Component equalisation = In a large organisation there are different persons
responsible for buying different components, causing differences in the levels of
different components as buyers buy in different pace and their activities are not
adequately coordinated. In a situation with too many components, the component
you have least determines the equalised level. If you have any components more
than the equalised level, those can be considered as waste. The difference
between the equalised level and the original forecasted level can be considered as
tolerance margin increasing agility. However, if the company prefers lean over
agility, this type of tolerance should be avoided.
Time based optimisation (Synchronisation) = In modern business, when new
product versions are introduced, there are a large number of tasks that must be
conducted. As time has become increasingly important aspect for business
success, time-based coordination of activities is important for total optimisation.
In this dissertation this coordination is also called synchronisation. Also, the
handling of component supply change, including component equalisation on time
basis, must be included in this synchronisation.
Liability = Company has contractual obligations for a certain period of a forecast
before they can stop buying certain components from a supplier. From a
supplier’s viewpoint, this gives a level of security for a certain period of time,
such as two months, allowing it to cut costs and adjust to changes. This liability
only applies to buyer company specific components.
11
14. Dynamic cut-off window = Buyer company has a natural goal of minimising the
liability of the amount of components it is obliged to buy. In order to optimise the
operations of buyer-seller cooperation, the information on critical issues must be
transferred as early as possible, for instance updating forecasts on a weekly basis.
This way of dynamically informing a supplier allows it to have time to react
accordingly. This in turn makes it possible to reduce the liability of the buyer.
Fixed cut-off window = Before starting a zero-series, product new version
changeover date is selected and fixed. This type of fixed cut-off window enables
suppliers to deliver the existing order plus liability. No further orders are placed
for the old material.
12
15. Contents
Abstract
Tiivistelmä
Acknowledgements 7
Abbreviations and key terminology 9
Contents 13
Introduction 15
1.1 Research background & motivation ........................................................ 15
1.2 Objectives and scope ............................................................................... 18
1.3 Research process ..................................................................................... 19
1.3.1 Action research ............................................................................. 19
1.3.2 Research context........................................................................... 20
1.3.3 Practical realisation ...................................................................... 22
1.4 Structure of the thesis .............................................................................. 23
2 Literature review 25
2.1 Manufacturing philosophies .................................................................... 25
2.1.1 Lean manufacturing and JIT philosophy ...................................... 25
2.1.2 ESP concept beyond JIT philosophy ............................................ 26
2.1.3 Agile manufacturing and leagility concepts ................................. 27
2.1.4 Manufacturing strategies and product life cycle ........................... 28
2.1.5 The innovator’s strategy ............................................................... 29
2.1.6 Summary of manufacturing philosophies ..................................... 30
2.2 Developing demand-supply network ...................................................... 32
2.2.1 Value oriented development for demand-supply network ............ 32
2.2.2 Manufacturing strategies affect demand-supply network ............. 36
2.2.3 The role of collaboration in demand-supply ................................. 40
2.2.4 Measuring demand-supply performance ...................................... 44
2.2.5 Purchasing automation challenge in product life cycle ................ 46
2.2.6 Optimisation of demand-supply with thinking of BI
automation .................................................................................... 48
2.3 Product change management................................................................... 52
2.4 Special characteristics of high-tech industries ........................................ 54
2.4.1 Challenges in forecasting ............................................................. 54
2.4.2 Telecom supply chain of case company ....................................... 55
2.4.3 Case Ericsson (analysed in 2002–2003) ....................................... 56
13
16. 2.4.4 Case Dell Corporation/Lucent Technologies (analysed in
2002–2003) ................................................................................... 58
2.4.5 Case Huawei Technologies (the new competition reality)............ 60
2.4.6 Other studies oriented by value differentiation or unique
advantage ...................................................................................... 61
2.5 Theory synthesis...................................................................................... 69
3 Results of the three action research cycles 73
3.1 Research Cycle 1 – minimising costs ...................................................... 75
3.1.1 Pre-Step ........................................................................................ 76
3.1.2 Diagnosis ...................................................................................... 77
3.1.3 Planning ........................................................................................ 77
3.1.4 Taking action ................................................................................ 80
3.1.5 Evaluation ..................................................................................... 81
3.2 Research Cycle 2 - shortening order delivery time ................................. 84
3.2.1 Pre-Step ........................................................................................ 84
3.2.2 Diagnosis ...................................................................................... 85
3.2.3 Planning ........................................................................................ 86
3.2.4 Taking action ................................................................................ 88
3.2.5 Evaluation ..................................................................................... 89
3.3 Research Cycle 3 - shortening product change time ............................... 91
3.3.1 Pre-Step ........................................................................................ 93
3.3.2 Diagnosis ...................................................................................... 94
3.3.3 Planning ........................................................................................ 94
3.3.4 Taking action ................................................................................ 95
3.3.5 Evaluation ..................................................................................... 96
4 Discussion 99
4.1 Answering research questions ................................................................. 99
4.1.1 Research question 1 ...................................................................... 99
4.1.2 Research question 2 .................................................................... 100
4.1.3 Research question 3 .................................................................... 102
4.2 Managerial implications ........................................................................ 103
4.3 Scientific implications ........................................................................... 105
4.4 Reliability and validity .......................................................................... 107
4.5 Research contribution & discussion ...................................................... 110
4.6 Future research ...................................................................................... 112
5 Summary 115
References 117
14
17. Introduction
1.1 Research background & motivation
Industrial globalisation has greatly changed high-tech companies while they have
created significant operations in multiple countries. Because poor visibility and
massive uncertainty are part of the operational nature, new challenges arise
continuously for companies who want to internationalise their demand-supply
network. The struggle to survive has become an integral part of each giant
company’s way of life (Hill, 2000). As the operations become more dynamic
(Wazed et al. 2009), the problems of the famous JIT (Just-In-Time) concept (Voss,
1987) are increasingly reported with the facts, even in Japan: zero-inventory
management is just a fiction (Hann et al., 1999), and JIT is not necessarily useful
for part suppliers (Naruse, 2003). Even Toyota Motor Corporation as a model of
operational efficiency within the auto industry, it also got its first annual operating
loss in 2009 after 70 years of enjoying healthy profits. Not as a symbol of
operational excellence, Toyota recall crisis of 2010 has prompted much criticism
in media circles, national business forums and automotive trade publications
(Piotrowski and Guyette 2010). Consequently, it is now time for new thinking.
For example, it needs to go against the mainstream and take current strategy to a
more extreme version of itself, before scaling back just a little bit (Schmitt 2007).
The research was initiated in 2002 during last economic downtime by
solution-finding for product change management in a famous international
company, the case company of this research, who operates as one the world’s
largest telecommunications infrastructure suppliers, and which continuously
suffers from inaccurate forecasting and dynamic demand in its innovative
manufacturing. As the nature of mobile infrastructure industry (Collin et al., 2005;
Heikkilä 2002), the system vendors have to be able to quickly respond to short-
term changes in demand. On the one hand, they are forced to have an in-built
ability to constantly adapt their supply chains to rapid and unexpected changes in
the markets or technologies (Raisinghani et al. 2002; Webster 2002). On the other
hand, the vendors are also expected to be fast and flexible while delivering
customised products and services with a high standard of delivery accuracy
(Alfnes and Strandhagen 2000; Småros et al. 2003; Knowles et al. 2005).
In the case company, the old way of doing things was to make a perfect
production plan based on a perfect forecast, at some point this did not work
15
18. anymore. In reality there was always some components missing, and production
stopped. As a consequence, scrapping costs became very high. There were
different product versions in different sites, with up to one year’s difference
resulting in sites being unable to help each other. In addition, more R&D people
were required to support the supply-chain and product changes became very slow,
almost out of control.
Figure 1 presents an example of the problem situation, relating to demand
fulfilment, during a one-year period in the case company. It shows how the
forecasts of one or two months were so different from the true demand fulfilled.
The example records a hopeless situation, in which such uncertainties make
product innovation through engineering changes as well as normal delivery of
customer order fulfilment extremely problematic. In other words, and to state the
problem for academic purpose, the intangible information flow in demand-supply
network cannot ensure physical product flow just-in-time at each step of the
manufacturing operation. Due to the bullwhip effect (Lee et al., 1997; Lee, 2002)
in material forecast and product delivery, it is even more frustrating when
utilising traditional purchase orders or long distance transportation. The tough
choice of a trade-off (such as inventory increase, change slow-down, delivery
delay, lost sales) has to be made due to such lead-time gaps in global operation
(Shahbazpour and Seidel 2006; Bozarth et al. 2009). It can be even worse when
product changes are included as extra uncertainties in this unsynchronised status
(Salmi and Holmström 2004).
Fig. 1. Challenge with monthly forecast and true demand.
16
19. In innovative businesses, the changes occur for most of a product’s life with great
impact to whole demand-supply network (Aitken et al. 2003; Dreyer et al. 2007).
It is unique to utilise the details about cases of product change management
constantly in the research of manufacturing operation, which was not seen in
previous attempts by others. It can include more factors than those studies only
dealing with product development (Knight, 2003; Guess, 2002) and demand-
supply operation (Bengtsson, 2002; Christopher and Peck 2004) alone, or mainly
at a conceptual and simulation-oriented level (Subramoniam et al. 2008; Falasca
and Zobel 2008; Koh and Gunasekaran 2006; Zhou 2006; Kemppainen and
Vepsäläinen 2004; Saab and Correa 2004). Under a complex business
environment as in Figure 2, the research was based on a simple clue from product
change implementation. It is then expected to equalise the amount of all material
in the whole supply operation at anytime and anywhere.
Fig. 2. Business operational environment of the research.
17
20. In the case company, there were simultaneous pressures to minimise costs,
shorten the product change period and quicken order delivery processes. In
addition, the case company had an aim to minimise scrapping costs in all
situations.
1.2 Objectives and scope
The research problem arises from the case company’s challenges in an
unpredictable business environment, where demand-supply forecasting is not
accurate enough. How to optimally manage product change process and demand-
supply chain in this type of environment? Companies phase pressures to
simultaneously be efficient, responsive and innovative, i.e. to minimise costs, and
shorten order delivery and product change periods. The research problem of this
dissertation is formulated:
How should companies optimise the product change process strategy in a
situation where there are simultaneous and variable pressures to be lean,
agile and innovative.
This research problem is addressed by focusing on product change process and
demand-supply chain optimisation of large global ICT companies operating in
business-to-business environment.
First, literature was reviewed to gain understanding on lean philosophy,
agility, and innovativeness and consequently to find potential solutions for the
research problem.
In order to obtain information for deeper analyses and conclusions, the
following research question were formulated.
RQ1 What are the effects for the product change process when costs are
minimised (Cycle 1)?
RQ2 What are the effects for the product change process when order delivery
period is minimised (Cycle 2)?
RQ3 What are the effects for the product change process when product
change time is minimised (Cycle 3)?
Action research method was utilised in the case company to find answers to these
above mentioned research questions. Each action research cycle, representing a
separate trial, seeks answers for one research question by going into one extreme
18
21. of minimising costs, diminishing order delivery time, or shortening product
change periods.
1.3 Research process
The aim of this study was to conduct practical analyses on the effects of changes
in essential parameters, namely inventory level, order delivery period, and
product change time. The effects were studied for a real demand-supply chain of a
significant international actor. Secondly, based on these analyses, this study
attempted to find new means of dealing with complex issues in the described
environment.
1.3.1 Action research
According to O’Brien (1998) action research can be used in practical situations
where the primary focus is on solving real problems. In addition, the researcher
was employed by a company to whom the studied aspects were of great
importance. Action research was chosen as a research method as it enables
combining research and ordinary business work within the studied organisation.
Action research is concerned with the resolution of organisational issues,
such as the implications of change together with those who experience the issues
directly. In action research the practitioners are involved in the research, and there
is a collaborative partnership between practitioners and researchers. In simple
terms, the researcher is a part of the research subject. Often action research is an
iterative process, often depicted as a spiral, of diagnosing, planning, taking
actions and evaluating. (Saunders et al. 2007).
Action Research is the process of systematically collecting research data
about an ongoing system relative to some objective, goal, or need of that system;
feeding these data back into the system; taking actions by altering selected
variables within the system based on the data and on the hypothesis; and
evaluating the results of actions by collecting more data (French et al., 1973).
Action research enables simultaneous utilisation of different research
methods and techniques (O’Brien 1998). According to Coughlan (2002) action
research requires that the researcher enters the culture, understands the common
values, and uses its language. This research method was chosen, even though
action research does not meet the verification criteria of positivitism, meaning
objective study as in natural sciences (Susman and Evered, 1978; Saunders 2007).
19
22. 1.3.2 Research context
Selected case company is a significant global actor in the ICT system business.
The researcher was employed by the company, thus having a good access and the
research was related to his everyday work. The global demand-supply chain of
the case company is studied in this thesis from the perspective of product change
process.
The research can be described to simultaneously include aspects of
worldwide business impact, rapid innovative pace, and high volume in operation.
There are many engineering changes during a product’s lifetime without a period
when new and old versions overlap as execution principle. Component changes in
products often happen at any time adding extra complexity for manufacturing
besides original demand uncertainty. Product versions were different more than
one year at some manufacturing sites before the research was launched.
The component logistics, as in the electronics industry in general, is
extremely complex due to a vast number of required components with long
production or delivery lead-times. For example, the lead-times may differ by days,
weeks (such as PWB and own specific integrated circuits), or even months due to
sea transportation (such as the cabinet). This causes bottlenecks or big inventories
in the supply network due to those time variances and real demand often not
matching with earlier forecasts. The case company had to combine push-based
supply chain and pull-based demand chain together as a mix to synchronise
production and delivery of all product parts with big lead-time gaps. Pull
principle was applied at internal steps of the production, as well as the delivery
end. Push principle had to apply for the supply end and keep the inventories to
absorb the impact of inaccurate forecast. Demand-supply network had to thus
have enough tolerance to avoid undesirable conditions, such as production stop
due to lack of key components.
Below list describes the challenges faced by the case company:
1. Both strategies of lean or agile thinking were not good enough as there were
some obvious drawbacks. For example, production lead time was at a level of
counting hours or days, which was not a critical step if comparing to months
or weeks for material supply. The wish of zero inventory or fast response is
hard to achieve constantly in dynamic demand situation. With whole demand-
supply network in consideration, not just the case company itself, lead time
gaps could not be solved by lean or agile principles alone. It was the
20
23. playground reality when product changes were to be also added into the
complexity.
2. Production can be described as a multiproduct / multistage stochastic pull
system (Askin and Krishnan 2009). Pull principle was applied from product
delivery till production start, in order to balance the pace and the flow of
manufacturing operations. When the gap of material supply occurred, such a
balance would be destroyed in a fire-fighting manner to take time for its
recovery. As an example, principles of popular theories were all checked but
with the product flow in FIFO (First-In-First-Out) mode at each step of
manufacturing, meant that not a same product was initiated, moved and
delivered in the operation to fulfil the demand at customer end. Observing in
various ways, the effects of different theories could be seen “virtually”, e.g.
MTO (Make-To-Order), ATO (Assemble-To-Order), DTO (Deliver-To-Order),
and even MTS (Make-To-Stock).
3. The main difficulty related to material supply and its liability for key
components due to long lead times. It could not be avoided and was a reality
for the case company if lead times were not possible to be shortened. For
example, new and old material in product change should be controlled well in
such a synchronisation. Especially, old components with lead time as weeks
could cause the liability as the amount for months to consume. Otherwise, it
could result in enormous scraping costs. It was the limitation to product
change and normal operations lean effect in mind. The liability was invisible
in MRP systems because of inaccurate forecast in the past, which was seldom
to be studied to reduce its effect.
The bottom-line was to deliver products to customers’ requirements (especially
having the changes of delivery amount or product configuration) at a high speed,
without means to develop efficient forecasting processes to manage demand
uncertainty. Whenever the volume of pull at delivery side was larger than the
amount of push at supply side, production had to be stopped due to missing
components. The case company had to find an alternative way to survive better in
the competition as everyone in the industry suffered by those same challenges.
In addition, multiple tiers of many companies were involved in the demand-
supply chain with international manufacturing operation. Faster transfer of
demand information or a more reactive planning was not enough to save
manufacturing companies as a physical process is inflexible in responding to
frequent plan changes in normal operation. When product changes added on this,
21
24. demand-supply planning practices became even more fragmented and frustrated.
There were no existing solutions available, academic or industrial, at the time.
1.3.3 Practical realisation
The research was mainly realised during the period of 2003–2006. The research
included three action research cycles. Each action research cycle sought answers
by going into one extreme of minimising costs, diminishing order delivery period,
or shortening product change periods. In practice, these research cycles included
the case company changing their business accordingly for each of these cases.
Conducting required changes in the case company were economically significant
trials. Figure 3 describes the research process.
Fig. 3. The research process.
Research Cycle 1 included the case company aiming all of its actions to
minimising costs. The case company executed a strategy of cost effectiveness.
Minimising inventory and scrapping costs required swift component control in the
whole demand-supply chain.
In research Cycle 2 the case company aimed at diminishing order delivery
period. In this trial, the case company aimed at strong concurrency in engineering
to get order delivery period as short as possible.
Research Cycle 3 concentrated on shortening product change period. The
case company executed a strategy of innovativeness making product changes as
fast as possible. The trial clarified whether a ready-product inventory could be
used to speed up product change.
During research cycles, every change case was recorded using change notes
(CN). Change notes compare the old and the new product versions, indicating all
changes in used components. CN also indicated the expectation when the changes
22
25. will be conducted. CN was common for all sites enabling to tell which site is
influenced.
Site specific implementation reports were utilised to record changes, the
implementation time and scrapping costs. Implementation report described all the
results from different sites. Both, implementation reports and change notes were
stored into a database.
There were over one hundred product change cases available within the
company at the time of research. The researcher selected three cases out of all
product changes, one for each cycle. The cases were important for business and
there was a significant change in the product.
Process improvements were made based on the three selected product change
cases individually. After the process improvements, it was checked whether the
targets set for that particular cycle was reached or not.
The researcher worked as the project manager for all the studied product
change cases. He was responsible for product change implementations, including
planning & informing all the sites, and cooperation between these sites, collecting
results, analysing and making conclusions.
1.4 Structure of the thesis
Chapter 1 describes the background information of this research straightforwardly
by using a true problem from industrial practices. The goal is to survive better
than others in the industry under inaccurate forecast. Because modern
manufacturing in global scale is more sophisticated than ever, it is essential to
define the scope and the limitation of this research precisely. It is aiming to be
beyond lean or agile manufacturing, as well as any improved versions currently in
use. The research approach is selected briefly from reviewing different
methodologies in order to obtain the advantages of the action research method.
This method enables developing modular solutions piece by piece in an
innovative way.
In Chapter 2, the literature review is conducted to collect applicable elements
from existing management science for further development. They are mainly
from the fields of manufacturing philosophies, operational performance of
demand-supply, product change management, and industrial case study.
The empirical research is stated in Chapter 3, and the results accomplished in
3 cycles of action research are presented. The key thoughts of each research cycle
are verified in order to ensure the research questions studied by sufficient details.
23
26. In Chapter 4, research questions are answered to summarise the thoughts on
flexible optimisation rather than choosing only one option and being stuck in the
middle. The key is applying multi-strategies in business environment as a
multidimensional playground. The validation and reliability of the research are
checked. The implications of research with its constructive contributions are
discussed for practical and academic evaluation. After summarising new
contributions of the research, the recommendations for future development are
also presented in order to continue the learning journey further for great success.
24
27. 2 Literature review
2.1 Manufacturing philosophies
Different manufacturing philosophies include, lean thinking, JIT (Just-In-Time),
agile manufacturing, and their derivates.
2.1.1 Lean manufacturing and JIT philosophy
Lean manufacturing, as practiced in the Toyota production system, was a
revolutionary change of just-in-time (JIT) philosophy to mass production
practices in the automotive industry (Haan et al., 1999). The conceptual model
can be like a continuously moving conveyor belt from the beginning of
production to the delivery of finished products. It aimed to provide cost-effective
production as its delivery of only the necessary quantity of parts at the right
quality, at the right time and place, while using a minimum amount of facilities,
equipment, materials and human resources. A time line from 1930 to 2006 about
its development within Toyota to form an overview of JIT can be found in
Holweg (2006).
However, the problems have been widely reported more and more as the
disadvantages of JIT in the dynamic business of global manufacturing nowadays:
– Limited to repetitive manufacturing
– Requires stable production level
– Does not allow much flexibility in the products produced
Seeking for the improvements, one example is the most efficient type of JIT
operation – Synchronous Manufacturing (Umble et al., 1996; Srikanth et al., 1997;
Doran, 2002), which can be a direction towards new JIT to solve the above
drawbacks. Synchronous manufacturing embodies many concepts related to
focusing and synchronising production control around bottleneck resources
(Frazier et al., 2000). Other common names for these concepts are the theory of
constraints (or simply TOC) and Drum-Buffer-Rope, which was introduced in
1984 by Eliyahu Goldratt in The Goal (Walker, 2002).
The Theory of Constraints (TOC) is an overall management philosophy that
aims to continually achieve more of the goal of a system. The key is to improve
schedule attainment performance and reduce inventories, as well as lead times
25
28. (Frazier et al., 2000). Drum-Buffer-Rope is a manufacturing execution
methodology, named for its three components.
– The drum is the physical constraint of the plant: the work centre or machine
or operation that limits the ability of the entire system to produce more.
– The buffer protects the drum, so that it always has work flowing to it. Buffers
in DBR have time as their unit of measure, rather than quantity of material.
– The rope is the work release mechanism for the plant. Pulling work into the
system earlier than a buffer time guarantees high work-in-process and slows
down the entire system.
It was also reported Drum-Buffer-Rope as the synchronisation for agility purpose
(Walker, 2002). This can support optimisation, possibly for both lean and agile
manufacturing as two different balancing points for the synchronisation.
However, few companies can keep the focus on bottlenecks (as they are hard
to identify or too often keep changing) to plan and control production. It cannot
become a popular way due to such a limitation from the Theory of Constraints
(TOC) as the base of synchronous manufacturing. In fact, the synchronisation
should not be related only to the constraints – it is more reasonable to act above
the business bottom-line if the tolerance is needed as a must from the view of
synchronisation.
2.1.2 ESP concept beyond JIT philosophy
In high-mix manufacturing, a new concept of Equalised and Synchronised
Production (ESP) has been researched by Toshiki Naruse for a revolution beyond
the Japanese Just-In-Time (JIT) system (Naruse, 2003).
According to Naruse (2003), the new system of ESP has the following
features in the development:
– ESP original concept one: Production guard strictly to customer needs is
inefficient.
– Hint: Need product inventory to separate production schedule from direct
link to the buyer’s orders.
– ESP original concept two: To fulfil the production division’s mission, daily
production output and production sequences must be stabilised, with
production output equalised among the various item numbers.
26
29. – The production Division’s mission:
– To maximise production efficiency by making and maintaining
improvements toward that end.
– To minimise inventory by working toward the goal of zero inventory.
For the JIT concept, the supplier or its warehouse must physically locate its plants
either within the manufacturer’s site or nearby. If located far away, it is hard for
them to make synchronisation well enough to meet the requirements of demand-
supply (specific volumes and delivery deadlines for specific product items).
However, Naruse (2003) claimed the ESP approach is the best way for suppliers
in various industries.
As a feature or a limitation from view of Naruse (2003), the system of ESP is
more for a parts supplier to deliver products made on its production lines to
multiple buyers / locations. JIT is more for a company to purchase material from
a parts supplier and assemble them to finished products, or a parts supplier to
built dedicated production lines synchronised with the production of
corresponding buyers. The ESP production system basically uses the periodic
reordering of variable amounts method. Both production and purchasing can use
the multiples of these equalised units. It also needs to ensure the supplier
implements synchronisation with the buyer’s delivery deadline. Shortening lead
time, using smaller lots and raising in-house production efficiency are all key
activities under ESP. Comparing with JIT of 100 percent response to orders from
customers, ESP emphasises maximising in-house production efficiency and
minimising inventory as its focus.
2.1.3 Agile manufacturing and leagility concepts
Because of the complexity of today’s supply chains, another direction of
operational improvements leading to agile manufacturing has been discussed
widely (more radical than the above lean-alternatives of synchronous
manufacturing or ESP). Other names include responsive manufacturing and
supply chain flexibility. The 1990s is associated with two important
considerations of agility and supply chain in a history review by Sharifi et al.
(2006). A summary of the literature on supply chain flexibility can be found from
Stevenson et al. (2007). There is also a list of the contributors relating to
flexibility / responsiveness / agility in Reichart et al. (2007).
27
30. Agile manufacturing is a vision of manufacturing that is a natural
development from the original concept of lean manufacturing (Gunasekaran,
1999). Yusuf et al. (1999) indicates the main driving force behind agility is
change. It is recognised as a necessary condition for competitiveness. The
comparison of lean supply with agile supply can be seen in the following Table 1
(Mason-Jones et al., 2000):
Table 1. The comparison of lean supply with agile supply.
Distinguishing attributes Lean supply Agile supply
Typical products Commodities Fashion goods
Marketplace demand Predictable Volatile
Product variety Low High
Product life cycle Long Short
Customer drivers Cost Availability
Profit margin Low High
Dominant costs Physical costs Marketability costs
Stockout penalties Long-term contractual Immediate and volatile
Purchasing policy Buy goods Assign capacity
Information enrichment Highly desirable Obligatory
Forecasting mechanism Algorithmic Consultative
However, it is very rare to see benchmark cases from famous companies for agile
supply operation as well as IT applications (Helo et al., 2006). More and more,
researchers are adjusting the concept backwards and forwards, using with a new
word, “leagility” – better to keep efficiency and flexibility always together. It is a
more balanced thinking to compare or combine both factors properly in business.
According to Mason-Jones et al. (2000) leagility is the combination of the
lean and agile paradigm within a total supply chain strategy by positioning the
decoupling point so as to best suit the need to respond to volatile demand.
2.1.4 Manufacturing strategies and product life cycle
Scholarly research in the manufacturing strategy field has moved its focus more
and more to the total impact on product life cycle, as well as to the trend to whole
supply chain in a global scale (Aitken et al., 2003). Aitken (2003) identified the
operational differences of demand-supply network needed in each phase of
product life cycle (PLC) as an interesting example of those multiple choices at
28
31. strategy level. The strategic effect from a higher level can provide a larger
tolerance to supply operation.
Holmström et al. (2006) reported external collaboration initiatives such as
Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and
Replenishment (CPFR) not being sufficient on their own to produce improved
efficiency and responsiveness. Firms need to actively co-ordinate internal
collaborative practices between functions to benefit from their development
projects with customers and suppliers.
As the view of Hilletofth et al. (2010), it has been always a big challenge
how to bring new product to the market faster as a competitive advantage, which
remains to be an essential need in high-tech industries discussed. In markets
where short product life cycles are the norm, delays in bringing products to the
market can have detrimental consequences to sales and profit. To remain
competitive in these environments, companies need to produce innovative, high
quality, highly value-added products and services and bring them quickly and
effectively to the market.
Hilletofth et al. (2010) emphasise two major issues need to be addressed:
– The need to develop innovative, value-adding products
– The necessity of bringing them quickly to the market.
2.1.5 The innovator’s strategy
With the additional interest of radical innovation in industries, a further review
was conducted of the innovator’s strategy (Christensen, 2003) about an
extraordinary way of competing by disruption in business, as well as its great
impact especially on the manufacturing operation. There are two kinds of
industrial innovation: Sustaining or disruptive innovation.
A sustaining innovation targets satisfying highly demanding customers by
incremental improvements in products with better performance, rather than what
was previously available. A disruptive innovation model shapes the strategies for
those new growth builders to win the fights.
To create a new value network on the third axis is called new-market
disruptions. According to Christensen (2003), it brings an opportunity for the
company to satisfy the customer well enough by squeezing the bubble out of
disruptive innovation. The innovation is thus leveraged by the value as business
driver focused clearly on the customers.
29
32. With its big impact, disruptive innovation can act as a force also in
manufacturing, for example, going to market as soon as possible to take more
risks than in normal time. This is the reason to use the innovation in
manufacturing strategies along with product life cycle changes as a new thinking,
which actually also happened in one of the cases in the action research.
2.1.6 Summary of manufacturing philosophies
This thesis utilises the following concepts from earlier research as theoretical
foundation:
1. New JIT to adopt postponement for a leaner efficiency as synchronous
manufacturing
Originally JIT was oriented for a repetitive manufacturing environment.
Synchronous manufacturing was developed for low-volume/high-mix
production. Concepts related to JIT operational strategy include lean and
postponement principles together with flexibility in the manufacturing
process. (Cusumano, 1992; Gunasekaran, 1999; Haan et al., 1999; Frazier et
al., 2000; Vokurka et al., 2000; Amasaka, 2002; Coronado M. et al., 2002;
Doran, 2002; Papadopoulou et al., 2005; Bhasin et al., 2006; Graman et al.,
2006; Holweg, 2006; Ruffa, 2008).
2. Agile manufacturing to achieve flexible and responsive operation
The concepts related to agile manufacturing are claimed to be the next steps
after the lean philosophy in production management evolution. Their focus is
to respond to customer needs and market changes faster while still controlling
costs and quality. These agile concepts are suitable for product-based
industries with unstable markets and volatile demand, as well as products
with short life cycles. (Brennan et al., 1999; Gunasekaran, 1999; Yusuf et al.,
1999; Rigby et al., 2000; Hoek et al., 2001; Little et al., 2001; Prater et al.,
2001; Welker et al., 2005; Sharifi et al., 2006; Swafford et al., 2006;
Reichhart et al., 2007; Stevenson et al., 2007).
3. The leagility to combine lean and agile characteristics
The definition of leagility, i.e. combining leanness and agility, was originally
developed to describe manufacturing supply chains. The basic idea behind
leagility is the existence of a decoupling point, which separates the lean
30
33. processes from the agile processes in the supply chain. Lean processes are
seen to be on the upstream side of the decoupling point, and agile processes
on downstream. A similar concept is applicable also within a company. Lean
and agile concepts can be applied at different stages of the same
manufacturing process, for different machines and parts, etc. In this case, a
level of buffer stock is maintained between lean and agile manufacturing
strategies. (Bonney et al., 1999; Naylor et al., 1999; Robertson et al., 1999;
Bolander et al., 2000; Hoek, 2000; Mason-Jones et al., 2000; Pagell et al.,
2000; Sahin, 2000; Takahashi et al., 2000; McCullen et al., 2001; Prince et al.,
2003; Christopher et al., 2002; Stratton et al., 2003; Corti et al., 2006; Hoque
et al., 2006; Stratton et al., 2006; Krishnamurthy et al., 2007; Mohebbi et al.,
2007).
4. Manufacturing strategy management focused for superior demand-supply
performance
Demand-supply performance is further studied for optimising, not only a
company, but also its ecosystem. Competitive advantages of global
manufacturing can be achieved if the supply chain has less organisational
boundaries. The key is to simultaneously aim for operational efficiency and
market responsiveness, including all parties. (Lummus et al., 1998; Banerjee,
2000; Golder, 2000; Sahin, 2000; Brassler et al., 2001; Olhager et al., 2001;
Christopher et al., 2002; Hinterhuber et al., 2002; Loch et al., 2002; Brown et
al., 2003; Stratton et al., 2003; Hui, 2004; Hallgren et al., 2006; Brown et al.,
2007).
5. Others: product innovation, agent-based modelling, IT implementation
proposal, research methodology
This group of concepts ensures the research supporting a wider knowledge
base. For example, the innovation through product changes is in the focus of
this research. The development of IT tools for optimising manufacturing
execution can be also important, as well as right methodology. (Papandreou et
al., 1998; Bajgoric, 2000; Davidrajuh et al., 2000; Thomke et al., 2000;
Corbett et al., 2001; Coronado M. et al. 2002; Coughlan et al., 2002; Forza,
2002; Mandal et al., 2002; Walker, 2002; Dooley et al., 2003; Jalote et al.,
2004; Ottosson, 2004; Ashayeri et al., 2005; Buxey, 2006; Helo et al., 2006;
Nilsson et al., 2006).
31
34. In order to ensure the literature review focusing on manufacturing optimisation,
the discussion includes synchronous manufacturing, Equalised and Synchronised
Production (ESP), the Leagility, Manufacturing Strategies in Product Life Cycle,
and the Innovator’s Strategy.
2.2 Developing demand-supply network
It has been many years as a popular thought that DCM (Demand Chain
Management) and SCM (Supply Chain Management) are not separate but
inextricably intertwined (Min and Mentzer 2000) The demand-supply network
management concept of Holmström et al. (1999) proved to be a useful tool in
analysing the demand and supply balancing mechanisms (Auramo and Ala-Risku
2005). Combining push-based supply chain and pull-based demand chain together,
the study is better focused directly on demand-supply network theory more
applicable to case company in the research. The reason is no major difference
between the demand and supply chain with respect to the network of
organizations involved, which are all to create, produce, and deliver customer
value. (Hilletofth 2010).
2.2.1 Value oriented development for demand-supply network
The target of developing demand-supply network is to maximise the overall value
generated.
Value as a key of winning in competition
According to the analysis by Chopra & Meindl (2001), the value is the difference
between what the final product is worth to the customer and effort the supply
chain expends in filling the customer’s request. The success key is the appropriate
management of all flows of information, and product, generating costs within the
supply chain. Monczka and Morgan (2000) identified those “critical six” as
follows to be the trend of developing demand-supply network:
– Increasing efficiency requirements
– Making use of information technology
– Integration and consolidation
– Insourcing and outsourcing
32
35. – Strategic cost management
– “Network” management.
For example, PC (Personal Computer) industry has many ways to organize the
value chain in a network manner. Curry and Kenney (1999) illustrated that the
traditional production-distribution channel (such as IBM and Compaq) co-existed
with new emerging structures represented by “local assemblers” and “direct
marketers” such as Dell. Such a complexity as global operation scale has been
also seen nowadays widely in other high-tech industries.
Ketchen et al. (2008) presented a tool as the best value supply chains
designed to deliver superior total value to the customer in terms of speed, cost,
quality, and flexibility. It is not just simply to create low costs, but also to
maximise the total value added to the customer. Relative to traditional supply
chains, best value supply chains also take much different approaches to key
functions such as strategic sourcing, logistics, information systems, and
relationship management.
Thinking as a networked way
Wu and Zhang (2009) introduced the value network perspective into the field of
business model study and discussed basic issues about business model such as
definition, elements and classification through the lens of value network. From
the perspective of value network, the definition of its business module is the
system connecting internal and external actors by value flows to create, deliver
and capture value:
– Value actors as the network nodes
– Value flows as the network relation
– Part of or the whole value network as the network structure.
In comparison with real business cases, Wu and Zhang (2009) summarised
business model innovations of value network as follows:
– Business model innovation based on actor change
– Business model innovation based on relation change
– Business model innovation based on network subdivision
– Business model innovation based on network extension
– Business model innovation based on network integration.
33
36. Gadde and Håkansson (2001) studied activity co-operation of JIT (Just-In-Time)
deliveries with numerous activities conducted by a large number of actors as a
network view. The complexity of strategising in networks is related to their
multidimensionality. Any change has some direct effects but also a number of
indirect effects, on other firms, impact on the actor’s performance. The focus is
emphasised on the interdependence among the activities conducted by customer
and supplier and call for more co-ordination than is needed when inventories
serve as buffers. The main issue in all network thinking is that “others” need to be
included. The second key aspect is related to time. The importance of others and
the crucial time dimension indicate that boundaries are key issues in all network
thinking.
Focus on demand or supply?
Esper et al. (2010) emphasised two primary sets of processes through which the
firm creates value for its customers by moving goods and information through
marketing channels: demand-focused and supply-focused processes. Historically,
firms have invested resources to develop a core differential advantage in one or
other of these areas—but rarely in both—often resulting in mismatches between
demand (what customers want) and supply (what is available in the marketplace).
Yusuf et al. (2004) also found supply chains (or demand-supply network) were
understood mainly in terms of long-term upstream collaboration with suppliers.
However, an equal amount of emphasis is then paid to downstream collaboration
with customers and even collaboration with competitors as a means of integrating
the total value creation process.
Hilletofth and Hilmola (2010) indicated management of the demand side
(DCM – Demand Chain Management) being revenue driven and focused on
effectiveness whilst the management of the supply side (SCM – Supply Chain
Management) having a tendency to be cost oriented and focus on efficiency.
Together these management directions determine a company’s profitability and
thus need to be coordinated, requiring a demand supply oriented management
approach. As the finding of Hilletofth (2010), it is important to promote the
coordination of DCM and SCM, which can occur within a particular company
and across the demand supply chain at different planning levels (strategic,
tactical, and operational).
From a survey result by Boonyathan and Power (2007), following outcomes
were found:
34
37. – Supply uncertainty is a more significant determinant of performance than
demand uncertainty.
– Closer relationships with trading partners are associated with higher levels of
performance.
– Uncertainty can be reduced by being more closely aligned with both suppliers
and customers.
Mason-Jones et al. (2000) emphasised that the success and failure of supply
chains are ultimately determined in the marketplace by the end consumer. Getting
the right product, at the right price, at the right time to the consumer is not only
the lynchpin to competitive success but also the key to survival. According to the
report from Ervolina et al. (2006), availability management process called
Available-to-Sell (ATS) is an example that incorporates demand shaping and
profitable demand response to drive better operational efficiency through
improved synchronisation of supply and demand. IBM has implemented an ATS
process in its complex-configured server supply chain in 2002. The realized
savings include $100M of inventory reduction in the first year of implementation
and over $20M reduction annually in the subsequent years.
New trend of operations management
As a strong trend, demand management should be more integrated in supply
operation to increase customer satisfaction and life cycle profit (Reiner et al.
2009). As the view of Frohlich and Westbrook (2002), the DCM strategy
appeared to be the best overall approach for manufacturers to follow and the
relatively few manufacturers that are already following this approach. As Ettl et al.
(2006) described, a demand-driven supply network (DDSN) is a system of
technologies and business processes that senses and responds to real time demand
across a network of customers, suppliers, and employees. DDSN principles
require that companies shift from a traditional push-based supply chain to a pull-
based, customer-centric approach.
Waters and Rainbird (2008) even claimed the demand chain and response
management is new direction for operations management. Supply chain
management would appear to be at the end of its lifecycle. Customers of all types
are expressing preferences based upon some degree of product-service
differentiation and not simply on cost. They suggested the supply chain is
obsolescent and should be replaced by a more proactive response system.
35
38. 2.2.2 Manufacturing strategies affect demand-supply network
Mason-Jones et al. (2000) presented that classifying supply chain design and
operations according to the Lean, Agile and Leagile paradigms enables the
companies to match the demand-supply type according to marketplace need. For
example, they could be mechanical precision products (lean); carpet manufacture
(agile); and electronics products (leagile).
Multiple strategy choices
Christopher and Towill (2000) summarised the differences on how to apply lean
or agile thinking for demand-supply network affected by manufacturing strategies.
The lean paradigm requires that ``fat'' is eliminated. However, the agile paradigm
must be ``nimble'' since sales lost are missed forever. An important difference is
that lean supply is associated with level scheduling, whereas agile supply means
reserving capacity to cope with volatile demand.
Lack of agile benchmark cases brings the difficulty to understand such a
concept clearly. As the view of Yusuf et al. (2004), the agility of a supply chain is
a measure of how well the relationships involved in the processes of design,
manufacturing and delivery of products and services. Monroe and Martin (2009)
described that agility in the supply chain is described as being able to “respond to
sudden and unexpected changes in markets. Agility is critical, because in most
industries, both demand and supply fluctuate more rapidly and widely than they
used to.
According to the explanation of Mason-Jones et al. (2000), leagile supply
chains already exist in the real world. Just as case company due to big differences
of material supply lead-time, there is decoupling point in demand fulfilment
process where order-driven way changed to forecast-driven way.
Design of demand-supply network to support strategies
Vonderembse et al. (2006) defined the characteristics for standard, innovative,
and hybrid products, and provided a framework for understanding lean and agile
supply chains. Lean supply chains (LSCs) employ continuous improvement
efforts and focus on the elimination of nonvalue added steps across the supply
chain. Agile supply chains (ASCs) respond to rapidly changing, continually
fragmenting global markets by being dynamic, context-specific, growth-oriented,
36
39. and customer focused. Hybrid supply chains (HSCs) combine the capabilities of
lean and agile supply chains to create a supply network that meets the needs of
complex products.
As the view of Vonderembse et al. (2006), early in their product life cycle,
innovative products, which may employ new and complex technology, require
ASC. As the product enters the maturity and decline phases of the product life
cycle, a LSC could be more appropriate. Hybrid products, which are complex,
have many components and participating companies in the supply chain. Some
components may be commodities while others may be new and innovative.
Hilletofth (2009) suggested that companies need to use several SC (Supply
Chain) solutions concurrently (i.e. develop a differentiated SC strategy) to stay
competitive in today’s fragmented and complex markets. The arguments in favour
is that there are no SC strategies that are applicable to all types of products and
markets and since companies usually offer a wide range of products and services
in various types of non-coherent business environments. In particular, Hilletofth
and Hilmola (2010) also emphasised a need for real life based industrial case
studies addressing how the various demand and supply processes influence each
other and how they can be coordinated across intra- and inter-organizational
boundaries. Thus, benefits to all parties should be aimed for developing win-win
solution in demand-supply network co-operation.
The differences in supplier selection were further studied by Chopra and
Sodhi (2004) how to plan the manufacturing in demand-supply network smarter:
When planning capacity, companies should select an efficient, low-cost supplier
for fast-moving (low-risk) items. In contrast a more responsive supplier better
suits slow-moving (high-risk and high-value) items. For example, Cisco tailors its
response by manufacturing fast-moving products in specialised, inexpensive but
not-so-responsive Chinese plants. High-value, slow-moving items are assembled
in responsive, flexible (and more expensive) U.S. plants.
Santoso et al. (2005) reported a stochastic programming model and solution
algorithm for solving supply chain network design problems of a realistic scale.
Existing approaches for these problems are either restricted to deterministic
environments or can only address a modest number of scenarios for the uncertain
problem parameters. Santoso et al. (2005) proposed a methodology to quickly
compute high quality solutions to large-scale stochastic supply chain design
problems with a huge (potentially infinite) number of scenarios.
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40. Lead time reduction as strategic effect
Amoako-Gyampah (2003) indicated that manufacturing strategy represents the
way a company plans to deploy its manufacturing resources and to use its
manufacturing capability to achieve its goals. Lead time has been recognised as a
very important issue in almost all strategy theories. It is one of the root-causes to
determine the choice of manufacturing strategies in many cases. From the view of
Sapkauskiene and Leitoniene (2010), speed as a competitive factor is gaining
more and more importance for companies involved in global market competition.
The company tends to compete for rapid response to consumer demand and new
products and technologies introduced to the market. This type of competition in
terms of reaction time is described as time based competition (TBC).
Comparing to lead time reduction in production, such an effort in demand-
supply network is often limited so as to bring big operational uncertainty and the
bullwhip effect significantly. The time gains so greater importance, as speed,
which is required by business and consumer expectations, continues to increase
even more (Sapkauskiene and Leitoniene 2010). Lyu and Su (2009) described the
challenges in demand-supply including uncertainty of customers’ demands, high
inventory levels and cost, inaccurate due date estimation, and slow response to
customer inquires. Lead time reduction is a critical issue which enables
manufactures to solve problems. They proposed extended master production
scheduling (MPS) system, developed using Internet technology, can be deployed
in a supply chain environment.
As similar philosophy focused for reducing lead time, Quick Response
Manufacturing (QRM) developed by Rajan Suri is a strategy that enables
companies to significantly improve their productivity and their competitive edge.
Suri (1998) presented the way how QRM has refined time based competition by:
– Focusing only on manufacturing.
– Taking advantage of basic principles of system dynamics to provide insight
into how to best reorganise an enterprise to achieve quick response.
– Clarifying the misunderstandings and misconceptions managers have about
how to apply time-based strategies.
– Providing specific QRM principles on how to rethink manufacturing process
and equipment decisions.
– Developing a whole new material planning and control approach.
– Developing a novel performance measure.
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41. – Understanding what it takes to implement QRM to ensure lasting success.
Suri (2002) claimed that JIT (Just-In-Time) was perfected by Toyota over 30
years ago. For certain markets, lean manufacturing has several drawbacks. Quick
Response Manufacturing (QRM) can be a more effective competitive strategy for
companies targeting such markets. Specifically, QRM is more effective for
companies making a large variety of products with variable demand, as well as
for companies making highly engineered products.
Suri (2003) explained why QRM has greater competitive potential and
described POLCA (Paired-cell Overlapping Loops of Cards with Authorization), a
material control system to be used as part of QRM. The combination of QRM and
POLCA will provide companies with significant competitive advantage through
their ability to deliver customised products with short lead times.
Suri and Krishnamurthy (2003) explained that POLCA is a hybrid push-pull
system that combines the best features of push/MRP systems and Kanban/pull
control, while at the same time avoiding their disadvantages. The flow of orders
through the different production cells is controlled through a combination of
release authorizations (High Level Materials Requirements Planning system or
HL/MRP) and production control cards known as POLCA cards (not part-specific
like a Kanban card). The release authorization times only authorize the beginning
of the work, but the cell cannot start unless the corresponding POLCA card is also
available. A POLCA card is a capacity signal, while a pull/Kanban signal is an
inventory signal. If there is no authorized job, then no job is started, even though
a POLCA card is available. It should be designed available capacities are not
significantly below the required levels.
From the description by Suri and Krishnamurthy (2003), there are Safety
Cards, which are only used to release POLCA cards that get stuck in the loop due
to occasional component part shortages. After a period of time, statistics from
these incidents will provide concrete insight into root causes of the shortages.
As their suggestions, the key metrics are measured as follows:
– The lead times for the products
– The throughputs of the cells
– The reliability of delivery between cells
– WIP inventories at various points in the system
– The on-time delivery performance of upstream and downstream cells in the
POLCA loops.
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42. Vandaele et al. (2005) also reported the implementation of an E-POLCA system
in a paperless – cardless – environment. It is a load based version for a multi-
product, multi-machine queuing network to determine release authorisations and
allowed workloads.
2.2.3 The role of collaboration in demand-supply
According to the explanation of Kaipia and Hartiala H (2006), manufacturing
companies need the collaboration with customers and suppliers to improve the
performance of demand-supply network. Better information-sharing can reduce
both the bullwhip effect and the operational risk (such as the level of safety
stocks).
Networked collaboration for better performance
Holweg et al. (2005) discussed that collaboration in the demand-supply network
comes in a wide range of forms, but in general have a common goal: to create a
transparent, visible demand pattern that paces the entire supply chain. Such
collaboration is for jointly creating the common pace of information sharing,
replenishment, and supply synchronisation in the system to reduce both excess
inventory and the costly bullwhip effect.
For example, Ryu et al. (2009) can identify types of demand information
according to their timestamp. There are three types of demand information
classified according to where they are located along the time-axis. These are
realised demand information, planned demand information, and forecasted
demand information. Two different information-sharing methods are defined
according to types of shared information and sharing procedures. One is the
‘planned demand transferring method (PDTM)’ and the other is the ‘forecasted
demand distributing method (FDDM)’.
Udin et al. (2006) proposed a collaborative supply chain management
framework. Normally, supply chain management (SCM) is a system that contains
multiple entities, processes and activities from suppliers to customers.
– The basic concept behind SCM is how the raw materials and information
flow from the supplier to the manufacturer, before final distributions to
customers as finished products or services.
40
43. – In addition, functional areas within the organisation also need information
that flows through the SCM in order for them to make a decision to produce
products.
– The capability of sharing and exchanging information is essential to improve
the effectiveness of the SCM.
Udin et al. (2006) provided a collaborative framework how to analyse the gap
between the current and the desirable position (benchmark) for its effective
implementation in organisation.
Heikkilä (2002) described about the collaboration oriented more by changing
from SCM (Supply China Management) to DCM (Demand Supply Management)
with following propositions:
1. Good relationship characteristics contribute to reliable information flows.
2. Reliable information flows contribute to high efficiency.
3. Understanding the customer situation and need and good relationship
characteristics contribute to co-operation between the customer and supplier.
4. Good co-operation in implementing demand chain improvement contributes
to high efficiency and high customer satisfaction.
5. High customer satisfaction contributes to good relationship characteristics.
Collaboration to reduce bullwhip effect
As explained by Ismail (2009), bullwhip effect is a major problem in supply
chains. It means the amplification of orders as you go up along the supply chain.
The bullwhip effect is a phenomenon that was discovered by Forrester (1958)
who realized that variations of demand increase up the supply chain from
customer to supplier, what was called the Bullwhip Effect or known as the
Forrester Effect. Holweg et al. (2005) also described that unpredictable or non-
transparent demand patterns have been found to cause artificial demand
amplification in a range of settings, which is also referred to as the ‘bullwhip’
effect’ (Lee et al., 1997; Lee, 2002). This leads to poor service levels, high
inventories and frequent stock-outs.
After studying three proposed scenarios, Bolarin et al. (2008) concluded that
collaborative structures improve the Bullwhip effect and reduce the total costs of
the supply chain in which these structures applied. Those are 3 scenarios in the
simulation: Traditional Supply Chain, VMI (Vendor Management Inventory)
(based on collaborative structures among the members that make up the Supply
41
44. Chain), and EPOS (Electronic Point of Sales). In the collaborative EPOS scenario,
the end consumer sales are sent to all members of the supply chain. Specifically,
in this strategy the end consumer sales may be used by each echelon for their own
planning purposes, but each echelon still has to deliver (if possible) what was
ordered by their customer (Disney et al 2004). The EPOS chain has proved to be
more efficient than the VMI and the traditional ones in reducing the Bullwhip
effect and in holding costs.
Susarla et al. (2004) argued that advances in information technology (IT) that
improve coordinated information exchange between firms result in a significant
impact on measures of operational efficiency such as time to market, inventory
turnover, and order delivery cycle time. To reduce bullwhip effect, IT can also
make it possible by exchanging information on a variety of parameters such as
demand and inventory related information, process quality information, feedback
from customers etc.
Collaborative risk management
Christopher and Lee (2004) noticed that many companies have experienced a
change in their supply chain risk profile as a result of changes in their business
models, for example the adoption of ‘lean’ practices, the move to outsourcing and
a general tendency to reduce the size of the supplier base. As their view, the
improvements in confidence can have a significant effect on mitigating supply
chain risk.
Snyder et al. (2006) researched about supply chain disruptions. It needs to
consider the risk of disruptions when designing supply chain networks. Supply
chain disruptions have a number of causes and may take a number of forms. They
presented a broad range of models for designing supply chains resilient to
disruptions. For example, these models can be categorised by the status of the
existing network: A network may be designed from scratch, or an existing
network may be modified to prevent disruptions at some facilities. Snyder et al.
(2006) emphasised that the companies may face costs associated with destroyed
inventory, reconstruction of disrupted facilities, and customer attrition (if the
disruption does not affect the firm’s competitors). In addition, the competitive
environment in which a firm operates may significantly affect the decisions for
risk mitigation. The key objective may be to ensure that their post-disruption
situation is no worse than that of their competitors.
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45. Goh et al. (2007) presented a stochastic model of the multi-stage global
supply chain network problem, incorporating a set of related risks: supply,
demand, exchange, and disruption. With the increasing emphasis on supply chain
vulnerabilities, effective mathematical tools for analysing and understanding
appropriate supply chain risk management are now attracting much attention.
They provided an optimal solution with profit maximisation and risk
minimisation objectives.
Thomas and Tyworth (2006) discussed about pooling lead-time risk by order
splitting. The policy of pooling lead-time risk by simultaneously splitting
replenishment orders among several suppliers continues to attract the attention of
researchers even after more than 20 years of extensive study. The research has
following major tracks:
– Modelling effective lead-time demand under a variety of stochastic
assumptions and enabling an assessment of the impact of pooling on reorder
points, stockout risk, safety stock, and shortages.
– Modelling cost tradeoffs on a comparison of the long run average total costs
for single-source versus dual- or multiple-source models under identical
conditions.
Thomas and Tyworth (2006) revealed two important and persistent limitations:
– The models do not give appropriate attention to transportation economies of
scale. Specifically, there are important gaps with respect to the true
magnitude of transportation cost, as well as the impact of order quantity
(weight), supply lines (distance), and mode (especially air versus ocean
shipments in a global setting) on transportation and incremental ordering
costs.
– The current theory that a reduction in average cycle stock is the key benefit of
splitting orders simultaneously considers only the buyer’s on-hand inventory
in the supply chain. The absence of in-transit inventory is an important
limitation, because simultaneously splitting an order among suppliers does
not reduce the combined amount of in-transit stock and cycle stock in the
system. Consequently, the only meaningful benefit of pooling lead times is to
safety stock from a total system-cost perspective.
Thomas and Tyworth (2006) also introduced other options such as a single
supplier to receive an order and then split it into smaller shipments released
43
46. sequentially. The long-term transportation commitments can also absorb some of
the demand variability at the consumer-facing point in the supply chain.
2.2.4 Measuring demand-supply performance
As the view of Jammernegg and Reiner (2007), supply chain performance
improvement should be measured by reduced total costs (transport, inventory
carrying and resources), as well as improved customer service (delivery
performance). For MTO (Make-To-Order) and ATO (Assemble-To-Order)
production, delivery performance (percentage of orders fulfilled within the
promised delivery time (or due date)) is used as measure of delivery reliability.
However, the trade-off between inventory cost and capacity cost has to be
considered. Reiner (2005) also discussed how performance measures derived
from total quality management (TQM) models could help to overcome the
limitations of financial measures. In such a context, process management and
customer orientation occupy a central position.
The performance of demand-supply network should be measured so as to
ensure its improvement accountable or at least visible. As one of other more
comparable options, it is also better to use existing key performance indicators for
a SCOR (Supply Chain Operations Reference) model, which can compare other
cases in this field. Here is an overview of SCOR model (Supply Chain Council,
2005):
SCOR-model key performance indicators
1. Customer focus
– Delivery performance
– Fill rate
– Order fulfilment lead time
– Perfect order fulfilment
– Supply chain response time
– Production flexibility
2. Internal cost focus
– Total supply chain management cost
– Cost of goods sold
– Value-added productivity
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47. – Warranty cost or returns
– Processing cost
– Cash-to-cash cycle time
– Inventory days of supply
– Asset turns.
Ho et al. (2005) emphasised the SCOR model is to help companies in managing
their supply chain. Process reference models integrate the mechanisms of
business process reengineering, benchmarking, and process measurement in a
cross-functional framework to helping companies to capture the “as-is” state of a
process and derive the desired “to-be” future status. However, Ho et al. (2005)
also indicated that SCOR does not provide a mechanism for measuring
uncertainty to enable a company to understand clearly the problems related to
uncertainty before the setting strategy.
Besides, Drzymalski and Odrey (2006) summarise a list of performance
metrics options from literature review, as well as ISO9001 and FEA (Federal
Enterprise Architecture) Consolidated Reference Model Document v2.0. Chan
(2003) presents following performance measurements as the suggestion. Apart
from the common criteria such as cost and quality, five other performance
measurements can be defined: resource utilisation; flexibility; visibility; trust; and
innovativeness.
Kaipia et al. (2007) introduced another option as the time benefit method,
which compares two potential collaboration modes as the following steps:
1. Describe the existing mode of replenishment process – the base case – and
one alternative mode.
2. Collect demand data for both alternatives to be examined.
3. Calculate the following for each item in the product range, and for both the
base case and the alternative solution.
4. Calculate for each item in the product range.
5. Graph for each product item in the product range the time benefit and
reordering amplification of demand.
For applying the thought from Kaipia et al. (2007) to product change
implementation, the most of key components (such as material supply normally)
belong to the base case and others belong to attentive case (such as VMI).
Furthermore, the trend of leading companies in high-tech industry has been
changed to using IT (Information Technology) solutions as a must in demand-
45
48. supply performance (Kauremaa et al. 2004). Auramo et al. (2005) found the IT
solutions to be divided into three categories, 1) transaction processing, 2) supply
chain planning and collaboration, and 3) order tracking and delivery coordination.
The role of information technology is shifting from a passive management
enabler through databases to a highly advanced process controller that can
monitor each activity (Gunasekaran et al. 2001). New idea or theory how to
measure the performance should be embedded in information technology tools as
IT-enabled research and development (Dong 2010).
It could improve real business in global scale and also bring reliable
academic value, which is a trend focused on how to leverage knowledge faster
and better than competitors (Thite 2003). In order to discuss such a trend, Auramo
et al. (2005) presented an explorative study about the benefits and their
observations of IT involvement in performance measurement. To gain strategic
benefits, the use of IT has to be also coupled with business process re-design. It is
a new normal of playground for business and a new interesting field for academic
research, which is so called IT enabled innovation (Watad 2009).
2.2.5 Purchasing automation challenge in product life cycle
Purchasing is a key activity in demand-supply operation especially hard in
dynamic product changes. Hilmola et al. (2008) suggested why a portfolio
approach of using different purchasing policies may be central to new intelligent
purchasing systems. A portfolio approach means lot for lot policy (L4L - The
order or run size is set equal to the demand for that period) may be useful in an
early phase of the product life-cycle, and later it may be an advantage to change
over to economic order quantity (EOQ) based ordering. Jammernegg and Reinera
(2007) described about the trade-off of inventory level in purchasing operation.
On the one hand, different types of inventory are necessary to buffer against
market and operational uncertainties but, on the other hand, inventory is
sometimes the result of inefficient management of the supply chain processes.
Therefore, inventory management has been a focal point of managing supply
chain processes.
As emphasised by Hilmola et al. (2008), accuracy of demand forecasting is
vital to switching point estimation. One potential method for tracking these
signals of that switching point was mentioned as the development of the GARCH
technique (proven useful in financial risk management and awarded the 2003
Nobel Prize in Economics). GARCH stands for Generalized Auto Regressive
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