St. Petersburg University
Graduate School of Management
Master in International Business Program
WORKING CAPITAL MANAGEMENT IN SUPPLY
CHAINS
Master’s Thesis by the 2nd year student
Concentration MIB-ILSCM
Nataliia G. Silaeva
Research advisor:
Nikolay A. Zenkevich, Associate Professor
St. Petersburg
2016
ЗАЯВЛЕНИЕ О САМОСТОЯТЕЛЬНОМ ХАРАКТЕРЕ ВЫПОЛНЕНИЯ
ВЫПУСКНОЙ КВАЛИФИКАЦИОННОЙ РАБОТЫ
Я, Силаева Наталия Геннадьевна, студент второго курса магистратуры направления
080200 - «Менеджмент», заявляю, что в моей магистерской диссертации на тему
«Управление рабочим капиталом в цепях поставок», представленной в службу
обеспечения программ магистратуры для последующей передачи в государственную
аттестационную комиссию для публичной защиты, не содержится элементов плагиата.
Все прямые заимствования из печатных и электронных источников, а также из
защищенных ранее выпускных квалификационных работ, кандидатских и докторских
диссертаций имеют соответствующие ссылки.
Я ознакомлен с действующим в Высшей школе менеджмента СПбГУ регламентом
учебного процесса, согласно которому обнаружение плагиата (прямых заимствований из
других источников без соответствующих ссылок) является основанием для выставления
за магистерскую диссертацию оценки «неудовлетворительно».
_______________________________________________ (Подпись студента)
________________________________________________ (Дата)
STATEMENT ABOUT THE INDEPENDENT CHARACTER OF
THE MASTER THESIS
I, Nataliia G. Silaeva, second year master student, program 080200 - «Management»,
state that my master thesis on the topic «Working capital in supply chains», which is presented to
the Master Office to be submitted to the Official Defense Committee for the public defense, does
not contain any elements of plagiarism.
All direct borrowings from printed and electronic sources, as well as from master theses,
PhD and doctorate theses that were defended earlier, have appropriate references.
I am familiar with the study process regulations at Graduate School of Management of
Saint-Petersburg State University, according to which finding of plagiarism (direct borrowings
from other sources without appropriate references) can be the reason for master thesis to be
evaluated as «unsatisfactory».
________________________________________________(Student’s signature)
________________________________________________ (Date)
2
АННОТАЦИЯ
ФИО студента
Силаева Наталия Геннадьевна
Тема магистерской
Управление рабочим капиталом в цепях поставок
диссертации
Высшая школа менеджмента
Факультет
Международная логистика и управление цепями поставок
Программа
2016
Год окончания
ФИО научного
Зенкевич Николай Анатольевич
руководителя
Описание цели, задач и основных результатов
Цель: разработка координирующей модели управления рабочим капиталом в совместной
цепочке поставок
Задачи исследования: идентифицировать пробелы в научных исследованиях управления
рабочим капиталом в цепи поставок; улучшить методологию управления рабочим капиталом
в цепях поставок через создание координирующей модели модифицированного
коллаборативного цикла оборота денежных средств в цепях поставок; проанализировать
координационное моделирование управления рабочим капиталом в цепях поставок,
используя подход тематического исследования.
Основные результаты: обнаружен пробел в исследованиях - управление рабочим капиталом в
совместных цепях поставок; создана координирующая модель управления рабочим
капиталом в цепях поставок с 3PL-провайдером в роли координатора децентрализованной
цепи поставок; результаты применения координационного моделирования показали
значительное сокращение издержек модифицированного коллаборационного цикла оборота
денежных средств в каждой из четырѐх коллаборативных цепочек поставок.
Управление рабочим капиталом, координационное
моделирование, коллаборация, 3PL провайдер, коллаборативная
Ключевые слова
цепочка поставок, модифицированный коллаборативный цикл
оборота денежных средств
3
ABSTRACT
Master Student's Name
Nataliia G. Silaeva
Master Thesis Title
WCM in supply chains
Faculty
Graduate School of Management
Program
International Logistics & Supply Chain Management
Year
2016
Academic Advisor's Name Nikolay A. Zenkevich
Description of the goal, tasks and main results
Goal: the development of coordinating model of WC management (WCM) in collaborative supply
chains
Research objectives: To identify the research gaps of WCM in supply chains based on the literature
review devoted to WCM in supply chains; to improve methodology of WCM in supply chains trough
creation of coordinating supply chain modified collaborative CCC (mCCCC) model; to analyze in
coordination modeling of working capital management in supply chains using case study approach.
Main results: Revealed research gap of coordinating mechanism of WCM in collaborative supply
chains; created coordinating supply chain mCCCC model with 3PL provider as a coordinator of a
decentralized supply chain; results of coordination modeling showed significant cost reduction of
running the mCCCC of each of 4 collaborative supply chain.
Working capital management, coordination modeling, collaboration,
Keywords
3PL provider, collaborative supply chain, modified collaborative cash
conversion cycle
4
Content
List of figures and tables .................................................................................................................6
Introduction .....................................................................................................................................7
1.
2.
3.
Theoretical review of financial supply chain .........................................................................10
1.1.
Collaboration in supply chains .......................................................................................10
1.2.
Financial supply chain management (FSCM) ................................................................ 17
1.3.
Working capital management as a FSCM perspective ...................................................23
1.4.
Cash conversion cycle (CCC) as a WC measure ............................................................ 27
Coordinating modified collaborative cash conversion cycle model (mCCCC) methodology
35
2.1.
mCCCC evaluation methods .......................................................................................... 35
2.2.
mCCCC evaluation models ............................................................................................ 37
2.3.
Data collection and industries studied ............................................................................47
2.4.
Research design ..............................................................................................................48
Coordination modeling case study analysis ...........................................................................50
3.1.
Case study of mCCCC scenario analysis ........................................................................50
3.2.
Case study of mCCCC coordination modelling ............................................................. 56
Conclusions ...................................................................................................................................68
References .....................................................................................................................................72
Appendices ....................................................................................................................................79
Appendix 1. The feasible solution for Case 1 ............................................................................79
Appendix 2. The feasible solution for Case 2 ............................................................................80
Appendix 3. The feasible solution for Case 3 ............................................................................81
Appendix 4. The feasible solution for Case 4 ............................................................................82
5
List of figures and tables
Figure 0.1. The scope of the research .............................................................................................. 8
Figure 0.2. The structure of the master thesis..................................................................................9
Figure 1.1.The evolution of supply chain management definition ................................................10
Figure 1.2. Four perspectives of supply chain management and logistics relationship ................11
Figure 1.3. Types of supply chain structure ..................................................................................12
Figure 1.4. Supply chain networks ................................................................................................ 13
Figure 1.5. Stages of collaboration achievement ...........................................................................14
Figure 1.6. The downstream and upstream directions of flows in supply chain ........................... 18
Figure 1.7. Intersections of FSCM with other disciplines ............................................................. 20
Figure 1.8. The definition difference of supply chain finance and financing supply chain ..........22
Figure 1.9. WC cycle flows ...........................................................................................................24
Figure 1.10. Summary of the studies of WC in supply chains ......................................................26
Figure 1.11. CCC approach ...........................................................................................................28
Figure 1.12. The relationship of WC cycle times and relative profitability ..................................30
Figure 1.13. Collaborative CCC framework .................................................................................31
Figure 1.14. Overlapping periods of CCC components ................................................................ 32
Figure 1.15. Modified CCC ...........................................................................................................34
Figure 2.1. Scenario analysis framework ......................................................................................38
Figure 2.2. Research design based on design-science approach ...................................................49
Figure 3.1. Implementation of scenario analysis to project collaborative supply chain ...............51
Figure 3.2. Implementation of scenario analysis to ICT collaborative supply chain ....................53
Figure 3.3. Implementation of scenario analysis to automotive collaborative supply chain.........54
Figure 3.4. Implementation of scenario analysis to P&P collaborative supply chain ...................55
Table 1.1. Different CCC calculations .......................................................................................... 29
Table 3.1. mCCCC and mCFC components for project collaborative supply chain ....................57
Table 3.2. Optimized collaborative CCC model for project industry ...........................................59
Table 3.3. mCCCC and mCFC components for ICT collaborative supply chain .........................59
Table 3.4. Optimized collaborative CCC model for Case 2 .......................................................... 61
Table 3.5. mCCCC and mCFC components for automotive collaborative supply chain ..............62
Table 3.6. Optimized collaborative CCC model for Case 3 .......................................................... 64
Table 3.7. mCCCC and mCFC components for P&P collaborative supply chain ........................65
Table 3.8. mCCCC and mCFC components for automotive collaborative supply chain ..............67
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Introduction
During the last decade the increased volatility in business environment followed by the
financial crisis of 2008-2009 and recent oil crisis increased the competition among the
companies. Companies seek additional ways to gain competitive advantage, decrease costs and
increase profitability. The common way of financing operations through credit and loans is no
longer easy accessible due to the financial crisis and tight economic situation thus companies try
to find new ways of operations financing.
One of ways to obtain competitive advantage through faster and more efficient financing
of supply chain (SC) is working capital management (WCM) in SCs as a part of financial supply
chain management (FSCM). (Sigurin, 2009). The WCM in SCs cannot be performed properly at
the intra-organizational level due to the fact that it involves liabilities at the inter-organizational
level of a SC and requires collaboration at all stages of a SC (Seifert, 2010).
From the academic perspective the WCM as a part of FSCM has gained a lot of attention
(Matyac, 2015). This approach was developed in the terms of a new research field – FSCM
which recently has gained a lot of attention. Viskari et al. (2012) predicted that there would be a
lot of studies dedicated to FSCM and particularly management of working capital (WC) in SCs
as a way to accelerate the cycle time of WC and increase the profitability of the company in
respond to recent financial volatility in business environment.
The subject of the master thesis is FSCM which defines by Hoffman (2005) as “located
at intersection of logistics, SC management, collaboration and finance, FSCM is an approach for
two or more organizations in a SC, including external service providers, to jointly create value
through means of planning, steering, and controlling the flow of financial resources on an interorganizational level”.
The object of the study is WCM in SCs which can be measured using WC ratios or
operational evaluation such as cash conversion cycle (CCC). Christopher and Ryals (1999)
defined WCM as one of the factors of shareholder value creation. Karl Max introduced the idea
of WC and Pirttilä (2014) identifies WC as “the capital of a business which is used in its day-today trading operations, calculated as current assets less current liabilities”. The WCM in SCs in
done using operational level measure of CCC, which modifications of concepts and evaluation
approach are developed in this master thesis. The scope of the research derives from three main
fields of FSCM, corporate finance and SC management and is illustrated at Figure 0.1.
7
Figure 0.1. The scope of the research
Source: Created by the author
The goal of the study is development of the coordinating model of WCM in collaborative
SCs. In terms of the goal stated the following objectives of the master thesis were set:
1. To identify the research gaps of WCM in SCs based on the literature review
devoted to WCM in SCs;
2. To improve the methodology of WCM in SCs through creation of coordinating
SC modified collaborative CCC (mCCCC) model;
3. To analyze coordination modeling of WCM in SCs using case study approach.
The methods applied for achievement of goals are design-science and archival
approaches. The results of methodology development are verified with the case study method –
the research based on four different cases – project, ICT, automobile and pulp&publishing
industries collaborative SCs.
The theoretical relevance of the master thesis is justified with the fulfillment of the
research gap identified as a result of theoretical review of WC in SCs – the absence of works
dedicated to the coordinating model of WCM in collaborative SCs. The managerial
applicability of the work is supported by the results of testing the concept and the model on four
different cases and advantages the companies can obtain using the developed coordinating model
to evaluate their length of collaborative cycle and ways of reduction of total financial costs
associated with the length of the cycle.
The structure of the research is following. The master thesis consists of introduction, 3
chapters, conclusion, references list and appendixes. The introduction reveals the goal, research
objectives and managerial problem of the master thesis. First chapter entails the theoretical
review of financial SC and identifies the research gap which corresponds to the first objective of
8
investigation of literature devoted to financial SC (FSC) and identification of research gaps in
FSC field. Chapter 2 consists of research approaches description and ways of the improvement
of the methodology of FSC and development of optimization model based on cost criterion
summarized in research design. Improved methodology of FSC then tested on four different
cases of collaborative SCs in Chapter 3. The main findings, theoretical and managerial
contribution of this master thesis alongside with the ways of future research are summarized in
conclusions. The structure of the research is illustrated in Figure 0.2.
Figure 0.2. The structure of the master thesis
Source: Created by the author
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1.
Theoretical review of financial supply chain
This chapter is dedicated to the justification of the theoretical relevance of the chosen
topic and to the identification of the research gap in the existing literature about WCM in SC.
The structure of literature review is following: the first goal is to identify all stages leading to SC
collaboration. This chapter also identifies the main perspectives if FSCM. Thirdly, there is a
theoretical foundation for one of dimensions of FSCM – WC. The CCC model and its
modifications are reviewed as a part of analyzing WCM in SCs.
1.1.
Collaboration in supply chains
The SC concept has steadily appealed the attention from both researchers and companies’
management due to increasing importance of the role it plays in companies’ performance.
Increasing demand and constant improvement of technologies increase the complexity of
business environment and these companies who can create and manage effective SCs can gain a
sustainable competitive advantage.
The development of SC and SC management definition has started in 1960s and the term
itself was firstly introduced by consultants in the beginning of 1980s. At this time the researchers
have started to align the processes which were perceived as separate such as transportation,
warehousing, purchasing, etc. In the 1980 to the beginning of 2000s the concept of SC was
integrated as “an integration of business processes from end user through original suppliers that
provides products, services and information that add value for customer and other stakeholders”.
(Lambert, Stock, Ellram, 1998)
Figure 1.1.The evolution of SC management definition
Source: Branch, (2009)
One of the most common definition of SC which is widely used in many studies is the
one stated by Jain (2009) – “SC is a dynamic process and involves the constant flow of
10
information, materials, and funds across multiple functional areas both within and between chain
members” which is elaborated from the definition provided by Mentzer et al.. (2001) – “SC is a
set of three or more entities (organizations or individuals) directly involved in the upstream and
downstream flows of products, services, finances and information from a source to a customer”.
The SC is closely associated with the SC management concept. One of the widely used
definitions to SC management is stated by the Council of SC Management Professionals – a
worldwide association of SC management professionals who defined the SC management as
“encompasses the planning and management of all activities involved in sourcing and
procurement, conversion and all Logistic Management activities. Importantly, it also includes
coordination and collaboration with channel partners, which can be suppliers, intermediaries,
third party service providers and customers. In essence, SC management integrates supply and
demand management within and across companies” (GSCMP, 2016).
This definition of SC management which will be used in this master thesis includes
logistics activities as a part of SC management. Although there are a lot of contradiction between
researches whether logistics is a part of SC management or there are two separate dimensions or
SC management is included into logistics. There are four different perspectives of SC
management and logistics relationship (Larson, Halldorsson, 2004).
Figure 1.2. Four perspectives of SC management and logistics relationship
Source: Larson, Halldorsson (2004).
According to Larson and Halldorsson (2004), the traditional view includes the SC
management into logistics as a part of it. The re-labelling is a view of substitution of logistics by
SC management. Intersection view for SC management and logistics relationship suggests that
SC management does not include business processes such as logistics, purchasing, inventory or
11
others. Rather this perspective is about strategic elements from all organizational perspective.
The last one – unionist view which incorporate logistics as a part of SC management will be used
in this thesis in compliance with the definition stated by GSCMP (2016) and supported by
Mentzer et al. (2001). According to Larson (1998) the logistics focuses on the processes at the
company level and the SC management operates inter-organizationally.
The concept of SC management includes the simple (direct) SC structure and its broaden
versions with multiple participants of the SC (Hugos, 2011). The simple version of the SC
consists usually of three members: a company, a supplier of this company and company’s
customers. The broaden versions of the SCs include several steps of the SC including financial
and service providers. Mentzer et al. (2001) provide three main types of SC relationships.
Figure 1.3. Types of SC structure
Source: Menzer et al., (2001)
The perceptions of the way the SC’s structures are also varied among researchers and
highlight two main approaches – integrated and dynamic. The concept of integrated model of SC
was introduced by Akkermans (2003) when he stated that SC consists of 5 participants which are
suppliers, companies (as a manufacturers), distributors (or warehouses), retailers and end
customers. Three types of flows are circulated through this network – physical (as goods or
services), information and financial. The financial flow has recently been highlighted as a
separate flow in SC processes and the new perspective was introduced – FSCM which is going
to be reviewed separately. These networks are supported by organizational structure of the
12
company and all participants of the SC, technologies which enables the business processes of the
SC.
Figure 1.4. SC networks
Source: Menzer et al., (2001)
Another view to SC process is reviewed by Harrison, Lee and Neale (2003) and is about
the dynamic nature of SC. The dynamic SC design should not only be about collaboration among
the companies but also about the dynamic processes of aligning capability chains.
The goal of the SC management is to align intra- and inter-organizational level of
company’s activities which allow effective management of all flows in SC and meet the demand
of its customers (Burgess, 2006). Thus, to achieve effectiveness a SC should be managed at the
system level. Mentzer et al. (2001) emphasize the critical role of systematic approach as a tool to
manage a SC in order to gain a sustainable competitive advantage not only for the participants of
a SC, but also for a whole SC which could be achieved through collaboration (Simatupang,
Sridharan, 2002).
1.1.1. Collaborative SC management. After the total integration of activities into
aggregated SC the role of it has increasingly started to play in gaining an advantage in a
competitive landscape. The importance of the SC proper management could affect not only the
operations but also the activities involved into business processes at all stages of value and SC
(Barratt, 2004).
13
To achieve the effectiveness and competiveness of SC the management should consider
several factors prior – fast information exchange, decreased lead-time alongside with decreased
transportation costs, responsiveness of goods or services. To meet these goals and improve the
financial and economic performance of the company the companies inside a SC should start to
collaborate with each other (Seifert, 2010).
According to Simatupang and Sridharan (2004) the SC collaboration assumes that the SC
members manage coordination among participants in order to meet customer demand by
spanning the boundaries. In other words “collaboration is a cooperative strategy of SC partners
with a common goal of serving customer through integrated solutions for lowering cost and
increasing revenue” (Simatupang et al.., 2004). Samaddar and Kadiyala (2006) in their research
add that knowledge exchange plays an explicit role in SC management collaboration. Besides,
Fawcett et al. (2011) state that the collaboration allows companies creating and managing unique
processes that bring value for companies by better meeting customer goals.
Most of the studies perceive the collaboration at the inter-organizational level, but
importance of collaborative networks inside the company is hard to be overestimated. Besides, in
the study of Simatupang et al.. (2004) the survey was conducted and most of the respondents
who have managed to achieve strong supplier-retailer collaboration stated that they have recently
enjoyed increasing sales, decreased lead time and increased on-time delivery and inventory
levels, etc. The process of achieving collaboration is consists of four stages which were stated in
the study of Spekman et al.., (1998).
Figure 1.5. Stages of collaboration achievement
Source: Spekman et al.., (1998)
At the first stage of open market negotiation companies start to discuss optimal prices
which result in adversarial relationships among participants. The stage of cooperation companies
start to decrease boundaries of information exchange and develop long-term contracts with some
suppliers. Spekman et al.., (1998) emphasize the fact that the beginning of the SC management is
at the stage of cooperation. Although it is still not enough to provide the goals of SC
management and the companies transit to the next part of collaboration achievement –
14
coordination. In addition to the previously opened information exchange channels participants of
the SC start specified material flows to provide better results. Although this stage is still fulfill
the requirements of the SC integration due to the lack of constant information exchange among
participants.
At the fourth stage of collaboration the integration of SC is finally achieved. The
integration means that all participants of the SC are involved in other participants’ business
processes. As for example of SC integration the members of SC not only the joint production
plan for the product but also sharing technologies available, creation future design and long-term
strategic actions. The final level of collaboration requires companies to trust each and share
information with their partners. (Horvath, 2001).
Anupindi and Bassok (1999) stated that the management of SCs deals with the flows of
information and physical goods while managing financial flows of the SC network. Höhn (2010)
pointed that the decisions of volumes and price of any pair of network members should be made
by a single decision maker who was all information available. In other words, the case of the
management of supply chain with a single decision maker is called centralized or integrated.
Otherwise, if there are many decision makers who do not have the holistic perspective of all
information available the supply chain management is decentralized.
According to Anupindi and Bassok (1999) the single decision maker management is
preferable because it optimized the SC with all information available of all members involved.
Furthermore as it was mentioned by Corbett et al. (2004) in the case of decentralized SC the
information holders often refuse to share private pieces of information which creates obstacles
for optimal SC performance. To paraphrase the decentralized SC is not efficient enough due to
incomplete cooperation which can affect profitability. Thus, Höhn (2010) concludes, the
centralized decision making can be used for sustainable competitive advantage achievement.
Popa (2013) in his study reflects that the integration of a bank or other financial
institution as a financial 3PL provider for FSCM should be investigated further. One of the main
reasons he provides is that to achieve collaborative management in the volatile economic
environment companies should not only exchange information flows but also integrate financial
ones which is possible when a bank or other financial institution is in a role of financial 3PL
operator for a collaborative supply chain which. The financial 3PL provider as a coordination
mechanism will be implemented in the coordination modeling of WCM in SCs and discussed in
details in chapter 2.2.
15
1.1.2. SC collaboration typology. Researches identify the types of SC collaboration
differently. The theoretical framework of joint ventures offered by Doz and Hamel (1998)
describes the inter-organizational collaboration as a development tool for new market
opportunities. The example of this type of collaboration could be describes with a situation when
a company which is seeking for a new market collaborate with a local one which provides
market knowledge and networks. Usually participants of this type collaborate at one point of the
SC to establish production or distribution economies of scale (Hennart, 1988).
Another type of collaboration among SC members refers to strategic alliances. Recently
this topic has received larger attention from the researchers but the field is still fragmented. The
most common definition of strategic alliances comes from the work of Spekman and Celly
(1995) and states that the strategic alliances assumed to be in a long-term perspective where
participants of the partnership share resources, technologies and information in order to gain a
competitive advantage for each other. It was mentioned by Li and Qian (2013) that strategic
alliances can be used for faster spread of new technologies, exploration and penetration of new
markets.
In the constantly changing business environment there are a lot of the companies who
would like to cooperate with other companies to response to these fast changes. For example,
with the rise of telecommunication technologies enables virtual collaboration to happen. There is
a situation where a company temporary is involved in a close collaboration with independent
entity because of this type of technology. Usage of telecommunication technology allows
companies involved to share and decrease costs, exchange skills and get access to global
markets. Bal et al. (1999) specifically mention that there are two stages of virtual collaboration –
information networks at the first stage and after the specific market goal was obtained the
dissolution of the collaborative network.
When it comes to long-term collaboration it is aligned with the strategies of the
companies which participate in the SC. Baratt (2004) in his study highlights three main strategies
of SC collaboration. Horizontal collaborative strategy or often called horizontal integration
happens when two companies which are not related but produce close to similar or similar
products create a collaboration in order to logistics resources or manufacturing capabilities
(Simatupang, Sridharan, 2002). Using horizontal integration may result in reduced operation
costs, improved procurement and overcome financial barriers for each company.
16
Lateral collaboration combines the advantages of both vertical and horizontal SC
collaborative strategies. Intermodal transportation mode is one of the examples of effective
lateral SC integration.
Another view on the collaboration is about splitting it into internal and external
dimensions. Internal collaboration is achieved on the intra-organizational level aligning the
different departures within the company such as marketing, customer development, SC and
others. External level of collaboration is based on the inter-organizational level and usually
connects manufacturer with its suppliers and customers. The common feature both collaborative
dimensions share is that at all levels including production planning, new products integration and
shared distribution. There are techniques that allow aligning of both levels of collaboration such
as Vendor Managed Inventory (VMI) and Collaborative Planning, Forecasting and
Replenishment (CPFR) (Flynn, Hou, Zhao, 2010).
Successful achievement of collaboration among partners is based on three main features
when at least one should be satisfied to ensure increased company’s performance. Spekman
(1998) in his study classified these features in three main factors of SC collaboration. The first
condition is information exchange which includes access to the data of the companies. The
second one is collaborative decision making in response to external factors using internal
capabilities. The last dimension is the ability of each SC member to take risks and share costs of
financial flows of the SC management. Recently researchers have started to highlight financial
flows as a separate flow alongside with information and physical flows of goods and services.
The financial flows of the SC are one of the main topics of this master thesis thus it is essential
to provide the review on one of the most recent study field in SC literature – FSCM.
1.2.
Financial supply chain management (FSCM)
In the era of SC integration there were two main flows of physical goods or services and
information flows inside the SC. The new studies in the beginning of 21st century revealed the
need of investigation into one more separate flow – financial due to increased speed of
constantly changing business environment and demand of faster cash-to-cash cycles as a
response.
Collaboration among partners of SC and SC management allows connection of all parties
of the SC such as suppliers, manufacturers, distributors, retailers, 3PL providers and financial
intermediaries. There are three main streams of flows into SC – downstream flow of cash,
17
upstream flow of goods or services and information flow which goes both directions (Gupta,
Dutta, 2011).
Figure 1.6. The downstream and upstream directions of flows in SC
Source: Gupta and Dutta, (2011)
Although the main focus of researchers’ attention is still paid to information and physical
goods or services flows the FSCM is gaining its fair amount of attention of the recent studies – in
the study of Wuttke et al. (2013) the financial flow is acknowledged as the one of the main
pillars that ensures the efficient performance of the SC. The authors also mention that in order to
achieve competitive advantage in SC management companies should have their concentration
not only on downstream but also on upstream financial flow. Randall and Farris (2009) support
this view and state that companies who are able to manage both directions could gain additional
competitive advantage based on cost reduction and increased profits.
Due to increased volatility in global economics companies that do not have efficient
FSCM will not be able to perform well in long-term conditions. Global financial crisis of 20082009 has boosted the investigations in the study of FSCM in order to provide techniques to
strengthen business processes inside SCs. Collaboration among the companies at this stage has
become increasingly important due to the fact the financial flows ensures constant performance
of all companies participating and most companies are seeking the way of financial optimization
at all stages of collaborative SCs (Fairchild, 2005). Some companies prefer managing its
financial flows themselves – others delegate the duties to 3PL providers or financial
intermediaries to achieve better collaboration in information and financial flows exchange.
18
1.2.1. Definition of FSCM. The literature dedicated to FSCM is scarce and most of the
research studies focus on one specific factor rather than pertain a holistic view. Most of the
financial papers dedicated to financial SC topics focus on the technical factors of the SC and
ignore its operations and strategic importance.
As it often happens in the science the researchers have not managed to evolve a common
definition of FSCM because many authors base their conclusions on different concepts
(Blackman, Holland, Westcott, 2013). The first definition was introduced by Killen&Associates
in 2000. They stated that the financial SC should be perceived as a chain where “parallels the
physical or materials SC and represents all transaction activities related to the flow of cash from
the customer’s initial order through reconciliation and payment to the seller” (Weiss, 2012). The
authors are mainly focused on intra-organizational nature of financial SC whereas other
researchers suggest interpretation of it also on the inter-organizational level (Popa, 2013).
One of the most cited definitions of inter-organizational financial SC is offered by
Hoffman (2005). He suggests the definition as “located at intersection of logistics, SC
management, collaboration and finance, FSCM is an approach for two or more organizations in a
SC, including external service providers, to jointly create value through means of planning,
steering, and controlling the flow of financial resources on an inter-organizational level”.
Management accounting studies interpret FSCM from the perspective of global value
chain as “a series of financial events that are based on financial and information flow between
members of global value chain” (Matayac, 2015). The technology also plays a role in connection
of financial flows with other flows as many of the studies of it operates the databases from
different IT platforms such as Enterprise Resource Planning SaaS. Many banks and consulting
services offer their clients optimization of FSCM to increase cash cycles and thus liquidity and to
fix risks.
In their study of investigating a financial SC strategy of Motorola company Blackman et
al.. (2013) state the definition of it as “a financial SC is the network of organizations and banks
that coordinate the flow of money and financial transactions via financial processes and shared
information systems in order to support and enable the flow of goods and services between
trading partners in a product SC”. This definition combines the features associated with the
financial flows in SCs and will be used in this master thesis in the further investigation of FSCM
perspectives.
19
1.2.2. Scope and perspectives of FSCM. To identify the scope and intersections of
financial SC in order to define the perspectives of this field Institute of SC Management linked it
with other disciplines such as SC management, corporate finance and risk management (ISCM,
2012). The intersections are illustrated in Figure 1.7. According to their study, there are two
main perspectives of FSCM: financing of trade operations and WCM in SC are included in
FSCM and risks of financing SCs relate to financial risk management in SCs.
Financial risk in SCs concerns risks of trade and possible supplier default risks. As for
FSCM, it includes pre- and post-shipment financial management and mutual activities such as
cash flow forecast and WCM in SCs (Sugirin, 2009)
Pre-shipment is a term of SC financing when a supplier or a buyer pays for products or
services right after the order came before the actual shipment happens. Thus, post-shipment
financial term means that supplier or buyer does financing operations after shipment to the
customer. According to Wuttke et al. (2013), pre-shipment includes “inventory finance,
advanced payments, trade enablement and WC financing” and post-shipment consists of such
financing activities as “factoring, reverse factoring, SCF, electronic payment platforms, trade
credit and trade settlement” (Camerinelli, 2009)
Figure 1.7. Intersections of FSCM with other disciplines
Source: Institute of SC Management, (2012).
The WC financing is about the amount of cash that is necessary for each stage of
company’s daily operations. Many companies lack sufficient WC due to late payments. To
20
overcome this kind of problem Sagner (2011) suggest financing WC through credits and loans
(secured or open-ended)), accounts receivable (A/R) – the amount of money a company should
receive after the purchase was made. Besides, pre-shipment finance activities also include
advance payments (A/E) – which perceived by some authors as the part of WC and inventory
finance, which is additional stock to prevent economic volatility and get constant cash flow.
Trade credit is one of the most widely used tactics for short-term SC financing. Yan
(2015) defines trade credit as “Under trade credit, a supplier adopts the complete financing
function, traditionally assumed by a third-party financial institution – hereafter referred to as the
bank – or by customer herself drawing from its own cash reserves”. Seifert et al. (2013)
identified six main reasons for supplier and buyer to initiate trade credit relationship – “capital
access, product market position, price elasticity, collateral value, credit information and nonsalvageable investment”. Seifert et al. (2013) also state that there are additional advantages for
buyer side in case trade credit conditions are proper – “transaction pooling, credit rationing and
control protection”.
Some researchers define SC Finance as the part of post-shipment activities. The direction
of this field is different in some studies; authors refer to the SC finance as a related field to
WCM, others perceive it as cash flows among the participants of the SC. Kouvelis and Zhao
(2011) provide the description of SC finance as “At the center of SCF is the management of WC
and financial flows, but equally important is the management of the respective information
across the SC and the documents and data involved that support these flows, such as POs,
invoices and payment approval processes”. Besides, the authors highlighted five main factors of
SC finance:
Using paper for documents is obsolete;
Automated and transparent flows of information;
Liquidity risk control;
Data is predictable;
Collaboration among all parts of SC.
Although for many researchers the definition of SC finance is still ambiguous. The main
difference between SC finance and financing SC is that SC finance has its base on IT solution
that provides the information exchange among all SC partners (Figure 1.8).
21
Figure 1.8. The definition difference of SC finance and financing SC
Source: Created by the author
According to Figure 1.8., banks in financing SC framework make a credit decisions using
information that buyer and supplier provide separately. On the other hand in SC finance there is
one bank that makes decisions about credit issue for both players of SC due to the fact that bank
receives information from both supplier and buyer through various IT platforms.
Post-shipment also includes one more practice of factoring. The definition of factoring is
that “factoring entails the sale of A/R to another firm, called the factor, who then collects
payment from the customer” (Pritchard, Mendez, 1990). Usually companies take the factoring
possibility when they need immediate cash and sale their A/R to third companies with a
discount.
Recently the reverse factoring has started to develop and now is one of the most common
practices of SC finance. Buyers hire a third party or financial intermediary such as bank to
finance invoices from suppliers. The scheme is following – a buyer approves invoices and a
supplier immediately starts to process an order with the support of a third party or a bank.
Reverse factoring brings advantages for both parties because suppliers shorten the time of A/R
with the help of financial intermediary and the buyer can prolong their payments without
negative effect on cash flows, WC and collaborative network.
1.2.3. Financial risks associated with SC. Different practices aimed to develop and
ensure financial flows in SCs have their risks that could happen internally and externally
depending on inter- or intra-organizational level. Previously the intersection of field showed that
the main financial risks associated with SC are supplier default risk and trade credit risks.
22
A company may face supplier default in case of external or/and internal disruptions.
Some of them may be the result of nature disasters, but mostly supplier default happens in case
of poor management and results in production failures and bankruptcy. In these situations a
buyer is forced to switch a supplier which may result in high switching costs, production and
sales losses, etc. Although in some cases a buyer can support a supplier in turbulence period but
there is no guarantee the companies in SC have completely fixed this risk. McKinsey&Company
in their report (2011) emphasize the fact that financial instability of one member of a
collaborative SC strongly affects other companies’ performance. Babich et al. (2007) also
mention that supplier default risks can be fixed by better SC network control.
Trade credit risks consists of two dimensions – supply (accounts payable (A/P)) and
demand (A/R) and can occur at different parts of SC (Yang, Dai, 2015). Furthermore, trade
credit is still the preferable source of short-term financing debt which increases the possibility of
risk to occur. To fix trade credit risk the companies use trade credit insurance from banks and
special services which decrease the volatility in a SC by providing a stable financial flow among
the participants. Although according to McKinsey&Company (2011) the financial crisis
increased the volatility of SCs and decreased efficiency of risk mitigating measures.
1.3.
Working capital management as a FSCM perspective
The WCM concept is one of the main perspectives connected to the FSCM and is the
main topic of this master thesis. The liquidity problem became especially important for most of
the companies because of the financial crisis of 2008-2009 and oil crisis that has started in 2014.
Christopher and Ryals (1999) defined WCM as one of the factors of shareholder value creation.
Karl Max introduced the idea of WC and Pirttilä (2014) identifies WC as “the capital of a
business which is used in its day-to-day trading operations, calculated as current assets less
current liabilities”. In other words it is “amount of cash that is tied up at each stage of SC”
(Viskari, 2012). WC connects with the short-term need of a company to maintain its business
processes. According to Brandenburg and Seuring (2010) companies that failed the cost and
WCM balance resulted having significant value losses.
There are two main perspectives of WC. The first one defines the ability of the company
to cover its short-term debt with current assets. Jones (2006) defines the concept of this WC
perspective and described it with the equation.
(1.1)
23
According to Jones (2006) current assets comprised of cash, total inventory, A/R,
securities and cash equivalents. On the other side, current liabilities refer to A/P, accruals, notes
payable and short-term debt. The positive result of WC means that the amount of cash the
company will receive in the next 12 months is bigger than that company needs to cover its
liabilities. The negative meaning of WC means that company will not be able to cover its shortterm debt (Monto, 2013).
Another perspective of WC is widely used on the most of the studies dedicated to
operational WC and comprises of total level of inventory, A/R and A/P. These components are
called WC operational components (Monto, 2013). According to Pirttilä (2014) the equation is
following:
(1.2)
The recent study of Talonpoika et al. (2014) include A/E as a separate component into
WC cycle (usually is a part of A/P) and this case will be reviewed in the next sections. Pirttilä
(2014) state that WC cycle describes the main parts of company’s performance connected with
financial flows.
Figure 1.9. WC cycle flows
Source: Pirttilä, (2014)
The cycle of WC starts with the inventory which consists of raw material inventory,
work-in-progress products and finished goods. At the transition point of every stage inventories
appear. A company may hold large amount of inventories for two main reasons – to avoid
stockouts and some customers require immediate deliver. However, large inventories in both
24
cases increase the amount of WC and low inventory may cause sales losses thus the SC
inventory optimization may increase the profitability of WC (Brodetskiy, 2015).
The next part of the cycle begins when a company sells its finished goods to a customer
and a customer pays for it. In a supplier – buyer relationship, with the flows of products or
services and information flow towards the customer occur – A/R occur on credit. In opposite
relationship of buyer – supplier with the flows of cash and information towards the supplier –
A/P happen (Jahfer, 2015).
The optimal level of WC depends on several factors – such as industry, competition,
economic situation and company’s strategy. Hill et al. (2010) run a survey to reveal the
dependency of WC on industry. The results are that companies that produce goods are tent to
have more inventories meanwhile service companies usually have low or now level of inventory
thus operational WC varies over industries. Al-Shubiri and Aburumman (2013) point the
industry factor as the most important external factor influencing the WC level.
WC can be negative and positive. When the value of WC is positive it means that the
amount of inventories and A/R exceed the level of A/P. The main advantage of high positive WC
is high levels of inventory and A/R can ensure ability of the company to cover its short-term
debt. The negative effect may result in high costs of inventory storage. The negative WC means
that short-term liabilities of the company exceed the level of inventory and A/R. This case
positively effect on low storage costs but too low inventory level may cause sales losses (Kroes,
Manikas, 2014).
To define the optimal level of WC for each company the WCM was implemented. WCM
depends on the strategy and policy of the company and WC Policy. There are three main types of
WC policy – aggressive – with high risks and possible high profitability, moderate – with
balanced risk and profitability and conservative – with low risks possible low profitability
(Baveld, 2012).
According to Viskari (2011) the scientific research fields related to WCM divide into four
groups. Three of the fields are mostly concerned with the financial aspects of WCM and
practices. The last one in centered with the operational WC and investigates its relationships with
SC management. This field includes the studies in empirical archival research – the case studies
from different industries and countries, qualitative conceptual research – focuses mainly on CCC
at all stages of collaboration in SC and mathematical modeling.
25
Viskari (2011) made the assumption which was confirmed lately that a lot of articles
about WCM will occur in time period of 2011-2014 due to the consequences of financial crisis
and approaches to fix future risks associated with economic turbulence. The WC investigation
approaches divide into two groups: based on different perspectives of the company’s
performance and investigation using CCC approach (Lind et al., 2012).
Figure 1.10. Summary of the studies of WC in SCs
Source: Created by the author
Hill (2010) mentions that previously each component of the WC cycle was estimated
separately, and in 1983 Sartoris developed an approach where all components were analyzed as a
whole. In the literature there are two approaches towards WCM – WC ratios and WC cycle time
(Pirttilä, 2014).
Pirttilä (2014) mentions that the ratios for assessing WC are “current ratio, current ratio,
quick ratio, net WC per net sales, net WC to total assets, and sales to net WC”. She also
mentions that these ratios have received a lot of criticism and a new measure circulating capital
ratio which is more dynamic was introduced.
The most frequently and considered as indicates of financial health and stability of the
company current ratio and quick ratio indicate the ability of the company to cover its short-term
obligations. The higher the ratio the more company is able to pay back its liabilities. The value of
these ratios equal more than two is preferable – in this case the company is able to cover its
26
liabilities without financial strain. Ratio which equals less than 1 assumes that a company is
unlikely to pay its short-term debt.
The operational approach to measure WC is WC cycle time – the CCC approach is one of
the main topics of this master thesis and modification and optimization of it in collaborative SCs
will be discussed in the next chapters.
1.4.
Cash conversion cycle (CCC) as a WC measure
The CCC concept was developed by Richards and Laughlin (1980) as a measure to
address the criticism of relative ratios as key indicators of a company’s profitability. According
to the authors CCC can be defined as: “The CCC, by reflecting the net time interval between
actual cash expenditures on a firm’s purchase of productive resources and the ultimate recovery
of cash receipts from product sales, establishes the period of time required to convert a dollar of
cash disbursements back into a dollar cash inflow from a firm’s regular course of operations”
(Richards, Laughlin, 1980).
There definition of CCC among researchers is ambiguous. Stewart (1995) defines CCC as
a “composite metric describing the average days required to turn a dollar invested in raw
material into a dollar collected from a customer”. Schilling (1996) in his study provides broaden
definition as “The CCC, which mirrors the operating cycle, measures the interval between the
time cash expenditures are made to purchase inventory for use in the production process and the
time funds are received from the sales of the finished products. This time internal is measured in
days and is equal to the net of the average age of the inventory plus the average collection period
minus the average of A/P”. One of the most recent definitions is provided by Hofmann and
Kotzab in 2010 - they define CCC as “as the time elapsed from the payment of cash for materials
or components through to the receipt of cash for sale of the finished product”.
One of the most cited definition which is used on financial dictionaries is stated in the
study of Pirttilä (2014). According to Pirttilä (2014) the definition of CCC is “the CCC presents
the length (in days) of time a firm has funds tied up in WC, starting from the payment of
purchases to the supplier and ending when remittance of sales is received from the customers”.
CCC as WC can be either negative or positive. Negative CCC means that the company
has a low amount of inventory and a company receives money from its customers before it has to
pay its A/R or make A/E. In other words, in negative CCC scenario, a company receives its A/R
before it should pay A/P. Many authors provide a view that the lower CCC the better and a
27
company can manage it cycles efficiently although the too low CCC can cause problems with
each component of CCC.
The CCC consist of three components – DIO – days of sales outstanding, DSO – days
sales outstanding and DPO – days payable outstanding and visualized at Figure 1.11.
Figure 1.11. CCC approach
Source: Adapted from Richards and Laughlin, (1980)
There are a lot of discussions for these components’ denominators among researchers.
Shin and Soenen (1998) developed the net trade cycle (NTC) framework – in their equation all
the components of CCC should be sales percentages – but as long as all denominators are
different – the modification is not useful. Farris and Hutchison (2002) and lately Ding et al.
(2013) introduce the calculation where inventories and A/P are divided by cost of goods sold
(COGS). The list of equations is structured in Table 1.1.
Back to 2003 there was a problem due to the fact that only in US GAAP the cost of goods
statement was obligatory in the profit and loss account although nowadays International
Financing Report Standards allow the cost of sales method and this equation can be applied.
Pirttilä (2014) notices that in the research of Hofmann and Kotzab they based the definition of
Farris and Hutchison (2003) equation but in reality they use the definition of NTC framework.
Although the revenue data is more available than cost of goods sold it can result in shorter CCC
due to the fact that revenue value is usually bigger than that of cost of goods sold (Ding et al.,
2013). To avoid the unintentional decrease the equation of Farris and Hutchison (2003) is used.
28
Table 1.1. Different CCC calculations
Source: Pirttila, (2014)
Several studies have investigated the relationship between WCM cycle time and found a
negative relationship between the CCC and the relative profitability of a company. The studies
point the companies can improve their profitability effectively managing WC, i.e. by shortening
the cycle time of WC (Deloof, 2003; García-Teruel, Martínez-Solano, 2007; Talha et al., 2010;
Yazdanfar, Öhman, 2014). However, the components of CCC and their impact on company’s
profitability have not been investigated enough and the correlation of these components is
ambiguous (Monto, 2013). Ruyken, Wagner, and Jonke (2011) also stated that in order to
increase profitability the relation between DIO and profitability and DSO and profitability
should be negative and assumes that relation of DPO and profitability should be positive. Figure
1.12 describes the diverse effect of the cycle times of WC – directly on profitability and
indirectly on sales and costs.
Monto (2013) also pays attention that WCM has inter-organizational context due to the
fact the cycle times are result of the decisions not only of one focal company. Besides, the
policies, strategy and management approach of companies and of WC in particular affect the
other members of the SC. To review the operation of WC cycle times for all participants of SC
the collaborative CCC concept was introduced.
29
Figure 1.12. The relationship of WC cycle times and relative profitability
Source: Monto (2013)
1.4.1. Collaborative CCC. In existing literature about WCM and SC management there
are few works investigating CCC at the level of collaboration among all SC partners. Although
there are financial studies that investigate the inter-organizational level of CCC using accounting
perspective but not from collaborative SC view. A lot of authors though state that CCC research
in SC should be done with a holistic point of view.
The collaborative CCC was introduced by Hofmann and Kotzab in 2010. In their study
they discuss that in collaborative SC the risks and costs spread through all stages of SC because
of late payments and changes in inventories. In their study they claim the fact that the reduction
of CCC only for one company in a SC does not add value to other SC participants. It is critical to
consider the bargaining power of each company in SC because if the company has a goal to
improve its CCC only at an intra-organizational level it may result in ethical disruptions for other
SC members. (Hofmann, Kotzab, 2010).
Optimization of collaborative CCC can be achieved not only buy reduce of inventories.
On the contrary, Hofmann and Kotzab (2010) state that “the synchronization of the
goods/material and financial (payment) flows within the SC is the key to reducing net WC and
enhancing value for the affiliated SC companies”.
30
The external management of collaborative CCC should follow the principles of CCC
objectives – increase the time of A/P as far as possible meanwhile getting cash from A/R as fast
as possible. At the intra-organizational level management should try to achieve the optimal level
of CCC minimizing the cost of WC at each stage (Hofmann, Kotzab, 2010). It is critical for all
SC members as it was stated in the part of collaborative SC to have equal power and trust among
SC partners. Figure 1.13 illustrates collaborative CCC.
Figure 1.13. Collaborative CCC framework
Source: Hofmann and Kotzab, (2010)
Figure 1.13 provides the internal – among the SC participants and external relationships –
that happen outside of collaborative SC. The collaborative CCC for whole SC is calculated as the
sum of every CCC in the SC, but the authors provide the justification of the approach to
calculate the collaborative CCC as the sum of all inventories in the SC plus the A/R of a
company 3 with the subtraction of A/P of company 1. Hofmann and Kotzab (2010) mention that
internal payments among the participants do not affect the collaborative CCC and are not
counted. If assume that there are three companies in collaborative SC the final equation is.
(1.4)
The limitations of the collaborative CCC approach include the difficulties in accounting
information exchange and the factor of competition also takes place if the company operates
with several suppliers and customers. To fix the limitations it is preferable to start the estimation
of collaborative CCC at the focal company level and continue it in the industries in which its
suppliers and customers operate.
The investigation and modification of collaborative CCC do not limit only at the study of
Hoffman and Kotzab (2010). In 2012 Viskari et al. introduced the new metric – Advanced CCC
31
while developing Financial cycle time model. The main improvement of the approach is that
ACCC takes into account not only the cycle times of WC but also consider the amount of capital
tied up in each stage of WC. Viskari et al. (2012) support the Hofmann and Kotzab view (2010)
that the internal payments should not be included in calculations and provide a view of
overlapping periods illustrated at Figure 1.14.
Figure 1.14. Overlapping periods of CCC components
Source: Viskari et al. (2012)
Based on the automotive industry SC, Viskari et al. (2013) developed the weights for
each CCC component based on the ratios of costs of each stage to total costs of whole SC. The
modification helps companies compare the participants of SC and are financial costs of
collaborative SC.
(1.5)
∑
∑
(1.6)
(1.7)
where AOC-adjusted operating cycle; k - stage number; n -
the
included in the operating cycle; Di - days of funds committed to AP; wk
number
of
stages
- weight of stage k; c
k
- cost for stage k; P- cost price.
Although due to the fact that ACCC uses only weighted days it turn a model into
theoretical approach instead of real life case. Besides, the ACCC calculations were done only at
the level of one order and efforts to transfer it to the inter-organizational level it will be
extremely time-consuming and may face the limitations of accounting information exchange.
32
Thus for optimization purposes it is preferable to use simple CCC. Although in the study of
Talonpoika et al. (2014) the modified version of CCC including A/E was stated.
The Financial cycle time model allows estimation of the costs of each stage of
collaborative SC and Viskari et al. (2013) introduced the formula of financial costs.
]
]
]
(1.8)
where c - annual cost of capital; d1 = DIO/365; d2 = DSO/365; d3 = DPO/365
1.4.2. Modified CCC. A lot of companies were affected by the financial crisis of 20082009 and recent economic turbulence. The availability of credit was not easy and companies
were obliged to find new sources of finance. Received A/E are one of the ways to finance
business processes. A/E were not included in the calculations of WC although a lot of companies
acknowledge the fact that A/E play significant role in WCM of these firms (Talonpoika et al.,
2014).
The new measure – modified CCC (mCCC) consider a new component in CCC
calculations – A/E in order to reflect the real cash flow movements and operational efficiency.
The measure was tested by the authors using sample of 108 companies listed at Helsinki stock
exchange and revealed a more accurate view on the WCM in these companies.
The mCCC is calculated in the same way as CCC does (1.3.). The new component of
days A/E outstanding is add to the calculation of the CCC and is analogous to other components.
A/E component follows the same logic as the DPO components does and provides the reduction
in time cash is tied up into the operating cycle. The formula of modified CCC is following:
(1.9)
(1.10)
The logic of the equation is that other components of the CCC remain the same and the
DAO is subtracted. A/E are often include in the total accrued expenses and payables but many
companies have started to separate A/E in their balance sheets. The process of mCCC is
illustrated at Figure 1.15.
33
Figure 1.15. Modified CCC
Source: Talonpoika et al. (2014)
The advantage of A/E is beneficial not only for the supplier who can receive cash when
there is a need in operating cycle and for the buyer who does not need to pay the whole price at
once. The benefits of smoothed cash flow can also please both SC participants. Furthermore, A/E
prevent supplier of a credit risk associated with a customer. For the customer, A/E can ensure the
availability of the product or service in different circumstances meanwhile preventing itself from
supply delays risk.
The empirical test of a new measure provided the results that A/E has significant impact
on WC cycle time and the efficiency is different if measured by CCC or mCCC. This measure is
especially important in industries where A/E is common practice – ICT, publishing, automotive
and project companies (Talonpoika et al., 2014). The authors also mention that “A/E could have
a significant role when aiming at efficient SC management and this should be studied further”.
To conclude the theoretical review of financial SC three main points are outlined:
There is a lack of literature to investigate the WCM from collaborative SC
perspective. This master thesis introduces the coordination mechanism of WCM
in SCs that includes financial 3PL operator as a coordinator of a collaborative
SC;
The literature review of CCC estimation models revealed a new approach of
modified CCC with new component DAO included. This master thesis reviews it
an holistic SC perspective;
This master thesis provides a study that uses goal and linear programming as a
tool for optimization of resources in collaborative SCs with mCCCC components
as variables and costs as criterion.
34
2.
Coordinating modified collaborative cash conversion cycle model
(mCCCC) methodology
The purpose of this chapter is to provide a holistic view of methodology of mCCCC
concept examination. In the first part of the chapter the new measure of cycle time evaluation is
introduced – modified collaborative CCC. The next part is dedicated to the adaptation of existing
scenario analysis framework to mCCCC evaluation. During adaptation several flaws in scenario
analysis framework appeared. The new coordinating mCCCC model is introduced to optimize
financial costs through mCCCC components from a holistic view.
2.1.
mCCCC evaluation methods
The research in this master thesis assumes to be a full, scientific and exploratory study.
Besides, the research is planned, organized and conducted using the positivist’s paradigm. The
master thesis applies design-science and archival approaches due to the fact the one of the main
objectives of the study is to provide the modification of two SC systems – modification of
existing methodology of cycle time in collaborative SC evaluation.
Design-science approach is a common method for the industry and science of
management (Geerts, 2011). The meaning was firstly mentioned by Simon, 1969. The purpose of
described approach is to provide structural solutions to practical problems (Simon, 1969). In later
research papers the investigators suggested the idea that the main aim of design-science approach
is theoretical development of a model that can provide solutions of the organizational problems
of the specific industry. This kind of solution can be direct, indirect or instrumental.
The outcome of design-science approach provides the solid theoretical base for model or
framework development, modifying and implementation. In accordance with Van Aken and
Romme (2009) there are three main points of design-science approach:
1.
“Research question are guided by the field problems;
2.
The research aims at solution-oriented knowledge;
3.
The explanation of research results is done through practice” (Daae, Boks,
2015);
Based on the critical literature review conducted in the first chapter, many of the previous
studies of collaborative SCs and cycle times applied design-science approach (Talonpoika et al.,
2014; Wuttke, Blome and Henke, 2013; Hoffman and Koztab, 2011; Randall and Farris, 2009;
35
Farris and Hitchison, 2002; etc.). Besides, the relevance of choosing design science approach
was proved by its practical implications and applicable outcomes.
Viskari (2012) in his study highlighted four stages of design-science approach:
1.
Problem justification
2.
Design development
3.
Solution representation
4.
Evaluation of the solution
Archival method is widely used in researches of management accounting (Hesford et al.,
2007). Moers (2006) defines archival approach as “an empirical study that uses archival data as
the primary source of data applying quantitative methods to analyze these data”. Archival data is
data which purpose originally was not for the academic research. The information could be either
for public or for confidential purposes. Besides, the requirement of archival data is to be
quantifiable (Monto, 2013).
Moers (2006) reveals a negative view towards the archival approach – the usage of public
sources is inadequate. In this master thesis this risk is fixed by using only official financial
statements of the companies with the annual reports and collecting all the information manually
(Monto, 2013). These actions ensure the reliability of data collected. Besides, the information
about the companies involved in collaborative SC is confidential and the names of the companies
participated are excluded from the data description.
According to Saunders, Lewis and Thornhill (2009) in the study about research methods
which can be applied to business inferred seven types of research methods: experiment, survey,
case study, action research, grounded theory, ethnography and archival research. In compliance
with the goal of master thesis, research questions and critical literature review the case method is
preferable one.
In the terms of design-science approach the modified model should be applied and
assessed based on the case study (Van Aken, Romme, 2009) Furthermore, the case study
approach of research method is implacable for this master thesis research due to the fact that it
assumes empirical investigation of theoretical statement or phenomena which contribute to real
life situations.
Yin (2003) in one of his studies derived four main types of case studies: single, multiple,
holistic and embedded (Saunders, Lewis and Thornhill, 2009). In accordance with the aim of the
36
study and in order to answer research questions the preferable case study method is holistic due
to the fact that the modified model is applied to the real life cases of the multinational
companies. Besides, they are evaluated as the whole in terms of authority – the collaboration
among all partners of the SC.
In the critical literature review it was stated that many authors used holistic case study
approach for the purposes of the development or modification of the existing models. The
method of mCCCC and the modification of the model are new to the research and should be
evaluated on real life problems to validate their applicability. Thus it is possible to conclude that
the master thesis research is conducted using holistic multiple-case study with design-science
and archival approach.
2.2.
mCCCC evaluation models
This part dedicates to the introduction of mCCCC concept and two models for the
evaluation of theoretical and managerial applicability of the mCCCC concept.
According to the literature review conducted and Talonpoika et al. (2014) suggestions for
future research the mCCC there is a research gap of applying the mCCC approach to a) SCs b)
collaborative SCs. One of the objectives of this thesis is to develop a collaborative mCCC
approach which is introduced further. Talonpoika et al. (2014) state that additional component of
mCCC is built using the same logic as other three components of CCC. Hofmann and Kotzab
(2010) developed collaborative CCC and the combination of mCCC and collaborative SC using
design-science introduced the new metric – modified collaborative CCC or mCCCC.
The calculation of mCCCC has similar logic as collaborative CCC does including the
overlapping periods of payments mentioned by Viskari et al. (2012) and is illustrated on the
example of three-stage collaborative SC of a supplier (1), manufacturer (2) and customer (3).
(2.1)
The same logic applies to the formula of total financial costs for each participant of SC
introduced by Viskari et al. (2012)
]
]
]
]
(2.2)
where c - annual cost of capital; d1 = DIO/365; d2 = DSO/365; d3 = DPO/365; d4 =
DAO/365
37
The mCCCC approach has the same advantages as mCCC does but on the collaborative
SC scale. From the side of mCCC the benefits of using mCCCC are – more precise calculation
of cycle time and thus WC associated with each stage of a SC. The collaborative approach
provide a holistic view to the SC and ensure information exchange and high level of trust which
is critical if there is an objective to improve the performance of a whole SC.
Similarly to collaborative CCC the main goal of mCCCC is to be close to 0 (or negative).
In the past the CCC measure of WC was used as a measure of efficiency and profitability of the
company and was reviewed in Chapter 1.4. At the company and SC level the zero or negative
mCCC or mCCCC value that the companies hold cash before payment to suppliers and have
ability to invest WC before the time of DPO and DAO expires.
The evaluation of applicability of mCCCC approach is done by two ways. The first one –
the adapted scenario analysis proposed by Randall and Farris (2009) allows validation the
efficiency of using DAO component in SC as Talonpoika et al. (2014) proposed. However, the
scenario approach has several critical limitations and the author of master thesis developed a new
model for mCCCC evaluation – optimization mCCCC framework. Both of the mCCCC
evaluations are tested empirically on the multiple case study data of 4 collaborative SCs.
2.2.1. mCCCC scenario analysis. The Randall and Farris (2009) in their study
introduced a scenario analysis framework which allows simulation of managerial actions towards
each component of CCC. The same logic is applied to the mCCCC concept and the scenarios for
each component. The practical application of the scenario modelling allows evaluation of the
impact of each scenario on the cycle time of WC and financial costs associated with the
collaborative SC. The framework of adapted scenario approach is illustrated at Figure 2.1.
Figure 2.1. Scenario analysis framework
Source: Randall and Fariss, (2009)
Each scenario describes the impact of the each component to the overall changes of the
mCCCC (2.1) and total FC, which is the sum of total financial costs of each member of a
collaborative SC (2.31). The initial amount of scenarios introduced by Randall and Farris is four,
but for the purposes of mCCCC evaluation the author of master thesis uses design-science
approach and adds the fifth scenario. The aim of fifth scenario is to evaluate the impact of DAO
38
component on collaborative SC conversion cycle and total financial costs. Thus there are five
scenarios to be applied to evaluate the mCCCC.
1.
Shortening the DIO component in collaborative chains
2.
Shifting the inventory from key customer to manufacturer
3.
Shortening the DSO component in collaborative chains
4.
Extension of DPO component in collaborative chains
5.
Extension of DAO component in collaborative chains
The objective function of scenarios is “is to optimize financial management-based cost
saving at the SC level” (Randall and Farris, 2009). A lot of studies of CCC differ in the terms of
what leverage affect the SC most. Moss and Stine (2003) state receivables are critical to affect
cycle time. Randall and Farris (2009) argue this view and based on their simulation research
state that inventories have the most significant impact on cycle time and WC costs because the
receivables are less manageable is SC members do opportunistic actions. They also point the fact
that for the company it is easier to manage its inventories and A/P rather than A/R. In addition
Bougheas, Mateut and Mizen (2009) suggest A/P is the most influential factor. The influence of
amount of accrued expenses (the balance sheet name of A/E) on collaborative mCCCC will be
studied using the scenario analysis.
Although after conducting the collaborative SC analysis using Randall and Farris
framework (2009) the author revealed three main problems of this method:
1. Using a 10-day reduction/addition approach makes sense only in case if all
companies did not optimized their components at all – during the case study some
of scenarios were not applicable and thus provide no managerial result. The
absolute change has it limits, for the managerial purposes it is better to use
relative measures (Field, 2009).
2. Reduction of DSO component does not provide the proportional increase for DPO
and DAO components and vice versa - the requirement of collaborative SCs
according to Viskari (2012) of overlapped payments is not satisfied. In other
words, the managerial actions of each scenario are done for each company
separately and do not provide a holistic view for the collaborative SC.
3. Each scenario is done separately and there is no optimization model which
integrates all the scenarios together in terms of collaborative SC.
39
To fix the problems of Randall and Farris model (2009) the author suggests the
improvement to existing methodology by development of coordination modeling called
optimization mCCCC model. The model is built using goal programming based on cost criterion
and is described in 2.2.2.
2.2.2. Coordinating mCCCC model description. The idea of mCCCC optimization has
at least two goals: the shortening of mCCCC cycle and reduction of total financial costs. In other
words, there are two functions that need to be minimized according to the literature review of
mCCC and developed concept of mCCCC. The basic linear programming (LP) is not suitable for
that purpose because the usage of this approach maximizes or minimizes one objective function
whereas goal programming (GP) minimized the fluctuations of objective functions values and
the realized results which is a satisfying solution (Tamiz, Jones, Romero, 1998)
GP characterizes as a special case of multi-objective optimization and is a part of multicriterion decision analysis. The basic concept of GP is stated by Orumie and Ebong (2014) in
their research study – the GP is “one of the oldest multi criterion decision making techniques
used in optimization of multiple objective goals by minimizing the deviation for each of the
objectives from the desired target”. In other words, the purpose of GP is to convert several
objectives into a single one (Ignizio, 1983).
The terminology of MCDM varies in different studies and Figueira, Greco and Ehrgott
(2005) introduced the most important definitions of goal programming. The classification below
includes the description of definitions’ application to the problem statement:
Decision Maker (DM) – refers to a person, organization, stakeholders, financial
intermediary for collaborative SC to whom the right to state the goal belongs. In this case the
author assumes there is a financial intermediary such as 3PL provider who took the management
of collaborative SC to optimize its costs and mCCCC;
Decision Variable (DV) – a factor through the DM performs the optimization. The
purpose of goal programming is to find all possible combinations of DVs in decision space
values to define the point where all constraints and goals are satisfied. In optimization mCCCC
model DVs are the components of mCCCC calculation (2.1.);
Criterion – is a measure of goodness of the optimization model. There are many
criterions to base a model on but such as cost, profit, time, etc. However, one of the main
purposes of mCCCC optimization is cost reduction thus the main criterion is cost. Adding more
criterions would be the problem statement of multi-criteria decision making (MCDM);
40
Objective – it is the criterion with the direction stated – either maximization or
minimization. In this master thesis the objective is to minimize the total financial costs function;
Goal – refers to a target level which the DM desires to achieve on criterion. The
optimization mCCCC model assumes the goals of optimization of total inventory, A/R, A/P and
A/E with the constraints of not exceeding existing level of costs;
Deviation Variable (DEV) – measures the difference of target level and the value
achieved in final solution. If the value is higher than that of target level than the difference is
positive DEV. Otherwise it is negative DEV;
Constraints – a set of restrictions of decision variables that should be satisfied in order to
ensure the reduction of costs. In this case the constraint is not exceeding existing level of costs.
A sign restriction limits the decision or DVs to take certain values within the range. The most
common used sign constraint is for variable to be nonnegative and continuous. The same
constraint applies to the optimization of mCCCC model;
Feasible region – Figueira, Greco and Ehrgott (2005) stays it as “the set of solutions in
decision space that satisfy all constraints and sign restrictions in a goal programming form the
feasible region. Any solution that falls within the feasible region is deemed to be implementable
in practice”.
The abovementioned definitions refer to the methodology of optimization mCCCC model
description. The problem statement of this framework is following – the DM (in this case either
or financial intermediary or consultancy) decided to minimize the objective total financial costs
function – total financial costs of the SC which is the sum of financial costs associated with each
mCCC cycle of every participant of 3-stage collaborative SC based on cost criterion using the
DVs of collaborative mCCCC.
The DVs of the model are components of mCCCC calculation (2.1.). Changing these
components using optimization mCCCC model it is possible to obtain the goals stated which are
the specific percentage decrease of total inventory (TI) and total A/R (TAR) and specific
percentage increase for total A/P (TAP) and total accrued expenses (TAE) of each member of a
collaborative SC. The constraint is a requirement of all variables to be nonnegative and
continuous as Figueira, Greco and Ehrgott (2005) recommend.
(2.3)
where x1 - DIO of supplier; x2 - DIO of manufacturer; x3 - DIO of customer; x4 - DSO of
customer; x5 - DPO of supplier; x6 - DAO of supplier.
41
The DM set constraints for costs of running cycle that no costs after f(z) optimization
should exceed their values before optimization in order to satisfy the requirement to provide the
centralized decision for decentralized supply chain.
(
]
(
(
]
(
]
(
]
(
]
(
]
(
]
]
(2.4)
(
]
(
(
]
]
]
(
(
]
(
]
(
]
(
]
(2.5)
(
]
(
(
]
(
]
]
(
(
]
]
(
(
]
]
(2.6)
The DM states the goals that need to be achieved through collaborative actions – the
value after specific percentage decrease of total inventory (TI) and total A/R (TAR) and the
value after specific percentage increase for total A/P (TAP) and total accrued expenses (TAE) of
each member of a collaborative SC.
(2.7)
(2.8)
(2.9)
(2.10)
where 1 – supplier, 2 – manufacturer, 3 - distributor
There are two main solutions which a model can get as a result of optimization – local
optimal (feasible) and global optimal (optimal) (Taha, 1971). The local optimal solution is any
element of a feasible region of the optimization problem. Feasible region is the diapason of all
solutions possible with satisfaction of all constraints. An optimal solution either minimizes or
42
maximizes the objective function (Taha, 1971). The restrictions for feasible solution are to find
first solution which improves the model and are following:
(2.11)
(2.12)
(2.13)
(2.14)
This constraints are for feasible solution and do not minimize the objective
function. In case if there is a feasible solution the researcher may proceed to the minimization of
total financial cost function. If there is no feasible solution the DM should take a priority of each
goal to be stated and as a result the DM has a compromised solution.
If there is no feasible solution which satisfies all the constraints Steuer (1986) suggests
trying to find compromise solution and to convert each inequality into flexible goal using DEVs
which can be violated if necessary with constraints (2.12.):
(2.15)
(2.16)
(2.17)
(2.18)
(2.19)
The nonnegative variables
and
are DEVs because they describe the deviations
below and above the right-hand side of constraint i and are dependent thus are unable to be basic
variables simultaneously. This limit means that in “any simplex iteration at most one of the two
deviational variables can assume a positive value. If the original ith inequality is one of the type
≤ and its
then ith goal is satisfied, otherwise, if ith
then ith goal is not satisfied”
(Cohon, 1978). In other words, the definition of both DEVs allows either satisfying or violating
the ith goal. This allows flexibility which is a feature of goal programming when it comes to
finding of compromise solution. A compromise solution aims to minimize as much as possible
the violation of each component.
In the model, given the fact that the first two constraints are the type of ≤ and the other
two are of the type pf ≥, the DEVs
represent the amount by which the goals are
43
violated. Hence, the solution seeks a compromise where following objectives are satisfied as
much as possible and the functions are minimized in compliance with the constraint equations of
the model.
Minimize
(2.20)
Minimize
(2.21)
Minimize
(2.22)
Minimize
(2.23)
There are two ways of prioritization of goals. There is either the weights method which is
a single objective function with a weighted sum of goals or preemptive method which first
optimizes the goal with the highest priority and eliminates one with the lowest, because
preemptive approach never compromises the quality of the higher-prioritized goal. However,
neither method is better that another one because each technique is suitable for different
purposes.
The weights method is the approach applied to the optimization model has 4 goals and
then the ith goal is:
Minimize
The parameters
(2.24)
are positive values that represent the DM’s decisions
about the relative importance of each goal. Hence, the determination of specific weights is highly
subjective. Thus goal programming is only feasible rather than optimum solution to the problem.
Taha (2005) states that “what goal programming does is to find a solution that simply satisfies
the goals of the model with no regard to optimization”.
The preemptive method uses the ranking approach where the DM should rank to goals in
order of importance. For example, the objectives are written as:
Minimize
(Highest priority)
(2.25)
:
Minimize
According to Taha (1971) “the variable
or
(Lowest priority)
(2.26)
is the component of the deviational variables
that represents goal i”. The process of solution obtaining considers the goal with the
highest priority
and elimination of the lowest
. The process differs from the weights
44
method in the way that the solution of lower-priority goal never degrades higher-priority
solutions.
The goals of optimization of TI, TAR, TAP and TAE are restricted with the percentage of
either decrease or increase stated by the DM and can vary from case to case. The advantage of
the optimization mCCCC model before scenario analysis is that it is adjusted to each case initial
characteristic instead of using the robust measure of 10 days as Randall and Farris (2009)
suggest.
In order to optimize the function the optimal solution constraints are set which means the
DM should specify goals as certain numbers to be achieved. In this model the specific numbers
are reduced TI and TAR and increased TAP and TAE.
(2.27)
(2.28)
(2.29)
(2.30)
where k – coefficient of % change set by the DM, 1,2,3,4 – goals to be achieved
All the constraints are set to achieve the main cost criterion – the minimization of the
objective cost function of total financial cost (TC) of running the mCCCC cycle which sums
financial costs (2.2.) of each participant’s costs of mCCCC in a collaborative SC of supplier,
manufacturer and distributor
(
]
]
(
(
]
(
(
]
]
(
(
]
]
(
]
(
(
]
(
]
]
(
]
(2.31)
where i =1, 2, 3. 1 – supplier, 2 – manufacturer, 3 – distributor; ci - annual cost of capital
45
The minimization of total financial cost function allows achievement of the best solution
with the constraints set which helps to optimize the financial flows of collaborative SC and get
access to working capital previously tied up in mCCCC cycle time. As a result, companies which
have troubles in accessing the traditional ways of financing operations – credit and loans can
now get cash freed from financial costs and reduced time of mCCCC.
2.2.3. Optimization mCCCC model design. The formulation of optimization mCCCC
model consists of several consequent parts. First of all, the DM sets the goals of TI, TAR, TAP
and TAE. Next, the DM decides to apply the optimization mCCCC model based on cost
criterion. Depending on whether the model with goals and constraints set has a feasible solution
the DM either minimize the total financial costs function (in case if the feasible solution is
found) or switch to any of two methods of goal programming to find a compromise solution.
First a researcher should find a feasible solution with variables (2.3) using constraints for
feasible solution developed in 2.2.1 with constraints (2.4-6); (2.11-14):
In case if there is a feasible solution which satisfied all constraints set the researcher
should proceed to the minimization of the objective function of collaborative financial costs
(2.31) using variables (2.3) with the target level of goals set as constraints (2.4-6); (2.27-30):
In case if there is no feasible solution the researcher should find a compromise by using
one of the goal programming approaches – either the weights method or preemptive
programming method with the following constraints (2.4-6); (2.11-14):
If apply the weights method than the prioritization of goals is following where the DM
sets the weights for each goal by himself with (2.24):
In case if the goals should be prioritized in terms of importance the DM should set the
rank and the researcher should implement minimize each goal in order of priority according to
(2.25-26).
As a result the model provides a holistic view of how the goals stated may be achieved
using mCCCC components based on cost criteria. The DM can fix the problems associated with
Randall and Farris (2009) scenario, set the goals which affect collaborative management of a SC,
obtain minimization of collaborative financial costs in diapason of goals set and reduce the
length of mCCCC which affects the efficiency and profitability of the company.
46
The coordination modeling allows the companies involved into supply chain to make
collaborative decisions when still being in decentralized conditions of SCs. The usage of
coordinating modeling with financial 3PL provider allows decision making of WCM in SCs on
the collaborative level of a 3-stage SC.
This section provides a theoretical design of optimization mCCCC model. Chapter 3
consists of practical application of both scenarios and optimization mCCCC model on 4
collaborative SCs of different industries. All calculations are made using Excel Solver Add-In
using GRG non-linear approach. Besides, the optimization mCCCC model can be calculated
using other programs such as MATLAB, TORA and others. The process of data collection and
industries studied is discussed in the next parts of Chapter 2.
2.3.
Data collection and industries studied
2.3.1. Data collection. Different sources for analysis and research are requirements of
design-science approach as long as for holistic case study method (Yin, 2003). The data
collection of this research is based on primary and secondary data.
The primary data for the master thesis goal and objectives was obtained from the
conversations with representatives of companies in 4 different collaborative SCs. The
conversations were face-to-face structured and conducted with the representatives of financial
department and operations/logistics departments. During the conversations the author has
managed to obtain the information about the structure and members of collaborative chains to
obtain the secondary data from open financial statements and annual reports of the companies
involved.
As for secondary data in their research Saunders, Lewis and Tornhill (2009) allocate
three types of secondary data: a survey conducted, multiple sources searched and documentary.
In compliance with archival approach (Moers, 2006) and for the purposes of goal and objectives
satisfaction the last type – documentary – is chosen and based on written materials. This kind of
documents usually includes key figures of SC performance and financial outcomes of this
performance in the form of the annual report. The secondary data of this type could be obtained
from the open sources of companies in the Investor Relations part. The relevance of this kind of
documents is sufficient due to the fact that every financial statement is audited before release and
all statements are completed in compliance with International Financial Reporting Standards
(IFRS) (Pirttilä, 2014).
47
The author made adjustments to the data to ensure the applicability of the model– the data
obtained from financial reports of members of 3-stage collaborative SCs of each industry
assumed to be dedicated to each specific collaborative SC with no other operations included.
2.3.2. Industries studied. The author has chosen for research purposes four collaborative
SCs from different industries. The criteria of choosing the industry for mCCCC evaluation and
application of optimization mCCCC model is based on Talonpoika et al. (2014) study where the
authors identify four main industries where the impact of A/E as a components of cycle time
calculations – project, ICT, publishing and automotive industries is the biggest.
The nature of project business assumes a lot of A/E because they are essential to finance
the long operating cycle. The amount of A/E in not constant though and is a subject of
negotiation between the supplier and the customer. A/E are usually the percentage of total price
and are paid in several parts during the project time.
According to the study of Talonpoika (2014) the ICT companies have the longest average
DAO. This value mostly belongs to the nature of the industry and by the constant dependence of
customers on ICT services. The main purpose of A/E in ICT industry is the amount of money
sufficient enough to finance the projects.
The pulp and publishing industry is closer to the end industry then the previous two
industries which are mainly focused on B2B business. The A/E in this industry are mainly from
the annual subscriptions. Due to the fact that the A/E in a form of annual subscriptions are very
popular among customers the publishing collaborative SCs may achieve negative mCCCC.
The automotive industry is one of the industries where A/E are widely practiced.
However the industry is interested by the fact that it was hit especially hard by the economic
crisis – the reduction of oil resulted in the decreased real income and decrease of demand
(McKinsey, 2011). Hence the companies in collaborative automotive SCs seek the ways of
reduction the cycle time of WC and decrease the costs associated.
2.4.
Research design
Monto (2013) suggests five stages of design-science approach – “identification of the
problem, development of the solution to the problem, application of the solution to the problem,
evaluation of the solution and introduction to main results and findings”. All process is
illustrated at Figure 2.2.
48
Figure 2.2. Research design based on design-science approach
Source: Created by the author
At the first stage of identification of the problem and objective the author does a
theoretical review of WC in FSCM. After identification of main sources, concept and models a
research gap reveals – there is no study of collaborative conversion cycle with A/E included. The
justification of relevance of the research topic is provided.
At the second stage, the author introduces the concept of mCCCC using design-science
and archival approaches. Besides, examination of described in literature models to evaluate the
cycle time of collaborative SCs revealed the flaws in the existing measures. To fix the problems,
the new model for mCCCC optimization is developed based on total financial cost criteria using
goal programming approach.
At the third and fourth stage the developed mCCCC concept is tested on four
collaborative SCs from different industries – project, ICT, publishing and automotive. Two
approaches are used – scenario analysis for justification of DAO component in collaborative SCs
and optimization mCCCC model for total financial costs minimization based on mCCCC
components as a variables, target levels of total inventory, total A/R, total A/P and total A/E as
goals and current levels of costs as constraints. The fifth part summarizes all parts and provides
justification of achieved goals of the master thesis. The managerial implications are provided
alongside with theoretical contribution and ways of future research.
49
3.
Coordination modeling case study analysis
This chapter provides the examination of the developed modified collaborative CCC
(mCCCC) on case companies and consists of two parts. The first part dedicates to examination
of mCCCC approach using adapted version of Randall and Farris (2009) scenario analysis for
collaborative SCs. The second part is devoted to evaluation of a newly developed model for
mCCCC management based on goal and linear programming approach using costs as main
Criterion.
3.1.
Case study of mCCCC scenario analysis
As it was discussed in Chapter 2, Randall and Farris (2009) in their study developed
scenario analysis framework which allows simulation of WC collaborative managerial actions
can influence the cycle time of WC and reduce the financial costs of running the cycle for each
company and collaborative CCC. The scenarios for mCCCC approach for each of case industries
are following:
1. Shortening the DIO component in collaborative chains
2. Shifting the inventory from key customer to manufacturer
3. Shortening the DSO component in collaborative chains
4. Extension of DPO component in collaborative chains
5. Extension of DAO component in collaborative chains
The collaborative SC which is a subject to this kind of analysis consists of three members
such as supplier, manufacturer and customer – in different cases it could be distributors, food and
non-food retailers or following companies in production cycle. The number of SC members is
not limited by only three participants and could be broaden at every stage of a SC although it is
critical to be sure that all participants are still under collaboration conditions to initiate WC
managerial action for a whole SC.
3.1.1. Project industry. The first industry to be analyzed is project. The industry
characterized by high collaboration and high level of accrued expenses for supplier to ensure the
production of essential components for manufacturer. The calculated data for this project
collaborative SC is done using equations (2.1) (2.2.) (2.3) is described in Figure 3.1. which
includes calculated mCCC components and financial costs for each member of a SC and total
modified collaborative CCC and financial costs tied up in each stage of mCCCC.
50
The dataset is characterized by high level of DIO component for all stages of
collaborative SC. DSO component is also high which means that there is a big time lap between
actual purchase of goods sold and money received by a seller which is not favorable for
minimizing mCCCC purposes. However, the DPO component is all cases bigger than DSO
which can be an indicator of the fact that all parties pay their suppliers longer than receives
money which one of the goals when optimizing mCCCC. DAO component is high for supplier
which could be explained by project features. The author applied 5 scenarios to this case and the
results are illustrated in Figure 3.1..
Figure 3.1. Implementation of scenario analysis to project collaborative SC
Source: Created by the author
According to Figure 3.1. the best scenario for managerial WC actions in terms of
reducing total financial costs is the shortening of DIO component because the value of financial
costs was reduced by 20% in compliance with the simulation results and the mCCCC is now
12% lower than before the actions which means that the cycle from inventory to cash is now
done faster. The reduction of inventories is possible when there is no threat of stockouts and
could be achieved through efficient control of inventory level and enhancing information
exchange to avoid the bullwhip effect.
51
Another possible scenario when the reduction of inventory is not possible – extension of
both DPO and DAO components in collaborative SC – the terms of internal payments. The
companies under collaboration conditions may agree to delay the payments ensuring the higher
speed of conversion cycle – these actions can bring reduction of mCCCC and mCFC by 4% and
16% retrospectively. Accrued expenses extension could be useful when the payments are making
too early to provide the availability of the product for the customers but if there are trust and
collaboration among the partners of the SC – if management decide to do this the results could
be the reduction of mCCCC by 4% and the reduction of costs by 11%.
3.1.2. ICT industry. Another case is from ICT collaborative SC. The industry is
characterized by high level of manufacturer inventory and low level of accrued expenses for the
customer because it usually buys finished goods. Calculated data for this ICT collaborative SC is
done using equations (2.1-4.) is described in Figure 3.2. which includes calculated mCCCC
components and financial costs for each member of a SC and total modified collaborative CCC
and financial costs tied up in each stage of mCCCC.
This collaborative SC is characterized by high level of inventory of the manufacturer
company and it pays its supplier late but receives the money from operations fast. The supplier
and manufacturer have low inventory of DSO and will not be reduced. The results of simulation
scenarios for ICT collaborative SC are illustrated at Figure 3.2.
The best result for reducing both financial costs and mCCCC for automotive
collaborative SC is also scenario of shortening the cycle time of inventories – after simulation
the costs decreases by 13% and the time to run the whole cycle reduces by 12%. The best
scenario is first again due to the fact that firstly all participants have long inventory days
components and secondly DSO component is already minimized in the manufacturer and
customer cases. It is important inference which can be made out of this case – switching
inventory from customer to manufacturer raises total financial costs by 10% and should not be
applied. This increase could be a result of high inventory storage costs and the fact that inventory
for manufacturer is already high – the high level of DIO component proves that.
DAO component plays a significant role in reduction of the cycle time of WC – 11%. The
result could be obtained by the lack of reduction of DSO component in Scenario 2 and not
complete reduction of inventories which customer has already shortened. Nevertheless, the
extensions of DAO component can be a great practice in case when there no managerial actions
for further reduction of inventories and A/R.
52
Figure 3.2. Implementation of scenario analysis to ICT collaborative SC
Source: Created by the author
3.1.3. Automotive industry. The same procedure as in two first cased was applied to the
case of automotive industry. The third automotive case reveals features of short inventory for the
customer who is a software company and has negative mCCC. That means that the customer
does not pay its suppliers that it purchases until the payment for these product is received. The
relation with the automotive industry is that the dealer takes the autos for realization and do not
need to hold high level of inventory because the demand is predictable but does accrued
expenses while making an order to ensure the autos available when needed. The scenario
analysis is illustrated in the Figure 3.3., although the reduction of DIO for supplier was not done
due to low inventory level and scenario 2 is skipped because of the same reason.
Due to the relationship between the manufacturer and a supplier in automotive industry
specifics the inventory reduction scenario is not the best. The shortening the time of A/R could
bring the serious reduction to financial costs – almost two times lower (-46%) and could be done
if the credit terms are reviewed and shortened.
53
Figure 3.3. Implementation of scenario analysis to automotive collaborative SC
Source: Created by the author
The DAO component provides the result second best in terms of costs reduction (-33%)
after the third scenario with shortening the DSO component for members of a SC. The usage of
DAO extension can be useful in case when the inventories cannot be shortened no more to
prevent stockouts or account receivable component shortening received its minimum. In this case
in terms of collaborative SC – information exchange and trust the members can agree to prolong
the cycle time of A/E in order to reduce the cycle time of WC and total financial costs for a
whole SC.
3.1.4. Pulp and publishing industry. The last case is about project business industry –
pulp and publishing SC. This case has a relatively high total financial costs of running relatively
short cycle compared to previous cases. The results of calculations of mCCCC components show
that the supplier has high level of inventory which is a common feature of a production
companies (in this case the supplier is a pulp manufacturer company). Besides, the credit terms
of the supplier for accrued expenses make them to pay later. The mCCCC components change
results according to scenario analysis are illustrated in Figure 3.4.
54
Figure 3.4. Implementation of scenario analysis to P&P collaborative SC
Source: Created by the author
The results of the case in pulp and publishing industry are controversial. The most
significant reduction of the mCCCC was done by the simulation of inventory decrease
managerial actions – by 14% but the costs were reduced by 6%. However, the most serious
reduction of costs is done while decreasing the level of DSO – 22% and the cycle was reduced
by 8%. The preferable option is still the DSO reduction because mCCCC formula consists of all
DIOs and only one DSO of a customer because of overlapped payments. The DAO component
plays a significant role in decreasing costs- 10%, this approach is preferable if there is no
opportunity to reduce the inventory and the companies in collaborative SC has not enough
bargaining power to change the terms of payments.
The scenario approach of Randall and Farris (2009) allows evaluation of change and
degree of contribution of each component to the cycle time and total financial costs. The purpose
of application of this framework was to test applicability of mCCCC to collaborative SCs. The
DAO component plays a significant role in reducing the costs associated with the collaborative
SC and the increase of this component brings a significant reduction to the mCCCC value. The
application of DAO extension scenario is preferable when there is no possibility to shorten cycle
time of inventories to avoid stockouts and no opportunity for further reduction of DSO
55
component because it is already optimized and shorten to minimum. In this case the usage DAO
component in terms of collaborative SC can bring its benefits. The variations of this component
alongside with others components affect the length of the cycle and for the companies with
accrued expenses the evaluation of collaborative SC using mCCCC is more precise rather that
common CCCC approach.
To fix the problems of Randall and Farris model (2009) which were describes in Chapter
2 the author suggests the improvement to existing methodology by development of new
framework that was described in Chapter 2 and is now tested on four cases in next paragraph of
Chapter 3.
3.2.
Case study of mCCCC coordination modelling
This paragraph dedicates to the evaluation and examination of developed mCCCC
methodology for resource optimization based on goal and nonlinear programming and costs as a
main Criterion which was described in Chapter 2. The newly developed methodology is tested
on four different cases and the minimization of objective function of total financial costs is
achieved in all cases with satisfaction of all constraints set.
3.2.1. Project industry. The intermediary or management, in other words Decision
Maker (DM), decides to apply collaborative CCC optimization framework to the existing
collaborative SC in project industry. The calculated data of mCCCC and mCFC components for
collaborative SC is illustrated in Table 3.1.
The DM has stated the following objectives for this collaborative SC in order to provide
faster mCCCC cycle and to achieve the goal of minimization of total financial costs of whole
SC. The DM sets the goals of total inventory and total A/R not exceed the existing costs and for
account payable and accrued expenses. Besides, the DM also requires that costs of each stage of
mCCCC (mFC) do not exceed its existing values. The first step is to find feasible (local optimal)
solution using mCCCC components as variables with satisfaction of all constraints.
56
Table 3.1. mCCCC and mCFC components for project collaborative SC
Source: Created by the author
There is a feasible solution which satisfied all the constraints (App. 1) and this means that
further optimization is possible. Now the formulation of the problem is more complex, because
the DM requires the participants of the collaborative SC to achieve the specific target levels of
total inventories, A/R, A/P and accrued expenses to improve the performance of the company by
reduced costs and the length of collaborative mCCCC:
Decrease of total inventories by 5% to 18 174$mln
Decrease of total A/R by 5% to 16 366$mln
Increase of total A/R by 5% to 15 005$mln
Increase of total accrued expenses by 5% to 8 017$mln
The formulation of the minimization the total financial costs function now is following:
Minimize
57
The optimal solution is found and all constraints are satisfied. The optimal results mean
that there is the best solution in the observed diapason. The results of the minimization of the
objective function using mCCCC components as variables are listed in the Table 3.2.
The model changed the mCCCC components as variables of mCFC function and the
results of application of the collaborative CCC optimization framework are following:
The objective function is minimized. The total financial costs are now decreased
by 38% and mCCCC is decreased by 42%. That means that in this case company
decreased the total amount of costs tied up in each stage of operating cycle and
now the cycle time is faster which can positively affect the performance company
in terms of liquidity and profitability.
The achievement of all goals stated. The optimized model has managed to achieve
all criteria which were stated by the DM – the reduction of total inventory and
total A/R by 5% and increase of total A/P and total accrued expenses by 5%.
The Criterion of not exceeding the current level of costs were satisfied fully.
Furthermore, the optimal solution provides the reduction of costs – for the
supplier the decrease is 82%, for manufacturer is 8% and for the customer which
inventory was increased the costs are still the same.
The optimized model allows having not only financial outcomes but also managerial
guidelines. The allocation of all components of total inventory, A/R, A/P and A/E are different
when the objective is minimized and if the SC operates in collaboration the resource relocation
may bring the goal results. For example the amount of inventory for supplier is reduced and the
inventory for manufacturer and retailer are increased but decisions based on the trust and
information exchange in collaborative SC bring better results if all the companies operate
separately – the example of this decision was reviewed in Chapter 3.1.
58
Table 3.2. Optimized collaborative CCC model for project industry
Source: Created by the author
3.2.2. ICT industry. The same procedure is applied to the case of ICT industry. The
assumptions of the model are that the DM decided to improve the WC metrics in the
collaborative SC in ICT industry and applies the optimization collaborative CCC model. The
calculated data of mCCCC and mCFC components for collaborative SC is illustrated in Table
3.3.
Table 3.3. mCCCC and mCFC components for ICT collaborative SC
Source: Created by the author
The DM sets the goals of total inventory and total A/R not exceed the existing costs and
for account payable and accrued expenses. Besides, the DM also requires that costs of each stage
59
of mCCCC (mFC) do not exceed its existing values. Similarly to case 1 the first step is to find
feasible (local optimal) solution using mCCCC components as variables with satisfaction of all
constraints. The formulation of a problem is following (in mln$):
The feasible solution is found and all constraints are satisfied (App. 6). If feasible
solution is found there is opportunity to found optimal solution while tighten the constraints. The
DM requires the participants of collaborative SC to achieve the following results in order to
decrease costs and improve collaborative financial cycle:
Decrease of total inventories by 5% to 13 212$mln
Decrease of total A/R by 5% to 2 194$mln
Increase of total A/R by 5% to 4 076$mln
Increase of total accrued expenses by 5% to 3 379$mln
The formulation of the minimization the total financial costs function now is following:
Minimize
60
The optimal solution is found and all constraints are satisfied (App.7). The optimal
results mean that there is the best solution in the observed diapason. The results of the
minimization of the objective function using mCCCC components as variables are listed in the
Table 5.
Table 3.4. Optimized collaborative CCC model for ICT industry
Source: Created by the author
The model changed the mCCCC components as variables of mCFC function and the
results of application of the collaborative CCC optimization framework are following:
The objective function is minimized and total financial costs function is reduced
by 21% with the mCCCC decrease by 24%. The optimization allows companies
involved in collaborative SC to spend less money on cycle operation and the cycle
time itself is reduced thus increasing profitability and liquidity of the companies
SC.
The achievement of all goals stated. The optimized model has managed to achieve
the entire criteria which were stated by the DM – the reduction of total inventory
and total A/R by 5% and increase of total A/P and total accrued expenses by 5%.
The criterion of not exceeding the current level of costs was satisfied fully.
Furthermore, the optimal solution provides the reduction of costs – for the
supplier the decrease is 41%, for manufacturer is 19% and for the customer which
inventory was increased the costs are still the same.
61
The important notification is the optimized model suggests cutting DSO days close to
zero. It means that customer should review its external credit terms of payments. The solution
suggests increase the amount of inventory thus the purchase could be paid and got at the closely
same day. The reduction of total inventory is achieved by the reduction of inventory of a
manufacturer and may be a result of more efficient allocation of resources – due to the fact that
inventories of supplier and customer increased the manufacturer can distribute some of the
inventories to them to achieve better results.
3.2.3. Automotive industry. To continue the methodology testing the optimization
collaborative CCC model applies to the next case of automotive industry. In order to increase the
performance of the whole SC and cut costs which are especially important in the terms of the
ongoing financial crisis the DM takes the decision to apply the model to the automotive
collaborative SC. The calculated mCCCC and mCFC are represented in Table 3.5.
Table 3.5. mCCCC and mCFC components for automotive collaborative SC
Source: Created by the author
The DM sets the goals of total inventory and total A/R not exceed the existing costs and
for account payable and accrued expenses. Besides, the DM also requires that costs of each stage
of mCCCC (mFC) do not exceed its existing values. The first step is to find feasible (local
optimal) solution using mCCCC components as variables with satisfaction of all constraints. The
formulation of a problem is following (in mln$):
62
There is a feasible solution which satisfied all constraints (App. 8) and this means that
further optimization is possible. In order to decrease costs for each stage of cycle time and get
the whole cycle faster, the DM set the following goals of:
Decrease of total inventories by 5% to 2 407$mln
Decrease of total A/R by 5% to 5 128$mln
Increase of total A/R by 5% to 2 804$mln
Increase of total accrued expenses by 5% to 3 751$mln
The formulation of the minimization the total financial costs function now is following:
Minimize
The optimal solution is found and all constraints are satisfied. The optimal results mean
that there is the best solution in the observed diapason. The results of the minimization of the
objective function using mCCCC components as variables are listed in the Table 3.6.
63
Table 3.6. Optimized collaborative CCC model for automotive industry
Source: Created by the author
The model has achieved all goals stated with satisfaction of all constraints. The
results of the optimized collaborative conversion cycle model are following:
The objective function of total financial costs is now minimized in diapason set.
The reduction of costs in terms of collaborative SC is 52% and the reduction of
mCCCC has achieved 70%. That means that for each mCCCC cycle the
collaborative SC now needs to pay nearly two times less money and the speed of
converting inventory to cash in collaborative SC increased for 70% which is
advantageous in terms of recent financial crisis.
The achievement of all goals stated. The optimized model has managed to achieve
all the Criterion the DM stated – the reduction of total inventory and total A/R by
5% to 9 709mln$ and 32 841mln$ retrospectively and increase of total A/P and
total accrued expenses by 5% to 8 057mln$ and 16 454mln$ retrospectively.
The criterion of not exceeding the current level of costs was satisfied fully.
Furthermore, the optimal solution provides the reduction of costs – for the
supplier the decrease is 67%, for manufacturer is 42% and for the customer which
cycle has increased nevertheless the reduction of costs is 95%.
The optimization collaborative CCC model allows revealing of possible hidden
opportunities. In the initial scenario most of the inventory was held at manufacturing facilities.
64
Now the inventory of customer increased which will help to increase the speed of cycle and
positively affect the profitability. The increase of inventory held for customer may reduce the
amount of A/R for customer due to the reduced time of receiving finished product – that means
that supplier faster gets money.
3.2.4. Pulp and publishing industry. The last case for optimization collaborative CCC
model is the collaborative SC from pulp and publishing industry. In order to getting the money
from purchases faster and especially to decrease costs associated with the collaborative
conversion cycle which are relatively high to relatively low mCCCC compared to other cases the
DM decided to apply the optimization collaborative CCC model. The calculation of mCCCC
components and CFC is summed in Table 3.7.
Table 3.7. mCCCC and mCFC components for P&P collaborative SC
Source: Created by the author
The DM sets the goals of total inventory and total A/R not exceed the existing costs and
for account payable and accrued expenses. Besides, the DM also requires that costs of each stage
of mCCCC (mFC) do not exceed its existing values. The first step is to find feasible (local
optimal) solution using mCCCC components as variables with satisfaction of all constraints. The
formulation of a problem is following (in mln$):
65
There is a feasible solution which satisfied all constraints (App. 9) and this means that
further optimization is possible. In order to decrease costs for each stage of cycle time and get
the whole cycle faster, the DM set the following goals of:
Decrease of total inventories by 5% to 9 709$mln
Decrease of total A/R by 5% to 32 841$mln
Increase of total A/R by 5% to 8 057$mln
Increase of total accrued expenses by 5% to 16 464$mln
The formulation of the minimization the total financial costs function now is following:
Minimize
∑
The optimal solution is found and all constraints are satisfied (App.7). The optimal
results mean that there is the best solution in the observed diapason. The results of the
minimization of the objective function using mCCCC components as variables are listed in the
Table 3.8. The model has achieved all goals stated with satisfaction of all constraints. The results
of the optimized collaborative conversion cycle model are following:
The objective function of total financial costs is now minimized in diapason set.
The application of mCCCC model allows reduction of total financial costs by
41% and the length of CCC decreased by 35%. The reduction allows companies
involved to operate and get money faster at lower costs.
66
The achievement of all goals stated. The optimized model has managed to achieve
all the Criterion the DM stated – the reduction of total inventory and total A/R by
5% to 9 709mln$ and 32 841mln$ retrospectively and increase of total A/P and
total accrued expenses by 5% to 8 057mln$ and 16 454mln$ retrospectively.
The Criterion of not exceeding the current level of costs was satisfied fully.
Furthermore, the optimal solution provides the slight reduction of costs for the
customer and for the manufacturer there is an increase of mCCC but the costs
remain at the same level.
Table 3.8. mCCCC and mCFC components for P&P collaborative SC
Source: Created by the author
The last case provides the optimization to the negative mCCC and negative financial
costs for supplier due to decrease of DIO component and increase of DPO and DAO
components. The negative value for mCCC means that the supplier in the case of optimized
collaborative SC model does not pay its suppliers until it receives money from the manufacturer.
The negative financial costs mean that the supplier finance its operations through low level of
inventories and while receiving the money from A/R it manages to delay the payments and A/E
to its suppliers.
The optimization mCCCC model was successfully tested on four collaborative SCs and
the results confirm the applicability of the model to real-life cases. The model allows companies
in collaborative SCs to achieve target levels of total inventory, A/R, A/P and A/E through
minimization of total cost function with the constraints of not exceeding the existing level of
costs for each participant of the collaborative SC.
67
Conclusions
Main findings. The main goal of this master thesis was development of the coordinating
model of WCM in collaborative SCs. The main objectives of this master thesis were: to identify
the research gaps of WCM in SCs based on the literature review devoted to WCM in SCs; to
improve the methodology of WCM in SCs through creation of coordinating SC modified
collaborative CCC (mCCCC) model; to analyze coordination modeling of WCM in SCs using
case study approach. The goal and main objectives of the research were successfully achieved
based on the results of empirical study of four collaborative SCs from different industries –
project, automotive, ICT and pulp&publishing.
To identify the research gaps of WCM in SCs based on the literature review devoted to
WCM in SCs. The theoretical review of WCM in FSCM revealed a few studies about WCM in
FSCM. Secondly, during the literature review showed lack of the studies dedicated to
implementation of modified CCC (Talonpoika et al., 2014) to collaborative SCs. Thirdly, based
on theoretical review using design-science approach the author introduced mCCCC concept that
provides a holistic view of WCM in collaborative SCs. During development of mCCCC
methodology the several flaws of existing models were revealed in terms of holistic view of
collaborative SC and the lack of coordination modeling devoted to WCM in SCs. To fix these
problems, the author developed a new model of mCCCC evaluation – coordinating mCCCC
model which financial 3PL provider as a coordination mechanism.
To improve the methodology of WCM in SCs through creation of coordinating SC
modified collaborative CCC (mCCCC) model. Using design-science approach the author
developed a new concept of cycle time evaluation for collaborative SCs. The improvement of the
concept is mainly concerned with more precise evaluation of WC cycle time in SCs – the DAO
component is added to the traditional calculation of CCC. Using design-science approach the
author developed a concept of modified collaborative CCC that should be used for the estimation
of WC in collaborative SCs especially for industries where A/E are common practice and often
are the enablers of the business processes – project, ICT, construction, automotive and others.
The existing models of collaborative CCC estimation have several flaws that prevent the
estimation of mCCCC from a holistic point of view. The scenario analysis which was adapted
for mCCCC evaluation purposes from Randall and Farris (2009) does not consider several
critical characteristics of collaborative SC – the lack of overlapped interim payments as Viskari
(2012) recommended, using 10-days measure is robust and does not reflect the measures of each
68
collaborative SC and each scenario is done separately thus no there is no model for mCCCC and
costs optimization. To fix these problems and to provide a holistic view the coordinating SC
mCCCC model (optimization mCCCC model) based on cost criteria was developed using goal
programming.
The optimization mCCCC assumes the coordination mechanism of a 3PL financial
provider who is a decision maker (DM) and states the goals to achieve concerning total
inventory, A/R, A/P and A/E with the main objective to reduce the total costs associated with the
whole cycle. The variables are the components of mCCCC with the constraints of no exceeding
the current level of costs for each member of collaborative SC.
Goal programming allows consideration of all the goals stated in one time if there is a
feasible solution. The developed methodology of optimization mCCCC model assumes the
possibility of lack of feasible solution and provides two methods of prioritizing the goals – the
weighted goals or preemptive programming. If there is a feasible solution the methodology
offers further minimization of total financial costs function in feasible diapason.
To analyze coordination modeling of WCM in SCs using case study approach. The
developed mCCCC concept was tested on four collaborative SCs – project, ICT, automotive and
pulp&publishing. The results are following – in all four cases the extension of DAO components
for 10 days for each player of collaborative SC resulted in reduction of cycle time of WC (-4%
for project industry, -11% for ICT, -7% for automotive and -8% for pulp&publishing) and
decrease of total financial costs of whole cycle (-11% for project industry, -3% for ICT, -33% for
automotive and -10% for pulp&publishing).
The usage of mCCCC concept for collaborative SCs is preferable due to the fact that first
of all, based on the results of Talonpoika et al. (2014) it is more accurate than CCC in terms of
estimation the cycle time of WC. Secondly, the usage of mCCCC and particularly the extension
of DAO component allow companies the reduction of the length of the WC cycle and total
financial costs in cases when the inventories and A/R are already optimized and/or could not be
shortened to avoid stockouts.
The optimization mCCCC model was tested on four same case industries as in the
previous part of justification of mCCCC concept. For all cases there were a feasible solution (see
App. 1, 2, 3, 4) thus minimization of total costs function was done. The application optimization
mCCCC model results in achievement of all goals stated by the DM in all cases, the reduction of
69
total financial costs in all cases (-38% for project industry, -21% for ICT, -52% for automotive
and -41% for pulp&publishing) and the decrease of mCCCC in all cases (-42% for project
industry, -24% for ICT, -52% for automotive and -35% for pulp&publishing).
Theoretical contribution and future research. There are two main theoretical
implication of this master thesis that correspond the main objectives justified by the empirical
study of four cases. Current studies outline the importance of WCM in SCs because companies
need to adjust their operations to volatile economic and financial environment.
Firstly, the research gap of lack of the study that connects mCCC approach and 3-stage
collaborative SCs in now fulfilled by the development of mCCCC concept. The usage of this
concept is preferable for the companies that have a high level of A/E in their operations because
it is provide more accurate view of cycle time and total financial costs.
Secondly, the author contributed improved the methodology of WC assessment in
collaborative SCs by introduction of the newly developed optimization mCCCC model that
provide a holistic view to the collaborative SCs. The developed methodology is suitable for 3stage collaborative SC and is applicable for usage for business, consultancy, 3PL or bank as an
intermediary or the decision maker. The coordination modeling – optimization mCCCC
modelling fixed all the flaws identified of the scenario analysis of collaborative evaluation of
cycle time of WC – the goals which are stated by the DM and reflect the purposes of mCCCC
and total cost function minimization.
There are two main ways of development for mCCCC concept and mCCCC optimization
model. The extension of mCCCC concept could be done with the additional component of tax
items. Besides, the integration of mCCCC with ACCC of Viskari (2012) may result in efficient
estimation of collaborative order cycle time. The further study of optimization mCCCC model
concerns the extension of number of members of SC. Besides, additional goals can be
implemented and the components can be weighted with the expert opinions or factor of crisis.
Managerial implications. The managerial actions towards working capital in
collaborative supply chains are critical for the operational level of the companies. Working
capital affects mostly every operation such as supply chain management, production,
procurement, finance and others. The companies have gained knowledge how to assess the cycle
time of working capital at intra-organizational level, but estimation of that at inter-organizational
level still causes difficulties for the companies involved in collaborative supply chains. This
70
master thesis provides insights into collaborative estimation of modified CCC and way of
holistic estimation of it using optimization mCCCC model.
The managerial implications of developed mCCCC concept are following. The
companies with large amount of A/E who participate in collaborative SC can more precisely
assess the length of cycle time of WC and total financial costs associated with the cycle. Besides,
the DAO component extension allows influence on the length of the cycle and costs when other
components which cannot be changed anymore to avoid stockouts or are already optimized.
The managerial implications of coordination modeling – optimization mCCCC model are
next. First of all, the coordination modeling with the implementation of financial 3PL provider
allows centralized WCM decision making for companies involved in collaborative SCs when
still being in decentralized conditions of SCs.
Secondly, the model allows simultaneous optimization of all components which can be
implemented in practice. Thirdly, the model minimized the financial costs associated with the
cycle with no exceeding the current costs of each player. Fourthly, the calculation of the model
allows not only obtaining the minimized costs but also the relocation of total inventory, A/R, A/P
and A/E for each member of collaborative supply chains to achieve the desired goals and
minimum of costs. The applicability of the model is wide – could be used by the DM in 3-stage
collaborative supply chains and for further academic development.
71
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Appendices
Appendix 1. The feasible solution for Case 1
Amount of money (in $ millions) of each component of mCCCC of various players in collaborative supply chain
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
r
y
ble
Payable
s
Supplier
3 463
5 112
1 613
4 075
6 309
20 263
0,0967
Manufacturer
6 989
5 266
2 585
2 204
13 534
36 582
0,0732
Retailer
8 678
6 849
10 093
1 357
76 524 103 442
0,1158
17 227
14 291
mCCCC components
DIO
DSO
Supplier
200
92
Manufacturer
188
53
Retailer
41
24
DPO
93
70
48
DAO
mCCC
mFC
73
126
185,5
22
149
269,0
5
13
9,5
MCCC mCFC
288
464
DPO
DAO
Total
19 130
mCCCC components
DIO
DSO
Supplier
200
92
mFC
Manufacture
188
53
r mFC
Retailer
41
21
mFC
Acievement of goals
Results
Total
19 130 <=
Inventory
Total A/R
16 366 <=
Total A/P
15 005 >=
Total A/E
8 017 >=
7 636
mCCC
initial data
mFC
135
80
77
128,4
70
22
149
269,0
48
5
10
-2,3
MCCC mCFC
236
395
GOALS
19 130
17 227
14 291
7 636
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
y
ble
Payable
s
Supplier
3463
5112
2328
4457
6309
20263
Manufacturer
6989
5266
2585
2204
13534
36582
Retailer
8678
5988
10093
1357
76524 103442
feasible solution
r
0,0967
0,0732
0,1158
79
Appendix 2. The feasible solution for Case 2
Amount of money (in $ millions) of each component of mCCCC of various players in collaborative supply chain
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
r
y
ble
Payable
s
Supplier
1 342
1 374
901
545
6 345
7 419
0,0818
Manufacturer 11 593
816
2 896
1 796
22 981
29 792
0,0474
Retailer
972
119
85
64
5 528
6 588
0,0338
2 309
3 882
mCCCC components
DIO
DSO
Supplier
77
68
Manufacturer
184
10
Retailer
64
7
DPO
52
46
6
DAO
mCCC mCFC
27
66
29
22
126
253
4
61
6
MCCC mCFC
254
288
mCCCC components
DIO
DSO
Supplier
79
68
Manufacturer
184
10
Retailer
63
7
DPO
52
46
6
DAO
mCCC mCFC
79
16
6,0
22
126
253,0
4
60
5,4
MCCC mCFC
202
264
Total
13 907
3 218
initial data
feasible
solution
Acievement of goals
Results
Total Inventory 13 907 <=
Total A/R
2 309 <=
Total A/P
3 882 >=
Total A/E
3 461 >=
GOALS
13 907
2 309
3 882
3 218
13 212
2 194
4 076
3 379
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
y
ble
Payable
s
Supplier
1 368
1 374
901
1 602
6 345
7 419
Manufacturer 11 592
816
2 896
1 796
22 981
29 792
Retailer
947
119
85
64
5 528
6 588
r
0,0818
0,0474
0,0338
80
Appendix 3. The feasible solution for Case 3
Amount of money (in $ millions) of each component of mCCCC of various players in collaborative supply chain
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
r
y
ble
Payable
s
Supplier
402
616
170
43
1 770
3 306
0,0625
Manufacturer
2 228
2 675
2 078
1 423
22 363
15 471
0,1010
Retailer
45
2 407
422
2 106
11 123
15 573
0,0628
Total
2 675
5 698
mCCCC components
DIO
DSO
Supplier
DPO
68
63
56
35
34
14
mCCCC components
DIO
DSO
Supplier
83
68
Manufacturer
36
63
Retailer
1
56
DPO
35
34
14
Manufacturer
Retailer
83
36
1
2 670
3 572
DAO
5
34
49
DAO
5
34
49
mCCC
mFC
111
11,5
32
35,0
-5
4,4
MCCC mCFC
138
51
initial data
mCCC
mFC
111
11,5
32
35,0
-5
4,4
MCCC mCFC
138
51
feasible
solution
Acievement of goals
Results
Total Inventory 2 675 <=
Total A/R
5 698 <=
Total A/P
2 670 >=
Total A/E
3 572 >=
GOALS
2 675
5 698
2 670
3 572
2 407
5 128
2 804
3 751
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
y
ble
Payable
s
Supplier
402
616
170
43
1 770
3 306
Manufacturer
2 228
2 675
2 078
1 423
22 363
15 471
Retailer
45
2 407
422
2 106
11 123
15 573
r
0,0625
0,1010
0,0628
81
Appendix 4. The feasible solution for Case 4
Amount of money (in $ millions) of each component of mCCCC of various players in collaborative supply chain
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
r
y
ble
Payable
s
Supplier
5 767
4 787
2 063
8 782
20 681
55 632
0,1104
Manufacturer
1 551 11 869
2 623
1 792
41 632
81 742
0,0538
Retailer
2 902 17 913
2 987
5 096
33 042
93 524
0,0925
Total
10 220
32 234
mCCCC components
DIO
DSO
Supplier
15 670
DPO
DAO
31
53
70
36
23
33
mCCCC components
DIO
DSO
Supplier
50
31
Manufacturer
14
53
Retailer
59
63
DPO
43
23
33
Manufacturer
Retailer
102
14
32
11 277
58
8
20
mCCC
mFC
39
46,2
25
83,0
9
280,1
MCCC mCFC
124
409
initial data
DAO
mCCC
mFC
63
-25
-120,4
8
36
83,0
20
69
277,9
MCCC mCFC
80
240
feasible
solution
Acievement of goals
Results
Total Inventory 9 709 <=
Total A/R
32 841 <=
Total A/P
8 057 >=
Total A/E
16 454 >=
GOALS
10 220
34 569
7 674
15 670
9 709
32 841
8 057
16 454
Account Account Accrued
Inventor
Receiva
s
expense COGS Revenue
y
ble
Payable
s
Supplier
2 822
4 787
2 447
9 566
20 681
55 632
Manufacturer
1 551 11 869
2 623
1 792
41 632
81 742
Retailer
5 336 16 185
2 987
5 096
33 042
93 524
r
0,1104
0,0538
0,0925
82
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