Knowledge Management strategies on service production.
A System Dynamics approach
Mauricio Uriona Maldonado Eng.
Post-Graduate Program in Knowledge Engineering and Management (EGC)
Fellow of the Programa Estudiante Convenio de Pós-graduaçao PEC-PG/CNPQ
Universidade Federal de Santa Catarina, Brazil (UFSC)
[email protected]
Renata Jorge Vieira Ms.C.
Post-Graduate Program in Knowledge Engineering and Management (EGC)
Universidade Federal de Santa Catarina, Brazil (UFSC)
[email protected]
Gregorio Varvakis Ph.D.
Post-Graduate Program in Knowledge Engineering and Management (EGC)
Universidade Federal de Santa Catarina, Brazil (UFSC)
[email protected]
Paulo Mauricio Selig Dr.
Post-Graduate Program in Knowledge Engineering and Management (EGC)
Universidade Federal de Santa Catarina, Brazil (UFSC)
[email protected]
Abstract
The service industry is gaining more space on regional and global markets. The main
characteristics that differentiate services and manufacturing industries which are intangibility,
simultaneity and non-stockability require Operations Strategy and Knowledge Management
Strategies to fit. This paper proposes to identify the non-linear relationships between
“Training Programs” strategies and long-term Financial results, through dynamic simulation.
The main issues regarding Service Operations Strategies, and Knowledge Management
Strategies are introduced, linking training programs with organizational knowledge creation.
The selected output indicators are: Income, Expenses, Balance, Explicit and Tacit Knowledge.
As a result, a System Dynamics model of a Software-development company’s service
production system is constructed, including a KM sub-model, a Workforce sub-model, a
Customer Management sub-model, a Financial Management sub-model, afterwards it is tested
in three different investment scenarios. The model suggests that investments in personnel
training are important for service production systems. The use of System Dynamics
methodology, techniques and tools improves decision-making for Operations Management,
facilitating the understanding of the system’s behavior and structure, especially the effects of
KM strategies on service production systems.
Keywords: Knowledge Management, Service Production, System Dynamics, Operations
Strategy.
1. Introduction
When service industry is discussed, it is common to find literature about the industry’s
positioning on world’s economy, evidently, the service industry has been growing in
importance in regional economies in the last decades, the main indicators of this grow are
GDP and labor occupation percentage. Both indicators have been increasing in developed
countries like the USA (CHASE et. al., 2004; FITZSIMMONS & FITZSIMMONS, 1997), as
well as in developing countries like Brazil (GIANESI & CORRÊA, 1994).
This is because the “service” concept was introduced in traditional manufacturing
companies, aiming to increase their core products’ value, and also in companies dedicated to
produce services, like the Hotel, Health, Software and Financial industries, generating value
through the delivery of intangibles (OLIVA & STERMAN, 2001).
According to Chase et. al. (2004) and Slack (2005) the boundaries between service
and manufacturing companies are getting more difficult to be identified, since both are
becoming more interconnected and integrated.
The main differences between service operations and manufacturing operations are
intangibility, simultaneity, and non-stockability (GIANESI & CORRÊA, 1994;
FITZSIMMONS & FITZSIMMONS, 1997). These differences, and specially the
“intangibility” issue, bring new concepts, like the “Knowledge Era”, “Knowledge Worker”
(DRUCKER, 1999) and “Knowledge Factories” (ROTH et. al., 1994).
This paper sheds some light about the effects produced by Knowledge Management
strategies in production service systems, considered to-be complex and non-linear, from a
System Dynamics perspective, a methodology that facilitates understanding of complex
systems behavior and structure, allowing, via computer-simulation, the introduction of new or
different organizational politics and the visualization of the effects produced (STERMAN,
2000).
2. Service Production Systems
Organization main goals are basically to “get and keep customers” and to “make a
profit” (BERRY, HILL & KLOMPMAKER, 1995). Both goals depend on the Production
System, which is responsible for producing goods and services in the organizations, therefore
Operations Strategy is vital for gaining competitive advantage and for delivering quality
services to customers (CHASE et. al., 2004).
For Chase et al. (2004) and Gianesi &Corrêa (1994) Operations Strategy refers to
plans and politics formulation, seeking the best use of operative resources, for supporting the
Firm’s strategy, by the production of goods and services that satisfies costumers’ needs
(SLACK, 2005).
Operations Strategy implies decisions related to production processes design and
supporting infrastructure for those processes (CHASE et al., 2004), namely: service project,
process-technology, facilities, capacity-demand, workforce, quality, customer management,
performance measurement, operations control and improvement systems, among others
(CHASE et. al., 2004).
According to Roth et. al. (1994), the competitiveness comparison basis have changed
since new types of non-tangible products are becoming more common, pushing organizations
to achieve a state called “customer-readiness”, influenced by new value-added sources like
organizational knowledge.
Terms like the “knowledge factory” (ROTH et. al., 1994), the “knowledge-creating
company” (NONAKA & TAKEUCHI, 1997) and the “knowledge worker” (DRUCKER,
1999; HAMMER et. al., 2004) refer to a new competitive priority in organizations, which is
to create organizational knowledge through learning in parallel with service production.
This paper suggests that in order to improve competitive advantage, organizations
must fit Operations Strategy and Knowledge Management strategies.
3. Knowledge Management strategies in service organizations
In recent years, many researchers have argued that the capability to create knowledge
is the most important source of competitive advantage (NONAKA & TAKEUCHI, 1997;
DRUCKER, 1999). The discipline which studies the theoretical approaches of organizational
knowledge creation is Knowledge Management (DALKIR, 2005).
There are many definitions of Knowledge Management (KM), for Davenport &
Prusak (1998) it is the “collection of process that aims to govern the creation, dissemination
and use of (organizational) knowledge, in order to reach organizational objectives”, for
Schreiber et. al. (2000) it is “a framework and tool set for improving the organizational
knowledge infrastructure, aimed at getting the right knowledge to the right people in the right
form at the right time”.
There are two major types of knowledge (POLANYI, 1966), tacit knowledge, which is
difficult to articulate, tending to reside “within the heads of knowers”; and explicit
knowledge, that has been captured in media, like text, audio or images (DALKIR, 2005;
NONAKA & TAKEUCHI, 1997). Thus, according to Dalkir (2005), roughly 80% of our
knowledge is in tacit form, leaving a 15 to 20% to explicit knowledge that has been captured
or codified.
It has been suggested that Knowledge Management strategies are more and more
important for organizations, in this sense, the knowledge creation process, namely the SECI
(Socialization, Externalization, Combination and Internalization) process is vital in order to
improve competitive advantage (NONAKA & TAKEUCHI, 1997).
Some strategies related to knowledge creation are the training, mentoring and tutoring
programs, where workers develop collaboration initiatives through knowledge sharing and
organizational learning.
In service production, training programs are more complex to manage, since they are
labor and knowledge intensive; on the other hand, service intangibility, simultaneity and nonstockability, provide a high uncertainty level and non-linear behavior; all of these, relying
heavily on worker’s tacit knowledge, thus, slowing the codification process, which is part of
the training program.
This paper present, through a System Dynamics simulation model, the influence that
training strategies could have in service organizations’ overall performance.
4. System Dynamics (SD)
System Dynamics (SD) was developed by J. Forrester in 1961 (FORRESTER, 1989),
as a methodology for understanding complex systems behavior, through soft and hard
simulation. According to Sterman (2000):
“System Dynamics is a perspective and set of conceptual tools that enable us to
understand the structure and dynamics of complex systems. System Dynamics
is also a rigorous modeling method that enables us to build formal computer
simulations of complex systems and use them to design more effective policies
and organizations”
It evolved from the application of control theory to the study of dynamic social
systems, its premise is that the behavior of a complex dynamic system is the result of the
structure (causal relationships, feedback loops and time delays) (STERMAN, 2000; OLIVA
& STERMAN, 2001).
Through SD modeling and simulation techniques, it is possible to develop new
understanding and mental models related to the complex system in study, thus, creating a
“Systems Thinking” view. In that sense, SD is strongly related to “systems thinking”
(FORRESTER, 1994; RICHMOND, 1994) that is “art and science of making reliable
inferences about behavior by developing an increasingly deep understanding of underlying
structure” (RICHMOND, 1994).
5. Insights from the practice field
Following that logic, this paper proposes to get some insights from practice, in order to
better understand Knowledge Management efforts in service production systems, using a
System Dynamics approach.
The study was made in a Software-development company in the city of
Florianopolis/SC, Brazil, whose core products are for the accounting market. The company is
structured in two main areas, Management, which is composed by Marketing and Financial
Areas, and Technical, composed by R&D, Mediation and Technical Support Areas.
The focus of this paper will be the company’s Technical Support, due to its
importance in the service delivery, and the complexity of the activities made by their
Technical staff. Specifically, the technical visits made to customers.
In order to create a simple but complete-as-possible model, there were selected two of
the decision areas presented by Chase et. al. (2004): a Customer Management Model (CMM)
and Workforce Management Model (WMM). Also, considering the two basic organization’s
goals, introduced on Point 2 (BERRY et. al., 1995): “to get and to keep customers” and to
“make a profit”, a Financial Management Model (FMM) will be selected too. In order to
analyze the dynamics of the model, a Service Production Model (SPM) will be also included.
And finally a Knowledge Management Model (KMM) composed by the tacit and explicit
concepts.
The complete System Dynamics’ Model is presented in Fig. 1, including the submodels: CMM, WMM, FMM, SPM and KMM. In the next point, each one of them will be
detailed and explained.
The model will be evaluated in three different scenarios related to Workforce Training
investment policy: Possibly Over-Optimistic (POO), Perhaps All Likely (PAL), and Possibly
Over-Pessimistic (POP).
The output variables selected for comparison purposes will be: Quantity of Customers,
Mean Monthly Income, Mean Monthly Expenses, Accumulated Income less Expenses,
Explicit Knowledge stock and Tacit Knowledge stock, those last two being non-dimensional
variables. The period for simulation was stated in 48 months.
In the SP Model, service demand depends on the comparison between the
competitors Lead Time and the own Lead Time. Service delivery depends on the quantity of
workforce and on its quality, through productivity.
In the CM Model, the input flow depends on a word-of-mouth (wom) multiplier and
on the satisfaction perceived on actual customers. In this model, satisfaction only depends on
the rate between new services inflow and service delivery outflow.
In the Workforce Model, the structure is as follows, the inflow of new workers
depends on the firing and additional hiring policies, the experienced workers depends on the
quantity of new workers and on the time for “gaining” experience through training, the
outflow depends on a rate of hiring workers each month. Fixed costs are dependable of
salaries and of number of trainings developed monthly.
In the FM Model, both income and expenses are calculated relying on the quantity of
services delivered, considering both variable and fixed costs.
In the KM Model, the explicit and tacit knowledge are modeled, considering the
“knowledge creation and transfer” to workforce in terms of monthly trainings. Also
considering the loss of “knowledge converted” produced by firing policy and by the 80/20
rule explained by Dalkir (2005).
Serv ice Production Model
Demanda de Serv ico
Contratacoes adicionais
Gerando demanda de serv ico
Prestando serv ico
Lead Time
~
Lead Time Concorrencia
FuncAmadores prestando serv ico
Demanda por cliente por mes
Produtiv idade Func Am
Func com experiencia
prestando serv ico
Customer Management Model
Produtiv idade Func Exp
Workf orce Model
Clientes
Taxa de Demissao
Contratacoes adicionais
Func com experiencia
Func Iniciantes
Ganhando clientes
Perdendo Clientes
Contratando
Boca a Boca
~
Ganhando experiencia
Demitindo Func
Taxa de perda de clientes
Taxa de Perda por LeadTime
Satisf acao
Quantidade de Força de Trabalho
Taxa Base de Perda
Lead Time
Gerando demanda de servico
Knowledge Management Model
Financial Management Model
Taxa de perda de conh exp
Receitas Acumuladas
Prestando servico
Rec Med
Conhecimento Tacito
Taxa de perda de conh tac
Ganhando R$
Preco
Saldo
ConhTac Perdido
Criac Conh Tac
Conhecimento Explicito
Despesa Acumulada
CVU
Conh Exp Perdido
Criac conh Exp
Gastando R$
~
~
Rap extracao ConhTac
Rapidez de extracao do ConhExp
Desp Med
CF
Taxa de Aprov eitamento
Quantidade de Força de Trabalho
No de Treinamentos
Salarios
Custos por Treinamento
Figure 1. SD Model of the Service Production System in study: Technical visits
1
Possibly Over-Optimistic (POO) scenario
This scenario presents ten (10) monthly trainings, considered to be high in training
investment; the results obtained are presented in Fig. 2 and Fig 3. Only after the first 48
months that income and expenses are balanced. The quantity of customers falls to 43 and then
rises back to 68. The summary of the results are presented in Table 1.
1: Ganhando R$
1:
2:
3:
4:
2: Gastando R$
3: Saldo
4: Clientes
40000
50000
100
1
2
1
2
3
1:
2:
3:
4:
20000
10000
70
2
2
1:
2:
3:
4:
0
-30000
40
3
3
4
4
1
3
1.00
Page 2
4
4
1
12.75
24.50
Months
36.25
48.00
5:15 PM Fri, Feb 16, 2007
Indicadores I - Escenario Otimista
Figure 2 – Financial and Customer indicators - POO
1: Conhecimento Explicito
1:
2:
2: Conhecimento Tacito
450
1
1:
2:
2
225
1
2
1
1:
2:
1
0
2
2
1.00
12.75
24.50
Months
Page 1
36.25
48.00
4:57 PM Fri, Feb 16, 2007
Untitled
Figure 3 – Knowledge indicators - POO
2
Possibly Over-Pessimistic (POP) scenario
This scenario presents one (1) monthly training, considered to be low in training
investment; the results obtained are presented in Fig. 4 and Fig. 5. The accumulated balance
after 48 months simulation surpasses –R$ 300.000, this is explained because Income was
always less than Expenses. Customers fall to 55 after 48 months simulation. The summary of
the results are presented in Table 1.
1: Ganhando R$
1:
2:
3:
4:
2: Gastando R$
3: Saldo
4: Clientes
40000
50000
100
2
3
3
2
1:
2:
3:
4:
1
3
20000
-150000
65
1
2
4
3
1
4
1:
2:
3:
4:
2
0
-350000
30
4
1.00
Page 2
4
1
12.75
24.50
Months
36.25
48.00
5:17 PM Fri, Feb 16, 2007
Indicadores I - Escenario Pessimista
Figure 4 – Financial and Customer indicators - POP
2: Conhecimento Tacito
1: Conhecimento Explicito
1:
2:
4
0
1
1
1
1:
2:
2
0
1:
2:
0
0
1
2
2
1.00
2
12.75
2
36.25
48.00
5:09 PM Fri, Feb 16, 2007
24.50
Months
Page 1
Untitled
Figure 5 – Knowledge indicators - POP
3
Perhaps all-likely (PAL) scenario
This scenario presents five (5) monthly trainings, considered to be reasonable training
investment, the results obtained are presented in Fig. 6 and Fig. 7. After the first 14 months
simulation, Income and Expenses are balanced, and customers quantity falls to 66.
1: Ganhando R$
1:
2:
3:
4:
2: Gastando R$
3: Saldo
4: Clientes
40000
75000
100
1
2
2
1
1:
2:
3:
4:
20000
30000
70
3
2
4
4
1
3
2
1:
2:
3:
4:
0
-15000
40
4
1
3
1.00
3
12.75
4
24.50
Months
Page 2
36.25
48.00
5:20 PM Fri, Feb 16, 2007
Indicadores I - Escenario Esperado
Figure 6 – Financial and Customer indicators - PAL
2: Conhecimento Tacito
1: Conhecimento Explicito
1:
2:
400
200
1:
2:
200
100
1
2
1
1
1:
2:
0
0
1
1.00
Page 1
2
2
2
12.75
24.50
Months
36.25
48.00
5:20 PM Fri, Feb 16, 2007
Untitled
Figure 7 – Knowledge indicators - PAL
The summary of the results for all three scenarios are presented in Table 1:
Item
Units
Customers
Customers
Mean monthly income
R$
Mean monthly expense
R$
Accumulated balance
R$
Accumulated Explicit
knowledge
w/o u.
Accumulated Tacit
knowledge
w/o u.
Source: Simulations on the SD model
POO
scenario
68
23.743
22.839
43.417
POP
scenario
55
15.647
22.107
-310.085
PAL
scenario
66
22.841
21.444
67.052
440
3
366
321
0
143
Table 1. Summary of the results obtained in the output indicators
6. Conclusions
The model presented in this paper helps managers and specially operations managers
to make decisions more securely, by gaining the flight-simulation capability to test different
policies and to analyze its results.
The model replicates some outcomes presented in real life, such as hiring and firing
policies and its effects on the organizational knowledge and the customer gaining-losing
dynamic. The model also captures the essence of the knowledge management dynamics,
related to investments in training as a positive reinforcing cycle aimed to obtain better service
quality.
Specifically, the best results (see Table 1) are obtained through high training
investment (POO policy), however the PAL scenario also presents relatively good results,
especially when considering that the investment in training is reduced at half of the latter,
since the Fixed Costs depend on the number of trainings.
Considering the model above, it is reasonable to sustain that System Dynamics
methodology, tools and techniques contribute positively to Operations Management, by
showing a broader view of production systems, and in service production systems in special,
were its core characteristics often difficult modeling and simulation.
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