Analyzing Training Programs from a KM perspective: A
System Dynamics model
Mauricio Uriona-Maldonado’, Gregorio Varvakis"
1 Research Affiliate at Research on Research Group (RoR).
Duke University. Durham, NC. 27710. United States. m-uriona@duke.edu
Knowledge Engineering Department. Federal University of Santa Catarina (UFSC)
Campus UFSC, P.O. Box 476. Florianopolis, SC. 88040-970. Brazil. grego@egc.ufsc.br
Abstract: Service operations depend intensively on human resources because of their
interaction with customers and suppliers and thus, feel the need to train their staff in
order to ensure organizational performance over time. Knowledge management may be
a framework for training programs since it addresses knowedge conversion from
explicit and tacit knowledge. This paper proposes that dynamic simulation may be used.
as a tool to model and analyze the knowledge management aspects associated with
training programs within an organization. Three scenarios are considered, relying on
the number of trainings per month. The model was built using data from a customer
support service of a software-house in Brazil and subsequently, tested using real data.
By simulating different scenarios, best decision making guidelines are provided to
reduce uncertainty and customer loss. It concludes that i) training programs can be
analyzed from a knowledge management perspective; ii) the knowledge conversion
process between tacit and explicit affects the effectiveness of training programs and
thus, the organization’s performance; and iii) system dynamics modeling helps service
managers to make decisions related to training programs, by providing micro-world
simulations in order to test and to analyze different strategies.
Keywords: Knowledge Management, Training and Development Programs, System
Dynamics, Service Operations.
Introduction
Service operations depend intensively on their human resource, because of the
interactions with both customers and suppliers in which value co-production is an
inherent property (Fitzsimmons and Fitzsimmons 2007; Maglio et al. 2009).
As Cook et al. (2002) points out only with the understanding of the undedying
principles of human interactions, service operations can be approached with the same
depth and rigor than on manufacturing ones.
Humans are the producers and users of knowledge, which is ultimately the primary
resource in service operations, opening different challenges to managers and academics
than those of manufacturing.
One of the most important aspects that need to be considered is how to keep the
organization’ s knowledge base, in other words, how to ensure that technical knowledge
distributed among the staff is not getting lost rather than creating new knowledge.
This is possible through the constant review of staff training and development, in order
to improve knowledge, skills and attitudes that could enhance organizational
effectiveness (Buckley and Caple 2008; Aguinis and Kraiger 2009). In the remainder of
this work, we use the term “training” to refer to both training and development efforts.
Pertaining to staff training, knowledge management has proven to be an effective
strategy since it addresses knowledge identification, acquisition, storage and transfer in
organizations (Davenport and Prusak 2000).
In service operations, training programs are highly complex, since the labor and
knowledge intensity of services; accordingly, service intangibility, simultaneity and
nor-stockahility, hinder the leaming process by affecting on the tacit-explicit
knowledge cycle.
Despite this increase, there is still little confidence regarding the scientific rigor of these
programs since poor empirical support.
According to Chen and Klimoski (2007) the lack of clear scientific rigor can hinder
knowledge creation and accumulation, thus, leading to inefficient use of human and
financial resources and loss of competitive advantages, even harming both employees
and the organizations that employ them.
Not only less than 5% of all training programs are assessed in tenms of financial benefits
for the organization as Aguinis and Kraiger (2009) point out, but the lack of scientific
rigor can hinder also, the effectiveness of those evaluations.
This paper proposes the use of dynamic simulation, specifically system dynamics, as a
tool to model and to analyze the knowledge management aspects associated with staff
training within an organization.
Simulation, as discussed in literature, is the process of building a model of a real system
and to conduct dynamic experiments with it (Pidd 1998; Robinson et al. 2004; Giaglis et
al. 2005).
In this sense, the customer service of a software-house in Florianopolis, Brazil was
modeled. The customer service process in software development industry has been
described as knowledge and labor-intensive (Uriona Maldonado 2008).
Through the use of a system dynamics model built in iThink', the contribution of this
work lies on shedding light over the intangible effects of tacit and explicit knowledge
that support the effectiveness of training programs over organizational performance.
Service Operations Systems
Organization main goals are basically to “get and keep customers” and to “make a
profit’ (Bery, Hill, and Klompmaker 1995). Both goals depend on the Production
" iThink is a registered trademark of Isee Systems Inc. (www.iseesystems.com).
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, Aquilano, and Jacobs 2004).
For Chase et al. (2004) and Gianesi & Corréa (1994) Operations Strategy refers to plans
and politics formulation, seeking the best use of operative resources, for supporting the
Finn'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, namely: service project, process-
technology, facilities, capacity-demand, workforce, quality, customer management,
performance measurement, operations control and improvement systems, among others
(Chase, Aquilano, and Jacobs 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-reaciness”, influenced by new value-
added sources like organizational knowledge.
Tens like the “knowledge factory’, the “knowledge-creating company” and the
“knowledge worker” refer to a new competitive priority in organizations, which is to
create organizational knowledge through leaming in parallel with service production
(Roth et al. 1994; Nonaka 1994; Hammer, Leonard, and Davenport 2004; Drucker
1999).
Training programs and Knowledge Management
Due to the constant increasing demands of the markets as well as the levels of
competitiveness, employees are forced to continuously update their knowledge, skills
and attitudes, and organizations to invest in training their staff in order to ensure
improved organizational performance (Chen and Klimoski 2007).
For Goldstein and Ford (2002) training programs are “the systematic approach to
affecting individuals’ knowledge, skills, and attitudes in order to improve individual,
team, and organizational effectiveness”.
As the amount of published literature referring on training programs grows, several
fields have researched this subject, from human resource management through
instructional design, human resource development, human factors and knowledge
Management, although they borrow heavily from theories developed in more basic
sciences, such as cognitive psychology (Aguinis and Kraiger 2009; Chen and Klimoski
2007); this paper will focus on the knowledge management perspective.
From an organizational knowledge creation approach, knowledge management
addresses knowledge identification, acquisition, storage and transfer in organizations
(Davenport and Prusak 2000; Dalkir 2005).
For Davenport and Prusak (2000) KM is the “collection of process that aims to govem
the creation, dissemination and use of (organizational) knowledge, in order to reach
organizational objectives”. Schreiber et. al. (2002) defines KM as “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”.
As Aguinis and Kraiger (2009) point out, training programs result in subtle improved
performance, sometimes hard to measure. Most of the performance comes from
“informal leaming” as Barber (2004) noted, were the tacit knowledge has a major
influence.
Tacit knowledge is difficult to articulate and resides “within the heads of knowers”; the
other type of knowledge is explicit knowledge, and represents knowledge that has been
captured in some kind of media, like text, audio or images (Dalkir 2005).
From the KM perspective, around and 80% of our knowledge is in tacit form, which
means that only 20% is knowledge that can be codified in order to share it with other
individuals. This means that training programs can be effective only when explicit
knowledge is converted into tacit knowledge, when employees develop an “intuitive
feel” (Barber 2004).
As Tharenou et al. (2007) suggests, there are few empirical studies showing the effects
of training programs over organizational performance. However, there are some studies
that will be described as followed.
Aragon-Sanchez, et al. (2003) surveyed 457 small and medium-sized companies in the
United Kingdom, the Netherlands, Portugal, Finland, and Spain. They established two
macro-indicators for organizational performance: i) effectiveness (i.e, employee
involvement, human resource indicators, and quality), and ii) profitability (i.e, sales
volume, benefits before interest and taxes). Their results indicated that on-the-job
training as well as in-house training were positively related to both indicators.
Ubeda Garcia (2005) studied 78 spanish companies with more than 100 employees.
This study related the organization’s training policies with four organizational results:
employee satisfaction, customer satisfaction, owner/shareholder satisfaction and
workforce productivity. The results suggested that policies oriented toward human
capital development were directly related to all four results.
Guerrero and Barraud-Didier (2004) surveyed more than 1500 human resource directors
of large firms in France and compared them with the finm’s financial information one
year later. The results suggested that 4.6% of the variance in financial performance was
Finally, Mabey and Ramirez (2005) surveyed 179 companies in the United Kingdom,
Denmark, France, Germany, Norway and Spain. Two main indicators were analyzed:
operating revenue per employee and cost of employees as a percentage of operating
revenue. Their results suggested that firms with management development programs
were more likely to have a positive relationship between management development and
financial performance.
The evaluation of training programs and its effects over organizational performance is
harder in service operations due to their intangibility and labor and knowledge intensity.
These studies were conducted using a survey approach; this paper proposes to use an
altemative approach for analyzing the impacts of training programs on performance,
through simulation experiments.
Experimental Design
Research Design
The methodological steps of this paper followed Fomrrester’s and Sterman’s
recommendations for system dynamics applications: problem identification, model
formulation, simulation and validation, and policy analysis (Forrester 1994; Sterman
2000).
Problem identification was done by using surveys and questionnaires. The company’s
goal was to identify the number of monthly trainings that would bring the best positive
outcomes.
Among the company, eleven stakeholders were interviewed in order to comprehend the
subtle dynamics of the company. The author of this work was in a management position
on this company for a period of 20 months, his experience also enriched data collection
from this step.
Model formulation was done using iThink software, as well as simulation and
validation.
And policy analysis was supported by the analysis of scenarios. Three scenarios were
considered, relying on the number of trainings per month.
Simulation Method Choice
The modeling and simulation method selected in this work was System Dynamics.
Created by J.W. Forrester in late 1950s, System Dynamics allows complex system
simulation through stock and flow metaphors (Forrester 1989).
The main principles of System Dynamics are that behavior of a complex dynamic
system is the result of its structure (causal relationships, feedback loops and time
delays) (Sterman 2000).
Often, the complexity of a system is simply related to the amount or components of a
system. However, it is dynamic complexity - the counterintuitive behavior of complex
systems that arises from the interactions of the agents over time (Forrester 1971) - the
unanticipated events or side effects that policy makers face when the system behaves in
a hardly predictable way.
The major effect of dynamic complexity over system behavior is what Sterman (2000)
defines as “policy resistance’ - the tendency of a system to defeat human-based
interventions by the system's response to the intervention itself (Sterman 2006) - in
other words, the system’s auto-regulation mechanism that seeks to re-establish the
“eatropic equilibrium” that was present on the system before any intervention was
made.
For Sterman (2006), it is our mental model that narrows our vision of the system, thus
blocking our awareness that there are other variables that provoke certain system
behaviors that would, at first glance, appear to be unanticipated. This narrowness
hinders our ability to make better decisions in order to impose certain mechanisms that
could change system behavior for our benefit.
Through SD modeling and simulation techniques, it is possible to develop new
understandings and mental models related to the dynamic complexity surrounding the
system in study. It is our assertion that dynamic complexity is a determinant factor on
service systems especially when intangible variables like knowledge and leaming are in
study.
Model-related aspects
The data was collected from a software-house in Florianopolis, Brazil, whose products
are targeted 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 customer service, due to its importance
for service delivery, and the complexity of the activities made by their Technical staff.
The model was built considering Customer, Workforce, Financial, Service Production
and Knowledge Management variables.
The Customer Management Model (CMM) was considered after Berry et al. (1995)
recommendations about the main goals of an organization “to get and to keep
The Workforce Management Model (WMM) serves to simulate the impacts of staff on
service production.
Benry et al. (1995) also sustains that another main goal of any organization is to “make a
profit’, thus a Financial Management Model (FMM) was also built.
In order to analyze the dynamics of the model, a Service Operations Model (SOM) was
included.
And finally, a Knowledge Management Model (KMM), including tacit and explicit
knowledge components that will simulate the effects of training staff over the service
system.
Model Formulation
The complete System Dynamics Model is presented in Fig. 1, including the sub-models:
CMM, WMM, FMM, SOM 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: Scenario 1 will consist of a single monthly training; Scenario 2 will
consist of 5 monthly trainings; Scenario 3 will consist of 10 monthly trainings; and
finally, Scenario 4 will consist of 15 monthly trainings.
The output variables selected for comparison purposes will be: Customers, Mean
Monthly Income, Mean Monthly Expenses, Accumulated Balance, Explicit Knowledge
stock and Tacit Knowledge stock, those last two being non-dimensional variables. The
period for simulation was stated in 100 months.
In the SO 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 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 employees
depends on the firing and additional hiring policies, the experienced employees depends
on the quantity of new employees and on the time for “gaining” experience through
training, the outflow depends on a rate of hiring employees 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. It also
considers the loss of “knowledge converted” caused by firing policies and the 80/20
knowledge mule explained by Dalkir (2005). This rule suggests that 80% of knowledge
is tacit and only 20% explicit. For simulation purposes, the model starts with a pre-
defined amount of tacit and explicit knowledge. When analyzing tacit and explicit
knowledge variables, the model will simulate increases on both knowledge stocks over
the initial pre-defined amount.
E=}) Service Operations Model 8
Q 5, na)
Lead Time
ExpertsProductvity
TraineeP roductivity
E=}s) Customer Management Model 8| oy Workforce Model 8
Cugtomers
Q Adatiboting
Gainingc otomers LossingC uftomers
Traine
WordofMouth
CustomefossRate
LossRatfPerLeadTime
satftachea
BaseLossRate
Lead Tire
GeneratingS erviceDerrand
Knowledge Management Model 6
Financial Management Model 8
eee
nec Hed
Tacitknowledge
LossingTk
Explciknowledge FKLYsSR ate
Expending
TkCodificaonSpeed
Desp Med
TotalWorkforce
ExCogtiationS peed Taxa de Aprov eitamento|
Salaries
Cost? erTraining
Figure 1. SD Model of the Service System
Simulation Results
Scenario 1 - One monthly training (IMT)
This scenario presents 1 (one) monthly training, considered to be low in training
investment; the results obtained are presented in Figure 2. When having a low
investment in training programs, the operational expenses are covered by incomes until
the 67° month. The company gains customers until the 67° month where it starts to
losing customers. Neither explicit nor tacit knowledge are created.
2 vex
-_—
Figure 2 - Results with one monthly training
Scenario 2 - Five monthly trainings (SMT)
This scenario presents five (5) monthly trainings, considered to be high in training
investment, the results obtained are presented in Figure 3. Financial results become
positive with this scenario. There is a moderate increase in customers and also a
moderate creation of explicit and tacit knowledge.
Figure 3 - Results with 5 monthly trainings
Scenario 3 - Ten monthly trainings (10MT)
This scenario presents ten (10) monthly trainings, considered to be high in training
investment; the results are presented in Figure 4. With ten monthly trainings the service
system also presents positive financial outcomes, however, for the first 25 months with
negative results. Customers’ increase is also observed as well a strong increase in
explicit and tacit knowledge.
Figure 4 -Results with 10 monthly trainings
Scenario 4 - Fifteen monthly trainings (15MT)
This scenario presents fifteen (15) monthly trainings, considered to be strong training
investment, the results of the simulation experiment presented in Figure 5. As same as
the financial behavior of the last scenario, this fourth scenario presents initially financial
losses a then a recovery starting in the 26" month. Customers increases moderately and
explicit and tacit knowledge present strong increases.
|
i
? q 2 Unites
Figure 5 - Results with 15 monthly trainings
The summary of the results are presented in Table 1:
Table 1. Summary of the simulation results
Item (*) Units 1MT 5 MT 10 MT 15 MT
Customers Customers 234 401 421 432
Monthly income R$ 43.657 52.018 53.231 54.207
Monthly expense R$ 45.416 47.300 48.886 50.453
Acc. Balance R$ -175.879 471.730 434.435 = 375.420
Acc. Explicit K. - 81 818 1.509 1.914
Acc. Tacit K. - 0 153 470 821
(*) All values considered at the 100™ month of simulation
Table 2 shows the comparisons of the results relative to Scenario 1 (1MT).
Table 2. Simulation results relative to Scenario 1 (MT)
Ttem (*) 1MT 5 MT 10MT 15MT
Customers 1 1.71 1.80 1.85
Monthly income 1 1.19 1.22 1.24
Monthly expense 1 1.04 1.08 1.11
Acc. Balance 1 4.68 4.47 4.13
Acc. Explicit K. 1 10.10 18.63 23.63
Acc. Tacit K. 1 153.00 470.00 821.00
(*) All values considered at the 100” month of simulation
For customers, for each percent point in Scenario 1, all other scenarios presented
increases, for 5MT an increase of 1.71, for 1OMT an increase of 1.80 and for 15MT an
increase for 1.85.
For monthly income, at the end of the 100" month, the increases were 1.19, 1.22 and
1.24 respectively for SMT, 10MT and 15MT.
Similarly, for monthly expenses, the relative values were 1.04, 1.08 and 1.11 for five,
ten and fifteen monthly trainings respectively.
In relation to the Balance, increase values were higher, 4.68 for SMT, 4.47 for 10MT,
and 4.13 for 1SMT. We highlight that the greatest value in this variable was in Scenario
2 (SMT). Meaning the profit for scenario 2 was the highest in relation to other
scenarios.
For explicit knowledge accumulation (EKA), SMT presented approximately 10 times
more EKA than 1MT, 18 times more in 10MT and 23 times more for 15MT.
Finally, tacit knowledge accumulation (TKA) for Scenarios 2, 3 and 4 were strongly
highest than the value of Scenario 1.
Discussion and C onclusions
Results shown in Section 5 comoborate training and development theory when
simulation results presents better performance after training programs have been
developed.
For base-case scenario 1 (1MT), major variables present a similar behavior than the one
found on real data from the company.
It is noted that with a single monthly training, the accumulation of new tacit knowledge
is not possible or at least it is not used for improving performance, meaning that pre-
defined amounts of tacit and explicit knowledge remain approximately constant.
Also for Scenario 1 (1MT) the difference between income and expenses produces a
negative belance after the 100” month simulation. This may be explained by the
inefficacy to improve performance in order to deliver services and in order to gain new
customers. At the end of the simulation, poor training investments produce a negative
overall outcome for the company.
For all other scenarios, 2, 3 and 4, financial results as well as intangible results (tacit
and explicit knowledge) present positive outcomes, suggesting a direct relationship with
the training programs variable.
In a qualitative analysis of results, it could be difficult to manage more than 10 monthly
trainings for the technical staff without prejudicing operational activities. Even if results
May appear originally appealing, other aspects like time for trainings must be
considered.
Accordingly, we conclude that an adequate training program for the company should
include between 5 and 10 monthly trainings, based on the results of the simulations.
In a broader sense, simulation models like the one presented in this paper help managers
and specially service operations managers to make more informed decisions, by gaining
a flight-simulation capability to test different policies.
The model replicated some outcomes presented in real business operations, such as
hiring and firing policies and its effects on organizational knowledge and the customer
gaining-losing dynamic. The model also captured the essence of the knowledge
Management dynamics, related to investments in training as a positive reinforcing loop
aimed at obtaining higher service quality.
Considering this, it is reasonable to conclude that System Dynamics methodology, tools
and techniques enhances decision making in service operations and especially when
intangible variables are at study.
Though, this paper culminates in the recommendation of using simulation techniques
for service operations systems, it calls for future extension of this research into the
specific details of knowledge conversion, i.e. the SECI model of Nonaka (1994), into
proximal trainee- and progranrlevels (e.g., trainers' support and instructional methods),
as well as contextual elements like work-related climate and supervisor support that
could influence on the knowledge conversion/creation process.
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