Knowledge-Based Production Management Model

Journal of Knowledge Management
Knowledge-based integrated production management model
Jorge Muniz, Edgard Dias Batista Jr, Geilson Loureiro,
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Knowledge Management, Vol. 14 Issue: 6, pp.858-871, https://doi.org/10.1108/13673271011084907
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Knowledge-based integrated production
management model
Jorge Muniz, Edgard Dias Batista Jr and Geilson Loureiro
Downloaded by Universidad del Norte At 06:03 19 June 2018 (PT)
Abstract
Purpose – This paper aims to propose a model of production management that integrates knowledge
management, as a third dimension, to the production and work dimensions and to identify factors that
promote a favorable context for knowledge sharing and results achievement in the production
operations shop floor environment.
Design/methodology/approach – The model proposed is built from opportunities identified in the
literature review.
Findings – The factors in the model integrate its three main components: knowledge management,
production organization and work organization, providing a representation of the dynamics of the
workplace and shop floor environment.
Practical implications – The proposed model and its factors allow managers to better understand and
to improve the organization activities, because it integrates knowledge management with the production
organization and work organization components of traditional models.
Jorge Muniz is Professor of
Production Management
and Edgard Dias Batista Jr
is Professor of Information
System, Statistics and
Transport Planning Field,
both in the Production
Department, São Paulo
State University,
Guaratinguetá, Brazil.
Geilson Loureiro is
Professor of Systems
Engineering at the
Laboratory of Integration
and Testing (LIT), Brazilian
Institute for Space
Research (INPE), São José
dos Campos, Brasil.
Originality/value – Literature acknowledges the role of knowledge as competitive advantage, but it is
still dealt in an implicit way within the traditional models of production management. This paper
proposes a model and factors that provide a favorable context for tacit knowledge sharing and results
achievement in the production operations shop floor environment. The model explicitly integrates
knowledge management with traditional models’ components.
Keywords Production management, Knowledge management, Knowledge sharing, Modelling
Paper type Conceptual paper
1. Introduction
Traditionally, production management models are comprised of two dimensions: the
technical dimension and the social dimension. The technical dimension refers to production
organization, hereafter called the P-dimension, processes, activities, types and physical
arrangement of equipment and to the flow of material that result in services and goods. The
social dimension refers to work organization, hereafter called the W-dimension.
However, knowledge management is recently gaining more attention from those subjects
related to organizational sciences (Serenko and Bontis, 2004; Paiva et al., 2007), especially
those related to improvement processes and incremental process innovation, more
specifically in the shop floor production operations environment. Recent papers on
knowledge management reinforce the need to research:
Received 29 December 2009
Accepted 30 April 2010
PAGE 858
j
B
Factors that affect the tacit knowledge in groups within the organizations (Erden et al.,
2008).
B
Methodologies for business improvement (Hazlett et al., 2005; Nonaka et al., 2006;
Fugate et al., 2009).
JOURNAL OF KNOWLEDGE MANAGEMENT
j
VOL. 14 NO. 6 2010, pp. 858-871, Q Emerald Group Publishing Limited, ISSN 1367-3270
DOI 10.1108/13673271011084907
B
Pragmatic guidelines on how the manager can develop favorable contexts in order to
encourage knowledge conversion processes within groups in the organization or within
the entire organization (Nonaka et al., 2006).
This paper aims to propose a model of production management that integrates knowledge
management, as a third dimension, hereafter called the K-dimension, to the P and
W-dimensions. In order to achieve the above-mentioned general objective, this paper has as
specific objective, which is to identify factors, in the K, P and W-dimensions, that promotes a
favorable context for knowledge sharing and results achievement in the production
operations shop floor environment, based on literature review.
In order to achieve the specific objective above, this paper is structured as following:
Section 2 reviews the traditional production management models. Sections 3, 4 and 5 review
models and processes of production organization, work organization and knowledge
management, respectively. This leads to the K, P and W factors described in Section 6.
Section 7 proposes the model. Section 8 draws conclusions. Section 9 anticipates potential
further research.
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2. Production management traditional models
Traditional production management models evolved from the classic, or Taylorist model, to
the socio-technical model.
In the Taylorist model, scientific administration and mass production focus on production
organization rationalization, seeking efficiency increase in order to obtain results, often
translated in terms of quantity and cost.
In the socio-technical model, Emery (1959) considers that people’s behavior at work
depends on their task structure and their task content. People’s performance and feelings
related to their tasks are important for their satisfaction with their jobs. Therefore, despite the
fact that the social and technical dimensions are identified as separate, both should be
‘‘jointly optimized’’ for obtaining results, while developing and integrating people. This
proposition is essential so that the socio-technical model cannot be considered as a social
experimentation exercise, but as a way of developing more effective organizations.
Both models are illustrated in Figure 1. The dashed line represents the permeability of
production operations and shop floor environment to external factors, such as, market and
technology aspects. The gray star that involves production organization and work
organization represents the existence of a well-defined and controlled set of factors to
conduct the production processes, such as standard time and best method emphasis
(Taylor, 1998). The cloud involving production organization and work organization illustrates
the existence of various factors related to people, such as, leadership or individual
satisfaction. Those factors, although acknowledged, do not have a prescribed treatment
such as the ones approached by the Taylorist model.
Figure 1 Taylorist and socio-technical models of production management
SOCIAL-TECHNICAL
PERSPECTIVE of
PRODUCTION MANAGEMENT
RESULTS
• Job Satisfaction
• Innovation
• Quality
• Time
• Quantity
• Cost
WORK
ORGANIZATION
PRODUCTION
ORGANIZATION
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VOL. 14 NO. 6 2010 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 859
Biazzo and Panizzollo (2000) state that the differences between the socio-technical and
Taylorist models are related to work organization, which, in the socio-technical model, it
should be as taken an advantage for the people’s intellectual and creative capabilities,
allowing continuous learning, generating recognition and social support, providing a clear
relationship with social life and social values of the workers, making the visualization of the
final product, ‘‘the big picture’’, possible, permitting the control on the results, minimizing
hierarchical differences and group composition to be heterogeneous.
The differences above also contain elements that require knowledge organization, and
suggest that the P-dimension and the W-dimension are not enough for representing the
increasingly important need for knowledge sharing among workers.
Factors influencing knowledge sharing among workers are little understood, probably due to
the emphasis on information technology (Bisalyaputra, 2004; von Krogh et al., 2000).
Procedures prepared by the workers themselves in order to enhance productivity, quality,
skills and understanding of the work place, attenuate the little involvement that bureaucracy
can bring (Adler, 1993).
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Knowledge management is definitely an opportunity to complement those traditional
models.
3. Production organization, the P-dimension
Production organization refers to processes, activities, types and physical arrangement of
equipment and material flow that result in services and goods in a production system (shop
floor production operations is the context of this paper).
According to Biazzo and Panizzollo (2000), the post-Taylorism transformations in the
automotive industry can be summarized into four models of production organization:
1. The Neo-Fordism is characterized by the introduction of advanced technologies of
manufacturing in a Taylorist organizational context.
2. The Volvo Udevalla represents a break with the Fordist tradition, given the fact that it
eliminates the assembly line and the ‘‘one minute job’’.
3. The Neo-Craft has been limited to the production of luxury and customized models.
4. The Toyota production system or lean manufacturing is based on waste minimization,
quality, teamwork, standardized processes and kaizen.
Although the above listed models were observed in the context of the automotive industry,
Biazzo and Panizzollo (2000) consider industry as a ‘‘microcosm’’, where the characteristics
of production organization are in general ‘‘crystallized’’ and can be observed. Various
models from that industry are also replicated in other industrial sectors such as mass
manufacturing in electronics industry and lean manufacturing trends in aerospace industry
(Murman, 2002).
It is still uncertain whether those models will coexist or whether they will converge into a
dominant model (Bartezzaghi, 1999; Fujimoto et al., 1997 in Biazzo and Panizzollo, 2000).
The main contemporary theory lines related to production organization come from a
combination of total quality management (TQM) with other theories, such as lean
manufacturing. Therefore, the limits of those theories are not clear and, there is frequently an
intersection among their main ideas (Berawi and Woodhead, 2005).
Prasad (1995, in Herron and Braiden, 2006) identified a relationship between lean
manufacturing tools and their effect on manufacturing competitiveness, on objectives
improvement, on productivity, and on operations control. He highlighted 5S, standard
operating procedure, problem-solving methods and quick changeover as examples of such
tools.
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PAGE 860 JOURNAL OF KNOWLEDGE MANAGEMENT VOL. 14 NO. 6 2010
4. Work organization, the W-dimension
Work organization refers to methods, job content and roles and responsibilities in a given
production system (shop floor production operations is the context of this paper). It focuses
on the social relationships among individuals and groups, their behavior, skills, capabilities,
feelings and other human aspects.
According to van der Zwaan (1975), it is impossible to draw limits between the technical and
social dimensions. Among the various work organization models, the Swedish
(semi-autonomous group) and the Japanese (enriched group) models deserve to be
highlighted. Table I presents the main differences between them.
This paper calls the Swedish model for work organization as semi-autonomous group and
calls the Japanese model as enriched group.
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The semi-autonomous group emphasizes group autonomy and flexibility, which allows the
participation of workers in the assignment and reviewing their roles and responsibilities in
order to improve the local and global results of their organization. The autonomy in a
semi-autonomous group may comprise work methods review, leader selection, tasks
distribution, target definitions. Volvo Udevalla is an example of a factory where the
semi-autonomous work organization took place (Sandberg, 1995).
The enriched group is based on responsibility and multi-skills of the local management, with
restricted autonomy to pre-defined roles stated by the organizational structure. Therefore,
the enriched model limits workers participation on assignment and review their roles and
responsibilities. In the enriched group, the worker has less autonomy than in the
semi-autonomous group but more autonomy than in the Taylorist worker arrangements. Lean
manufacturing groups are organized according to the enriched group model.
5. Knowledge management, the K-dimension
Knowledge management refers to the management of the various knowledge conversion
processes in the production system (shop floor production operations is the context of this
paper).
Knowledge management, as a theme, has been receiving much attention from various
production management areas. This evidences its multidisciplinarity and its complexity
(Alvesson and Kärreman, 2001). For example, knowledge management contributes to
stimulate innovation and continuous improvement by using existing knowledge within the
organization (King et al., 2008).
Table I Models of work organization
Swedish (semi-autonomous group)
Japanese (enriched group)
Semi-skilled workers with high initial training
Semi-skilled workers with generally high training
starting qualifications
Work tied to the production cycle
Highly repetitive work; cycle times around one
minute on the assembly lines, around five
minutes in the machining area where
multi-machine work is the norm
No partial autonomy for the teams through JIT
design
Strong hierarchical structures, group leader
appointed by management, no group self
regulation
Work totally uncoupled from the production cycle
Wholistic tasks with long work cycles (. one
hour)
High partial autonomy for teams through process
layout
De-hierarchization with elected speaker and self
regulation of group affairs
Source: Hancké and Rubstein in Sandberg (1995)
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VOL. 14 NO. 6 2010 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 861
According to Serenko and Bontis (2004), knowledge management is a relatively new field of
the general theory of administration. Many of its concepts are still evolving (Nonaka et al.,
2006; Pasternack and Viscio, 1998 in Hazlett et al., 2005).
Using Kuhn’s (1996) scientific paradigm, Hazlett et al. (2005) consider knowledge with two
main focuses (Figure 2):
1. Management: called the organic paradigm, based on the importance of communication
and tacit knowledge. It emphasizes the people.
2. Information systems: called the computational paradigm, based on the importance of
databases. It emphasizes information technology.
Various organizational programs are underlined by the theme ‘‘knowledge’’, and many are
backed by standards (ISO 9000, TS 16949, ISO 14000, SA 8000). However, the theme is little
explored and treated modestly, marginally and implicitly in the models of production
management.
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According to Nonaka (1994), new knowledge always originates in people. Nonaka rescues
Polanyi’s concept of tacit knowledge, the knowledge that is personal, knowledge that cannot
be objectively articulated in order to be shared with others. On the other hand, explicit
knowledge is expressed in words and data that can be formally and systematically shared
with other people. Nonaka presents a model based on the social and interpersonal
interactions that represents the four ways of conversion between tacit and explicit
knowledge (see Table II), called the SECI processes: socialization (exchange of
experiences among people); externalization (record and formal availability of the
knowledge for other people); combination (integrated contents explicitly available that
generates new knowledge); and internalization (people acquiring the knowledge through
ways already formalized and registered).
Those continuous and dynamic interactions between the knowledge conversion modes
result in increasing levels of knowledge, forming the so -alled Knowledge Spiral (Figure 3).
Authors have defended that only the explicit knowledge can be managed, captured and
kept up to date (von Krogh et al., 2000; Gilmour, 2003). However, the same authors indicate
that better results can be achieved with the existence of a favorable context, stimulated by
actions that are focused on tacit knowledge sharing and people’s integration, facilitating the
exchange and learning of new knowledge. This favorable context is hereafter called Ba (von
Figure 2 Knowledge focuses
Management
(Organic Paradigm)
Knowledge
Information system
(Computational Paradigm)
Sources: Adapted from Spender and Scherer (2007); Hazlett et al. (2005)
Table II Knowledge conversion process – SECI
To
From
Tacit
Explicit
Tacit
Explicit
Socialization
Internalization
Externalization
Combination
Source: Nonaka (1994)
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PAGE 862 JOURNAL OF KNOWLEDGE MANAGEMENT VOL. 14 NO. 6 2010
Figure 3 Knowledge spiral
Knowledge
Level
S
E
C
I
S
I
E
C
Source: Adapted from Nonaka (1994)
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Krogh et al., 2000). When the organization formalizes and makes such actions explicit,
obtaining the Ba is improved.
Alvesson and Kärreman (2001) and Gourlay (2006) perform a critical analysis of Nonaka’s
work. They acknowledge the comprehensiveness of the knowledge conversion processes
proposed. However, they argue that Nonaka treats the subject ‘‘management’’ in a vague
way and still rooted on the Taylorist idea that only the manager ‘‘adjusts the direction, selects
the participants, provides the interaction environment, establishes guidance and schedule
to projects and supports the innovation process’’ (Alvesson and Kärreman, 2001). Nonaka’s
work is empirically founded on a product and development context. Garvin (1993) indicates
the lack of prescriptive elements, mainly those related to process performance
measurement and management.
Despite the criticism, Nonaka’s work has been applied to empirical studies in many contexts,
such as the analysis of:
B
organizational knowledge creation processes in the automotive industry (Vaccaro et al.,
2009; Dyck et al., 2005);
B
influences of the adoption and use of the information on communication in organization
learning (Lopez-Nicolas and Soto-Acosta, 2010); and
B
confirmatory factors were conducted to test Nonaka’s model with the Japanese middle
managers (Nonaka et al., 1994), among others.
6. The K, P and W factors
The K, P and W dimensions have relevant factors that promote the Ba for the processes of
knowledge conversion in the production operations shop floor environment. Factors in the K,
P and W dimensions are hereafter called the K, P and W-factors, respectively.
Relevant P-factors are the use of the following tools that promote the use of worker’s
knowledge and involvement. The tools contribute for the control and improvement of the
daily activities of production workers. They are: problem solving methods (Garvin, 1993;
Kolb, 1984); standard operating procedure (Bartezzaghi, 1999; Ohno, 1988; Spear and
Bowen, 1999); 5S (Ohno, 1988); Poka Yoke (Ohno, 1988; Black, 1991) and quick
changeover (Black, 1991; Shingo, 1989).
Relevant W-factors are objectives, structure, communication, training, and incentives.
According to Smith (2001), the most important factors for converting tacit knowledge into
explicit knowledge, in the shop floor operation environment, are: objectives, communication
and incentive. Worley and Doolen (2006) say that interdepartmental communication is the
most common issue for converting tacit knowledge into explicit knowledge. Work
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VOL. 14 NO. 6 2010 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 863
organization models catalyze collective and individual learning, job satisfaction and
productivity (van der Zwaan, 1975). Also, autonomy, in the daily routine and improvement
production activities, is related to knowledge creation, storage, sharing, use and
assessment. Many factors related to work organization are relevant to promote the Ba.
Training, for example, cannot be separated from learning in the workplace (Darrah, 1995).
Objectives represent a measurable way to relate the work of the group to the achievement of
results, indicating progress, establishing priorities and justifying the claim for material and
time resources to be used in problem solving and improvement projects.
Structure encompasses the specification of roles and responsibilities of people in the
working group, that is, group members, group leader and direct supervision, and, also, the
availability of material and time resources. The formal organization of people, material
resources and time allocation stimulate the initiative and the autonomy of the group
members to seek support and to meet for creating, sharing, using and assessing new ideas
of improvement and results gained. Barton and Delbridge (2006) state that the role of the
supervisors and shop floor operators is important for continuous improvement and for the
plant innovation process, and emphasizes the importance of teamwork and empowerment.
They also state that supervisors must translate the company’s global objectives into
objectives specifically to working groups, as a workforce motivation element.
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Communication is the process by which, ultimately, ideas and feelings are transmitted from
people to people, from people to group, or from group to group (including to supporting
groups, e.g. maintenance and logistics), making the social interaction necessary to
knowledge management possible.
Training is the skills development in production activities by emulating situations similar to
working situations. Training shall provide the working groups with knowledge to action
(Nonaka, 1994).
Incentive is related to stimulus to carry out action, such as, to motivate shop floor operators
to make suggestions for improvement in the workplace.
Nakano (2007) classifies Nonaka’s ideas as analytical-constructivist, in other words,
knowledge is considered as the creation process from people interaction and provides a
larger range in the description, analysis and understanding of the phenomenon.
Relevant K-factors are based on the work of Nonaka (1994). They are the processes of
socialization, externalization, combination and internalization. The institutionalization of
Nonaka’s processes as factors to create the Ba is expected to:
B
support socially built knowledge;
B
stimulate cooperation and teamwork;
B
emphasize the importance of transferring and transforming knowledge from personal to
organizational and from tacit to explicit; and
B
stimulate interactive work on problems (try and error) as a learning process.
Table III presents the acronyms assigned to each dimension and factor.
Table III Acronyms assigned to factors and dimensions
Knowledge management
K-dimension
Production organization
P-dimension
Work organization
W-dimension
Factor
Code
Factor
Code
Factor
Code
Socialization (tacit ! tacit)
Externalization (tacit ! explicit)
Internalization (explicit ! tacit)
Combination (explicit ! explicit)
SOC
EXT
INT
CBN
Problem-solving method (PSM)
Standard operating procedure
5S
Poka Yoke
Quick change over
PSM
SOP
5S
PY
QCO
Objective
Structure
Communication
Training
Incentives
Personal characteristics
OBJ
STR
COM
TRN
INC
PCH
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PAGE 864 JOURNAL OF KNOWLEDGE MANAGEMENT VOL. 14 NO. 6 2010
7. K-PMM model – the knowledge-based and integrated production management
model
Traditional product management models have two dimensions, human or social,
represented by the work organization is the W-dimension and a technical dimension is
represented by the production organization, the P-dimension.
The P and W-dimensions capture, essentially, the explicit structure and behavior of the
production management system. Such a system has also a tacit structure that is
progressively converted into explicit as it is better understood. Tacit knowledge exists, is
important and needs to be formally included in a model of production management system,
especially to model shop floor environment relationships.
The knowledge conversion process acknowledges the importance of a tacit knowledge and
focuses on the various processes of conversion of such knowledge into explicit and other
tacit knowledge and vice versa.
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This paper proposes the K-PMM model, including a third dimension in a production
management model. The K-PMM is a model of production management, focused on shop
floor operations, and has three dimensions, the K, P and W dimensions. In the model it is
proposed that these three dimensions must be integrated.
The P, K and W dimensions of the K-PMM model were translated into factors. The factors
were chosen focusing on the shop floor environment and, especially, for demonstrating the
relevance of including knowledge as a third dimension of production management. The
factors chosen in this work are the ones that promote the Ba, a favorable context that
facilitates knowledge conversion processes.
7.1 Model justification based on a literature review
Figure 4 relates papers and factors mentioned in them in order to improve the Ba. The fact
that K, P and W factors are mentioned in those papers suggests that a production
management model for promoting the Ba for shop floor workers should have the three K, P
and W integrated dimensions. This evolves the traditional production management models
presented in Section 2, adding a third dimension to them, and, reinforcing the need that
these three dimensions must be integrated (Figure 5).
Figure 4 Papers and factors identified in them
K - Dimension
Code
References
van der Zwaan 1975
Sandberg 1995 (Org)
Emiliani 1998
Peltokorpi et al. 2007
Nonaka 1994
Garvin 1993
Bartezzaghi 1999
Bisalyaputra 2004
Nonaka et al. 2006
Easterby-Smith; 1997
Spender and Scherer 2007
Biazzo and Panizollo 2000
Emery 1959
Smith 2001
Wiig 1994
Woorley and Doolen 2006
Kulkarni et al. 2007
Ohno 1988
Shingo 1989
Spear and Bowen 1999
SOC
EXT
INT
W – Dimension
CBN
OBJ
STR
COM
TRN
P - Dimension
INC
j
PCH
PSM
SOP
5S
QCO
j
PY
VOL. 14 NO. 6 2010 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 865
Figure 5 Dimensions for promoting the Ba
Knowledge
Management
(K-Dimension)
Work
Organization
(W-Dimension)
(P-Dimension)
Production
Organization
Favorable
Context
(Ba)
R
E
S
U
L
T
S
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7.2 Model description
The K-PMM is depicted in Figure 6. The K-PMM promotes the integration of P, K and W
dimensions because it is formally concerned with the tacit and explicit knowledge
conversion modes, incorporating them to the procedures and assessing, by measures, their
use in the shop floor knowledge identification and sharing activities.
The star involving production organization and work organization represents the set of
defined, controlled and integrated factors for carrying out production management in a way
that creates the Ba. As in the taylorist and socio-technical models, the dashed line
represents the permeability of production operations shop floor environment to external
factors, such as, market, strategic and technology aspects reflected in the production
processes.
The inclusion of the SECI conversion process and the knowledge spiral (Nonaka, 1994), in
Figure 6, formalizes the integration of knowledge management with the traditional
production management model. Figure 6 highlights the need for measures and procedures,
related to results or to the factors presented in Section 6, establishing a dynamic relationship
of cause and effect between the factors and the obtained results.
In the K-PMM, production organization focuses on the definition, management and
improvement of the production processes, by the application of pragmatic tools for the
critical analysis and implementation, by the operators themselves, targeting results such as
Figure 6 Knowledge based production management model (K-PMM) with dimensions and factors
INTEGRATED PERSPECTIVE
of PRODUCTION
MANAGEMENT
WORK
ORGANIZATION
S E
W-Dimension
1. Objective
2. Structure
3. Communication
4. Training
5. Incentive
6. Personal
Characteristics
j
I C
Ba
PRODUCTION
ORGANIZATION
j
PAGE 866 JOURNAL OF KNOWLEDGE MANAGEMENT VOL. 14 NO. 6 2010
RESULTS
K-Dimension
1. Socialization
2. Explicitation
3. Combination
4. Internalization
Measurables
and
Procedures
P-Dimension
1. Problem Solving Method
2. Standard Operating
Procedures
3. 5 S
4. Poka Yoke
5. Quick change Over
• Job Satisfaction
• Innovation
• Quality
• Time
• Quantity
• Cost
reduction in number of defects, in manufacturing time, in time of product model changeover
during production, in costs, and in rework hours.
The use of the P-factors enhances operators learning, by systematically seeking
improvement in the production environment. Lean manufacturing and mass production
were considered when selecting such factors. In order to promote the Ba integrated to the
production work routine, the use of P-factors not only requires socialization, externalization
and internalization of knowledge (K-factors), but also the implementation and use of the
W-factors.
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In the K-PMM, work organization includes the level of autonomy at the various hierarchical
levels of the people on the shop floor, for the definition, management and improvement of the
production processes, in order to create opportunities for those people to develop
pro-activity, learning and creativity to implement incremental innovative solutions. Attention
must be given to cooperation and communication, to the incentives and training needs and
to operators’ development, in order to get results for the organization and for the operators
themselves. Among these results, the number of suggestions implemented; percentage of
people participating in improvement projects; percentage of trained people who used, in a
real work situation, the knowledge acquired; percentage of people who have a qualified
substitute; percentage of people who declare themselves sufficiently motivated and
satisfied can be highlighted as an example.
The W-factors to promote the Ba, presented in Section 6, support the interaction between the
operators and the organization, by sharing measurable objectives, by work and
communication structure and by training and incentives. For the selection of those
factors, two work organization models were considered: the semi-autonomous models and
the enriched model.
The W-factors adopted in the K-PMM contribute to organize people in order to get the best
from operators’ knowledge and to obtain results. They are adequate to the production
environment. It is intended, with the use of those factors, to enhance people involvement in
order to get the organization objectives, systematically, by the creation, retrieving, sharing
and using knowledge. The factors consider the needs of the group members when
executing their routine and improvement activities, outlining: ‘‘who can help to do what’’,
material and time resources availability, communication among group members and among
the group and other people in the organization, training required by the various activities and
by the operation of the production machinery, and incentives.
The K-dimension as presented in Figure 6, promotes the integration between the P and
W-dimensions, because it is formally concerned with the tacit and explicit knowledge
conversion modes, incorporating them to the procedures and assessing, by measures, their
use in the shop floor knowledge identification and sharing activities. Therefore, K-PMM
recognizes the spontaneous and collective knowledge generation process and the
workforce flexibility for the operation of shop floor machinery and for a better communication
among the people involved.
In Figure 6, the integration of K, P and W dimensions lead to improvement activities, such as:
problem solving, kaizen projects, waste reduction, standard operation procedure
elaboration and review. Those activities are the result of people’s interaction in a working
group and of their knowledge application in the production environment. Kaizen
improvement activities, applied continuously, incrementally and in a participative way for
obtaining results, are in line with the socio-technical model. Brunet and New (2003) state that
kaizen activities must be outside contractual scope. However, as mentioned in Section 6,
related to the W-factors, there must be formal support (W-STR, structure) and time allocation
(W-COM, communication by meetings) for improvement activities. Therefore, kaizen
activities must be carried out, routinely, for improvement, without conflict with production
objectives (e.g. pieces produced per day).
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VOL. 14 NO. 6 2010 JOURNAL OF KNOWLEDGE MANAGEMENT PAGE 867
8. Conclusions
In the context of the shop floor production operations environment, this paper proposed a
production management model which integrates knowledge management as a third
dimension, hereafter called the K-dimension, to the already traditional P and W-dimensions
(see Figure 7)
Factors on the K, P and W-dimensions that promote a favorable context for knowledge
sharing and results achievement in the production operations shop floor environment were
identified based on literature review. Factors were initially identified in the literature. Those
factors were presented in Section 6.
Literature review (see Figure 4) showed that those factors, and therefore their corresponding
dimensions, are relevant to the model and are integrated.
In Section 7, a model with the three K, P and W integrated dimensions is proposed.
By translating the P, K and W dimensions of the model into factors that promote a favorable
context for the knowledge conversion process in the shop floor operations environment, the
integration of the dimensions was demonstrated.
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The K-PMM developed is robust to different work and production organization models and to
the various functions related to shop floor environment. Ongoing researches conducted by
the authors in the automotive, electronics and glass industries suggest this statement.
The K-PMM expands the scope of the manager over the reality of his work. This enhances
the analysis of this reality and, therefore, contributes for his decision making process.
9. Further work
The present paper suggests potential for developing a diagnostic tool to identify which
factor should be further developed in order to get the favorable Ba.
The scope extension mentioned above and the deepening of the understanding of the
integration of K, P and W dimensions suggest the creation of a tool to diagnose qualitatively
and quantitatively the situation of a plant that wants to promote a Ba.
The K-PMM provides the basis for building a diagnostic tool for assessing the presence of
the factors in a given shop floor production operation environment and orientate the formal
integration of people, physical production means and knowledge. The tool would allow that
the needs of the people and of the production system can be jointly met by the use of the
Figure 7 Integrated P, K and W dimensions in the K-PMM
Knowledge
Production
Work
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sources of knowledge, in a formal process, for waste reduction and other incremental
improvement. Tacit knowledge can be particularly of difficult management, but the tool
would enhance the creation and operation of a favorable context for the use of the operator
knowledge.
K-PMM was conceived for the shop floor production operations environment, where direct
work force is predominant, with focus on the application of experience and skills of operators
in the assembly line of the automotive sector. As future work, it is also proposed the analysis
of the application of the K-PMM in other sectors, such as the electrical-electronic and
chemical, and also in areas with more specialized workforce, not completely automated,
such as maintenance and tooling groups.
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About the authors
Jorge Muniz is Professor of Production Management at São Paulo State University (UNESP),
Brazil. He holds a doctorate research at the Production Department, São Paulo State
University (UNESP) and a Master in Production Engineering, São Paulo University (USP). He
has worked for many years in the Brazilian automotive industry in production management
assignments. Jorge Muniz is the corresponding author and can be contacted at:
[email protected]
Edgard Dias Batista Jr is Professor of Information System, Statistics and Transport Planning
Field at the Production Department, São Paulo State University (UNESP), Brazil. He holds a
doctorate title in Computation and System Engineering, Federal University of Rio de Janeiro
(COPPE/UFRJ) and a Master in System Analysis and Application, National Space Research
Institute (INPE), Brazil. He has been a consultant in public transport planning department for
the government.
Geilson Loureiro is a Professor of Systems Engineering at INPE (Brazilian Institute for Space
Research) and ITA (Technological Institute of Aeronautics). He works also as a consultant to
global Brazilian companies such as EMBRAER and VALE. He worked as a post doctoral
associate at Massachusetts Institute of Technology (MIT) in the NASA Concept of
Exploration and Refinement project for deriving system of systems architectures for human
and robotic solar system exploration. Geilson has already published two books on complex
systems concurrent engineering.
To purchase reprints of this article please e-mail: [email protected]
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