Int. J. Knowledge Management Studies, Vol. 4, No. 3, 2010
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Knowledge-based integrated Production Management Model applied to automotive companies Jorge Muniz Jr.* and Edgard Dias Batista Jr. Production Department, São Paulo State University, Av. Ariberto Pereira da Cunha 333, Guaratinguetá, SP 12.516-410, Brazil Fax: +55 12 3123 2855 E-mail:
[email protected] E-mail:
[email protected] *Corresponding author
Geilson Loureiro INPE – Brazilian Institute for Space Research, LIT – Laboratory of Integration and Testing, ITA – Technological Institute of Aeronautics, Av. dos Astronautas, 1758, Caixa Postal 515, São José dos Campos, SP 12227-010, Brazil E-mail:
[email protected] Abstract: This paper aims to examine the relevance of a production management model, in the shop-floor operations environment, that integrates the dimensions of production organisation (lean and mass production), work organisation (enriched and semi-autonomous groups) and knowledge management. A theoretical model has been applied to automotive companies to verify model adherence. Each of those dimensions has been described by factors. Shop-floor personnel interviews were conducted to confirm the factors relevance to that company. Results have shown that the model represented the reality of those companies concerning the researched dimensions. The factors allow managers to promote a favourable context for knowledge sharing. Keywords: knowledge management; work organisation; production organisation; production model; worker; automotive companies; tacit knowledge. Reference to this paper should be made as follows: Muniz Jr., J., Batista Jr., E.D. and Loureiro, G. (2010) ‘Knowledge-based integrated Production Management Model applied to automotive companies’, Int. J. Knowledge Management Studies, Vol. 4, No. 3, pp.301–318. Biographical notes: Jorge Muniz Jr. is a Professor of Production Management at Sao Paulo State University (UNESP), Brazil. He holds a doctorate research at the Production Department, Sao Paulo State University (UNESP) and he holds a Master’s Degree in Production Engineering from Sao Paulo University (USP). He has worked for many years in the Brazilian Automotive Industry in Production Management assignments. Actually, he is researching about knowledge management in production systems.
Copyright © 2010 Inderscience Enterprises Ltd.
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J. Muniz Jr. et al. Edgard D. Batista Jr. is a Professor of Information System, Statistics and Transport Planning Field at the Production Department, Sao Paulo State University (UNESP), Brazil. He holds a doctorate title in Computation and System Engineering from Federal University of Rio de Janeiro (COPPE/UFRJ) and he holds a Master’s Degree in System Analysis and Application from National Space Research Institute (INPE), Brazil. He has been consultant in the Public Transport Planning to government. Geilson Loureiro is a Professor of Systems Engineering at INPE (Brazilian Institute for Space Research) and ITA (Technological Institute of Aeronautics). He also works as a consultant to global Brazilian companies such as EMBRAER and VALE. He worked as a postdoc associate at MIT (Massachusetts Institute of Technology) in the NASA Concept of Exploration and Refinement project for deriving system of systems architectures for human and robotic solar system exploration. He has already published two books on complex systems concurrent engineering.
1
Introduction
There is a consensus on the role of knowledge as an organisation’s competitive advantage; however, the issue is explored poorly in management practice (Nonaka et al., 2006; Nonaka and Peltokorpi, 2006). However, recently, knowledge management is gaining much attention from those subjects related to organisational 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, common in the automotive industry. Recent papers on knowledge management reinforce the need to research: •
factors that affect the tacit knowledge in groups within the organisations (Erden et al., 2008)
•
methodologies for organisation improvement (Hazlett et al., 2005; Nonaka et al., 2006; Fugate et al., 2009)
•
pragmatic guidelines on how the manager can develop favourable contexts to encourage knowledge conversion processes within groups in the organisation or within the entire organisation (Nonaka et al., 2006)
•
integrating the concepts of knowledge management, production and work organisation (Muniz et al., 2010).
Traditionally, production management models are composed of two dimensions: the technical dimension and the social dimension. The technical dimension refers to production organisation, 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 organisation, hereafter called the W-dimension. This paper aims to examine the relevance of the Knowledge-based Integrated Production Management Model (K-PMM) (see Muniz et al., 2010 for model description and detailing), in the shop-floor production operations environment, that integrates
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knowledge management, as a third dimension, hereafter called the K-dimension, to the P and W-dimensions. To achieve the above-mentioned general objective, this paper has the following specific objectives: •
to identify factors, in the K-, P- and W-dimensions, that promote a favourable context for knowledge sharing and results achievement in the production operations shop-floor environment, based on exploratory case study
•
to show that these factors, and therefore, their corresponding dimensions, are integrated
•
to discuss the relevance of the model with the three K, P and W integrated dimensions in the scope of the automotive shop-floor production operations activities, where the case study took place.
To meet the above-mentioned specific objectives, this paper is structured as follows. Section 2 presents the K-PMM and its K-, P- and W-factors. Section 3 presents the method, Section 4 presents the results and analysis of the case study carried out to confirm factors relevance and integration discussed in Section 5. Section 6 draws conclusions.
2
K-PMM: The Knowledge-based integrated Production Management Model
Traditional production management models have two dimensions, a human or social dimension represented by the work organisation, the W-dimension and, a technical dimension, represented by the production organisation, the P-dimension. The P- and W-dimensions capture, essentially, the explicit structure and behaviour 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. Many authors have defended that only the explicit knowledge can be managed, captured and kept updated (von Krogh et al., 2000; Gilmour, 2003). However, the same authors indicate that better results can be achieved with the existence of a favourable context, stimulated by actions that are focused on tacit knowledge sharing and people integration, facilitating the exchange and learning of new knowledge. This favourable context is hereafter called Ba (von Krogh et al., 2000). When the organisation formalises and makes explicit such actions, obtaining the Ba is potentialised. This evolves the traditional work and production management models, adding a third dimension to them, and, reinforcing the need that these three dimensions must be integrated (Figure 1). Knowledge management is the set of systematic, formal and deliberate actions to capture, preserve, share and reuse tacit and explicit knowledge created and used by people during routine and improvement productive processes, generating measurable results for the organisation and for the individuals (Muniz et al., 2009). This definition is used in this work due to its adequacy to the Operations Management.
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The K-PMM (Muniz et al., 2010) is a theoretical model, which is depicted in Figure 1. 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. Figure 1
Dimensions for promoting the Ba (see online version for colours)
The star involving Production Organisation and Work Organisation 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. 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 (Table 1). Table 1
Knowledge conversion process – SECI To
From
Tacit
Explicit
Tacit
Socialisation
Externalisation
Explicit
Internalisation
Combination
Source: Nonaka (1994)
The inclusion of the SECI conversion process and the knowledge spiral (Nonaka, 1994), in Figure 2, formalises the integration of knowledge management with the traditional production management models and highlights the need for measures and procedures, related to results or to the factors presented in Section 4, establishing a dynamic relationship of cause and effect between the factors and the obtained results. The K-dimension, as presented in Figure 2, 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 recognises the spontaneous and collective knowledge generation
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process and the workforce flexibility for the operation of shop-floor machinery and for a better communication among the people involved. Figure 2
Knowledge-based integrated Production Management Model (K-PMM) with dimensions and factors (see online version for colours)
Source: Muniz et al. (2010)
Relevant P-factors are the use of the following tools that promote the use of worker 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); 5S (Ohno, 1988); Poka Yoke (Ohno, 1988; Black, 1991) and Quick Changeover (Black, 1991; Shingo, 1989). 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. To promote the Ba integrated in the production work routine, the use of P-factors require not only socialisation, externalisation and internalisation of knowledge (K-factors), but also the implementation and use of the W-factors. Relevant W-factors are objectives (Smith, 2001), structure, communication (Worley and Doolen, 2006), training (Nonaka, 1994; Darrah, 1995) and incentives (Smith, 2001). The W-factors to promote the Ba support the interaction between the operators and the organisation, by sharing measurable objectives, by work and communication structure and by training and incentives. For the selection of those factors, two work organisation models were considered: the semi-autonomous models and the enriched model.
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The W-factors adopted in the K-PMM contribute to organise people to take the better 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 to get their and organisation’s objectives, systematically, by the creation, retrieving, sharing and use of 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 between the group and other people in the organisation, training required by the various activities and by the operation of the production machinery and incentives. Muniz et al. (2010) conclude that the theoretical model K-PMM and its factors may influence the Ba creation because they: •
support the socially built knowledge
•
stimulate the cooperation and teamwork
•
emphasise the importance of transferring and transforming knowledge from personal to organisational and from tacit to explicit
•
stimulate interactive work on problems (try and error) as a learning process
•
suggest that a production management model for promoting the Ba for shop-floor workers should have the three K, P and W integrated dimensions as proposed in the K-PMM and its factors.
Other ways the model and its factors may influence the Ba are discussed in Sections 4 and 5.
3
The exploratory case study method
With the objective of confirming the factors relevance and integration in the shop-floor environment, it was carried out an exploratory case study in four different automotive plants belonging to different autoparts makers. Each autoparts maker is closely related and supplies to at least one large automaker. This section describes how the case study was carried out. Qualitative Case Study method (Yin, 2008) was used in this study. It is defined as an empirical research method that investigates a contemporary phenomenon within its real context, what allows a better understanding of the phenomenon. In this exploratory study, this understanding is obtained by literature research and by capturing the perspectives and viewpoints of the operators, of the production supervisors and of representatives of the human resources department in the various plants. The selection of the plants to be studied is based on theoretical aspects, on the selection of a limited number of ‘polar’ cases, in which the process of interest is “transparently observable”, to get a balance between research complexity and data volume (Eisenhardt, 1989). The plants belong to companies whose headquarters are in four different countries, they are: American, French, Japanese and Swedish plants. The plants were classified (‘polar’ types) in terms of work organisation and production organisation, according to Table 2.
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Criteria for choosing the plants to be studied Work organisation
Production organisation
Semi-autonomous
Enriched
Swedish
Japanese
Lean
Plant S
Plant J
French
American
Mass
Plant F
Plant A
Plants selection was based on the following criteria: autoparts makers from different origins; medium to small size; belonging to the region of Paraiba Valley (one of the most developed industrial areas in Brazil) in São Paulo state in Brazil; representatives of ‘polar’ production organisation models (lean and mass manufacturing) and work organisation models (enriched and semi-autonomous groups), developed in the automotive industry. Plants in Table 3 were then selected. Table 3
Characteristics of the plants studied
Plant Headquarter’s country Inauguration year Lean manufacturing starting year Number of automakers served Direct labour Indirect labour Number of assembly lines Product
J Japan 2002 2003 1 10 23 7 Indicators sets
S Sweden 1998 2000 7 540 84 19 Air-bag
A USA 1973 2001 4 150 180 2 Automotive pipes
F France 2000 2001 5 164 88 4 Bumper
It must be highlighted that the Japanese autoparts maker belongs to the Toyota group and supplies only to that automaker. The data-collecting procedure used was, according to semi-structured interviews, conducted by the authors, with open questions (Appendix A). Interviews in four plants were carried out. Stemler (2001) defined Contents Analysis technique as a systematic, replicable technique for compressing many words of text into fewer content categories based on explicit rules of coding qualitative approach to data analysis of narratives and observations. Bardin (2008) includes the means of the fragment of the answers more than the explicit words. The technique uses qualitative approach to data analysis of narratives and observations and was based on: •
the meaning and explanation interviewees attributed to each one of the three dimensions
•
the relationship between answers and factors.
Each transcribed interview was reviewed by the respective interviewee and the authors consider a meaning unit as words, sentences or paragraphs containing aspects related to each other through their content and context.
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Table 4 illustrates the Contents Analysis performed in sample answers. For example, Human Resource representative (Plant S) indicates training (W-TRN) and socialisation (K-SOC) factors in the fragment of the answer “train the other colleagues”. Table 4
Examples of content analysis
Meaning unit
Condensed meaning unit
Code
Often, an operator is selected to learn and train the other colleagues on the machine on a day-by-day basis during production time Operator knowledge sharing is essentially based on communication, but many times it is informal and, consequently, there are losses
To learn and train the other colleagues
Training (W-TRN) Socialisation (K-SOC)
Operator knowledge sharing is essentially based on communication
Socialisation (K-SOC) Communication (W-COM)
What happens in the reality is that During the shift change it is during the shift change it is informal and initiative-driven informal and initiative-driven. Operator tells what happened For example, an operator tells what during his work happened during his work in the previous shift and what his action was. They seek an understanding of what occurred
Personal Characteristics (W-PCH) Socialisation (K-SOC) Communication (W-COM) Problem Solving Methodology (P-PSM)
Source: Adapted from Graneheim and Lundman (2004)
The interviews were recorded, transcribed and analysed by the Contents Analysis technique. The reason for carrying out the Contents Analysis in two stages, first based on dimensions and second based on factors (Sections 4 and 5), is to possibly identify other factors not initially identified in the theoretical model (see Muniz et al., 2010). Table 5 shows the code of the factors set to K-, P- and W-dimensions. Table 5
Acronyms assigned to factors and dimensions
Knowledge management K-dimension
Production organisation P-dimension Factor
Factor
Code
Socialisation (Tacit → Tacit)
K-SOC Problem Solving Method P-PSM Objective (PSM)
W-OBJ
Externalisation (Tacit → Explicit)
K-EXT Standard operating procedure
P-SOP
Structure
W-STR
Internalisation (Explicit → Tacit)
K-INT 5S
P-5S
Communication
W-COM
Combination (Explicit → Explicit)
K-CBN Poka Yoke
P-PY
Training
W-TRN
Quick change over
Code
Work organisation W-dimension Factor
P-QCO Incentives Personal characteristics
Code
W-INC W-PCH
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The operator, production supervisor and the human resources representative were selected based on Convenience Sampling Method (Rea and Parker, 2005). Table 6 summarises the characteristics of the interviewees. Table 6
Characteristics of the interviewees
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Results and analysis
This section presents the results of the case study. These results come from the analysis of the interview answers and from the feedback collected in the final consolidation meetings in each of the four plants. Muniz (2007) reported the complete data set (interview answers and feedback collected). The Contents Analysis technique was used for identifying, in the interview answers, the factors. Each answer can be associated to more than one factor. The 382 registered answers generated 955 citations associated to factors. Table 7 presents the number of citations made by a given professional function in each of the plants studied. Table 8 presents the number of citations of factors in a given dimension in each of the plants studied. Table 7
Number of citations per professional function in each of the plants studied Plant
Professional function
F
Total
112 (28%)
99 (32%)
299 (31%)
203 (50%)
177 (58%)
484 (51%)
29 (22%)
92 (23%)
29 (10%)
172 (18%)
129 (100%)
407 (100%)
305 (100%)
955 (100%)
S
J
A
Operator
43 (38%)
45 (35%)
Production supervisor
49 (43%)
55 (43%)
Human resources representative
22 (19%) 114 (100%)
Total Table 8
Number of citations of factors on each dimension in each of the plants studied
Dimension to which cited factor belongs
Plant S
J
A
F
Total
Knowledge (K)
32 (28%)
39 (30%)
129 (32%)
87 (29%)
287 (30%)
Production (P)
28 (25%)
18 (14%)
90 (22%)
46 (15%)
182 (19%)
Work (W) Total
54 (47%)
72 (56%)
188 (46%)
172 (56%)
486 (51%)
114 (100%)
129 (100%)
407 (100%)
305 (100%)
955 (100%)
K-factors are cited more frequently than P-factors. This reinforces the fact that a production management model is not complete if it does not include knowledge management, translated in this paper by the K-factors. Figure 3 also shows that most cited: K-factors are SOC and INT; P-factors are SOP and PSM and W-factors are COM and TRN. It is important to highlight that the research does not mean to evaluate the plants. It just intends to compare the interviewees’ perception of the factors presence in each plant. The same answer profile in Figure 3 is also observed when the answers are analysed per organisational function (Figure 4), whether operator or production supervisors or human resources representative in each of the plants. It needs to be highlighted that individual answers from production supervisors contain more factors from the three different dimensions in the same answer. This suggests that they have a greater perception of the importance of the three dimensions integration in production management, once they themselves are production managers. On the other hand, the
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number of factor citations from human resources representatives is lower than from the other two functions, even for questions related to work organisation (social dimension). It can be justified by the fact that their activities are more related to labour regulatory law than to the day-by-day activities of the shop floor. Figure 3
Number of factor citations per plant (see online version for colours)
Figure 4
Number of factor citations per organisational function (see online version for colours)
From the results obtained in the Plants S and J, the initial set of factors (see Muniz et al., 2010) and the interview content were revised. It is important to highlight that the research does not mean to evaluate the plants. It just intends to assess the people perception of the factors presence in each factory. Table 9 presents the number of citations of K-factors in answers containing, coincidently, also any other (P or W) factor in that same answer. Table 9 shows how individual K-factors integrate to P- and W-factors and how individual P- and W-factors integrate with all K-factors. For example, in the 137 answers where the W-factor TRN was cited, the K-factors: SOC was cited 73 times; EXT, 5; INT, 68; CBN, 1. All K-factors were, then, cited 147 times in those 137 answers where TRN was found. One answer may have a K-factor cited more than once.
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Table 9
Number of citations of K-factors in answers containing, coincidently, also any other (P or W) factor in that same answer W-Dimension
K-Dimension
OBJ × K.
STR × K.
P-Dimension
COM × K.
TRN × K.
INC × K.
18
64
73
18
PCH × K.
PSM × K.
SOP × K.
5S × K.
3
28
31
5
PY QCO × K. × K.
SOC
7
0
0
EXT
1
6
23
5
11
3
17
18
2
0
0
INT
2
17
18
68
6
2
10
40
3
0
0
CBN
0
0
4
1
0
0
0
4
0
0
0
Total
10
41
109
147
35
8
55
93
10
0
0
Total
21
60
162
137
68
38
58
94
28
1
1
OBJ
STR
COM
TRN
INC
CHT
MSP
SOP
5S
PY
ZD
W-Dimension
P-Dimension
The low number of citations of the K-factor, CBN, is due to the difficulty of this knowledge conversion process to operators. Such a process assumes a close relationship of: the people involved, written records (work instruction and procedure) and the environment where those people work. The process happens by the combination of explicit sources of knowledge resulting in a “new written record”. The immediate example is the operator familiarity with the many sources of explicit knowledge and the operator’s contribution for reducing written records by combination or elimination of redundancies. Most of the answers where CBN was cited came from indirect technical personnel, without operator connections. Table 9 shows a large number of citations of a K-factor in answers already containing other dimension factors. This suggests the integration among such coincident factors. Because of the inherent complexity of approaching all factors in the shop-floor operation environment at the same time, Table 9 can indicate factors that should be prioritised. For example, W-factor TRN applied to operators to learn the P-factor PSM, supported in day-by-day real situations, to avoid the recurrence of undesirable result, is a convenient opportunity for the promotion of the K-factor SOC of the knowledge of the involved operators and of the K-factor INT of the learning of this process by them. The case study showed that K-factors are integrated to P-factors and W-factors. Most integrated factors are: K-SOC × W-COM, K-SOC × W-TRN, K-INT × W-TRN, all combinations of K-SOC, K-EXT, K-INT with P-PSM and P-SOP. The integration can be interpreted as follows. W-COM can be ruled by explicit instructions in organisation regulations but include tacit-to-tacit knowledge conversion represented by the K-SOC. The relationship between K-SOC and W-COM comprises the interactions either within the working group or between the working group and the supporting areas and other groups. An example of the presence of such a relationship is when presenting practices or difficulties, sharing knowledge and best practices among groups. W-TRN can happen when informal Socialisation (K-SOC) mechanisms take place and when explicit knowledge is internalised by the operators in the Internalisation (K-INT) process. The relationship of K-SOC and W-TRN was mentioned in the
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interviews. Examples of such relationship are: training of a fresher operator by an experienced one and practical interactions among operators during the day-by-day activities. Either improvement activities represented by P-PSM or routine activities represented by P-SOP requires tacit-to-tacit knowledge conversion as in meetings or when a more experienced worker trains a novice, tacit to explicit when a procedure is updated as a result of the problem-solving process or when a procedure is prepared by an experienced worker, and explicit to tacit when learning process takes place in either improvement or routine activities. The relationship between the P-SOP, with either K-EXT or K-SOC, is present when using an SOP as an instrument for knowledge diffusion. For example, an operator, when preparing a working instruction with its own language, contributes to the tacit to explicit knowledge conversion process, increasing the efficiency and effectiveness of other operators training. The approval process would include their peers, in addition to the traditional engineering and safety approvals. The use of a real problem in the tacit knowledge conversion process (K-SOC) among operators is also much cited in the answers. However, despite the majority of companies researched having a formal method of problem analysis and solving (P-PSM), in general, the operators are not trained to make use of it or do not know it and, therefore, rarely use it. Training operators in problem-solving techniques may lead to productive process incremental innovations. It can also be inferred from Table 9 that the P-PSM is closely related to P-SOP and both must have an intrinsic and explicit relationship with W-TRN and W-COM, carried out by the procedures of the production management system. Relationships of P-PSM and P-SOP with W-TRN and W-COM stand out. In the answers that present citations of both K-EXT and W-TRN, it is mentioned the use of registers, with a language accessible by the operator background level, theoretical training with written material, multi-functionality motivation and fresher workers integration. It is also mentioned that even having written registers or training material, it is still necessary oral instructions (social contact).
5
K-PMM model relevance
Exploratory Case study in four automakers plants with ‘polar’ work and production organisation models demonstrated the relevance of including knowledge as a third dimension of the production management model and also that the three dimensions are actually very much related and integrated. Exploratory Case study showed that, regardless of the work and production organisation model adopted by the plant, the relative importance given to the P-, W- and K-factors remains the same. Case study also showed that, regardless of the function performed by the interviewees, the relative importance given to the P-, W- and K-factors remains the same. Case studies showed that K-factors must be integrated to P- and W-factors to model production management, especially in the shop-floor operations environment. In the K-PMM, Production Organisation 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
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such as reduction in number of defects, in manufacturing time, in time of product model changeover during production, in costs and in rework hours. In the K-PMM, work organisation considers 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, 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, to get results for the organisation and for the operators themselves. From these results, it can be highlighted, for example: 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. The W-factors to promote the Ba, presented in Section 2, support the interaction between the operators and the organisation, by sharing measurable objectives, by work and communication structure and by training and incentives. For the selection of those factors, two work organisation models were considered: the semi-autonomous models and the enriched model. The W-factors adopted in the K-PMM contribute to organise people to take the better 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 to get their and organisation’s objectives, systematically, by the creation, retrieving, sharing and use of 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 between the group and other people in the organisation, training required by the various activities and by the operation of the production machinery and incentives. The K-dimension, as presented in Figure 2, promotes the integration of the P- and W-dimensions, because it is formally concerned with the tacit and explicit knowledge conversion modes, incorporating them into the procedures and assessing, by measures, their use in the shop-floor knowledge identification and sharing activities. Therefore, K-PMM recognises 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 2, the integration of K-, P- and W-dimensions leads to improvement activities, such as problem solving, kaizens projects, waste reduction, standard operation procedure elaboration and review. Those activities are the result of people 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 4, 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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Conclusions
In the context of the shop-floor production operations environment of the automotive industry, this paper proposed and showed the relevance of a model of production management that integrates knowledge management, as a third dimension, hereafter called the K-dimension, to the already traditional P- and W-dimensions. Factors on the K-, P- and W-dimensions that promote a favourable context for knowledge sharing and results achievement in the production operations shop-floor environment were identified based on the literature review and case study. Factors were initially identified in the literature and confirmed by the answers in the case study. These factors were presented in Section 4. By translating the P-, K- and W-dimensions of the model into factors that promote a favourable context for the knowledge conversion process in the shop-floor operations environment, the integration of the dimensions was demonstrated. Case study in four automotive plants with ‘polar’ work and production organisation models demonstrated the relevance of including the K-dimension in production management models and the integration among K-, P- and W-factors. The K-PMM is robust to different work and production organisation 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 or her work. This enhances the analysis of this reality and, therefore, contributes for his or her decision-making process. In the consolidation meetings, the K-PMM was presented together with the interview analysis results. Up to that moment, the professionals involved did not know about the model and its factors. In those meetings, also recorded, the professionals agreed with the factors in the model due to their coherence and adequacy to reality. The following citation illustrates that: “... (The Model) identifies our needs, that figure (...ex. Figure 3...) is not mentioning absolute quantities, if good or bad, but it, in this situation it indicates that we lack those that are little cited or commented. (The Model) really adheres to reality. This touched me. (...) I am looking from the beginning, observing... It does not look it was done for the four plants. It looks it is telling our own reality [Production Supervisor, Plant J].”
In the sequence, Operator [Plant S] reinforced the fact that: “In many points, I had the feeling it talks only about our company”. As observed during the case study, formal application of all factors presented in the paper does not happen in practice due, probably, to: •
complexity for integrating and implementing K-, P- and W-dimensions
•
inaccurate definitions of factors and professional functions (operator, supervisor and human resources representative)
•
influence of other factors not driven by the shop-floor activities for promoting Ba (manager, time pressure and lack of importance given to shop floor)
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focus on routine and production targets does not give space to experimentation to meet the expectation of the involved people
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lack of fidelity of traditional production management models to translate shop-floor production operations management.
Feedback from the interviewees in the case study suggests potential for developing a diagnostic tool to identify which factor should be further developed to get the favourable 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.
Acknowledgements The authors acknowledge the recommendations made by the reviewers. The authors particularly thank Ana Cristina Limongi França (USP), Piotr Trzesniak (UNIFEI), Paulo T.M. Lourenção (UNIVAP) and Guilherme A. Plonsky (USP) for their advices and invaluable assistance during the research. Acknowledgements should be made to the financial support from Taubate University (CONSUNI 028/2003) and Sao Paulo State University (PROINTER/PROPe 052/2006).
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Appendix: Semi-structured interview Dimension Question W
Which factors are considered important for the work of the operators on the shop floor? What helps to improve, to create synergy and pro-activity in the group of the operators on the shop floor? Which factors are considered to be relevant in the productive process?
P
Which factors are considered significant in the improvement of the productive process in its organisation? Which factors contribute most in the integration between operators and the productive process? How to deal with such factors? What abilities and knowledge are important for the involvement and contribution of the operators in the production?
W and P
What does motivate the operators in the continuous improvement of the production? Which practices promote the integration between operators and the production? Which difficulties do exist for the integration between operators and production? Which factors contribute most in the integration between operators and the productive process? How to deal with such factors? Does your organisation get benefits from the application of operators’ knowledge? How? Does it produce results? In your opinion, is the operators’ knowledge important? Why is the operator knowledge important for the production? How do the operators create new knowledge in the production? How do the operators get new knowledge?
K
How are the operators trained? How do the operators teach their abilities to each other? (Socialisation) How the practical knowledge of the operators is formally recorded? (Externalisation) How the learning of the recorded knowledge is promoted? (Internalisation) Consider the knowledge had been recorded. For example, standard operating procedure. Is there some example that 2 or more “pieces” of knowledge have been combined? (Combination)
