model of converting tacit knowledge into explicit …...model of converting tacit knowledge into...

Journal of Theoretical and Applied Computer Science Vol. 9, No. 3, 2015, pp. 25–34ISSN 2299-2634 (printed), 2300-5653 (online) http://www.jtacs.org

Model of converting tacit knowledge into explicitknowledge on the example of R&D department of themanufacturing company, including evaluation ofknowledge workers’ usefulness

Małgorzata Sliwa, Justyna Patalas-MaliszewskaFaculty of Mechanical Engineering, University of Zielona Góra, Poland

{m.sliwa, j.patalas}@iizp.uz.zgora.pl

Abstract: This article attempts to create a model of converting tacit knowledge into explicit knowledgewith Bayes algorithm for the research and development department in a manufacturing company.Based on the reference works, there have been proposed the characteristics of knowledge con-version process, with the focus on the factors supporting sharing tacit knowledge. The sourcesof tacit knowledge in the research and development department in a manufacturing companywere identified, and then mechanisms of its collection were proposed. As a result, Bayes algo-rithm was implemented to perform the conversion process of the collected tacit knowledge intoexplicit knowledge. By stimulating knowledge workers to share tacit knowledge, the organisationincreases its know-how value, by formalized specialist knowledge available for other procedures.The model is illustrated by the example of business practice. As a result, it is assumed to receivemeasurable benefits, i.e. reductions of cost, corrections, complaints and faster completion of asimilar project, optimal selection of workers. It presents directions for further work, including theIT implementation of the presented model and its verification.

Keywords: knowledge map, tacit knowledge, explicit knowledge, research and development department, man-ufacturing company, Bayes algorithm

1. IntroductionKnowledge-based economy requires continuous improvement, development and expertise

collection from enterprises. According to Drucker [1], it is a resource being the basis forthe largely innovative action. Differences between the book value and the market value arepresent primarily in innovative companies [2], in which infrastructure and technological fa-cilities cannot function without proprietary recipes or specialized knowledge. The value of anorganization depends on its employees and their knowledge.

The process of collecting explicit knowledge is a set of activities involving the collection,processing and transfer of information exchanged on the knowledge and stored in the formof rules, procedures and instructions. In contrast, systematization and recording tacit knowl-edge is a process that requires commitment and motivation of employees [3]. The difficultiesassociated with obtaining tacit knowledge (mostly equated with the experience of knowledgeworker) encourage development of a company strategy supporting the externalization.

This article attempts to build a model of converting tacit knowledge into explicit knowl-edge, by using Bayes algorithm for the research and development department in a manufactur-

26 Małgorzata Sliwa, Justyna Patalas-Maliszewska

ing company. Based on the reference works, the characteristics of the knowledge conversionprocess have been prepared. For that purpose, sources of tacit knowledge were identified inthe research and development department in the manufacturing company, then the mecha-nisms of its collection were proposed. The formulated model is illustrated by the exampleof business practice. They also simulated evaluation of the usefulness of the employee andtheir knowledge when working on a similar project. The conclusions include directions forfurther work, including the IT implementation of the presented model and its verification inmanufacturing companies.

2. Conversion of tacit knowledge into explicit knowledgeThe knowledge conversion definitions available in the reference works establish the re-

lationship between tacit knowledge and explicit knowledge. They relate to the knowledgeworkers: their education, professional and life experience [4]. If this knowledge is to be usedmore broadly than just by people who possess it, the organization needs solutions to supportcommunication and sharing. In the well-known SECI model, according to I. Nonaka and H.Takeuchi [5], there are four ways of knowledge transformation (see Fig. 1):

— tacit knowledge to the tacit one (socialization),— tacit knowledge to the explicit (externalization),— explicit knowledge to the explicit one (combination),— explicit knowledge to the tacit one (internalization).

Figure 1. Transforming knowledge - SECI model. Based on [6]

The knowledge conversion process can be defined analogously to the process of knowl-edge externalization, which is to express tacit knowledge in the explicit knowledge form (e.g.in a dictionary of concepts, procedures and other) [7]. Other factors are also important whichcan help and encourage employees to share tacit knowledge with the organization and its

Model of converting tacit knowledge into explicit knowledge. . . 27

members. By identifying tacit knowledge, its attempted formalization would increase theintellectual capital of the company, and thus its competitiveness. Improving the system ofacquiring knowledge, that enables them to keep it in a durable form (from the point of viewof the organization), it can positively affect labor optimization in carrying out projects orresearch. The success in this case would give benefits connected with reducing expendituresrelated to the project, optimization of human resources, fewer corrections or complaints andfaster project completion (ahead of time).

In this case, it would focus on a correct assessment of the employee team’s suitabilityfor the project strategy in the organization. Then you can say that continuous improvementarising from the knowledge possessed would positively affect further cooperation within theorganization. It is assumed there are direct benefits of cooperation with the new employees,mainly in adapting to the team, where you can expect faster initiation, perusal of "well-known"approach to problems and action based on the organization rules. Also, HR department couldmore easily gather a potential team of knowledge workers, bearing in mind what character-istics of the employee are welcome to support the knowledge sharing. Structured knowledgewould be useful when working on similar projects. Namely, the transfer of basic knowledgewithin the group and outside (in the organization) would reduce the unnecessary and repetitiveactions and "discovering" something again. Benefits are also envisaged when choosing theteam by the project manager who will be dedicated to strategic projects with a short lead time.Then, in turn, they would develop the project with experienced workers or people certain oftheir success.

M. Morawski [8] emphasizes the differences between the source of success in the organi-zation of the old type (following rules and principles, avoiding mistakes), and the new type,where what really matters is ingenuity, commitment and self-development. In his paper, T.Kowalski [9] refers to the claim that knowledge workers unwillingly receive orders, they aredifficult to clearly systematize their mode of work, and achieve the best results in cooperationwith others in the networks of contacts. The knowledge employee should [8]:

— obtain new skills, experience and contacts,— gain expertise by taking part in training, foreign exchange, supported by an appropriate

certificate,— have access to highly specialized information and data,— perform the tasks independently, selecting appropriate methods,— teamwork based mainly on informal relationships and free discussion.

Based on the reference work analysis, the model of converting tacit knowledge into ex-plicit knowledge using Bayes algorithm for the research and development department in amanufacturing company was developed.

The article describes each step of the model in detail (see Fig. 2) and shows a practicalexample of its use.

3. Model of converting tacit knowledge into explicit knowledge usingBayes algorithm for the research and development department in amanufacturing companyWithin the confines of the first step of the defined model (see Fig. 2), it is necessary to

determine the source of tacit knowledge in the R&D department in a manufacturing com-pany. The primary source of the company knowledge is constantly learning by practice,


Figure 2. Model of converting tacit knowledge into explicit knowledge, using Bayes algorithm on theexample of Rand D department in a manufacturing company

which consists mainly of experience or training and observation [10]. The basic source oftacit knowledge in the company includes:

— knowledge acquired during independent action when performing tasks and projects,— knowledge acquired and observations made during the tests and studies,— analysis of complaints (product, process, which seems to be consistent),— feedback, interviews and other forms of replies from the customer and user of the product

or service,— team brainstorming and consideration the problem from a different perspective.

There are several groups of employees in the manufacturing company who can be con-sidered knowledge workers. They are employees of the R&D department or the engineeringdepartment, but also the of quality control department, maintenance and administrative staff.The proposed model relies on the choice of knowledge workers according to the followingcharacteristics - determined as X0 field [11, 12, 13]:

— altruism - x1

— commitment - x2,— common knowledge - x3,— experience - x4,— professional education - x5,


— access to procedures - x6,— access to databases - x7.

The second step of the model (see Fig. 2) includes defining the methods of obtainingtacit knowledge in the R&D department in the manufacturing company. The following areproposals to acquire tacit from the defined knowledge workers:

— recording the activities of experienced employee,— interview with a specialist and procedure steps,— involvement of specialists from external companies,— creating a site where knowledge workers can note down their observations,— preparation of knowledge acquisition forms [4].

The obstacles for obtaining knowledge from specialists include individualism and a desireto preserve the autonomy of knowledge workers [14].

In the third step, it is proposed to use Bayes algorithm to classify the collected tacitknowledge. The fundamental verification of the correctness of assumptions behind the createdmodel will take place by means of the probabilistic method. The proposed classification isan effective, simple Bayesian system based on statistics. It enables to predict the conditionalprobability of object belonging to a particular class or decision.

Bayes statement P (C \X) =P (C \X)P (C)

P (X)(1)

Where: P (C \ X) is the conditional probability (a posteriori) of event C: a class of tacitknowledge, provided the incident X (with X - characteristics of knowledge worker).

P (Ci) = si/s (2)

Where:s is the number of objects in the training set: the number of employees si is thenumber of objects in the class C: the number of knowledge workers in R&D department ofmanufacturing company, For C = (x1, x2, ..., xn), is value:

P (X \ Ci) = P (x1 \ Ci) ∗ P (x2 \ Ci) ∗ ... ∗ P (xn \ Ci) (3)

P (xk \ Ci) = sik/si (4)

Where: sik is number of objects of C class, for which attribute Ak is equal to xk,si is the number of all objects of C class in a given training set.

In the model it was assumed that when determining the training set it is necessary to usethe evaluation table (YES / NO, where YES is marked 1, NOT by 0) for each xn parameter (seeTab. 1). Arranging the training set to identify the knowledge workers, the project manageror a designated person (expert) evaluates the employee or the team as per the occurrence ofa given parameter, answering positively (logical marking "1") or negative (logical marking"0").

Collecting the satisfactory amount of information in the training set will provide an exem-plary database for the finished model. The designed tool, after determining the appropriateinput parameters, could support management of the strategic project and of the team of knowl-edge workers by a less experienced or a new managing person in the given environment.


Table 1. Breakdown of ratings for the individual parameters

No of knowledge worker x1 x2 x3 x4 x5 x6 x712

. . .n

The fourth stage defines converting the acquired tacit knowledge into its formal studyin the form of materials, i.e. procedures, operating instructions, scripts, brochures, trainingmaterials, database, library (paper, electronic) and a multimedia presentation.

According to the report called "Sharing knowledge in the Corporate Hive", cited by J.Falzagic [4], you can distinguish the following benefits of knowledge acquisition and sharing(in the bracket, the value quoted is the percentage of respondents pointing to the process) forthe company:— efficient exchange of information among employees - 56%— better customer service - 40%— less duplication of activities - 36%— fewer bottlenecks - 34%— integration of business units - 33%— increased productivity - 29%— more efficient decision-making - 7%.

One should, however, remember that the organization must be prepared for losing theemployee and try to keep the most added value, by intercept the employee’s knowledge, andthen formalizing it. For that purpose, strategies of the knowledge management are adopted.Gruczynska-Malec and Rutkowska [15] describe the three main strategy types: codification,personalization and bridge which differ of the kind of the dominant knowledge (table 2).

Table 2. Knowledge management strategies

Strategy DescriptionCodification strategy OBJECTIVE: collecting, archiving, processing and transmis-

sion of knowledge, refers to explicit knowledge, it is the atti-tude to reusable knowledge, and the dominant relationship ishuman-technology

Personalization strategy OBJECTIVE: to improve communication, cooperation andmutual support of staff, improve knowledge sharing and de-velopment; the strategy applies to tacit knowledge, the domi-nant relationship is human-human

Bridge strategy (It combinesthe features of these two

strategies)

OBJECTIVE: to improve access to explicit and implicitknowledge, the verified knowledge is combined with theknowledge of the people and dominant relationship is thesociotechnique-related rone

The fifth step of the proposed model, based on the following analysis of reference works,defines the benefits of its implementation in the enterprise. The following were distinguished:cost reduction, faster completion of the project or task, fewer corrections and complaintsconcerning the completed project and reduction of the necessary personnel, allocated to thetask. The table below presents a practical implementation of the proposed model of converting


tacit knowledge into explicit knowledge using Bayes algorithm in the R&D department in amanufacturing company.

4. Case studyThe R&D department analysed belongs to a manufacturing company in the SME (small,

medium enterprises) sector of the automotive industry, which manufactures pneumatic com-ponents used in brake systems and suspensions of vehicles. The R&D department receives anew project from a key customer from time to time. The current problem is the new functionalrequirements (quality), which include a safety valve produced on a mass scale, otherwiseknown as test point. Inside the brass body, there is a plunger, the rubber gasket (NBR, o-ringtype) of which cooperates with the inner wall of the body, thereby giving the required tightnessto it (see Fig. 3). At lower working temperatures, rubber hardens and does not exhibit highdeformation, so it is a problem to maintain an appropriate level of tightness in the circuit.The aim of the new project is to choose the right manufacturing technology, and to performthe appropriate tests and measurements that will indicate the achievement of the assumedparameters.

Figure 3. The safety valve being the subject of a new project in the R&D department in a manufactur-ing company

The R&D department employs 5 workers. Also the extreme conditions are assumed (be-longing to set D0), including the characteristics of the project performed in the R&D depart-ment:

D1: company size: SME, current employment: about 70 workers,D2: project budget : 150,000 PLN,D3: project duration: 6 monthsD4: project assumptions: reverse engineering based on the incomplete prototype withtechnical drawings, standard restrictions, functional guidance from the client,D5: project’s theme: the pneumatic control valve,D6: project representative > 28 years,D7: R&D manager: 15 years in the team, 17 years of experience on a similar position,


D8: number of projects carried out: 3-4 / year.According to the established model of converting tacit knowledge into explicit knowledge

in the research and development department, using a Bayes algorithm, the five steps definedallow for verifying the suitability of the model supporting the conversion of tacit knowledge.

In the first step there was the source of tacit knowledge identified in the R&D departmentin the manufacturing company. During the new project implementation, the team anticipatesan unprecedented situation and problematic issues. is the following are assumed: a designof new structural nodes, combination of the materials which have not cooperated before, thecustomer has declared new functional requirements. Because of newly implemented techno-logical solutions in the automotive industry, the problems are not described in the referenceworks, therefore, the knowledge about them is obtained on an ongoing basis. Using CAD,CAM will enable to create a simulation and a prototype in the R&D laboratory. The company,in practice, organizes and conducts interdepartmental meeting, so most of the problems will beresolved by a group of workers not taking direct part in the project development, (technologyand quality department workers), to give their ideas and alternative solutions.

In the second step, the methods of collecting tacit knowledge in the R&D department in themanufacturing company are defined. When working on the design of a new valve, the R&Dteam will work with a group of university researchers specializing in plastic materials. Theconducted consultations were aimed at supporting the optimum choice of structural materialsused in the project. The analyzed company could simultaneously benefit from the help ofcompanies supplying the designed components to select the appropriate mixture of plasticgranules. The key parameters pre-defined by the customer were the resistance of the materialto a wide range of temperatures and the presence of oil derivatives.

In the third step, the tacit knowledge, which can be found in the R&D department in themanufacturing company is classified appropriately. Based on the experience related to similarprojects, the R&D manager, along with the head, is able to verify the engineering staff withrespect to their suitability to carry out the new tasks and the generate new knowledge in asimilar field i.e. the materials science of plastics. Then, there was a set of training datapresented in Table. 2 created, with 10 objects. All verified knowledge workers must referpositively or negatively to x1 − x7 attributes, which define the individual parameters of everywhite-collar worker and their access to information. Then, an experienced supervisor decidesthat the employee is necessary in this project, based on the object presence in the class.

Table 3. The training set

No of knowledge worker x1 x2 x3 x4 x5 x6 x7 affilation1 1 1 0 1 1 0 0 12 0 1 1 0 0 1 1 13 0 0 1 1 1 1 0 04 1 1 0 1 0 1 1 15 1 1 1 0 1 0 0 06 1 0 1 0 1 1 1 07 0 1 1 1 1 1 0 18 0 1 1 0 1 1 1 19 1 1 0 1 1 0 0 0

10 0 1 1 1 0 1 1 1

During the new employee’s usefulness evaluation, its necessary to specify their attributes.


The test object is characterized by the following parameters: X = (altruism: x1 = ”1” com-mitment: x2 = ”1” common knowledge: x3 = ”0”, experience: x4 = ”0”, education:x5 = ”1”, access to procedures: x6 = ”1”, access to databases: x7 = ”1”) Determination ofthe unconditional affiliation of the object to the class Ci,i = 1, 2: P (C1) = 0.6 P (C2) = 0.4Calculation of the probability P (X \Ci) is the product of individual conditional probabilities:

P (xk \ Ci) = si,k/si

P (X \ C1) = (2

6) ∗ (6

6) ∗ (2

6) ∗ (2

6) ∗ (3

6) ∗ (5

6) ∗ (4

6) = 0, 0102

P (X \ C1) ∗ P (C1) = 0, 0102 ∗ 0, 6 = 0, 00617

P (X \ C2) = (3

4) ∗ (2

4) ∗ (1

4) ∗ (2

4) ∗ (4

4) ∗ (2

4) ∗ (1

4) = 0, 0059

P (X \ C2) ∗ P (C2) = 0, 0059 ∗ 0, 4 = 0, 00234

(5)

The knowledge worker has qualified for C1 class which means, he is included into theteam working on the commissioned task.

In the fourth step, there are new procedures and instructions for the R&D department in themanufacturing company defined. Within the confines of new task, there are relevant data inthe field of materials science obtained. It is necessary to create a form for the key informationabout the cooperating materials used, such as: specification of the mixture composition (plas-tic granules), dimension tolerances, maximum leakage, airtightness at low work temperatures,with a graphical representation of dependencies.

In the fifth step, there is the evaluation of the model of converting tacit knowledge intoexplicit knowledge in the R&D department in the manufacturing company made. One shouldverify the usefulness of the acquired explicit knowledge for working at similar tasks. In theanalysed case, the group of selected R&D employees should acquire new knowledge whichwill be kept in the forms and properly categorised in the internal database. Based on existingcontracts with the customer, it can be argued that in the near future the company will receivea similar order. The confirmation of the benefits will be to estimate reduction of the workingtime when compared to a similar project, savings in the project budget and the funds spent onresearch and consultations with specialists.

5. ConclusionsThe value of the organization is not only the facilities, but also its knowledge, influence

on market value and brand reputation. Companies seek to gain tacit knowledge collected byknowledge workers through the use of strategies to support the knowledge conversion. Apartfrom ensuring the good development conditions to support sharing knowledge and access todatabases, it is necessary to pay attention to the informal surroundings and relationships withinthe employees’ team. By stimulating white-collar workers to share tacit knowledge, the or-ganization expands its know-how value by formalized knowledge of specialists, available forother procedures. These activities are important even in the case of losing the so-called. "keyspecialists", the creation of a new employees’ team for a strategic project, but also when work-ing with similar projects. After creating similarly rich database and optimizing the proposedmodel supporting the transfer tacit knowledge, you could apply it successfully in commercialknowledge-based organizations. As a result, it is expected to gain measurable benefits forthe manufacturing enterprise, such as reduction of costs associated with the project, fastercompletion of a similar project, reduced modifications, complaints and optimum personnel


selection process. Further research concerns the verification of the proposed models in reallife. It is also assumed that these actions could have a positive impact on the competitivenessgrowth among medium-sized manufacturing companies with their own research and develop-ment departments.

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Zeszyty Naukowe Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach. Administracja iZarzadzanie, pp. 229–244, 2013.

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[4] Fazlagic, J.: Innowacyjne zarzadzanie wiedza. Difin, Warszawa, 2014.[5] Nonaka, I., Takeuchi, H.: The knowledge-Creating company. How Japanese Companies Create

the Dynamic of Innovation. Oxford University Press, New York, 1995.[6] Jashapara, A.: Zarzadzanie wiedza. Polskie Wydawnictwo Ekonomiczne, Warszawa, 2006.[7] Bogdanienko, J.: W pogoni za nowoczesnoscia. Wybrane aspekty tworzenia i wprowadzania

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[11] Haua, Y.-S., Kimb, B., Leec, H., Kimc, Y.-G.: The effects of individual motivations and socialcapital on employees’ tacit and explicit knowledge sharing intentions. International Journal ofInformation Management, p. 356–366, 2013.

[12] Hunga, S.-Y., Durcikova, A., Lai, H.-M., Lin, W.-M.: The influence of intrinsic and extrinsicmotivation on individuals’ knowledge sharing behavior. International Journal Human-ComputerStudies, p. 415–427, 2011.

[13] Yua, Y., Haob, J.-X., Dongc, X.-Y., Khalifa, M.: A multilevel model for effects of social capitaland knowledge sharing in knowledge-intensive work team. International Journal of InformationManagement, p. 780–790, 2013.

[14] Mironski, J.: Wyzwania zarzadzania wiedza w zespołach wirtualnych. E-mentor, 57, 2014.[15] Gruczynska-Malec, G., M., R.: Strategie zarzadzania wiedz. Modele teoretyczne i praktyczne.

Polskie Wydawnictwo Ekonomiczne, Warszawa, 2013.

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