Iran aerospace industries' KM approach based on a comparative study: a benchmarking on successful practices
The Authors
Mostafa Jafari, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Peyman Akhavan, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Mehdi N. Fesharaki, Computer Department, Malek Ashtar University of Technology, Tehran, Iran
Mohammad Fathian, Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract
Purpose – The main objective of this paper is to develop a knowledge management (KM)approach in Iran aerospace industries based on the findings through the analysis of successful practices in KM area.
Design/methodology/approach – A qualitative case study technique has been used in this paper for data collection and analysis. For that, “grounded theory” research approach has been selected by which the collected data from successful organizations in KM adoption are categorized and analyzed. The extracted concepts were deployed in Iran aerospace industries to present a KM approach through benchmarking.
Findings – The overall results from the case studies analysis were positive, thus reflecting the appropriateness for benchmarking. The extracted concepts clarify how to develop KM approach in an organization. This approach has been applied in a large case study in Iran and is supported by practical implementation in Aerospace Industries Organization (AIO), one of the most important high-tech industries in Iran.
Practical implications – This paper provides a helpful roadmap for practitioners in implementing KM through out the organizations and especially in large-scale ones. This helps to ensure that the essential issues are covered during design and implementation phase. For academics, it provides a common language for them to deploy a KM approach in the organizations.
Originality/value – This study is probably the first to provide a benchmarked integrated KM approach based on the critical success factors extracted by analysis in a multi case study research. This study further opens up new lines of research and highlights implications for KM efforts through benchmarking. It gives valuable information and guidelines which hopefully will help the leaders to deploy KM in their organizations.
Article Type:
Case study
Keyword(s):
Knowledge management; Benchmarking; Aerospace industry; Iran.
Journal:
Aircraft Engineering and Aerospace Technology: An International Journal
Volume:
79
Number:
1
Year:
2007
pp:
69-78
Copyright ©
Emerald Group Publishing Limited
ISSN:
0002-2667
Introduction
Knowledge management (KM) is known as a systematic, goal-oriented application of measures to steer and control the tangible and intangible knowledge assets of organizations, with the aim of using existing knowledge inside and outside of these organizations to enable the creation of new knowledge, and generate value, innovation and improvement out of it (Wunram, 2000).
Knowledge has always been central in the functioning of society. However, in today's “knowledge economy” organizations are increasingly aware of the need for a “knowledge focus” in their organizational strategies as they respond to changes in the environment. For many organizations, this has meant that the character of knowledge has changed towards a more objective, theoretical knowledge with a focus on the codification of knowledge into systems (Bell, 1999).
It is necessary to say that we are now changing steadily from an information age to a knowledge age, where knowledge has been recognized as the most important aspect in human life. Individuals and organizations are starting to understand and appreciate knowledge as the most valued asset in the emerging competitive environment. Knowledge is a powerful tool that can make changes to the world. It is now considered as the main intangible ingredient in the melting pot that makes innovation possible (Syed-Ikhsan and Rowland, 2004).
KM creates a new working environment where knowledge and experience can easily be shared and also enables information and knowledge to emerge and flow to the right people at the right time so they can act more efficiently and effectively (Smith, 2001).
For a deeper understanding of the KM processes, an attempt to express the hidden meaning of data, information and knowledge is necessary. Data mean a set of discrete and objective facts concerning events. Therefore, they can be construed as a structured record of transactions within an organization. Information is data with attributes of relevance and purpose, usually having the format of a document or visual and/or audible message. Knowledge is linked to the capacity for action. It is intuitive, therefore hard to define. It is linked to the users' values and experience, being strongly connected to pattern recognition, analogies and implicit rules (Joia, 2000).
Meanwhile by the comparison of different definitions of “KM” the following aspects of high relevance are resulted during KM adoption (Wunram, 2000): “Exploitation of existing knowledge, Creation of new knowledge, Process orientation, Goal orientation, Value orientation, Improvement orientation, and Innovation orientation”.
KM is about interventions in the organizations' knowledge base, which by definition includes individual and collective intellectual assets that help an organization to perform its tasks (Probst et al., 2000). It undergoes regular changes that constitute organizational learning (Senge, 1990). A review of the early KM literature shows that raw technical approaches drew the initial interest, but are not sufficient to produce the desired outcome of KM (Davenport and Prusak, 1998). While intranets and information repositories may provide means for people, they are not good in helping people apply the new knowledge in the context of process work (Massey et al., 2002). Therefore, every KM initiative has not only technical aspects, but also involves people and processes.
Considering the importance of aerospace industries and the role of knowledge in these kinds of organizations, KM in the design of aerospace systems addresses the question of how designers can share, capitalize, and re-use knowledge in an effective and reliable way. KM is situated in groups, organizations, and communities, playing different roles in the design process. Design of aerospace systems has specific properties, such as dealing with complexity, traceability, maturity of knowledge, interaction between experts, awareness of the status of information, and trust in knowledge (Boy and Barnard, 2003).
Nowadays, aerospace organizations are technologically experienced, and they have spent multi million dollars for reaching the advanced knowledge, advanced equipments and machines to which millions of people entrust their lives every year. So technical information and knowledge has to be completely identified, captured, stored and shared effectively between the experts.
In this paper, we show how to develop a KM approach for Iran Aerospace Industries Organization (AIO) through the findings of a multi case study research analysis. The authors implement a qualitative technique for data analysis at the first part of the research, and then deploy the findings of research to AIO as benchmarking for developing a KM approach.
Methodology
In the methodological approach for this study, the authors adopted a qualitative research design due to their need for rich data that could facilitate the generation of theoretical categories that could not derive satisfactorily from existing data. In particular, due to the exploratory nature of this research and the interest of authors in identifying the main subjects, events, activities, and influences that affect the progress of KM in the organizations, they selected the grounded theory style of data interpretation, which was blended with the case study design.
Grounded theory approach (GT) is a highly systematic general methodology used for the data collection and analysis of any sort of data. Its purpose is the generation – not verification – of explanatory theory of basic common pattern in social life, by continuously comparing data (Glaser, 2001). GT rests on notion that the world is socially organized in latent patterns, which will emerge if researched properly (Glaser, 2003). A key concept to GT is that of the main concerns of participants involved in a substantive area. GT considers the continual processing and resolving of that concern to be the prime mover of participants' behavior (Glaser, 1998). GT therefore aims at surfacing these latent social patterns via the conceptualization of the opinions, actions, etc. of these participants (Glaser, 2003). GT generates theory from minimum prior knowledge. As an inductive method, it seeks to discover theoretically relevant issues from data, rather than from existing theories, preconceived notions or professional interests. By entering a research field with as few predetermined ideas as possible, increases the theoretical sensitivity of the researcher (Glaser, 1978). Since, no researcher can possibly obliterate all the previously theories learnt, the trick is to line up what one takes as theoretically plausible with what one finds in the substantive field via an emergent fit (Glaser and Strauss, 1967).
Data used in this paper comes from a longitudinal study during a two-year period examining knowledge establishment processes in different companies. Finally, some more famous corporations which were successful in KM adoption were selected. This research paradigm, which was based on an in-depth qualitative study, has some similarity to ethnography (Atkinson and Hammersley, 1994) and other forms of research that derive their theoretical insights from naturally occurring data including interviews or questionnaires (Marshall and Rossman, 1989). Especially, the researcher should intervene in the results of project on a matter of genuine concern to them on which they have a genuine need to take action. Research data and insight are gained alongside or on the back of the intervention.
The data collected over the two years of the intervention have derived from different papers, journals, books, reports and also through browsing the internet. During these interventions, the expressed experiences, views, action-centered dilemmas, and actual actions of selected companies have been recorded as research data. The data analysis for the research consists of four stages:
- accumulating different data;
- developing an in-depth case history of the company activities from the raw data that provided all the information;
- open coding and subsequent selective coding the in-depth case history for the characteristics and origin of KM process in the company; and
- analyzing the pattern of relationships among the conceptual categories.
In the first stage of the data analysis, chronological descriptions of the project's activities were constructed with respect to KM process in the companies, describing how it came about, when it started, who was involved (rank of authority in the company), the level of involvement, and the major outcomes. Through this work, an in-depth case history of the project was completed. The second stage of analysis involved coding the in-depth case history with respect to its characteristics, origin and effects. This was a highly iterative procedure that involved moving between the in-depth case history, existing theory, and the raw data (Glaser and Strauss, 1967).
The data were subjected to companies, cyclical, evolving interpretation and reinterpretation that allow patterns to emerge. The GT is based upon the researchers' interpretation and description of phenomena based on the actors' subjective descriptions and interpretations of their experiences in a setting (Locke, 2001). This “interpretation” strives to provide contextual relevance (Silvermann, 2000).
For each case, many reports and data were collected. After reviewing all data, some of them were selected. In this analysis we were to answer:
- Why did these companies have to apply a KM program?
- What are the essential issues of KM program in these companies?
- What are the critical success factors of KM program for a company?
For that, data from 14 successful companies in KM program were collected; Ernst & Young, Hewlett-Packard, BusinessEdge Solutions (www.businessedge.com), Microsoft, Teltech, Siemens, JPL, Compaq, Xerox, Chrysler, IBM, Phonak, Ford motor, and Rolls-Royce were our selected companies.
In the next step, through selected input data and by categorizing and combining them, main concepts were understood and their specifications distinguished.
Concepts are the basic units of analysis since it is from conceptualizations of data. Corbin and Strauss (1990) state:
Theories can't be built with actual incidents or activities as observed or reported; that is, from “raw data.” The incidents, events, happenings are taken as, or analyzed as, potential indicators of phenomena, which are thereby given conceptual labels. If a respondent says to the researcher, “Each day I spread my activities over the morning, resting between shaving and bathing,” then the researcher might label this phenomenon “pacing” as a main concept. As the researcher encounters other incidents, and when after comparison to the first, they appear to resemble the same phenomena. Only by comparing incidents and naming like phenomena with the same term can the theorist accumulate the basic concepts for theory.
Distinguishing the relations between concepts and axial and selective coding are the next stages of this step. Literature comparison with the results of each stage is the main mechanism of emerging and appearing new ideas and concepts. This will be continued until saturation stage. In this stage new cases will not add any new concept to the findings. Table I shows the specifications of this part of the research.
Data analysis
By analyzing input data of selected companies, some concepts were found for answering the questions of research about critical issues of KM success for developing a KM approach. The extracted concepts have been shown in Table II. Now here we discuss more about some important concepts.
From the strategic point of view, each company has some strategies for reaching its objectives. For being successful on implementing KM system in the organization, knowledge efforts and knowledge strategies should be aligned completely and correctly with organization strategy.
Success of every program and planning in the organization depends directly on CEO support and commitment. Of course a KM program also needs complete CEO support for being successful in implementation.
For developing knowledge in the organization there should be some centers to lead knowledge activities. This can be done through knowledge committees, communities of practice, knowledge teams and network of experts. For spreading knowledge policies and totality of knowledge in the organization, employees should become completely and deeply familiar with knowledge concepts. So, training programs are very important for an organization which is to conduct KM.
The other extracted concept is reengineering. The process of “reengineering” involves the breaking of old, traditional ways of doing business and finding new and innovative ways, and from the redesigned processes, new rules emerge that determine how the processes will operate (Hammer, 1990). Considering Business Process Reengineering (BPR) definition, usually the processes in the organizations have not been well designed. Now if we want to establish a KM system on a weak foundation, knowledge efforts will be failed. So, BPR helps the organization to decentralize and define a value-oriented structure, in that KM system can be implemented correctly. Knowledge sharing plays an important role on implementing and executing KM system. Knowledge sharing can often be done effectively by regular or event-triggered knowledge sharing occasions. Regular means repeated at specific intervals while event-triggered means at specific events like, e.g. a project's end, coming up of a new technology, etc. Of course knowledge sharing between employees needs a strong culture, trust and also transparency in all over the organization. The political and cultural surroundings are known from the analysis of knowledge culture because effective KM cannot take place without extensive behavioral, cultural, and organizational change.
There is a need to initiate according the changes. This especially aims at creating an environment where knowledge sharing is encouraged. In 3M, for example, a company that has been successfully innovating for years, all employees can use 15 percent of their working time for pursuing their own dreams. This arrangement clearly points out the interest of the management in culture openness and knowledge creation, especially regarding innovation, and the company has been successful with this. So, organizational culture should be considered as an important driver for KM systems.
Great and important programs in the organization need to be executed first on a pilot, then results should be studied, and possible amendments should be done through feedbacks. Then, the program can be generalized and executed in all over the organization on condition that taking suitable results from the pilot. KM system should also be executed first in a pilot for taking the best results.
Knowledge audit is the other extracted concept. Knowledge audit is defined as a survey measuring knowledge re-use and communication, cultural receptiveness to KM and valuing of knowledge, KM opportunities, and deficiencies, gaps and problem areas and is very important in KM systems.
Now, we discuss on KM architecture. KM architecture is the other main concept that has been extracted through the case study analysis. An organizational architecture can be defined as a complex, multi-dimensional construct expressing principles that guide how the organization is to be designed, that is, how the elements of the business model are actually organized and executed. Knowledge architecture can also be defined as a logically set of principles and standards which guides the engineering (high level design, detailed design, selection, construction, implementation, support, and management) of an organization's KM system infrastructure. So the companies which are to design their KM system should be really sensitive to construct their knowledge architecture correctly and robustly.
The above-mentioned factors affect on success of KM system directly or indirectly and have also effects on each other. Table II shows these items in a concise way. These findings show that organizations should design their knowledge architecture in an effective way and align all their knowledge strategies with organizational strategies. Knowledge sharing is necessary and network of experts should be organized in the company for leading knowledge efforts.
CEO support and commitment plays a very important role in KM systems. Some factors such as BPR, decentralization, trust and transparency are directly dependent to CEO support and commitment.
Knowledge identification and knowledge capturing are also important in a KM system and storage the knowledge of organization should be applied for both tacit and explicit knowledge. Continuous training for employees should be applied through seminars, training courses, and conferences. The role of educations should not be forgotten in training programs.
For knowledge sharing, transparency in all over the organization and also a strong culture and good atmosphere of cooperation between employees are necessary. Also, trust factor enables KM efforts and also helps knowledge sharing. Network of experts are also known as the enablers of KM system. These networks lead knowledge activities through scientific committees, communities of practice, knowledge teams and knowledge centers and drive knowledge efforts in the organization.
Knowledge management in aerospace organizations
Aerospace and defense companies are facing increased pressure to boost efficiency as they cope with shifting demands for new aerospace systems in a highly competitive environment, but the inherent complexity of aerospace and defense means that companies cannot achieve significant efficiency gains unless they provide a global workforce with streamlined access to highly technical information. This requires a unified content value chain, where information can be easily shared within and between relevant organizations (Documentom solutions for the aerospace and defense industries, 2004).
The design and construction of knowledge is incremental. Aerospace systems are designed over time. They are tested, modified several times and certified. The resulting observation product, usually called experience feedback, is provided to designers who use it to modify their current understanding of the artifacts they have designed. Knowledge about these artifacts becomes progressively mature through this incremental process.
To reduce costs, most of aerospace organizations have increased their outsourcing to suppliers of subassemblies (such as engines, structures, landing gear and avionics) and concentrating on their core competencies of design, assembling and marketing aircraft. At the same time, they have made efforts to reduce, reorganize and rationalize their supply base. Thus, KM in the supply chain has also become critical (Bozdogan et al., 1998; Gostic, 1998; Allen et al., 2002).
KM in the design of aerospace systems addresses the question of how designers can share, capitalize and re-use knowledge in an effective and reliable way. KM is situated in groups, organizations and communities, playing different roles in the design process. Design of aerospace systems has specific properties, such as dealing with complexity, traceability, maturity of knowledge, interaction between experts, awareness of the status of information, and trust in knowledge (Boy and Barnard, 2003).
NASA defines KM in this way:
Knowledge management is getting the right information to the right people at the right time, and helping people create knowledge and share and act upon information in ways that will measurably improve the performance of NASA and its partners.
This means providing access to information at the time people need it to make the best decisions possible for mission safety and success (Holcomb and Keegan, 2002).Aerospace systems are increasingly challenging to manage, and system interactions are growing more complex. An integrated KM approach could access knowledge that is currently spread across many people and organizations, addressing issues of the:
- social domain;
- cognitive domain;
- information domain; and
- physical domain.
This pattern has been extracted from an article published by Jafari et al. (2006), in which the KM dimensions have been mapped by network warfare techniques. Network-centric warfare (NCW) is an emerging theory of war in the information age. It is also a concept that, at the highest level, constitutes the military's response to the information age (Cebrowski, 2002). The term network-centric warfare broadly describes the combination of strategies, emerging tactics, techniques, and procedures, and organizations that a fully or even a partially networked force can employ to create a decisive war fighting advantage (Garstka, 2000).
AIO knowledge management approach: benchmarking from the best practices
The importance of aerospace industries is known for every country. The AIO leaders have also understood the vital role of KM in aerospace industries and that is why KM efforts have seriously been started in Iran aerospace industries. The necessity of developing a comprehensive KM approach for Iran aerospace industries was the best incentive for the authors of this research. The most important dimensions of KM system have been considered in this approach through the findings of the first part of the research including best practices in successful corporations (Figure 1).
AIO managers believe that if they do not start managing their organizational knowledge, they will repeat their mistakes and worse, they will never learn from their successes and can never grasp their great vision, launching the Iranian National Satellite to capture the space.
By now, Iranian engineers and scientists have spent many years working on an important national project - launching the first Iranian National Satellite - and have learned from the senior members and eventually mentored junior team members. Through these years, AIO's knowledge base and abilities are growing up. Today's engineers and scientists may work years on a project and then move on. Individually, they may gain a lot of knowledge, but that knowledge remains in their minds with them and is not captured or passed on broadly for future missions. New employees are tossed into a maelstrom of project implementation and expected to perform without any substantial introduction to organizational processes, history, culture, and lessons learned.
In this way, AIO's knowledge is believed as the organization's primary, sustainable source of competitive advantage. Considering physical assets age, today's workforce is mobile, and technology is quickly bypassed. However, the leaders of AIO believe that knowledge in AIO can endure. This knowledge is a fluid mix of aerospace experience and know-how that allows AIO's employees to strive for achieving the great wish of launching the Iranian National Satellite through continuous learning and sharing of this knowledge between the experts.
Those companies whose cultures promote knowledge-sharing and individual learning have high employee retention, attract high-quality employees, and have a workforce that focuses on fixing the problem rather than fixing the blame.
KM is the spark that will ignite AIO's ability to get the most from the investments have been made in its workforce and information technology, and to harness the considerable intellectual capital within the organization and its partners. Implementations in KM build upon technology and information to help and guide AIO through the complexities of working with different teams and making ever-more-complex decisions. AIO has many of the key ingredients to making KM succeed: a highly intelligent workforce, a need to learn in order to succeed, and some technical infrastructure.
Considering the large amount of knowledge in AIO, and strong belief of top managers about knowledge as the most important capital and the most important output of AIO, the managers decided to establish a KM system in the organization. The commitment of senior managers and their strong wish and belief on it, helped movement towards a knowledge-based organization.
Usually, management efforts in an organization are geared toward survival, while making a profit and KM efforts are no different. There are several key characteristics that should exist in a learning organization and the AIO leaders are sensitive enough about them.
Leadership in the knowledge enterprise is responsible for practicing strategic planning and systems thinking approaches, making best use of resources, fostering a culture that encourages open dialogue and team learning, and finally, for encouraging and rewarding risk taking, learning, and knowledge sharing.
Considering the findings of this research about the best practices done in successful corporations in KM area, a systematic KM approach was developed for AIO which will be explained as follows.
After vision definition, Iranian National Satellite launch was considered as the main and the most important objective, and then strategies were selected and defined including organizational and knowledge strategies while were discovered by the findings of this research as the important concepts in KM adoption.
Knowledge strategy is defined as the strategies that facilitate knowledge objectives in the organization. Every organization that is to establish a KM system has some main objectives and specially knowledge objectives. Knowledge strategies show how an organization can reach its knowledge objectives in an effectively way. Knowledge strategies in AIO were defined by close relationship between strategic research center and knowledge centers.
BPR was discovered as the other main concept for KM systems. As the traditional and hierarchical structure of the organization was not agile enough, so BPR was started in the organization. Moving towards a process-based organization with a horizontal structure were the main targets of BPR in AIO. The BPR project resulted focus on process, especially R&D processes. Meanwhile the organizational structure was decentralized and designed in a way that, process teams could communicate with each other as fluent as possible.
One of the most important challenges in AIO was available organizational culture which has also distinguished as an important concept in the case studies. Resistance against the change is a main topic in cultural changes. This problem was being solved through an action plan. Different sessions and meetings were hold for common clarity of the subject. In these sessions, the importance of KM system and its functions were completely explained and the necessity of moving towards this system was explored and discussed. Also many seminars were hold, and different bulletin and magazines were published to help employee and managers get familiar with KM and its role in different organizations, especially in aerospace industries. All these plans were helping in acceptance the change and cultural changes.
Owing to the necessity of training and developing the human resources in a knowledge-based organization, AIO started training programs for the employees. These programs were followed in different domains and especially academic educations in the universities. Short-term training (during the work) and training seminars were some other programs for developing human resources in AIO.
In addition to training, many other programs are followed in AIO. As some programs faced with some limitation at the first sages, they should be executed in small dimensions, which had been explored in this research as pilot. So some pilots were selected and the programs were deployed in them. One of these programs was reward system. After the pilot program, the results were explored for getting feedback. The weaknesses and strengths of the programs were monitored, and finally after some amendments, those programs were deployed to all around the organization.
Knowledge centers, knowledge sharing and knowledge committees were distinguished as the other main concepts. In this way, each department of AIO was equipped by a developed research center that all R&D activities were directed by it. All these research centers could share knowledge and communicate with each other horizontally. Also a KM department was organized in each of these research centers and a CKO (chief knowledge officer) was appointed in order to settle and arrange knowledge and also facilitate knowledge sharing inside and outside of the organization.
Gradually, knowledge committees were established in different scientific groups and knowledge sharing process was started seriously between different committees. These knowledge committees had been emerged from different departments of the organization. They had different meetings with each other continuously and regularly in order to share their experiences and scientific findings in the best way. In this way, the network of experts was gradually formed in AIO.
Meanwhile different programs such as knowledge identification, knowledge capturing, and storage of knowledge were defined. Knowledge repositories, knowledge committee, and network of experts were established in order to share the knowledge more strongly and swiftly.
The available knowledge in the organization should be identified and captured in the best way. This knowledge should be organized and codified in order to better utilization in the organization. Meanwhile, saving the knowledge of organization (tacit and explicit) is one of the most important elements of a KM system. Skill databases, expertise database, and storage of tacit and explicit knowledge of the organization are as important as the other elements of KM systems. If an organization cannot store its knowledge truly, the most important property of the organization (knowledge) may be missed easily. Personal KM is the first important factor in knowledge storage that can be applied through a suitable documentation system. Knowledge bases are also vital for knowledge storage. Of course for aerospace industries which are equipped with a large amount of knowledge, these factors are very crucial and should be considered carefully. All these items were distinguished as some important findings of the first part of the research and considered in KM approach of AIO.
Also all departments started to extract their knowledge map in order to identify and capture their knowledge in a better way. Transparency in all official relations in the organization and especially financial transparency were deployed in all over the organization in order to motivate employees and reinforce their trust and participation as have been discovered as the main concepts through the case studies analysis.
The enterprise should have a structure that facilitates personal interactions and supports communities of practice to capture tacit and explicit knowledge within the organization.
From the information technology point of view, it is necessary to say that the overarching purpose of information technology (IT) is to increase productivity in the workplace. To that end, IT departments now assemble complex systems of specialized hardware and software applications to serve the varied and distinct information needs within the company. As technology was subsequently adopted and embraced by large businesses and organizations such as AIO - and then customized to meet their growing information-handling needs – emphasis switched from group communication to more sophisticated ways of inputting, organizing, storing, and retrieving the burgeoning mountains of data.
Now emphasis is swinging back from data storage and manipulation toward interpersonal communication as the most effective means of exchanging knowledge.
There are many ways in which IT inherently understands the model of the knowledge network and may be able to save itself some work in the long run by helping to establish strong technical bases for them. Circumstances define the choice of technology as the knowledge network begins and gets up to speed. The longer range considerations of the technology were tied to the purpose of the knowledge network in AIO too.
Knowledge networks exist primarily for learning, but they have many other purposes. They are formed to manage and complete projects, to generate new ideas and innovations, and by educating and inspiring their members, to stimulate more productive activity in the workplace. Software design is becoming increasingly specialized to serve a wide variety of specific interactive and collaborative purposes, and though many companies include their products under the broad banner of “online community tools,” they all lean toward serving certain types of group needs over others that are possible through information technology.
Considering the importance of KM architecture, it has been developed as a system integrator between the other concepts. As earlier discussed, KM architecture can be defined as a logical set of principles and standards which guides the engineering (high-level design, detailed design, selection, construction, implementation, support, and management) of an organization's KM system infrastructure. KM architecture focuses on KM by a systematic approach and integrates all factors related to KM to prepare a suitable architecture for knowledge in the organization. Therefore, the companies that are to design their KM system should be sensitive to construct their KM architecture correctly and robustly as was considered in AIO too.
Figure 1 shows the conceptual KM approach for Iran aerospace industries. This figure represents the knowledge cycle including knowledge identification, knowledge capturing and storage, knowledge audit and knowledge sharing in the outer layer.
Management, human resources, organizational dimensions, and culture are the main topics which cover some other related concepts.
Management issue includes support and commitment of CEO, strategic planning and money spending. All these topics are related to “management” closely.
Organizational structure, transparency, decentralization, and centers of knowledge are some important issues which are linked to organizational dimension.
Human resources topic includes some concepts such as knowledge committees, network of experts, conferences and knowledge sharing, and finally, the culture topic points to some important issues such as trust, transparency, alignment of knowledge strategies by organizational strategies and knowledge sharing.
It is clear that knowledge sharing is nearly repeated in all topics. This is because of the importance of knowledge sharing in KM domain. By knowledge sharing, knowledge can increases and survives in the organization. Knowledge can sediment in the organization through knowledge sharing that can cause knowledge creation.
The political and cultural surroundings are known from the analysis of knowledge culture because effective KM cannot take place without extensive behavioral, cultural, and organizational change. This especially aims at creating an environment where knowledge sharing is encouraged. This arrangement clearly points out the interest of the management in culture openness and knowledge creation, especially regarding innovation. Since, most knowledge processes are on a more or less voluntary basis and knowledge is to a large degree personal, there should be a culture of motivation, a sense of belonging, empowerment, trust, transparency and respect within an organization before people really start engaging themselves in developing, sharing and using knowledge. It requires a culture in which people are respected, based on the knowledge they have and the way they are putting it to use for the organization.
Pilot, information technology, training programs and BPR are located at the middle of the figure. They are crucial factors which should be considered with the other concepts for developing a successful KM approach; and finally KM architecture is located at the center of figure which shows the important role of it in linking of the different concepts in KM.
The author's emphasis on this factor can be clarified through the definition of KM architecture as discussed earlier. Knowledge architecture was defined as a logically set of principles and standards which guides the engineering of an organization's KM system infrastructure. So the organizations and especially aerospace ones, which are to design their KM system, should be really sensitive to construct their KM architecture.
Conclusion
The aerospace industry produces high-value added products and services that can act as a knowledge base for other manufacturing industries too. Owing to the large amount of knowledge and intellectual capital inside the aerospace industries, applying an effective KM approach is a vital necessity for this kind of industries in different countries. In this paper, we followed a benchmarking for developing a KM approach in Iran aerospace industries through analysis of some successful corporations.
A qualitative case study technique has been used in this paper for data collection to gain insights into the topic being investigated. For that, “grounded theory” research approach has been selected by which the collected data from real case studies (successful organizations in KM adoption) were categorized and analyzed through specific stages. The extracted concepts demonstrated critical success factors of KM system within organizations. These concepts were benchmarked to develop a KM approach in Iran aerospace industries. The suggested approach can act as a roadmap for the leaders who are to establish KM in their organizations.
Mostafa Jafari
Peyman Akhavan
Mohammad Fathian
Figure 1KM approach in Iran aerospace industries
Table ISpecifications of the research first part
Table IICritical issues of knowledge management
References
Allen, M. (2002), A Study to Examine the Future of Turboprop Aircraft, The George Washington University and Back Aviation, Washington, DC, .
Atkinson, P., Hammersley, M. (1994), Handbook of Qualitative Research, Sage, Thousand Oaks, CA, .
Bell, D. (1999), The Axial Age of Technology Foreword: The Coming of the Postindustrial Society, Basil Book, New York, NY, .
Boy, G., Barnard, Y. (2003), "Knowledge management in the design of safety-critical systems", available at: www-eurisco.onecert.fr/Wise/Publication/ECS03_BoyBarnard_Final.pdf, .
Bozdogan, K. (1998), "Architectural innovation in product development through early supplier integration", R&D Management, Vol. 28 No.3, pp.163-73.
Cebrowski, A.K. (2002), "New rules, new era pentagon must embrace information age", Defense News, October 21-27, .
Corbin, J., Strauss, A. (1990), "Grounded theory research: procedures, canons, and evaluative criteria", Qualitative Sociology, Vol. 13 No.2, pp.3-21.
Davenport, T.H., Prusak, L. (1998), Working Knowledge – How Organizations Manage What They Know, Harvard Business School Press, Boston, MA, .
Documentom solutions for the aerospace and defense industries (2004), EMC Corporation, available at: www.documentum.co.uk, .
Garstka, J. (2000), "Network-centric warfare: an overview of emerging theory", PHALANX: The Bulletin of Military Operations Research, Vol. 33 No.4, .
Glaser, B.G. (1978), Theoretical Sensitivity: Advances in the Methodology of Grounded Theory, Sociology Press, Mill Valley, CA, .
Glaser, B.G. (1998), Doing Grounded Theory: Issues and Discussions, Sociology Press, Mill Valley, CA, .
Glaser, B.G. (2001), The Grounded Theory Perspective: Conceptualization Contrasted with Description, Sociology Press, Mill Valley, CA, .
Glaser, B.G. (2003), The Grounded Theory Perspective II: Description's Remodeling of Grounded Theory Methodology, Sociology Press, Mill Valley, CA, .
Glaser, B.G., Strauss, A.L. (1967), The Discovery of Grounded Theory, Aldine, Chicago, IL, .
Gostic, W.J. (1998), "Aerospace supply chain management", MIT Sloan School of Management, Boston, MA, .
Hammer, M. (1990), "Don't automate, obliterate", Harvard Business Review, July-August, pp.110.
Holcomb, L., Keegan, B. (2002), Strategic Plan for Knowledge Management, NASA Publication, .
Jafari, M., Fathian, M., Akhavan, P., Fesharaki, M.N. (2006), "Mapping network warfare techniques to KM", KM Review, Vol. 9 No.4, pp.28-33.
Joia, L.A. (2000), "Measuring intangibles corporate assets: linking business strategy with intellectual capital", Journal of Intellectual Capital, Vol. 1 No.1, pp.68-84.
Locke, K. (2001), Grounded Theory in Management Research, Sage, Thousand Oaks, CA, .
Marshall, C., Rossman, G. (1989), Designing Qualitative Research, Sage, London, .
Massey, A.P., Montoya-Weiss, M.M., O'Driscoll, T.M. (2002), "Knowledge management in Pursuit of performance: insights from nortel networks", MIS Quarterly, Vol. 26 No.3, .
Probst, G., Raub, S., Romhardt, K. (2000), Managing Knowledge, Wiley, Chichester, NY, .
Senge, P. (1990), The Fifth Discipline: The Art and Practice of the Learning Organization, Currency/Doubleday, New York, NY, August, .
Silvermann, D. (2000), Doing Qualitative Research, Sage, Thousand Oaks, CA, .
Smith, R. (2001), "A roadmap for knowledge management", available at: www2.gca.org/knowledgetechnologies/2001/proceedings, .
Syed-Ikhsan, S.O.S., Rowland, F. (2004), "Benchmarking knowledge management in a public organization in Malaysia", Benchmarking: An International Journal, Vol. 11 No.3, pp.238-66.
Wunram, M. (2000), "Concepts of the CORMA knowledge management model", available at: www.corma.net, .
Further Reading
Davenport, T. (1996), "Knowledge management at Hewlett-Packard", available at: www.mccombs.utexas.edu/kman/hpcase.htm, .
Davenport, T. (1997a), "Knowledge management at Ernst & Young", available at: www.mccombs.utexas.edu/kman/E&Y.htm, .
Davenport, T. (1997b), "Knowledge management at Microsoft", available at: www.mccombs.utexas.edu/kman/microsoft.htm, .
Davenport, T. (1998), Teltech: The Business of Knowledge Management Case Study, McCombs School of Business, University of Texas, Austin, TX, .
Graham, A.B., Pizzo, V. (1996), "A question of balance: case studies in strategic knowledge management", European Management Journal, Vol. 14 No.4, pp.338-46.
Jennex, M. (2005), Case Studies in Knowledge Management, Idea Group Publishing, Hershey, PA, .
Pudlatz, M. (2002), Case Study: The Siemens ICN Knowledge Management Challenge, available at: www.knowledgeboard.com/cgi-bin, .
About the authors
Mostafa Jafari an Assistant Professor of Industrial Engineering Department in Iran University of Science and Technology (IUST), Tehran, Iran, with BE in Mechanical and ME in Productivity and PhD in Industrial Engineering from IIT, Delhi. Working in area of strategic planning, BPR, knowledge management, with more that 20 research paper and five books in area of industrial engineering. Mostafa Jafari is the corresponding author and he can be contacted at: mostafajafari2006@yahoo.com
Peyman Akhavan received his MSc degree in Industrial Engineering from Iran University of Science and Technology, Tehran in 2003, and currently is PhD student in the same university. His research interests are in BPR, Knowledge Management, Information Technology, and Strategic Planning. He has published 1 book and has more than 20 papers in different conferences and journals. E-mail: akhavan@iust.ac.ir
Mehdi N. Fesharaki
received his MSc degree in Electrical and Computer Engineering from Sharif University of Technology, Tehran in 1986, and the PhD degree in Computer Engineering from University of NSW, Australia in 1994. He is currently an Associated Professor in the Department of Computer at the Malek Ashtar University of Technology at Tehran. His research interests are in Knowledge Management, Knowledge Engineering, IT-based Operations Management, Decision Making, and Meta-heuristics. He has published more than 20 papers in the international conferences and 7 papers in international journals. E-mail: fasharaki@mut.ac.irMohammad Fathian an Assistant Professor of Industrial Engineering Department in Iran University of Science and Technology (IUST), Tehran, Iran, with MSc and PhD in Industrial Engineering from Iran University of Science and Technology. Working in area of information technology, electronic commerce, knowledge management, with more that 18 research paper and three books in area of industrial engineering and information technology. E-mail: Fathian@iust.ac.ir
