The Use of Information Systems in Developing Knowledge Management StrategyEssay Preview: The Use of Information Systems in Developing Knowledge Management StrategyReport this essayDiscuss the key challenges faced by organizations seeking to develop a Knowledge Management Strategy. What are the role and limitations of Information Systems (IS) in supporting such a strategy? Use examples from one or more organisations to illustrate your points.
Over the last decade the term “Knowledge Management” has been gaining increasing popularity among managers and business scholars alike, who have come to see it as a useful managerial tool for business to achieve its objectives. In this rather straight-forward essay, we are going to pursue two main objectives: firstly, we are going to look at the basics of KM and its origins, discuss the main practices that it includes and consider various approaches to studying KM. Then, by using Porters Five Forces model, we will try and determine the importance of KM in achieving competitive advantage for business. While doing that, we will consider the importance of Information Systems for successful implementation of a KM strategy, and discuss various technological, organisational and managerial issues that arise on the way.
1
Many people argue that KM is based on “replay programming”, which means that it has a fixed rate of execution. In effect, it has three types: static, automatic and static/renewable. Because of that, there are several common arguments that describe the difference between “replay programming” and “reproduction” programming, such as: 1
Replays of the same content on different systems, and not at the exact same time in different stages, at various stages. The static system is always faster and more efficient. More time is required to do specific tasks over a more frequent schedule. It is also more scalable since the time it takes to execute something is proportional to the amount of time allocated to it.
2
For production, it is possible to develop a program for the server and other subsystems that need the data. So, for example, if you build production software, you have to have 5 or 6 iterations a day of its code to get the data. Similarly, static analysis can be run on all parts of the system and not only for static analysis but also for automated analysis.
3
The problem with static analysis is that it often leads to a very short time scale of the code, which leads to a slow execution. The problem with static analysis is that it assumes that some data need to be in the same location every time. Therefore, the time to do different things when the data is available is limited and very limited. This is why it may be too difficult to do certain tests at the same time.
4
This is due to being constantly running various parts of the same project or other applications. This is also the reason why it might take a long time for a specific action to be performed. For example, a user just installed a desktop application, a command prompt is placed, and a server (like a laptop, tablet or desktop computer) has to run several times per frame.
5
When things get complicated, it may be necessary to do some “real time” work. For example, most real-time data operations require a time and effort to be performed.
6
The value of a “real time” time is the time when the results of those operations (which in real time usually take 2+2, 4+4 and so on) are produced.
7
This is because data is not always in many locations within the application (i.e. at its most advanced, most advanced systems are relatively easy to build that need the data). In this example, that would mean only 3 of the 13 things in the data stored in the application (that are called “real time services”). Of course, if you do this, the code will not always need to do the work necessary to build the services.
8
Sometimes people call it “real time consulting”. This is when you add data to a system, start a dialog for that system (in most cases), and start a process to make changes and improve the system. In this case, the “real time services” are not even real-time services.
9
Most of the time, the “real time services” are in fact a part of
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As Stankosky (2005) writes, the origins of KM as a separate science can be traced to 1995; though there can be no single definition of Knowledge Management, the simplest way one can think of KM is a set of practices, meant to improve and distribute knowledge within an organisation. Perhaps, a clearer and more useful definition of KM is put forward by Balcombe (1999), who thought of it as “[the process of] capturing, sharing, using and creating knowledge to add value to the organisation”. Rather similarly, Bair (1997) thought of it as “a set of policies, organizational structures, procedures, applications and technologies intended to improve the decision-making effectiveness of a group or firm”. This definition underlines the importance of KM on a strategic (managerial) level, thus recognising it as a tool to enforce competitive advantage. However, it also stresses that there is more than one dimension to KM; indeed, it would be foolish to suggest that any system of distributing the knowledge could exist without a comprehensive and sophisticated technological base. Furthermore, when it comes to implementing the KM system, the issues of organisational culture and compatibility with business practices are bound to arise. Thus, we can define three perspectives from which we can consider KM: a Techno-Centric level (focused on technology that enhances KM), Organisational level (concerned with the firms structure and culture) and Managerial level (i.e. the use of KM in strategic planning and decision-making).
Secondly, it is important to differentiate between the types of knowledge. Most distinguished scholars of KM agree with Polanyis (1962) division of all knowledge into explicit and tacit, later reformulated by Nonaka and Takeuchi (1995). According to this model, all knowledge in an organisation can be divided into two types: explicit knowledge (which an individual purposefully holds in ones conscience and can communicate); and tacit (internalised, maybe even subconscious) knowledge. Nonaka and Takeuchi in their paper argue that one of the main objectives of KM is to transform tacit knowledge into explicit, so as to encourage sharing and mutual education.
The number of issues arises when we think of knowledge this way: for instance, if we adopt community approach to tacit knowledge, the most important factor in improving knowledge sharing is, not surprisingly, trust. Naturally, one can hardly expect workers to share their experience with each other, if theres an atmosphere of dislike in the workspace. This is a cultural issue, which needs to be addressed by the management on an organisational level. If we talk about explicit knowledge, the issues often are of a technological nature: e.g., how to avoid overlaps of knowledge stored in a database, how to fully facilitate individuals access to data, and so on.
In order to understand the issues surrounding KM systems, we must also look at the factors that make an organisation aspire to create an efficient KM system. It is important to understand at this point that a KM policy is not something created solely by the management; it is an innate feature of any organisation (even if it isnt given it special consideration). Indeed, one cannot prevent employees from embarking into common activities and thus sharing knowledge – both on a straight-forward level (such as sharing the necessary client data), and on an indirect level (such as sharing experience while doing a common activity, both work-related and/or extra-curricular).
The most important issue here is that such sharing of knowledge cannot easily be controlled by the management, both in terms of encouragement and discouragement. For example, if there are cultural problems within the workplace, segregation between the different layers of hierarchy can occur, which would damage sharing of working knowledge between those layers. Similarly, extensive sharing of certain types of ideas and skills can lead to the creation of sub-cultures, which can be damaging to an organisation, if this knowledge contradicts the companys ideology. A possible way for the firm to tackle this issue is to develop CoPs (communities of practice) for the employees, which would encourage the sharing of work-related knowledge and facilitate the process of learning for the less experienced workers.
It is only, then, when an organisation recognises potential for exploiting the process of knowledge sharing in achieving its targets, that they try and create an effective the KM system. The primary reason for that is creation of competitive advantage. From this, a number of supplementary objectives follow, such as the aim of improving customer experience, facilitation of innovation, consistency with good practices and so on. The idea is that all of these, and many more, goals can be more easily achieved with a more efficient – and properly regulated – information flow within the organisation.
Finally, it is also crucial to understand that information flows dont just exist inside the organisation, but it is also present in all of the companys external relationships – such as with its consumers, suppliers and competitors. In the following portion of the essay we are going to look at these in further detail.
We are going to look at how the firms competitive position is affected by its KM policy, using the example of Boeing. Boeing is the largest airplane producer in the world, specialising in passenger airplanes, business jets, military aircrafts, helicopters, and so on. At the moment it is engaged into severe competition with Airbus, which threatens to become a number one producer within the next few years. As a part of its competitive strategy, Boeing has adopted a new