Those devoted to managers acting in sociotechnical systems are more complex ... Keywords: knowledge base, uncertain knowledge, reasoning, expert system,.
Cite this paper as: Orłowski C., Sitek T., Supporting Management Decisions with Intelligent Mechanisms of Obtaining and Processing Knowledge, [In:] LECTURE NOTES IN COMPUTER SCIENCE. Nr 6277 Berlin Heidelberg: Springer-Verlag, 2010, p. 571-580
Supporting Management Decisions with Intelligent Mechanisms of Obtaining and Processing Knowledge Cezary Orłowski1, Tomasz Sitek1 Gdańsk University of Technology, Department of Management and Economics, ul. Narutowicza 11/12, Gdansk, Poland {cezary.orlowski, tomasz.sitek}@zie.pg.gda.pl
1
Abstract. This paper is a summary of the authors’ work on a model of support for decision processes in organisation management. Key decisions in organisations are often made without sufficient knowledge and by people lacking the proper qualifications and experience. Without full support in decision processes, true threats to organisations are created. Therefore, to support decision processes in numerous organisations, apart from experts in their field, knowledge-based systems (expert) are applied. Those designed to support decision processes in technical systems are well described and applied. Those devoted to managers acting in sociotechnical systems are more complex and generally difficult to describe. An example of such a system for the needs of an IT organisation has been presented in this paper. The authors identify possible problems of an IT organisation and introduce a decision system supplemented with applicable elements in technical and sociotechnical systems. Keywords: knowledge base, uncertain knowledge, reasoning, expert system, information technology
1
Decision processes in management
Making decisions is a complex management process strongly influencing an organisation’s performance. Its dynamic character in a multidimensional institutional system results in multipronged decisions that require wider knowledge from a decision-maker. It entails more responsibility, and in the event of defective decisions – considerable costs. An example of complex conditions for decision making is running an organisation where business decisions are made. These conditions are one consequence of an increase of the technical and technological level in existing companies that are treated as complex sociotechnical systems. Such systems require modern and precise methods of decision making [1].
1.1
The essence of management decisions
In management a decision process is defined as the choice of one action from a number of solutions available at the time. A decision process can also be defined as a
conscious restraint on making a choice – which may also be treated as a choice [2]. Therefore, a decision process may be described as a number of mental operations leading to the solution of a decision issue. No matter how different the definitions are in the literature regarding the number of such operations or their names, it is always a sequence of actions in the form of an algorithm – the subsequent steps shall be a direct effect of the previous steps. A decision process consisting of five steps is shown in figure 1. It includes the definition of the situation, analysis, research of alternatives, choice and implementation.
DEFINITION OF THE SITUATION
ANALYSIS
RESEARCH OF ALTERNATIVES
CHOICE
IMPLEMENTATION
DECISION MAKER
Fig.1. Diagram of a decision process [3]
Therefore, making decisions is a fundamental management process, conducted regardless of the level or type of activity. It should be based on the decision-maker’s consideration of all available circumstances (to minimise the risk level). Such circumstances may be divided into two groups [4]: objective – information available at the given moment, originating from the surrounding data (e.g. statistical), reports, indicators (i.e. KPI - Key Performance Indicators); generally, they are elements of a company's information system, subjective – aspects dependent on a manager’s experience related to his/her educational background, but also intuition; so-called soft factors. It is unlikely that all the mentioned factors are available at a given moment in the organisation’s reality. Therefore, management decisions are made with uncertainty and risk. Such an approach results from the following threats (appearing in decisionmaking in sociotechnical systems): lack of sufficient information or information of low quality, data and information do not constitute knowledge yet; the available information may be difficult to transform into knowledge or such a process may be too timeconsuming or/and too expensive, the available knowledge may have some defects, such as: uncertainty, lack of precision, incompleteness, management personnel may have no experience in the given area; no good practices are implemented, processes in a company are immature (they are not repeatable), no decision support.
1.2
The need for support for decision processes in management
In view of the decision problems identified above, it must be assumed that a number of decisions made in an organisation are defective. A required solution would be a decision-making model where, together with the decision-maker, there is a second entity – a field expert. Such a model is presented in figure 2 below.
Fig.2. Decision model with the decision support aspect
The model consists of two entities and the links between them together create an environment for decision making. The constant need for decision support for a manager who is about to make a decision is a key assumption here. Each decision, before being made, should be consulted with an expert from a given field (or a few). The decision-maker should pass to the expert the status of knowledge about the problem and the conditions related to it. He should also deliver description of the required status as precisely as possible (quantified – if needed). Only after such consultation, a decision-maker can make a decision possessing the received suggestions. Nevertheless, there are a few fundamental drawbacks of such an approach:
How can we gain experts from a given field? How can we verify the “quality” of experts? (the issue of trust) Can we count on the full availability of experts? How should we act if there is contradictory information from a few experts?
These doubts illustrate the need to design a model where the above-mentioned problems would be reduced or eliminated. A model has been presented in this paper that aims at supporting decisions in IT organisations. This model may be implemented in an organisation where complex management decisions are made. The concept described in the next chapter relies on expert-based systems (also known as expert systems).
2
The concept of support for management decisions
An expert system is defined as a information technology system containing specialised knowledge on a given area of human activity, giving advice on issues that the user has difficulties to cope with [5]. Such systems provide advice, guidance, and diagnosis concerning problems from the field they are used in. They are able to reason from the resources of properly formalised knowledge, where knowledge bases are separated from reasoning modules. There is no explicit algorithm for solving decision problems.
2.1
A decision support model based on the expert system concept
In the context of the mentioned defects in the decision-making module, the weakest link is the human factor. Expert systems, in direct cooperation with experts, in some way eliminate defects. The decision-making model mentioned before may therefore be supplemented with an additional element – knowledge bases together with instruments supporting the obtaining and processing of knowledge. The scheme of this model is presented in figure 3 below.
Fig.3. A decision model including the application of a knowledge-based system. It should be noted (fig. 3) that the decision process proceeds in a similar way to that in fig. 2. In this case, before making the decision there is interaction with an expert system, not an expert. This system is able to support a decision maker by providing a valuable suggestion or advice. However, making such a decision relies on knowledge (and its quality) from the knowledge bases of an expert system. Therefore, a key aspect of building such a model is the adequate preparation of knowledge and the development of the methods of processing it.
3
The proposed model of decision support in management
Expert systems are applied in numerous fields of human activity. The majority of recorded applications practise decision support for technical systems in reality.
A distinctive feature of technical systems is the extensive availability of data that is processed into knowledge. Such data usually come from measurement systems or data bases. They are of objective and indisputable character. The performance of technical systems using these data is clear and well-described. From the point of view of obtaining and processing the knowledge (based on such data) the bases created are complete and are based on rules and/or facts. Contrary to technical systems, social systems are not fully recognised and therefore, hard to describe. This results from fragmentary knowledge or total lack of it. Very often, the obtained knowledge is imperfect. It is also difficult to obtain dependable or sufficiently precise knowledge. It is rarely complete. Therefore, it is difficult to forecast the management of an organisation in such circumstances. Decisions that have to be made in such circumstances must allow for knowledge resulting from objective sources of information as well as any “soft” aspects that are difficult to measure. It also becomes necessary to treat an organisation (a company) as a sociotechnical system [6]. This paper presents a decision support system in a management area taking into consideration problems arising from its social nature. The authors’ work is an attempt to adapt solutions from technical systems to social and sociotechnical environments. 3.1
IT companies as an environment for a decision support model
The proposed model is designed to be a general solution. It was developed as a solution to be applied in any organisation where decisions are made in conditions requiring support. These organisations may be companies, non-commercial organisations, science and education entities [7]. 3.2
Knowledge division
Knowledge in expert systems is usually presented via facts and rules. Technical systems aim at complete knowledge models, so the base of rules should include a description of all possible states resulting from a number of combinations of input variables. Physically, there are usually two types of structure – a fact base and a separate rule base. In social systems, this kind of knowledge division proves to be too simple. Rule knowledge may come from various sources, which leads to e.g. possible incoherency. It may appear to be partially uncertain (experts may show their doubts in some aspects). From the point of view of the criteria applied in technical systems, this should thus be rejected. However, in management, this knowledge may prove to be a valuable reference for a decision maker, therefore, it should be kept. Nevertheless, it is important to properly differentiate the knowledge of different “ranks”. Assuming the application of knowledge in the desired form (certain, complete) and imperfect knowledge, priorities for them should be set. A reasoning machine should always use certain knowledge first. In order to have the possibility to set such a chronology, it is necessary to establish a proper logical division of
knowledge. It is recommended to activate a number of separate bases. The diagram of such a division is presented in figure 4.
Fig. 4. Division of knowledge resources into dedicated bases and the pattern of knowledge flow [8]
Instruments supporting obtaining knowledge during consultations with experts should place each rule or fact in the proper base with the proper tag. As an example, uncertain knowledge should have established certainty factors (CF). This division is not assumed to be constant, knowledge should be continuously monitored and after meeting the given conditions may be moved from one base to another [8]. 3.3
Quality of knowledge
Imperfection of knowledge obtained in sociotechnical systems has already been mentioned. It needs to be said whether this results from the nature of an organisation or from the improper selection of experts. Perhaps knowledge in the same area from a different source would lack defects. The question about the quality of knowledge leads to questions about trust for the developed model for supporting management decisions. Should the knowledge that is obtained go directly to production bases and act as a base for conducted reasoning? It seems that the risk related to making possible wrong decisions is too high. The model should establish appropriate mechanisms for the verification of the obtained knowledge. In order to minimise the risk of introducing “garbage” into knowledge bases, a mechanism of a so-called knowledge buffer was developed. This concept is schematically presented in figure 5. Adding knowledge Verifying knowledge
EXPERT
BUFFER
PRODUCTION
Fig.5. Concept of the function of a knowledge buffer [8]
MANAGER
A knowledge buffer assumes that for each knowledge base there is its second instance. Such a copy is identical with the original base as far as aim, structure and links with other bases are concerned. A set of such base-copies creates a nonproductive environment where the new knowledge should be moved. It acts as a quarantine for rules and facts which, for a given time, are verified in isolated structures (unavailable for a reasoning machine). It is assumed that the knowledge in a buffer is public. All experts participating in a project have access to it. Therefore, they can assess, comment on and evaluate chosen rules and facts finally leading to their rejection or acceptance and their forwarding to productive bases. 3.4
Cooperation with experts
The extent of obtaining knowledge in the presented model is tightly dependant on positive experiences from cooperation with experts. This aspect of building a model seems "softer". It is hard to describe due to the fact that a lot depends on the experts themselves and links between them and the project. The authors’ observations confirm that cooperation with specialists involved in building a system for decision support in management differs from cooperation with external experts. It is proved that deficiencies in obtaining knowledge result from its incompleteness on the specialists’ side as well as low effectiveness of knowledge engineering (e.g. too long sessions become arduous for experts). “System - person introducing knowledge” interaction must be automatic. The proposed model envisages that such problems might appear. An environment for direct contact with experts is distinguished by [9]: proper logic of the obtaining knowledge process (optimal with regard to the time criterion and to applying criteria which is hard or impossible to measure e.g. ease of the process of adding knowledge based on the authors’ experience), a user interface that is ergonomic and resistant to errors, where an expert is at each step supported by the system. It must be noted that such problems do not occur within technical systems. Within these, the knowledge is based on objective data, e.g. measurements, and not on the necessity to develop special procedures with the application of sociotechnics. 3.5
Reasoning from uncertain and imprecise knowledge
The awareness of possible imperfections in knowledge must be transferred into proposals for solving such a problem in a given model. There are numerous methods of how to deal with this knowledge, distinguished by: incompleteness, lack of precision, uncertainty.
Such problems rarely occur in technical systems as imperfect knowledge is rejected due to the need to provide high quality. If the model is to be a base for decision support in sociotechnical systems, the large part of the available knowledge base may be considerably incomplete. Should we use such a base? Yes, because there may be no other option. Therefore, we need to be aware of less precise advice and suggestions. The proposed model assumes the application of diffuse logic (for imprecise knowledge) and certainty levels (for uncertain knowledge). It is vital that knowledge may require the application of both these methods simultaneously. Therefore, correct reasoning will only then be possible if imperfections are noticed already at the stage of obtaining knowledge. The order of applying methods is also important here (first collecting knowledge in a diffuse form, then analysis aimed at indicating the uncertainty level).
4
Verification of the model
The model is a projection of reality designed on theoretical grounds. Its relevance will be proved only when it is verified. A set of experiments was conducted for the proposed solution. They aimed at showing the correctness of certain assumptions and at determining the direction of future work. The authors also wished to check if the model can be used in organisations from various branches of industry (the model was developed relying on experiences in IT organisations). It was established that during the research special emphasis would be put on the following aspects: Relevance of dividing knowledge bases and knowledge flow (in the area of obtaining knowledge), Ensuring quality of the added knowledge (in the area of obtaining knowledge), Developing methods of cooperation with experts (in the area of obtaining knowledge), Analysis of reasoning mechanisms (in the area of processing knowledge). The first experiment was conducted for a prognostic technical system. The model was applied in predicting the concentration of pollution in the air . Knowledge of chemical concentrations of pollution in the air with measured meteorological parameters was collected from experts in this field in a few iterations. The rule and fact bases were prepared, a buffer mechanism was applied, a simple application model was implemented. It was forwarded to experts who, after adjusting and comparing the results with other models, evaluated the project very positively. The second experiment was conducted for a sociotechnical system. It was applied for decision support in IT companies in the area of selecting the proper technologies (methods/devices) to manage an IT project. Three groups of input variables that should influence such decisions were distinguished:
The maturity of organisations conducting the project – in order to precisely understand the influence of this variable on the choice of methodology to manage IT projects, surveys and laboratory examinations were conducted; a maturity scale was established (based on the one used in the CMMI assessment model [10]) factors determining the evolution of IT organisations were analysed; the example rule (general form, Prolog notation): End_state :- Begin_state, Transition_processes. Client maturity – analyses of client behaviour conducted in this field with a survey gave knowledge about client capacities (e.g. their adjustment and adequacy for the project) [10], Client_maturity :- Client_adequacy, Client_fit. The entropy (disorganisation) of the project – it was assumed that specific examples of IT projects would be analysed – implementing corporate architecture; such projects are characterised by high complexity, therefore, they require the precise choice of project management devices and methods; variables were established based on the TOGAF standard [11]; the example rule: Project_entropy :Architecture_Type, Generated_Documentation, Area_Of_Management_Developement. The variables were pre-processed and determining factors were defined for each of them. In addition, dedicated knowledge bases were created. It must be noted that this is a sociotechnical system. Such a multilevel model allowed for the verification of all the established presumptions, at the same time indicating further work.
5
Summary
The paper is a summary of the authors’ work on the model of decision support in management. Based on experiences gained during cooperation with IT companies, the main problems of IT managers or project managers were identified. Key decisions in organisations are made in circumstances far from perfect – without sufficient knowledge in the given field, often by people lacking the proper qualifications and experience. Therefore, the lack of decision support may be a true threat. The need for such support may be realised through the assistance of IT systems. The best solution is a class of systems based on obtained and formalised expert knowledge developed in the previous century. These systems were designed to perform properly reasoning from well-defined knowledge. Unfortunately, managers do not act in such comfortable conditions. Therefore, the proposal to supplement the classic decision model in management with an expert device will only be justified if such a concept is adopted in sociotechnical systems. The designed model was verified through a series of experiments that confirm theoretical presumptions. These experiments prove that the model might be applied
both in technical and sociotechnical systems. However, one question remains unanswered: can this model be applied in any organisation? In order to confirm this, more research (experiments) needs to be conducted. Recipients of this solution should also be considered. It might appear that for some companies or institutions a change of the classic decision model is too serious a revolution. They might not be able to integrate their own processes with the applied tool, for example due to the immaturity of such processes. For further research, the authors have chosen organisations that seem able to best accommodate such projects – intelligent organisations (also known as learning organisations). In such organisations, knowledge is regarded as the key resource and its conscious processing might be supported with a dedicated tool.
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