Supporting Management Decisions with Intelligent

Mechanisms of Obtaining and Processing Knowledge

Cezary Orłowski1, Tomasz Sitek1

1Gdańsk University of Technology, Department of Management and Economics,

ul. Narutowicza 11/12, Gdansk, Poland

{cezary.orlowski, tomasz.sitek}@zie.pg.gda.pl

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

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

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.

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 decision-

making 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 time-

consuming 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.

DEFINITION OF THE

SITUATION

ANALYSIS

RESEARCH OF ALTER-NATIVES

CHOICE

IMPLEMEN-TATION

DECISION MAKER

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.

Fig.5. Concept of the function of a knowledge buffer [8]

EXPERT BUFFER PRODUCTION MANAGER

Adding knowledge

Verifying

knowledge

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 non-

productive 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.

References

1. Czermiński, J.: Decision Support Systems in Company Management. Wydawnictwo Dom

Organizatora, Toruń (2002)

2. Runiański, J.: Before Decission. PWE, Warszawa (1965)

3. Leigh, A.: Perfect Decision-Making. Rebis, Poznań (1999)

4. Koźmiński, A.K., Piotrowski, W.: Management. Theory and Practice. PWN, Warszawa

(2002)

5. Mulawka, J.: Expert Systems. Wydawnictwa Naukowo-Techniczne, Warszawa (1996)

6. Orłowski, C.: Introduction. In: Orłowski, C. (ed.): Management of Information

Technology. Status and Prospects. Pomorskie Wydawnictwo Naukowo-Techniczne, Gdańsk

(2006)

7. Sitek, T., Orłowski, C.: Model of Management of Knowledge Bases in the Information

Technology Evaluation Environment. In: Wilimowska, Z. (ed.): Information Systems

Architecture And Technology: Models of the Organisation's Risk Management. Oficyna

Wydawnicza Politechniki Wrocławskiej, Wrocław (2008)

8. Sitek, T., Orłowski, C.: Knowledge Base in Multi-agent System for the Evaluation of

Information Technology - Division of Knowledge and Knowledge Bases. In: Knosala, R.

(ed.): 11th Conference "Computer Integrated Management". Oficyna Wydawnicza

Polskiego Towarzystwa Zarządzania Produkcją, Opole (2008)

9 Orłowski, C., Rybacki, R., Sitek, T.: Methods of Uncertain and Incomplete Knowledge

Acquisition In Knowledge Processing Environment. In: Knosala, R. (ed.): 12 Conference

Computer Integrated Management, vol. 2. Oficyna Wydawnicza Polskiego Towarzystwa

Zarządzania Produkcją, Opole (2010)

10 Carnegie Mellon University, Software Engineering Institute, The Capability Maturity

Model: Guidelines for Improving the Software Process. Addison-Wesley Professional,

Boston (1995)

11 Chabik, J., Orłowski, C., Sitek, T.: Intelligent Knowledge-Based Model for IT Support

Organization Evolution. In: Szczerbicki, E., Nguyen, H. T. (eds): Smart Information and

Knowledge Management: Advances, Challenges and Critical Issues, Studies in

Computational Intelligence, vol. 260. Springer-Verlag, Berlin Heidelberg (2010)

12 Orłowski, C., Sitek, T.: Implementation of Enterprise Architecture Project as the

Verification of Knowledge Bases in Multi-agent System. In: Orłowski, C., Kowalczuk, Z.,

Szczerbicki, E.: Application of Information Technology in Knowledge Management.

Pomorskie Wydawnictwo Naukowo-Techniczne, Gdańsk (2009)