A framework for the requirements of capturing, storing and reusing information and knowledge in engineering design

International Journal of Information Management 22 (2002) 263–280

A framework for the requirements of capturing, storing and reusing information and knowledge in engineering design B.J. Hicks*, S.J. Culley, R.D. Allen, G. Mullineux Department of Mechanical Engineering, Faculty of Engineering & Design, University of Bath, Bath BA2 7AY, UK

Abstract Data, information and knowledge are very important commodities for organisations. The effective utilisation of these ‘commodities’ is increasingly the only way for organisations to achieve and sustain competitive advantage. In the field of mechanical engineering there are vast numbers of information and knowledge sources that are utilised throughout the design of an artefact or system. These may include documentation, component catalogues, past designs, new technologies, complex methodologies as well as a whole range of informal and formal sources developed through discussions and meetings. The effective utilisation and application of these information and knowledge commodities help enable the generation of feasible design alternatives and assist the decision-making process, which ultimately determines the success of the designed artefact. This paper discusses data, information and knowledge, providing formal definitions and an understanding of the relations and limitations of these resources. This understanding enables the development of better mechanisms and procedures for the capture and reuse of information and knowledge in engineering design. In particular, the approach of this work is to consider the intended reuse and level of application for knowledge in order to determine the requirements for its acquisition. Using this approach, an overall framework for the requirements of capturing, storing and reusing information and knowledge in engineering design is generated. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Information; Knowledge; Decision making; Creativity

1. Introduction In Today’s competitive and expanding global marketplace, competitive advantage lies with those organisations that can produce products of increased quality, reliability and performance, whilst reducing costs and bringing the product to the marketplace sooner. Ultimately, one of the key issues in better achieving all of these aspects is the use, reuse and handling of information *Corresponding author. Tel.: +44-1225-826-456. E-mail address: [email protected] (B.J. Hicks). 0268-4012/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 8 - 4 0 1 2 ( 0 2 ) 0 0 0 1 2 - 9

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264 Specification

Libraries

Concept

Company Public Private Government Academic

Patent Office

Government

National International

Reports Research Centres Design Centres DTI Market Research Applications Reports

Institutions Associations Societies Academic Professional BSI CBI Design Council

Advertising Press Press Releases Magazines (Non Specialist)

Journals Professional Trade

Visits Exhibitions Trade Fairs Conferences Seminars

Embodiment

Personal

Detailed design

Contacts Colleagues Experience Reps Consultancy (External)

Standards ASME ASTM BSI HSE ISO

Company

Customer

Product Spec Tech Sales New Prod Data Previous Schemes

Periodic Surveys Enquiries Direct Involvement

Drawings Data Handbooks

Supplier Agents Competitors Trade Brochures Data Books CD-ROM Disk Internet Catalogue

Other Internet Literature Review

Product

Fig. 1. Information sources used during the design process.

(Moran, 1999). At the various stages of the traditional design process (Ullman, 1992; Pahl & Beitz, 1996; Pugh, 1990) the engineer uses many sources of information. These include supplier information, catalogued information, records of previous designs and new technologies. The vast range of possible sources is illustrated in Fig. 1, taken from Allen, Hicks, and Culley (2000), and all contribute to the design of effective, economical and elegant products. From Fig. 1, it is clear that engineering like many other disciplines, relies heavily on information in order to achieve its core activities. Because of this high dependency on information, many companies can obtain significant improvements in performance and efficiency of delivering their service or product by the adoption of traditional information systems or knowledge management systems (Hicks, 1993). These systems are generally considered to comprise a set of interrelated computer-based elements that retrieve, process, store and distribute information to support activities at either an enterprise level or inter-enterprise level. This support is provided through improved methods and processes for decision making and control within an organisation, as well as the provision for accurate and current information (Curtis, 1991; Laudon & Laudon, 1996). One major problem, faced by many organisations is identifying what knowledge and information to capture (Redman, 1996), and once identified, what levels or extents of capture are required in order that the information or knowledge is truly useful. For information or knowledge to be truly useful individual elements must be available, authentic, applicable and accessible, as defined by Turner (1978). In engineering design, massive amounts of information and knowledge are used during (Boston,

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1998) and produced by the design process (Christian & Seering, 1995), therefore the resolution of such issues is very important. In addition to this, complex interaction and informal aspects of the process magnify and complicate the levels of knowledge and information interchange (McMahon, Pitt, Yang, & Sims Williams, 1995). Examples of informal processes include negotiations between members of the design team, customers and suppliers. This volume and diversity of information; both used and generated, is one of the major issues that frustrates the development of methods that deal with the capturing, organisation and reuse of design information and knowledge. The work described in this paper deals with development of a framework for the management of information and knowledge within engineering design. The management of these resources encompasses the requirements for the capture, processing, storage and reuse of knowledge and information sources. The paper discusses the utilisation of information and knowledge in engineering design and the reliance of decision-making processes on these commodities. Formal definitions for data, information and knowledge are developed and the differences, limitations and relations between them described. Because of the manner in which many practitioners interchange the words data, information and knowledge it is unclear what the differences are between them (Hicks, 1993). This complicates and confuses the identification and development of mechanisms for the capture, storage and reuse of each resource. Following the development of formal definitions, various subsets of information and knowledge are defined, and the hypothesis that the intended reuse and level of application determine the requirements for the capture of knowledge and information is discussed. These definitions, classifications and methods are used to generate a framework for the requirements of various levels of knowledge and information management within engineering design.

2. Data, information and knowledge in the design process Design can be considered to be an information process or an information transformation process (Ognjanovic, 1999; Hubka, 1988). The various design states each contain different extents of information, however, the process of transformation from one information state to another is the result of a decision process, driven by knowledge and information, as shown in Fig. 2. The application of knowledge and information is necessary because explicit, limited information is not a sufficient basis for decision making (Wilson, 1993; Targett, 1996). In addition to this, further knowledge is generated through consideration of one or more aspects of information and/or knowledge, and it is this knowledge which is used for decision making. In engineering design this additional knowledge will typically infer a measure of some quantity or quality between options or against a predetermined requirement (Samuel & Weir, 1999). For the transformation of the design from one state to another, information about the existing state is available (this must not be confused in the initial state specification, where the information pertains to the desired characteristics of the final state) and information regarding possible solutions, or procedures for transformation to another state, as well as knowledge about the best processes and procedures to the next state. In addition to this, there is a creative aspect to the transformation process. For the creative aspects the authors propose two types of creative activity; adaptive and inventive, which are adapted from Blessing (1994). Adaptive creativity involves the adaptation and extension of existing knowledge to a new situation, whilst inventive creativity is purely original, it

Creativity

Knowledge

Information

Adaptive

Inventive

Adaptive Creativity

Knowledge

Information

Creativity

Knowledge

Information

Knowledge

Information

Adaptive

Inventive

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! Requirements !

!

Information !State 1 Decision

Specification

Concept

Embodied solution

Final design

Information State 2

Information State 3

Information State 4

Information State 5

Decision

Decision

Decision

Fig. 2. Design as an information–knowledge process.

may involve radical new fundamental principles or methods in order to achieve an existing or new function. These three capacities generated through the creativity, knowledge and information phases of the transformation can then be used for evaluation and decision making. The culmination of this phase is the transformation of the design solution to its next state. This transformation is achieved when the information state and all of its information subsets have been fully populated and unambiguously define the new state. The process diagram illustrated in Fig. 2, demonstrates that both information and knowledge contribute significantly to the transformation and creation of the designed artefact, for all aspects but the inventive element of the creativity phases. Therefore, knowledge and information provide for the basis of all possible decisions, and decision processes. In fact upward of 80% of design is adaptive or variant (Pahl & Beitz, 1996), which does not require the inventive aspects of creativity, resulting in a process that is particularly reliant on information and knowledge. Therefore, an improved process and better final design can be achieved through the efficient and effective utilisation of information and knowledge resources for engineering design. In order to best utilise this information and knowledge it is necessary to provide effective means for its identification, capture, storage and reuse. This identification and capture not only pertains to individual information or knowledge elements but also the extents of supplementary elements necessary in order that the information or knowledge can be used meaningfully and unambiguously. This supplementary information or knowledge is defined and referred to as ‘meta-knowledge’ in later sections. The capturing of information and knowledge generally involves the generation of an electronic representation. Benefits of electronic representations include improved storage, access, concurrency, availability

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and searching (Automotive Engineering, 1992; Culley & Webber, 1992; Encarnac,a* o & Lockemann, 1987). The key aspects for this include the processing, format and organisation of information and knowledge elements. These are necessary to capture the relationships between elements and provide a structure for the electronic representation, the latter of which will influence the availability of the resource for reuse. For reuse the designer requires the ability to search for relevant information and knowledge elements and once located, the ability to acquire all relevant or related information elements. This ability is severely restricted by the number of proprietary formats and structures, which prevent homogenous or open access searching of various repositories (Allen et al., 2000).

3. Relationship between data, information and knowledge Within different fields of research many authors have developed definitions for data, information and knowledge (Benyon, 1990; Wilson, 1987; Devine & Kozlowski, 1995; Tomiyama, 1995), and are reviewed extensively by Court (1995) within the context of engineering design. Court concludes that information is comprised of a number of data parts and their descriptions, and that knowledge is the ability of the individual to understand information and describes the manner in which they handle, apply and use it in a given situation. This corresponds with work in the management sector, which defines knowledge as information within people’s minds (Davenport & Marchand, 1999). Now these definitions imply that knowledge is solely a withinperson attribute or capacity. However, it is not the knowledge itself which is a within-person capacity but the generation of the knowledge. Consequently, based on their work in the field of engineering design, the authors propose two aspects to knowledge production; knowledge processes and knowledge elements. Knowledge elements are produced by knowledge processes, which are generated by an individual through the understanding, assimilation and application of information and other knowledge elements. These two aspects to knowledge production and their corresponding definitions, are perhaps more closely aligned with the definition used by Marsh (1997), which considers knowledge to consist of the assimilation of related information addressed in the context of a frame of reference. Where this assimilation and frame of reference form the knowledge process, as proposed earlier in the text. These many and varied definitions, combined with the fact that data, information and knowledge are often considered to be synonyms of one another (Collins, 1998) severely frustrate the ability to identify information or knowledge, and develop requirements for their capture. The authors consider that whilst each is related there are differences between them, and these differences hold the key to better enabling their effective identification, capture and reuse of these resources. The following sections classify and define each resource as well as describing their relations within the context of engineering design. An overview of the relations and hierarchy between data, information and knowledge is depicted in Fig. 3. 3.1. Data Data is usually considered to be textual, either numeric or alphabetical (http://dictionary. cambridge.org, 2001). Some authors’ detail structured and unstructured data (Miller, Honavar, &

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Knowledge process Understanding / perspective

Context

Data

Decision

Knowledge Knowledge Element

Formal Structured representation

Data Information Informal

Data

Unstructured representation

Unreliable for decision making purposes

Fig. 3. Relationships between data, information, knowledge and decision making.

Barta, 1997), however, it is arguable that any representation of data is structured, whether it is computer information stored in a file or a stack of paper based documents, these are both indirectly structured or ordered. For the purposes of this work, data is considered to be structured and represent a measure such as a quantity. 3.2. Information A number of authors provide discussions on the definition of information, often with respect to data (Checkland, 1981; Wall, 1986; Holstrom, 1971). Evaluation of these works by the authors, leads to a definition of an information element as ‘describing a fact’, where the fact is an occurrence of a measure or inference of some quantity or quality. The fact does not have to be true and fair, it may be subjective or objective and is merely a record of the measure or inference undertaken. In order to describe a fact, an element of information must provide meaning and an appropriate measure. These two aspects to an information element can be termed a subject or a descriptor, which provides the meaning, and a predicate or value that holds the measure, typically a data element. Examples of information may include the mass of a component, the delivery date, the colour or even the texture or a particular item. Information is therefore the sum of a data element and one or more context descriptors, where the context descriptor(s) clarify the meaning of the data element, and are themselves one or a combination of data elements. To put this into perspective a sentence may comprise several information elements. For the purposes of this work, the authors define two classes of information: formal and informal. This distinction is also made by other authors (McMahon et al., 1995; Wall, 1986; Hollingum, 1987), although formalised and accepted definitions have yet to be fully documented (Culley & Allen, 1999). However, a review of these classifications, by the authors, purports that

B.J. Hicks et al. / International Journal of Information Management 22 (2002) 263–280 Textual (structured)

269

Textual (unstructured)

Formal

Informal Pictorial (unstructured)

Memory Pictorial (structured)

Expression

Verbal (explanative)

Verbal (conversational)

Fig. 4. Classes of formal and informal information.

the primary difference between informal and formal information is the structured nature of formal information, although both classes may share common mechanism for their exchange. Further to the distinction between formal and informal information, various subcategories for types of formal and informal information in engineering design are proposed. These categories relate to the structure and representation of the information being conveyed and are depicted in Fig. 4. These have been developed from studies by the authors into the common representations for the exchange of information in the engineering design process. 3.2.1. Formal information Formal information is an element of information that provides a specific context and measure. It provides a structure or a focus so that individuals exposed to it may infer the same knowledge from it, such as formal education, where the content and order is prescribed. In order to achieve this, formal education is structured and sufficiently decomposed to describe all the necessary information, which includes facts and relations, upon which the inferred knowledge is based. For the purposes of communication, formal information can be sub-classified into three categories that relate to the representation or conveyance of the information. *

*

Textual (structured) may be numeric, alphabetic or symbolic, or a combination of each. The medium could be paper based, electronic or any other medium capable of explicit visual conveyance of the information. In terms of its structure the textual representation is expressed in an accepted language or symbolic format, and is logically constructed to convey the context or subject and the predicate (measure or value). Typically, in engineering these may be formal documents or published materials. Pictorial (structured) information is considered to be any visual image conforming to an accepted standard. These may include diagrams, two- and three-dimensional engineering drawings, and flow charts. As in the case of textual information, pictorial elements are independent of medium.

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Verbal (explanative) is information that has been conveyed in a logical structured manner. Its content provides detailed information elements with clearly defined subjects and predicates from the outset. This is typically used for descriptions or explanations between individuals in the design team.

3.2.2. Informal information Informal information is considered by the authors to encompass unstructured information. The majority of which is either personal information or information that is developed through interaction between two or more individuals. Here the subjects and predicates may not be clearly defined; the information may change dynamically as content is altered or added. Indeed this varied and dynamic information set provides for the generation of various knowledge perspectives for the individuals taking part, and it is this variation that both stimulates and develops the creative and decision-making processes. *

*

*

*

*

Textual (unstructured) information may be expressed in shorthand, personalised notation or an accepted language. Although, it may follow no logical progression, contain incomplete information sets or utilise pointers and references which are truly meaningful only to the author(s). In addition to this, the topic and context may not be clearly defined and may have been assumed by the individuals during the process of creation. Pictorial (unstructured) information is similar to textual unstructured information, in that it may follow no logical order or progression, may contain incomplete information sets and may not conform to any accepted or practiced standards. These may include sketches, annotated notes or outlined process diagrams. Verbal (conversational) is a dynamic process, where discussions may include information subjects and predicates that are not clearly defined and change as the conversational topic(s) evolve during the process. Information sets are added, removed or altered as the discussion progresses and more often than not subjects are assumed. Memory information is considered to be those elements that are within-person. Such elements may be generated from past experiences, both formal and informal. Their content and relevance might be unclear or ill defined until stimulated by an external source or situation. Expressions include both physical expressions and intonations in the voice. Both of which indicate a predicate about a certain subject. These could denote approvals, indifferences or dislikes, all of which are powerful contributors to decision making and may be as a result of some knowledge or information held within-person, although they may not be truly quantifiable (formally).

3.3. Knowledge As discussed earlier in Section 2 and shown in Fig. 3, the authors propose two aspects to knowledge, and in particular its generation. These include the knowledge element and the knowledge process. The knowledge process is the procedure(s) utilised by the individual to infer the knowledge element from information, other knowledge elements or a combination of each. These knowledge processes are generally within-person processes, which complicate and frustrate the ability to formalise many of the knowledge processes. This is because of the inherent variances

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across situations and options between individuals for within-person activities (Culley & Allen, 1999). In contrast to this, knowledge elements can easily be represented, this is due to the fact that knowledge elements are in fact conveyed as information (Boston, 1998), which can be explicitly defined. The following sections discuss these two aspects to knowledge generation with respect to decision making. 3.3.1. Knowledge elements Knowledge elements are inferred from one or more elements of information. This information can be formal or informal. Although, because of the nature of informal information and the inherent differences between within-person processes, the knowledge generated or inferred by various individuals from the same elements of informal information may well be different and may not be true and fair (the wrong end of the stick syndrome). This is because the subject and predicate may not be clearly defined, and the fact that informal information processes are dynamic in terms of their content. For the purpose of this work, knowledge is deemed true and fair if it accurately represents the intended subject and the predicate of the information from which it was inferred, and it is free from bias. If informal information is used to infer knowledge for decision making processes then the validity and reliability of the knowledge cannot be guaranteed. This significantly reduces confidence in the knowledge providing a poor basis for decision making. In order to ensure a high level of confidence in the inferred knowledge then it must be based on clearly defined fact (formal information), not interpretation of fact as in the case of informal information. 3.3.2. Knowledge processes Knowledge elements suitable for decision-making purposes are considered by the authors to be perspectives of formal information. These perspectives are the particular inferred relations of the information to the individual for the application considered, and constitute the knowledge inference process. This perspective is dependent on factors such as the environment, the role of the person in the organisation, the knowledge base of the person, and the function of the person at that particular moment, as well as short-term and perhaps long-term goals. The knowledge may be derived or generated from a single element of information or may be generated comparatively from one or more elements of information. These knowledge processes are considered to be within-person processes. However, if these inference processes can be formalised in their entirety and their application clearly defined, then it is possible to reconstruct and represent the conversion or interference process. This is certainly desirable where knowledge is to be continually generated from a similar perspective or from various instances of information and/or knowledge elements. In the case of engineering science these processes can be considered to be scientific practices or procedures (Ehrlenspiel, 1997).

4. Capture and storage of data, information and knowledge For the most part data is rarely captured in isolation, it is usually a subset of information, where a context or descriptor applies to individual data elements or collectively to a pool of data, such as a field in a database. Consequently, the capture of information and knowledge are the two

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capacities dealt with in this work. In today’s world of computationalism, capturing generally results or produces a representation in an electronic format, capable of being held in a computer. Consequently, all forms of representation are ultimately numerical (machine level). Although interpretation of this, through software applications enables text, images, videos and sounds to be represented. This enables the three primary types of formal information, detailed in Fig. 3, to be directly created or converted into an electronic format. This construction or transformation process is enabled by many standard input/output devices, such as keyboards, microphones, scanners and cameras, controlled by various software applications. Once acquired these objects are then stored and organised. However, because of the prerequisite to use proprietary software to interpret and regenerate the information elements many different formats for storage and reproduction are available. As a consequence, access to various information and knowledge repositories is limited purely to those users with software that can accommodate the respective formats. These proprietary software environments provide for various data structures to organise the information elements within the repositories. These structures are necessary to replicate relations, networks or hierarchies between each element (Elmasri & Navathe, 1994). Such models may include time/date, user, project, organisation or application-specific dependencies. Many relationships can be defined; the objectives of which are to provide a logical structure for the content, better enabling the designer to update, search and retrieve information as required. In the case of informal information all the categories can be captured electronically, although memory items must be transformed to one of the other categories of informal information. However, capturing informal information is severely frustrated by the fact that the subject and often the predicate may not be clearly defined. In fact, in many conversational situations the subject may be assumed. This makes relevance a problem; if the information is to be reused or available to the organisation then conversion or some form of pre-processing is often required. This aims to either formalise the informal information or provide supplementary formal information to clarify the informal elements. Examples of such techniques may include the taking of minutes for a meeting or the use of comment statements in software code. Although, there are instances where future utilisation of the information may be limited to the individuals, present during its generation process, and therefore in such instances the assumed subjects and often illogical flow of the process may be recaptured later by the individuals, and thus may not need to be pre-processed. Examples include log books, personal notes, rough sketches and emails. Knowledge elements are typically conveyed as formal or informal information, thus inferred knowledge may be captured and represented in the same manner as information. In the case of knowledge processes, providing they can be formalised in their entirety and the objective of their process well defined (this is the subject of the knowledge element produced) then representation is possible. This representation can either be a description of the process, or a functional representation of the process. The former provides a documented procedure for generating the knowledge elements, whilst the latter provides a mechanism for the automatic generation of the particular knowledge element. These automated procedures are often required where repetitive or iterative tasks are considered. In engineering design these procedures are often incorporated into modelling and simulation tools enabling standard analysis or performance measures to be generated. Therefore, descriptive knowledge processes can be captured and represented using the

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same mechanisms as information, whilst procedural knowledge may only be represented in an environment which provides for the interrogation of the process. In conclusion, data, information and some elements of knowledge can be captured. However, the purpose or intended use of the captured materials will significantly affect the level and extent of material that is necessary in order to acquire truly useful information or knowledge, and describe the limit of its applicability. These issues are discussed in the next section and a classification for knowledge levels and applicability proposed.

5. Reuse of data, information and knowledge When dealing with information the term reuse can relate to the repeat utilisation of the same information for the same or similar tasks, or can relate to the further use or extension of information for a different purpose. Studies have shown that the reuse of information in engineering design is predominantly for the evaluation of measurable quantities, qualities or performance (Burgess, Shahin, & Sivaloganathan, 1998), the distribution of engineering data models between CAD systems (Finger, 1998) and the evaluation of decision processes (design audit trail) (Checkland, 1981; http://www.fnc.co.uk, 2000). A problem within design reuse in engineering practice is the lack of formal guidelines or approaches to enable designers to reuse design information (Duffy & Legler, 1999). Consequently, the authors propose four generic classes of reuse: decision making, descriptive, measurement and distribution. *

* * *

Decision making describes previous decision processes, which encompasses the decision outcome, the alternatives and the basis of the decision process. Descriptive elements describe or classify an object, an event or a process. Measurement represents the value of a particular aspect of an object, an event or a process. Distribution may include elements from each of the above categories, but has been processed specifically for exchange between a number of individuals, environments or processes. This distribution class is only applicable to formal information elements, because these conform to a standard or formalised language which can be exchanged or parsed between environments.

For all categories other than decision making, providing that the integrity and completeness of the information is good then the information is suitable for reuse. However, information reuse for the evaluation of previous decision processes and in particular design processes, requires that additional information regarding the decision alternatives and the various drivers or considerations also be captured. This is necessary in order to capture and represent the true intent of the original decision. These considerations may include conditions such as economics, both global and local, machinery or production schedules, supplier issues, distribution, environmental and skills base to name but a few. The reuse of knowledge in engineering design typically aims to make available the experiences of individual knowledge or organisational knowledge from previous design activities in order to better inform and enable future design activities. Much of this knowledge will pertain to the decision processes associated with the various design phases. Such decisions might include the selection of design alternatives, utilisation of new technologies, suppliers or the specification of

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engineering components. These are not meant to be exhaustive but merely illustrate a number of key uses. Therefore, reuse of knowledge will typically involve its application to new situations, where these situations may or may not be familiar. Consequently, the authors propose four states of applicability. Here the knowledge is applied to a given problem, the extent and manner in which the knowledge is applied is dependent on the familiarity of the situation and the level of direction required. Self-direction represents the independent application of knowledge by either an individual or a process, whilst external direction represents the application of knowledge by an individual or a process under the guidance or supervision of one or more additional individuals or processes. In the case of engineering design this may include other designers or standards such as BS8888 (2000). 1. 2. 3. 4.

Application Application Application Application

to to to to

unfamiliar situations self-directed. unfamiliar situations externally directed. familiar situations self-directed. familiar situations externally directed.

In addition to these four states of application, the authors have identified four levels of knowledge element, shown in Fig. 5 with associated states of applicability. If these levels of knowledge are combined with the various levels of direction required for application then eight states are generated, illustrated in Fig. 6. Only eight states are generated because general principles

Knowledge level

Level of applicability

General principles

Extrapolation of knowledge

Applicable to unfamiliar situations and domains

Generic knowledge Applicable to unfamiliar situations for a particular domain

Specific knowledge Applicable to familiar situations within a particular domain

Cases Applicable to a specific situation within a particular domain

Fig. 5. Knowledge levels and states of applicability.

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Application

Extent of capture

! Formal information ! Descriptive Requires clearly ! Textual (structured) defined subject and ! predicate for each Decision making element. ! Pictorial Multiple elements ! (structured) may be necessary Measurement of to represent the ! quality or quantity context. and true ! Verbal intent,, as well as Distribution prevent ambiguity ! (explanative) ! ! Informal information ! Memory ! Requires Descriptive ! Textual complete record (unstructured) of informal ! information and Pictorial ! (unstructured) related Decision making information ! elements in order Verbal ! (conversational) to capture the true intent and ! Measurement of context Expression quality or quantity ! ! ! Knowledge level (processes and elements) ! -Requires vast metaUnfamiliar knowledge about assimilation ! situations Self-directed for all situations and domains ! Generic principle Unfamiliar External -An external supplies metasituations directed knowledge for all domains ! ! -Requires extensive metaUnfamiliar knowledge about assimilation ! situations Self-directed for all situations in domain General knowledge ! -An external supplies metaUnfamiliar External knowledge for domain situations directed ! ! -Requires limited metaFamiliar knowledge for specific ! situations Self-directed assimilation Specific knowledge situations and domains ! -An external supplies Familiar External ! specific meta- knowledge situations directed -No meta-knowledge ! Specific situations Self-directed required for application, ! although knowledge describing limitations is ! Case knowledge desirable Specific External situations directed ! -Limit of application determined by an external !

Processing

275

Computational Capture

Computational Storage

Standard input/output devices: Mouse Keyboard Microphone Scanner Camera Tablet

Standard databases MSACCESS Oracle DB2 SYBASE Mark-up languagesXML, SGML Spreadsheets

Formalise subject and predicate

Provide supplementary formal information to clarify informal elements

Knowledge elements must be formalised for reliable decision making

Informal knowledge can be used for descriptive and measurement, although backward propagation may be necessary

Standard input/output devices: Mouse Keyboard Microphone Scanner Camera Tablet

Standard input/output devices:

Mouse Keyboard Microphone Scanner Camera Tablet

Proprietary databases Data archives Libraries Emerging standards

Case based support systems Knowledge bases and knowledge management systems Proprietary knowledge definition languages Decision support systems

Fig. 6. Requirements for capture of information and knowledge for various levels of application and reuse in engineering design.

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and generic knowledge are not restricted to familiar situation, whereas specific knowledge and case knowledge are restricted to familiar situations only. At the highest level, generic principles or general knowledge is applied to unfamiliar situations. Here the applicability of individual knowledge elements to the new situation is not an issue because they are generic. However, decisions are rarely taken based upon a single aspect of knowledge, and it is likely that the necessary knowledge can only be generated through the application of a combination of knowledge elements. Consequently, knowledge regarding the assimilation of knowledge elements is required (knowledge about knowledge). Such metaknowledge must be formalised if the knowledge is to be generated in a self-directed manner. Although this may be unfeasible for levels of knowledge beyond specific knowledge elements due to the vast extents of knowledge elements required. These vast extents are necessary to represent the sheer number of possible knowledge elements and combinations. Therefore, an individual, such as the designer, almost always supplies this meta-knowledge for levels beyond specific knowledge. In the case of specific and case knowledge elements, which are to be applied self-directed then knowledge regarding applicability and assimilation can be captured. For such situations the formalisation of accepted knowledge processes, such as best practices, or the explicit capture of knowledge as derived by an individual is required. In the latter case the individual must be qualified to undertake such knowledge derivation, and must share the same goals or perspective as those members of the organisation who will utilise or reuse the knowledge. Finally, for generic or specific knowledge applied to familiar situations it is required that knowledge describing the limitations or scope of applicability be made available, so that misapplication does not occur.

6. Requirements for information and knowledge capture, storage and reuse The previous sections discuss the various types of informal and formal information, knowledge processes and elements with respect to the requirements for reuse in an organisation, and in particular the field of engineering design. The results can be combined to illustrate the various categories of information and knowledge, their applicability and requirements for reuse. These requirements include the extents of capture necessary to represent the context and true meaning of an information or knowledge element, any pre-processing operations required to formalise the elements, an overview or possible mechanisms for electronic capture and examples of possible storage environments. These are summarised in Fig. 6. This framework provides an indication of the requirements for the level and extents of captured material necessary for the different categories of information and knowledge. These various categories relate to the purpose or intended use of the resource. This differentiation outlines the requirements for each category, which better enables the development of methods that deal with the capture and application of the various capacities. These methods will generally be industry or company tailored, although they may use common software modules. This tailoring is necessary to meet the specific needs of the organisation, its individuals and often its customers. For the utilisation of information and knowledge for engineering design there will always be a requirement for designer intervention and intuition, not least for the creative aspect of the process, but also for the assimilation and application of the vast amounts of knowledge. In addition to

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Knowledge

Information and knowledge produced by decisions

Information and knowledge for decision making

Decision

Semantic backward interpretation of Knowledge element

Perspective or specific understanding

Information

Context or subject descriptor

Data Fig. 7. Bi-directional information and knowledge transformation processes for decision making.

this, there are always going to be problems with incomplete information, which has to be interpreted. Computational processes cannot as yet infer bi-directionally in the same manner as within-person. This bi-directional inference is illustrated in Fig. 7, there will always be the need for continuous conversion of knowledge into information and information into knowledge (Devine & Kozlowski, 1995). In the case of backward propagation between knowledge and information, the content of the information is subjective rather than objective. It is this subjectivity that enables individuals to freely think, although the final result may not be true and fair. Language dictates how individuals express themselves and consequently how they think. This is also the case for computers where binary arithmetic forms the basis of their computational dexterity. Because computers are arithmetically driven they are restricted to evaluating truth states in order to determine a single best outcome. However, within-person processes can be mimicked or modelled computationally but the level of modelling abstraction will always limit them. Reuse of knowledge is frustrated by semantics. The knowledge may not be structured and specific, and for the particular application may require an altered perspective. Here the individual must dynamically step from knowledge back to information and then generate another perspective which may provide knowledge for the new or unfamiliar situation. This new perspective is generated from semantic interpretation of the old perspective, also shown in Fig. 7. This activity is dependent on the extents of the framework provided by the lowest level language

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of the individual or process, and once again requires the involvement of the engineer or certain individuals in the process.

7. Conclusions In modern engineering, an organisations’ information and knowledge base is fast becoming the key to sustainable competitive advantage in all sectors. Until recently, companies could expect to succeed based upon the individual knowledge of a number of strategically positioned individuals. Increasingly, however, competitive advantage is only gained by making individual knowledge, both current and past, available to the entire organisation. The embodiment of organisational knowledge is the experience of the employees combined with the tangible information and knowledge repositories of the organisation. This embodiment or collection of knowledge and information is frustrated by issues over reliability, true intent and applicability. This work firstly discusses the lack of formal, consistent definitions for information and knowledge in engineering design. This inadequacy is identified as one of the key issues to be resolved, if effective methods are to be developed for the acquisition and management of each resource. To address this, formal classifications for data, information, knowledge and their relations, within the context of engineering design are developed. In the development of this classification various categories of information, both formal and informal are proposed. Furthermore, for the generation of knowledge, a distinction is made between the knowledge generation process and the knowledge element that is produced by this process. Following the development of these classifications, the work introduces the hypothesis that intended application or reuse drives the requirements for the acquisition and management of information and knowledge. To develop these requirements, various classes of reuse for information in engineering design are illustrated, these include: descriptive, measurement, decision making and distribution. In the case of knowledge, four levels of knowledge element are proposed, which define the limits of their applicability: general, generic, specific and case knowledge. These classifications for formal and informal information, knowledge elements and processes, are used to generate a framework that indicates the limits of applicability for each class, as well as outlining the requirements for acquisition, capture and electronic storage. The work also identifies that for higher levels of knowledge elements and process, such as general principles or generic knowledge, extensive meta-knowledge is required. This meta-knowledge describes the assimilation and applicability of knowledge elements. Because of the vast levels of possible knowledge elements and combinations, this meta-knowledge is best generated from within-person processes rather than computational processes. Further to these considerations, the need for continuous conversion of information to knowledge and knowledge to information is highlighted, and the fact that such processes cannot be effectively performed computationally are discussed. In order to provide effective utilisation of information and knowledge, an organisation must make the best use of electronic information–knowledge repositories and processes as well as the within-person sources of knowledge and information. This work proposes various classes of information and knowledge and develops requirements for their acquisition, capture and reuse. This provides for the identification of information and knowledge types and their intended application. This better enables an organisation to formalise the requirements for capturing,

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storing and reusing particular information and knowledge, and develop specific methods for their management.

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