International journal of computer integrated manufacturing , tập 23, số 11, 2010

International Journal of Computer Integrated Manufacturing Vol 23, No 11, November 2010, 957–967 Knowledge value chain: an effective tool to measure knowledge value Yang Xu* and Alain Bernard IRCCyN, Ecole Centrale de Nantes, Nantes, France (Received 21 September 2009; final version received 19 May 2010) Knowledge value is a significant issue in knowledge management, but its related problems are still challenging This paper aims at discussing how knowledge value changes in the knowledge evolution process and develops a knowledge value chain (KVC) to measure knowledge value By applying the notions of knowledge state and knowledge maturity, the knowledge finite state machine (KFSM) and knowledge maturity model (KMM) are introduced to characterise the KVC Based on these concepts, knowledge value is measured by calculating the difference between two maturity states rather than by direct calculation This point of view of knowledge value, the construction of KVC and the association of knowledge value and knowledge maturity are insightful for both researchers and practitioners Keywords: knowledge management; knowledge value; value chain Introduction Nowadays, more and more enterprises and entrepreneurs realise that knowledge plays an important role in business success and that knowledge management is becoming a core activity The capacity of knowledge management becomes a crucial issue for companies, and essential for enterprise competitiveness (Bernard and Tichkiewitch 2008) However, although ‘knowledge is power’ was spelled out more than 400 years ago (Bacon 1597), it is still easier said than done, and people can hardly control and measure knowledge as they can electrical or mechanical power Therefore, there is a growing need to represent this ‘power’ in an explicit way and specify the process during which this ‘power’ works Before companies became aware of the importance of knowledge management, knowledge activities were usually ill-defined, and as a result, knowledge innovation, application and abandonment mostly happen without rigid control One of the main purposes of knowledge management is to standardise and formalise knowledge changing and transmission processes, so as to follow the rules concerning knowledge and to control knowledge in order to improve production activities (Leonard-Barton 1995) As tacit knowledge, which exists in the form of mental models, beliefs, experience or other forms of know-how of individuals, it is not easy to convey in formalised patterns and is usually represented and stored using storytelling (Guerra-Zubiaga and Young 2008) *Corresponding author Email: Yang.Xu@irccyn.ec-nantes.fr ISSN 0951-192X print/ISSN 1362-3052 online Ó 2010 Taylor & Francis DOI: 10.1080/0951192X.2010.500677 http://www.informaworld.com The backbone of knowledge management is to ensure the availability of knowledge to all people or cases that may require it ‘Availability’ means not only an extensive, living and sharable knowledge base accessible to all users within an organisation, but also the probability and ease to satisfy requirements, and the degree of satisfaction In recent years, many researchers have made important contributions to measure such ‘satisfaction’ Ahn and Chang (2004) have introduced a KP3 methodology to assess the contribution of knowledge to business performance and they have established logical links between knowledge and business performance through product and process concepts Chen et al (2009) have integrated analytical network processes and balanced scorecards to measure knowledge management performance, so as to compare an organisation’s knowledge management performance with its rivals and to improve its knowledge management activities Bernard and Xu (2009) have developed an integrated knowledge reference system to describe the knowledge evolution process in product development and show the mutual valueadding process between knowledge and product Wen (2009) has constructed a model to measure knowledge management effectiveness by using focus groups, analytic hierarchy process and questionnaire analyses Liu et al (2005) have made some empirical surveys to compare the effectiveness of different knowledge management systems 958 Y Xu and A Bernard As knowledge is inherently difficult to measure, former researchers mostly assess the outcomes associated with knowledge, such as the performance of a knowledge management system or how knowledge could contribute to business performance, instead of measuring knowledge directly There is, therefore, a lack of direct focus concerning the problem of knowledge value, which is a key point and a bottleneck in knowledge management To achieve this goal, this paper will discuss issues of knowledge value and how knowledge could be evaluated and acted upon in production activities Knowledge value When talking about ‘knowledge is power’, intuitively, people are aware of the fact that knowledge can make things better Furthermore, when saying ‘better’, it can be ‘a little better’ or ‘much better’, so people are eager to know ‘to what extent are things better’ As a result, a measurement should be introduced Unfortunately, people think that a measurement is one of the most difficult parts of the knowledge management field (Ruggles 1998) and Liebowitz and Wright (1999) even stated that it was not clear whether knowledge could be measured In order to challenge this issue, the primary problem is, above all, ‘to measure what’ This paper proposes the term ‘knowledge value’ as what is to be measured in considering ‘how powerful knowledge is’ Consequently, we come to the question: what exactly is ‘knowledge value’? ‘Value’ is a flexible term that is used in a variety of domains with different meanings For example, in economics, it means the market worth or estimated worth of commodities, services, assets or work; in mathematics, the output of a function is called the value; in psychology, value explains why people prefer or choose some things over others, i.e the explanation of an individual’s preferences in life goals, principles and behavioural priorities (Renner 2003) From these different meanings of ‘value’, we may conclude that ‘value’ describes how people positively or negatively evaluate things and concepts, and the reasons used in making their decisions Based on the fundamental meanings of ‘value’, this paper uses the term ‘knowledge value’ to characterise ‘the ability of knowledge that can make things better’ From the point of view of knowledge lifecycle (Birkinshaw and Sheehan 2002), at the beginning, knowledge cannot always meet the requirement, which is determined by a given context Knowledge has to experience an evolution process to augment its ability, i.e its value, so as to arrive at a state which can meet the requirement and solve the problem The notion of ‘maturity state’ is thus introduced to represent such a state and it integrates the knowledge itself within the context Before starting the survey on knowledge value and knowledge maturity, some insightful arguments on information value and information maturity should be presented, as knowledge is usually linked to information and information is a concept that is similar to knowledge in some points of view Sillince (1995) argued that information value depends on the probability of information transfer from one person to another within an information system and is affected by whether such a transfer is costless or not By modeling three types of information (related information, alternative information and unrelated information), some useful formulations are deduced, which make is possible to discover which organisational or market forms are most able to deliver high information value through empirical testing Zhao et al (2008) clearly distinguished information value from information quality and defined it as ‘(Benefits of having information) / (Resource spending on storing and retrieving)’ which is equal to ‘(Quality Relevance) * Saving’ Then they developed an assessment system integrating information characteristics, the Bayesian Network theory, and conditional probability statistical data to evaluate information value For the concept of information maturity, the Meta Group proposed a five-level model of information maturity, which enables organisations to assess their information management practices (MIKE 2010) and Blanco et al (2007) presented a tool called PIQUANT to illustrate an information maturity management model which can manage information types within different workspaces during the design process Knowledge is not simply information, so the notions of knowledge value and knowledge maturity differ from the concepts introduced above, and they will be introduced in detail in the following sections with explicit and formal definitions 2.1 Features of knowledge value Knowledge is different from traditional resources, having many features which make it difficult to judge its value (Stewart 1997) For example, knowledge can be used without being consumed and some knowledge can only be sold once For traditional power such as petrol, sales personnel can calculate how much is consumed by clients and take this parameter as an index in evaluating its value But for knowledge power that can only be sold once, such as an idea, people can hardly evaluate its value by estimating how much it is expected to be used For example, the value of International Journal of Computer Integrated Manufacturing software depends on the number of times it is used once bought and different clients may use it with different frequencies Sometimes some frequencies can be obtained, such as the ‘impact factor’ of a scientific paper or journal, but when such a ‘frequency’ or ‘the number of times’ is not available, it will be more practical to consider ‘knowledge value’ as the value it provides each time it is used instead of its ‘whole’ value that the knowledge could provide in its lifecycle Another feature is that the values of different kinds of knowledge are, for the most part, not comparable, for example, how can we compare the value of ‘specialised’ knowledge (e.g the formula of diet pills) and ‘common’ knowledge (e.g doing exercises to keep fit)? Both of them are useful for a similar purpose and may have a same effect Although people would have to pay more to buy diet pills than jogging along a river, we cannot say ‘common’ knowledge is less valuable than ‘specialised’ knowledge But in reality, why should we always pay more for ‘specialised’ knowledge? It is not because ‘specialised’ knowledge is more valuable than ‘common’ knowledge but because it costs more, and people may think something that costs more is more valuable, which might not be true In practice, admittedly, cost and time both have a great impact on knowledge evaluation, as knowledge should always be acquired within the financial budget and a tolerable time delay, and conflicts between planned cost/time and actual cost/time may result in some risks, so a comprehensive investigation should be based on value/cost/risk This paper mainly focuses on the issues of value, and further research will take cost and risk into consideration The third feature is that knowledge value can be estimated only when knowledge is used and can hardly be judged in advance For example, when we go to a lecture or take a course, we are not able to answer the questions ‘Is it valuable to you? How much value could it bring to you?’ before we have heard the content Sometimes, statistical studies based on experience or other methods might be used to expect the value of an object, but it is not the case for knowledge, because judgments from other people (or other cases) may vary greatly from each other As a result, ‘knowledge value’ can not be ‘predicted’, and it is defined on condition that objectives are already known In other words, knowledge value should be simulated within a context that is known From these features, which are still far from covering all features of knowledge value, we may reach the following conclusions when defining knowledge value: Knowledge value is the value of its one-time use Knowledge value does not have a direct link with its cost 959 Knowledge value is assessed on the condition that its context is already known The statement that ‘knowledge value is the value of its one-time use’ means that in the present proposition of knowledge value, knowledge reuse is not considered However, it does not mean knowledge reuse has no link with knowledge value; in fact, the number of times specific knowledge can be reused (reused directly or reused after being revised or created in the view of knowledge lifecycle) is an important parameter In other words, ‘the value of one-time use’ means knowledge reuse is not considered when evaluating the knowledge in ‘the current’ case, but it may influence the ‘future’ value of knowledge When considering this ‘future’ value, current/past values, current/past contexts and future contexts should be taken into account, and this will be a very interesting topic in the coming research projects Moreover, knowledge could exist in both explicit and tacit forms, or as Chilton and Bloodgood (2008) pointed out that knowledge has ‘its degree of tacitness’ As explicit knowledge, is codified and formally stored in specific media, people may find some numerical parameters to characterise a given aspect, for example, people can use ‘bit’ to measure the quantity of a kind of explicit knowledge such as electronic documents or even audio-visual files The Impact Factor (IF) founded by the Institute for Scientific Information (ISI) is also a well-known parameter to evaluate the importance of a specific type of explicit knowledge – published scientific papers On the other hand, tacit knowledge can hardly be represented objectively People are not often aware of the fact that they are using the tacit knowledge they possess, so it is more difficult to recognise how valuable it is Tacit knowledge is quite personal, as it can only be gained through personal experience and transferred through personal contacts People may have their own judgement of it: whether they possess it, whether they are using it, whether it is important in helping them to carry out certain actions or tasks, and more confusingly, whether it is useful and valuable for others As a result, the objective or absolute measurements are actually very limited in ‘measuring’ knowledge value We may even say that knowledge does not have a philosophically absolute value that is independent of the context, such as a stone of kg which is always ‘5 kg’ regardless of the time, the site or the actor Furthermore, it is also difficult to capture the ‘outcome’ of knowledge activities directly and objectively as people could easily fail to extract real causal relationship between a knowledge activity and ‘its’ outcomes, so we should sometimes rely on parameters that are subjective, depending on individuals, 960 Y Xu and A Bernard organisations and cases Thus, we may conclude that knowledge value should always be studied and measured in context 2.2 Knowledge value in context The proposition that knowledge has value is ancient, and arguments about knowledge value emerged thousands of years ago Pritchard (2007) has analysed several problems concerning knowledge value, including the primary value problem for knowledge (the Meno problem concerning why knowledge is more valuable than mere true belief), the secondary value problem (concerning the issues of why knowledge is more valuable than any proper subset of its parts) and the tertiary value problem (why knowledge is of more value to us than whatever falls short of knowledge) Despite philosophical analysis of knowledge value, nowadays, knowledge value is also recognised in a business context, for example, a pair of shoes is worth more than leather plus rubber, and it is knowledge that augments the value of products in this manufacturing process This example indicates a clear fact that knowledge value is revealed in the process of product development, thus, this paper limits the studies of knowledge value to within such a context Definition The knowledge context is characterised by a space of: C ¼ O1 Â O2 Â Á Á Á Â On where Oi ¼ foi1 ; oi2 ; ; oij g Oi is the ith attribute of the context and oij is the jth value of attribute Oi Intuitively, a knowledge context could be regarded as a super-cube which characterises the environment of knowledge activities, and it may have different axes in different cases In order to explain how this knowledge context space can be concretely applied, this paper has chosen three main aspects: participants (PA), knowledge status (KS) and product lifecycle (PL) Thus, we have: C ¼ PA Â KS Â PL; ci ¼ fpai ; ksi ; pli g where ci C is a specific context, and pai PA, ksi KS, pli PL Each set has several values, i.e elements, and they are illustrated as follows: (1) PA ¼ {director, manager, engineer, technician, operator} PA refers to the human factor Knowledge is a thing which is always linked to people, and different people may regard the same piece of knowledge differently For example, given a piece of knowledge that describes the selling strategy of a competitor, the manager of an enterprise may regard it as crucial but the operators working in the manufacturing unit may think it useless PA has five elements, i.e values, which are derived from the common hierarchical structure of a company All providers, users and workers of knowledge are treated as ‘human factors’, and they correspond to one of these five elements When a human factor is assigned one value, it does not merely refer to the name of the job, but the different levels of points of view and functions, from a strategic outline to a concrete operation (2) KS ¼ {initial, intelligent} ordered, organised, usable, KS refers to how knowledge is organised and the five elements are illustrated as follows: Initial The knowledge is scattered unsystematically, such as the original data of a market investigation Ordered The knowledge has already been stored in text files and in formal forms, but at a relatively low integration degree and data redundancy exists For example, the numerical data of collected questionnaires belong to this status Organised The knowledge is structured logically, and can be maintained and managed by effective mechanisms Organised knowledge abstracts the core information of knowledge Results after synthesis, classification and calculation belong to this status, e.g., the average income per family in area X is $60,000 per year, the average number of cars owned per family in area Y is 1.2, etc Usable The description and organisation about knowledge is user-oriented, in other words, usable knowledge is rather descriptive and is able to describe phenomena which have been analysed and concluded according to the desires of users E.g., people of area X tend to use fuelefficient cars rather than pursuing high performance in speed, young people of area Y are more interested in car design than car size, etc Intelligent Knowledge and production activities are integrated comprehensively, and knowledge acts as a motivating power with a certain degree of intelligence General conclusions and suggestions for decision making belong to this status Intuitively, when knowledge is abstract and descriptive it is close to the ‘Intelligent’ side and when it is International Journal of Computer Integrated Manufacturing more likely to be in code or digital forms it approaches the ‘Initial’ side KS may seem to be similar to the data-information-knowledge-wisdom (DIKW) hierarchy (Rowley 2007), but the difference is that, when presenting DIKW, people mainly aim to explain how to understand those literally abstract concepts (they not exist physically) in a more comprehensive way, using illustrations, examples or metaphors People may be interested in exploring how the chain is constructed and in clarifying the fuzzy transitions between different concepts For example, Hey (2004) examined the transitions between data, information and knowledge which link them as a DIKW chain Similarly, ‘initial’ knowledge seems like ‘data’ and ‘intelligent’ knowledge has a sense of ‘wisdom’ However, this paper emphasises how knowledge is organised, rather than the relationship between different knowledge statuses There is another important difference between the description of knowledge status and DIKW chain The DIKW chain implies an evolution process, in other words, wisdom is better or more advanced than data In our proposition of knowledge status, the five elements are ‘equal’, that is to say, for example, ‘intelligent’ knowledge status is not always superior to ‘ordered’ status (3) PL ¼ {information gathering, design, development and testing, manufacturing, sales, service} PL links the evolution of knowledge with product development In product development, knowledge is inseparable from a product, so the product lifecycle should be introduced For example, a product design does not have the same purpose in the realisation phase as in the selling phase Currently, the concept of product lifecycle no longer emphasises just financial matters in an enterprise applying for business planning and management It has been more broadly used as an engineering term to describe a comprehensive approach in managing enterprise performance (Ma and Fuh 2008) It is usually integrated with knowledge management (KM) methods especially in dynamic and collaborative environments (Thimm et al 2006) and different stages of a product lifecycle have different knowledge requirements (Xu and Bernard 2009) The axis of PL enables the integration of product lifecycle management (PLM) within the context PLM is the process of managing the entire lifecycle of a product and it integrates people, products and processes to form an all-encompassing system that can provide companies with an overview of product development Typically, PLM aims at improving product development processes and involves activities such as information gathering, conception, design, manufacturing, sales and services Figure shows the wheel of PLM Figure 961 The wheel of PLM The phase of Information Gathering mainly includes investigation, data collection and analysis, etc The phase of Design mainly includes requirement specification, product definition, general conception, detail design, embodiment, etc The phase of Development and Testing mainly includes prototype simulation, acquirement and adjustment of technical parameters, product validation, etc The phase of Manufacturing mainly includes product manufacturing, assembly, packing, etc The phase of Sales mainly includes advertising, selling, product/service delivery, etc The phase of Service mainly includes maintenance, after sales support, product retirement and recycling, etc PA, KS and PL are the three axes chosen in this paper to characterise the knowledge context in the following studies These three are chosen to perform as an example of how the knowledge context space can be implemented and other different attributes can be chosen to characterise specific contexts Not all the other attributes can be treated in exactly the same way as PA, KS and PL are, because different attributes have different possible values, but the idea of constructing a knowledge context space is the same: the knowledge context is characterised by Cartesian coordinates consisting of several attributes and elements of each attribute correspond to given points on the axis 2.3 Knowledge state The notion of state is usually applied to simplify problem characterisation by dividing a continuous 962 Y Xu and A Bernard developing process into discrete stages When former researchers describe knowledge activities by knowledge states, they usually build the knowledge value chain model with a linear series of knowledge stages and steps (Lee and Yang 2000, Wong 2004) However, those models are limited in their linear structure and when different stages may have ambiguous boundaries, they will bring uncertainty as well As a result, this paper proposes a knowledge finite state machine (KFSM) to represent the knowledge evolution process Definition A knowledge finite state machine (KFSM) is a hextuple hQ, S, K, d, s0, Fi, where: Q is a finite and non-empty set of knowledge states si; S is a finite and non-empty set of manipulations required to change the knowledge states; K is a finite and non-empty set of knowledge required to change the knowledge states, including two subsets Ka (knowledge available, namely the knowledge existing in the knowledge base) and Ki (knowledge imported, namely the knowledge that needs to be acquired from the outside); the knowledge fragments are noted by k, and k K d is the state transition function: d: Q S K ! Q, and when a transition from si to siþ1 happens with the ‘right’ manipulation and knowledge, it is called an effective state transition; s0 is an initial state, which is an element of Q; F is the set of final states, which is a subset of Q, and there is at least one state sn F The KFSM provides us with a method to describe the knowledge evolution process in a more flexible and general way When a knowledge evolution process starts with s0 and ends up with sn, the aim of KM activities is to eliminate the difference between s0 and sn, and this target can be accomplished by bridging the gap between si and siþ1 step by step 2.4 Knowledge maturity When characterising an evolution process, the notion of maturity is usually referred and used to describe the varying states of a thing during its development Maturity has different meanings in different domains, such as: Biology The age/stage when an organism can reproduce Geology A measurement of a rock’s state in terms of hydrocarbon generation Psychology A person responds to their circumstances or environment in an appropriate manner, being aware of the correct time and place to behave and knowing when to act in serious or non-serious ways Software engineering To what extent is it planned how to things, mainly described by the capability maturity model (CMM) (Paulk et al 1995) Inspired by these interesting applications of the term ‘maturity’, we will introduce the maturity of knowledge based on the notion of knowledge state As knowledge is context sensitive, given a knowledge state, it may be less mature in one situation than another For example, a mathematical theory is mature for a professor (as the knowledge is ready for reuse in solving some problems), when it is not mature enough for a pupil (as the knowledge must be illustrated by some simple means so that the pupil may understand and apply it) Consequentially, the knowledge maturity described in this paper associates the knowledge with the context, and is defined as follows: Definition Knowledge maturity describes the state of knowledge within a specific context, noted as: mi ¼ ðsi ; ci Þ where si is a knowledge state in KFSM and ci is a specific context The knowledge value chain The evolution of knowledge is a challenging topic that has aroused much interest Nonaka and Takeuchi (1995) proposed a spiral process which is regarded as a basic model to describe how knowledge develops in its lifecycle 3.1 Knowledge maturity model In fact the subject of a knowledge maturity model (KMM) has already incited interest among researchers, and they have conducted insightful surveys on KMM For example, Markow (2004) proposed a KMM consisting of four levels: the Process cycle, Roles, KIM (Knowledge Insight Model), and Inner mechanism; Robinson et al (2006) constructed a knowledge management maturity roadmap of five stages: Start-up, Take-off, Expansion, Progressive, and Sustainability The main idea of these existing KMM mainly comes from CMM, which is mostly applied in the field of software engineering CMM was originally intended International Journal of Computer Integrated Manufacturing as an objective evaluation and served as a tool to measure the performance of various software engineering contractors It measures the organisation’s current state by providing a set of goals, a checklist indicating what the organisation should accomplish to reach a higher level and is now one of the most recognised models in industry Through a number of applications and experiences, it has been shown to be well-suited for organisations when characterising their key processes The basic idea of CMM is a five-level ladder consisting of the initial, repeatable, defined, managed and optimised levels, and the existing KMM is also based on this idea of ‘level-ladder’ Here are several limitations of the structure of ‘level-ladder’ when describing knowledge activities in product development: The direction of maturity development is always ‘up’ According to CMM, we can say that when the company is at level 3, it is more mature than when it is at level 2, and the company’s aim is always to progress from a low level to a higher level, i.e the direction is always ‘up’ However, does knowledge always go ‘up’? For example, the mathematical formula ‘x þ y ¼ y þ x’ is the induction of ‘1 þ ¼ þ 1, þ ¼ þ 3, etc.’ and it can describe a general mathematical law, thus, it is supposed to be at a ‘higher’ level and more mature than ‘1 þ ¼ þ 1, þ 5 þ 3, etc.’ But when this knowledge is expected to be taught to pupils in their first year of primary school, ‘x þ y ¼ y þ x’ should be transferred to ‘1 þ ¼ þ 1, þ ¼ þ 3, etc.’ In this case, the direction of knowledge evolution is ‘down’ and knowledge at a lower level is more mature All tasks of one level should be accomplished in order to go to the next In CMM, the companies should climb the ‘ladder’ level by level; however, as knowledge is an active thing, can it not transfer its maturity status by ‘leaping’ or ‘making a detour’? For example, some people may follow this sequence: ‘have an idea -4 write it down -4 realise it’, but others can make a direct leap from ‘idea’ to ‘action’ The higher level overlays the information of the lower level But for knowledge, is the knowledge in a ‘less mature’ status no longer useful? In the traditional understanding about maturity levels, the information of a less mature status is overlaid by a more mature status For example, in the constructing process of a manufacturing system, from the traditional view point, the result (a system that is working) is more mature than design drafts However, knowledge contained in 963 the design drafts could be also valuable, such as knowledge about ‘why Part X is designed like that’, but the manufacturing system itself does not communicate this knowledge: in other words, the knowledge is overlaid by the result which is more mature Given a same manufacturing system, different participants need different knowledge For engineers whose duty is to maintain the system, they need the knowledge about ‘why Part X is designed like that’, but for technicians who are focusing on operating the system, what is useful for them is knowledge about ‘how to use Part X’ In order to overcome the limitations above, the KMM introduced in this paper implies the idea of multi-dimension rather than linear structure As defined in Section 3.2, knowledge maturity is not only determined by the knowledge state itself, but also by its context Together with KFSM introduced in Section 3.1, the KMM will serve as a base for the knowledge value chain 3.2 Knowledge value chain The concept of value chain was described and popularised by Porter (1996) as a value-adding process in which an organisation might engage Based on this understanding, more researchers have continued to improve the knowledge value chain (KVC) with their own emphasis Holsapple and Singh (2001) introduced a knowledge chain model comprising five primary activities that an organisation’s knowledge processors perform in manipulating knowledge resources, with four secondary activities that support and guide their performance King and Ko (2001) proposed an information/knowledge value chain which is based on three important levels at which value enhancing activities are conducted Eustace (2003) developed it as a model that integrates different perspectives from various interest groups Carlucci et al (2004) modelled KVC as a series of stages of KM Wang and Ahmed (2005) developed a KVC which incorporates eight types of KM processes and five kinds of KM enablers Those outstanding researchers mainly organised the knowledge value chain by arraying different levels, describing the stages of an organisation’s activities, from acquiring knowledge to using it However, existing knowledge value chains are mainly descriptive This paper differs from former perspectives as it regards the knowledge value chain as a sequential flow of knowledge in which knowledge value increases, and thus aims at proposing a model based on the knowledge value chain to measure knowledge value and survey the mutual impact between knowledge and 964 Y Xu and A Bernard product It is in an explicit form to describe the knowledge evolution process By applying KVC in knowledge activities, critical nodes of the evolution process could be revealed and different evolution ‘paths’ could be compared to achieve an optimised solution This paper characterises KVC with KFSM and KMM, and Figure shows a KVC ‘s0 ! s1 ! s2 ‘which characterises the knowledge evolution process in a 3-dimension super-cube By instantiating the elements of the three axes with values, an example is set up to illustrate the KVC Given s0: Results of marketing investigation s1: Production plan s2: Selling plan o10 ¼ technician, o20 ¼ ordered, o30 ¼ information gathering o11 ¼ engineer, o21 ¼ organised, o31 ¼ conception o12 ¼ manager, o22 ¼ usable, o32 ¼ sales The KVC ‘s0 ! s1!s2 ‘ represents the following process: (1) In the information gathering stage, data analysts (technician) process the data coming from investigations by transformation, filtration, calculation, etc., then the knowledge gathered in the market is transferred to the expected selling quantity of cars (2) Based on the results of marketing investigation s0, the production schedule s1 is proposed, e.g., in the next season, 10,000 cars with a 1.6L engine and 20,000 cars with a 2.0L engine will be produced (3) The selling plan s2 is established by the manager according to previous results Figure A KVC based on KFSM and KMM In this KVC, the selling plan s2 is the final state, as the aim of the investigation is to help optimise the selling plan It should be noted that the selling plan is influenced by a production schedule as well, because the actual production capability of an enterprise is limited so the ideal selling plan can not be obtained Thus, s1 is a critical node of the KVC It is also possible that the KVC might have different critical nodes which form different ‘paths’ Furthermore, KVC could be split into several sub-KVC according to practical needs In a KVC, a knowledge state si is considered to be more mature than sj when its maturity states mi is ‘closer’ to the final maturity state mn, namely its distance jmi – mnj is smaller Especially, mn is regarded as completely mature This distance is related to the knowledge value and will be discussed in the following section Knowledge evaluation based on knowledge value chain Based on the KVC introduced above, we have a brief idea about knowledge value: knowledge value increases during its evolution process and the value is greater when the knowledge is ‘closer’ to the end (target) The notion of knowledge value is defined as follows: Definition Knowledge value represents the knowledge evolution degree in a KVC, noted as V(si) Generally, V(s0) ¼ 0, V(sn) ¼ Unlike length with ‘meter’, information with ‘bit’, or price with ‘dollar’, there is still not a specific unit to assess knowledge value, because people have not yet found a metric by which knowledge value can be added physically For this reason, this paper does not propose a unit either, but uses the percentage to measure the degree of knowledge evolution Given an initial state s0 and a final state sn to arrive, there is a distance jsn7s0j, and knowledge value is the power to ‘travel the path’ (we may take as a metaphor petrol that is providing power to a car when it is travelling on the road) As there is no physical unit measuring knowledge value, the usage of percentages is suitable A simple hypothesis of measuring knowledge value using percentage is that: if knowledge k1 can ‘make the travel further’ than k2, then k1 has a higher value than k2, e.g., if k1 can achieve 70% of the path jsn7s0j and k2 can achieve 50%, then k1 has a higher value As proposed above, KVC is a framework to characterise knowledge evolution in a given context, and therefore it can serve as a base to describe and measure knowledge value By associating knowledge International Journal of Computer Integrated Manufacturing evolution with knowledge maturity, knowledge value could be measured by knowledge maturity, in other words, when knowledge maturity states change from mi to miþ1, knowledge values change from V(Si) to V(Siþ1) The procedure of calculating the difference of two maturity states, namely jmi7mjj, is as follows: (1) Supposing n indicators are considered in calculating jmi7mjj As an example, let us take n ¼ 3, and the three indicators chosen are financial cost f, the consumed time t and the risk r To unify the three indicators, the calculating formulae are as follows: (a) The financial cost f ¼ [(money spent in this step)/(the total cost of the lifecycle)]6 100% (b) The consumed time t ¼ [(time spent in this step)/(the total time of the lifecycle)]6100% (c) The risk r ¼ (17abg) 100%, where a, b and g are calculated as follows: (i) Suppose x1, x2, x3 are the incremental steps of the elements in PA, KS and PL respectively, and the ‘incremental step’ means the number of step-by-step changes E.g the incremental step of ‘technician to manager’ is 2; the incremental step of ‘usable to organised’ is 1; the incremental step of ‘design to service’ is (ii) Suppose the success rates of one leaping degree of the elements in PA, KS and PL are y1, y2, y3 respectively The success rates indicate that risks happen in states changes As we know, when knowledge states are changing, the unexpected may happen Different reasons may cause yi 1, such as: (1) For PA: incomplete efficiency of execution, different understandings, etc (2) For KS: loss of data, calculation errors, semantic differences, etc (3) For PL: deviation during the product lifecycle evolution, e.g incomplete realisation of the design (iii) a ¼ yx1 ; b ¼ yx2 ; g ¼ yx3 : (2) The difference between the two maturity states is the weighted mean of f, t and r: 1036 Table G.Y Kim et al Definition of elements in the resource sub-schema Element name RItem Figure Definition ResourceType Size Cost MHEOperationPlan MaxStackHeight ResourceQty Resource type (box, rack, MHE, etc.) Resource dimensions (width, depth, height) Fixed, FPM, labour cost data Material handling device Stack height (number) Total number of used resources Plant sub-schema PLM integrator The first module in DFW is PI The main role of PI is the exchange of P3R information across the modules of DFW as well as PLM systems Therefore, PI can extract P3R information from diverse legacy systems in a company, such as PDM and MPM PI has a function that converts P3R information to a standard format based on ISS Subsequently, based on the converted information, it is possible to import information into other PLM systems and upload and download document files PI consists of a PLM adapter and a digital factory adapter The digital factory adapter manages the standard format based on ISS The PLM adapter extracts P3R information and should be developed individually for each information management system in PLM because each commercial system has a different development environment and method for information representation Figure shows the concept of PI Layout builder A new concept for layout design and generation is proposed in this article because it is not easy to manage the factory model and factory resource models in current PLM systems 1037 International Journal of Computer Integrated Manufacturing Table Definition of elements in the plant sub-schema Element Name Station Figure Definition PlID PlName Version AreaType Unit ChildStationRelation ParentStationRelation StationAreaInfo FactoryObject Archive Property Description Station Id Station name Station revision version Station type (storage, dock, etc.) Used unit Child station Id Parent station Id Representing coordinate information Factory object info (wall, door, column, etc.) Attribute of geometry file Station property Station description P3R relation sub-schema The plant layout can be classified by area and factory object with locational information The factory has various areas such as storage areas, stations, receiving areas, shipping areas, etc The plant layout including the storage and receiving areas can be divided into smaller areas Each area has factory objects including walls, columns, roofs, trusses, machines and equipment Area and factory objects each have locational information A plant layout can be a combination of areas, manufacturing resources, and 1038 Table G.Y Kim et al Definition of elements in the P3R relation sub-schema Element name P3RModel P3RID Process Product Resource Plant PEngPlanning ReEngPlanning BaseUnitInfo WorkDayPerYear WorkDayPerWeek ProductionVolume ShiftsPerDay HoursPerShift BaseInfo Figure Definition P3R relation ID P3R relation base information Assigned product information Assigned resource information Assigned plant information Product engineering plan information Resource engineering plan information Unit information (year, month, day, etc.) Manufacturing planning information The concept of PI factory objects that are assigned to various areas Areas and objects can be managed by locational information In this study, using the basic concepts discussed earlier, LB is developed The function of LB is factory layout design using ISS and the respective physical model files that are managed in PLM systems In this module, first, each factory object is imported from the corresponding PLM system Second, engineers can select each object, which will be addressed to layout, and manually set the locational and rotational information for that object With the selection of each object, engineering can address various objects ranging from industrial objects to architecture objects, e.g machines, robots, walls, roofs After every factory object is addressed, the integrated layout can be exported to an XML file This file can be loaded up to a commercial CAD program for plant design In this article, FactoryCAD from Siemens PLM Software is used to import the generated layout design Simulation builder The final module in DFW is SB Commercial simulation software is used for verifying logistical or process planning in the manufacturing engineering area But, it takes a lot of time and effort to build a simulation model, i.e gathering and inputting data A discrete event simulation model for manufacturing flow analysis consists of products, resources, and plants or workstations based on processes This means that a simulation model has a process-centred data structure Hence, the concept of process-centred data structures is similar to that of P3R relations The P3R relation sub-schema can be used in the simulation model Thus, based on these concepts, SB is developed for automatically building a simulation model for manufacturing flow analysis through managed P3R information Figure describes the concept of SB Through this module, engineers can generate a International Journal of Computer Integrated Manufacturing Figure 1039 The concept of SB simulation model that can be imported to commercial simulation software, FactoryFlow from Siemens PLM Software, by defining basic simulation information such as the production volume, production schedule, and standard activities for logistics, and generating part routing information from the P3R relation subschema Also, engineers can generate the file for specifying coordination information of each workpoint This is required for the target commercial simulation software that is used in this article Implementation of DFW In this study, modules of DFW have been developed: PI, LB and SB DFW was developed on the NET Framework using the Cþ programming language It has been developed using APIs and a development toolkit both of which are provided by commercial PLM systems In this study, PLM adapters for SmartTeam of Dassault Systems are developed Then, LB and SB for commercial CAD systems and simulation software are developed Figure 10 shows the main user interface of the PI of DFW, as developed in this study The two listboxes on the left of the figure show project-related information and detailed data on projects The upper list-box shows project data, and the lower list-box shows P3R information that is related to the project selected in the upper list-box Thus, an engineer and user can check current and previous project data using these modules The five tree-views located at the middle and right sides show the product, process, resource, plant and relations pertaining to the selected project data With the structural information displayed in the tree-view, an engineer can check the product, process, resource, plant and relations in P3R information The engineer and user can extract P3R information using PI The two buttons located in the bottom area are for the other two modules of DFW, viz SB and LB The user interface of SB is illustrated in Figure 11 It consists of three buttons and three list views The three buttons are for creating the basic model for simulation software and inserting data in the created model The last button is for creating the point of work, such as machines, storages based on extracted information The interface of LB developed in this research consists of a list box for displaying factory areas, object-related information and functions for editing, i.e locational and rotational information on areas and objects Thus, an engineer can survey and select areas and objects through displays in the list box The 1040 G.Y Kim et al Figure 10 The PI of DFW Figure 11 The SB of DFW engineer can edit locational and rotational information on the selected area or object Figure 12 shows the interface of LB Figure 12 The LB of DFW Case studies In this study, two cases that include engineering analysis and logistical planning have been considered 1041 International Journal of Computer Integrated Manufacturing with regard to DFW In the first case, the products, processes, resources and plants are managed by SmarTeam, which is a commercial PDM system of Dasaults Systems Engineering analysis including the simulation of the ‘Desk’ manufacturing process in an assembly shop is performed The second case is for simulation in the press shops of a Korean automobile manufacturer It is possible to obtain quantitative data from this real instance of application ‘Desk’ production P3R and planning data For product manufacture, basic data on production planning are necessary This article considers the production volume of time unit, production volume, material handling time-unit, days per year, days per week, shifts per day and hours per shift as the product planning data Through this production planning, Table Product BOM data Part Id PRD00001 SUB00003 SUB00001 PR00001 PR00002 PR00003 SUB00002 PR00004 PR00005 PR00006 PR00007 PR00008 Figure 13 engineering analysis is performed using DFW ‘Desk’ is the example product used in this article It is composed of two panels, four legs and supporters for each panel These parts are assembled by washers and bolts Table shows the BOM structure used in this article The number of times a part is used in the desk is defined in Table The process data consist of the ‘Assembly leg’ and ‘Assembly sub-panel’ processes The process-planning data comprise of operations and units of work The manufacturing process of Desk is composed of four operations: ‘Operaton01’, ‘Operaton02’ and ‘Operaton03’ ‘Operaton01’ can be divided into more detailed work Hence, in this study, ‘Unit work’ is defined for representing detailed work The manufacturing process planning data used in this article are described in Figure 13 There are many manufacturing resources for the production of a product, such as racks, boxes Figure Product name Part name Quantity/product Desk Sub-assembly Main frame Main panel Leg 5-mm bolt Sub-frame Sub-panel Washer 3-mm bolt Sub-panel supporter Brace Process planning data 1 16 1 12 1042 G.Y Kim et al 14 shows the specification of the manufacturing resources used in this article regarding racks and boxes for part storage The plant is composed of Figure 14 Logistical operation planning data Figure 15 P3R information managed by SmarTeam receiving areas, storage areas, stations and shipping areas Logistical workers unload sub-assemblies and parts purchased from various suppliers in the International Journal of Computer Integrated Manufacturing ‘Receiving area’ The ‘Central store’ stores stocks of parts and sub-assemblies purchased from suppliers based on the safety stock policy In the case of ‘Station01’, ‘Station02’, ‘Station03’ and ‘Station04’, workers assemble the desk at each station following the process planning data Figure 14 shows the planning data for logistical operations, from origin to destination, and material flow using material handling equipment and resources, i.e forklifts and tractors In each movement, the quantity of transferred parts is as described in Figure 14 The parts and sub-assemblies that are unloaded at the ‘receiving area’ are transferred to the storage area by material handling equipment Then, based on the process planning data, parts and sub-assemblies are supplied from storage to each station by material 1043 handling equipment Finally, the final products are shipped from the ‘Shipping area’ In this article, the defined process planning and logistical planning data are verified using DFW Application of DFW Figure 15 shows a snapshot of SmarTeam for managing product, process, resource and plant information (Dassault Systemes 2008) P3R information including process and logistical operation planning data is managed in PDM, as illustrated in Figure 15 The first step of integrated engineering analysis is the execution of PI to collect product, process, resource and plant information from SmarTeam and other systems Then, PI generates the informational structure based on ISS With the information extracted by PI, engineering analysis can be executed Figure 16 shows an example of the result of the execution of LB using FactoryCAD and Teamcenter Visualisation from Siemens PLM Software Areas and objects can be handled by locational information using LB Thus, it is possible to edit and generate the plant layout design In this article, SB is applied to validate logistical operation planning data With the simulation model generated by SB, an engineer can execute simulations without data gathering and input Figure 17 shows the result of the execution of the simulation model generated by SB using FactoryCAD/FLOW from Siemens PLM Software Automotive press shops The proposed DFW was applied to automotive press shops in a Korean company as the other case study An automotive press shop produces various kinds of panel for the chassis For this case, all related Figure 16 Example of the execution of LB in DFW Figure 17 Example of simulation results through commercial software under DFW 1044 Table G.Y Kim et al The P3R information of a case study, the automotive press shop Products Product Part P1 and P2 P3 P4 P5 P6 Table Processes 106 parts (mixed) 76 parts 17 parts 37 parts 51 parts Resources Manufacturing Processes Non-manufacturing processes MHE Container Blanking Line, Stamping Line A-1, A-2, B, C Receiving dock, coil storage, AS/RS, part (rack) storage, ship dock Four kinds of fork lifts, four kinds of crane, conveyor 59 kinds of racks The quantitative effects of case study In manual material flow analysis (man-hours) DFW analysis (man-hours) Savings (%) 10 20 30 10 20 30 0 40 40 80 20 20 40 50 50 50.0 100 25 100 225 0 96 100 100 98.2 100 50 100 250 585 100 50 152 186 96 50 39.2 68.2 Requirements analysis Define problems in a project Define the objectives and scope of simulation Sub-total Data preparation Collect data for simulation Analyse data for simulation Sub-total Simulation modelling Design the simulation model Implement the simulation model Verify and validate the simulation model Sub-total Alternatives’ simulations and analyses Define alternatives Model and simulate alternatives Analyse results and final decisions Sub-total Total information on products, processes, resources and plants is also managed by SmarTeam Table shows the composition of P3R information for automotive press shops DFW performed simulation using this P3R information Table shows the benefits from applying the proposed DFW to an automotive press shop Especially, these benefits focused on time savings (man-hours) regarding simulation As one can see in Table 6, the total time of simulation activities was reduced down to 68.2% because various amounts of time for data preparation and simulation modelling could be saved using the proposed DFW Table shows comparisons of the number of steps and computational times between general simulation and simulation by DFW Conclusions PLM is an innovative manufacturing paradigm for effectively integrating all manufacturing-engineering contents and business processes For implementing integrated and concurrent engineering under a PLM environment, DFW is proposed, which includes three modules and one integrated P3R schema: PI, SB, LB and ISS As a case study, an engineering analysis is concurrently undertaken for desk production using DFW, as developed in this study It is possible to concurrently and effectively perform diverse engineering analyses, including those in the area of manufacturing engineering The time and cost are reduced especially dramatically because the required information is managed in PLM systems DFW can not only reduce unnecessary effort for data exchange between mixed systems and data gathering through PI and ISS but also enable an efficient engineering environment for validating the plant layout and various planning data through LB and SB As a result, DFW can be a good method for achieving time and cost savings in various engineering activities In conclusion, further research on DFW is required to support production areas Especially, a method of extending ISS to consider production information is required With an extended schema, PLM information can be more easily exchanged throughout the product lifecycle International Journal of Computer Integrated Manufacturing Acknowledgement This work was supported by the Innovate Korea R&D program of Ministry of Knowledge Economy, Korea References Arndt, F.W., 2006 The digital factory planning and simulation of production in automotive industry In: J Braz, H Arau´jo, A Vieira, and B Encarnac¸a˜o, eds Informatics in control, automation and robotics I Dordrecht, The Netherlands: Springer, 27–29 Bracht, U and Masurat, T., 2005 The digital factory between vision and reality Computers in Industry, 56 (4), 325–333 Burkett, M., Kemmeter, J., and O’Marah, K., 2002 Product lifecycle management: what’s real now [online] Available from: http://www.sinexsolutions.com/Articles/AMR.pdf [Accessed December 2009] Choi, S.S., Yoon, T.H., and Noh, S.D., 2010 XML-based neutral file and PLM integrator for PPR information exchange between heterogeneous PLM systems International Journal of Computer Integrated Manufacturing, 23 (3), 216–228 Chryssolouris, G., et al., 2004 Towards the internet based supply chain management for the ship repair industry International Journal of Computer Integrated Manufacturing, 17 (1), 45–57 CIMdata, 2002 Product lifecycle management: empowering the future of business [online] Available from: http:// www.cimdata.com/publications/pdf/PLM_Definition_0210.pdf [Accessed 22 June 2010] CIMdata, 2006 Digital manufacturing in PLM environment [online] Available from: http://www.cimdata.com/publications/pdf/Delmia_Whitepaper.pdf [Accessed 22 June 2010] Dassault Systemes, 2008 ENOVIA SmarTeam Offering [online] Available from: http://www.3ds.com/products/ enovia/portfolio/enovia-v5 /enovia - smarteam/offering/ [Accessed 22 June 2010] Eastman, C and Augenbroe, G., 1998 Product modelling strategies for today and the future In: Proceeding of the Life-Cycle of Construction Innovations: CIB Working Conference, 3–5 June 1998 Stockholm, 191–207 Faro Technologies, 2006 Volvo: press line simulation based on LS 3D scanning [online] Available from: http:// www.faro.com /content.aspx? ct¼uk& content¼news& item¼575&tab¼3 [Accessed December 2009] Gamberi, M., Manzini, R., and Regattieri, A., 2009 An new approach for the automatic analysis and control of material handling systems: integrated layout flow analysis (ILFA) International Journal of Advanced Manufacturing Technology, 41, 156–167 Gunpinar, E and Han, S.H., 2008 Interfacing heterogeneous PDM systems using the PLM services Advanced Engineering Informatics, 22, 307–316 Iwata, K., et al., 1995 A modeling and simulation architecture for virtual manufacturing systems Annals of the CIRP, 44 (1), 379–383 Kim, B., et al., 2009 A layout- and data-driven generic simulation model for semiconductor Fabs IEEE Transactions on Semiconductor Manufacturing, 22 (2), 225–231 Kim, G.Y., et al., 2008 XML-based concurrent and integrated ergonomics analysis in PLM International Journal of Advanced Manufacturing Technology, 39, 1045–1060 1045 Kim, H., et al., 2006 A framework for sharing product information across enterprises International Journal of Advanced Manufacturing Technology, 27 (5–6), 610–618 Lau, H.C.W., et al., 2002 An innovative scheme for product and process design Journal of Materials Processing Technology, 123 (1), 85–92 Lee, S.H., Cho, H.B., and Jung, M.Y., 2000 A conceptual framework for the generation of simulation models from process plans and resource configuration International Journal of Production Research, 38 (4), 811–828 Legner, C and Vogel, T., 2007 Design principles for B2B services – an evaluation of two alternative service designs In: Proceeding of IEEE International Conference on Services Computing, – 13 July 2007, Salt Lake City, UT, 372–379 Makris, S., et al., 2008 On the information modeling for the electronic operation of supply chains: a maritime case study Robotics and Computer-Integrated Manufacturing, 24 (1), 140–149 Mills, A., 1998 Collaborative engineering and the internet Society of Manufacturing Engineers, Dearborn, MI Ming, X.G., et al., 2008 Collaborative process planning and manufacturing in product lifecycle management Computers in Industry, 59 (2–3), 154–166 Noh, S.D., et al., 2002a Using virtual manufacturing technologies for continuous verification of products, processes and resources in the manufacturing preparation of automotive companies In: The 35th CIRPInternational Seminar on Manufacturing Systems, 12–15 May 2002, Seoul, Korea, 245–252 Noh, S.D., Kim, D.Y., and Park, Y.J., 2002b Virtual manufacturing for an automotive company (III) – construction and operation of a virtual paint shop IE Interfaces, 15 (4), 356–363 Noh, S.D., Lee, C.H., and Hahn, H.S., 2001 Virtual manufacturing for an automotive company (II) – construction and operation of a virtual body shop IE Interfaces, 14 (2), 127–133 Object Management Group, 2000 Product data management enablers specification version 1.3 [online] Available from: http://www.omg.org/cgi-bin/doc?formal/2000–11-11 [Accessed December 2009] ProSTEP iViP, 2006 OMG PLM services – a leap forward in product data communication Available from: http:// www.prostep.org/en/standard-info/omg-plm-services html [Accessed December 2009] Park, T.K., et al., 2005 Virtual manufacturing for an automotive company (V) – parametric modeling of the digital general assembly shop using object-oriented methods IE Interfaces, 18 (1), 94–103 Rachuri, S., et al., 2008 Information sharing and exchange in the context of product lifecycle management: role of standards Computer-Aided Design, 40, 789–800 Sapuan, S.M., Osman, M.R., and Nukman, Y., 2006 State of the art of the concurrent engineering technique in the automotive industry Journal of Engineering Design, 17 (2), 143–157 Sly, D and Moorthy, S., 2001 Simulation data exchange (SDX) implementation and use In: Proceedings of the 2001 Winter Simulation Conference, 9–12 December 2001 Arlington, TX, 1473–1477 Wenzel, S., Jessen, U., and Bernhard, J., 2005 Classifications and conventions structure the handling of models within the digital factory Computers in Industry, 56 (4), 334–346 Yeh, S.C and Yoy, C.F., 2002 STEP-based data schema for implementing product data management system International Journal of Computer Integrated Manufacturing, 15 (1), 1–17 International Journal of Computer Integrated Manufacturing Vol 23, No 11, November 2010, 1046–1058 Characterisation of collaborative decision making processes Anne Seguya*, Daniel Noyesb and Philippe Clermontb a Universite´ de Bordeaux, IMS – UMR 5218 CNRS, Talence, France; bUniversite´ de Toulouse, LGP – ENIT/INPT, Tarbes, France (Received November 2009; final version received July 2010) This paper deals with the collaborative decision making induced or facilitated by information and communication technologies (ICTs) and their impact on decisional systems After presenting the problematic, we analyse the collaborative decision making and define the concepts related to the conditions and forms of collaborative work Then, we explain the mechanisms of collaborative decision making with the specifications and general conditions of collaboration using the modelling formalism of the GRAI method Each specification associated to the reorganisation of the decisional system caused by the collaboration is set to the notion of decision making centre Finally, we apply this approach to the e-maintenance field, strongly penetrated by the ICTs, where collaborations are usual We show that the identified specifications allow improvement of the definition and the management of collaboration in e-maintenance Keywords: collaborative decision making; ICTs (information and communication technologies); maintenance; e-maintenance Introduction This paper outlines the collaborative decision making supported by information and communication technologies (ICTs) We study the decision making mechanisms when collaboration between actors is needed in order to define collaboration modes and their creation conditions In the current economic context where companies must continuously improve their performance, while having less and less time to react and make choices, decisions: become increasingly complex, concern all decisional levels at the organisation, require more and more high volume of information and knowledge The use of ICTs tools such as personal computers, PDAs (Personal Digital Assistant), Internet and Intranet access , that allow to access and exchange of all type of information is one solution implemented to answer to these companies’ requirements Thanks to an easy access to more information and many communication channels and forums, the actors can collaborate, share and increase their knowledge and so, improve their decision making ICTs have strongly modified the companies functioning introducing new concepts such as e-service, collaborative work, sharing of knowledge and *Corresponding author Email: anne.seguy@u-bordeaux1.fr ISSN 0951-192X print/ISSN 1362-3052 online Ó 2010 Taylor & Francis DOI: 10.1080/0951192X.2010.506654 http://www.informaworld.com involving many changes in the organisation and the functioning of the underlying decisional systems The introduction of ICTs within an organisation offers the possibility for the actors to collaborate and communicate and it can produce redeployments of missions or powers, changes of the current balances , leading positive or negative effects In this context, we discuss new results on the mechanisms of creation and functioning of collaborative decision making with ICTs tools New characteristics about the creation, the functioning and the performance of collaboration in organisations are proposed in this paper in order to guarantee the progress of collaboration in an existing organisation Collaborative decision making mechanisms can occur in various contexts: design in concurrent engineering (Pol et al 2007), management of supply chains, collaborative manufacturing (Wang 2009), maintenance organisations In maintenance – in most cases – the interventions occur in an emergency context (failure, accident, production breakdown) involving rough constraints for the creation and the execution of collaboration A sharing of knowledge from different areas is often required to carry out maintenance interventions (computer and mechanic, for example) This pooling of knowledge often leads to collaborations We have chosen this maintenance area and the e-maintenance field to illustrate our developments although the corresponding results are International Journal of Computer Integrated Manufacturing generic and transposable to other situations such as new product development that require the collaboration of several skills The paper is divided in three main sections Section contains a short presentation of the studied mechanisms of collaborative decision making A literature review of existing works compared to our approach is also proposed In section 3, an analysis is carried out by an identification of the main characteristics of these collaboration mechanisms The purpose of this analysis is to help the progress of collaboration and to contribute to the idea that collaborations are beneficial to the organisation Then, in section 4, an application of our analysis of a collaborative situation is proposed in the e-maintenance field Finally, some conclusions and prospects of this work are expressed Collaborative decision making and ICTs In an organisation, ICTs can affect all organisational levels and change the environment of decision makers and actors by: an enrichment and improvement of information, skills and expertise, an expansion of action zones, the feasibility of remote work ICTs can help decision making by allowing actors an easier access to remote and relevant information and knowledge The creation of ad hoc collaboration is made possible by ICTs, in order to improve decision making and to act remotely on properly instrumented processes These changes can affect: the amount of information to consult and / or to treat, the volume of communications and exchanges, the relations between actors, the frequency of solicitations Before analysing in depth the impacts of ICTs, we are going to define the mechanisms of collaborative decision making 2.1 Definition of decision In the literature, there are several definitions of the term ‘decision’ For Rey-Debove and Rey (2004) and Office of Quebec (2008), the decision is ‘the action to decide, to judge, to adopt a final conclusion on a dispute point or to choose a solution in response to a problem’ Kast 1047 proposes a more precise definition of the term, synonymous with ‘choice between several existing options, each with different consequences, the choice being made according to specific criteria of selection’ (Kast 1993) The decision is often a complex activity for which ICTs may have different effects If we consider, for example, the decision making process defined by Simon (Simon 1960), ICTs can help: to search information on the problem to be solved, to design possible solutions, to evaluate different solutions and to choose among them, to control the implemented decision 2.2 Collaborative work and collaborative decision making Collaborative work can be considered as ‘an activity of a group of actors working together, with a common goal, sharing resources and communicating’ Several forms of collective works exist (Winer and Ray 1994, Lubich 1995, Kvan 2000): cooperation: a simple juxtaposition of individual and sporadic activities, without sharing an objective, collaboration: shared production and shared objectives, where each actor performs a part of the work with resources, benefits or risks sharing, codecision: a joint decision following the involvement of several actors with sharing resources and goals, and where each actor is involved in the decision making In our work, we consider the collaborative decision making as ‘the realisation of a set of activities by a group of actors working together and sharing a common objective and resources, an activity leading to a decision’ (Seguy 2008) Collaboration modes can be differentiated according to space criteria (local or remote collaboration) and time criteria (synchronous or asynchronous collaboration) (Schmidt 1990, Rodden 1991) In this article, we focused on the synchronous and remote collaboration, which could evolve to an asynchronous and remote collaboration in case of temporary unavailability of one of the actors 2.3 Decision’s modes Collaborative work can lead to different decision making situations according to the geographical 1048 A Seguy et al situation of the actors and to the collaborative conditions These situations depend on the actor’s decision power, the actor’s knowledge and the activity’s degree of complexity Three collaborative decision cases can be distinguished: local decision: the local actor keeps his decision making power After consulting remote actors or databases, he is the only one to decide, external decision: the local actor transfers the decision making power to a remote actor; the decision is not made by the local actor but by another remote actor, distant from the action site, multiactor decision: several actors (local and remote) work together and share their knowledge to make a joint decision The distinction between local decision and external decision is related to the fact that in the first case, the e-service contributions are just a way to strengthen the local actor decision, while in the second case, it is a true decision transfer to another actor with another decision frame These situations are detailed in section 3, with particular attention to the last situation ‘multiactor decision’ which requires special conditions for a correct implementation of the collaboration Before presenting the detailed multiactor decision, a short bibliographic part is presented to compare and position our approach in relation to existing works 2.4 Literature review In this paper, we propose an analysis of collaborative works and we specify the criteria for collaborative work Activities of collaborative decision making are complex, like presented in the last part This is the reason why, we think it is important to specify in detail the realisation conditions of collaborations in order to help collaborative processes and to guarantee the performance of the processes Before the presentation of our work, we have analysed the literature to compare existing works to our approach and to show the specificity and the contributions of our work We can notice here that concerning this matter, few works can be compared to ours However, some research fields and some works can still be presented here An important field of collective works is led in design, with the design of new products, the management of designer’s teams, the project management (Girard and Robin 2006, Zha and Du 2006, Chen et al 2008, Keeney 2009, Movahed-Khah et al 2010) In this field, some works are at the intersection with the knowledge management field and deal with the formalisation and the reuse of knowledge developed for a design project (Robin et al 2007) In this article, the authors present a modelling of collaborative knowledge in order to help the management of design project In this purpose, a modelling of collaborative process is proposed; the process is decomposed in several steps and also there is a description of the collaborative exchanges and their context The aim of this work is to specify all the elements characterising a collaborative process, knowledge included, in order to more specify and more manage the design activities Other field where collective works are studied and where there are management solutions of collective works is the supply chain management and the virtual enterprise (Mo et al 2003, Camarinha and Afsarmanesh 2007, Galasso and Thierry 2009) In Camarinha and Afsarmanesh (2007), the authors propose a modelling framework of a collaborative organisational network This model is positioned at a too macroscopic level, so that it is not operational for the success of collaborative work within organisations In the literature, there are some works on models of networks or of collaborative organisations, but no proposition about conditions to be met in order for collaboration to be conducted properly and be beneficial to the organisation The aim of collaboration is to help the organisation to improve its performance To this end, it seems necessary to propose an analysis of collaborative processes in order to define some characteristics ensuring that collaboration goes smoothly In our work, we have similar objectives to the ones of Robin (Robin et al 2007), related to the characteristics and specifications of collaborative processes The analysis proposed aims at formalising the different stages of development, progress and ending of a collaborative process in order to supervise and to improve the occurrences of collaboration within an organisation These characteristics can be used for reorganisation or daily management of each collaboration We place this work at an operational level of a decisional organisation Collaborative decision making analysis The mechanisms of collaborative decision making can be analysed from general terms to conditions of collaboration For this purpose, we take the modelling of the decisional system and the decision centre as physical support of the decision making in an organisation International Journal of Computer Integrated Manufacturing 3.1 Basic representation: decision centre The concept of a decision centre (DC) was defined in the GRAI method (Roboam and Pun 1989, Doumeingts et al 1998) to model the decisional system of an organisation (Chen et al 1997) We expose then this DC concept and some modifications that we brought there A DC is a ‘set of activities with the same horizon and same period, having to be executed following the same set of objectives given by a single decision frame’ (Doumeingts et al 1978) A DC can be associated to a time management in terms of (Roboam 1993): horizon: time interval considered to construct the decision, period: validity duration of the decision A DC is composed of a decision frame defined by a higher hierarchical DC, the objectives to achieve and the constraints to respect (Doumeingts et al 1978) Then, a DC receives information of follow-up and coordination of follow-up from upstream and downstream DCs A DC can also receive information of synchronisation from a DC belonging to the same decisional level These definitions are completed by Doumeingts and Marcotte (Doumeingts et al 1995) with the integration of decision variables and selection criteria available for the DC Using GRAI formalism, we have extended the modelling principles of a decision centre (DC) (Seguy et al 2009) in order to lead to the formalism proposed in Figure Some new labels are adopted: a mission frame: all the characteristics defining the DC mission, a decision frame: all the resources associated to the DC In this way, a DC is made up of: Figure Modelling of a decision centre 1049 a mission frame for bringing together the items that the DC receives from the upstream DC in terms of objectives, constraints, decision variables and selection criteria, a decision frame composed of all tangible and intangible resources (informational and human) whose decision centre has to achieve its mission, results that represent the production of the decision centre to the downstream decision centre(s), a downstream follow-up which is the information from the downstream decision centre(s), an upstream follow-up which is the feedback given to the upstream decision centre(s), the time management with the horizon and period (H/P) which depends on the decisional level associated to the DC The instrumentation of a DC with ICTs tools can lead changes in its characteristics: reinforcement of the decision frame: contributions of remote information, knowledge , increase of processing capacity , improvement of upstream and downstream follow-up and communication, modifications, often not scheduled, of the mission frame with changes of relationships with surrounding DC (upstream, downstream or even of the same decisional level) 3.2 Reorganisation principle An analysis of the collaborative decision making is proposed using this representation of a DC We consider that the collaboration of DC leads to bring together the DCs and so, to create a collaborative decision centre (CDC) according to the principle presented in Figure In Figure 2, the reorganisation principle of DCs is presented when there is DCs collaboration via ICTs tools, in case of a multiactor decision (see section 2.3) The grouping of DCs (DC1 and DC2) involved in the collaboration led to the creation of a collaborative decision centre (CDC) which decisional characteristics (mission frame, decision frame, ) are deducted from those of two grouped DCs and mission dedicated to the collaboration established by the original DC (DCO) This reorganisation induces some changes in the initial decisional system, which can be more or less important Before the collaboration, each DC (DC1 and DC2) has its own decisional characteristics (mission frame, decision frame) The mission frame has often a common part with a higher hierarchical DC 1050 A Seguy et al nevertheless related and cannot be understood separately, as it is shown in the approach developed in the following paragraphs In the GRAI method, a DC is associated to a decisional level (same horizon and same period) and carries out activities having the same horizon and period The CDC must also respect this GRAI rule and has a horizon and a period according to the mission of collaboration and to the DCs which collaborate 3.3 Collaborative decision making specifications To study thoroughly the formation of a CDC, different configurations of collaboration have been explored and we have identified the key characteristics of collaboration of DCs conditioning the creation and the life of a CDC Figure Reorganisation principles After the grouping, the created CDC has its own characteristics and decision rules which must reflect the mission to achieve, the consistency of the CDC working and its integration into the initial decisional system The connection of DCs corresponds to a set of relationships relating the characteristics of the initial DCs (DC1 and DC2) and the mission dedicated to the collaboration (MO) to the characteristics of CDC resulting from the collaboration The CDC characteristics can be formalised with the following mathematical relationships: 3.3.1 Global frame The prerequisite for any collaboration is the definition of the global frame of this collaboration The creation of a CDC must be authorised and approved by an upstream DC or a higher hierarchical DC For every DC of the lower level, the higher hierarchical DC must define: the grouping methods: communication channel, protocols , the missions that can be processed by a CDC, the allowed freedom degrees to facilitate the decision making, the DCs with which it is possible to collaborate and their characteristics, the validity of the CDC duration: limited in response to a special need or sustainable in response to a durable need the mission frame M0 ¼ f(MO,M1,M2) divided into: O0 ¼ f1 (OO, O1, O2) C0 ¼ f2 (CO, C1, C2) DV0 ¼ f3 (DVO, DV1, DV2) SC ¼ f4 (SCO, SC1, SC2) the decision frame D0 ¼ g(D1,D2) divided into: K0 ¼ g1 (K1, K2) Tool0 ¼ g2 (Tool1, Tool2) Mat0 ¼ g3 (Mat1, Mat2) the results: R0 ¼ h(RO,R1,R2) the follow-up: upstream follow-up: F0up ¼ jðFUpO ; FUp1 ; FUp2 Þ downstream follow-up: F0Down ¼ kðFDownO ; FDown1 ; FDown2 Þ time management with the horizon/period: horizon: H0 ¼ fðHO ; H1 ; H2 Þ period: P0 ¼ f(PO, P1,P2) a one-time activity whose occurrence date can be expected or unexpected, a periodic activity requiring regular collaboration These decisional characteristics have been defined independently as proposed in the GRAI method, the basis of our approach These characteristics are The creation of a CDC, while within the general frame, can be planned or unplanned and in this case unexpected This authorisation to collaborate may be: decided by a higher hierarchical DC to improve performance of lower DCs, induced by strategic decisions of the management to change the engagement and the management of human resources In addition, this authorisation may concern: [...]... jobs(batches ), (i ¼ 1, 2, , N , where i is the index of all jobs, M: total number of stages, (j ¼ 1, 2, , M ), where j is the index of all stages, Mj: total number of machines in the jth stage, (m ¼ 1, 2, , Mj ), where m is the index of machines in the jth stage, K: total number of time units to be considered, (k ¼ 1, 2, , K ), where k is the index of all time units, tij: the processing time of job i... 01 ), http://zone.ni.com/devzone/ cda/tut/p/id/6183 Pappas, M ., Karabatsou, V ., Mavrikios, D ., and Chryssolouris, G ., 2006 Development of a web-based collaboration platform for manufacturing product and process design evaluation using virtual reality techniques International Journal of Computer Integrated Manufacturing, 19 (8 ), 805–814 Parkinson, M.B ., Reed, M.P ., Kokkolaras, M.P ., and Panos, Y ., 2007... Integrated Manufacturing, 18 (7 ), 601–609 Scapin, D.L and Bastien, J.M.C ., 1997 Ergonomic criteria for evaluating the ergonomic quality of interactive systems Behaviour and Information Technology, 16 (4–5 ), 220–231 Vigano, G ., Mottura, S ., Greci, L ., Sacco, M ., and Boer, C.R ., 2004 Virtual reality as a support tool in the shoe life cycle International Journal of Computer Integrated Manufacturing, 17 (7 ), 653–660... International Journal of Management Science, 31 (6 ), 451–457 Guerra-Zubiaga, D.A and Young, R.I.M ., 2008 Design of a manufacturing knowledge model International Journal of Computer Integrated Manufacturing, 21 (5 ), 526–539 Hey, J ., 2004 The Data, Information, Knowledge, Wisdom Chain: The Metaphorical link Intergovernmental Oceanographic Commission December 200 4, Paris, France Holsapple, C.W and Singh, M ., 2001... Management, 28 (4 ), 246–258 International Journal of Computer Integrated Manufacturing Vol 2 3, No 1 1, November 201 0, 968–978 Computer integrated reconfigurable experimental platform for ergonomic study of vehicle body design Z.M Bi* Department of Engineering, Indiana University Purdue University Fort Wayne, Fort Wayne, IN 4680 5, U.S.A (Received 4 January 2010; final version received 18 May 2010) A computer integrated. .. Bernard, A and Xu, Y ., 2009 An integrated knowledge reference system for product development CIRP Annals, 58 (1 ), 119–122 Birkinshaw, J and Sheehan, T ., 2002 Managing the knowledge life cycle MIT Sloan Management Review, 4 (1 ), 75– 83 International Journal of Computer Integrated Manufacturing Blanco, E ., Grebici, K ., and Rieu, D ., 2007 A unified framework to manage information maturity in design process International. .. Z.M ., Lang, S.Y.T ., Orban, P ., and Verner, M ., 2007 An integrated design toolbox for tripod-based parallel kinematic machines ASME Journal of Mechanical Design, 129 (8 ), 799–807 978 Z.M Bi ETC, 2009 Marker placement guide, Entertainment Technology Center, Carnegie Mellon University, www.etc.cmu edu/projects/mastermotion/Documents/markerPlacement Guide.doc Jun, Y ., Liu, J ., Ning, R ., and Zhang, Y ., 2005... assembly process planning International Journal of Computer Integrated Manufacturing, 18 (6 ), 442–451 Kobe, G ., 1995 Reconfigurable interior buck Automotive Industries, 175 (2 ), 180–183 Lee, M.W ., 1999 High touch - A new strategy for ergonomic product design Advances in Occupational Ergonomics and Safety, 3, 3–8 Mavrikios, D ., Karabatsou, V ., Fragos, D ., and Chryssolouris, G ., 2006 A prototype virtual... Liebowitz, J and Wright, K ., 1999 Does measuring knowledge make ‘cents’? Expert Systems with Applications, 17 (2 ), 99–103 Liu, S.C ., Olfman, L ., and Ryan, T ., 2005 Knowledge management system success: empirical assessment of a theoretical model International Journal of Knowledge Management, 1 (2 ), 68–87 Ma, Y and Fuh, J.Y.H ., 2008 Product lifecycle modelling, analysis and management Computers in Industry,... Implementation of new Product Development System in a Winery The Electronic Journal of Knowledge Management, 2 (1 ), 77–90 Xu, Y and Bernard, A ., 2009 Knowledge organization through statistical computation: A new approach Knowledge Organization, 36 (4 ), 227–239 Zhao, Y ., Tang, L.C.M ., Darlington, M.J ., Austin, S.A ., and Culley, S.J ., 2008 High value information in engineering organisations International Journal of