Peter Nielsen

Grzegorz Bocewicz
email: bocewicz@ie.tu.koszalin.pl

Simulation study of the volatility of order sizes and their impact on the stability of a simple manufacturing environment

Keywords: Simulation study, order sizes, discrete event

Customer ordering behaviorin the form of size and timing of orders are critical for manufacturing systems such as make-to-order or assemble-to-order systems. To be competitive a Make-to-order manufacture must be flexible, both with regards to volume and product mix. A particularly critical parameter in determining the flexibility needs in the case of responsive manufacturing environments is the distribution of order sizes. In this paper several methods for describing the volatility of order sizes are presented. A discrete event simulation of a single server batch manufacturing system is subsequently conducted using various distributions of order sizes. The aim is to investigate 1) which measures for order size volatility best relates to the volatility of the output rate from the manufacturing system 2) how does the output stability from the system deteriorate as order sizes become more volatile.

Introduction

The performance of planning approaches within Manufacturing Planning and Control to a large extend depends on the planning foundation [START_REF] Nielsen | Analyzing and evaluating product demand interdependencies[END_REF]. This foundation is for the purpose of this paper be limited to a simple model consisting of external demand / supply conditions, internal structure (planning approach and customer order decoupling points, Bill-of-Material/Bill-of-Resources structure and Product family structure). The internal structure should match the external conditions and enable an effective and efficient transformation of input from suppliers (materials and resources) to matching customer requirements [START_REF] Vollmann | Manufacturing Planning and Control for Supply Chain Management[END_REF]. For simplicity's sake this paper will focus on the orders arriving to the systems and more specifically to the size of orders rather than the timing of their arrival. The timing of orders rightfully falls in the domain of demand forecasting modeling, where this paper focuses on the response to the order sizes themselves. The considered system will be a simple single server system responding directly to customer orders. This system could correspond to a simple make-to-order manufacturing system. This paper has three main contributions. First, a method for evaluating the variation of order sizes is discussed. Second, a discrete event simulation study of a single server system is conducted to investigate the impact of order size volatility on a make-to-order system. Third, various measures for describing order size volatility are compared to the stability of the output from the manufacturing system to identify which order size volatility measure best predicts the volatility of the output. The remainder of the paper is structured as follows. First, a brief literature review is presented. Second, a new method for evaluating order size volatility is presented. Following this a simulation study of the impact of order size volatility on the output is presented. This is followed by a section discussing the experimental results before finally conclusions and further research is presented.

Literature review

Orders form the foundation for demand. However, they typically act in an unpredictable manner [START_REF] Nielsen | Analyzing and evaluating product demand interdependencies[END_REF], and are as a consequence typically aggregated to give stability and to serve as a planning input [START_REF] Silver | Inventory Management and Production Planning and Scheduling[END_REF]. Wijngaard [START_REF] Wijngaard | On Aggregation in Production Planning[END_REF] identify four normal dimensions for aggregation: Types of products, production stages, capacities and time. Of these the types of products and time are the two typical used dimensions when aggregating demand information, as seen in e.g. Vollmann et al. [START_REF] Vollmann | Manufacturing Planning and Control for Supply Chain Management[END_REF]. Especially the time dimension is often used so that orders are aggregated over time in a way that ensures that the aggregated demand information can be translated into allocations in the internal manufacturing system structure [START_REF] Vollmann | Manufacturing Planning and Control for Supply Chain Management[END_REF]. The further up-stream the customer order decoupling point is placed, the more a company will be reacting directly to the customer ordering information as structured in the customer order [START_REF] Berry | Linking Systems to Strategy[END_REF], [START_REF] Olhager | Strategic positioning of the order penetration point[END_REF]. This also means that the planning approach is adapted according to the customer ordering decoupling point [START_REF] Olhager | Beyond Manufacturing Resource Planning (MRPII): Advanced models and methods for production planning, chap[END_REF] and that impact of variation in order sizes will depend on the context. The consequence is that while it may be possible to allocate resources, materials and balance manufacturing lines pre-ante based on aggregate forecasts of demand translated into demand rates for individual products, the system it-self will in fact be reacting to a customer ordering behavior that may differ significantly from the assumptions. So while aggregate demand may be stable [START_REF] Zotteri | The impact of aggregation level on forecasting performance[END_REF] and comply with standard assumptions such as stationary demand rates [START_REF] Brander | Lot sizes in a capacity constrained facility-a simulation study of stationary stochastic demand[END_REF] the output may in fact be unstable or dependent. Because of this, it is relevant to investigate the behavior of customer demand in the form of the order sizes and the impact on a simple manufacturing system. This also has a number of further implications for e.g. line balancing that is highly sensitive to the distribution of orders [START_REF] Nicholas | Competitive Manufacturing Management: Continuous Improvement. Lean Production and Customer-focused Quality[END_REF]. In the case of line balancing it will be of significant value to have not just a clear picture of overall volumes and mix of orders, but also have a clear idea of the size of orders per product received. The more volatile the order sizes, the more difficult line balancing becomes [START_REF] Nicholas | Competitive Manufacturing Management: Continuous Improvement. Lean Production and Customer-focused Quality[END_REF]. For these reasons it is critical to have a clear picture of the volatility of order sizes faced by the company and how the manufacturing system reacts to these. This paper focuses on the second point.

Simulation study of the volatility of order sizes and their impact on the stability of a simple manufacturing environment 3

Method for evaluating order size volatility

An often used term to describe the volatility of a given distribution is the Coefficient of Variance (CV), the standard deviation of the observed values, divided by the mean of the observed values. CV can be adapted to describe the volatility of order sizes by = , where σ Q is the standard deviation of order sizes and µ Q is the mean order size. This term is particular suitable if the observations are i.i.d. from e.g. the normal distribution. However, since the likelihood of normality is limited we use another term to establish the volatility of order sizes [START_REF] Nielsen | Towards an analysis methodology for identifying root causes of poor delivery performance[END_REF]. The measure of variation is established as the following. First, sort the orders for the period by size. Second, calculate the accumulated demand for the sorted set of orders, i.e. the increase to total demand by adding the next order. Third, calculate the relative amount of total demand for all percentiles of orders. In the ideal situation of constant order sizes one order will proportionally increase the accumulated demand as much as any other orders. This means that e.g. 25% of the orders should amount to precisely 25% of the total demand. The higher the CV, the more one would expect the percentiles to deviate. Figure 1 illustrates the proposed measure of order size stability, with the three percentiles 90%, 95% and 98% illustrated on the bottom graph with respectively a green, blue and red line. The order size distributions shown in figure 1 are 2,000 observations from a normal distribution, with a mean of 1000 and standard deviation of 300 (CV=0.3) and a uniform distribution with minimum 200 and a maximum of 1800 (approx. CV=0.46). The bottom graphs in figure 2 illustrate the concept of variation of order size, while the histograms illustrate the distributions. The curved graphs are the accumulated demand with orders sorted by order size. The lower the corresponding value is for a given percentile, the further from the assumption of constant order size is from actual distribution and the higher the CV will be. As such the percentiles become substitute measures describing the relative skewness of a given distribution. The more the distribution deviates from symmetry the lower values will be achieved for a given percentile.

Simulation of impact of volatility on planning environment stability

The experimental setup has three aspects, the various data sets of order sizes used, the simulated manufacturing system and the measures used to describe the input and output. The considered manufacturing system consists of a single server processing orders from an infinite queue taking orders FIFO. The server processes the orders as batches and the orders exit the system as a batch when all the order has been processed. The server has the following properties:

• Process times dependent on order size, and stem from a i.i.d. normal distribution, with a situational mean of * and standard deviation * , where Q j is the order size of order j, and PrT is the average processing time per unit, and σ PrT is the standard deviation per processed unit. Processing times are i.i.d.

• Setup time is constant and independent of order size and set to * .

• Orders are batch processed and no units of the order are released until the whole order is completed. • PrT is in the simulation set to 1 min. /unit and is set to 0.3*PrT. Order sizes are drawn from the following distributions (and rounded to integers):

• sets from normal distributions with CV's 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, (labeled respectively norm_set_1:6) • sets from uniform distributions with limits 18:22, 15:25, 12:28, 9:31, 6:34 and 3:37 (labeled respectively uni_set_1:6) • 1 set from a an exponential distribution (exp_set) • 1 set with constant order size (cst_set) All 14 order size distributions have a mean (µ Q ) of 20 units.

To evaluate the stability of the order sizes for the various distributions the order size distribution scheme proposed in the previous section is used in combination with the CV Q . The order distribution scheme allows for the calculation of any number of Simulation study of the volatility of order sizes and their impact on the stability of a simple manufacturing environment 5

percentiles of order sizes and their contribution to the total processed demand. In this case the 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 98% and 99%-percentiles were chosen. To evaluate the stability of the manufacturing system under investigation the following measures are proposed:

, = , ,
, where σ V,i is the standard deviation of total demand processed (i.e.

volume in units outputted) in a time interval of i and µ V,i is the mean demand processed in time interval i.

, = , ,
, where σ N,i is the standard deviation of the number of orders processed in a time interval of length i and µ N,i is the mean number of orders processed in a time interval of length i.

Experimental results

For data analysis and data generation the statistical software package R [START_REF][END_REF] is used, while Enterprise Dynamics© is used for the discrete event simulation. A simulated manufacturing run was conducted for 200 hours of manufacturing time. The results of the output variation and order size characteristics are shown below in table 1 and figure 3 and the order size distributions' variation are shown in figure 2. Simulation study of the volatility of order sizes and their impact on the stability of a simple manufacturing environment 7

Table 1. The descriptive statistics for both the order size distributions and the output stability of the manufacturing system per 1 hour.

The processing and setup time should on average allow for 2 orders per interval (i.e. 2 orders per hour) and an output of 40 units per hour. As can be seen the output is quite naturally more unstable for the more unstable order size distributions. While CV N,1 and CV V,1 seem to be more or less perfectly correlated, there seems to be a marked difference when the exponentially distributed order sizes are considered. This is clearly seen from table As can be seen from table 2 strong correlation is found between all the order size stability measures. The strongest relations are for CV N,1 found in CV Q and the values for 50%-80% percentiles. This means that the CV N,1 is equally well described by the simple measure of CV Q and the percentiles. For CV V,1 the higher values (80%-95%) of the percentiles of order size distribution, better describe the variation of the output in form of total volume. So while CV Q is a an adequate predictor for the CV N,1 behavior, the order size volatility measures better describe the CV V,1 .

Another interesting aspect that merits investigation is whether the output is in fact also independently distributed. To investigate whether the output is in fact independently distributed the autocorrelation function is used [START_REF] Box | Time Series Analysis: Forecasting and Control[END_REF]. The conclusion is that both output measures are in fact highly dependent for almost all distributions. This could have big implications if e.g. the aim is to balance a production line [START_REF] Nicholas | Competitive Manufacturing Management: Continuous Improvement. Lean Production and Customer-focused Quality[END_REF], as dependently distributed output could indicate poor balance and difficulties in achieving a proper performance. Using Pearson correlation measures it is clear that there is a significant correlation (better than 0.01) between the input volatility and the lag 1 autocorrelation. This strongly indicates that while the input order sizes may independently distributed (as is the case for all but the constant order sizes), the volatility of order sizes actually has a strong impact on the output stability of the manufacturing system. So if a particular output rate is desired the volatility of order sizes must be taken into account. This is critical for any system reacting directly to customer orders as the dependencies of output directly influences the need to buffer as the need for buffering to achieve the same performance (e..g delivery time) increases as distributions go from independent to dependent. This can explain deviations in real life systems between expected and actual output, while having a relatively constant demand rate. Another observation is that the dependencies of output are quickly reduced after only a few periods (almost no lags greater than 1 are significant). This would seem to indicate that the disturbances tend to be absorbed by the system over time. The time horizon needed for the dependencies to disappear could be a valid measure for a reasonable delivery time for the system.

Conclusions and further research

A number of significant conclusions can be reached based on the results presented in this paper. First, the new measures for order size volatility are robust estimators of volatility. Second, as order size volatility increases, the output volatility naturally increases also. Third, the need to aggregate in time to achieve a given stability of output is increasing disproportionally to the increases in order size volatility. Fourth, while the order sizes are i.i.d. the output is actually dependently distributed. The conclusion must by necessity be that the volatility of order sizes must be taken into account. Future research will focus on finding the aggregation of time (planning period length) needed to ensure that a given order size volatility can be used as input to a manufacturing system and give an output that can be considered stable.

Fig. 1 .

 1 Fig. 1. Top: Histogram of order sizes from a normal (left) and uniform (right) distribution.Bottom: The corresponding accumulated demand profiles.

Fig. 2 .

 2 Fig. 2. The accumulation of demand graph for all 14 data sets used in the simulation study. Each line represents a different set of order size distributions.

Fig. 3 .

 3 Fig. 3. The quartiles from the 14 data sets used in the simulation study. Top left graph indicates color of each experiment.

Table 2 :

 2 [START_REF] Berry | Linking Systems to Strategy[END_REF]. This seems to indicate that the manufacturing system is more sensitive to asymmetrically distributed order sizes. Correlation values between order size volatility measures and output volatility.

			Percentiles
		0.30	0.40	0.50	0.60	0.70	CV Q
	CV N,1	-0.972	-0.980 -0.987 -0.991 -0.993 0.992
	CV V,1	-0.927	-0.943 -0.957 -0.972 -0.985 0.987
		0.80	0.90	0.95	0.98	0.99
	CV N,1	-0.987	-0.97 -0.951 -0.936 -0.93
	CV V,1	-0.995	-0.997 -0.991 -0.984 -0.981


Allen T Alexander
email: a.t.alexander@ex.ac.uk

Stephen J Childe
email: s.j.childe@ex.ac.uk

A Framework for the Transfer of Knowledge between Universities and Industry

Keywords: Knowledge transfer, universities, industry, tacit knowledge

This paper describes research to examine the process of knowledge transfer between universities and industry, where the transfer of knowledge can be a valuable source of innovation for a company, in terms of new product development (radical innovation) but also as a source of knowledge for process or product improvement (incremental innovation). The view is adopted that the most useful knowledge for industry is knowledge that leads to action, known as tacit knowledge. However, tacit knowledge is seen as the most difficult type of knowledge to transfer. The paper builds on the research in this area of strategic knowledge management and uses case-study style research to review a framework that shows how knowledge can be codified for transfer, transferred and then assimilated. The paper concludes with comments about the use of the framework and directions for future research.

Purpose

Companies in business and commerce need innovation to develop and compete. One source of innovation is by adopting ideas and techniques that are developed or simply better understood in institutions such a universities, other research bodies or higher education institutions (HEI). This exhibited itself in the first part of this century with 'collaborative manufacturing enterprises' joining other organisations, in networks where continuous improvement and incremental innovation practices could be developed and shared [START_REF] Chapman | From continuous improvement to collaborative innovation: the next challenge in supply chain management[END_REF]. In this decade the focus has broadened to Open Innovation, which dispenses with the "old" linear model of innovation and promotes "the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and to expand the markets for external use of innovation, respectively" [START_REF] Chesbrough | Open Business Models: How to thrive in the new innovation landcape[END_REF]. Based in a university that works closely with industrial partners, the researchers were keen to understand the process of knowledge transfer so that it could be improved, but found little substantiated theory on managing knowledge flows.

This paper presents a practical framework that focuses on the act of transferring knowledge from one party to another and is set in the context of knowledge transfer activity between higher education and industry. The framework is structured around the well-known categories of tacit and explicit knowledge [START_REF] Polanyi | The Tacit Dimension[END_REF] where implicit knowledge exists between tacit and explicit forms [START_REF] Liebowitz | Key ingredients to the success of an organization's knowledge management strategy[END_REF]. The framework has practical relevance to managers and participants in knowledge transfer projects and can be used in the planning stages of a knowledge transfer; can be used to analyse actions during the activity of transferring knowledge and can be used to review a completed knowledge transfer.

To explore if this framework worked in practice semi-structured, face-to-face interviews with expert respondents from recently completed knowledge transfer projects (25 interviews representing 19 completed projects) were undertaken. This work builds on a definitive list of the channels of knowledge transfer, developed by Alexander & Childe [START_REF] Alexander | Innovation: A Knowledge Transfer Perspective[END_REF].

Context

The drive to gain competitive advantage fuels businesses development worldwide. According to Grant [START_REF] Grant | Toward a knowledge based theory of the firm[END_REF] knowledge has become the most important of a firm's resources and authors such as Teece, Drucker, Cohen all talk of the importance of achieving a knowledge society. Competitive Advantage is seen to come from the transfer of external or new knowledge into a company as an important source of Innovation [START_REF] Bessant | External Knowledge: A review of the literature addressing the role of external knowledge and expertise at key stages of business growth and development[END_REF]. Universities are important sources of knowledge and a number have shifted their strategy from only pursuing research and teaching students to position research and knowledge transfer activities as their first priority. If companies are able to gain competitive advantage from working with universities then effective ways of transferring knowledge are required [START_REF] Schartinger | Knowledge interactions between universities and and industry in Austria: Sectoral patterns and determinants[END_REF]. Chilton & Bloodgood [9 p.76] state "a stream of research needs to investigate moving tacit knowledge directly into outcomes". Further Meier states there is a "lack of research on which knowledge management practices are most useful in order to transfer different types of knowledge" [10 p.17] . By understanding the performance of the different channels of knowledge transfer using the framework, the flows of knowledge between the two organisations can be considered and actions can be undertaken to improve the likelihood of the success of any particular knowledge transfer.

For industry to be able to exploit knowledge, our previous work has shown that that knowledge is needed to support action. We therefore take the view in this study that for useful exploitation, a successful transfer of knowledge would be one that resulted in the transfer of tacit knowledge [START_REF] Alexander | Innovation: A Knowledge Transfer Perspective[END_REF].

Typologies of Knowledge

Beckman [11 p.23] defines knowledge as "reasoning about information and data to actively enable performance, problem solving, decision making, learning and teaching". Polanyi's definition of knowledge [START_REF] Polanyi | The Tacit Dimension[END_REF] distinguishes between tacit or theoretical knowledge and explicit, recognised or scientific knowledge. Grant [START_REF] Grant | Toward a knowledge based theory of the firm[END_REF] states that tacit knowledge is "knowing-how" and explicit knowledge "knowingabout".

Companies are collectives of humans and to some extent they learn accordingly. If we consider Piaget's theory of child learning [START_REF] Piaget | The language and thought of the child[END_REF], as a child gains new experiences or learns new things they reflect these back to existing experiences in order to be able to comprehend and understand them. This is not only a reflection of how children learn, adults use the same method of reflection and assimilation, using like experiences to process new information and to turn it into tacit knowledge. This was identified by Scribner [START_REF] Scribner | Knowledge at Work[END_REF] whilst studying the collective learning of workers employed within a dairy. It is this cognitive absorption that occurs within the transfer of explicit knowledge to tacit knowledge (according to Polanyi in 1966 and later Scribner in 1985 amongst others) and at this point of cognition that the knowledge takes on the "ability to act".

An argument can be made that explicit knowledge, in the form of instruction manuals, is an explicit representation of tacit knowledge and as long as the instructions are clear enough to follow, explicit knowledge should provide an ability to act. From that point of view, the ability to act is not solely dependent upon tacit knowledge. However Scibner etc argue that the instructions replicated in a manual (or articulated in process and procedures for dairy workers) are explicit and still require the cognitive absorption, assimilation with like experiences and reflection in order for them to be used at create an ability to actwhich is the development of tacit knowledge. Scribner offers that, in developing the tacit knowledge, employees often then abandon explicit instructions and explicit knowledge, in favour of improved routines and personalised actions.

The literature suggests a spectrum of views, ranging from those that believe that knowledge is seated in the knower and therefore cannot be transferred at all, and those that believe that knowledge can be externalised and therefore transferred. We take the view that tacit knowledge can be transferred but that this is hard to achieve. It subscribes to the view of Chilton & Bloodgood who suggest a continuum in which fully tacit knowledge is completely embedded and fully explicit knowledge is entirely codified and that the remainder of the knowledge in the world lies somewhere upon this continuum. This realises that tacit knowledge can be transferred, but this transferrable knowledge is not located at the extreme, "tacit pole". It also recognises that explicit knowledge can include aspects of know-how (or tacit knowledge) in relationship to an instructional manual and this perspective is not located at the extent of the "explicit pole" bounding the continuum.

This representation of a tacit-explicit knowledge continuum is criticised by Tsoukas [START_REF] Tsoukas | Do we really understand tacit knowledge?[END_REF] and Gourlay [START_REF] Gourlay | Conceptualizing Knowledge Creation: A Critique of Nonaka's Theory[END_REF]. They argue that the interpretation of the tacit to explicit continuum or at least the one presented in the 'SECI' process developed by Nonaka & Takeuchi [START_REF] Nonaka | The Knowledge Creating Company[END_REF] is incorrect and does not respect the original explanations of tacit and explicit knowledge presented by Polanyi [START_REF] Polanyi | The Tacit Dimension[END_REF]. They explain that instead of tacit knowledge existing and then being converted, through the process of socialisation, into explicit knowledge, the two types of knowledge exist simultaneously and represent "two sides of a coin" instead of two ends of a continuum. To illustrate this they suggest that there are two sort of awareness and that each state of awareness relates to a type of knowledge. They use an example of driving a nail. The person holding the hammer focuses on the head of the hammer and the head of the nailthis is their focal awareness. The person hammering is not consciously aware of how the hammer feels within their hand or how their muscles feel as they bring down the head of the hammerthis is their subsidiary awareness. Tsoukas [START_REF] Tsoukas | Do we really understand tacit knowledge?[END_REF] likens focal awareness to explicit knowledge and tacit knowledge to subsidiary awareness. He argues that as tacit knowledge is within one's subsidiary awareness it cannot be separated from the person and is therefore intangible. This perspective can only promote the transfer of knowledge via personnel movement.

Tsoukas [START_REF] Tsoukas | Do we really understand tacit knowledge?[END_REF] and Gourlay [START_REF] Gourlay | Conceptualizing Knowledge Creation: A Critique of Nonaka's Theory[END_REF] however are arguing that the conversion between tacit and explicit knowledge, explained in the SECI process of knowledge creation, does not actually create new knowledge and they do not mention the transfer of knowledge.

The second example provided by Tsoukas [START_REF] Tsoukas | Do we really understand tacit knowledge?[END_REF] may help to understanding how tacit knowledge can be transferred in this context. When examining a cavity, a dentist's primary focus is on the pointed probe in their hand and the view they can obtain using the mirrorthe subsidiary awareness is the feel of the probe in their hand and the feedback they get as they move the probe into the cavity etc. If the dentist becomes unable to see the end of the probe, nor view the inside of the cavity, their focus shifts to their subsidiary awareness -to the feel of the probe in their hand and the physical resistance presented by the cavity. This is built through reference to experience and suggests that differing levels of tacit and explicit knowledge can exist, depending on the situation. The continuum explained above recognises the polarised position of transfer of pure tacit and this would reflect a sole focus on subsidiary awareness. Likewise it recognises the transfer of purely explicit knowledge, which would represent the transfer of only focal awareness. The continuum suggests that there are a range of intermediate states where tacit knowledge can transfer to some extent, and this would represent a blend of focal awareness and subsidiary awareness.

A revised continuum is therefore presented in Figure 1.

Fig. 1. The Tacit to Explicit Continuum

Tacit Implicit Explicit

At the left-hand end the knowledge is tacit and cannot be transferred as it is entirely within the knower. This represents a predominance of subsidiary awarenesslike the dentist who is operating 'blind'. The right-hand end is explicit, fully codified knowledge and with a dominance of focal awareness, with little realisation of tacit knowledge and subsidiary awareness.

Liebowitz & Beckman [START_REF] Liebowitz | Knowledge Organisations: What every manager should know[END_REF] introduce a third property of knowledge that they suggest lies between tacit and explicit, that of 'implicit' knowledge.

According to Beckman (17 p.p. 1-4):

 Tacit (residing in human mind, or organisations) is accessible indirectly with difficulty (through knowledge elicitation and observation of behaviour);

 Implicit (residing in human mind or organisation) is accessible through querying and discussion (but informal knowledge must be first located and then communicated);

 Explicit (residing in document or computer) is readily accessible, as well as documented into formal knowledge sources that are often well organised.

The inclusion of implicit knowledge may help to understand the stages of a knowledge transfer. In considering actual knowledge transfers and therefore exploring if this model works in practice, the inclusion of the term implicit knowledge may make it easier to recognise the transition between tacit and explicit.

In this paper, Tacit Knowledge is defined as "knowledge that is resultant from both the cognition of information and the interaction with experience and encompasses the ability to act" [START_REF] Clinton | Using Corporate Universities to facilitate Knowledge Transfer and Achieve Competitive Advantage: An exploratory model based on media richness and type of knowledge to be transferred[END_REF] while "explicit knowledge can be expressed in words and numbers and shared in the form of data, scientific formulae, specifications and manuals" [START_REF] Civi | Knowledge Management as Competitive Advantage[END_REF]. Implicit knowledge exists between these categories (that represent the poles on a continuum) and refers to the start of codification of tacit knowledge or the refocus toward subsidiary focus, where the knowledge starts to become structured or organised [START_REF] Liebowitz | Key ingredients to the success of an organization's knowledge management strategy[END_REF]. The framework proposed within this paper draws on these definitions.

A Framework for Knowledge Transfer.

Whilst it is possible in some cases to transfer knowledge at the tacit level, from one person's deep understanding and ability direct to another's, this is likely to take considerable time (for example apprenticeships) and is rather limited as a source of innovation. Alternative channels facilitate transfer at different levels on the continuum between tacit and explicit. For example, as a member of staff from a university prepares to transfer knowledge, they use their intellectual ability organise their knowledge on a subject (making it implicit) and then they codify their knowledge into an explicit state -language, information, data or text that can be used for the transfer. The transfer occurs with the participation of the recipient, a member of the commercial organisation, who must then learn and understand the new knowledge in the context of the organisation and develop the experience to apply the knowledge in action. This processing is shown in Fig. 1. (As knowledge transfer is a two directional activity, there is also a transfer occurring in the reverse direction, although for simplicity this reciprocity is not shown in Fig. 2.)

One route to enable knowledge transfer, for which there is extensive agreement, involves codification of tacit knowledge prior to transfer. For example, as an instructor prepares to teach a class they begin to assemble the knowledge they possess on the teaching subject. This is the translation or codification stage where tacit knowledge retained within the instructor is first made implicit (organised, structured and ready to transfer) and then fully codified (in words, language, demonstration, images etc) as it is transferred [16 p. 9] This is an example of teaching or education and not knowledge transfer, but is relevant, to explain how knowledge is codified in a simplistic way.

Explicit knowledge could manifest in the form of data or text. The working definition of explicit knowledge above refers to "words and numbers and shared in the form of data, scientific formulae, specifications and manuals". the data to make it information. The theory suggests data with capta becomes information and that with the addition of cognitive structures and some form of longevity this becomes knowledge. This progression from data through to knowledge could be considered to be similar, in as much as there is linearity in the process of conversion, to the progression from explicit to tacit as explained by Sveiby [START_REF] Sveiby | The New Organisational Wealth[END_REF]. The key step in Sveiby's suggested process that converts explicit into tacit knowledge is, for one thing, the addition of the "ability to act" or to "apply it". He goes on to argue that each time a codification or translation occurs, and the parties begin to transfer explicit information, a potential exists to lose a component of that knowledge through the interpretation stage. The act of codification between tacit and explicit can be represented on a linear scale (similar to the progression between data and knowledge referred to in 'knowledge-based management') as can the cognition (or interpretation) that occurs as the knowledge is re-codified by the recipient, to add the ability to act / application. There is a significant amount of research that considers the intellectual processes that occur to embed this ability to act. These include the referencing of new knowledge to other personally embedded experiences that are similar that allow the cognition and embedding of this knowledge [START_REF] Argyris | Organizational Learning: A theory of action perspective[END_REF]. Another process referred to is the repetition of 'like actions' that lead to individuals being able to digest and therefore vary their work patterns to accommodate local efficiencies in cognitive processes [START_REF] Scribner | Knowledge at Work[END_REF].

All of the perspectives considered above suggest that the transfer of knowledge is complex. A way to reflect the perspectives of the authors above, who are trying to create explanation around the act of transferring knowledge, whilst trying to define the properties of knowledge, is to create a way to visualise the subject. Epp & Price [START_REF] Epp | Family Identity: A Framework of Identity Interplay in Consumption Practices[END_REF] suggest that a "sensitising framework" could be one way to enable people to visualise and comprehend an intangible. Wacker [START_REF] Wacker | A definition of theory: research guidelines for different theorybuilding research methods in operations management[END_REF] also suggests that some form of a framework or mental model is a good way of visualising theory.

A framework for review has therefore been developed to aid practitioners and people who will become involved in knowledge transfer to understand how the properties of knowledge can change during and transfer of knowledge. This is shown in Figure 3.

For simplicity the framework takes only two stakeholders into account; the University and the Company and does not consider the additional stakeholders promoted by Etzkowitz [START_REF] Etzkowitz | The triple helix of university-industry-government: Implications for policy and evaluation in Report[END_REF] and Stevens & Bagby [START_REF] Stevens | Knowledge transfer from universities to business: returns for all stakeholders Organisation[END_REF]. This is because normally the third stakeholder (the government) and the fourth stakeholder (society) do not directly become engaged in the actual process of transferring knowledge.

The framework portrays the transfer of knowledge between Universities and Industry or commercial organisations, firstly in the form of tacit-to-tacit knowledge shown at the top of the framework and toward the lower half in the form of explicit exchange of knowledge, importantly in two directions from the university to the company and reciprocally between the company and the university. According to Polanyi [START_REF] Polanyi | The Tacit Dimension[END_REF] the transfer of tacit knowledge is hard to achieve; this is represented by the size of the transfer arrows in the model, but no scalable relationship is inferred by the relative size of the arrows.

The lateral arrows represent the properties of knowledge and show a shift in the types of knowledge progressing from tacit (at the top), through implicit to explicit (at the bottom). The left hand arrow relates to codification and cognition as does the right hand arrow. The top of each arrow represents high levels of tacit knowledge, resulting from experiences and education and laden with the ability to act. The bottom represents a dominance of explicit knowledge. This continuum exists between the poles of "entirely tacit" knowledge and "entirely explicit" knowledge, however there is never a state where either no explicit knowledge exists, and vice versa. This corresponds to the view of Tsoukas [START_REF] Tsoukas | Do we really understand tacit knowledge?[END_REF] and Gourlay [START_REF] Gourlay | Conceptualizing Knowledge Creation: A Critique of Nonaka's Theory[END_REF].

Real examples, where knowledge was transferred at the tacit level, and also those where the route from tacit-to-tacit is via codification, explicit transfer and understanding or assimilation were used to validate this framework. The purpose of this research is to create a guideline that identifies which channel is likely to transfer tacit knowledge most effectively. This is achieved by referencing the work of authors such as Schmoch et al [START_REF] Schmoch | Wissens und Technologietransfer in Deutschland[END_REF], and Schartinger et al [START_REF] Schartinger | Knowledge interactions between universities and and industry in Austria: Sectoral patterns and determinants[END_REF] and then triangulating their findings against real examples of typical knowledge transfer projects (to understand the transfer of knowledge within each type of channel) and reflecting on the framework. The results can be used to influence the choice of knowledge channel for both industrial managers and academic institutions.

Methodology

A detailed research protocol was established as part of this strategic knowledge management research -a research field that unlike its sibling "Knowledge-based Management (an evolution of information systems), is still in it infancy and lacks robust, empirically tested theory. In relation to operations research, and the three dominant systems perspectives (hard, soft and critical), in general terms a "hard systems perspective" employs only a positivist approach to study "objective data" (which can be likened to seeking only explicit knowledge) whereas, a "soft" systems perspective however treats knowledge in a more phenomenological way as being "tacit, generated and consumed in social action… and it is assumed that this knowledge is Innovation" ([28] p. 388). By combining methodologies into a dualist, social constructivist approach and seeking mode 2 knowledge creation [START_REF] Gibbons | The New Production of Knowledge: The dynamics of science and research in contemporary societies[END_REF], a robust three-step data collection protocol was established around participant enquiry and participant interaction. The research instrument was derived (within step 1) by reference to a Delphi-style expert panel, made up of 10 'innovation-focussed' policy executives and subsequently posing a broad-range question to a community of practice. Step 2 involved data collection using semi-structured interviews lasting 90 minutes, in certain cases followed by a second 60 minute interview, undertaken after a period of interviewer reflection. Each interview was transcribed and returned to the subject for approval before being summarized in a partially-coded in-case tabulated summary. To ensure that the data collected was representative a sample size and selection criteria were develop for the second and third parts of the study. In total 19 completed projects were chosen for review in 25 interviews, not all with an operational management focus, but taken pan-sector and across a range of innovation disciplines. Reliability, validity and generalisability were expressly considered as was the role of experimental control for this study, which looks to build theory, before subjecting it to deductive-style testing within further research.

Findings

The knowledge transfer projects that were studied included the development of a spin-out company exploiting engineering-based research in software analysis and three patents involving a range of research, from novel techniques in spectroscopy to a bio-science application.

In the 19 interviews undertaken in step 2, all respondents acknowledged that the framework did reflect the way that knowledge transfer occurred within their projects. The 6 interviews taken in two further case studies (step 3) representing triangulated stakeholder opinions agreed that the framework typically represented how knowledge transferred as well. A summary of the interviews and results is shown in Table 1.

A good example of a response that affirmed the framework in detail is a "joint supervision" case study (TRMU) focussed on the development of materials for a museum of local culture. The respondent stated that the transfer took place over a long period and they felt that tacit knowledge transferred at the outset as face-to-face interviews and subsequent transcripts were developed. This led to the development of explicit material that could then be displayed in the form of teaching materials in local schools. Further explicit material were developed in the form of a booklet that was circulated within the local community and also uploaded to the internet.

Analysing the content of the response and referring it back to the framework it could be suggested that the transfer of tacit-to-tacit knowledge via face-to-face communication was one act of knowledge transfer, between interviewer and interviewee. In relation to the preparation of the media and dissemination in the form of a booklet etc, this could be considered as a second act of knowledge transfer and be identified as tacit-to-explicit prior to transfer, however this is not true knowledge transfer as this element of the project is only one-directional. This project does relate back to the framework and the responses have confirmed the aim of the framework, which is to enable the subject to consider the implications of transferring different types of knowledge.

In multiple interviews from a case in contract research and consultancy (RDEP) the second interviewer states the "flows reflect the model but there was a mix of tacit and explicit in varying proportions during the project. There was an exchange of tacit at the beginning. As the project became more defined, with more knowledge then the University could provide more focussed knowledge into the products, so it started off with having mostly tacit knowledge, with some explicit and as the project moved on the knowledge became more explicit". This reflected a deeper response than some of the interview candidates had made, when reviewing the framework.

The other respondent from the same project stated "the framework is OK -I have never thought of it like that I guess, we mostly transfer [knowledge] across the big arrow at the bottom as companies can't wait or can't afford the tacit bit". The third respondent from this case stated the "first process is the transfer of tacit knowledge to explicit knowledge from industry to university, second is the transfer of the tacit knowledge from the university staff into explicit knowledge in the product and the third is the transfer of our explicit knowledge to explicit knowledge for the industrial partner. Also each transferred tacit knowledge to tacit knowledge at the beginning, then tacit knowledge was transferred to explicit in the form of the product design specification and then we transferred explicit to explicit knowledge in the prototype". During the interview response above the design engineer traces their finger across the framework picture to correspond with the flows that they are verbalising.

Interview Number

In the third patent project (ATT3) the respondent indicates that "there was definitely a flow of information from tacit to explicit, but the whole model was definitely skewed to the left-hand side; any release of knowledge to industry was done in a stage managed way". In patent 2 (MALA) the respondent felt that "the whole model [framework] was going on, but there was probably more explicit knowledge" and in patent 1 (SYNG) the respondent agreed "it is basically what happened, we have tacit knowledge about fungi and fungicides that we used to test the patent from [name] and it proved to work and we then presented the work in explicit form to the company, the company has taken up the knowledge and it will become tacit with them".

Contract research & consultancy 1 (KELL) suggested the framework "reflects the study. At the beginning the children had tacit knowledge which was shared with the university staff; the university staff also have some tacit knowledge. The children's knowledge in terms of diaries, measurements etc was codified, understood and then the data was collected and written down in the form of a report. This knowledge is then passed to the company".

Three of the respondents also suggested improvements to the framework. The respondent that had been interviewed because of their experience of networks to transfer knowledge (EDSN) felt that the framework reflected knowledge transfer in networks but to differing extents and this related to each particular network. The respondent went on to suggest a modification to the framework, so that instead of pure transfer of knowledge occurring on the x-axis (horizontally) a time line could be superimposed to show how the transfer of tacit knowledge changed over the duration of the knowledge transfer.

The academic responding from their experiences of consultancy (RDEP) affirmed the model represented their particular knowledge transfer "almost". They then described key stages of their particular transfer with reference to steps within their project. The first step was explained as being the translation of tacit knowledge into explicit knowledge as the industrial partner defined the problems and sent documents relating to context and needsa brief or scope of works. The second step was explained as the University translating the explicit knowledge within the brief into tacit knowledge (to develop a proposal of what they intended to undertake). The next step was the codification of the tacit knowledge into a material artefact (where drawings were prepared, further codified into the software and printed on an additive layer rapid prototyping machine to create the end product) in the form of a prototype and finally, the transfer of explicit information between the parties as the prototype and report are exchanged to complete the project.

The professor and co-author responding in respect to the joint academic publications (AEBS) stated "on a simplistic level I can understand how this model applies to the project but I see knowledge transfer as more circular and fluid. I think the transfer was not as linear as the model implies; knowledge can happen on various different levels with more or less tacit and explicit knowledge being exchanged at one time, which is why I think the model has to be put into context. It needs to take into consideration time. I can see how a circular model of knowledge transfer can happen in an hour and can also take several months. Also I think that generally the model has to be put into context in the problem situation and that it is important to define outcomes". The comment relating to the simplistic level is important as this framework can only really work at a high level where management guidelines and review instruments are often most effective -they operate best at a simplistic level and taken too literally can be misleading.

It is important to understand that the framework has a broad range of applicationsit can be used to take a "snapshot" of how knowledge is being transferred at a particular point in time, but it can also be used in planning, to create an ambition of transferring mostly tacit knowledge or it can be used in reflection to consider how knowledge flowed in a project, perhaps as part of a post project review process or lessonsrealised exercise.

In summary the responses received from the interviews taken from completed knowledge transfer projects suggest that the framework is a helpful map to enable interviewees to reflect on their particular the study has knowledge transfer projects.

We have demonstrated that the framework stimulates the thoughts of professionals focussed around what types of knowledge flow between a company and a higher education. All of the respondents were able to relate this framework to their real life examples of completed knowledge transfer projects that were promoted by their respective companies, universities or government organisations as successful examples. Each respondent either acknowledged the pertinence of, or affirmed in detail or with suggestions how the framework could be related to their particular project. The only exception to this arose during the interview with the company respondent representing a project to develop a 3-dimensional CAD facility within a traditional boat building firm. When asked if the framework reflected the flow of knowledge in the project the respondent replied "No, the knowledge transfer was the reverse of this model" however there were two interesting notes made by the interviewer relating to this statement. These were that the subject was unable to articulate how flows could act in reverse and that the subject could not elaborate around the framework and became dismissive". The most likely answer to this, is that the respondent had failed to grasp the two-directional nature of the framework (and was referring to the reverse flow as right to left and not left to right) or that the term reverse relates to there being only explicit knowledge in the heads of the project participants, which then became tacit as the project progressed. It would seem that without reference to this respondent further a clear explanation cannot be offered, however it does seem that the framework has still achieved what it set out to do, which was to make the respondent consider the way in which knowledge transferred in their project.

In obtaining more than 12 responses this research proposes that the framework could form the basis of a useful construct, which when applied to a phenomenon (where company and academic personnel promote the success of a knowledge transfer without reference to objective measures or detailed comparative analysis), improves the understanding of what is occurring within the projects.

The responses (including some that looked to try to improve the way the framework represented knowledge transfer) demonstrated to the researchers that the framework has achieved what it set out to do. A summary of the results can be seen in Table 1.

It stimulates professionals' thought into what types of knowledge flow between a company and a higher education and allows people to visualise knowledge (which is often overlooked due to the difficulty in visualising it). All of the respondents were able to relate this framework to their real life experience of knowledge transfer projects undertaken by their respective companies, universities or government organisations.

Each respondent either acknowledged the pertinence of the framework, or affirmed its usefulness in detail, or affirmed while adding suggestions how the framework could be related to their particular project.

Practical Application of the Framework

The framework and the understanding gained from the knowledge transfer cases will lead to a tool that can be applied to stimulate innovation by allowing managers to select the most appropriate channels for transferring knowledge into companies from universities, using such channels as staff secondment, jointly supervised projects, consultancy, contract research etc. The research also allows universities to configure their offerings to industry in order to tailor their activities to offer the maximum benefit according to the situation and the type of knowledge to be transferred.

As a particular example studied, a process-based outsourcing service company undertook a two year joint-supervision project (within a UK-specific grant funded scheme entitled Knowledge Transfer Partnership -KTP). The aim of the project was two fold: to review the contract and order fulfilment capability within the company (which at the outset turned-over around £500k per annum and employed 12 staff working across shop-floor and IT-based service provision) and undertake an Activity Based Costing appraisal to highlight the contracts with the greatest yield and to redesign the order fulfilment processes to greater increase the contract yield, whilst downscaling the sales and marketing activity for poorly performing contracts.

As a result of the project the company reported an increase in net profits of more than £300k which they attributed to improved operational efficiency (28%), targeting only profitable contracts (27%), longer duration contracts (33%) and increased production capacity (27%).

When questioned the company respondents stated "The people who fund the project [in reference to the grant body] want explicit knowledge because they want to be able to measure it. Some explicit knowledge is needed, but the essence of the project, and its greatest benefit, is the transfer of tacit knowledge". Whilst reflecting on the framework the university respondent stated that "most of [the knowledge transferred] is in the implicit stage and it happens on a continuum". The company respondent, in this case the Managing Director, stated "[the knowledge flows] were definitely happening but at different rates and in numerous forms, some of which is still going on [the interview took place 6 months after project completion] and I would say that we are at the implicit stage for embedding (on right-hand axis of the framework)".

Conclusions and future research

The conclusion for this research is that by referencing experts in this area and by studying the outcomes of participation in collaborative projects, transferring knowledge back and forth between industry and higher education, we have developed a useful and practical visualisation framework. Within an immature research landscape, where main contributors in this area relate to the barriers to; benefits from and motivations for knowledge transfer, we have contributed to theory. By focussing on certain attributes or properties of knowledge the framework can be used to aid in planning a knowledge transfer activity and visualising how knowledge might flow during the project. This will in-turn affect decisions on governance (where a partnering style of governance can lead to more tacit knowledge being transferred [START_REF] Perkmann | How firms source knowledge from universities: Partnerships versus contracting Creating Wealth from Knowledge: Meeting the Innovation Challenge[END_REF]), geographic location of the knowledge partner (in relation to the ability to hold face to face meetings [START_REF] Alexander | Innovation: A Knowledge Transfer Perspective[END_REF]) amongst a number of other factors.

Future research in the this area will focus on balancing the extensive qualitative data collected within this study with more objective measurements of performance, again taken across completed knowledge transfer project as the second, more deductive phase of theory development (according to [START_REF] Carlile | The Cycles of Theory Building in Management Research[END_REF]).

In terms of further application of this research, a study developing this framework into a set of management guidelines for policy makers, managers and participants within knowledge transfer projects has already been undertaken and the results are awaiting publication. It is planned to extend these guidelines further by incorporating them into a policy decision-making tool based on innovation management capability and innovation channel suitability.

Fig. 2 .

 2 Fig. 2. Pre-transfer and post-transfer processing of knowledgeThe Information Systems research of Checkland & Howell[START_REF] Checkland | Information, systems and information systems[END_REF] develops a routine or set of steps to translate data into knowledge. Whilst this has 'knowledge-based management' origins, as opposed to 'strategic knowledge management', the theory may still be relevant. Each step (or process) relates to the capture of data, being pure figures or text and the subsequent undertaking of capta, the act of placing relevance to

Fig. 3 .

 3 Fig. 3. The Assessment Framework

Table 1 .

 1 Summary of results

		Code	Knowledge Transfer Channel (number)	Acknowledge ment	Affirmed
					in detail	with suggestions
			KEY RESPONDERS
	N/A	N/A	Graduate Employment		N/A
	1	UEME	Joint Conference		X
	2	ATT1	Spin Out (1)		X
	3	ICO3	Spin Out (2)		X
	4	SIMP	Spin out (3)		X
	5	AEBS	Professional Journal			X
	6	EDSN	Network 1			X
	7	3DAC	Joint Supervision 1	X
	8	TRMU	Joint Supervision 2		X
	N/A	N/A	Training & CPD		N/A
	9	PATC	Collaborative Research		X
	10	KELL	Contract Research &		X
			Consultancy (1)
	N/A	N/A	Shared Facilities		N/A
	11	SYNG	Patent 1		X
	12	MALA	Patent 2	X
	13	ATT3	Patent 3		X
	14	ARGA	Joint Venture 1	X
			MULTIPLE RESPONDERS
	15 16	QINE QINE (2)	Joint Venture 2	X	X
	17 18 19	RDEP (1) RDEP (2) RDEP (3)	Consultancy (2) Contract Research &		X X	X


Hajime Mizuyama
email: mizuyama@ise.aoyama.ac.jp

How Innovation and Improvement Ideas Are Created for a Production System by a Kaizen Team: A Protocol Analysis

Keywords: Collective intelligence, continuous improvements, creative problem solving, innovation, mental models, mental space, protocol analysis 47

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The competitiveness of a production system has a dynamic nature and hence should be maintained through innovations and continuous improvements. How to accomplish them effectively is not simply a matter of choosing an optimal one from a given set of solutions, but is rather the matter of creating the solutions themselves as well as finding the concrete problems to solve. This means that introducing innovations and improvements to a production system should be regarded as another ill-defined creative problem solving process carried out somewhat tacitly by a cross-functional Kaizen team. Consequently, it is not clear how the process should be operated and managed, what kind of supports are possible and effective, etc. to enhance its productivity. Thus, a deeper understanding of the process is strongly demanded.

One may understand the creative problem solving process carried out by a Kaizen team as an idea generation process similar to a well-studied brainstorming session [START_REF] Strobe | Why Groups Are Less Effective than Their Members: On Productivity Losses in Idea-Generating Groups[END_REF][2] [START_REF] Taggar | Group Composition, Creative Synergy, and Group Performance[END_REF]. However, this view captures only a partial aspect of the process: The members of the Kaizen team not only generate and enumerate innovation and improvement ideas suitable for the target production system but also deepen and share the understanding on the production system as well as evaluate and elaborate the ideas.

Further, the three aspects of the process seem to be mutually coupled tightly together in a synergetic way. Hence, the possible synergy among the aspects should also be well understood to be capable of managing and supporting the process effectively, and it requires a process model suitable for studying the synergy. Thus, this paper proposes a new process model for describing how innovation and improvement ideas for a production system are being created through creative discussion among the members of a Kaizen team from all the three aspects.

Although it is usually not formally externalized during the discussion, the understanding (shared among the team members) on the target production system should form a certain mental model. Thus, the second aspect of the creative discussion can be captured as a kind of group model building [START_REF] Vennix | Group Model-Building to Facilitate Organizational Change: An Exploratory Study[END_REF] [START_REF] Vennix | Group Model Building: Facilitating Team Learning Using System Dynamics[END_REF]. However, the structure of the mental models created here seems to be more qualitative and vague than the system dynamics model, which is widely used in ordinal group model building sessions. Hence, an original model structure is developed for the proposed process model. Further, each innovation or improvement idea can be considered as corresponding to how the mental model representing the current status of the production system should be changed. Thus, the process model captures the first and the third aspects of the creative discussion as actions of introducing new mental models and evaluating and refining existing mental models.

Accordingly, the proposed process model treats the team-based creative problem solving process as a sequence of updating a set of mental models of the target production system shared by the team members through exchanging utterances among them. Further, it utilizes a protocol analysis technique [START_REF] Gero | An Approach to the Analysis of Design Protocol[END_REF][7] [START_REF] Sakao | Inside a PSS Design Process: Insights through Protocol Analysis[END_REF] in order to follow the process based on the utterances. In addition, the applicability and utility of the proposed process model and accompanying protocol analysis approach are also confirmed by actually capturing an example practice of discussing how to improve a two-stage assembly cell by them. The analysis reveals some interesting characteristics of the team-based creative problem solving process.

In the remainder of the paper, section 2 presents the proposed process model of the team-based creative problem solving process, and section 3 applies the proposed model to a case and draws some relevant insights into the process. Finally, section 4 concludes the paper.

2

Process Model of Team-Based Creative Problem Solving

Mental Space and Mental Models

In the situation considered here, a Kaizen team is in charge of conducting innovations and continuous improvements for a production system. The Kaizen team is assumed to be properly motivated and cross-functional having different background knowledge. At least some of the members are familiar with the current status of the target production system. The process model to be proposed below formally conceptualizes the creative discussion among the team members for collectively creating innovation and improvement ideas for the production system. It outlines the discussion as a process of producing some output, like a manufacturing process. However, what the discussion process produces is not visible unfortunately. In the case of a manufacturing process, the subject is a visible raw material, part or subassembly, and what transform it are some operations like machining, assembling, etc. Analogically, in the case of the discussion process, an invisible concept on the target production system can be regarded as the subject, and interactions, such as exchanging utterances, among the team members can be deemed as transforming operations. Hence, the key is how to envision the invisible subject and how they are transformed through the discussion process.

Fauconnier [START_REF] Fauconnier | Mental Spaces[END_REF][10] introduced a schema called mental space for visualizing the subject of verbal communication. In order to utilize the aforementioned analogy, the process model proposed here applies this schema and captures the team-based creative problem solving process as a sequence of updating a shared mental space through utterances. The contents of the mental space considered here are limited to those related to the production practice conducted in the target production system, and are called production system mental models (PSMMs). PSMMs can be stated as intersubjective concepts on the target production system. The PSMMs that appear in the team-based creative problem solving process can be classified into the following two classes:

Real PSMMs: Each of them is a concept regarding the production practice actually conducted or experienced in the target production system.

Imaginary PSMMs: Each of them is a concept made by changing a part of another PSMM. It is a candidate concept of how the target production system should be innovated or improved.

Characteristics of Production System Mental Models

The mental space shared by the team members contains several PSMMs as described later and each of them is characterized by a number of attributes and the relationships among them. The attributes and relationships can be qualitative and abstract. For enabling a posteriori analysis on the scope of discussion, the attributes of a PSMM are classified into the following three layers:

Objective Layer (OL): This layer includes observable facts in the PSMM.

Subjective Layer (SL):

This layer contains unobservable feelings, thoughts and mental tasks of the operators in the PSMM.

Judgmental Layer (JL):

This layer gathers interpretations or evaluations on the PSMM of the Kaizen team members.

Further, since the practice of creative problem solving is composed of problem finding and solving activities, the attributes are also distinguished into the following two categories: Problem-Related (PR): An attribute in this category is related to a problematic situation in the target production system. Solution-Related (SR): An attribute in this category is concerned with a solution to a problematic situation in the system.

Most attributes of a PSMM are not stand-alone but are related to one another. During the creative discussion process, the team members can elaborate a PSMM not only by adding a new attribute to it but also introducing a new relationship among its attributes. Here, the possible relationships among the attributes of a PSMM are first classified into directional, hierarchical and non-directional: Directional relationships include those from a cause to its effect, from a premise to its consequence, from a reason to its evaluation or interpretation, from a problem to its solution, etc. Hierarchical relationships appear between an abstract characteristic and its detailed features. Whereas, a pair of analogically mapped characteristics or alternative solutions are connected by a non-directional relationship. Then, the relationships can be deemed as corresponding to the following knowledge handling actions:

Ordinal inference: This action is represented in the proposed model by adding a new directional relationship.

Generalization/Specialization: A new hierarchical relationship appears when this action is taken.

Analogical-mapping/Alternative-seeking: Taking this action corresponds to introducing a non-directional relationship.

State Transitions through Utterances

In the proposed process model, the process of creating innovation and improvement ideas for the target production system through creative discussion among the team members is captured as follows: First, it starts with the initial mental space, called State 0, where there is only an empty real PSMM. Then, as shown in Figure 1, the state is being updated step by step by utterances exchanged among the members. For example, a new attribute is added to an existing PSMM; a pair of attributes are connected by a relationship; a new PSMM is introduced, etc. The PSMMs are numbered in the order of appearance to the mental space. Since the shared mental space itself is not visible, it must be reverse-engineered from the recorded utterances exchanged during the creative discussion. The proposed approach applies a protocol analysis technique for this purpose: After irrelevant parts are screened out from the utterances, they are sorted out into meaningful elements. Then, each utterance element is encoded in terms of on which PSMM it is talking about and what attribute and relationships it adds to the PSMM, as described in the next section.

3

Case Study

Case Overview

A two-stage assembly cell for a product (a lego block car) is used as an example production system, and four university students are engaged in the experiment. The experiment is composed of some production sessions and discussion sessions. In a production session, two of the participants actually experience operating the cell and produce 16 products, and the other two members observe the operation as analysts.

The assignment of the roles to the participants is determined by the participants themselves. In the following discussion session, which is about one hour long, they discuss how to enhance the production rate of the assembly cell. The innovation and improvement ideas created in the discussion session are actually introduced into the cell, and then the next production session is started. The fee paid to the participants is set proportional to the highest production rate of the production sessions performed in the experiment, and which is announced to them before starting the first production session. It is left up to the participants how to organize the discussion session. However, the number of ideas introduced to the cell after each discussion session is limited up to three, so that they would concentrate on refining ideas rather than simply enumerating simple ones. The discussion sessions are video and voice recorded.

Encoding Discussion Sessions

The transcript of the utterances exchanged in the first and the second discussion sessions is analyzed according to the proposed state transition model. All the utterances are first extracted from the recorded data and those which are judged not directly related to the creative problem solving practice are screened out. If a certain statement is repeated, only the original one appearing first is included in the analysis and the rests are omitted. The remaining utterances are then divided into elements so that each of them should have a separate meaning. Finally, each utterance element is encoded in terms of the attribute, relation and PSMM introduced in the previous section, respectively. Table 1 shows a part of the result. For example, the attribute added by utterance element 1 is classified as a problem-related one of PSMM0 in the judgmental layer, and that added by utterance element 2 is encoded as a problem-related one of the same PSMM but in the objective layer. Further, it is considered that the second element introduced a directional relationship from the second to the first attribute and thus functioned as an ordinal inference. There are more parts to be inserted in cell 1 than cell 2. OL-PR Directional (6, 1) 0

It is observed during the analysis that the participants themselves sometimes become confused on which PSMM they are currently talking about. Because of this, encoding is most difficult in terms of PSMM. If this analysis can be made online, visualizing the result and feeding it back to the participants will help them prevent themselves from getting lost and stay aware of where they are.

OL-PR

SL-PR

JL-PR

JL-SR

SL-SR

OL-SR Utterance Elements

Fig. 2. Sequence of added attributes in discussion session 1.

OL-PR

SL-PR

JL-PR

JL-SR

SL-SR

OL-SR Utterance Elements

Fig. 3. Sequence of added attributes in discussion session 2.

Outline of Discussion Sessions

Figures 2 and 3 represent the sequence of added attributes in the first and second discussion sessions, respectively. Although repeated attributes are omitted, an overview of how the scope of discussion changes along time can be obtained from the figures. What is interesting in the figures is that the scope does not always move from abstract (JL) to concrete (OL) and from problem (PR) to solution (SR) but it moves in the opposite direction almost equally frequently. Further, the distribution of attributes across the scopes does not seem to be stable over the whole discussion process. This implies that the discussion process is composed of several distinguishable phases. Table 2 shows the number of attributes added to each scope during the sessions. It is noticed that the distribution is quite similar between sessions 1 and 2. For example, many attributes are located in the objective layer (OL) in both sessions. It suggests that this layer is used as the main field of elaborating problems and solutions. Further, there are more solution-related attributes than problem-related ones in the objective layer (OL), but the opposite is true in the subjective layer (SL). This interesting feature suggests that objective solutions tends to bring about subjective side effects in one's mind and vice versa. Tables 3 and4 show the frequencies of scope changes in sessions 1 and 2, respectively. The focus of discussion moved from a scope corresponding to the row to the next one corresponding to the column. The distributions shown in the tables are also similar to each other. Further, as conjectured above, the focus moved frequently within the objective layer (OL) as well as from/to the problem-related scope in the subjective layer (SL-PR). It is also noticed that the focus sometimes moved from the solution-related scope in the objective layer (OL-SR) to the solution-related one in the judgmental layer (JL-SR) and then to various ones. This suggests that a judgment of a member given to a concrete solution functions as a watershed of the discussion flow.

Fig. 1 .

 1 Fig. 1. Overview of proposed state transition model.

  14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

Table 1 .

 1 Example utterance elements and their codes (translated from Japanese).

	ID	Utterance element	Attribute	Relation	PSMM
	1	The assembly operation in cell 1 is more difficult than that in cell 2.	JL-PR		0
	2	The idle time of operator 2 is almost as long as his working time in each cycle.	OL-PR	Directional (2, 1)	0
	3	Inserting operation of operator 1 is sometimes imperfect.	OL-PR		0
	4	There are moments when an operator changes the holding angle of a part.	OL-PR		0
	5	What about making it possible to insert the part without changing its holding angle?	OL-SR	Directional (4, 5)	1
	6

Table 2 .

 2 Number of attributes added in each scope.

		JL-PR	SL-PR	OL-PR	OL-SR	SL-SR	JL-SR
	Session 1	8	10	17	21	3	7
	Session 2	4	10	20	44	8	11
	Total	12	20	37	65	11	18

Table 3 .

 3 Frequencies of scope changes in discussion session 1.

		JL-PR	SL-PR	OL-PR	OL-SR	SL-SR	JL-SR
	JL-PR	2	1	3	2	0	0
	SL-PR	0	2	4	3	0	1
	OL-PR	2	2	6	5	1	1
	OL-SR	1	3	2	9	1	5
	SL-SR	0	1	0	1	0	0
	JL-SR	2	1	2	1	1	0

Table 4 .

 4 Frequencies of scope changes in discussion session 2.

		JL-PR	SL-PR	OL-PR	OL-SR	SL-SR	JL-SR
	JL-PR	0	1	0	2	1	0
	SL-PR	0	0	4	3	2	1
	OL-PR	0	1	7	10	0	2
	OL-SR	3	5	5	21	4	5
	SL-SR	1	1	2	4	0	0
	JL-SR	0	2	2	3	1	3

Acknowledgments. This research was partially supported by the Japan Society for the Promotion of Science, Grant-in-Aid for Creative Scientific Research 19GS0208 and Grant-in-Aid for Scientific Research (B) 20310087.

Figures 4 and5 visualize the upper triangular matrices showing between which attributes relationships are given in sessions 1 and 2, respectively. Each blacked out box represents that one of the relationships defined earlier is introduced between the attributes corresponding to the row and the column. The distributions of the black boxes in the figures are even surprisingly similar to each other. That is, there are many local clusters along the diagonal line as well as some plots away from the line, which represent occasional relationships between attributes far away from each other.

Those attributes to which relationships are introduced even from far downstream may be regarded as functioning as the main topics of discussion.

Knowledge Handling Actions

This subsection studies the knowledge handling actions taken in the discussion sessions. Table 5 sorts out the frequencies of the ordinal inference actions, that is, the directional relationships introduced in the sessions. Each of them derives an attribute in the scope corresponding to the column from another in the scope corresponding to the row. The table shows that this action is quite frequently taken between various scopes, which are not only from problem to solution but also within problem, within solution and even from solution to problem.

Table 6 summarizes the frequencies of the generalization/specialization actions, that is, the hierarchical relationships defined in the sessions. Each of them connects an abstract characteristic in the scope corresponding to the row and its detailed feature in the scope corresponding to the column. It is observed in the table that this action is also frequently taken and, in most cases, the detailed feature lies in the objective layer (OL). This action is usually used within problem or within solution but between problem and solution. Table 6. Frequencies of generalization/specialization actions.

JL-PR

Table 7 shows the frequencies of the analogical-mapping/alternative-seeking actions, that is, the non-directional relationships mentioned in the sessions. Each of them bridges between a pair of attributes in the scopes corresponding to the row and column. Since the relationship is non-directional, the table is a lower triangular matrix. The table shows that this action is frequently taken and it is mostly within solution. It implies that this action is an effective vehicle for exploring the solution space. It is further observed that this action can be used not very often but also within problem and even between problem and solution.

Table 7. Frequencies of analogical-mapping/alternative-seeking actions.

JL-PR

Conclusions

This paper proposed a state transition model for analyzing how a Kaizen team collectively creates innovation and improvement ideas for a production system, and captured actual sessions of creative discussion according to the process model. The analysis shows that the team-based creative problem solving practice is not a simple unidirectional process from abstract to concrete and from problem to solution. It is also observed that knowledge handling actions, such as ordinal inference, generalization, specialization, analogical-mapping, alternative-seeking, are frequently used in the process.

A future research direction is to reveal standard features of the team-based creative problem solving process by conducting more case studies. Further, it is also an interesting challenge to develop the proposed process model into a systematic tool for supporting the discussion process of creating innovation and improvement ideas for a production system. Possible directions of this include cognitive support by using the model as a computerized minute, automatic proposition of potentially effective knowledge handling actions, creativity support by combining the process model with TRIZ [START_REF] Savransky | Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving[END_REF], etc.