ARTICULATION AND APPROPRIATION OF COLLECTIVE KNOW-HOW IN THE
STEEL INDUSTRY:
EVIDENCE FROM BLAST FURNACE CONTROL IN FRANCE
Nathalie Lazaric
[email protected] LATAPSES CNRS, 250 rue A. Einstein,
06 560 Valbonne Sophia Antipolis, France
Pierre-André [email protected]
CEPN-IDE CNRS 99 av J-B Clément,
93430 Villetaneuse, France
and
Marie-Laure Massué
Reims Management School, 59 rue Tattinger
51100 Reims, France
Abstract
In this article, we use the implementation of an expert system to improve blast furnace control in the French steel industry to illustrate the problem of knowledge articulation/codification. Blast furnace related knowledge still largely takes the form of empirical know-how in general and expert know-how tied to specific individuals in particular. Therefore, the articulation/codification of knowledge in this field is a difficult task requiring the identification and selection of ‘best practices’ for the purpose of codification. This process, in turn, affects daily routines and creates new forms of non exclusive generic knowledge that make use of local knowledge. These new forms of generic information reinforce the tendency to appropriate private knowledge currently prevailing in Usinor, a large French steel company, and contrast sharply with the tradition of knowledge sharing between firms that predominated until the end of the nineteenth century.
Key words : blast furnace, articulation, knowledge, codification, steel industry.
Acknowledgments : This paper has benefited from various discussions notably in EAEPE conference (Berlin, November 2000) in Nice “Knowledge workshop” (December 2000) and in Paris Sceaux “New Economy” conference (May, 2001). The authors are grateful for comments from M. Becker, W. Dolfsma, F. Tell, P. Nightingale, J. De Bandt, P.Petit and N. Greenan among others. Usual caveat apply.
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“It will at once be evident that on this point the position will be different with respect to different
kinds of knowledge ; and the answer to our question will be there fore largely turn on the relative
importance of the different kinds of knowledge ; those more likely to be at the disposal of particular
individuals and those which we should with greater confidence expect to find in the possession of an
authority made up of suitable chosen experts. If it is today so widely assumed that the latter will be in
a better position, this is because one kind of knowledge namely, scientific knowledge, occupies now so
prominent a place in public imagination that we tend to forget that it is not the only kind that is
relevant. It may be admitted that, so far as scientific knowledge is concerned, a body of suitably
chosen experts may be in the best position to command all the best knowledge available-though this is
of course merely shifting the difficulty to the problem of selecting the experts. What I whish to point
out is that even assuming that this problem can be readily solved, it is only a small part of the wider
problem” (Hayek, 1945, p. 521)
“Experts of any kind tend to look at the world in terms of a very limited number of variables-indeed,
that is a reasonable definition of what it means to be an expert. The training and experience of
experts equip them to deal with movement along some very particular trajectories, but not others. The
old aphorism that an expert is a person who knows more and more about less and less conveys an
important truth, one that has serious implications for the understanding of technological change”
(Rosenberg, 1986, p. 23)
Introduction
Much attention has recently been paid to the articulation and codification of knowledge
(Cohendet and Steinmuller, 2000; Cowan and Foray 1997; Cowan, David and Foray, 2000;), on the
grounds that "the increase in the stock of useful knowledge and the extension of its application are the
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essence of modern economic growth” (Kuznets, 1966). The debate, which has been very heated, is
open to a variety interpretations as indeed are its implications (see Knudsen, 2000; Nightingale,
2001). Instead of reviewing the entire debate we will attempt to illustrate the degree and ways in
which it applies to the French steel industry. We believe that sectoral differences are an important part
of the story and could help explain the degree and potential of transforming different kinds of
knowledge, be they scientific or empirical (Rosenberg, 1982; Pavitt, 1984; Balconi, 1993, 1999;
Divry and Lazaric, 1998; Saviotti, 1998).
In the steel industry, the main challenge lies in decontextualising the local knowledge
anchored in experts. Such experts often belong to different ‘communities of practice’. They therefore
tend to use localised jargons and vary widely in the way they carry out their tasks and interpret
technical phenomena. In order to shed more light on these issues we will focus on the different stages
of knowledge articulation and codification and integrate organizational dynamic and social links as
driving forces in the process and not as minor variables. The reason for this is that cognitive and
political dynamics are interlinked and the nature of their co-evolution can be crucial to the evolution
of the knowledge itself ( Coombs and Hull, 1998 ; Rochhia and Ngo Mai, 1999; Simon, 1999 ;
Cohendet and Llerena 2001 ; Dosi, Nelson and Winter, 2001). Articulation and codification
transform the way in which communities habitually represent knowledge and share it between their
members at different levels: new knowledge representations come into play at both the individual and
the collective level, while new objectives concerning knowledge accumulation and knowledge
preservation enter the organizational level.
In this article, we first try to show the ways in which the articulation and codification of
knowledge undermines traditional daily routines relating to the handling of blast furnaces. We
emphasise the fact that the knowledge associated to the workings of the blast furnace is mainly
empirical and difficult to master in its entirety. We also review the basic concepts of routine,
knowledge articulation and codification in order to clarify them.
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Secondly, we take our argument a step further by providing an overview of the French steel
industry. Codification in this sector was directly related to its recovery. In this context, we examine
Usinor’s organizational context and economic situation in some detail. Thirdly, we discuss the firm’s
Sachem project, which saw the introduction of an important programme of knowledge articulation and
codification in a number of stages.
Fourthly, we analyse the impact of this process, placing particular emphasis on the
organisational and cognitive effects of generic knowledge implementation. The crucial role played by
the blast furnace experts becomes apparent here. Similarly, the importance of human skills and tacit
knowledge for the system in its present form but also in its evolution also become clear.
Fifthly, we turn to the debate on knowledge in order to discuss the different forms of
knowledge involved in the process and to see whether this case study may demonstrate a change of
behaviour within the steel industry involving a new and more centralized locus of cooperation.
Finally, we conclude by reviewing the sectoral dynamic our case study highlights and the
organizational and social forces involved in knowledge articulation and codification.
I) Articulation and codification of empirical know-how in the steel industry
We begin by discussing why blast furnace related knowledge is still largely empirical in its
form, thereby increasing both the difficulties associated with its generalisation and the degree of
uncertainty in process control. Any attempt to disentangle this knowledge from individual and
collective practices by articulating parts of them disturbs daily routines. Articulation paves the way
for codification and can only be achieved by making the relevant practices explicit within different
“communities of practice”. We will examine these issues at both the empirical and the analytical level
and then attempt to devise a theoretical framework with which to explain such changes.
1) Empirical know-how and the blast furnace
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The blast furnace is used for smelting and is capable of producing different grades of steel.
The procedure involves coke, charred coal, different types of ore, hot air and gas being introduced into
the furnace and then smelted. Dross is then produced by a process of “decarburization” and
“dephosphorization”. Since the contents of the smelted scraps varies, the melted metal must be
analysed immediately in order to determine which gasses should be added to it and at what
temperatures. Many of the thermal, chemical and mechanical phenomena taking place in this kind of
large reactor are far from being well understood ( Rosenberg, 1982) .
Although some of the physical-chemical reactions inside the blast furnace have been
described by mathematical models, scientific ways of describing the relevant chemical and physical
reactions remain incomplete. However, the scope of such models is severely limited by the fact that
most of the reactions cannot be observed. To summarize briefly, we can say that the blast furnace is a
very complex structure that relies on empirical know-how and still represents a ‘black box’, in that
very few physical or mathematical models have been developed to describe what happens inside it
(Rosenberg, ibid; Steiler and Schneider, 1994). This lack of understanding enhances the importance of
human judgement, tacit knowledge and labour skills. This last point is widely recognised in the
literature:
“One might naively regard a blast furnace as a deterministic chemical system, but in fact, its
behaviour is stochastic. Many aspects of a furnace, its interior lines, the placement of tuyeres, the
quality of raw materials, the degree of scaffolding, etc –exert an elusive but consequential effect on
fuel consumption. Consequently, if one builds a taller blast furnace it is not immediately obvious
whether its coke rate indicates the systematic effect of increasing height or is distorted by unusual
random circumstances” (Allen, 1983, p. 12)
Consequently, existing knowledge of the workings of the blast furnace may be difficult to
generalise because it relates to a number of different technological artefacts. In the circumstances, any
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attempted generalisation is confronted with the problem of learning from partial experience involving
great uncertainties- a considerable difficulty for knowledge comparisons. This also means that the
day-to-day control of a blast furnace is based on a very costly trial and error process. Let us examine
this point in more detail.
The conversion process carried out by blast furnaces is continuous and takes up to 8 hours from
the introduction of the ores to the smelting stage. Any interruption is extremely costly and thus
prohibited unless an emergency occurs. However, a blast furnace tends to work more or less as
planned. Regular control is very important for both the smelting quality1 and the working life of the
blast furnace, which, on average, is 15 years.2 If the flow of ores is not regular, it can provoke an
above average erosion of the furnace’s internal brickwork and tank. It can also cause deficient casting
due to an iron notch. A number of problems can arise during this process, the most notorious
occurring when ores do not tap properly and flow on one side of the tank. This happens when ores are
insufficiently fluid and therefore create a kind of dome preventing gasses from moving up the
furnace’s throat. The ductility of the metal, which is affected by this problem, is a very important
quality of the final product and depends on the ability to carry out timely additions of oxygen and
other gases. If intervention is limited in any way, ores and smelt scraps can suddenly sink back and
cause a number of other problems, including obstructing the tuyeres and triggering explosions or gas
emissions. Two things must be checked continuously to ensure the smooth operation of a blast
furnace:
(1) the quality of the smelting scraps and their proper repartition inside the throat;
(2) the temperature inside the tank.
Team operators responsible for the continuous control of the blast furnace must be able to solve
problems and make quick decisions. However, this ability depends not only on the integration of
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many pieces of articulated knowledge but also on the operators’ understanding of what makes a ‘good
process’ and what must be done to improve control. One way to achieve the latter is to reduce the
uncertainties associated with the process. To put it another way, in order to gain a better
understanding of what happens inside a blast furnace, the firm must open the “black box” and try to
analyse the causal links between the various parameters coming into play in different contexts and
situations.
2) Routinization, knowledge articulation and codification: a theoretical framework for
understanding the blast furnace
The starting point of our attempt to come to grips with the operation of a blast furnace is provided
by the concept of routine. In defining this notion, Nelson and Winter (1982) emphasize that a
“routinized” organisation can be understood by reference to specific competencies. These encompass
and embody different types of know-how, knowledge and part of the social context in which they are
embedded. Different forms of know-how are memorised using a variety of mechanisms (different
kinds of equipment, tools, procedures, data, human know-how, etc.). In order to illustrate the interplay
between various forms of know-how we use the notion of “repertoire”: just as a more or less talented
acting troupe interprets different plays in its repertoire more or less successfully, teams in the steel
industry avail of and can mobilise different kinds of know-how. These can remain inactive or, when
eventually activated, produce pig and cast iron.
An activated routine is an expression of the repertoire and can be judged on the basis of technical
indicators such as casting delivery, steel quality, raw material and energy input and process fluidity.
Collective activity involves procedures and rules and uses artefacts and know-how as well as skills.
The collective ability to solve problems and co-ordinate different incidents is highly dependent on the
existing repertoire. The co-ordination of different routines is sometimes compared to a “circuit” that
must work “smoothly” (Lazaric and Mangolte, 1999). This means that some degree of cognitive
coherence between different kinds of repertoires is necessary before they can operate successfully.
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Effective co-ordination also depends on the relevant “motivational/relational context” (Winter, in
Cohen et al., 1995), which includes the “good will” of individuals, the prevailing interests and latent
conflicts and the discretionary aspect of behaviour within organisations.
Social relations and potential conflicts can, in fact, disturb the smooth operation of routines
and the expression of individual repertoires. Attempts to streamline individual behaviour in a
hierarchical structure through the exercise of authority or on the basis of particular incentive structures
have limited power, because forcing individuals to participate in a collective undertaking is difficult.
Individual agents have a certain amount of autonomy over their actions as well as their own way of
interpreting rules and modulating their effort, as Leibenstein (1987) put it. That is, although
individuals may have the competence to solve a problem, they may fail to do so simply because they
do not want to. In this case, a lack of individual and collective dedication can thwart cognitive co-
ordination and prevent the circuit from working smoothly.
Overall performance in the casting process is highly dependent upon:
(a) the state of cognitive repertoires, that is the accumulated stock of knowledge, and
(b) the social and relational context in which the repertoires are activated.
Most firms in this sector have looked into technical solutions, such as the implementation of
expert systems, in an attempt to improve blast furnace regularity. An expert system, however,
involves the transformation of all the knowledge stored in a particular firm, including its previous
repertoires, and therefore affects both organisational memory and routine activation. This raises a
number of questions as to where this knowledge is stored, who its carriers are, how it can be extracted
etc. As has been recently pointed out by a number of authors, this problem is far from trivial:
“It’s familiar enough that business firms and other organisations ‘know-how to do things-things like
computers to fly us from one continent to another. On second thoughts what does this mean? Is there
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not a sense in which only a human mind can possess knowledge? If so, can this proposition somehow
be squared with the idea that organisations know-how to do things? And if organisational knowledge
is a real phenomenon, what are the principles that govern how it is acquired, maintained, extended
and sometimes lost?” (Dosi, Nelson and Winter, 2000, p. 1)
Indeed the maintenance of collective knowledge through the articulation and memorisation of
best practices is not neutral. It can generate specific assets for a firm by rendering the product of
human experience more “manageable” and by contributing to the selection of routines and practices
located within the organisational memory: “The degree of articulation of anything that is articulable is
partially controllable” (Winter, 1987, p.174). Let us examine this point further: in line with the quote,
we argue that knowledge is “articulable” and eventually “articulated” when the knowledge of some
person or some organisation is made explicit by means of language. “Articulated knowledge” can be
rendered explicit through language. Language, in this context, refers to a system of signs and
conventions that allow the reproduction and storage of knowledge in such a way that it can then be
communicated and transferred between individuals3.
The process of articulation involves the extraction of knowledge from the person carrying it and
the transformation of personal knowledge into a generic form (Winter, ibid; Mangolte, 1997).
Although some forms of knowledge can benefit from it, parts of tacit knowledge may defy articulation
and be poorly reproduced and communicated.4 In other words, only a small fraction of articulable
knowledge can in fact be articulated. Moreover, the degree to which articulation will actually be taken
up as an option may differ radically between firms, depending on the associated costs and benefits
accruing to a particular firm, on its strategic vision and the importance it places on the building of
capabilities (Teece, 1998 ; Zollo and Winter, 2000).
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Articulation, however, is distinct from knowledge codification5 and may be considered as the
product of this preliminary step:
“Knowledge codification, in fact, is a more restrictive notion with respect to knowledge articulation
processes. The latter is required in order to achieve the former, while the opposite is obviously not
true. Actually the fact that in most cases articulated knowledge is never codified bears witness to the
argument of increasing costs to be incurred when scaling up the effort from simply sharing individual
experience to developing written reports, manuals or process-specific tools” (Zollo and Winter, 2000,
p. 17).
Following Zollo and Winter (ibid), we will take ‘routinization’, articulation and codification to
be three interlinked stages. The three forms can exist and evolve together. So, codification, while not
excluding ‘routinization’ and the presence of tacit knowledge, may well produce new useful insights
and therefore enrich old repertoires or create new ones. However, as we shall see in the case of
Usinor, this co-evolution is far from perfect because encoding has to face the problem of practice
selection in the presence of different technological visions. Moreover, relational and social aspects are
fundamental to the preservation of consensus between the different “communities of practice” co-
existing in a company6. Such communities play an active role by helping determine both the validity
and the content of the relevant knowledge and by contributing to or questioning the articulation
process. Let us discuss this point in the context of the French steel industry, an industry that has
undergone important industrial change during the last two decades.
II) The industrial and organizational contexts of knowledge articulation in Usinor
In order to understand the French steel industry in general and its recovery in particular, we start
by presenting a concise overview of the sector. The creation of the Usinor group and the associated
integration of different companies generated a number of opportunities for changing old routines. In
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the circumstances, it was thought that the articulation and codification of old practices would be
valuable in that it could provide a common tool and lead to a shared vision of the blast furnace
process.
1) Concentration in the steel industry
The need to articulate and codify Usinor’s know-how emerged at the end of the 70s, a time of
serious economic difficulties for the French steel industry.7 Shrinking markets and the emergence of
new competitors made substantive improvements in productivity and product quality crucial to the
survival of the industry8. The sector, in France as well as abroad, faced a protracted period of
structural excess capacity during the first half of the 1980s. This triggered a selection process and,
later, a series of mergers amongst the survivors aimed at consolidating existing industrial positions. In
this context, Usinor, using state aid available at the time, absorbed a large part of the existing French
companies. So, for example, Sacilor, an old rival, became part of the group in 1986 (see table 1). The
French State, Usinor’s majority shareholder, progressively disengaged from Usinor following
privatisation in 1995. This change also enabled Usinor to take over foreign firms, such as Arvedi in
1998, Ekosthal and Cockeril in 1999.
As the above table shows, the creation of Usinor is a recent event. The group is the outcome of a
combination of various formerly competing organisational entities. The creation of a global strategy
for a firm of this kind was far from trivial both because local plants were very decentralised and
powerful and because top management was slow in centralising its own activity (Godelier, 1997). The
participation of the French State as principal shareholder and the introduction of new managers that
had previously been working in very different sectors of the economy also triggered some important
changes. Moreover, the firm’s staff composition changed as employee turnover inside the group
increased and a new organizational culture was introduced by Mr Levy, the company’s new CEO,
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brought in from the petroleum industry. Over time, Usinor became increasingly centralised, as
common goals, practices and a common technological vision gradually took hold and introduced
some unity between the various plants. Creating a shared understanding of the technology and a
shared representation of the blast furnace was part of this strategy.
2) The need to create a coherent organisational entity inside Usinor
By the mid 1980s, the group had still not become an effective organizational entity and experts
using different routines had yet to develop a sufficient degree of mutual understanding. Decentralised
plants and the pervasiveness of forms of knowledge associated with the localised and sometimes
conflicting experiences of working with individual blast furnaces, led to the continued existence of
different ways of understanding the same problems. Although both articulated and tacit knowledge
were present, they varied across plants and different communities of practice co-existed.
Consequently, a variety of dialects coexisted within the company and, with them, different beliefs and
know-how about blast furnace operation. Each plant depended on the personal vision of its blast
furnace experts, the authoritative figures discussing, articulating, transmitting and validating
knowledge.9 These experts were a lot more powerful than their hierarchical grade and status inside the
plant warranted. Another crucial problem for the company was memorisation of knowledge in a
context of rapidly declining manpower.
Overall, four major reasons prompted Usinor to articulate and codify its knowledge:
1) The need to create unified capabilities and articulated knowledge across the group and to channel
all local knowledge into a common project in order to create shared semantics between experts
(“to be sure that we are talking about the same thing”). Moreover, apart from the linguistic
problem, the firm had to establish a shared tool, which would ensure problems were dealt with in
similar ways and lead to a common understanding of blast furnace operation. To summarize, the
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firm had to unify the variety of dialects present in the different “communities of practice” co-
existing within Usinor.
2) The firm made 98,000 employees redundant between 1977 and 1990 (mainly through a
retirement policy). Following this period, the company hired very few new employees and
therefore, the average age of the group’s employee population rose dramatically (around 50% of
Usinor’s employees will have retired by 2010). In this context, the need to save existing
collective knowledge became crucial, given that most of its human holders were soon to
disappear!
3) The need to improve the quality of the process by introducing real time intervention, thus
ensuring the rapid resolution of problems occurring during the early stages of metal fusion (the
cost of guiding the blast furnace in a plant like Sollac represents 56% of the total costs of steel
production).
4) Usinor’s merger and acquisition activity increased the concentration of R&D centres. For
example, IRSID,10 which was meant to carry out fundamental research for all French firms in the
steel industry, became, as a matter of fact, an integrated Usinor R&D centre and a de facto a
private R&D centre. This large laboratory had extensive experience in the domain of artificial
intelligence and played an active role in the implementation of the Sachem Project.
We shall return to this latter project and examine it in more detail in the next section.
III) The Sachem project in Usinor
The background to our case studies will help us provide a good depiction of the Sachem project.
Following this preliminary stage, we will describe the gradual constitution of an ‘epistemic
community’, in charge of establishing a shared vocabulary among the ‘community of practice’. This
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vocabulary plays an active role in the articulation of knowledge and helps identify the critical
practices. Codification takes the process a step further by selecting the ‘best practices’. The problem at
hand is to create models that can codify such practices in the first instance and then re-translate them
into natural language so that the proposed system can be agreed upon and socially validated by the
members of the organization.
1) The methodological approach
In order to collect consistent information about Usinor’s Sachem project, we had to carry out a
series of face-to-face interviews during the course of a year (in fact, between August 1997 and June
1998). During this first stage, we tried to collect information about Sachem from as many
representatives of the sets of actors involved as possible. We therefore conducted interviews with:
- two blast furnace experts,
- the Sachem manager,
- a knowledge manager,
- a R&D department manager, two engineers and a researcher from IRSID,
- a representative individual from Sachem’s user group (composed mainly of operators and
technicians), and
- a retired chief executive of the human resources department.
Each interview took a total of 4 hours on average: a first long face-to-face session (between
two and three hours long) was supplemented by a further discussion at a later stage aimed at
completing the previous interview (mostly face-to-face but also via telephone in some cases).
It is important to note that all interviews were summarised and incorporated into a general
research report. This was then discussed with the project’s manager in order to validate its general
vision and technical aspects, but also in order to avoid the dissemination of confidential information
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contained in the Sachem methodology. Following the collection of the qualitative information, we
also gathered general information from newspapers and books in order to enhance our understanding
and knowledge. In October 1999, a member of our team finalised important details with Sachem’s
manager. Discussions at this stage were informal and focused on specific issues on which we required
detailed documentation (for information we felt was incomplete, notably training policies).
Let us now turn to the Sachem project and trace it from its infancy to its maturity during the
1980s in Usinor.
2) Knowledge articulation in Usinor
The tradition of memorising long standing practices in order to improve processes is not new in
the steel industry. In France, many books provide descriptions of the empirical practices associated
with the blast furnace.11 In the case of Usinor, knowledge was articulated through the launch of
‘Xperdoc’ in 1989. This established a common word-list and was aimed at creating a shared
vocabulary among blast furnace experts. Shared semantics were seen as the first step in the process of
harmonising different practices, beliefs and repertoires and were meant to constitute a common
reference for the group. This “hand-book”12 was used by the experts and created an “epistemic
community”, which, in turn, generated discussion and the mutual acknowledgement of different
practices.13 “Xperdoc” had the following objectives:
(1) To bring the experts together by giving them the opportunity to meet (“Xperdoc” in this way
gave them a chance to exchange views and discuss their practices);
(2) To establish an acknowledgment of different practices and help institute personal trust
between experts.
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This latter point was far from trivial: the creation of a ‘common reference’ depended on a
prior selection of best practices, which was difficult to implement. The firm needed to establish a
climate of trust before it could achieve a real acknowledgement of practices among experts. As
Steinmuller recognises:
“The availability of collective memory within an epistemic community also allows the reduction of
remembering solutions to problems that have been previously discovered and documented. This
permits a reduction in effort devoted to re-invention. For such gains to be realised the codification of
solutions need not to be complete. Instead the purpose of the collective memory is to ‘signal’ the
availability of the previous work and a solution that might arise again within the community. The
gains from this process are both collective and individual and therefore are incentive compatible with
efforts to contribute to the group memory project. Note that it is important for the group to have some
degree of trust and mutual regard for such system to work. Otherwise individuals will not regard the
‘solutions’ or ‘ideas’ of others to be worth the time it takes to become aware of and evaluate them in
a new context” (Steinmuller, 2000, p.367).
‘Xperdoc’ brought changes to the usual ways of doing things and was understood in different
ways by the experts who had shaped their personal vision through their own experience of the
workings of the blast furnace. Moreover, Xperdoc produced a radical change in organisational
memory and in the knowledge activated daily in the firm, by questioning old repertoires and social
links surrounding the activation of these repertoires.
3) Identification of ‘best practices’
In 1987, the Sachem initiative came to light mainly due to the efforts of a part of the staff who
believed that artificial intelligence could help memorise a large part of the knowledge held by experts.
This idea was only implemented in 1990 under the supervision of Francis Mer, the company’s top
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manager, who was particularly interested in this new tool in his efforts to improve Usinor’s
productivity.
During the course of year, over a hundred artificial intelligence applications were tested in
collaboration with Usinor’s R&D centre, IRSID. The implementation of artificial intelligence had
benefited from European subsidies and a 40-strong team spent 5 years working towards the creation of
systems and their diffusion inside the group. Following this first stage, 17 technical solutions were
selected, among which the Sachem project.14 This latter was first implemented in October 1996,
following a long period of discussion within the company which focused mainly on the following
crucial questions:
- What kinds of knowledge have to be articulated and stored?
- How should practices be selected?
- How can such practices be transposed into a new tool?
- How can losing tacit knowledge be avoided as the importance of articulated knowledge increases?
In order to identify the ‘best practices’ and key ‘know-how’, 13 experts were chosen among
those who had co-operated in the writing of “Xperdoc”. Experts were selected according to know-how
and location in order to ensure a fair representation of the various types of knowledge prevailing in
the different plants (Sollac Fos, Dunkerque, Lorfonte Patural, etc.). The team worked with six
“knowledge engineers” in order to extract the “core know-how” and articulate it (400 interviews were
conducted).
Before the extraction process took place, an interpretative model15 was created in order to gain
an understanding of expert know-how. Blast-furnace experts based this interpretative model on a
collection of representative case studies. The samples exemplified the different ways in which experts
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understood the blast furnace and the ways in which they solved problems. This preliminary work took
10 months (from September 1991 to June 1992) and allowed identification of the crucial know-how
activated daily. Consequently, the perimeter of expertise was delimited and the relevant expertise was
diffused among the experts. This process of disclosure also entailed a selection of knowledge from the
activated daily know-how. Two important conditions had to be fulfilled before knowledge could be
selected, validated and eventually codified.
First, knowledge had to be generic, that is to say, it had to be sufficiently broad to remain
meaningful once it had been dissociated from its local context and specific use. Variety amongst
experts and their associated expertise, which stemmed from different plants, helped identify the
broader strands of knowledge.
Second, the knowledge had to be identified and acknowledged as ‘true’ by all the experts
involved. The knowledge had to be deemed useful to and employable by operators before it was
codified. A knowledge manager clearly recognised this point: “the automation of knowledge is the
transformation of true expertise into useful one”. Consequently, at this stage, parts of the pre-existing
knowledge were lost while others were accentuated (validation reinforces the parts collectively judged
as crucial to the detriment of those deemed too tacit or local to be selected).
4) Selection and validation of ‘best practices’
The selected know-how was classified and indexed between April 1992 and May 1995.
During this stage, called AED,16 key concepts and their articulation were registered in order to
introduce consistent problem solving. For example, 150 potential blast furnace anomalies were
identified and classified according to their position in the steel making process (quality, gas, wall,
temperature decline, permeability, etc.). On the basis of this knowledge, specific problems (notably an
unusual temperature increase) could be identified systematically, operators alerted and specific
recommendations made on the actions to be implemented.
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Following the process of translating words into codes, the stage of knowledge
acknowledgement and validation was launched.17 Blast furnace experts had to recognise their codified
know-how, which had been radically transformed by computation. This stage was crucial, as it
allowed experts to verify whether the codes did in fact represent what they had intended to articulate
in the first place. Knowledge validation was a long and difficult stage as consensus had to be reached
before it was concluded. Individual meetings, which saw experts having one-to-one discussions with
knowledge engineers, and a collective one, including all the experts, took place. Local know-how had
been radically transformed as the “knowledge engineers” had changed the way experts represented
their own expertise. As it happened, parts of the general knowledge codified in the expert system had
ceased to be meaningful to some of the experts. As a result, the knowledge engineers designed a
linguistic model (in natural language) in order to translate the code. The experts were thus able to
recognise their own expertise and acknowledge it collectively. Similarly to the interpretative model,
which had translated the experts’ articulated knowledge into codes, the linguistic model converted
codes into words. Different models had to be created because different levels of abstraction and local
knowledge were required depending on their use.
The different levels of knowledge created by Sachem are depicted in the following table (table
2), which also shows the break introduced by the automation of knowledge.18 The different stages of
the Sachem project are summarised in the table 3.
IV) Cognitive and organisational consequences of the Sachem project
The consequences of articulation and codification were very important because actual
knowledge content changed drastically, thus forcing some blast furnace experts to modify long
standing beliefs and their usual interpretation of technical phenomena. One would expect some degree
17
18
19
of resistance to this process. In fact, co-operation in the knowledge identification and validation
processes proved crucial and was successful because the blast furnace experts were acknowledged as
the organizational knowledge carriers and their existing expertise was validated inside the ‘epistemic
community’. The company also allowed the experts to play an active role in the adaptation and update
of the system in order to integrate minor changes and improve its daily performance.
1) Deep transformation of the content of knowledge
Articulation and codification entails a radical change in knowledge because it involves the
selection of parts of all available know-how. Moreover, its affects the content of knowledge, as, in
practice, the traditional expertise anchored in an expert’s routines is alive. A first transformation
occurs when experts put their practices and parts of their tacit know-how into words. This
‘explicitation’ creates articulated knowledge, which entails a first selection of know-how. Parts of
know-how are too dependent on practices prevailing in local plants: these cannot be articulated and
resist extraction because of their ambiguity and fragility (deeply tacit knowledge and personal
judgement may defy codification by virtue of being too personal). As Dreyfus emphasizes knowledge
may be difficult to disembody :
“Experts generally know what to do because they have a mature and practiced understanding. When
deeply involved in coping with their environment, they do not see problems in some detached way and
consciously work at solving them. The skills of experts have become so much a part of them that they
need be no more aware of them than they are of their own bodies” ( H. and S. Dreyfus, 1986, p. 44).
A second transformation takes place when articulated knowledge is turned into code, as
technicians in the area it affects have their own ways of representing and selecting knowledge: parts of
knowledge may be deemed useful simply because of the nature of particular technical parameters
embedded in the expert system. In other words, the nature of the container is far from being neutral
and can in fact change the knowledge content itself by including unnecessary bits of know-how while
20
excluding others. Consequently, the outcome is not a simple translation of existing knowledge into
code but also a reformulation produced by the knowledge engineers and validated by the experts. Each
stage of the transformation, which takes place in the handover from live expertise and activated
knowledge to the memory of an outsider and from one outsider to another, entails a change in the
preserved knowledge. This is neither a perfect equivalent nor a total substitute of the knowledge
carried in the different memories. As a knowledge engineer recognised:
“It is the same relation that the painter has with the nature he observes. He magnifies it. The
knowledge engineer describes the know-how of the expert; his art is to capture live expertise in order
to reveal it and make it consistent outside of him”.
Hatchuel and Weill (1992) brilliantly describe this deep transformation of knowledge and
argue that the container transforms knowledge content because each language has its very own ways
of representing things. Repeated transmission through a variety of languages will always involve
some losses as codes differ radically across languages. Moreover, articulation and codification are in a
way unpredictable because they are largely based on individuals’ willingness to participate in a
process that is likely to depart from their initial experience (most implemented codes differ
substantially from the original individual representations of particular technical problems). This is
precisely the reason why the translation into natural language and the validation by experts that took
place following the codification process was crucial to the Sachem project: it prevented experts from
feeling they lost their original know-how once they had passed it on to the knowledge engineer.19
2) Revision of prior beliefs and reinforcement of empirical know-how
An important contribution of the Sachem project was that it brought about a change in the
way operators and experts themselves understood the blast furnace process. As we have already
emphasised, the blast-furnace experts’ know-how is generally empirical and has not yet be captured in
19
21
its entirety by a scientific model. Implementation of the Sachem codes meant that some parts of pre-
existing know-how were validated and acknowledged, whereas other prior beliefs were discussed and
collectively rejected. In this way, the newly validated know-how, a subset of that previously
employed, in itself contributed to the improvement of the company’s empirical knowledge.
For example, before Sachem was set up, a phenomenon called ‘fluidisation’, involving an
increase in temperature above the ores was often observed. This was connected to the suspension of
coke iron ore and limestone flux and was associated with a fall in different ores. In the ‘Fos sur Mer’
plant two beliefs prevailed about this phenomenon:
Belief 1: Experts believed that fluidisation never occurred in ‘Fos sur Mer’
Belief 2: When descending ores and fluidisation were detected, experts did not associate it with
fluidisation and the temperature increase was attributed to a different phenomenon.
After Sachem came into use, prior beliefs were revised, notably:
Belief 1: Fluidisation did take place in ‘Fos sur Mer’
Belief 2: When fluidisation occurred, fluidisation had preceded the descent of ores by an hour.
In practice, the system provided a robust tool against which empirical know-how could be
tested in order to improve and generalise it. As the process unfolded, prior beliefs were gradually
redefined: what had been known to be true started appearing to be only partially so (especially
through the discovery of new causal links between technical events). The causal links connecting
separate technical events, which used to be tacit and intuitive, were tested and systematically proven.
By this way, the articulation process led to more robust beliefs and unquestionable know-how within
the firm by changing the experts’ and operators’ local cognitive representations. The Sachem project,
also led to the creation of generic knowledge. However, this was not simply based on technical
parameters but depended vitally on the social co-operation of the blast furnace experts, the main
22
holders of this knowledge. In other words, the way in which this tool was used was crucial to its
survival, as we will see in the following section. Usinor’s management was aware of these
fundamental issues and let the experts play an active role in this process.
3) The co-evolution of tacit and codified information: a continuously updated system and a
permanent activation of individual skills
Sachem is able collect systematic data (with the aid of over 1000 sensors placed inside the blast
furnace), interpret and qualify them. The system continuously compares the collected information
against a reference situation and can, in this way, make certain predictions, detecting problems and
making recommendations to operators. It provides a global vision in real time, a selection of
interesting indicators and some solutions. Operators retain a certain amount of autonomy in decision-
making, as they are able to ask the system to provide explanations on a number of the phenomena it
detects. The system has been set up in such a way as to avoid automatic guidance, which would risk
reducing operators to a passive role and thereby undermine their ability to solve problems.
In order to stimulate the operators’ energy and attention, the company introduced an important
training policy (‘off the job training’). This was put in place in 1995, before Sachem had begun
operating effectively. In 1995, a team of operators and blast furnace experts was created in order to
anticipate potential problems with Sachem and to encourage interaction with the new technological
artefact. The training policy put an end to the old apprenticeship system and particularly affected the
transmission of know-how from experts to newcomers.20 Prior to the introduction of the expert
system, it took 10 years of ‘on the job training’ to become a confirmed operator. Now only 3 years of
work within the firm are required to reach operational status and 5 to become a confirmed operator.
The new training policy meant that experts and operators themselves had to gain an explicit
20
23
understanding of their own know-how. In fact, they were required to recognize why they were solving
particular problems and had to justify their repertoires. This transformed their knowledge and
expertise, that had been largely based on “knowing how” and activated new pieces of detached
knowledge, based more on “knowing that”21.
The training policy was implemented in order to avoid a “cognitive prosthesis syndrome”. In
other words, it was meant to ensure that operators and experts remained constantly vigilant and active
and did not become too confident in the new technological artefact. In fact, the system still requires
direct input from users and blast furnace experts for its updates. Moreover, the system’s
recommendations resulting from its interpretation of the data (especially the identification of causal
links between different events) and the operators’ ultimate decisions are still compared and
scrutinized. The gaps between the system’s recommendations and the operators’ decisions are
systematically deconstructed in order to detect divergences. This analysis allows the database to be
enriched and updated. Parts of the tacit know-how that were not articulated by the operators and blast
furnace experts and were considered insignificant during the second stage of articulation (in 1992),
are gradually incorporated into the system. Updates are formalised in an annual meeting with the
operators, foremen and experts. Experts play a crucial role in this context as they formalise and
systematise the results of the divergence analysis of users (operators). Moreover, they introduce new
articulated knowledge that will later be turned into codes by the knowledge managers. The system’s
knowledge base is therefore constantly enhanced by new articulated knowledge, a process that
prevents it from becoming rapidly obsolete. The constant activation of human skills is very important
because, as Dreyfus reminds us, the coupling of human skills and machine capacity is crucial in order
to make use the expert system’s entire potential (Dreyfus, 1972). In fact, the expert system is unable
to deal with new situations and solve novel problems. Its abilities are limited by its existing,
previously articulated, knowledge. In the absence of integration of new pieces of knowledge and of
their codification, the system would, in the long run, become outdated.
21
24
Let us now turn to the impact the creation of generic knowledge had not only on the Sachem
project but on the steel industry as a whole.
V) Knowledge appropriation in the steel industry : an open discussion
Usinor’s articulation and codification of knowledge is also symptomatic of the reinforcement of
private appropriation within the steel industry. This point has to be emphasized because it stands in
stark contrast to the ancient tradition of knowledge sharing and ‘collective invention’, which prevailed
at the end of the nineteenth century. Nevertheless, the very same period that saw knowledge become
internalised in Usinor also witnessed the establishment of new areas of co-operation. These displaced
the ancient locus of ‘collective invention’ (now tightly located within Usinor’s epistemic community)
and put the creation of generic knowledge in its place. We shall examine these different aspects more
depth.
1) Knowledge sharing and ‘collective invention' in the blast furnace: changes in firm behaviour
during the twentieth century
The steel industry has attracted the attention of many observers because its traditions of
knowledge dissemination inherited from the past are very important. Allen (1983) introduced the
notion of ‘collective invention’ to characterise a knowledge sharing attitude whereby inventors
disseminate their knowledge instead of protecting it, in order to benefit from knowledge disclosure. In
the United States as well as in England, the greatest improvements in the blast furnace process were
achieved primarily through classic ‘learning by doing’ and local experimentation rather than research
and development inside firms:
25
“During the nineteenth century, non profit institutions and firms undertaking R&D were of little
importance as sources of invention for the iron industry. The British and American governments
funded no research, and university faculty published scarcely any findings. Firms did not maintain
research departments and seem rarely to have expended any appreciable quantity of resources with
the main intent of advancing knowledge or improving products and processes. What is more
remarkable is that the independent inventor was also not the major supplier of new inventions to the
iron industry. Some major improvements can, of course, be attributed to famous inventors –to the hot
blast to Neilson, the Bessemer converter, and to the basic process to Thomas and Gilchrist. However,
if one examines a sector like the blast furnace industry and determines the inventions whose diffusion
were important for the growth in efficiency, it proves impossible to attribute their discovery to any
single inventor. Certainly, no one received a patent for many of these advances. Thus the increase in
furnace height and blast temperature that were so important for productivity growth in England’s
Cleveland district evolved through that actions of many individuals over a twenty years period.(…)
Such experiences are usually dealt with by labelling them as ‘the accumulation of minor
improvements’ ”(Allen, ibid, p. 1-2).
Allen showed how potential entrants could benefit from this knowledge during the building of
a new furnace: the information was available to both professional associations and the labour market,
where rotation contributed to the diffusion of knowledge. So, many advances in the steel industry
were the outcome of collective efforts. For example, the idea that capping the blast furnace could lead
to the use of waste gases as fuel or for the reduction of limestone was invented collectively (Allen,
ibid).22 In this context, knowledge sharing, far from being an exception, was the generalised rule:
“Each individual has some cherished bit of knowledge, some trade secret which he hoards carefully.
Perhaps by sharing it with others, he might impart useful information; but by an open discussion and
interchange he would almost for certain, learn a dozen things in exchange for one given away.
22
26
General increase of knowledge would give general improved practice, most likely a larger use of the
materials in which a manufacturer is interested” (Muntz, 1909 in Allen, ibid, p. 19).
This behaviour evolved when oligopoly became the rule in manufacturing in every country.
By this way the transformation of the industry in the twentieth century, aimed to take advantage of
economies of scale and scope, led firms to concentrate their efforts on private research and
development programmes. In this new context, collusion and knowledge exchange ceased to be the
rule and were gradually replaced by secret policies and closures.
In France, the jealous protection of secrets eventually predominated although it proved
difficult to implement. Leaks23 and the disclosure of generic information to technical and professional
associations did not disappear altogether (Jollivet, 1999).24 In practice, however, the concentration of
French firms and the change in their legal status during the 70s led to the gradual reinforcement of
private knowledge appropriation, not only between rival firms but also within companies. At the same
time, an important knowledge memorisation programme was implemented and coincided with the
creation of internal R&D centres. The IRSID R&D centre for example, now integrated in Usinor,
directed its resources towards the company’s exclusive needs, despite its historical mission, which
was oriented towards knowledge disclosure to the French steel industry as a whole.25
In Usinor, the change of attitude towards knowledge dissemination was in line with the
centralisation movement that took place inside the firm and was aimed at building a common identity
within the group (see above) and to increase blast furnace control.
Let us now discuss the different states of knowledge present in the company in order to assess the
changes that followed the introduction of generic knowledge into the different communities of
practice.
23
24
25
27
2) Generic, local and universal knowledge in Usinor: a discussion
Sachem generalised local know-how and selected some of the daily-activated routines. The firm
benefited from this transfer of knowledge and has, in the last few years, improved its productivity,
increased operator reactivity, seen a global improvement in casting quality and a decrease in steel
making related incidents. In 1996, these gains were estimated to amount to 0.18 % of global
turnover.26 From a long-term perspective, the creation of strategic assets involving the memorisation
of most of the company’s know-how is difficult to evaluate in simple monetary terms. The creation of
‘generic knowledge’ is also a strategy of ‘knowledge disclosure’, which prevents the company from
losing crucial technical know-how and helps improve its capabilities. Generic knowledge is key to
this process as it broadens the original know-how created inside the various communities of practice.
As Nelson emphasises:
“In many cases specific practice is buried deep in the structure and custom of the host institution and
is difficult to disentangle. In this cases technology, or at least a particular technique, is more like a
private good that a latent public good (....) Generic knowledge not only tends to be germane to a wide
variety of users and user, but technological communities and professions tend to grow around such a
knowledge, which then become the stock in trade of the insiders of a field” (Nelson, 1993 p. 12).
Generic knowledge can also be applied to a broad range of domains that lie outside its original
locus of creation (i.e. the original community of practice). Our case studies show, however, that
generic knowledge is not a public good.27 According to the literature, for knowledge to qualify as a
public good, any agent should be able to use it as universal information without incurring any costs
and without having to make any investment in its duplication. When knowledge is considered as a
public good it is seen as a set of ideas or universal information that can be spread indefinitely to a
large number of agents. This simple step (consisting in the reduction of knowledge to information and
26
27
28
to a quasi-public good) proves very problematic with Sachem. What we have observed is that the
creation of generic knowledge does not generate a free access to technology, but on the contrary,
enhances collective appropriation within a particular group. This means knowledge held by the
‘epistemic community’ cannot be simply reduced to “knowing that” and conscious information.
Moreover, “knowing how” is still activated in the update of the system, as an ingredient aimed to
avert the system’s rapid obsolescence, as we saw above.
In effect, Usinor must preserve its specificity and technological advantage vis-à-vis its
competitors and generic knowledge is a means to achieve it. Consequently, the articulation and
codification processes are also compatible with the reinforcement of control required for firms to
benefit from Schumpeterian rent (Teece 1998).28 This is one of the reasons why generic knowledge in
the steel industry may be articulated, codified and tightly controlled, notably inside the ‘epistemic
community’. Compared to the old dialects and practices prevailing in individual plants before the
implementation of Sachem, generic knowledge is, of course, more likely to be redeployed and
articulated into more widely shared categories. It is therefore more likely to be shared and
homogenously distributed and also more likely to be centralized (Lazaric and Marengo, 2000). This
kind of knowledge usually stands in contrast to the local knowledge involved in daily practices and
dispersed inside the firm, as Fleck recognizes:
“The local practical knowledge is highly contingent to the particular firm concerned. It deals with the
specific ‘knowledge base’ built up over many years of experience in carrying out the firm’s business.
Moreover, this local component is usually distributed among many operatives involved in day-to day
activities. In many cases, this knowledge is tacitly embodied in skills and practices which have been
gradually formed over a period and are not available in any other centralized or formalized form.”
( Fleck, 1994, pp. 641-642).
28
29
The differences between the different states of knowledge (local, generic and universal) are
discussed and summarized in the following table (table 4).
As the above table shows, the implementation of the Sachem project introduced a certain
amount of generic knowledge and therefore reduced the dissemination of collective know-how inside
the industry. The centralisation of best practices that followed Sachem is a good example of a new
compromise between tacit and universal knowledge and a change in the locus of collective invention,
previously dispersed in many different organizational entities and spread out in the sector.
Conclusion
In our view, the articulation and codification of knowledge in the French steel industry is a
manifestation of the new behaviour that has come to dominate the sector. This is driven by the
willingness to capture value from knowledge assets, to improve understanding of the knowledge
associated with the workings of the blast furnace and to increase the furnace’s productivity and
reliability. One could conclude briefly that this is an example of the end of ‘collective invention’ in
the sector: collective know-how is now more likely to be held by private firms intentioned to protect
their knowledge instead of sharing it with their competitors. In effect “collective invention” has been
gradually displaced around the ‘epistemic community', preventing knowledge dissemination among
steel competitors. In Usinor’s case, it was a new technical tool that changed the role of the blast
furnace experts and created non-exclusive knowledge. Experts have now ceased to be the only
disseminators of knowledge and have a new part to play in the update and maintenance of existing
organizational knowledge. The organisational consequences of this process are not neutral, of course,
as they create centralised know-how and generic knowledge, parts of which could be tradable outside
the firm. In other words, capabilities have changed: they are now less dependent on human holders
and are anchored to the organisation and its shareholders.
30
Social context and managerial input play an active role in changing the content of knowledge
(from personal and private to shared and generic), by creating the climate of trust and mutual regard
required to achieve true co-operation between individuals and active participation. Participation,
which is essential to the creation of a new representation of the blast furnace, involves renouncing old
beliefs (concerning technical events such as fluidisation), once prevalent amongst different
‘communities of practice’. The system relies substantially on the permanent combination of local and
generic knowledge for its improvement, as parts of it are revised by adding new causal links between
technical events. The implementation of a training policy has helped this combination by allowing the
system’s performance to improve through permanent activation and update. In order to prevent a rapid
fossilisation of the company’s know-how, live expertise, human skills and tacit knowledge are all
required: they are fundamental to moving the system beyond the stage of codification and are crucial
to its constant enrichment. New pieces of knowledge, which originally were deemed to defy
articulation, were in fact articulated over time as new causal links between technical events were
discovered. Nevertheless human beings and machines are not substitutes but complements and human
ability is always required for solving new problems: generic and local knowledge are two faces of the
same coin and are both necessary in order to improve the technological tool and organizational
repertoires inside the company.
________________________________
Notes
A “speedy guidance” produces a molten metal that is “too hot” (i.e. including too much silicon), whereas a
“slow guidance” can produce a “cold” molten metal (with insufficient silicon and too much sulphur), which is
unusable.
31
2 Blast furnaces are extremely expensive: building one costs around EUR 300m and repair costs can reach
EUR 150m. The casting process alone accounts for 56% of Usinor’s average steel making costs. This is one of
the reasons why improvements in casting and blast furnace guidance are crucial.
3 Natural language has a more precise definition because its grammar produces a certain degree of consistency
between the various signs. On this important debate, see Choamsky (1988), Piaget (1970) and Polanyi (1958).
4 Traditional know-how may be difficult to articulate as it is affected by the way in which its “master”
transmits his know-how during training and apprenticeships and because of the presence of important tacit
know-how. For example, Verry (1955) reviews the sensorial know-how involved in the steel industry’s casting
process. Before 1914, rollers in the Ardennes region were directly involved in the temperature control of metal
before it was put into the rolling mill. They directly observed the colour of the metal, which can take a variety
of colours ranging from pale straw (225°) to brownish purple (260°), dark purple (280°) and sea-green (350°).
Six different shades of red were known. Rollers would strike a piece of dry wood on the hot metal and
determine the metal’s temperature by observing the reaction (slippery wood indicated 350°, smoking wood
indicated 400° and a spark indicated 425°). Even though some of this know-how was written down and
articulated in a book, it did not entirely capture the knowledge held by the rollers. The refined perception of
colours remained unformulated and deeply tacit. Acquiring this skill required a long apprenticeship with a
master.
5For a debate on codification, see Cowan and Foray (1997), Cowan, David and Foray (2000) and Nightingale’s
(2001) reply. Nightingale argues that tacit and codified knowledge are not substitutes and presents some
doubts on the way in which codification may precede articulation, most notably via a “codebook”.
6 For a debate on the notion of “community of practice”, see Brown and Duguit (1991), Wenger (1998) and,
more recently, Cohendet and Llerena ( 2001).
7 See L. Boy et al (1983), who describe this crisis and the actions taken by the French state to help various
firms in France.
32
8 For a description of the Italian case during this period, see Balconi (1993).
9 As Godelier (ibid) insists, Usinor drew mainly on an internal market and on the basis of length of service
rather than seniority. The elders in fact were the repositories of the company’s memory and were the only
people capable of introducing younger employees into the company and providing them with ‘on the job
training’.
10 The acronym stands for “Institut de Recherche de la SIDérurgie Française” (the French steel industry
research institute).
11 The work of Thierry (1940) is a good example of an attempt to describe empirical knowledge (i.e. a
description of the different situations and incidents that may occur during the process) in order to disseminate
it, by producing general guidelines based on the study of local experience. During the 1980s, the need to
overcome this limitation lay behind Corbion’s construction of a kind of dictionary of the steel industry’s
ancient language in an attempt to make it more explicit (Corbion, 1989). Corbion started off with 730 words
but many additions were gradually made to the original so that by the mid-80s the dictionary contained 1,760
words. By the end of the 90s the figure had reached 12,000. The aim of this glossary was of course to
articulate the industry’s language and to record different dialects and expressions in order to preserve the
social and linguistic identity of its holders.
12 Hatchuel and Weil deal with the same problem in the case of an expert system. Handbooks are usually the
first stage in a longer process and are intended to transfer, translate and articulate current practices through a
support instrument known to all contributors. For a more general discussion, see also Cowan and Foray (1998).
13 According to Mansell and Steimuller ( 2001) the notion of epistemic community has been firstly developed
by Haas (1992). Recently this notion has been used by Steinmuller 2000, Cohendet and Llerena, (2001) and
Cowan, David, Foray (2000). For these latter the notion concerns small groups of “ agents working on a
commonly acknowledged subset of knowledge issues and who at the very least accept a commonly understood
procedural authority as essential to the success of their knowledge activities” (Cowan et al. p. ).
33
14 The acronym that gives its name to the project stands for “Système d’Aide à la Conduite des Hauts
fourneaux En Marche” (aid system for the control of a working blast furnace).
15This was called AEG (the acronym stands for “general analysis of expertise”).
16 The acronym stands for ‘detailed analysis of expertise’.
17For a description of a similar way of knowledge acknowledgment and knowledge validation during the
process of a implementation of a knowledge based system, see de Jongh et al.( 1994).
18 More specific and technical details about KADS (Knowledge Acquisition and Documentation Systems) are
reviewed in a recent article by N. Shabolt (1998).
19 The way experts co-operated in the construction of Sachem and their involvement in the process of
extraction and articulation of their own expertise may appear curious to external observers. Several arguments
can explain their co-operation. First, experts and operators were sensitised to the fact that part of their own
memory had to be preserved and communicated to others. Most of the experts were also encouraged to transfer
their know-how before reaching retirement and were proud to participate in the passing of knowledge to
younger employees. Secondly, operators, technicians and blast furnace experts in the steel industry belong to
the same ‘community of practice’. The latter are very powerful because of their long-standing experience
(their expertise is essentially based on ‘on the job training’ and not necessarily validated by diplomas). Their
own legitimacy is entirely based on their personal knowledge and not on hierarchy. To the experts,
participation in the Sachem project meant that the company acknowledged their empirical knowledge and
validated their extensive work.
20 It is now impossible to hire unskilled operators, as all newcomers must already be endowed with a technical
diploma called BTS (brevet de technician supérieur), which can only be obtained after the completion of
secondary school.
34
21 Know-how usually refers to the total of unconscious and conscious knowledge. Know-that is described as the
conscious level and as knowledge that can be articulated. For a more in depth discussion on this distinction,
originally introduced by Ryle (1949), see Loasby (1999).
22 See Rosenberg (1982) on the use of different forms of fuel for the blast furnace and on ways to reduce fuel
consumption, and Jollivet (1999) on the introduction of Pulverised Coal Injection as a way of reducing coke
input into the French blast furnaces.
23 For an amusing story on the French steel industry, see Fuchs (1980), who shows how a Japanese company
got hold of most of the Dunkerque plant’s plans and strategies during the 1970s.
24 For more details on the active role played by technical and professional associations, see Jollivet (1999).
25 Even if some of IRSID’s projects constitute fundamental research (such as physical and mechanical
properties, different types of corrosion, secondary processing and surface treatments, etc.), most of them are
directed towards Usinor’s private needs and relate to the application of artificial intelligence.
26 This is Usinor’s own estimate.
27 Since Arrow made his contribution, many parts of the production process have in practice been assimilated
into information. More recently, Romer gave new life to the debate by considering information as a quasi-
public good. Here private individuals who respond to market incentives can control many ideas (Romer,
1993). By contrast with private competencies, the principal advantages of quasi-public goods derive primarily
from their unlimited extension beyond the holder: “when the person dies, the skills are lost, but not a non rival
good that this person produces- a scientific law; a principle of mechanical, electrical or chemical engineering;
a mathematical result; software; a patent; a mechanical drawing; or a blueprint-lives on after the person is
gone”(Romer, 1990). Two main attributes are necessary for a good to qualify as quasi-public: it must be ‘non
rival’ and ‘non exclusive’. In order to be non exclusive, quasi-public goods, must be available for use
regardless of the availability of human support. The perfect example of a non rival good, according Romer, is
35
a design, because ‘once a design is created, it can be used as desired in as many productive activities as
desired’.
30 “Intellectual property systems have been strengthened since the 1980s, both in the US and abroad. Moreover,
intellectual property is not just important in the new industries- such as microelectronics and biotechnology- it
remains important in pharmaceuticals and chemicals and its receiving renewed interest in more mature
industries such as petroleum and steel” (Teece, 1998, p. 57).
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Table 1 : The creation of the Usinor group
Years USINOR SACILOR
Forges and Aciéries du
Nord et de L'Est
Forges and Aciéries de Denain-Anzin
1948 Usinor Sollac is created with a concentration in Lorraine
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1951 Creation of Sidelor
1953-1966
Merger of Usinor and Lorraine-Escaut Creation of Lorraine- Escaut
1968 Creation of Wendel Sidelor
1974 Creation of SACILOR (merger of all existing plants)
1970-1980 French Steel Concentration: Usinor and Sacilor(many plants are shut down, including Valenciennes Longwy and Denain)
New manager, Mr Etchegaray, brought in from a different industry
1981 French state intervention in the two firms(90 % French state participation)
New manager: Mr RH. Levy
1986 Merger and birth of the public group Usinor-Sacilor
1995 Usinor-Sacilor becomes a private group
1997 Group renamed Usinor
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Table 2 : The Sachem methodology
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Table 3 : A summary of the different stages of the Sachem project
Years Main objectives Technical name of the sub-project and process of knowledge’s transformation
1989 Shared vocabulary among blast furnace experts Xperdoc(First stage of knowledge articulation)
1990 .Exploration of various opportunities provided by artificial intelligence
.Choice of the Sachem methodology
1991-1992 . Delimitation of the blast furnace expertise with representative case studies
AEG( Second stage of knowledge articulation )
1992-1995 . Selection of core know-how
. Encoding process and validation by blast furnace experts
. Official acknowledgments of the ‘epistemic community’ in Usinor
AED( Codification)
1996 . Sachem implementation in Usinor Sachem
Table 4: The different states of knowledge in the steel industry
Types of knowledge
Charac-teristics ofknowledge
Local Generic Universal
Localisation . Knowledge is anchored to a blast furnace and some experts
. Knowledge is partly extracted from local context, diffused to each plant, and usable by various experts.
. Knowledge is considered as information, a public good
Diffusion . Circulation and communication of this knowledge is rather limited because decentralized ( transfer of knowledge is mainly under the control of the apprenticeship’s system)
. Knowledge is spread inside the ‘epistemic community’ of the blast furnace and centralized inside Usinor
. Circulation and appropriation of knowledge may be limited by property rights and institutional protection or by agent calculation.
Properties
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. Tacit . Exclusive but partly shared inside a small ‘community of practice’
. Articulated and codified
. Non exclusive and shared inside the ‘epistemic community’ . Partially rival (Sachem is used in Usinor and controlled in order to avoid its expansion to competitors).
. Non rival . Non exclusive
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