Tacitness, codification of technological knowledge and the organisation of industry☆
Introduction
This paper sheds light on the wave of codification of technological knowledge which has occurred over the last 20 years due to the availability at increasingly low cost of electronic automation technologies and measurement instruments and on the way this process affected the organisation of industry. It illustrates the results of a wide ranging investigation, which was undertaken by visits to factories and direct unstructured interviews to managers, technical experts and operatives of firms active in numerous manufacturing sectors, such as various branches of the engineering and metal working sector, the semiconductor, steel, chemical, automotive, textile, footwear, and printing industries.1 Parts of the investigation are presented here in the form of case studies.
In a historical perspective, this is the second epochal wave of codification, the first one having been realised by applying the Taylorist principles of scientific management; it is complementary to the first, bearing upon those areas, only slightly affected by the Taylorist rationalisation, where the production flow could not do without the discretionary activity of skilled workers, and is leading to the progressive reduction of craft and manual work in the direct process of manufacturing.
As is well known, from the beginning of the 20th century the Taylorist and Fordist reorganisation of factories carried out the codification of production knowledge previously held by workers, by clearly articulating and subdividing it into elementary tasks (through the time and motion studies), easily transmissible via operational protocols. The control of knowledge was thus transferred from the heads of skilled workers to the Planning Department (Coriat and Dosi, 1998) and individual skills were transformed into organisational competences incorporated in operational and higher order co-ordination routines.
Taylorist principles and methods were especially suited to be applied to those operations that consisted of a set of decomposable acts each of which was inherently simple and repetitive. Since the intelligence needed to carry them out lay mainly in the ability to plan and to integrate their sequence, the opportunity existed to transfer this systemic intelligence from the traditional skilled workers to a specialised department, rationalising the manufacturing process by breaking it down into elementary tasks to be assigned to unskilled workers. This situation characterised especially the assembling stage of most products, and the Taylorist rationalisation gave rise to the most significant increases in productivity precisely in the realisation of the products which required a particularly demanding assembling phase, like cars or sewing machines. But in the processing phases many operations inherently relied upon the application of a specific cognitive competence acquired by skilled workers through experience. The codification of their tacit knowledge and the routinisation of these activities could not be accomplished without the deployment of technological means capable of measuring those physical phenomena (like the temperature of steel or the uniformity of doping on silicon slices) that the skilled workers used to evaluate by sensory organs (as, in the case of the temperature of steel, by recognising the “right” colour of molten metal, or in the case of the doping, the “right” distance of smoke rings in the doping furnaces), and also of controlling and guiding intelligently the production flow, thus substituting human judgement and effort. The current wave of codification of technological knowledge, and the intelligent automation movement that it supports, thus, is grounded upon the availability at lower and lower cost of the means of quantifying the underlying, micro-level physical transformations and the flow of materials through the process of production.
Computer-based automation (CBA) is currently reshaping all manufacturing activity, since assembling is also being affected, even though at a much slower pace than processing. In fact manipulations that are simply accomplished by humans are often not easily realisable by robots and the cost of automating assembling operations still often outweighs the benefits.2 But in the most advanced computerised assembly lines, which are still rare, the tasks assigned to the operatives have become about the same as those performed in the processing lines, which are thus at the forefront of a general transformation of the shop floor.3
The main role operatives have acquired is that of monitoring and controlling the action of intelligent machines fully freed from human appendixes. Since various kinds of anomalies may occur, they act mainly as problem solvers, while they no longer participate in the physical manipulation of some material or artifact. Moreover, the rapid change of the products supplied implies that always new problems arise.
It is important to note that the formalisation and proceduralisation of technological processes has opened the way to an unprecedented opportunity of continuously improving them. This does not mean that the scientific laws underpinning the functioning of technologies are always understood (see Balconi, 1998), but simply that the modelling of physical processes helps to introduce improvements, compared to when their functioning was based on the tacit and opaque knowledge embodied in the heads of the operatives. Add to this the effects of the use of computers in the design activity (CAD), which, facilitating the standardisation of the components of the objects to be designed and an easy retrieval of the solutions already in existence, enables one to concentrate efforts on the true novelties and to rapidly create new virtual objects, thus lowering the cost and increasing the speed of innovation—an activity which, in spite of this, absorbs ever more resources, having become crucial in order to compete in the marketplace. Thus, a growing proportion of employees is engaged in the problem solving tasks of designing new artefacts, planning their manufacturing cycle and bringing them to the marketplace.
The crucial role of problem solving points to the fact that codification of technological knowledge does not absolutely imply that human tacit competences—meaning knowledge and abilities that are inherently embodied in individuals—have ceased to be important.4
Finally, the empirical investigation addresses the issue of the impact of codification, CBA and the increasing complexity of technological knowledge on the division of labour among firms.
In Section 2 a basic conceptual framework is illustrated and the benefits and costs of codification of technological knowledge are discussed. Section 3 develops a taxonomic analysis of skills, which is useful in understanding the change of the nature of work due to codification of technological knowledge. Section 4 presents empirical evidence—through the cases of steel, semiconductors and metal working—of the obsolescence of traditional skills in manufacturing, of the spread of codification of technological knowledge and of the complementary relationship of the latter to tacit knowledge of industrial practice. Section 5 discusses the connection between the increasing recourse to outsourcing by firms, which has taken place in the last decades, and codification/automation/growth of fixed costs and growing complexity of knowledge. It suggests a story of co-evolution of technological knowledge, cost structures, transaction costs and social division of labour. The empirical reference is the mechanics industry, but the driving forces underlying the reorganisation of this sector seem quite general, as the results of other studies suggest. Section 6 presents some conclusions.
Section snippets
The main notions
In this section I shall first delineate a conceptual framework to clarify the main characteristics of the process of codification of technological knowledge.
To do so, I shall refer to some important contributions published in a special issue of the journal Industrial and Corporate Change (Vol. 9, No. 2, 2000), which was devoted to “sharpening the distinction between information and knowledge” (Cohendet and Steinmueller, 2000), by providing a clear conceptualisation of the activities of
Tacit skills and codification of technological knowledge: some stylised facts
On the whole, codification of technological knowledge has brought with it a deep change in the types of skills required to carry out manufacturing activity.
Tacit skills which have been substituted by codified know-how and have become obsolete in most modern manufacturing processes are those relying on the perceptions of sensory organs or manual ability. They serve to recognise and assign meanings to stimuli (raw data inputs) emitted by objects and to manipulate them in order to obtain the
Steel
To illustrate the changes occurred over the last 30 years due to the progressive codification of technological knowledge formerly embodied in skilled workers, I start focusing on a steel industry segment—the production of special steel from scraps by electric furnace—especially characterised by low volumes and high product variety.
With the electric furnace steel is made by smelting scraps and then refining the liquid metal, by decarburising and dephosphorising it and carrying out the other
Codification, computer-based technologies (CBT) and vertical disintegration
The codification of technological practices and the use of CBT together with the accelerated pace of innovation significantly impacted the decisions of firms regarding their boundaries, leading them to vertical disintegration. This aspect is examined here with reference to the metal working sector, but the empirical evidence provided by other sectors, such as the electronic industry, show a very similar tendency (as emerges from Sturgeon (1997)).
Conclusions
This paper has discussed the new paradigmatic role played by humans in a manufacturing world reshaped by the widespread adoption of CBT and by the related codification of technological knowledge. Putting succinctly, in modern factories people no longer “do” things, but control that things are done correctly by automatic equipment. In controlling, they must exert some judgement and their ability in understanding and assessing anomalous situations is based both on some formal education (computer
Acknowledgements
I would especially like to thank an anonymous referee who greatly helped me in clarifying the conceptual framework.
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This paper has been produced within the DYNACOM research project, funded by Targeted Socio-Economic Research (TSER), under the Fourth Framework Programme, European Commission.