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Organizational foundations of intelligent manufacturing systems — the holonic viewpoint
Organizational foundations of intelligent manufacturing systems the holonic viewpoint John Mathews IR Research Centre, University of NSW, Sydney NSW 2052, Australia (email:j.mathews(ir:unsw.edu.au)
This paper demonstrates that intelligent manufacturing systems can be designed so that they form part of a larger class of technical systems which are structured along ‘hoionic’ lines. The properties of the holonic organizational architecture are brought out, and applied to the description of intelligent manufacturing systems (IMS). This brings out the fundamental organizational features of IMS structures, abstracting them from other issues to do with standardization, technical configurations or costs, and enables a clearer understanding of the sources of their superior performance over traditional, functionally structured and centralized manufacturing systems. Finally, the use of the holonic paradigm as a design tool is illustrated, with a view to further developing the organizational clarity and effectiveness of intelligent manufacturing systems. Keywords:
intelligent
manufacturing
system. holonic system. work organization
Introduction The application of intelligence to improve the efficiency and effectiveness of manufacturing systems is a topic of long-standing discussion in the engineering literature. The phrase intelligent manufacturing system (IMS) is now widely used to indicate a range of design innovations that seek to apply microprocessor-based intelligence to many facets of the manufacturing operation’. Indeed, the term IMS is now applied to a major program of international collaborative research, sparked in 1989 by Japan and now involving firms and governments in three continents’. In all this discussion, the emphasis is overwhelmingly on the technical details of intelligence applications (such as finding common interfaces for the elements of machining cells), and not on a probing of the organizational foundations of various forms of IMS. That there are options in organizational design is clear enough. There is, on the one hand, the extreme of the -unmanned factory’, in which automation and robots take over all activities, and the design goal is to take all skill out of work to place it in computer-controlled routines. Experience to-date with such approaches has shown them to be brittle, rigid and very expensive; yet this is still in many ways the dominant approach to manufacturing systems design”. Another extreme is provided by the goal of ‘human-centred’ manufacturing technology, which seeks to place human skill at the centre of the operations of machines4. It is the complexity generated by traditional organizational structures of many technical systems, including
manufacturing systems, that is driving designers to find organizational frameworks. In many alternative technological systems, a concern to break down rigidity and exponentially expanding complexity can be detected in moves to decompose systems into smaller, more manageable units which behave as mini-systems in their own right, but which can be coordinated as subsystems of a central intelligent super-system. These trends can be detected in large-scale software engineering systems, in telecommunications systems, in robotic systems, and in many other technological systems. In all these cases, microprocessors are providing the technical foundation for a decentralization of control by embedding ‘intelligence’ in the sensors, operating units and peripheral elements of the larger systems. We shall look closely at some examples below. A consistent organizational architecture can be discerned here, in which complex systems need to be decomposed into simpler sub-systems, and where the sub-systems themselves can be treated as autonomous systems possibly containing further sub-systems of their own. This is the architecture that Herbert Simon discussed extensively under the term ‘ordered hierconceived as a device for getting archical system’, things done efficiently’. It was Arthur Koestler who took the matter further and drew attention to this common structure in what he called ‘holonic’ systems. In his 1967 book The Ghost in the Machine, he emphasized that the system derived its potency from the capacity of individual units to perform their own, relatively autonomous functions, while system integrity was maintained through an
Intelligent
manufacturing
systems: J Mathews The term I would propose is ‘holon’, from the Greek holos = whole, with the suffix on which, as in proton or neutron, suggests a particle or part.’ (Koestler, 1967, p. 48)“.
intelligence coordinating the activities of the individual unit&. Such holonic architectures have been developed for overall
system
computer systems, control hierarchical
where the traditional commandrelationships have given way to
Koestler’s conceptual and terminological innovation has been all but ignored by organizational theorists. But its relevance to the organizational innovation that is currently underway in both business and technical systems, is surely apparent, as is the scope of its application. In this paper, we apply the conceptual innovation to the case of IMS. The organizational architecture of holonic systems can be described as follows’. Holonic systems may be considered to consist of scalar chains of holonic entities, where at each level the reference entity can be considered to subsist as part of a higher-level system and to contain lower-level subsystems of its own. At each level, tasks are allocated to holons according to a given design, such as parallel processing. This determines the fundamental relations between holons at any level, between holons at different levels, and between holons and the overall system. There are three basic structural features of holonic systems which are taken to be definitional:
Computer programs were whole-part relationships. originally developed along strikingly hierarchical lines, with ‘master programs’ calling up ‘slave’ routines and sub-routines as circumstances warranted. These systems suffered from the rigidity that now bedevils manufacturing systems organized along the same lines. The new holonic architectures for computer systems embody what is called ‘object-oriented programming’, where the ‘objects’ are independent, semi-autonomous routines or data sets that come into play depending on the use to which the program, or machine, is put. ‘Objects’ communicate by sending each other messages to perform operations. ‘Objects’ may learn from experience, in strong contrast with traditional computer program routines’. While such non-hierarchical models have been deployed for many years in data processing, their use in manufacturing has been limited by the very much greater complexity encountered in processing materials as opposed to pure data. However, the advent of ‘intelligent manufacturing’ brings the concept of enterprise modelling and process representation that much nearer reality.
1. Relative automony of holons Holons need to be self-sufficient if they are to have what might be termed coherence and integrity. At a minimum, they need to be equipped with a model of the activity they are required to perform by the over-arching system; they need to have their own capacity to collect data to ensure that the set task is completed to a sufficient standard; they need to be possessed of ‘intelligence’, meaning processing power and the ability to vary their response depending on circumstances; and they need to be equipped with a mechanism for continuous improvement, or learning. They may contain sub-systems that similarly display holonic qualities. 2. System dependence Holons are not expected to operate with absolute autonomy. Rather, they are required to function within the constraints, and subject to the direction, of a super-system that provides all necessary external coordinating inputs. Systemic order (as opposed to control) is obtained through the coordinated activity of holons each performing their own operations, but within the operational context established by a super-systemic coordinating mechanism. Thus, holons are not expected to determine their tasks for themselves - these are given by the overall system design. But how they accomplish their tasks is up to them (given the existence of standardized protocols, operating procedures and so on).
Holonic organizational architecture Koestler introduced his notion of a holonic organizational architecture in the following way: The first universal characteristic of hierarchies is the relativity, and indeed ambiguity, of the terms ‘part’ and ‘whole’ when applied to any of the sub-assemblies . . A ‘part’, as we generally use the word, means something fragmentary and incomplete, which by itself would have no legitimate existence. On the other hand, a ‘whole’ is considered as something complete in itself which needs no further explanation. But ‘wholes’ and ‘parts’ in this absolute sense just do not exist anywhere, either in the domain of living organisms or of social organisations. What we find are intermediary structures on a series of levels in an ascending order of complexity: sub-wholes which display, according to the way you look at them, some of the characteristics commonly attributed to wholes and some of the characteristics commonly attributed to parts . . . The members of a hierarchy, like the Roman god Janus, all have two faces looking in opposite directions: the face turned towards the subordinate levels is that of a self-contained whole; the face turned upward towards the apex, that of a dependent part. . . This ‘Janus effect’ is a fundamental characteristic of sub-wholes in all types of hierarchies. But there is no satisfactory word in our vocabulary to refer to these Janus-faced entities: to talk of sub-wholes (or sub-assemblies, sub-structures, sub-skills, subsystems) is awkward and tedious. It seems preferable to coin a new term to designate these nodes on the hierarchic tree which behave partly as wholes and wholly as parts, according to the way you look at them.
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3. Holonic concatenation: recursivity Holonic systems consist of holons at various levels, but system integrity calls for these holons to be structured along similar lines, so that there can be meaningful aggregation from level to level. This implies that holonic structures be self-similar or
4
Intelligent manufacturing recursive. For example, data management needs to follow the same format within different holons, as do message protocols to allow holons to communicate at each level and between levels. Since fractal geometry displays such self-similarity, it provides an attractive metaphor for this property, such as in the notion of the ‘fractal factory’“. Self-similarity provides the technical foundation for systemic coherence. We shall use these three properties of holonic autonomy, system dependence and recursivity, as defining the characteristics of holonic systems. We wish to characterize organizations or systems that have a holonic basis, as instances of ‘holonic organizational architectures’. Depending on the degree of interdependence of holons and system, and the number of holonic levels within any given system, there will be a variety of such architectures. In each, there will be different intensities of information exchange and knowledge generation.
Three faces of holonic systems The description of the behaviour of holonic systems makes use of a triad of relations and properties. There are the properties of the holons themselves (which we term firstorder properties); there are the properties that inhere in the relations between holons at any level, arising from their interaction (which we term second order properties); and there are third order properties that emerge at the systemic level. For example, the functionality of holonic systems can be described using this triad. There are first order functional issues, to do with the operations of the holons themselves. In the case of manufacturing, these might refer to the parts produced by various machining centres. There are second order functional issues, to do with the functional relations between holons (e.g. how they cooperate to accomplish a complex task). In the case of manufacturing, this might refer to how several machining cells are combined to produce a coherent sub-assembly. And there are third order functions which treat the system in its totality, calling for systemic judgments such as generating new holons to deal with new situations and dispensing with holonic functions no longer needed. In the case of manufacturing, this could involve judgments at the level of a total production system, such as the capital investment needed to make it flexibly able to accommodate a variety of product these functionalities answer the types. In short. questions: what do holons do; how do they interact; and why are some holons used and not others. we may consider the coordination of Likewise, holonic systems through the same triad of relations. There is first order coordination of tasks conducted within holons themselves; there is second order coordination carried out by integrative functions across and between holons; and there is third order coordination which changes system parameters and structures to achieve system-wide coordination.
systems: .I
Marhews
It is in drawing attention to the necessity for these different levels of functionality, coordination, learning, flexibility and so on, that rests the principal virtue of holonic architecture as a design tool. The behaviour of holonic systems has both static and dynamic features which can be traced back to the holonic design. The static features include such issues as control, flexibility and reliability: Centralizedidecentralized control Recognizing that complete centralized control is impossible in any complex system, and is in any case brittle and subject to catastrophic failure, holonic systems ensure that control is shared between holons themselves, through their relative autonomy, and a systemic coordinating mechanism which takes responsibility for steering the system as a whole. Thus, management functions inhere in each level of the system, as in the notion of autonomous production management units (PMS) operating at each level of the system”‘. Systemic flexibility The principal virtue of holonic systems lies in their flexibility and adaptability. Unlike centralized systems, which need to alter their entire internal dynamic to make even a small adaptation, holonic systems adapt through individual holons making changes ‘in the small’; when aggregated across all holons affected by the shift in circumstances, and propagated across the system by the coordinating centre, the system itself can be seen to have adapted ‘in the large’. Likewise, changes initiated from higher levels, can be implemented rapidly by relatively autonomous holons making their own internal adjustments. Systemic reliability Systemic reliability of holonic systems lies in their capacity to keep functioning, even in the absence of one or more individual holons which may have broken down. It is the reliability of the total system that is at issue, rather than the operation of any particular holon. Dynamic features of the behaviour of holonic systems concern their capacity to adjust their behaviour over time, as circumstances change. This involves notions of adaptation and responsiveness; organizational learning; and organizational renewal (autopoiesis). We shall consider this aspect of the examples.
Example:
holonic robotic system
Let us illustrate these concepts with the notion of a holonic robotic system. The traditional approach to the design of robotic systems has been to devise an overall controlling program which sends instructions as needed to individual robots, in standard hierarchical fashion. Robots are manipulative devices that require a processing unit to read instructions (given originally in punched card or tape format, then by magnetic tape, and now by and an electromechanical unit to computer disk),
Intelligent manufacturing systems: J Mathews produce thk required movement. More sophisticated robots come equipped with their own sensors (such as television cameras) to provide feedback as to whether the robot is producing the required actions or not. This in turn calls for more sophisticated processing capabilities, to take into account the feedback and modify the instructions accordingly. In the conventional organizational architecture, informed by notions of centralized coordination, the processing unit grows larger, and the sensors and actuators are all connected to this unit. As the number of sensors and actuators increases, this wiring apparatus gets ever more complex. Designing the wiring arrangements has become more and more of a headache for robotics producers as the capabilities of their machines expend. A solution has been sought by leading Japanese robotics producers in the form of holonic architecture, in what is described as the ‘holonic manipulator”‘. In place of a single, centralized processor, the holonic machine builds autonomous local controllers into its mechanical structure. In one example, produced in the Department of Mechanical Engineering at the University of Tokyo, a manipulator has been built with four degrees of freedom for its joints, and six degrees of freedom for grippers. The gripper is equipped with six touch sensors and vision sensors which can identify simple shapes of objects located in front of the gripper. The machine is made ‘holonic’ by equipping the gripper controllers and joint controllers with pre-processing capability, allowing them to adjust their own activity. The total machine is coordinated by a processor now called a ‘supervising processor’ which receives only functional parameters (e.g. calculated actions) rather than raw data from sensory inputs. In the new version, all pre-processed data is now transmitted via a single universal communication line, regardless of the number of sensors and actuators. The reduction in data transmission, and in data complexity, achieved by the holonic architecture, is prodigious. Moreover, the advantages accumulate as the robotic device gets more complejt. (Many other examples of such ‘holonic’ robotic systems could be given. As part of the international IMS program, for example, in the ‘Holonic Manufacturing System’ development stream, Hitachi have designed and built a robotic assembly holon consisting of system components of autonomous modules with distributed control”.) Organizational
features
of the holonic manipulator
The Japanese call such a robotic system a ‘holonic manipulator’ because ‘the built-in controller of the manipulator works not only as a subsystem of the supervisory system but also as a stand-alone feature’“. Thus, the manipulator control system is at once a system in its own right, and a subsystem of the larger robotic system. The manipulator controls are activated by the overall robotic controller, which maintains coordination and
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robotic system integrity at all times. There is no question of manipulator control units acting on their own in contradition to the requirements of the overall system. Thus, the manipulator controls, as sub-systems, are dependent for their operation on the total system operation. The design of the manipulator ensures that control is not centralized, but is in fact distributed to the manipulator CPUs located in each gripper and joint. The design of the control system then becomes one of developing algorithms framed within decomposed systems, in which computation or processing is allocated to local levels. This is the basis of the hardware and software streamling of the holonic manipulator. It needs to be matched by a communications system that functions within such a decentralized environment. The robotic system described by Hirose has been designed so that there are three levels of coordination, entailing three communication interfaces or protocols. These cover the manipulator level (i.e. sending and receiving data), the level of task synchronization, and the level of network-wide coordination. This corresponds to the triad of functionality and coordination described above. These three levels apply whatever the point of reference may be. Thus, there is no reason in principle why each manipulator may not be decomposed into semi-autonomous, semi-dependent mini-manipulators, where each of these acts as a subsystem, transmitting and receiving data, and the manipulator CPU acts as the system. Thus, the structure of the system is preserved as we move from level to level; it obeys the condition of recursivity. Unlike conventional robotic systems, which are programmed to achieve a particular task or series of tasks, the holonic manipulator allows for much more general programming. Each manipulator can be programmed with general functions (turn, twist, push, pull, etc., depending on the number of degrees of freedom) which can be particularized by the overall controller interpreting a specific global task in terms of the basic operations of the manipulators. Thus the flexibility of the holonic system is enhanced by an order of magnitude, through this customization organizational feature. An important advantage of the holonic device lies in its improved reliability. As Hirose puts it: ‘Since most of the information flow is localised within the built-in controller level, a local accident rarely propagates to the system creating an accidential system failure”‘. Finally, the holonic manipulator can be considered as a learning system. Each local manipulator CPU can be programmed to seek optimal solutions to its own task, within a given operating environment. To the extent that such solutions can be stored locally, the manipulators can be said to be endowed with learning ability. These are the major organizational features which we may deduce from the description of the holonic manipulator. Robots do not operate alone. Generally, they are combined to form a work cell, and the various work
Intelligent manufacturing systems: J M&hews cells are themselves combined to produce an entire production line. These, in turn, are combined to produce a multi-product factory. And so the process of holonic concatenation, producing more levels in a holonic manufacturing sytsem, goes on.
Design of intelligent manufacturing holonic factory
systems:
A total system for intelligent manufacturing would start at the level of a given range of products, and the technical possibilities for parallel segmentation of the flow of these products through a production system. Holonic production cells would then be allocated the task of producing a given range of products. These cells would consist of skilled ‘knowledge workers’ operating machining centres, AGVs and robotic systems, each of which would be considered as a holonic entity in its own right. Further decomposition would proceed along the lines of the holonic robotic system considered above, with holonic control centres operating peripheral parts of the robotic system. At each level of this system, the brief for engineering design teams would be to ensure that components, systems, cells and the entire production super-system be relatively independent and autonomous, yet use protocols and interfaces that are ‘self-similar’ at each level. This means ensuring that feedback loops are established at each level, and that each holonic entity has the processing intelligence required to respond to feedback data and change its operating procedures accordingly. The cellular manufacturing paradigm comes into its own when cells are viewed as holonic entities combining to form a total production system - the holonic factory. The production system is built up as a holonic concatenation, from factory to production line to work cell to holonic manipulator. The point about such a nested structure is that each level is treated as semiautonomous and possessing its own intelligence. And within each level, there are numerous independent entities. Many designs have been advanced for such a structure, such as that by Ranta et al.‘” utilizing a nested sequence of what they call Production Management Units (PMU). Starting now from the perspective that manufacturing cells need to be structured as holons, we may translate the holonic conditions outlined above into criteria governing the design of viable intelligent manufacturing systems based on cellular architecture: 1. System structure: scalar chain of cells From the perspective of the total production system, cells represent sub-systems each of which is dedicated to the production of a sub-set of products in its own viable, or sustainable fashion. Each cell is specialized to the needs of its specific product range, within the operating context of the needs of the total production system. The design of the overall system structure needs to be concerned
with total throughput, supply links and their synchronization (logistics), customer links, and the maintenance of quality, timeliness and reliability. 2. Relative autonomy of cells Each cell is conceived as an independent entity, meaning that it must span a sufficient number of operations to produce a well-defined product, and be endowed with sufficient resources and technology to operate relatively independently. (Hence synonyms such as ‘focused factory’, ‘fractal factory’l”.14,“, Holonic coherence calls for steps within a process to be linked, wherever possible, by direct contiguity. Process coherence calls for each cell to have as much control over the production of its designated set of products as is compatible with system-wide coordination. It is the focus provided by this process coherence, and the capacity for measuring quality and productivity by the cell members themselves, that underscores the organizational advantages of cellular production. The design of such systems has two faces: technical and social. From the technical perspective, it becomes a process of efficiently allocating products to cells in such a way that waiting time and machinery interfaces arc reduced to a minimum, providing the cell with sufficient technical autonomy. (Indeed, the algorithms designed for this task constitute the bulk of the engineering literature on ‘group technology’.) From the social perspective, it is a matter of ensuring that groups of operators assembled within the cell have sufficient contiguity, skills, information, autonomy and authority to operate effectively as a team. 3. System dependence Cells do not decide for themselves what their work will be. Tasks are presented to them by the overall system management. This is what guarantees system integrity. The relative autonomy of cells lies in their being able to choose how best to fulfil the tasks they are given, rather than in choosing the tasks themselves. 4. Decentralization of control The essence of cellular manufacturing is that cells are self-managing entities. They dispense with traditional methods of supervision in which operators are presented with individual tasks rather than with whole processes. This property can be are called process intent, since cell members presented with the goals they are expected to reach, but not with the detailed instructions needed to achieve them. (A description of this process at Australian industry is given in work in MathewsIS.) The key to successful decentralized control of such systems lies in their information architecture. In a holonic system, the behaviour of each level is determined partly by the autonomous operation of units at that level; partly by the overall parameters supplied by the level above; and partly by the data (in aggregated form) provided by the level below.
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concatenation:
These decisions can only be taken in the light of monitoring of system-wide aggregate operating data, matched against a scanning of the organizational environment and the review of strategic plans. (Zelenylh described this phenomenon in the context of the, Japanese firm, Kyocera. Because of the dynamic variation in work cell size and function, Kyocera called their teams ‘amoebae’. While the spirit of this nomenclature is well taken, nevertheless, it is probably unlikely that highly skilled cell members would appreciate being called amoebae .)
recursivity
Cells should be designed so that they share critical architectural features, such as the use of performance critiera, communication protocols and the formation and dismantling of sub-systems which themselves share holonic features. Recursivity is what facilitates system aggregation (e.g. of performance criteria), enabling the central coordinator to produce a reliable aggregate picture of total system functioning and make adjustments accordingly .
6. Systemic flexibility A significant organizational gain lies in the capacity of cells to switch production rapidly from one product line to another, as called for by changing customer orders. Cells need to be equipped with sufficient machines, control units and skills, to be able to effect such switches without the need for long periods of downtime. This is the important customisation property of holonic systems. 7. Systemic reliability Cellular production is an example of an organizational architecture based on parallel flows, in which breakdowns in any one cell or flow (e.g. due to machine malfunction) do not disrupt the flow of products through other cells. At the same time, preventive maintenance carried out by cell members themselves will minimise the occurrence of such breakdowns - emphasizing the fact that cell members need to span sufficient operating and maintenance skills as are necessary to endow the cell with autonomy. 8. Systemic efficiency In cellular production, it is not the efficiency of utilization of individual machines or machining centres that counts, so much as the efficiency of the total system. It is the task of central coordinating mechanism to collect the data needed to compute overall efficiency, and to feed relevant data back to cells which need to make adjustments. It is this self-adjusting mechanism of cells, conditioned by central coordination,, rather than local optimization, that is the real organizational driving force of cellular production. 9. Systemic learning Individual cells are themselves powerful engines of learning through cross-skilling, with cell members learning new skills from each other as they shift operations internally. But on its own this would be a micro gain. System design needs to take care to establish a set of rules for the adjustment of individual cells according to circumstances, and for this set of rules themselves to be adjusted in the light of experience. This is second order learning, which will come into its own in the era of intelligent manufacturing. autopoiesis 10. Self-regeneration: Likewise, it is the task of overall system management to judge when the time is ripe for dismantling of an existing cell or for creation of a new one.
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Concluding
remarks
In this paper, we have pursued three lines of reasoning. First, we have demonstrated that intelligent manufacturing systems can be designed so that they fall within a wider class of ‘holonic’ technical systems, defined by .their adherence to a holonic organizational architecture. Thus, links are established between the design issues faced in manufacturing with those faced in cognate disciplines, such as holonic software engineering (‘object-oriented programming’). Secondly, the organizational significance of holonic architecture has been brought to the fore, through the argument that the efficiency and effectiveness of manufacturing systems can be traced back to their organizational foundations. While many designs for intelligent manufacturing systems organized along ‘holonic’ lines have been advanced (such as those by Hitachi12, The Finnish VTT’O, and by others such as O’Hare”, with his concept of ‘distributed artificial intelligence’), the emphasis is such papers has been on technical considerations rather than on organizational fundamentals. This paper has sought to demonstrate that the fundamentals count. Thirdly, it has been argued that the holonic framework offers not just a descriptive framework, within which current engineering attempts to develop intelligent systems may be analysed (e.g. from the perspedtive of the triad of coordination and functionality), but as a prescriptive tool to aid design. The essence of the holonic approach, as applied to the design of intelligent manufacturing systems, lies in its formulation of the system as a nested sequence of selforganizing and self-managing entities, each of which has intelligence built into it and the systemic authority to use this intelligence to adjust its behaviour in the light of operating circumstances. Design, then, focuses on the need to create systems of coordination that operate in first order, second order and third order mode, with full alignment achieved between holonic levels through the insistence on observing holonic selfsimilarity. The argument of this paper can be summarized by saying that intelligent manufacturing systems built along these holonic lines are likely to demonstrate superior efficiency and effectiveness, through their ability to cut through the complexity that overwhelms
4
Intelligent
systems designed along lines of conventional tional architecture.
organiza-
References Kusiak, A Intelligent Manufacturing Systems, Prentice Hall. Englewood Cliffs. NJ (IYYO) Proceedings Internationul Workshop on Intelligent Manufacturing Systems, IMS International Technical Committee, Sydney. Australia (22 February 1993) Lund, R et al. Designed to Work: Production Systems and People. PTR Prentice Hall, Englewood Cliffs, NJ (1993) Symon, G ‘Human-centred computer-integrated manufacturing’. Comput. Integrated Manuf. Syst.. Vol 3 No 4 (1990) pp 223-220 Simon, H ‘The architecture of complexity’, Proc. Am. Phil. Sot.. Vol I06 (lY62) pp 467382 Koestler, A The Ghost in the Machine, Hutchinson. London (1967) (Danube edition. with new preface: 1976) Yom-don, E Object-Oriented Systems Design: An Integruted Approach. Prentice Hall. Englewood Cliffs, NJ (1994) Mathews, J ‘Holonic organisational architectures’. Human Systems Manugement (forthcoming)
manufacturing
systems:
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9 Warnecke, H-J The Fractal Company: A Revolution in Corporute Culture, Spinger-Verlag, Heidelberg (1993) (Translation from the German Die Fraktale Fahrik lYY2) IO Ranta, J, Gras-Pietro, G M and Eloranta, E ‘Requirements for advanced manufacturing systems and competing strategies in different industries: Some issues and questions of a possible future IMS program’, Proc. Int. Workshop on Intelligent Manufacturing Systems, IMS International Technical Committee. Sydney, Australia (22 February 1993) 11 Hirose, M ‘Development of the holonic manipulator and its control’. Proc. 29th IEEE Conf. Decision und C’ontml, Honolulu. HI (December 1990) pp Yl-96 12 Matsumoto, Y ‘Current intelligent manufacturing system activities at Hitachi Ltd’, Proc. 2nd Int. Symposium on /MS. Tokyo. Japan (March 1903) 13 Skinner, W Manufacturing: The Formidable C‘ompetitive Weapon John Wiley, New York (1985) 14 Harmon, R and Peterson, L Reinventing the Fuctory: Productivity Toduy Free Press/Collier Rreukthroughs in Munufacturing Macmillan, New York (1090) 15 Mathews, J Catching the Wurje: Workplaw Reform in Australia. Allen & Unwin. Sydney. 11-R Press, Cornell University (1994) 16 Zeleny, M ‘Amoeba: The new generation of self-managing human systems’, Human Systems Manage., Vol Y No 2 (I 990) pp 57-S’) 17 O’Hare, G ‘Designing intelligent manufacturing Systems: a distributed artificial intelligence approach’. Comput. in Ind., Vol I5 ( IYYO) pp 17-25
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Integrated
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This paper demonstrates that intelligent manufacturing systems can be designed so that they form part of a larger class of technical systems which are structured along ‘hoionic’ lines. The properties of the holonic organizational architecture are brought out, and applied to the description of intelligent manufacturing systems (IMS). This brings out the fundamental organizational features of IMS structures, abstracting them from other issues to do with standardization, technical configurations or costs, and enables a clearer understanding of the sources of their superior performance over traditional, functionally structured and centralized manufacturing systems. Finally, the use of the holonic paradigm as a design tool is illustrated, with a view to further developing the organizational clarity and effectiveness of intelligent manufacturing systems. Keywords:
intelligent
manufacturing
system. holonic system. work organization
Introduction The application of intelligence to improve the efficiency and effectiveness of manufacturing systems is a topic of long-standing discussion in the engineering literature. The phrase intelligent manufacturing system (IMS) is now widely used to indicate a range of design innovations that seek to apply microprocessor-based intelligence to many facets of the manufacturing operation’. Indeed, the term IMS is now applied to a major program of international collaborative research, sparked in 1989 by Japan and now involving firms and governments in three continents’. In all this discussion, the emphasis is overwhelmingly on the technical details of intelligence applications (such as finding common interfaces for the elements of machining cells), and not on a probing of the organizational foundations of various forms of IMS. That there are options in organizational design is clear enough. There is, on the one hand, the extreme of the -unmanned factory’, in which automation and robots take over all activities, and the design goal is to take all skill out of work to place it in computer-controlled routines. Experience to-date with such approaches has shown them to be brittle, rigid and very expensive; yet this is still in many ways the dominant approach to manufacturing systems design”. Another extreme is provided by the goal of ‘human-centred’ manufacturing technology, which seeks to place human skill at the centre of the operations of machines4. It is the complexity generated by traditional organizational structures of many technical systems, including
manufacturing systems, that is driving designers to find organizational frameworks. In many alternative technological systems, a concern to break down rigidity and exponentially expanding complexity can be detected in moves to decompose systems into smaller, more manageable units which behave as mini-systems in their own right, but which can be coordinated as subsystems of a central intelligent super-system. These trends can be detected in large-scale software engineering systems, in telecommunications systems, in robotic systems, and in many other technological systems. In all these cases, microprocessors are providing the technical foundation for a decentralization of control by embedding ‘intelligence’ in the sensors, operating units and peripheral elements of the larger systems. We shall look closely at some examples below. A consistent organizational architecture can be discerned here, in which complex systems need to be decomposed into simpler sub-systems, and where the sub-systems themselves can be treated as autonomous systems possibly containing further sub-systems of their own. This is the architecture that Herbert Simon discussed extensively under the term ‘ordered hierconceived as a device for getting archical system’, things done efficiently’. It was Arthur Koestler who took the matter further and drew attention to this common structure in what he called ‘holonic’ systems. In his 1967 book The Ghost in the Machine, he emphasized that the system derived its potency from the capacity of individual units to perform their own, relatively autonomous functions, while system integrity was maintained through an
Intelligent
manufacturing
systems: J Mathews The term I would propose is ‘holon’, from the Greek holos = whole, with the suffix on which, as in proton or neutron, suggests a particle or part.’ (Koestler, 1967, p. 48)“.
intelligence coordinating the activities of the individual unit&. Such holonic architectures have been developed for overall
system
computer systems, control hierarchical
where the traditional commandrelationships have given way to
Koestler’s conceptual and terminological innovation has been all but ignored by organizational theorists. But its relevance to the organizational innovation that is currently underway in both business and technical systems, is surely apparent, as is the scope of its application. In this paper, we apply the conceptual innovation to the case of IMS. The organizational architecture of holonic systems can be described as follows’. Holonic systems may be considered to consist of scalar chains of holonic entities, where at each level the reference entity can be considered to subsist as part of a higher-level system and to contain lower-level subsystems of its own. At each level, tasks are allocated to holons according to a given design, such as parallel processing. This determines the fundamental relations between holons at any level, between holons at different levels, and between holons and the overall system. There are three basic structural features of holonic systems which are taken to be definitional:
Computer programs were whole-part relationships. originally developed along strikingly hierarchical lines, with ‘master programs’ calling up ‘slave’ routines and sub-routines as circumstances warranted. These systems suffered from the rigidity that now bedevils manufacturing systems organized along the same lines. The new holonic architectures for computer systems embody what is called ‘object-oriented programming’, where the ‘objects’ are independent, semi-autonomous routines or data sets that come into play depending on the use to which the program, or machine, is put. ‘Objects’ communicate by sending each other messages to perform operations. ‘Objects’ may learn from experience, in strong contrast with traditional computer program routines’. While such non-hierarchical models have been deployed for many years in data processing, their use in manufacturing has been limited by the very much greater complexity encountered in processing materials as opposed to pure data. However, the advent of ‘intelligent manufacturing’ brings the concept of enterprise modelling and process representation that much nearer reality.
1. Relative automony of holons Holons need to be self-sufficient if they are to have what might be termed coherence and integrity. At a minimum, they need to be equipped with a model of the activity they are required to perform by the over-arching system; they need to have their own capacity to collect data to ensure that the set task is completed to a sufficient standard; they need to be possessed of ‘intelligence’, meaning processing power and the ability to vary their response depending on circumstances; and they need to be equipped with a mechanism for continuous improvement, or learning. They may contain sub-systems that similarly display holonic qualities. 2. System dependence Holons are not expected to operate with absolute autonomy. Rather, they are required to function within the constraints, and subject to the direction, of a super-system that provides all necessary external coordinating inputs. Systemic order (as opposed to control) is obtained through the coordinated activity of holons each performing their own operations, but within the operational context established by a super-systemic coordinating mechanism. Thus, holons are not expected to determine their tasks for themselves - these are given by the overall system design. But how they accomplish their tasks is up to them (given the existence of standardized protocols, operating procedures and so on).
Holonic organizational architecture Koestler introduced his notion of a holonic organizational architecture in the following way: The first universal characteristic of hierarchies is the relativity, and indeed ambiguity, of the terms ‘part’ and ‘whole’ when applied to any of the sub-assemblies . . A ‘part’, as we generally use the word, means something fragmentary and incomplete, which by itself would have no legitimate existence. On the other hand, a ‘whole’ is considered as something complete in itself which needs no further explanation. But ‘wholes’ and ‘parts’ in this absolute sense just do not exist anywhere, either in the domain of living organisms or of social organisations. What we find are intermediary structures on a series of levels in an ascending order of complexity: sub-wholes which display, according to the way you look at them, some of the characteristics commonly attributed to wholes and some of the characteristics commonly attributed to parts . . . The members of a hierarchy, like the Roman god Janus, all have two faces looking in opposite directions: the face turned towards the subordinate levels is that of a self-contained whole; the face turned upward towards the apex, that of a dependent part. . . This ‘Janus effect’ is a fundamental characteristic of sub-wholes in all types of hierarchies. But there is no satisfactory word in our vocabulary to refer to these Janus-faced entities: to talk of sub-wholes (or sub-assemblies, sub-structures, sub-skills, subsystems) is awkward and tedious. It seems preferable to coin a new term to designate these nodes on the hierarchic tree which behave partly as wholes and wholly as parts, according to the way you look at them.
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3. Holonic concatenation: recursivity Holonic systems consist of holons at various levels, but system integrity calls for these holons to be structured along similar lines, so that there can be meaningful aggregation from level to level. This implies that holonic structures be self-similar or
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Intelligent manufacturing recursive. For example, data management needs to follow the same format within different holons, as do message protocols to allow holons to communicate at each level and between levels. Since fractal geometry displays such self-similarity, it provides an attractive metaphor for this property, such as in the notion of the ‘fractal factory’“. Self-similarity provides the technical foundation for systemic coherence. We shall use these three properties of holonic autonomy, system dependence and recursivity, as defining the characteristics of holonic systems. We wish to characterize organizations or systems that have a holonic basis, as instances of ‘holonic organizational architectures’. Depending on the degree of interdependence of holons and system, and the number of holonic levels within any given system, there will be a variety of such architectures. In each, there will be different intensities of information exchange and knowledge generation.
Three faces of holonic systems The description of the behaviour of holonic systems makes use of a triad of relations and properties. There are the properties of the holons themselves (which we term firstorder properties); there are the properties that inhere in the relations between holons at any level, arising from their interaction (which we term second order properties); and there are third order properties that emerge at the systemic level. For example, the functionality of holonic systems can be described using this triad. There are first order functional issues, to do with the operations of the holons themselves. In the case of manufacturing, these might refer to the parts produced by various machining centres. There are second order functional issues, to do with the functional relations between holons (e.g. how they cooperate to accomplish a complex task). In the case of manufacturing, this might refer to how several machining cells are combined to produce a coherent sub-assembly. And there are third order functions which treat the system in its totality, calling for systemic judgments such as generating new holons to deal with new situations and dispensing with holonic functions no longer needed. In the case of manufacturing, this could involve judgments at the level of a total production system, such as the capital investment needed to make it flexibly able to accommodate a variety of product these functionalities answer the types. In short. questions: what do holons do; how do they interact; and why are some holons used and not others. we may consider the coordination of Likewise, holonic systems through the same triad of relations. There is first order coordination of tasks conducted within holons themselves; there is second order coordination carried out by integrative functions across and between holons; and there is third order coordination which changes system parameters and structures to achieve system-wide coordination.
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It is in drawing attention to the necessity for these different levels of functionality, coordination, learning, flexibility and so on, that rests the principal virtue of holonic architecture as a design tool. The behaviour of holonic systems has both static and dynamic features which can be traced back to the holonic design. The static features include such issues as control, flexibility and reliability: Centralizedidecentralized control Recognizing that complete centralized control is impossible in any complex system, and is in any case brittle and subject to catastrophic failure, holonic systems ensure that control is shared between holons themselves, through their relative autonomy, and a systemic coordinating mechanism which takes responsibility for steering the system as a whole. Thus, management functions inhere in each level of the system, as in the notion of autonomous production management units (PMS) operating at each level of the system”‘. Systemic flexibility The principal virtue of holonic systems lies in their flexibility and adaptability. Unlike centralized systems, which need to alter their entire internal dynamic to make even a small adaptation, holonic systems adapt through individual holons making changes ‘in the small’; when aggregated across all holons affected by the shift in circumstances, and propagated across the system by the coordinating centre, the system itself can be seen to have adapted ‘in the large’. Likewise, changes initiated from higher levels, can be implemented rapidly by relatively autonomous holons making their own internal adjustments. Systemic reliability Systemic reliability of holonic systems lies in their capacity to keep functioning, even in the absence of one or more individual holons which may have broken down. It is the reliability of the total system that is at issue, rather than the operation of any particular holon. Dynamic features of the behaviour of holonic systems concern their capacity to adjust their behaviour over time, as circumstances change. This involves notions of adaptation and responsiveness; organizational learning; and organizational renewal (autopoiesis). We shall consider this aspect of the examples.
Example:
holonic robotic system
Let us illustrate these concepts with the notion of a holonic robotic system. The traditional approach to the design of robotic systems has been to devise an overall controlling program which sends instructions as needed to individual robots, in standard hierarchical fashion. Robots are manipulative devices that require a processing unit to read instructions (given originally in punched card or tape format, then by magnetic tape, and now by and an electromechanical unit to computer disk),
Intelligent manufacturing systems: J Mathews produce thk required movement. More sophisticated robots come equipped with their own sensors (such as television cameras) to provide feedback as to whether the robot is producing the required actions or not. This in turn calls for more sophisticated processing capabilities, to take into account the feedback and modify the instructions accordingly. In the conventional organizational architecture, informed by notions of centralized coordination, the processing unit grows larger, and the sensors and actuators are all connected to this unit. As the number of sensors and actuators increases, this wiring apparatus gets ever more complex. Designing the wiring arrangements has become more and more of a headache for robotics producers as the capabilities of their machines expend. A solution has been sought by leading Japanese robotics producers in the form of holonic architecture, in what is described as the ‘holonic manipulator”‘. In place of a single, centralized processor, the holonic machine builds autonomous local controllers into its mechanical structure. In one example, produced in the Department of Mechanical Engineering at the University of Tokyo, a manipulator has been built with four degrees of freedom for its joints, and six degrees of freedom for grippers. The gripper is equipped with six touch sensors and vision sensors which can identify simple shapes of objects located in front of the gripper. The machine is made ‘holonic’ by equipping the gripper controllers and joint controllers with pre-processing capability, allowing them to adjust their own activity. The total machine is coordinated by a processor now called a ‘supervising processor’ which receives only functional parameters (e.g. calculated actions) rather than raw data from sensory inputs. In the new version, all pre-processed data is now transmitted via a single universal communication line, regardless of the number of sensors and actuators. The reduction in data transmission, and in data complexity, achieved by the holonic architecture, is prodigious. Moreover, the advantages accumulate as the robotic device gets more complejt. (Many other examples of such ‘holonic’ robotic systems could be given. As part of the international IMS program, for example, in the ‘Holonic Manufacturing System’ development stream, Hitachi have designed and built a robotic assembly holon consisting of system components of autonomous modules with distributed control”.) Organizational
features
of the holonic manipulator
The Japanese call such a robotic system a ‘holonic manipulator’ because ‘the built-in controller of the manipulator works not only as a subsystem of the supervisory system but also as a stand-alone feature’“. Thus, the manipulator control system is at once a system in its own right, and a subsystem of the larger robotic system. The manipulator controls are activated by the overall robotic controller, which maintains coordination and
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robotic system integrity at all times. There is no question of manipulator control units acting on their own in contradition to the requirements of the overall system. Thus, the manipulator controls, as sub-systems, are dependent for their operation on the total system operation. The design of the manipulator ensures that control is not centralized, but is in fact distributed to the manipulator CPUs located in each gripper and joint. The design of the control system then becomes one of developing algorithms framed within decomposed systems, in which computation or processing is allocated to local levels. This is the basis of the hardware and software streamling of the holonic manipulator. It needs to be matched by a communications system that functions within such a decentralized environment. The robotic system described by Hirose has been designed so that there are three levels of coordination, entailing three communication interfaces or protocols. These cover the manipulator level (i.e. sending and receiving data), the level of task synchronization, and the level of network-wide coordination. This corresponds to the triad of functionality and coordination described above. These three levels apply whatever the point of reference may be. Thus, there is no reason in principle why each manipulator may not be decomposed into semi-autonomous, semi-dependent mini-manipulators, where each of these acts as a subsystem, transmitting and receiving data, and the manipulator CPU acts as the system. Thus, the structure of the system is preserved as we move from level to level; it obeys the condition of recursivity. Unlike conventional robotic systems, which are programmed to achieve a particular task or series of tasks, the holonic manipulator allows for much more general programming. Each manipulator can be programmed with general functions (turn, twist, push, pull, etc., depending on the number of degrees of freedom) which can be particularized by the overall controller interpreting a specific global task in terms of the basic operations of the manipulators. Thus the flexibility of the holonic system is enhanced by an order of magnitude, through this customization organizational feature. An important advantage of the holonic device lies in its improved reliability. As Hirose puts it: ‘Since most of the information flow is localised within the built-in controller level, a local accident rarely propagates to the system creating an accidential system failure”‘. Finally, the holonic manipulator can be considered as a learning system. Each local manipulator CPU can be programmed to seek optimal solutions to its own task, within a given operating environment. To the extent that such solutions can be stored locally, the manipulators can be said to be endowed with learning ability. These are the major organizational features which we may deduce from the description of the holonic manipulator. Robots do not operate alone. Generally, they are combined to form a work cell, and the various work
Intelligent manufacturing systems: J M&hews cells are themselves combined to produce an entire production line. These, in turn, are combined to produce a multi-product factory. And so the process of holonic concatenation, producing more levels in a holonic manufacturing sytsem, goes on.
Design of intelligent manufacturing holonic factory
systems:
A total system for intelligent manufacturing would start at the level of a given range of products, and the technical possibilities for parallel segmentation of the flow of these products through a production system. Holonic production cells would then be allocated the task of producing a given range of products. These cells would consist of skilled ‘knowledge workers’ operating machining centres, AGVs and robotic systems, each of which would be considered as a holonic entity in its own right. Further decomposition would proceed along the lines of the holonic robotic system considered above, with holonic control centres operating peripheral parts of the robotic system. At each level of this system, the brief for engineering design teams would be to ensure that components, systems, cells and the entire production super-system be relatively independent and autonomous, yet use protocols and interfaces that are ‘self-similar’ at each level. This means ensuring that feedback loops are established at each level, and that each holonic entity has the processing intelligence required to respond to feedback data and change its operating procedures accordingly. The cellular manufacturing paradigm comes into its own when cells are viewed as holonic entities combining to form a total production system - the holonic factory. The production system is built up as a holonic concatenation, from factory to production line to work cell to holonic manipulator. The point about such a nested structure is that each level is treated as semiautonomous and possessing its own intelligence. And within each level, there are numerous independent entities. Many designs have been advanced for such a structure, such as that by Ranta et al.‘” utilizing a nested sequence of what they call Production Management Units (PMU). Starting now from the perspective that manufacturing cells need to be structured as holons, we may translate the holonic conditions outlined above into criteria governing the design of viable intelligent manufacturing systems based on cellular architecture: 1. System structure: scalar chain of cells From the perspective of the total production system, cells represent sub-systems each of which is dedicated to the production of a sub-set of products in its own viable, or sustainable fashion. Each cell is specialized to the needs of its specific product range, within the operating context of the needs of the total production system. The design of the overall system structure needs to be concerned
with total throughput, supply links and their synchronization (logistics), customer links, and the maintenance of quality, timeliness and reliability. 2. Relative autonomy of cells Each cell is conceived as an independent entity, meaning that it must span a sufficient number of operations to produce a well-defined product, and be endowed with sufficient resources and technology to operate relatively independently. (Hence synonyms such as ‘focused factory’, ‘fractal factory’l”.14,“, Holonic coherence calls for steps within a process to be linked, wherever possible, by direct contiguity. Process coherence calls for each cell to have as much control over the production of its designated set of products as is compatible with system-wide coordination. It is the focus provided by this process coherence, and the capacity for measuring quality and productivity by the cell members themselves, that underscores the organizational advantages of cellular production. The design of such systems has two faces: technical and social. From the technical perspective, it becomes a process of efficiently allocating products to cells in such a way that waiting time and machinery interfaces arc reduced to a minimum, providing the cell with sufficient technical autonomy. (Indeed, the algorithms designed for this task constitute the bulk of the engineering literature on ‘group technology’.) From the social perspective, it is a matter of ensuring that groups of operators assembled within the cell have sufficient contiguity, skills, information, autonomy and authority to operate effectively as a team. 3. System dependence Cells do not decide for themselves what their work will be. Tasks are presented to them by the overall system management. This is what guarantees system integrity. The relative autonomy of cells lies in their being able to choose how best to fulfil the tasks they are given, rather than in choosing the tasks themselves. 4. Decentralization of control The essence of cellular manufacturing is that cells are self-managing entities. They dispense with traditional methods of supervision in which operators are presented with individual tasks rather than with whole processes. This property can be are called process intent, since cell members presented with the goals they are expected to reach, but not with the detailed instructions needed to achieve them. (A description of this process at Australian industry is given in work in MathewsIS.) The key to successful decentralized control of such systems lies in their information architecture. In a holonic system, the behaviour of each level is determined partly by the autonomous operation of units at that level; partly by the overall parameters supplied by the level above; and partly by the data (in aggregated form) provided by the level below.
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concatenation:
These decisions can only be taken in the light of monitoring of system-wide aggregate operating data, matched against a scanning of the organizational environment and the review of strategic plans. (Zelenylh described this phenomenon in the context of the, Japanese firm, Kyocera. Because of the dynamic variation in work cell size and function, Kyocera called their teams ‘amoebae’. While the spirit of this nomenclature is well taken, nevertheless, it is probably unlikely that highly skilled cell members would appreciate being called amoebae .)
recursivity
Cells should be designed so that they share critical architectural features, such as the use of performance critiera, communication protocols and the formation and dismantling of sub-systems which themselves share holonic features. Recursivity is what facilitates system aggregation (e.g. of performance criteria), enabling the central coordinator to produce a reliable aggregate picture of total system functioning and make adjustments accordingly .
6. Systemic flexibility A significant organizational gain lies in the capacity of cells to switch production rapidly from one product line to another, as called for by changing customer orders. Cells need to be equipped with sufficient machines, control units and skills, to be able to effect such switches without the need for long periods of downtime. This is the important customisation property of holonic systems. 7. Systemic reliability Cellular production is an example of an organizational architecture based on parallel flows, in which breakdowns in any one cell or flow (e.g. due to machine malfunction) do not disrupt the flow of products through other cells. At the same time, preventive maintenance carried out by cell members themselves will minimise the occurrence of such breakdowns - emphasizing the fact that cell members need to span sufficient operating and maintenance skills as are necessary to endow the cell with autonomy. 8. Systemic efficiency In cellular production, it is not the efficiency of utilization of individual machines or machining centres that counts, so much as the efficiency of the total system. It is the task of central coordinating mechanism to collect the data needed to compute overall efficiency, and to feed relevant data back to cells which need to make adjustments. It is this self-adjusting mechanism of cells, conditioned by central coordination,, rather than local optimization, that is the real organizational driving force of cellular production. 9. Systemic learning Individual cells are themselves powerful engines of learning through cross-skilling, with cell members learning new skills from each other as they shift operations internally. But on its own this would be a micro gain. System design needs to take care to establish a set of rules for the adjustment of individual cells according to circumstances, and for this set of rules themselves to be adjusted in the light of experience. This is second order learning, which will come into its own in the era of intelligent manufacturing. autopoiesis 10. Self-regeneration: Likewise, it is the task of overall system management to judge when the time is ripe for dismantling of an existing cell or for creation of a new one.
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Concluding
remarks
In this paper, we have pursued three lines of reasoning. First, we have demonstrated that intelligent manufacturing systems can be designed so that they fall within a wider class of ‘holonic’ technical systems, defined by .their adherence to a holonic organizational architecture. Thus, links are established between the design issues faced in manufacturing with those faced in cognate disciplines, such as holonic software engineering (‘object-oriented programming’). Secondly, the organizational significance of holonic architecture has been brought to the fore, through the argument that the efficiency and effectiveness of manufacturing systems can be traced back to their organizational foundations. While many designs for intelligent manufacturing systems organized along ‘holonic’ lines have been advanced (such as those by Hitachi12, The Finnish VTT’O, and by others such as O’Hare”, with his concept of ‘distributed artificial intelligence’), the emphasis is such papers has been on technical considerations rather than on organizational fundamentals. This paper has sought to demonstrate that the fundamentals count. Thirdly, it has been argued that the holonic framework offers not just a descriptive framework, within which current engineering attempts to develop intelligent systems may be analysed (e.g. from the perspedtive of the triad of coordination and functionality), but as a prescriptive tool to aid design. The essence of the holonic approach, as applied to the design of intelligent manufacturing systems, lies in its formulation of the system as a nested sequence of selforganizing and self-managing entities, each of which has intelligence built into it and the systemic authority to use this intelligence to adjust its behaviour in the light of operating circumstances. Design, then, focuses on the need to create systems of coordination that operate in first order, second order and third order mode, with full alignment achieved between holonic levels through the insistence on observing holonic selfsimilarity. The argument of this paper can be summarized by saying that intelligent manufacturing systems built along these holonic lines are likely to demonstrate superior efficiency and effectiveness, through their ability to cut through the complexity that overwhelms
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systems designed along lines of conventional tional architecture.
organiza-
References Kusiak, A Intelligent Manufacturing Systems, Prentice Hall. Englewood Cliffs. NJ (IYYO) Proceedings Internationul Workshop on Intelligent Manufacturing Systems, IMS International Technical Committee, Sydney. Australia (22 February 1993) Lund, R et al. Designed to Work: Production Systems and People. PTR Prentice Hall, Englewood Cliffs, NJ (1993) Symon, G ‘Human-centred computer-integrated manufacturing’. Comput. Integrated Manuf. Syst.. Vol 3 No 4 (1990) pp 223-220 Simon, H ‘The architecture of complexity’, Proc. Am. Phil. Sot.. Vol I06 (lY62) pp 467382 Koestler, A The Ghost in the Machine, Hutchinson. London (1967) (Danube edition. with new preface: 1976) Yom-don, E Object-Oriented Systems Design: An Integruted Approach. Prentice Hall. Englewood Cliffs, NJ (1994) Mathews, J ‘Holonic organisational architectures’. Human Systems Manugement (forthcoming)
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9 Warnecke, H-J The Fractal Company: A Revolution in Corporute Culture, Spinger-Verlag, Heidelberg (1993) (Translation from the German Die Fraktale Fahrik lYY2) IO Ranta, J, Gras-Pietro, G M and Eloranta, E ‘Requirements for advanced manufacturing systems and competing strategies in different industries: Some issues and questions of a possible future IMS program’, Proc. Int. Workshop on Intelligent Manufacturing Systems, IMS International Technical Committee. Sydney, Australia (22 February 1993) 11 Hirose, M ‘Development of the holonic manipulator and its control’. Proc. 29th IEEE Conf. Decision und C’ontml, Honolulu. HI (December 1990) pp Yl-96 12 Matsumoto, Y ‘Current intelligent manufacturing system activities at Hitachi Ltd’, Proc. 2nd Int. Symposium on /MS. Tokyo. Japan (March 1903) 13 Skinner, W Manufacturing: The Formidable C‘ompetitive Weapon John Wiley, New York (1985) 14 Harmon, R and Peterson, L Reinventing the Fuctory: Productivity Toduy Free Press/Collier Rreukthroughs in Munufacturing Macmillan, New York (1090) 15 Mathews, J Catching the Wurje: Workplaw Reform in Australia. Allen & Unwin. Sydney. 11-R Press, Cornell University (1994) 16 Zeleny, M ‘Amoeba: The new generation of self-managing human systems’, Human Systems Manage., Vol Y No 2 (I 990) pp 57-S’) 17 O’Hare, G ‘Designing intelligent manufacturing Systems: a distributed artificial intelligence approach’. Comput. in Ind., Vol I5 ( IYYO) pp 17-25
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