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Factors Governing the Evolution and Diffusion of CIM
Copyright © I FAC Man-~Iachine Svstems, Oulu , Finland, 1988 '
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PAPERS
FACTORS GOVERNING THE EVOLUTION AND DIFFUSION OF CIM R. U. Ayres and
J. Ranta
Computer Integrated Manufacturing (C I,vl) ProJfct, I nlemalional Inslill/le for Apphed Syslems Ana~)'sls (IIASA) Laxenblllg, Awlria
Abstract , There are now about 800 FXS (flexible manufacturing systems) in the world, These systems have some interesting technological and economic properties, which reflect technological constraints and partly also explain different applications and diffusion channels, Apart from these technological and economic factors, there exist a lot of organizational , institutional and other social factors, which also control the diffusion process of ClM-technologies, The paper analyzes some of these key factors, Keywords,
ClK; flexible manufacturing automation; impact assessment; diffusion,
INTRODUCTION: TECHNOLOGICAL PROGRESS VS, DIFFUSION It is important not to confuse the processes of technological innovation and diffusion/adoption, Although they commonly overlap to some degree, they are driven by different forces and controlled by different mechanisms, The adoption/diffusion process sometimes occurs alone, as where a product or process is essentially fully developed before it reaches the marketplace, An example of the latter might be a new drug such as penicillin or a chemical additive, such as tetraethyl lead (TEL), A variant of this situation occurs where complex technologies are developed and introduced in distinguishable "generations", as in the case of comme r cial jet aircraft or early computers, At the other extreme is a technology, like the telephone or the electric light, that is introduced in relatively primitive for m and continuously improved thereafter, Here progress and diffusion occur in parallel, and interact closely, Computer-integrated manufacturing (CIM) is an example of the latter type, In fact, the term "diffusion" may even be misleading, since it seems to imply a single center where CIX technology is created, and from which it spreads out spatially in all directions, This image is clearly wrong, inasmuch as there are many such centers, in scores of institutions located on several continents, The most that can be said in terms of identifying "leaders" and (by implication) "followers", is that leadership in the underlying electronics technology does not necessarily coinCide, or co-locate, with leadership in downstream applications technology, This is as unusual pattern, in historical terms, The purpose of this paper is to identify the elements of CIX and to decompose the complex changes that can be observed -- insofar as possible into distinguishable elements of progress and diffusion, It is also interesting to examine the interaction between technological progress and the diffusion/adoption process in the case of CI X, MANUFACTURING PRODUCTIVITY At the aggregate level advances in CIM technologies are reflected in manufacturing productivity, Of course, manufacturing productivity (defined in terms of output per unit factor input) has been
increasing more or less continuously since the beginning of the industrial revolution, In the earliest period great gains were achieved simply by specialization of tasks or "division of labor", as exemplified by Adam Smith's famous pin- making factory, A century later Frederick Taylor and others formalized and systematized the notion of reducing a complex Job to a series of simple tasks that could, in turn, be scientifically analyzed and optimized, Further gains resulted from the substitution of water power or steam engines for animal or human muscles for the driving of machines, This permitted substantial increases in machine size and speed, Machine tool capabilities, in terms of "degrees of freedom" and accuracy, increased dramatically during the 19th century, culminating with the development of "production" type grinding machines in the first decade of this century, These changes were both faci l itated and necessitated by the introduction of harder and stronger engineering materials, initially the substitution of steel for wrought or cast iron, and later the introduction of many specialized alloys (e,g, "high speed" steel ) and synthetic carbides for cutting and grinding, The latter development, alone, is re s ponsible for gains in ma c hining speed of the order of 600-fold since the 1860's, ' Further gains in manufacturing produc tivity resulted from the substitution of individual electric drive for centralized shaft power in the first third of the present century, Finally , the moving assembly line and the mechanical integration of a large number of machine tools l inked by a transfer line yielded further gains, culminating with the large but terminally inflexible automobile engine plants built in the 1950's and 60 ' s ,
' This figure refers to a standard steel axle, approximately 100 cm in length and 10 cm in radius, It is derived by combining two sources, For the period 1859-1895 productivity for steel axles increased by a factor of about 5,5 according to a specific study by the US Department of Labor in 1898, For the period 1900-1970, machining time for an axle of the dimensions noted declined from 105 minutes to less than 1 minute, according to data from Sandvik Steel Co, (Coromant Div,) , cited by the American Machinist 100th Anniversary issue (1077) ,
R. U. Ayres and J. Ranta
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However the last-named development seems to have signified the end of an era. While technological progress in "conventional" production technologies such as those noted above has not ceased by any means, the economic gains to be had from further increments in ma c hine size, speed or accuracy seem to be less and less significant. Two factors seem to be responsible. One is the increased competition in U.S. and world markets resulting from the rise of Japan and the other East Asian export-oriented economies. This has destroyed the postwar hegemony of General I(otors in the (world) auto industry. It has also made obsolete GI('s formerly dominant strategy of gradual "managed" innovation (the annual model-change ), with its emphasis primarily on exterior appearance . In current market conditions the relative importance of performance and quality vs. style has increased sharply, requiring a correspondingly greater emphasis on manufacturing. The second, and related, trend is towards increased product complexity, not only in the auto industry, but throughout manufacturing industry (Ayres, 1988). The consequences of the two trends noted above are also two-fold. In the first place, the rate of change of product design has accelerated, in the auto industry and elsewhere. Yhereas in the 1950's and 1960's the auto industry phased-in design changes rather slowly so as to permit mass production facilities a 20-year useful life before major renovation and retooling, this is no longer possible. But, on the other hand, increased complexity has made the de sign process increasingly expensive and risky . This led to the so-called "productivity dilemma", an apparent contradiction between the need to cut manufacturing costs by maximizing standardization and specialization and the need to keep introducing new and improved products (Abernathy, 1978) . The possibility of a way out of this dilemma through increasing the flexibility (economies of scope) of manufacturing technology was not clearly recognized until this decade", although the facilitating technologies -- computers and numerically controlled mac hines -- began filtering into the manufacturing world about thirty years ago. Ye return to the issue of flexibility later. ELEKENTS OF COMPUTER-INTEGRATED I(ANUFACTURING (CI I() For purposes of this paper, CII( is taken to comprise the whole range of programmable, computerdriven technologies -- from machines to information systems -- that are beginning to make their appearance in factories (a nd offices of manufacturing firms ). From the perspective of the factory floor, these technologies have the effect of e liminat ing "hands on" contact of the workers wi th the product or workpiece, and sharply reducing or eliminating the role of humans as machine controlle rs, materia ls handlers, assemblers or inspectors . From the perspective of higher
2 The importance of flexibility was not re cognized explicitly until the publication of Abernathy's book libidJ, which focussed attention on the problems associated with its absence. For some other discussions of the issue see IAyres & Miller 1982 ; Ayres & I(iller 1983; Goldhar & Jelinek 1983; Ayres 198 4; Brodner 1985; Bullinger et al 1985; Talaysum et a!. 1986; Talaysull. et a!. 1987J. The importance of economies of scope in economics has also received theoretical attention during the last decade, in the context of its implications for "contestability" of markets (e . g . BaullOl 1982; Farrow 1985J.
management, they can be regarded as assistance to and extensions of traditional methods of human decision-making, but with far greater informationhandling and information reduction power than previous generations of office automation. The underlying motivation for this substitution of computer-driven machines for human hands and eyes is a matter of conjecture. Surveys of managers seem to reveal a wide variety of justifications, ranging from direct labor savings to notions about increasing "flexibility". A major factor which has received relatively little attention, thus far in the management literature is the increasing need to control errors and produc t defe ct s arising from human carelessness
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Numerically controlled (NC) machine tools, and follow-on technologies such as Co mputer Numerical Control (CNC) , and Direct Numerical Control (DliC). Robots (programmable manipulators). Flexible manufacturing cells (FMC's) and Flexible Manufacturing Systems (F XS's) consisting of groups of programmable machines linked by programmable materia ls handling devices, such as robots. Computer-AIded-Manufacturing (C AM ) and related operating systems such as Materials-ResourcePlanning (!(RP ). Computer-AIded-Design (C AD ), Co mpu te r-AidedProcess-Planning (C APP ) and Computer-AidedEngineering (CAE ) systems to assist managers in planning functions.
In addition to systems, there are a variety of devices, such as micro-processors, machine controllers, sensors, servo-mechanisms, communications interfaces and sensoryinterpretation
Factors Governing the Evolution and Diffusion of elM speed increases over the past century (cited above) have been documented in terms of the time required to machine a steel axle of given dimensions. Comparable measures of performance for NC machine t ools, robots and so forth can certainly he conceived, but again, this has not been done s ystematically in practice, so far as we are aware . Lacking more direct measures , we are forced to fall back on a somewhat roundabout argument. As noted above , the common element in all CIM technologies is elec tronic data sensing and processing . It can be argued , indeed, that CIM is "driven" by computer technology ( in its most general sense) . It was, after all, the advent of the electronic computer in t he 1940's and early 50's that induced people like Norbert Wiener and John Diebold to imagine that c omputers could, eventually , control manufacturing proce sses (Wiener, 1948; Diebold, 1952 ). In fact, fears of the potential adverse impact of automation on employment surfaced as early as the mid-1960's ~ , long before any measurable effects (if , indeed, any s uc h effec ts are even now observable ). Actual l y , computers per se were only applied in the continuous proc ess industries to any significant e xtent before the 1970 ' s . Numerical controls, as applied to machine tools, only involved some of the c omputer peripherals such as magnetic tape readers/ writers until the advent of the mini c omputer . The large scale application of compu t er technology in the fabrication industries required the mi c roprocessor, which was introduced to the marketplace in 1971 and first applied to the c ontrol of machine tools , robots, transfer lines, etc . in the 1974-75 period . Machines controlled direc tly by mini- and micro-c omputers were di s tinguished from their predecessors by the CNC designation. This was the facilitating breakthrough for CIM. Some quantitative measures of technologi c al progress in the electronics sector are given in an acc ompanying paper (Ranta , 1988) . In effec t, we therefore suggest that the most appropr i ate measures of technological capability for CIM , as app l ied to the fabrication industries, are derived from its "backward linkages" to the elec troni c s se c tor (semiconductors, computers and tele c ommunicat i ons equipment ). A s c hematic diagram showing these relationships is given in Fig . 1. The "motive" effect of technological progress in the ~ R OGII ""'IIIl"'lil."
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Fia . 1 . For ewa r d (and backward) li nkages between motive and carrier techno logies.
, A major study of the problem was published i n 1966 by the National Commission on Technology, Automa t i on, and Ec onomic Progress. although the section on automation in the fabricating industries was foc ussed almost entirely on the impact of IC ma c hine t ools and automatic assembly [Schwartz & Prenting 1966J .
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electronic industries scarcely needs further discussion . These industries have also been the earliest adopters of advanced computerized manufacturing technologies . The machine tool building industry has contributed less and less to manufacturing technology in rec ent decades. Meanwhile , the applications engineering and systems integration are increasingly c arried out by downstream users, especially auto , truck, farm equipment and aircraft manufacturers . THE FLEXIBILITY PROBLEM As noted above, the "productivity dilemma" noted by Abernathy (op.cit . , 1978 ) created an imperative to increase capital savings through economies of scope, which requires greater flexibility of capital equipment and organization . A Flexibili t y implies the ability to produce a number of variants of the basic product, including new designs and "custom" versions . Il also implies competitiveness with regard to quality , short delivery times , small batch sizes and responsiveness to the market . In general terms, the flexible factory i s one with a cost structure that is insensitive (or inelastic) to variations in volume and product c onfiguration . Needless to say, the goal is to inc rease flexibility without increasing capital costs or sacrificing product quality . Strategies (or means ) to increase flexibility are not limited to the use of programmable automation in plac e of fixed automation . In addition, there are organizational c onsiderations, financial considerations and logistic factors . The latter are discussed, for instance, in (Ranta & Wandel , 1988) . We limit the present discussion to techno l ogical means alone . The first step towards flexibility i s to provide for modular product design . This phase necessitates an investment in a computer aided design system (CAD). The CAD s ystem decreases the total delivery time and gives possibilities to generate different variations and design c hoi c es in a rapid and cost-efficient way . The second step usually is to enhance manufac turing flexibility . In this phase , us ually, a subcontracting network is built up . A possible approach is to utilize a flexible manufactur i ng system (FMS ). This is a major investment . The prerequisites for a successful implementation of FMS are a clear product strategy, with a welldef i ned foc us and different i ation. Suc h systems can de c rease delivery times and even inc rea s e production capac ity without los s of flexibi li ty . In summary, there are s everal diffe r en t types of flexibility , each related t o a spe c ifi c strategi c function . We can classify them as follows : Design flexibility focuses on varia t ions i n product form . It permits specialized and cus tomized versions of a product to be drawn up on short notice to respond to te nders . Koreover, design flexibility also makes it poss ible to introduce product changes rapidly on the factory floor . In a broader sense, a part of the design flexibili ty is also the c apab i lity to plan production schedules quickly and change them as needed . This guarantees rapid all - over delivery times and rapid confirmation of orders . A modular des ign philosophy is needed t o maximize the benefi ts of flexible manufacturing systems (FMS ).
Alote that the " dilemma" was not c reated by Abernathy, although he was the first to articulate it clearly .
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R. C. Ayres and
Manufacturing flexibility focuses on variations in product configuration, timing and volume. This Deans that the manufacturing process has a capability to make small batch sizes, to make variations, and to minimize throughput time . Material supply flexibility focuses on variability of product configuration, time and volume of purchased materials. It is needed to guarantee the flexibility of the whale manufacturing logistic chain. It is a common practice to have semiintegrated subcontractors and just-in-time deliveries for purchased parts. However, just-intime supply of raw materials (e.g. bar stock) is not suitable for custom-designed parts, bought in small quantities only for a particular order. In this case a more project-oriented supply function together with a flexible supply network is needed . Distribution flexibility involves the inventory, transport and administrative means of creating flexibility in the place, time, size and assortment of deliveries to achieve customer satisfaction. These four functional flexibilities interact in a complex way . Figs. 2 and 3 illustrate the basic situation.
Des ign .nd
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Ranta existence of enough pallets, fixtures and tool magaZines. Moreover, the physical configuration of the mach ines must no inhibit such changes; process flexibility implies the possibility of producing members of the part family in any convenient order . This requires machine flexibility as well as supporting planning flexibility; product flexibility implies an easy shift to a new product or a new part family; and production flexibility implies minimal barriers to a change in production volume. in the routing of the workpieces. in tooling sequences etc .; it is also referred to as routing and sequenc ing flexibility of structura l fleXibility. In any case. is reflects the basic structural limitations of the system and it is related to the properties of the transportation system. warehousing system, interfacing system. systems control and software modularity.
Of course, it is possible to define the above notions in still greater detail. There are many different definitions, but these brief cha racterizations provide some insight before the economic tradeoffs are considered .
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FKS CAPABILITIES:
Distribution
Fig. 2. Network of flexibility.
The tou t respons.e time
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Maintenance Spire part supply
Fig. 3. Flexibility factors.
One can easily recognize that in order to decrease the total response time the most important phases are in order processing and planning and design as well as distribution. On the other hand, the ability to make variations is mainly provided by design, planning and manufacturing systems as well as by the subcontracting network. Complexity is provided mainly by planning and manufacturing. Volume flexibility and batch size flexibility are mainly related to the manufacturing and subcontracting network, but other functions are also essential. ~ith regard to manufacturing flexibility, it is convenient to identify several components (see also Son & Park, 1983; Ylmaz & Daris, 1987; Slack, 1987; Gerwin, 1987):
machine flexibility implies easy changeability of workpieces and tools. This requires the
1972 VS. 1987
Unfortunately, for reasons noted above , it is not a simple matter to measure increases (over time) in the "flexibility quotient" of CD! systems in any straightforward way. One can, however, note a significant increase, over time, in the capabilities of the "leading edge" systems reported occasionally in the engineering literature with respect to "part family" size, part complexity, lead time, etc . The first successful FKS system in the U.S. was the "Omnicontrol" system developed by Sundstrand Corp . beginning in the late 1960's (Cook, 1975). It began round-the-clock operation in 1972 in the Roanoke, Va. plant of Ingersoll - Rand Corp., a manufacturer of hydrauliC pumps, hOists, winches and other eqUipment. It consisted of six NCmachine tools: two 4-axis milling ma chi nes, two 5axi s milling machines and two 4-axis drills (i bid. ). Parts were transferred from machine to machine by pallets on a transfer-line. Each machine was served by a tool-changing carousel with up to 60 tools, selected by the controll ing computer (an I BM 360/30) (i bid). The Sundstrand syst em as a whole accommodated 500 different tools (200 at a time) and produced up to 500 different prismatic parts ( 16 at a time ) in sizes up to 1 me t er c ube. It was manned by 3 operators and a supervisor per shift. Equiva lent output in a conventional shop would have required 30 machines and operators, plus supervisors. One of the key features of the "Omnicontrol" system was a CRT display screen, next to each machine, describing the piece being worked on and the progress made (ibid). It is interesting to co ntrast the "Omnicontrol" system with one of the most recent FKS systems, designed and built by Ingersoll Mi lling Machine Co. (Rockford, Ill.) for its own use ( ~arndorf & Merchant, 1986. Ingersoll Co .• 1986 ). It began operation in 1983 with a cell conSisting of 5 NC milling centers (MC's ) to produce heads, gearboxes and brackets. A second cell consisting of 3 MC' s and a third with 4 vertical l ather completed the FKS in 1987. The FKS also incorporates an
Factors (;OHTllillf{ lhe E\"oiulioll alld Diffusioll of CI\' inspection station with 2 Zeiss Mauser coordinate measuring machines, also computer controlled. They included a CNC materials handling system with automatic loading capability. Control is exercised by a hierarchy of computers. At the top is an IBX 3090/200 mainframe, which is the host for the corporate management information system. This system includes the master schedule (order, entry and confirmation together with all significant dates), the bill of materials, the geometric models generated on CADAX, a master scheduling program known as a "dispatch list", and other factory-wide planning programs. The FXS itself is controlled by a VAX -750 computer in 2-way communication with the IBM 3090/200. The VAX reports back to the mainframe data needed by the KlS system, i nc 1ud i ng personne 1 stat ist ics, work progress reports, tools inventory, machine condition, et c. The $ 20 million Ingersoll system was designed to manufacture 25.000 different parts types annually, with 70% in lot sizes of one, and 50% of which are unique and will never be made again. The syst em replaces 40 stand-alone ma c hine s and reduces the number of operators by 75%, although the primary economic benef i ts were expected to be in the reduction of overhead costs, espeCially work-inprogress. The 1987 Ingersoll FXS is obviously many times more flexible than the 1972 model Sundstrand system. However a true comparison must be multidimensional. A suitable methodology remains to be developed. THE DIFFUSION OF CIM The diffusion of a teChnology, suc h as CIM, may be regarded as a s pe cial case of a more general social diffusion process, viz. the diffusion of ideas, language, or lifestyle. One may also identify "similar" diffusion processes, suc h as the spread of an infectious disease or a pest. However, while these processes may offer some useful analogs, they also la c k two key features of the process of technological diffusion . The first is the element of choice and deci s ion .... hile people do "choose" whether to adopt a new concept, a new slang word, or a new hairstyle, much of the decision process is subconscio us and is probably governed by factors such as the extent to whi ch people want t o behave like others, unlike others, or just don't care. \{bile not necessarily easy to determine in advanc e for a particular indiVidual, such things c an probably be determined with fairly high ac cu racy for large populations. In the case of an infectious disease, of course, there is no conscious c hoice involved, exce pt to the extent that people can control the extent of their exposure. If the latter is not controllable, the rate of spread is essentia lly deterministic. By contrast, the adoption of a new product (other than an "i mpu lse" ite m) does generally involve comparison, evaluation and explicit choice. In most cases, there is not only a choice of "brands" or suppliers, but als o a generic choice among alternative strategies or systems at several "nested" levels of abstraction. For instance, a factory manager may choose among robot vendors only after making prior ch oices among robot archite ct ures (rectangular , cylindrical, spherical, revo l ute ), drive systems (pneumatiC. hydrauliC, electriC). and even between robots, ACV's. transfer lines, or human workers in the parti c ular application . This multi-level choice i s always a factor in the adoption decision. Because it is so complex, the
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market success of a new product is extremely difficult to predict. Hence, new consumer products are seldom introduced without extensive market studies -- often involving distribution of the actual product in selected "typical" localities. Based on the results of such studies, changes in the product -- or its packaging and presentation are often made before full scale introduction. New producer goods or processes, unfortunately, cannot be pretested in this manner because the major cost is the development itself, not the distribution and marketing. Thus, even with the most careful attention to all controllable factors, many R&D efforts result in failure, at least from an economic perspective. Sometimes the amounts of money involved are extremely large, as in the case of GM's "Saturn" project. Once a new product has passed this initial hurdle, however, the uncertainties with regard to acceptance seem to be sharply reduced. In effect, there is often a "yes/ no" reaction from the marketplac e that is quite hard to predict, at least on the basis of currently available methods. On the other hand, once the level of adoption has passed a certain point -- rule-of-thumb puts it at about 5% of the "potential" market -- the remaining question is to estimate the subsequent trajectory of the penetration. In effect, this involves choosing among a set of available forecasting models, using the information gained from the early adoption history to make the selection and to parametrize the chosen model (e.g. Mahajan & Wind, 1986) . Unfortunately, most of the available diffusion models do not deal with the case of interest here, since they all assume, at least implicitly, that the "product" remains unchanged after the diffusion process starts. Yet, in the real world of CIM technology there is an active, conscious, feedback between the primary technology developer and the market. While controlled market-testing is seldom pOSSible, the early adopters of a new, evolving industrial technology (such as CIX) do provide a constant stream of useful information to the developers and manufact urers of producer goods, both in terms of the performance and reliability of the eqUipment and systems that have been put in service, and the ever-more clearly understood (and changing) needs of their customers. A further complication is the fact that the technology in the case of ClM, (and other cases as well ) arises from a variety of sources, not least of whi ch are the early users themselves. Early users of NC machines, robots and FMS systems, for instance, needed to learn how to use these things effect ively. In many cases, this depended on the development of speCial-purpose software, which remains proprietary (i. e. it does not diffuse to other potential users ). It also depends on organizational learning whi c h, by its nature, is not easily transferable. Another important source of basic technology, possibly the major one in this case, is the electronics industry, especially semi conduc tors and computers. Having made these points, it must be emphasized that the dominant mechanism governing diffusion is the calculus of costs vs benefits. While technological information may not have been available equally around the world as recently as 30 years ago, when NC machines began to diffuse, this cannot be assumed today. In the major industrialized countries, at least, there cannot be significant differences in terms of basic technical information availability. Hor does it make sense to assume that decision-makers in one country are more or less rationa l than those in another. Any observed" lags" in diffusion between countries must, therefore, be explained by differences in the economic structure or the socio-political
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environment . Real labor costs, interest rates, energy costs, inventory ratios, domestic market size, labor, anti-trust and trade policy, currency stability, fiscal , monetary and tax policy and a host of other factors do differ from country to country. Diffusion rates will obviously vary from sector to sector, also for rational reasons. Here the factors involved are more -micro' than -macro'. Some typical company level factors and design issues are descr i bed be 1ow. Usually the starting point for economi c analysis is capacity. Typically there are variations in the required volume, as well as expected (or unexpected ) future changes in product mix. In small and medium size companies increase of production capacity through capital sharing c an be the most important justification for investment in a flexible manufacturing system. Thus the first economic goal is volume flexibility: the ability to share c apacity among products according to demand fluctuations. To achieve an economic operation, there are three critical factors: (1) a steady and high level of total volume, (2) a high machine availability, and (3) a suitable product mix. The second important economic goal is the ability to vary the product within general parameters. These parameters define a "part family". Usually a larger part family means less total product ion capacity for the same capital cost . This conflicting situation is usually presented graphically by a picture like Fig . 4. The maximum size of the part family is restricted by technological factors as well as by economic factors. The larger the part family is, the more tool changes and setups are necessary, which decreases the effective production time and thus also the annual volume.
and.J. Ranta A third economi c goal for flexibility is minimum batch size, to minimize work-in-progress. A small batch size will also decrease total delivery time . But again, smaller batch sizes decrease maximum production capacity because of tool changes, etc. Short delivery times and decreased work-in-progress are usually related to each other and are usually realized by a suitable compromise of batch sizes, part family, and system capacity. Each of the flexibility goals has its tradeoffs, of co urse . One of the aims of the design is to have an overall cost/benefit ratio as high as possible. Usually we can observe the following Simple tradeoffs: 1.
I ncre ase of part fami ly will in c r ease the need for machine flexibility as well as process and production flex ibili ty , because of t he reqUired routing possibilities and new dimensions; will increase sofLw~re costs, because more NCplograms are needed as we 11 as more integration software; device or hard ware costs will increase, because more pallets, fixtures, storage space , robot capacity are needed; wi ll decrease materia l cost s and capital fixed in storage, work- in-progress and inventory.
2.
Increase of volume or ca pacity will increase hardware and machinery costs (other factors held const ant ); wi ll increase auxiliary device costs; will i ncreas e the need for production and process flexibility, due to increased tooling reqUirements; will increase software costs, because of more complex syste ms control .
3.
Increase of Part complexity
Volume
will inc rease the need for machine and product flexibility; will i ncrease software costs , because of more complex part programs and a more complex systems contr o l integration; will increase too l, pallet and fixture costs.
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4.
Decrease of batch size wi ll increase the need for process and producti on flexib il i t y; wil l i ncrease software costs, because of a more complex systems c ontrol; will increasp. auxi liary device costs, because more (and more complex ) pallets are needed .
Plrt flm lly f1e. ibil, tv
Fig. 4. Flexibility vs. volume .
A factor related to the s ize of the part family is the complexity of the parts, or simply the number of different surfaces needed for each part. Other things being equal, an increas e in comp l exity will decrease the maximum production volume because of time-consuming tooling and set-up operations. The more complex the parts the system is able to produce, the larger the part family can be. The complexity of parts made by a given system is also restricted by many technological and economic factors. In any case, an investment to increase a llowed part complexity ca n be seen as an investment for the future, to cope with potential future market changes.
Supplementary data on observed relationships between these factor s is presented in (Sheinin & Tchijov , 1988), and (Tchijov & Alabian, 1988). CONCL UDING REMARKS In summary, the imperative for increased flexibility depends on the nature of the product and the market . even with in a single country. There is no suc h imperative. for inst ance. in the case of products -- such as factories, power plants. office bUildings and bridges -- that have always been custom-designed. Increased flexibility has nothing to contribute i n these cases. At the other extreme, there is also no imperative for i ncrea sed flexibi lit y in the case of co mponents like ball and roller bearings. nuts and bolts , e l e ct ri cal connectors , light-bulbs, sparkplugs. fractional horsepower motors , or a number of other products
Factors Cmerning the E\'olulion alld Diffusion of Cl M that are, and are likely to remain, highly standardized. In fact, the domain of flexible manufacturing is generally considered to be restricted to parts normally produced in families of at least ten to a hundred and batches of less than a thousand or so. These figures are impreCise, but certain products are clearly excluded: tools and dies and industrial patterns, at the low end, and anything truly standardized at the other end of the spectrum. It is for these reasons, for instance, that the auto industry uses robots primarily in the final assembly process (for welding and painting ) , but not for parts manufa c turing or subassembly opera t ions. In the case of flexible (i.e. robotic) assembly, the economic restrictions are even tighter. Since even the most flexible machine cannot compete in flexibility with a human worker, the minimum batch size necessary to justify the investment in automation is considerably larger: in the thousands. In fact, product (model> families of the necessary size are parti c ularly characteristic of cameras, watches and consumer electronics. All of these are industries dominated by Japan. Thus it is not surprising, and certainly not an indication of "lag" on the part of other count ries, that Japan has far more robots than other industrial countries, a very large percentage of which (40%) are involved in assembly tasks. By contrast, the U.K. and ~est Germany have, respectively 9.7% and 8.6% of their robot s doing assembly tasks (Tani,
7
There is some evidence, in fact, that there are really two categories of FXS systems, differentiated along the above lines (Sheinin & Tchijov, 1988). This difference in use would inCidentally, explain the otherwise surprising results observed by Jaikumar and reported in his widely publicized article in Harvard Business Review (Jaikumar, 1986). Summarizing a survey of 35 FXS' in the U.S. and 60 in Japan he noted that the average number of parts made by the U.S. system was 10, with an average annual volume of 1.727. On the other hand, the average number made by the Japanese FXS' was 93, with an average annual volume of 258. Jaikumar asserts that this statistic proves that US manufacturers were using FXS "the wrong way". However it seems possible that he was inadvertently comparing applies and oranges. Although he claims that the two samples "had similar machines and did similar work", his Japanese sample was very heavily weighted by FXS' used for machine tool production, while none of the U. S. FXS' were used for this purpose. (The Ingersoll system described earlier was not included). The U.S. sample was heavily weighted, on the contrary, by firms like Caterpillar and Deere, which are volume manufacturers of standardized items of heavy equipment. It may well be true that U. S. industry has lagged in the application of flexible automation , but the data available to date do not yet unambiguously support such a conclusion. It also shows the difficulties to make solid conclusions regarding benefits and costs of CI~ technologies.
1987) .
In as much as flexible automation occupies the middle of a spectrum ranging from labor-intensive "job shops" at one end and capital-intensive automatic transfer - systems at the other, the middle ground of batch production is, itself, quite heterogeneous. From the labor-intensive job soop end of the spectrum it may make se nse to extend the technology in the direction of highly flexible but co mpact and inexpensive low-volume systems. From the mass-produc t ion end of the spectrum, it may make sense to move in the direction of increasing some variability in high-capacity systems. These two cases are illustrated in Fig. 5.
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Abernathy, ~. J. (1978). The Productivity Dilemma. Johns Hopkins University Press, Baltimore, MD. Ayres. R. U. (1984). The lIext Industrial Revolution. Reviving Industry Through Innovation. Ballinger, Cambridge, ~. Ayres, R. U. (1988). Complexity, reliability, and design: manufacturing implications. ~nufacturing Review, Vol. 1, Spring 1988. Ayres, R.U., Miller, S.M. (1982). Robotics & conservation of human resources . Technology in SOCiety, i, pp. 181-197. Ayres, R.U., Miller, S.M. (1983). Robotics. Application & Social Implications. Ballinger Publishing Co., Cmabridge, ~. Baumol, ~. J. (1982). Contestable markets. An uprising in the theory of industry structure. American Economic Review, 72, March 1982, pp. 1-15.
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REFEREIiCES
ma nu :acturing systems.
Brodner, P. (1985). Skill-based production - the super ior concept to the factory. Frod. I nt. Conf. on Factory of Future, Fraunhofer Gesellschaft, Stuttgart, 1985, 6 pp. Bullinger, H.J., ~arne c ke, H.J., Lentes, H.P. (1 985 ). Toward the factory of the future. Proc . Int. Conf. on Factory of Future, Fraunhofer Gesellschaft, Stuttgart, 1985, pp. XXIX-L1V. Cook, !I.H. (1975). Computer-managed parts manufacture. Scientific American, 232, 2, February 1975, pp. 22-29. Diebold, J. (1952). Automation: Advent of the Automatic Factory. Van Bostrand, New York. Farrow, S. (1985). Flexible manufacturing: ~rket structure & market failure. Journal of Policy Analysis and ~nagement, 4, pp. 585-605. Gerwin, D. (1987). An agenda for research on the flexibility of manufac turing processes. IJOPM, z..... 1, pp. 38-49. Goldhar, J.D., Jelinek, M. (1983). Plans for economies of scope. Harvard Business Review, November-December 1983, pp. 141-148.
R. C. Ayres and Hac kamack, L.C. (1965). The productivity criteria quotient (PCQ) special report # 570. American Machinist, 109, 12, June 7, 1985, pp . 129-140. Ingersoll lUlling !(achine Co. (1986). Computer integrated flexible manufacturing - 1986. Presentation to CASA/SKE FMS-86 Plant Tour, March 6, 1986. Jaikumar, R. ( 1986). Postindustrial manufacturing. Harvard Business Review, November-December 1986, pp. 69-79. Mahajan , V., 'iind, Y. (1986). Innovation diffusion models of new product acceptance . Ballinger, Cambrdige, KA . Ranta, J. (1988). Impact of electronics and inf or mation technology on the future trends and applications of CU! technologies. IlASA, li.ay 1988. Ranta, J. and 'iandel S. (1988). Economies of scope and design of flexibility in manufacturing logisti c systems. Proc. 5th International 'iorking Seminar on Production Economics, February 22-26, 1988, Igls, Austria. Schwartz, E.S ., Prenting, T.O. (1966). Automation in the fabricating industries. In: The Outlook for Technological Change and Employment. Appendix I, pp. 290-361. National Commission on Technology Automation and Economic Progress, 'iashington , D.C. Sheinin R., Tchijov , 1. (1988). Flexible manufa c turing systems (FKS ) diffusion and advantages. 'iP-88-29 , IIASA. Slack, N. (1987). The flexibility of manufacturing systems . I JOPJI!, 7, pp. 35-45. Son , Y. K., Park, C. S. ( 1983 ). Economic measures of productivity, quality and flexibility in advanced manufacturing systems. J. of Manuf. Systems, 6 , 3, pp. 193-206. Talaysum A. T., Hassan, M. Z., Goldhar, J. D. (1986 ). Scale vs. Scope Considerations in the CIMlFKS Factory. A. Kusiak (e d. ), E1sevier Science Publ ishers B. V., North-Holland. Talaysum, A. T., Hassan, M.Z., Goldhar, J.D. (1987). Unce r tai nty reduct ion through flexible manufa cturing. IEEE Trans. Eng. !(ang . , EM-34, 2, pp. 85-91. Tan i, A. <1987> . An international comparison of industrial robot penetration. IIASA 'iorking Paper 'iP- 87-125. Tchij ov, 1., Alabian, A. Flexible manufact uring systems (F MS ): !(ain economic features. Forthcoming IIASA 'iorking Paper, July, 1988. 'ia rndorf, Paul R., Merchant, M. Eugene (1986). Development and future trends in computer integrate d manufa cturing in the U.S.A. ~ Technology Management, Vol . 1., Nos 1/2, 1986 (pp. 161 - 178). 'iiener, N. ( 1948). Cybe rneti CS. Control and Co mmunications in the Animal and the Ma chi ne . 'iile y, N. Y. Ylmatz, O. S., Daris, R. D. (1987). Flexible manufa ct uring systems - characteristics and assessment . Eng. Mgm. I ng . , 4, pp. 209-212.
J. Ranta
I'LE:-JL'~I
PAPERS
FACTORS GOVERNING THE EVOLUTION AND DIFFUSION OF CIM R. U. Ayres and
J. Ranta
Computer Integrated Manufacturing (C I,vl) ProJfct, I nlemalional Inslill/le for Apphed Syslems Ana~)'sls (IIASA) Laxenblllg, Awlria
Abstract , There are now about 800 FXS (flexible manufacturing systems) in the world, These systems have some interesting technological and economic properties, which reflect technological constraints and partly also explain different applications and diffusion channels, Apart from these technological and economic factors, there exist a lot of organizational , institutional and other social factors, which also control the diffusion process of ClM-technologies, The paper analyzes some of these key factors, Keywords,
ClK; flexible manufacturing automation; impact assessment; diffusion,
INTRODUCTION: TECHNOLOGICAL PROGRESS VS, DIFFUSION It is important not to confuse the processes of technological innovation and diffusion/adoption, Although they commonly overlap to some degree, they are driven by different forces and controlled by different mechanisms, The adoption/diffusion process sometimes occurs alone, as where a product or process is essentially fully developed before it reaches the marketplace, An example of the latter might be a new drug such as penicillin or a chemical additive, such as tetraethyl lead (TEL), A variant of this situation occurs where complex technologies are developed and introduced in distinguishable "generations", as in the case of comme r cial jet aircraft or early computers, At the other extreme is a technology, like the telephone or the electric light, that is introduced in relatively primitive for m and continuously improved thereafter, Here progress and diffusion occur in parallel, and interact closely, Computer-integrated manufacturing (CIM) is an example of the latter type, In fact, the term "diffusion" may even be misleading, since it seems to imply a single center where CIX technology is created, and from which it spreads out spatially in all directions, This image is clearly wrong, inasmuch as there are many such centers, in scores of institutions located on several continents, The most that can be said in terms of identifying "leaders" and (by implication) "followers", is that leadership in the underlying electronics technology does not necessarily coinCide, or co-locate, with leadership in downstream applications technology, This is as unusual pattern, in historical terms, The purpose of this paper is to identify the elements of CIX and to decompose the complex changes that can be observed -- insofar as possible into distinguishable elements of progress and diffusion, It is also interesting to examine the interaction between technological progress and the diffusion/adoption process in the case of CI X, MANUFACTURING PRODUCTIVITY At the aggregate level advances in CIM technologies are reflected in manufacturing productivity, Of course, manufacturing productivity (defined in terms of output per unit factor input) has been
increasing more or less continuously since the beginning of the industrial revolution, In the earliest period great gains were achieved simply by specialization of tasks or "division of labor", as exemplified by Adam Smith's famous pin- making factory, A century later Frederick Taylor and others formalized and systematized the notion of reducing a complex Job to a series of simple tasks that could, in turn, be scientifically analyzed and optimized, Further gains resulted from the substitution of water power or steam engines for animal or human muscles for the driving of machines, This permitted substantial increases in machine size and speed, Machine tool capabilities, in terms of "degrees of freedom" and accuracy, increased dramatically during the 19th century, culminating with the development of "production" type grinding machines in the first decade of this century, These changes were both faci l itated and necessitated by the introduction of harder and stronger engineering materials, initially the substitution of steel for wrought or cast iron, and later the introduction of many specialized alloys (e,g, "high speed" steel ) and synthetic carbides for cutting and grinding, The latter development, alone, is re s ponsible for gains in ma c hining speed of the order of 600-fold since the 1860's, ' Further gains in manufacturing produc tivity resulted from the substitution of individual electric drive for centralized shaft power in the first third of the present century, Finally , the moving assembly line and the mechanical integration of a large number of machine tools l inked by a transfer line yielded further gains, culminating with the large but terminally inflexible automobile engine plants built in the 1950's and 60 ' s ,
' This figure refers to a standard steel axle, approximately 100 cm in length and 10 cm in radius, It is derived by combining two sources, For the period 1859-1895 productivity for steel axles increased by a factor of about 5,5 according to a specific study by the US Department of Labor in 1898, For the period 1900-1970, machining time for an axle of the dimensions noted declined from 105 minutes to less than 1 minute, according to data from Sandvik Steel Co, (Coromant Div,) , cited by the American Machinist 100th Anniversary issue (1077) ,
R. U. Ayres and J. Ranta
2
However the last-named development seems to have signified the end of an era. While technological progress in "conventional" production technologies such as those noted above has not ceased by any means, the economic gains to be had from further increments in ma c hine size, speed or accuracy seem to be less and less significant. Two factors seem to be responsible. One is the increased competition in U.S. and world markets resulting from the rise of Japan and the other East Asian export-oriented economies. This has destroyed the postwar hegemony of General I(otors in the (world) auto industry. It has also made obsolete GI('s formerly dominant strategy of gradual "managed" innovation (the annual model-change ), with its emphasis primarily on exterior appearance . In current market conditions the relative importance of performance and quality vs. style has increased sharply, requiring a correspondingly greater emphasis on manufacturing. The second, and related, trend is towards increased product complexity, not only in the auto industry, but throughout manufacturing industry (Ayres, 1988). The consequences of the two trends noted above are also two-fold. In the first place, the rate of change of product design has accelerated, in the auto industry and elsewhere. Yhereas in the 1950's and 1960's the auto industry phased-in design changes rather slowly so as to permit mass production facilities a 20-year useful life before major renovation and retooling, this is no longer possible. But, on the other hand, increased complexity has made the de sign process increasingly expensive and risky . This led to the so-called "productivity dilemma", an apparent contradiction between the need to cut manufacturing costs by maximizing standardization and specialization and the need to keep introducing new and improved products (Abernathy, 1978) . The possibility of a way out of this dilemma through increasing the flexibility (economies of scope) of manufacturing technology was not clearly recognized until this decade", although the facilitating technologies -- computers and numerically controlled mac hines -- began filtering into the manufacturing world about thirty years ago. Ye return to the issue of flexibility later. ELEKENTS OF COMPUTER-INTEGRATED I(ANUFACTURING (CI I() For purposes of this paper, CII( is taken to comprise the whole range of programmable, computerdriven technologies -- from machines to information systems -- that are beginning to make their appearance in factories (a nd offices of manufacturing firms ). From the perspective of the factory floor, these technologies have the effect of e liminat ing "hands on" contact of the workers wi th the product or workpiece, and sharply reducing or eliminating the role of humans as machine controlle rs, materia ls handlers, assemblers or inspectors . From the perspective of higher
2 The importance of flexibility was not re cognized explicitly until the publication of Abernathy's book libidJ, which focussed attention on the problems associated with its absence. For some other discussions of the issue see IAyres & Miller 1982 ; Ayres & I(iller 1983; Goldhar & Jelinek 1983; Ayres 198 4; Brodner 1985; Bullinger et al 1985; Talaysum et a!. 1986; Talaysull. et a!. 1987J. The importance of economies of scope in economics has also received theoretical attention during the last decade, in the context of its implications for "contestability" of markets (e . g . BaullOl 1982; Farrow 1985J.
management, they can be regarded as assistance to and extensions of traditional methods of human decision-making, but with far greater informationhandling and information reduction power than previous generations of office automation. The underlying motivation for this substitution of computer-driven machines for human hands and eyes is a matter of conjecture. Surveys of managers seem to reveal a wide variety of justifications, ranging from direct labor savings to notions about increasing "flexibility". A major factor which has received relatively little attention, thus far in the management literature is the increasing need to control errors and produc t defe ct s arising from human carelessness
o o
o o
Numerically controlled (NC) machine tools, and follow-on technologies such as Co mputer Numerical Control (CNC) , and Direct Numerical Control (DliC). Robots (programmable manipulators). Flexible manufacturing cells (FMC's) and Flexible Manufacturing Systems (F XS's) consisting of groups of programmable machines linked by programmable materia ls handling devices, such as robots. Computer-AIded-Manufacturing (C AM ) and related operating systems such as Materials-ResourcePlanning (!(RP ). Computer-AIded-Design (C AD ), Co mpu te r-AidedProcess-Planning (C APP ) and Computer-AidedEngineering (CAE ) systems to assist managers in planning functions.
In addition to systems, there are a variety of devices, such as micro-processors, machine controllers, sensors, servo-mechanisms, communications interfaces and sensoryinterpretation
Factors Governing the Evolution and Diffusion of elM speed increases over the past century (cited above) have been documented in terms of the time required to machine a steel axle of given dimensions. Comparable measures of performance for NC machine t ools, robots and so forth can certainly he conceived, but again, this has not been done s ystematically in practice, so far as we are aware . Lacking more direct measures , we are forced to fall back on a somewhat roundabout argument. As noted above , the common element in all CIM technologies is elec tronic data sensing and processing . It can be argued , indeed, that CIM is "driven" by computer technology ( in its most general sense) . It was, after all, the advent of the electronic computer in t he 1940's and early 50's that induced people like Norbert Wiener and John Diebold to imagine that c omputers could, eventually , control manufacturing proce sses (Wiener, 1948; Diebold, 1952 ). In fact, fears of the potential adverse impact of automation on employment surfaced as early as the mid-1960's ~ , long before any measurable effects (if , indeed, any s uc h effec ts are even now observable ). Actual l y , computers per se were only applied in the continuous proc ess industries to any significant e xtent before the 1970 ' s . Numerical controls, as applied to machine tools, only involved some of the c omputer peripherals such as magnetic tape readers/ writers until the advent of the mini c omputer . The large scale application of compu t er technology in the fabrication industries required the mi c roprocessor, which was introduced to the marketplace in 1971 and first applied to the c ontrol of machine tools , robots, transfer lines, etc . in the 1974-75 period . Machines controlled direc tly by mini- and micro-c omputers were di s tinguished from their predecessors by the CNC designation. This was the facilitating breakthrough for CIM. Some quantitative measures of technologi c al progress in the electronics sector are given in an acc ompanying paper (Ranta , 1988) . In effec t, we therefore suggest that the most appropr i ate measures of technological capability for CIM , as app l ied to the fabrication industries, are derived from its "backward linkages" to the elec troni c s se c tor (semiconductors, computers and tele c ommunicat i ons equipment ). A s c hematic diagram showing these relationships is given in Fig . 1. The "motive" effect of technological progress in the ~ R OGII ""'IIIl"'lil."
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, A major study of the problem was published i n 1966 by the National Commission on Technology, Automa t i on, and Ec onomic Progress. although the section on automation in the fabricating industries was foc ussed almost entirely on the impact of IC ma c hine t ools and automatic assembly [Schwartz & Prenting 1966J .
3
electronic industries scarcely needs further discussion . These industries have also been the earliest adopters of advanced computerized manufacturing technologies . The machine tool building industry has contributed less and less to manufacturing technology in rec ent decades. Meanwhile , the applications engineering and systems integration are increasingly c arried out by downstream users, especially auto , truck, farm equipment and aircraft manufacturers . THE FLEXIBILITY PROBLEM As noted above, the "productivity dilemma" noted by Abernathy (op.cit . , 1978 ) created an imperative to increase capital savings through economies of scope, which requires greater flexibility of capital equipment and organization . A Flexibili t y implies the ability to produce a number of variants of the basic product, including new designs and "custom" versions . Il also implies competitiveness with regard to quality , short delivery times , small batch sizes and responsiveness to the market . In general terms, the flexible factory i s one with a cost structure that is insensitive (or inelastic) to variations in volume and product c onfiguration . Needless to say, the goal is to inc rease flexibility without increasing capital costs or sacrificing product quality . Strategies (or means ) to increase flexibility are not limited to the use of programmable automation in plac e of fixed automation . In addition, there are organizational c onsiderations, financial considerations and logistic factors . The latter are discussed, for instance, in (Ranta & Wandel , 1988) . We limit the present discussion to techno l ogical means alone . The first step towards flexibility i s to provide for modular product design . This phase necessitates an investment in a computer aided design system (CAD). The CAD s ystem decreases the total delivery time and gives possibilities to generate different variations and design c hoi c es in a rapid and cost-efficient way . The second step usually is to enhance manufac turing flexibility . In this phase , us ually, a subcontracting network is built up . A possible approach is to utilize a flexible manufactur i ng system (FMS ). This is a major investment . The prerequisites for a successful implementation of FMS are a clear product strategy, with a welldef i ned foc us and different i ation. Suc h systems can de c rease delivery times and even inc rea s e production capac ity without los s of flexibi li ty . In summary, there are s everal diffe r en t types of flexibility , each related t o a spe c ifi c strategi c function . We can classify them as follows : Design flexibility focuses on varia t ions i n product form . It permits specialized and cus tomized versions of a product to be drawn up on short notice to respond to te nders . Koreover, design flexibility also makes it poss ible to introduce product changes rapidly on the factory floor . In a broader sense, a part of the design flexibili ty is also the c apab i lity to plan production schedules quickly and change them as needed . This guarantees rapid all - over delivery times and rapid confirmation of orders . A modular des ign philosophy is needed t o maximize the benefi ts of flexible manufacturing systems (FMS ).
Alote that the " dilemma" was not c reated by Abernathy, although he was the first to articulate it clearly .
4
R. C. Ayres and
Manufacturing flexibility focuses on variations in product configuration, timing and volume. This Deans that the manufacturing process has a capability to make small batch sizes, to make variations, and to minimize throughput time . Material supply flexibility focuses on variability of product configuration, time and volume of purchased materials. It is needed to guarantee the flexibility of the whale manufacturing logistic chain. It is a common practice to have semiintegrated subcontractors and just-in-time deliveries for purchased parts. However, just-intime supply of raw materials (e.g. bar stock) is not suitable for custom-designed parts, bought in small quantities only for a particular order. In this case a more project-oriented supply function together with a flexible supply network is needed . Distribution flexibility involves the inventory, transport and administrative means of creating flexibility in the place, time, size and assortment of deliveries to achieve customer satisfaction. These four functional flexibilities interact in a complex way . Figs. 2 and 3 illustrate the basic situation.
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Ranta existence of enough pallets, fixtures and tool magaZines. Moreover, the physical configuration of the mach ines must no inhibit such changes; process flexibility implies the possibility of producing members of the part family in any convenient order . This requires machine flexibility as well as supporting planning flexibility; product flexibility implies an easy shift to a new product or a new part family; and production flexibility implies minimal barriers to a change in production volume. in the routing of the workpieces. in tooling sequences etc .; it is also referred to as routing and sequenc ing flexibility of structura l fleXibility. In any case. is reflects the basic structural limitations of the system and it is related to the properties of the transportation system. warehousing system, interfacing system. systems control and software modularity.
Of course, it is possible to define the above notions in still greater detail. There are many different definitions, but these brief cha racterizations provide some insight before the economic tradeoffs are considered .
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One can easily recognize that in order to decrease the total response time the most important phases are in order processing and planning and design as well as distribution. On the other hand, the ability to make variations is mainly provided by design, planning and manufacturing systems as well as by the subcontracting network. Complexity is provided mainly by planning and manufacturing. Volume flexibility and batch size flexibility are mainly related to the manufacturing and subcontracting network, but other functions are also essential. ~ith regard to manufacturing flexibility, it is convenient to identify several components (see also Son & Park, 1983; Ylmaz & Daris, 1987; Slack, 1987; Gerwin, 1987):
machine flexibility implies easy changeability of workpieces and tools. This requires the
1972 VS. 1987
Unfortunately, for reasons noted above , it is not a simple matter to measure increases (over time) in the "flexibility quotient" of CD! systems in any straightforward way. One can, however, note a significant increase, over time, in the capabilities of the "leading edge" systems reported occasionally in the engineering literature with respect to "part family" size, part complexity, lead time, etc . The first successful FKS system in the U.S. was the "Omnicontrol" system developed by Sundstrand Corp . beginning in the late 1960's (Cook, 1975). It began round-the-clock operation in 1972 in the Roanoke, Va. plant of Ingersoll - Rand Corp., a manufacturer of hydrauliC pumps, hOists, winches and other eqUipment. It consisted of six NCmachine tools: two 4-axis milling ma chi nes, two 5axi s milling machines and two 4-axis drills (i bid. ). Parts were transferred from machine to machine by pallets on a transfer-line. Each machine was served by a tool-changing carousel with up to 60 tools, selected by the controll ing computer (an I BM 360/30) (i bid). The Sundstrand syst em as a whole accommodated 500 different tools (200 at a time) and produced up to 500 different prismatic parts ( 16 at a time ) in sizes up to 1 me t er c ube. It was manned by 3 operators and a supervisor per shift. Equiva lent output in a conventional shop would have required 30 machines and operators, plus supervisors. One of the key features of the "Omnicontrol" system was a CRT display screen, next to each machine, describing the piece being worked on and the progress made (ibid). It is interesting to co ntrast the "Omnicontrol" system with one of the most recent FKS systems, designed and built by Ingersoll Mi lling Machine Co. (Rockford, Ill.) for its own use ( ~arndorf & Merchant, 1986. Ingersoll Co .• 1986 ). It began operation in 1983 with a cell conSisting of 5 NC milling centers (MC's ) to produce heads, gearboxes and brackets. A second cell consisting of 3 MC' s and a third with 4 vertical l ather completed the FKS in 1987. The FKS also incorporates an
Factors (;OHTllillf{ lhe E\"oiulioll alld Diffusioll of CI\' inspection station with 2 Zeiss Mauser coordinate measuring machines, also computer controlled. They included a CNC materials handling system with automatic loading capability. Control is exercised by a hierarchy of computers. At the top is an IBX 3090/200 mainframe, which is the host for the corporate management information system. This system includes the master schedule (order, entry and confirmation together with all significant dates), the bill of materials, the geometric models generated on CADAX, a master scheduling program known as a "dispatch list", and other factory-wide planning programs. The FXS itself is controlled by a VAX -750 computer in 2-way communication with the IBM 3090/200. The VAX reports back to the mainframe data needed by the KlS system, i nc 1ud i ng personne 1 stat ist ics, work progress reports, tools inventory, machine condition, et c. The $ 20 million Ingersoll system was designed to manufacture 25.000 different parts types annually, with 70% in lot sizes of one, and 50% of which are unique and will never be made again. The syst em replaces 40 stand-alone ma c hine s and reduces the number of operators by 75%, although the primary economic benef i ts were expected to be in the reduction of overhead costs, espeCially work-inprogress. The 1987 Ingersoll FXS is obviously many times more flexible than the 1972 model Sundstrand system. However a true comparison must be multidimensional. A suitable methodology remains to be developed. THE DIFFUSION OF CIM The diffusion of a teChnology, suc h as CIM, may be regarded as a s pe cial case of a more general social diffusion process, viz. the diffusion of ideas, language, or lifestyle. One may also identify "similar" diffusion processes, suc h as the spread of an infectious disease or a pest. However, while these processes may offer some useful analogs, they also la c k two key features of the process of technological diffusion . The first is the element of choice and deci s ion .... hile people do "choose" whether to adopt a new concept, a new slang word, or a new hairstyle, much of the decision process is subconscio us and is probably governed by factors such as the extent to whi ch people want t o behave like others, unlike others, or just don't care. \{bile not necessarily easy to determine in advanc e for a particular indiVidual, such things c an probably be determined with fairly high ac cu racy for large populations. In the case of an infectious disease, of course, there is no conscious c hoice involved, exce pt to the extent that people can control the extent of their exposure. If the latter is not controllable, the rate of spread is essentia lly deterministic. By contrast, the adoption of a new product (other than an "i mpu lse" ite m) does generally involve comparison, evaluation and explicit choice. In most cases, there is not only a choice of "brands" or suppliers, but als o a generic choice among alternative strategies or systems at several "nested" levels of abstraction. For instance, a factory manager may choose among robot vendors only after making prior ch oices among robot archite ct ures (rectangular , cylindrical, spherical, revo l ute ), drive systems (pneumatiC. hydrauliC, electriC). and even between robots, ACV's. transfer lines, or human workers in the parti c ular application . This multi-level choice i s always a factor in the adoption decision. Because it is so complex, the
5
market success of a new product is extremely difficult to predict. Hence, new consumer products are seldom introduced without extensive market studies -- often involving distribution of the actual product in selected "typical" localities. Based on the results of such studies, changes in the product -- or its packaging and presentation are often made before full scale introduction. New producer goods or processes, unfortunately, cannot be pretested in this manner because the major cost is the development itself, not the distribution and marketing. Thus, even with the most careful attention to all controllable factors, many R&D efforts result in failure, at least from an economic perspective. Sometimes the amounts of money involved are extremely large, as in the case of GM's "Saturn" project. Once a new product has passed this initial hurdle, however, the uncertainties with regard to acceptance seem to be sharply reduced. In effect, there is often a "yes/ no" reaction from the marketplac e that is quite hard to predict, at least on the basis of currently available methods. On the other hand, once the level of adoption has passed a certain point -- rule-of-thumb puts it at about 5% of the "potential" market -- the remaining question is to estimate the subsequent trajectory of the penetration. In effect, this involves choosing among a set of available forecasting models, using the information gained from the early adoption history to make the selection and to parametrize the chosen model (e.g. Mahajan & Wind, 1986) . Unfortunately, most of the available diffusion models do not deal with the case of interest here, since they all assume, at least implicitly, that the "product" remains unchanged after the diffusion process starts. Yet, in the real world of CIM technology there is an active, conscious, feedback between the primary technology developer and the market. While controlled market-testing is seldom pOSSible, the early adopters of a new, evolving industrial technology (such as CIX) do provide a constant stream of useful information to the developers and manufact urers of producer goods, both in terms of the performance and reliability of the eqUipment and systems that have been put in service, and the ever-more clearly understood (and changing) needs of their customers. A further complication is the fact that the technology in the case of ClM, (and other cases as well ) arises from a variety of sources, not least of whi ch are the early users themselves. Early users of NC machines, robots and FMS systems, for instance, needed to learn how to use these things effect ively. In many cases, this depended on the development of speCial-purpose software, which remains proprietary (i. e. it does not diffuse to other potential users ). It also depends on organizational learning whi c h, by its nature, is not easily transferable. Another important source of basic technology, possibly the major one in this case, is the electronics industry, especially semi conduc tors and computers. Having made these points, it must be emphasized that the dominant mechanism governing diffusion is the calculus of costs vs benefits. While technological information may not have been available equally around the world as recently as 30 years ago, when NC machines began to diffuse, this cannot be assumed today. In the major industrialized countries, at least, there cannot be significant differences in terms of basic technical information availability. Hor does it make sense to assume that decision-makers in one country are more or less rationa l than those in another. Any observed" lags" in diffusion between countries must, therefore, be explained by differences in the economic structure or the socio-political
6
R. L
:hITS
environment . Real labor costs, interest rates, energy costs, inventory ratios, domestic market size, labor, anti-trust and trade policy, currency stability, fiscal , monetary and tax policy and a host of other factors do differ from country to country. Diffusion rates will obviously vary from sector to sector, also for rational reasons. Here the factors involved are more -micro' than -macro'. Some typical company level factors and design issues are descr i bed be 1ow. Usually the starting point for economi c analysis is capacity. Typically there are variations in the required volume, as well as expected (or unexpected ) future changes in product mix. In small and medium size companies increase of production capacity through capital sharing c an be the most important justification for investment in a flexible manufacturing system. Thus the first economic goal is volume flexibility: the ability to share c apacity among products according to demand fluctuations. To achieve an economic operation, there are three critical factors: (1) a steady and high level of total volume, (2) a high machine availability, and (3) a suitable product mix. The second important economic goal is the ability to vary the product within general parameters. These parameters define a "part family". Usually a larger part family means less total product ion capacity for the same capital cost . This conflicting situation is usually presented graphically by a picture like Fig . 4. The maximum size of the part family is restricted by technological factors as well as by economic factors. The larger the part family is, the more tool changes and setups are necessary, which decreases the effective production time and thus also the annual volume.
and.J. Ranta A third economi c goal for flexibility is minimum batch size, to minimize work-in-progress. A small batch size will also decrease total delivery time . But again, smaller batch sizes decrease maximum production capacity because of tool changes, etc. Short delivery times and decreased work-in-progress are usually related to each other and are usually realized by a suitable compromise of batch sizes, part family, and system capacity. Each of the flexibility goals has its tradeoffs, of co urse . One of the aims of the design is to have an overall cost/benefit ratio as high as possible. Usually we can observe the following Simple tradeoffs: 1.
I ncre ase of part fami ly will in c r ease the need for machine flexibility as well as process and production flex ibili ty , because of t he reqUired routing possibilities and new dimensions; will increase sofLw~re costs, because more NCplograms are needed as we 11 as more integration software; device or hard ware costs will increase, because more pallets, fixtures, storage space , robot capacity are needed; wi ll decrease materia l cost s and capital fixed in storage, work- in-progress and inventory.
2.
Increase of volume or ca pacity will increase hardware and machinery costs (other factors held const ant ); wi ll increase auxiliary device costs; will i ncreas e the need for production and process flexibility, due to increased tooling reqUirements; will increase software costs, because of more complex syste ms control .
3.
Increase of Part complexity
Volume
will inc rease the need for machine and product flexibility; will i ncrease software costs , because of more complex part programs and a more complex systems contr o l integration; will increase too l, pallet and fixture costs.
Clp«:lty
4.
Decrease of batch size wi ll increase the need for process and producti on flexib il i t y; wil l i ncrease software costs, because of a more complex systems c ontrol; will increasp. auxi liary device costs, because more (and more complex ) pallets are needed .
Plrt flm lly f1e. ibil, tv
Fig. 4. Flexibility vs. volume .
A factor related to the s ize of the part family is the complexity of the parts, or simply the number of different surfaces needed for each part. Other things being equal, an increas e in comp l exity will decrease the maximum production volume because of time-consuming tooling and set-up operations. The more complex the parts the system is able to produce, the larger the part family can be. The complexity of parts made by a given system is also restricted by many technological and economic factors. In any case, an investment to increase a llowed part complexity ca n be seen as an investment for the future, to cope with potential future market changes.
Supplementary data on observed relationships between these factor s is presented in (Sheinin & Tchijov , 1988), and (Tchijov & Alabian, 1988). CONCL UDING REMARKS In summary, the imperative for increased flexibility depends on the nature of the product and the market . even with in a single country. There is no suc h imperative. for inst ance. in the case of products -- such as factories, power plants. office bUildings and bridges -- that have always been custom-designed. Increased flexibility has nothing to contribute i n these cases. At the other extreme, there is also no imperative for i ncrea sed flexibi lit y in the case of co mponents like ball and roller bearings. nuts and bolts , e l e ct ri cal connectors , light-bulbs, sparkplugs. fractional horsepower motors , or a number of other products
Factors Cmerning the E\'olulion alld Diffusion of Cl M that are, and are likely to remain, highly standardized. In fact, the domain of flexible manufacturing is generally considered to be restricted to parts normally produced in families of at least ten to a hundred and batches of less than a thousand or so. These figures are impreCise, but certain products are clearly excluded: tools and dies and industrial patterns, at the low end, and anything truly standardized at the other end of the spectrum. It is for these reasons, for instance, that the auto industry uses robots primarily in the final assembly process (for welding and painting ) , but not for parts manufa c turing or subassembly opera t ions. In the case of flexible (i.e. robotic) assembly, the economic restrictions are even tighter. Since even the most flexible machine cannot compete in flexibility with a human worker, the minimum batch size necessary to justify the investment in automation is considerably larger: in the thousands. In fact, product (model> families of the necessary size are parti c ularly characteristic of cameras, watches and consumer electronics. All of these are industries dominated by Japan. Thus it is not surprising, and certainly not an indication of "lag" on the part of other count ries, that Japan has far more robots than other industrial countries, a very large percentage of which (40%) are involved in assembly tasks. By contrast, the U.K. and ~est Germany have, respectively 9.7% and 8.6% of their robot s doing assembly tasks (Tani,
7
There is some evidence, in fact, that there are really two categories of FXS systems, differentiated along the above lines (Sheinin & Tchijov, 1988). This difference in use would inCidentally, explain the otherwise surprising results observed by Jaikumar and reported in his widely publicized article in Harvard Business Review (Jaikumar, 1986). Summarizing a survey of 35 FXS' in the U.S. and 60 in Japan he noted that the average number of parts made by the U.S. system was 10, with an average annual volume of 1.727. On the other hand, the average number made by the Japanese FXS' was 93, with an average annual volume of 258. Jaikumar asserts that this statistic proves that US manufacturers were using FXS "the wrong way". However it seems possible that he was inadvertently comparing applies and oranges. Although he claims that the two samples "had similar machines and did similar work", his Japanese sample was very heavily weighted by FXS' used for machine tool production, while none of the U. S. FXS' were used for this purpose. (The Ingersoll system described earlier was not included). The U.S. sample was heavily weighted, on the contrary, by firms like Caterpillar and Deere, which are volume manufacturers of standardized items of heavy equipment. It may well be true that U. S. industry has lagged in the application of flexible automation , but the data available to date do not yet unambiguously support such a conclusion. It also shows the difficulties to make solid conclusions regarding benefits and costs of CI~ technologies.
1987) .
In as much as flexible automation occupies the middle of a spectrum ranging from labor-intensive "job shops" at one end and capital-intensive automatic transfer - systems at the other, the middle ground of batch production is, itself, quite heterogeneous. From the labor-intensive job soop end of the spectrum it may make se nse to extend the technology in the direction of highly flexible but co mpact and inexpensive low-volume systems. From the mass-produc t ion end of the spectrum, it may make sense to move in the direction of increasing some variability in high-capacity systems. These two cases are illustrated in Fig. 5.
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Abernathy, ~. J. (1978). The Productivity Dilemma. Johns Hopkins University Press, Baltimore, MD. Ayres. R. U. (1984). The lIext Industrial Revolution. Reviving Industry Through Innovation. Ballinger, Cambridge, ~. Ayres, R. U. (1988). Complexity, reliability, and design: manufacturing implications. ~nufacturing Review, Vol. 1, Spring 1988. Ayres, R.U., Miller, S.M. (1982). Robotics & conservation of human resources . Technology in SOCiety, i, pp. 181-197. Ayres, R.U., Miller, S.M. (1983). Robotics. Application & Social Implications. Ballinger Publishing Co., Cmabridge, ~. Baumol, ~. J. (1982). Contestable markets. An uprising in the theory of industry structure. American Economic Review, 72, March 1982, pp. 1-15.
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J. Ranta