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Information and Control in Computerized Manufacturing Systems
INFORMATION AND CONTROL IN COMPUTERIZED MANUFACTURING SYSTEMS J.J. Talavage and M.M. Barash Purdue University, West Lafayette, Indiana, U. S. A .
may include parts , carts, tools , spind l es , as well as other components.
ABSTRACT Specifications are provided for a sufficient set of information which can be the basis for the design of a wide c l ass of coordination and control policies for Computerized Manufacturing Systems. INTRODUCTION Computerized Manufacturing Systems (CMS) is the term used here to describe manufacturing facil i ties which also have been called "F l exible Manufactur ing Systems," "Variable Mission Manu facturing Systems," "Computer-Managed Parts Manufacturing Systems " "Multiple Station Manufacturing Syste~s , " etc. The components in such systems cons i sts of four major types: machine too l s with their too l ing and fixturing , material handling system , auxiliary equipment, and control/computer configuration . The deve l opment of CMS has been based on the perception that it is an idea l production system for products made in batches of less than fifty . Such products apparently comprise about 70 percent of al l metalworking manufactur ing . Within the production environment, the attributes of a CMS that are important to company management include average production rate and long - term machine and system re liability. These meas u res are significant to control of CMS production within the context of a larger manufacturing organizat i on. We will retu r n to this viewpoint later . Other more detailed aspects of the system, such as the l ocation of each part and tool, t h e number of operations performed on each part , and so forth, may be said to specify the current status of the system . Informat i on concerning this current status obtained by observat i on or measurement can be emp l oyed for the relative l y short term purpose of coordinating and contro l ling the flow of movable enti ties within the CMS. Such entities 279
In genera l, it is not economical to provide material handling systems which provide a separate path of trans port between every possible pa ir of destinations . Thus , it will usual l y be necessary to make choices when moving system entities regarding which ones are to be moved, when they are to be moved, and by what route. The complexity of the choice decision varies depending on the type of mate r i al handlin g system . For example, these decision are simple ones for "random access" systems such as circular conveyors, but may be quite complex for "addressable" delivery systems in which carts may be moved by various routes to assigned desti nations . Whether these choices are made by a man or by use of computer ized decision algorithms, the choices will be intelligent only if appropri ate and accurate information on the current status of the system is avail able on which to base the decision. Our NSF - spons ored project has , as one aspect of the p r oject , r esulted i n the development of a simulati o n for an existing CMS at Ca terpillar Tract or Company. This simulation incorporates decision rules for movement of both parts and carts. The development of such computerized algorithms p rovide d us with the appreciati on for the in formation requirements of such rules . We are now in the process of develop ing a general - purpose simulator called GCMS which can model any existing or proposed CMS . Such a simulator must allow for a wide variety of decision rules to be incorporated into the mode l by the user. To do this, it was necessary to specify and provide all the information in an appropriate form that such a user might need. The comp l ete specifications are available and are summarized below. Such a list of info r mation specifications is not only of i n terest to simulation model e r s , but also to system designers who must p r ovide for the collection and distribution o f such information .
280
J. J. Talavage and M. M. Barash
INFORMATION FOR DECISIONS It should be clear that certain information is necessary in order to invoke a given system decision rule . However, two other aspects of the relationship between information and decision rules should not be overlooked. One of these aspects has to do with which information must be known in order to determine which, if any, of the decision rules should be invoked at a given time. That is, the state of the system must be identified to determine whether it is among that set of states in which one (or more) of the decision rules need be exercised. The other aspect of the relationship is more definitive and concerns the changes that need be made to the information structure as a result of some decision. All three of the above aspects of the information/decision relationship are considered later for an example deci sion ru l e. The full set of decision rules for any CMS has at least six elements including decision rules for: 1.
introducing raw castings into the system,
2.
finding the next operation for a part,
3.
selecting a station to per form an operation,
4.
selecting a material handling entity,
5.
scheduling movements of mate ial handling entities,
6.
selecting the next part to be machined from a queue of parts at a machine .
Each of these rules is incorporated into the GCMS simulator and, depending on the system structure, may be invok ed to obtain efficient system opera tion. As a result of our simulation efforts (and real world experience at Cater pillar), we believe that a minimal set of data for employing the above rules is contained in the "system descrip tion" and the "status vector" of GC MS. The system description consist of five parts corresponding to the five major types of system components. These component - types are the material handling system (MHS) configuration, parts, stations , pallets, and MHS devices. Table 1 shows the system description in terms of arrays with a brief state ment about the contents of each array element . From the standpoint of vol-
ume of information, it is especia l ly important to note that each array may be duplicated as many times as there are members of a set of components. For example, our simulator a l lows a maximum of thirty stations, and so there may be up to thirty arrays of the type STATN . The system status can also be describ ed in terms of the five component types. This is shown in the form of annotated arrays in Table 2. The system status completely identi fies the dynamic system state at any time, and the system description displays the important static system characteristics. It seems reasonable to take the union of these two sets of information as a sufficiently large information source on which to base initiation and execution of the six decision rules shown earlier. Let us consider as an example the initiation and execution of the third decision rule in the previous list; name l y, the rule for selecting a station to perform an operation. The decision rule is initiated by a change in the status of some part in the system. In particular, by the change in the assignment of a next operation as shown in array element 8 in the array PARST. Execution of the deci sion rule may assume the existence of a set of available stations . This question of existence is assessed by checking the first and the twenty first element of each STAST array in the system status. In flexible sys tems, a number of stations might exist for performing the given operation. One version of this rule for those systems would be to select the high est priority station for this opera tion, whether it is available or not . That priority is available from array ROUTE in the system description, where stations performing the same opera tions are listed in order of their priority. In any case, the result of applying this decision rule will be to affect the system status. In particul ar, element number 9 of array PARST in the system status will be updated to its new value . In summary, the design of short term coordination and control algorithms for a given CMS depends on the particular structure of the CMS . We believe that the relatively small set of data provided in Table 1 and 2 is a suffi cient basis for the specification of such algorithms for any present or proposed CMS structure.
Information and control in computerized manufacturing systems
TABLE 1
- 281
System Description
Part Description 2
1
PADIS part type
decision rule when finding next operation
maximum number allowed in system
4
3
priority for part type
decision rule when finding a cart
5 target production for each shift
50
1 ...
ROUTE part type
sequence of operations
for each operation list of 9 stations to perform operation
Station Description 1
STATN station no.
2
number of different operations performed
number of on-shuttle queue positions
4
5 decision rule shuttle to when picking track time part from [also track shuttle to machine time]
3 number of off-shuttle queue positions
6
7
8
shuttle to machine time
track position on - shuttle
track position off- shuttle
OPER operation no.
time
RESTA
(t ime) for part s to be removed
station type for reliability distribution
51
50
2
1
9
decision rule 1
3
2
station number for operation
time left for operation on part at station
Pallet DescriEtion
1 PALL pallet types
2-1 0
11
part types it can be used for
number that exist
number in system busy
Cart DescriEtion 2
1
CART cart type
number that exist
number of pallet-holding positions
RECAR cart number
cart number o - do not for all as- remove parts signments in 1 - remove breakdown parts
3 average speed over all portion of track
4 decision rule to use when moving
decision point decision point where cart moves where cart for repair returns
Track DescriEtion 1
TRACK decision points
2 - 5
5 portions of track it can move across
6
station number successors speed for if station exist to this conveyor decision pt.
J. J. Talavage and M. M. Barash
282
Track Description (continued) node B distance only for correct ing decision points
DISTA node A
TABLE 2
System Status
Part Status 1
PARST part number
part type
priority
8 NPART
3
4
pallet number
cart no. when assigned
2
10
9
next station
no. operations left to do
1.0 - on -shuttle 2 . 0 - off - shuttle 0 - neither 2 - 6
1
STAST station number
+ - last starting time idle - - operation completed but part still in machine
7
current opera tion
next opera tion -1. 0 when done
11
12 - 43
time entered system
list of operations completed 12 - 20
7 - 11
designated on-shuttle queue (Part #)
o-
21
6
5 current station
desi g nated off-shuttle queue
general queue
22
o - active 1 - down
time available in case of breakdown
Cart Status 2
1
CARST
current location
4
3
time of passing
current speed o - stopped
destination
789
6
o - idle 1 - picking up part 2 - dropping off part 3 - waiting for part 4 - out of way of another cart
part number
part number
12 - 50
11
time when available
current route
Pallet Status 2
1
PALST pallet number
+ - part number if l oaded 0 - empty
i f empty
station number at which i t resides
Track Status 1
TRAST decision point
o - active 1 - down
5 cart type
2
time when available
3 type
part number
10
o - active 1 - down
'Information and control in computerized manufacturing systems
Information gathering and processing, as described above, and actions taken upon it are functions of the CMS control subsystem; they ensure optimal execution of system's assignment. However, for the Computerized Manufacturing System to perform optimally as part of the total production process, two conditions must be met: the system must be provided with all its needs, and the assignment given to it must be one which best meets production process requirements at the given time. Thus, for example, raw materials, tools, etc., must be available as needed without maintaining excessive inventories, and the products of the system must be those most needed in, e.g., the assembly section, and again, without inventory buildup. Similar considerations apply to provision of software, fixtures, maintenance, etc. There is need for constant monitoring of all the variables throughout the entire production process from orders to shipments, on-line evaluation of status and preparation of forecasts for different time horizons, and for selection of best decisions for corrective action. These problems have always existed in manufacturing, and their severity has been continually increasing with the growing complexity and size of industrial enterprise. The volume of information to be processed daily in an industrial enterprise is very lar ge. A plant employing only 200 persons may execute 4 , 000 transactions a day [lJ. There is reason to suspect that the number of transactions increase faster than the number of productive "units", or persons in this case. Theoretically this can be based on the fact that the number of links between all units, productive and controlling (management) increases faster than the number of units. Glushkov [2J shows that this indeed is the case. For example, if we assume that each transaction has ten arithmetic operations requiring 10 seconds each, an enterprise haridling manually , i.e., without electronic computers, 4,000 transactions a day requires 14 "management" persons, or 7 percent of the total work force. For a large country, such as the USA or USSR, the number of arithmetic operations in national economy is according to Glushkov 10 16 a year. To handle them manually, 10 billion persons are required, which is 50 times the population of the country, or 100 times its work force. One may draw the conclusion that to simplify manage ment, enterprises should be kept small. However, economies of scale as a rule are against small enterprises. More over, while volume of information in the enterprise would decrease if each were small, number of potential links
283
between enterprises would vastly increase. The electronic computer has, of course, changed the situation drastically . Even at a speed of only 10 6 operations per second, a medium computer can easily handle all the manage~ent information in a sizeable enterprise. The problem now is not the computing ability of the hardware, but proper structuring of the information and control system. In the last decade computers have gained increasing acceptance in the management environment and such concepts as "management information systems" have arisen. In most cases they have distinguished themselves by unexceptional performance and often outright failure. Without going into details, one can identify as the main cause of this state of affairs the absence, as yet, of a basic, theoretically underpinned methodology for the construction of such management information and control systems. Recently, however, some progress has been made. A fairly detailed conceptual (but not theoretical) presentation of computerized production information and con trol system has been offered to the public [3,4J. Theoretical work is also beginning to take form, such as, for example by Doumeingts [lJ. It is not the purpose of this paper to discuss the features of such management tools which can be found in the original sources. We shall, however, show that only if effective and reliable production information and control systems are available can Computerized Manufacturing Systems offer their full potential. Existing production manageme nt syste ms , including those based on computers, are applied to the conventional mode of manufacturing , because it is still by far the dominant mode. A conven tional machine tool employed in batch manufacturing "acts" upon the rroduct about 5 percent of the total time the product spends in the shop . Thus, if the entire machining cycle re qui res 2 hours, the product will "be around" for 40 hours. In a CMS, with a utilizati on factor of only 50 percent (which is not difficult to achieve and can be greatly exceeded), the part leaves after 4 hours. The system's internal control can respond instantaneously to any changes in the original schedule, but to be truly effective, the instructi on for such change must reach the system without delay, otherwise numerous "wrong" products will be made, and there will be a shortage of "correct"
284
J. J. Talavage and M. M. Barash
ones. Not only instruction for the CMS has to be issued, but also for all supporting activities, such as delivery of the correct raw materials, tools, fixtures and possibly even software. If CMS is to become the main mode of manufacture (for batch production), the ability to issue such instructions at practically a moment's notice (from higher management, sales, etc.) must be the standard operating procedure, and not just an emergency feature. Without this ability, the enterprise would have to maintain vast inventories, economically prohibitive, and in many cases may be unable to meet customer demand even with them. To be fully effective, the system must be all-encompassing. Manufacturing interacts with all other activities of the enterprise, such as design, research, quality assurance, sales, finance, and personnel, with a definite permissible maximum "signal delay time" in each link, which is vastly shorter in automated than in conventional manufacturing. Inside the manufacturing domain, the activity of the CMS is closely linked with other activities, such as overall plant monitoring and control, maintenance, purchasing, stores control, master production schedule planning, etc., etc. The complexity of the algorithmic structure (not to mention the actual programs) required to create a reliable and foolproof cybernetic system meeting the above demands is staggering. It is almost axiomatic that such system will be hierarchial, and possibly distributed at lower levels. The fundamental problem is how to ensure that each hierarchy level passes on the critical information, which poses the question as to what is "critical." Lack of imagi-
References 1.
Doumeingt s, Guy. "Hierarchal Production Control System Using Decision Making Procedures." Proceedings of the 14th Annual Meeting and Technical Conference of the Numerical Control Society, March 13-16, 1977, Pittsburgh, Pennsylvania, pp. 239-266.
2.
Glushkov, V. M. "Introduction into Automated Production Management Control Systems." (Vvedeniye v ASU), Tekhnika, Kiev, 1972 (Russian).
3.
Orlicky, Joseph. Material Requirements Planning, McGraw-Hill, New York, 1975.
4.
IBM Corporation. "COPICS - Communications Oriented Production Information and Control System." Vols. I-VIII. White Plains, New York, 1972. nation (or intuition) on the part of human subordinates and superiors has been the downfall of many organizations. How does one build an "imaginative" computer system? Clearly, the limits to flexible automation lie on our ability to create its total controls. It is planned that the NSF-sponsored project into CMS, ongoing at Purdue University, will in its later phases include the management information and control aspects of the operation of a group of systems, and their interaction with the production environment. We hope that with completion of the project we shall be able to present a version of the management information and control system philosophy suited for the CMS mode of manufacturing.
may include parts , carts, tools , spind l es , as well as other components.
ABSTRACT Specifications are provided for a sufficient set of information which can be the basis for the design of a wide c l ass of coordination and control policies for Computerized Manufacturing Systems. INTRODUCTION Computerized Manufacturing Systems (CMS) is the term used here to describe manufacturing facil i ties which also have been called "F l exible Manufactur ing Systems," "Variable Mission Manu facturing Systems," "Computer-Managed Parts Manufacturing Systems " "Multiple Station Manufacturing Syste~s , " etc. The components in such systems cons i sts of four major types: machine too l s with their too l ing and fixturing , material handling system , auxiliary equipment, and control/computer configuration . The deve l opment of CMS has been based on the perception that it is an idea l production system for products made in batches of less than fifty . Such products apparently comprise about 70 percent of al l metalworking manufactur ing . Within the production environment, the attributes of a CMS that are important to company management include average production rate and long - term machine and system re liability. These meas u res are significant to control of CMS production within the context of a larger manufacturing organizat i on. We will retu r n to this viewpoint later . Other more detailed aspects of the system, such as the l ocation of each part and tool, t h e number of operations performed on each part , and so forth, may be said to specify the current status of the system . Informat i on concerning this current status obtained by observat i on or measurement can be emp l oyed for the relative l y short term purpose of coordinating and contro l ling the flow of movable enti ties within the CMS. Such entities 279
In genera l, it is not economical to provide material handling systems which provide a separate path of trans port between every possible pa ir of destinations . Thus , it will usual l y be necessary to make choices when moving system entities regarding which ones are to be moved, when they are to be moved, and by what route. The complexity of the choice decision varies depending on the type of mate r i al handlin g system . For example, these decision are simple ones for "random access" systems such as circular conveyors, but may be quite complex for "addressable" delivery systems in which carts may be moved by various routes to assigned desti nations . Whether these choices are made by a man or by use of computer ized decision algorithms, the choices will be intelligent only if appropri ate and accurate information on the current status of the system is avail able on which to base the decision. Our NSF - spons ored project has , as one aspect of the p r oject , r esulted i n the development of a simulati o n for an existing CMS at Ca terpillar Tract or Company. This simulation incorporates decision rules for movement of both parts and carts. The development of such computerized algorithms p rovide d us with the appreciati on for the in formation requirements of such rules . We are now in the process of develop ing a general - purpose simulator called GCMS which can model any existing or proposed CMS . Such a simulator must allow for a wide variety of decision rules to be incorporated into the mode l by the user. To do this, it was necessary to specify and provide all the information in an appropriate form that such a user might need. The comp l ete specifications are available and are summarized below. Such a list of info r mation specifications is not only of i n terest to simulation model e r s , but also to system designers who must p r ovide for the collection and distribution o f such information .
280
J. J. Talavage and M. M. Barash
INFORMATION FOR DECISIONS It should be clear that certain information is necessary in order to invoke a given system decision rule . However, two other aspects of the relationship between information and decision rules should not be overlooked. One of these aspects has to do with which information must be known in order to determine which, if any, of the decision rules should be invoked at a given time. That is, the state of the system must be identified to determine whether it is among that set of states in which one (or more) of the decision rules need be exercised. The other aspect of the relationship is more definitive and concerns the changes that need be made to the information structure as a result of some decision. All three of the above aspects of the information/decision relationship are considered later for an example deci sion ru l e. The full set of decision rules for any CMS has at least six elements including decision rules for: 1.
introducing raw castings into the system,
2.
finding the next operation for a part,
3.
selecting a station to per form an operation,
4.
selecting a material handling entity,
5.
scheduling movements of mate ial handling entities,
6.
selecting the next part to be machined from a queue of parts at a machine .
Each of these rules is incorporated into the GCMS simulator and, depending on the system structure, may be invok ed to obtain efficient system opera tion. As a result of our simulation efforts (and real world experience at Cater pillar), we believe that a minimal set of data for employing the above rules is contained in the "system descrip tion" and the "status vector" of GC MS. The system description consist of five parts corresponding to the five major types of system components. These component - types are the material handling system (MHS) configuration, parts, stations , pallets, and MHS devices. Table 1 shows the system description in terms of arrays with a brief state ment about the contents of each array element . From the standpoint of vol-
ume of information, it is especia l ly important to note that each array may be duplicated as many times as there are members of a set of components. For example, our simulator a l lows a maximum of thirty stations, and so there may be up to thirty arrays of the type STATN . The system status can also be describ ed in terms of the five component types. This is shown in the form of annotated arrays in Table 2. The system status completely identi fies the dynamic system state at any time, and the system description displays the important static system characteristics. It seems reasonable to take the union of these two sets of information as a sufficiently large information source on which to base initiation and execution of the six decision rules shown earlier. Let us consider as an example the initiation and execution of the third decision rule in the previous list; name l y, the rule for selecting a station to perform an operation. The decision rule is initiated by a change in the status of some part in the system. In particular, by the change in the assignment of a next operation as shown in array element 8 in the array PARST. Execution of the deci sion rule may assume the existence of a set of available stations . This question of existence is assessed by checking the first and the twenty first element of each STAST array in the system status. In flexible sys tems, a number of stations might exist for performing the given operation. One version of this rule for those systems would be to select the high est priority station for this opera tion, whether it is available or not . That priority is available from array ROUTE in the system description, where stations performing the same opera tions are listed in order of their priority. In any case, the result of applying this decision rule will be to affect the system status. In particul ar, element number 9 of array PARST in the system status will be updated to its new value . In summary, the design of short term coordination and control algorithms for a given CMS depends on the particular structure of the CMS . We believe that the relatively small set of data provided in Table 1 and 2 is a suffi cient basis for the specification of such algorithms for any present or proposed CMS structure.
Information and control in computerized manufacturing systems
TABLE 1
- 281
System Description
Part Description 2
1
PADIS part type
decision rule when finding next operation
maximum number allowed in system
4
3
priority for part type
decision rule when finding a cart
5 target production for each shift
50
1 ...
ROUTE part type
sequence of operations
for each operation list of 9 stations to perform operation
Station Description 1
STATN station no.
2
number of different operations performed
number of on-shuttle queue positions
4
5 decision rule shuttle to when picking track time part from [also track shuttle to machine time]
3 number of off-shuttle queue positions
6
7
8
shuttle to machine time
track position on - shuttle
track position off- shuttle
OPER operation no.
time
RESTA
(t ime) for part s to be removed
station type for reliability distribution
51
50
2
1
9
decision rule 1
3
2
station number for operation
time left for operation on part at station
Pallet DescriEtion
1 PALL pallet types
2-1 0
11
part types it can be used for
number that exist
number in system busy
Cart DescriEtion 2
1
CART cart type
number that exist
number of pallet-holding positions
RECAR cart number
cart number o - do not for all as- remove parts signments in 1 - remove breakdown parts
3 average speed over all portion of track
4 decision rule to use when moving
decision point decision point where cart moves where cart for repair returns
Track DescriEtion 1
TRACK decision points
2 - 5
5 portions of track it can move across
6
station number successors speed for if station exist to this conveyor decision pt.
J. J. Talavage and M. M. Barash
282
Track Description (continued) node B distance only for correct ing decision points
DISTA node A
TABLE 2
System Status
Part Status 1
PARST part number
part type
priority
8 NPART
3
4
pallet number
cart no. when assigned
2
10
9
next station
no. operations left to do
1.0 - on -shuttle 2 . 0 - off - shuttle 0 - neither 2 - 6
1
STAST station number
+ - last starting time idle - - operation completed but part still in machine
7
current opera tion
next opera tion -1. 0 when done
11
12 - 43
time entered system
list of operations completed 12 - 20
7 - 11
designated on-shuttle queue (Part #)
o-
21
6
5 current station
desi g nated off-shuttle queue
general queue
22
o - active 1 - down
time available in case of breakdown
Cart Status 2
1
CARST
current location
4
3
time of passing
current speed o - stopped
destination
789
6
o - idle 1 - picking up part 2 - dropping off part 3 - waiting for part 4 - out of way of another cart
part number
part number
12 - 50
11
time when available
current route
Pallet Status 2
1
PALST pallet number
+ - part number if l oaded 0 - empty
i f empty
station number at which i t resides
Track Status 1
TRAST decision point
o - active 1 - down
5 cart type
2
time when available
3 type
part number
10
o - active 1 - down
'Information and control in computerized manufacturing systems
Information gathering and processing, as described above, and actions taken upon it are functions of the CMS control subsystem; they ensure optimal execution of system's assignment. However, for the Computerized Manufacturing System to perform optimally as part of the total production process, two conditions must be met: the system must be provided with all its needs, and the assignment given to it must be one which best meets production process requirements at the given time. Thus, for example, raw materials, tools, etc., must be available as needed without maintaining excessive inventories, and the products of the system must be those most needed in, e.g., the assembly section, and again, without inventory buildup. Similar considerations apply to provision of software, fixtures, maintenance, etc. There is need for constant monitoring of all the variables throughout the entire production process from orders to shipments, on-line evaluation of status and preparation of forecasts for different time horizons, and for selection of best decisions for corrective action. These problems have always existed in manufacturing, and their severity has been continually increasing with the growing complexity and size of industrial enterprise. The volume of information to be processed daily in an industrial enterprise is very lar ge. A plant employing only 200 persons may execute 4 , 000 transactions a day [lJ. There is reason to suspect that the number of transactions increase faster than the number of productive "units", or persons in this case. Theoretically this can be based on the fact that the number of links between all units, productive and controlling (management) increases faster than the number of units. Glushkov [2J shows that this indeed is the case. For example, if we assume that each transaction has ten arithmetic operations requiring 10 seconds each, an enterprise haridling manually , i.e., without electronic computers, 4,000 transactions a day requires 14 "management" persons, or 7 percent of the total work force. For a large country, such as the USA or USSR, the number of arithmetic operations in national economy is according to Glushkov 10 16 a year. To handle them manually, 10 billion persons are required, which is 50 times the population of the country, or 100 times its work force. One may draw the conclusion that to simplify manage ment, enterprises should be kept small. However, economies of scale as a rule are against small enterprises. More over, while volume of information in the enterprise would decrease if each were small, number of potential links
283
between enterprises would vastly increase. The electronic computer has, of course, changed the situation drastically . Even at a speed of only 10 6 operations per second, a medium computer can easily handle all the manage~ent information in a sizeable enterprise. The problem now is not the computing ability of the hardware, but proper structuring of the information and control system. In the last decade computers have gained increasing acceptance in the management environment and such concepts as "management information systems" have arisen. In most cases they have distinguished themselves by unexceptional performance and often outright failure. Without going into details, one can identify as the main cause of this state of affairs the absence, as yet, of a basic, theoretically underpinned methodology for the construction of such management information and control systems. Recently, however, some progress has been made. A fairly detailed conceptual (but not theoretical) presentation of computerized production information and con trol system has been offered to the public [3,4J. Theoretical work is also beginning to take form, such as, for example by Doumeingts [lJ. It is not the purpose of this paper to discuss the features of such management tools which can be found in the original sources. We shall, however, show that only if effective and reliable production information and control systems are available can Computerized Manufacturing Systems offer their full potential. Existing production manageme nt syste ms , including those based on computers, are applied to the conventional mode of manufacturing , because it is still by far the dominant mode. A conven tional machine tool employed in batch manufacturing "acts" upon the rroduct about 5 percent of the total time the product spends in the shop . Thus, if the entire machining cycle re qui res 2 hours, the product will "be around" for 40 hours. In a CMS, with a utilizati on factor of only 50 percent (which is not difficult to achieve and can be greatly exceeded), the part leaves after 4 hours. The system's internal control can respond instantaneously to any changes in the original schedule, but to be truly effective, the instructi on for such change must reach the system without delay, otherwise numerous "wrong" products will be made, and there will be a shortage of "correct"
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J. J. Talavage and M. M. Barash
ones. Not only instruction for the CMS has to be issued, but also for all supporting activities, such as delivery of the correct raw materials, tools, fixtures and possibly even software. If CMS is to become the main mode of manufacture (for batch production), the ability to issue such instructions at practically a moment's notice (from higher management, sales, etc.) must be the standard operating procedure, and not just an emergency feature. Without this ability, the enterprise would have to maintain vast inventories, economically prohibitive, and in many cases may be unable to meet customer demand even with them. To be fully effective, the system must be all-encompassing. Manufacturing interacts with all other activities of the enterprise, such as design, research, quality assurance, sales, finance, and personnel, with a definite permissible maximum "signal delay time" in each link, which is vastly shorter in automated than in conventional manufacturing. Inside the manufacturing domain, the activity of the CMS is closely linked with other activities, such as overall plant monitoring and control, maintenance, purchasing, stores control, master production schedule planning, etc., etc. The complexity of the algorithmic structure (not to mention the actual programs) required to create a reliable and foolproof cybernetic system meeting the above demands is staggering. It is almost axiomatic that such system will be hierarchial, and possibly distributed at lower levels. The fundamental problem is how to ensure that each hierarchy level passes on the critical information, which poses the question as to what is "critical." Lack of imagi-
References 1.
Doumeingt s, Guy. "Hierarchal Production Control System Using Decision Making Procedures." Proceedings of the 14th Annual Meeting and Technical Conference of the Numerical Control Society, March 13-16, 1977, Pittsburgh, Pennsylvania, pp. 239-266.
2.
Glushkov, V. M. "Introduction into Automated Production Management Control Systems." (Vvedeniye v ASU), Tekhnika, Kiev, 1972 (Russian).
3.
Orlicky, Joseph. Material Requirements Planning, McGraw-Hill, New York, 1975.
4.
IBM Corporation. "COPICS - Communications Oriented Production Information and Control System." Vols. I-VIII. White Plains, New York, 1972. nation (or intuition) on the part of human subordinates and superiors has been the downfall of many organizations. How does one build an "imaginative" computer system? Clearly, the limits to flexible automation lie on our ability to create its total controls. It is planned that the NSF-sponsored project into CMS, ongoing at Purdue University, will in its later phases include the management information and control aspects of the operation of a group of systems, and their interaction with the production environment. We hope that with completion of the project we shall be able to present a version of the management information and control system philosophy suited for the CMS mode of manufacturing.