- Email: [email protected]
CALM — computer aided logistics management
CALM -- COMPUTER AIDED LOGISTICS MANAGEMENT H. F. Finley and R. P. Hyland
ABSTRACT Logistics functions directly support the principal operation. In the international petroleum industry they mainly are maintenance, materials and transport. CALM is a microcomputer system which integrates the arrangement of these functions. It operates at the plant level. There are six CALM subsystems: l) equipment control; 2) maintenance work control; 3) materials control; 4) transport control; 5) budgeting and costs and 6) management reporting. CALM is a collection of relational data bases and application programs. Its modules are distributed and are under user control. Data bases are designed for three organizational levels: I) local area networks on microcomputers at the user level (CALM proper); 2) relational data bases on minicomputers at the plant level and 3) relational data bases on main frame computers at the corporate level. This is said to be a 3-1evel computer system. Data base design is optimized over the three levels. System integration requires strict discipline over the design of identification codes (equipment, spare parts, job plans, job orders, failure reports, etc.). The paper suggests standard code formats which have proved to be successful in industry. Besides CALM, integrated systems from four multinational oil companies are discussed. Design errors experienced in these projects are described. Specific design precautions are emphasized.
REYWORDS Logistics; microcomputers;
relational data base; operations support; distributed data processing.
INTRODUCTION Logistics functions directly support the principal operation. The major ones are maintenance, materials and transport. While there are others, these three have the most immediate influence on operational success. They are the ones most difficult to manage. Logistics operations are carried out on a massive scale in the international petroleum industry. Individual operations are large; up to 4,000 maintenance personnel, 250,000 materials articles, hundreds of on-road and off-road vehicles, marine craft, helicopters and fixed wing aircraft. Operating conditions are harsh: North Sea storms, Arctic temperatures, Middle Eastern deserts, tropical jungles. Maintaining the equipment, storing and moving the materials and keeping the operations going are not easy tasks. Petroleum companies were among the first to develop computer systems to help management cope with logistics problems. Sophisticated systems came on the scene in the early 1950s. The first ones were oriented to keeping track of what was going on. We call them "bean counting" systems. They emphasized collecting, recording and storing data but, in general, were not analytical nor interpretive.
SYSTEM REQUIREMENTS The ideal management system is one that is fully integrated. We do not know of any company that has achieved this ideal but several are close to it. The principal shortfall is the integration of financial accounting. The structure of traditional accounting systems makes integration awkward at best. There are 6 modules which must be developed in order to achieve full integration.
They are:
equipment control module -- oriented to the equipment being operated and maintained. Typical components are the equipment register, job plan dictionary, lubrication specifications, various equipment condition data bases, a failure report data base, an equipment run-log module. work control module -- oriented to the people who maintain the equipment. Typical components are a standard job plan data base, a data base containing standard equipment servicing frequencies, the work order module, a module for creating job plans, a module for developing
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PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING
short-, medium- and long-range job schedules, the timekeeping system, a maintenance contract data base and a capability to charge manhours, materials and other job costs to work orders and from there to end accounts. materials control module -- oriented to the materials and spare parts used to maintain the equipment. Typical components are the materials catalog, the master stock ledger file, an inventory control capability, a surplus materials control module, the warehouse materials requisition module, the materials requisition module, the purchasing system, a capability to track open purchase orders and a capability to charge materials costs to user accounts including maintenance work orders. transport control module -- oriented to the transport fleet that hauls the workers who maintain the equipment and the tools and materials they require for their work. Components vary by type and scope of operation. Typical ones for large onshore/offshore operations provide the capability to schedule trips, match vehicles to trip load requirements and develop load lists. They may include a trip ticket data base and a system of tariffs and rules for charging users (for example, who pays for dead heading). budgeting and cost reporting module -- oriented to financial control of people, materials and transport operations. The work order data base, the maintenance contracts data base, the materials master stock ledger file and the transport trip ticket data base are subledgers in the financial accounting system. They are part and parcel of financial accounting and their structures must be correctly designed to accommodate this integration. This is where most existing logistics systems fail; adequate integration with financial accounting has not been achieved. management reporting module -- oriented to exception reporting of significant events and situations which merit management attention and correction. Pareto's law (the 80/20 principle) should be strictly followed. Various "hot lists" and "hit parades" should be produced. Detailed reporting should be scrupulously avoided. Most existing systems fail in this respect. They continue to emphasize bean counting. The significant few become lost in the trivial many. Finley (1985A) described a series of maintenance performance indicators and how an electronic spreadsheet can be used to process the data.
DATA BASE TECHNOLOGY Most existing logistics management systems are written in the COBOL language and are application specific. There is little flexibility for change. Integration is complex, difficult and sometimes, because of structural deficiencies, impossible. Today's approach is to use a DBMS (data base management system) to create subject data bases which are separate from application programs. The latter use the data in the data bases but do not destroy them. Each application program is stand-alone and independent of all other application programs. The management system is infinitely flexible for changes, enhancements and growth. On several occasions (Finley, 1984, 1985A, 1985B) we have said that all existing logistics management systems which are not built with data base technology are obsolete. We continue to hold that position.
SYSTEM DESIGN ERRORS Starting about 1979, several of the large multinational petroleum companies began converting their logistics management systems to subject data bases and separate application programs. We have had the opportunity to audit four of these projects. It is interesting that we found the same design errors in all four systems. In each situation the companies had to back track and correct the errors at a cost of $100,000 to $250,000. These errors were: ..
failure to recognize the difference between an equipment and the function it performs. There must be two modules in an equipment register. We call them: ..
FINLIST -- a data base containing the functional, process-related information. is the FIN (functional identification number) code.
..
SPINLIST -- a data base containing the equipment technical information. SPIN (specific plant identification number) code.
The key
The key is the
failure to recognize the difference between a job plan and a work order. Job plans used to be saved for reuse under a work order code number. These code numbers had no relationship to the content of the work. As a result, the plan could not be found for retrieval. trying to incorporate failure reporting within work order processing. This approach mixes equipment control with work control. They are two different things. Effective failure reporting can only be accomplished with a stand-alone failure reporting system.
FINLEY & HYIAND: Calm -- Computer Aided Logistics Management
67
failure to recognize Pareto's law. Rather than reporting only significant information, most systems incorporated saturation reporting. The result was a mass of virtually unusable data which discouraged potential users, hampered analysis and progressively undermined data gathering efforts. asking for too much feedback information. This approach focused attention on trivial information, thus compounding the problems of user confidence and serious analysis. trying to use the critical path method to schedule daily maintenance jobs. cannot be made to work.
This approach
These were errors common to all four companies. Additional errors were made by individual firms. For example, one company tried to build the material control system using the manufacturer's part number rather than a specific company commodity code. Given the lack of logical structure in this number and the frequency of number changes, the result was chaos in materials management.
CALM - COMPUTER AIDED LOGISTICS MANAGEMENT
Our company is developing CALM to be used in individual plants and operations. Examples of the latter include small to medium size petroleum refineries and petrochemical plants, NGL and LNG plants, offshore production platforms, drilling vessels. Basically, we are downsizing mainframe systems to fit on microcomputers. Hardware and software constraints limit CALM to operations of 1,000 to 3,000 pieces of equipment, 12,000 to 20,000 material articles and I00 to 250 repairmen. Only a few plants and operations exceed these limits. Today CALH is only partly finished.
We have:
done exhaustive research into coding requirements. of information (data) elements.
We have developed a comprehensive list
done extensive data modeling. We have determined which data elements should be in which data base modules and have specified primary and secondary keys. We have developed a Data Dictionary. developed a comprehensive data base strategy. We have identified which data bases will be owned by which organizational functions, which functions will share which data bases and where the data bases will be physically located. developed the initial data base modules. These include the FINLIST, SPINLIST, MATCAT (materials catalog), FAS (failure analysis system), RUNLOG, JBUDGET (a long-range work budgeting and planning tool) and LPIPLAN (a series of logistics performance indicator data bases). These modules are fully integrated. ..
developed application programs for advanced technical and cost analyses.
When finished, CALM will provide to small plants and operations the same powerful logistics management capabilities provided by the the main frame systems employed by the multinational giants. Specific modules already are in use in several plants and offshore production operations. CALM is evolutionary and can be implemented step by step. The first (and usually the most difficult) step is to get the equipment register and materials catalog in proper order. This may require 4 months for a small system to 12 months for a larger system.
MANAGEMENT BENEFITS The principal benefit management receives from CALM (or any other similar logistics management system) is not the bookkeeping capabilities (although, this is certainly an important benefit) but rather is the management discipline imposed by the integrated structure. Careful adherence to the philosophies embedded in CALM can lead to substantial reductions in direct logistics costs and to sharp increases in equipment availability. We regularly see cost reductions of 25 percent to 35 percent and reductions in equipment downtime of 35 percent to 50 percent. Scientific management of logistics is good business.
CALM provides the basis for this scientific
management.
REFERENCES Finley, H. F. (1985A).
Monitor Maintenance Performance with an Electronic Spreadsheet. Hydrocarbo n Processing, 64, 66-68.
Finley, H. F. (1985B).
Switch to Knowledge-based Maintenance and Reduce Costs. to Hydrocarbon Processing.
Finley,
H. F .
(1984).
HSS submitted
Maintenance Management Systems for the 1990's. Proceedings of the Energy Sources Technology Conference~ Plant Maintenance Workshop. New Orleans.
A MICROCOMPUTER BASED DESIGN OF A CELLULAR MANUFACTURING SYSTEM
S. A. Irani and S. K. Khator Department of Industrial and Management Systems Engineering University of South Florida Tampa, FL 33620
ABSTRACT This paper presents a new heuristic for machine-component cluster formation in Group Technology. The method eliminates user judgment and data suppression required by some methods in the literature. It reduces row and column manipulation to the determination of a particular sequence of listing of the machines. With this approach the problems of exception elimination and bottleneck machines are not severe. Test problems rated as "difficult" for the other methods are easily solved by this heuristic. KEYWORDS Machine-component cluster formation; Group Technology; Occupancy Value heuristic; Bottleneck Machine problem. STATEMENT OF THE PROBLEM Greene and Sadowski [I] describe several problems that will be faced by companies desirous of implementing group technology-based manufacturing cells. These include: a) Determining the exact number of cells to replace the existing system. b) Deciding which machine types will be assigned to the remainder cell, to be shared by all cells. c) Locating the cells to minimize the volume of intercellular material flow. d) Placing a limit on the maximum number of machines to be placed in any cell. e) Assigning the components among the cells so as to minimize the number of components that must visit more than one cell for processing. Machine-component matrix clustering algorithms can offer guidelines for the solution of these problems. UTILITY OF MACHINE-COMPONENT CLUSTER FORMATION METHODS These methods use part routing information to develop a machine-component matrix. By convention, the rows in the matrix will represent the machines and the columns will represent the components. However, in practice, the reverse representation is more convenient since the number of components will usually far exceed the number of machines. A machine-component matrix will be an array of Os and Is.
An entry A.. ij = I at the intersection of row "i" and column "j" will indicate that machine "i" processes component "j". If A.. = O, ij then there is no relationship between the machine and component. The clustering algorithms considered in this paper operate on the assumption that the machine-component array can be partitioned into matched groups of machines and components. These will appear as clusters along the diagonal of the matrix as shown below. This visual presentation of the independent sets of machines and components can provide useful guidelines for planning the cells. ~ C
I
M
c
~M
A
M = Machines C = Components
OR
A
A
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IRANI & KHATOR: A Microcomputer Based D e s i g n o f C e l l u l a r M a n u f a c t u r i n g System
69
LITERATURE REVIEW The Rank Order Clustering algorithm [2,3] uses a positional weighting technique to alternately rearrange the rows and columns of the matrix in order of decreasing rank. A simplified version of this algorithm, the Direct Clustering algorithm [4], eliminates the sensitivity of the ranking procedure to the configuration of the initial matrix. Both these array-based methods are affected when several machines are common to one or more clusters. The authors describe such disperslon-causing entries in the matrix as either "exception operations" or "bottleneck machines." These algorithms fall to yield visible clusters when inherent dispersion of entries exists. The analyst needs to analyse intermediate matrix outputs to identify them. Fresh runs of the algorithms must be made with those entries suppressed. Such prior assumptions bias the solution. It is difficult for the analyst to perform visual identification. Further, an algorithm is expected to indicate the exceptions and bottleneck machines. These two algorithms require preliminary elimination of the entries that would improve the quality of the solutions they produce. The Matrix Transformation algorithm given by Chen and Chang [5] eliminates user Judgement in the selection of the matrix entries that should be eliminated. However, it requires the user to specify the machines and components to ignore during analysis by making them row and column border members, respectively. OBJECTIVE OF THE OCCUPANCY VALUE HEURISTIC The Occupancy Value method is a heuristic developed to eliminate the manual adjustment of intermediate resultsin the algorithms described in References [2], [3], [4] and [5]. It assumes that the analyst has no prior knowledge about "bottleneck machines" or "exception operations" in the initial matrix. The matrices which were used for the Rank Order Clustering and Direct Clustering algorithms were ideal for testing this new method. Results indicate that (a) the heuristic guarantees cluster formation when no machlne-sharlng exists in the initial matrix, (b) cluster identification is greatly facilitated even when limited machine-sharlng is inherent in the data. Further enhancements in this new heuristic will accommodate a larger number of bottleneck machines. But, while an algorithm for a larger number of bottleneck machines is worthwhile, it negates the basis for using Group Technology. AN APPROACH TO THE BOTTLENECK MACHINE PROBLEM A bottleneck machine creates high interaction between several clusters. The exception operation is essentially a bottleneck machine causing interaction between any pair of clusters through operations on fewer components. The large number of components using a bottleneck machine constitutes the problem in cluster formation. The problem complexity increases with the number of bottleneck machines. The previous algorithms concentrate on those machines which will need to be shared among the clusters. So prior identification becomes necessary to suppress them from further analysis in order for the algorithms to work. In contrast, the Occupancy Value method seeks to use the nonbottleneck machines in the input matrix. Theoretically, a minimum of two machines is required to define a cluster, regardless of any additional machines that may be shared. The clustering of components using the group of non-bottleneck machines they use will be sufficient. Operations on the bottleneck machines required by these components will appear dispersed outside the clusters. This will not affect the clarity of the clusters formed, as opposed to the initial outputs obtained by the Rank Order or Direct Clustering algorithms. Significant improvement in the clarity of clusters formed has resulted from this simplified clustering method. NOTATIONS USED
{Ck}:
kth cluster indiagonallzedmachlne-component matrix
{Mk}:
set of machines that constitute cluster {Ck}
{Kk}:
set of components that constitute cluster {Ck}
{Rj}:
set of machines contained in the route of the component number J
{Ms}:
superset of machines introduced into the diagonallzed machlne-component matrix till the last iteration
{Li}:
set of components that visit machine i for processing
n:
number of clusters constituted by the non-bottleneck machines (k = 1,2,...,n) in the input machine-component matrix.
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PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING
THEORETICAL BASIS FOR THE OCCUPANCY VALUE METHOD The process of cluster formation wlth the OV method is mainly the ordering of all the machines In a machine superset {Ms}. This superset is a union of all machine sets representing the n clusters existing in the input data ({Ms} = {MI} U {M2} ... U {Mn}).
The entry of the components
associated with the machines in a particular cluster depends on the choice of machines entered into {Ms}. The machines in the route, {Rj}, of component j will be contained in the set {M k} if it occurs In {Kk}.
All the machines in {M k} may not occur in the route of a component J that belongs to {Kk}.
The routes of the components that belong to {K k} will be represented by several subsets of machines derived from {Mk}.
Hence, a three-step procedure for cluster formation can be
developed. Step I - Start by selecting a subset of machines that can be expected to belong only to a particular {Mk}. Step 2 - Select all components whose routes are completely contained in this subset only to enter
{Kk}. Step 3 - If {M k) is incomplete, return to step I to enlarge the previous subset of machines. This is preferably done by replacing {Mk} by {Mk}U{R j} where JE{Kk). The procedure terminates when all machines have been entered in {Ms}.
Implicitly, the routes of
all the components in the initial matrix can now be represented by this set of machines. THE SEED COMPONENT Machines must be entered into {Ms } to Ca) initiate and (b) continue cluster formation. a component whose machines are added to {Ms}.
A SEED is
This is preferable to random selection of
machines. A minimum of one component at least will enter the current cluster this way. Subsequent seeds will usually be components whose routes are partially contained in {Ms}. number of machines added by each seed to {M s } may vary.
The
This method assists In reducing cluster
formation to a one-dlmensional problem of ordering machines in {Ms}.
Currently, the computer
program has no provision for machine duplication when a NEW SEED uses machines introduced earlier into {MS}. The reason is that the clarity of the clusters formed around the non-bottleneck machines has proved sufficient on all test problems. CRITERION FOR SEED SELECTION Mj has been defined as the number of operations that remain to be performed on component J.
It
can also be interpreted as the number of machines in {Rj} that remain to be introduced into {Ms}. The SEED component is one whose route contains the least number of machines not contained in {Ms}. That is, it has the minimum value of Mj for all components in the usage llst {L I} of some machine "i". The minimum Mj value criterion for seed selection reduces combinatorial complexity and, hence, the possibility of incorrect machlne-component assignments among the clusters. Consider two components, one of whloh, A, uses only machine I ({R A} = {I}) whereas the other, B, uses machines ; and 2 ({R B} = {1,2}).
If SEED = A, then other components using machine ] only can be selected.
However, if SEED = B instead, then any component whose route is {I}, {2} or {1,2} or {2,1] can be selected. Ordering a large number of components to prevent cluster dispersion is more difficult. The minimum Mj criterion prevents the selection of any SEED with a large number of machines that have not been entered into {Ms}.
Control on the number of new machines entered limits the number
of part routings that can be developed from the new {Ms} formed. SEED SELECTION WITH THE CURRENT MACHINE RULE The minimum Mj criterion for SEED selection is not sufficient for ensuring cluster formation when
[ R s E E D } ~ { M k} instead of the case where {RSEED} = {Mk}. selection as SEED.
Several components can tie for
For example, both components I and 2 tie with Mj ~ 2, as shown below:
IRAIVI & KEATOR: A Microcomputer
(R,)
fl
U-$,1 - (A)
whereas
Based Design of CellularManufacturingSystem
(MS1 - e since
(R21 0
neither
71
machine E or F has been entered
into
It would be desirable to enter (R,) into (MS 1 as it will complete the machine set (MB). (MS). The computer progrm uses a Current Machine Rule which (a) retains the componentwith the lowest ~~ value ss the SEED and (b) ensures that this SEED is drawn from the usage list (Li) of the current
machine
“i”
entered
SEED - 1, corresponding seed has entered (MS).
into
(MS).
In this
to component 1, after
case,
of machineA, (LA),
the usage list
the last
Yields
machine,A , in the route of the Previous
SOLUTIONTO THE BOTTLENECK MACHINEPROBLEM Suppose
(Mk) and (Mb) are two sets
of machines
that
constitute
the Clusters
{Ok) and (CL)*
respectively: No bottleneck
Case 1: (Mk) n (ML) = 0. formation Case 2: (Mk)0
will
not pose
machine(s)
(ML1 * 0. Bottleneckmachine(s)
iscare)
(a)
values
of of
one of
not belonging
to
being
shared
the bottleneck (b) select analysis:
them as the next SEED.
components
Common to the tW0 ClUsters.
place
heuristic will perform two tasks: IC,), and eliminate them from further lists
iscare)
OluSte:
a problem.
The next (Ck) or
cluster
by the clusters.
The
machines in One Cluster, say some component from the usage formed will
be (CL)
{CL) would not have changed.
since Hence,
the Occupancy Value heuristic, the bottleneck machines and exception operations influence Clustering of the non-bottleneck machines. They will influence only in which the clusters appear along the diagonal. When a NEWSEED has been selected,
its
of
entered
the usage
lists
of
all
machines
route
is not previously
MNEWSEED+ OLD SEED becomes the next SEED.
Otherwise
ccmPonantsin the usage list
that
Of the machine
immediately into
NEWSEED
yielded
merged with
(MS) for
[MS).
an OLD SEED.
introduces
machines
the Mj with
do not the order
A scan
is made
If MOLDSEED < required by
it.
DESCRIPTION OF THE COMPUTER PROGRAM The OV heuristic has been coded in BASIC and run on a microcomputer. While the complete program has about 200 lines of code, the logic is contained in under 70 lines. The preliminary portion of the program accepts data for a run of the heuristic from the keyboard or an existing file on diskette. A facility for data correction prior to filing exists. The output portion of the program reprints the printouts of the Initial and Final Machinecomponent matrices. It recognizes the need to handle (a) a large number of machLnes and (h) number of components that exceeds the number of machines.
a
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PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING
EXAMPLE PROBLEM The heuristic presented In the paper was tested using a rather difficult matrix used by King [3]. The results of this problem are shown below:
~(3( I N I T I A L MACHINE-COMPONENT HATRIX ~(~(
K3( FINAL MACHINE-COMPONENT MATRIX ~(3( HACH 1NE
HACHINE I
COMP
1234567899 I I I 1 I I I 1 1 1
I 2 3 4 5 6 7 8 ? 18 1I 12 13 14 15 16 17 18 19 28 21 22 23 24
I I I 1 1234
I I I I I
1 I
I I I I I
l I I I I I I I I
I
I I I I I I I I I I
I
1 1 l I
I 1
I
I I
1 I
1
C(~t'IP 19 17 28 I 2 23 18 6 7 8 24 21 3 4
1 ,4573120
I l I 1 1
l
I 1 I 1 3 2 6 8 9 4 ,
1 l 1 1 1 I
1
1
I1 1 11
22 18
II 11
14 16 5 12 9 II 13 15
I,I1 1
(~
k_J
= Exceptions
Ill IIII I
I I!
IIII
Two o p e r a t i o n s , corresponding t o the machine-component p a i r s 13-23 and 7-7, were the e x c e p t i o n s . The ROC a l g o r i t h m r e q u i r e d these e n t r i e s to be suppressed b e f o re i t produced the f i n a l s o l u t l o n . The OV heuristic produced the diagonalized matrix in a single scan. Component
19 was chosen as the SEED as it was the last component with minimum Mj = I.
entered {Ms}.
The usage list {L 4} constitutes
the only interactions
between {CI} and {C2}.
Machine 4
the components in {Cl}. Components 23 and 7 create Component 23 became the next seed, instead of
component 18 even though M23 = MI8 = I after {Ms} = {4,5,7}. Component 7 dld not qualify as seed since {{R7} N{Ms}}' ~¢ at this point. {R23}U{Ms} yielded {M s } = {4,5,7,13}.{L13} yields {C 2} since SEED selection proceeds by the Current Machine Rule (CMR). even though M10 = M14 = M16 = M22 = 2. SEED selection. be selected.
{C3} was SEEDed by component 24
Again, this is due to the sequential scan performed for
It causes the highest numbered component satisfying the minimum Mj criterion to
CMR easily formed {C3} and {C4} since both clusters are independent.
Results are
identical to those from the ROC algorithm. CONCLUSION This paper has described an efficient heuristic for machlne-component cluster formation in group technology. It demonstrates a method for reducing the problem of dlmensionallty. A method that can identify clusters of non-bottleneck machines helps the analyst concentrate on the allocation problem for the bottleneck machines only. Further experimentation Is in progress. REFERENCES Greene, T. J. and R. P. Sadowski (1980). Loading the Cellularly Divided Group Technology Manufacturing System. Fall IE Conf. Proe., pp 190-195. King, J. R. (1980). Machlne-component Group Formation in Production Flow Analysis: An Approach Using a Rank Order Clustering Algorithm. Int. J. of Prod. Res., Vol. 18, No. 2, pp. 213-232. King, J. R. (1980). Machlne-Component Group Formation in Group Technology. Omega, Vol. 8, No. 2, pp 193-199. Chan, H. M. and D. A. Milner (1985). Direct Clustering Algorithm for Group Formation In Cellular Manufacture. J. of Manuf. Sys., Vol. I, No. I, pp. 65-74. Chen, D. S. and J. Chang (1985). A Matrix Transformation Algorithm for System Decomposition and Classification. Comput. &. Ind. Engng., Vol. 9, Suppl. I, pp. 213-217.
ABSTRACT Logistics functions directly support the principal operation. In the international petroleum industry they mainly are maintenance, materials and transport. CALM is a microcomputer system which integrates the arrangement of these functions. It operates at the plant level. There are six CALM subsystems: l) equipment control; 2) maintenance work control; 3) materials control; 4) transport control; 5) budgeting and costs and 6) management reporting. CALM is a collection of relational data bases and application programs. Its modules are distributed and are under user control. Data bases are designed for three organizational levels: I) local area networks on microcomputers at the user level (CALM proper); 2) relational data bases on minicomputers at the plant level and 3) relational data bases on main frame computers at the corporate level. This is said to be a 3-1evel computer system. Data base design is optimized over the three levels. System integration requires strict discipline over the design of identification codes (equipment, spare parts, job plans, job orders, failure reports, etc.). The paper suggests standard code formats which have proved to be successful in industry. Besides CALM, integrated systems from four multinational oil companies are discussed. Design errors experienced in these projects are described. Specific design precautions are emphasized.
REYWORDS Logistics; microcomputers;
relational data base; operations support; distributed data processing.
INTRODUCTION Logistics functions directly support the principal operation. The major ones are maintenance, materials and transport. While there are others, these three have the most immediate influence on operational success. They are the ones most difficult to manage. Logistics operations are carried out on a massive scale in the international petroleum industry. Individual operations are large; up to 4,000 maintenance personnel, 250,000 materials articles, hundreds of on-road and off-road vehicles, marine craft, helicopters and fixed wing aircraft. Operating conditions are harsh: North Sea storms, Arctic temperatures, Middle Eastern deserts, tropical jungles. Maintaining the equipment, storing and moving the materials and keeping the operations going are not easy tasks. Petroleum companies were among the first to develop computer systems to help management cope with logistics problems. Sophisticated systems came on the scene in the early 1950s. The first ones were oriented to keeping track of what was going on. We call them "bean counting" systems. They emphasized collecting, recording and storing data but, in general, were not analytical nor interpretive.
SYSTEM REQUIREMENTS The ideal management system is one that is fully integrated. We do not know of any company that has achieved this ideal but several are close to it. The principal shortfall is the integration of financial accounting. The structure of traditional accounting systems makes integration awkward at best. There are 6 modules which must be developed in order to achieve full integration.
They are:
equipment control module -- oriented to the equipment being operated and maintained. Typical components are the equipment register, job plan dictionary, lubrication specifications, various equipment condition data bases, a failure report data base, an equipment run-log module. work control module -- oriented to the people who maintain the equipment. Typical components are a standard job plan data base, a data base containing standard equipment servicing frequencies, the work order module, a module for creating job plans, a module for developing
65
66
PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING
short-, medium- and long-range job schedules, the timekeeping system, a maintenance contract data base and a capability to charge manhours, materials and other job costs to work orders and from there to end accounts. materials control module -- oriented to the materials and spare parts used to maintain the equipment. Typical components are the materials catalog, the master stock ledger file, an inventory control capability, a surplus materials control module, the warehouse materials requisition module, the materials requisition module, the purchasing system, a capability to track open purchase orders and a capability to charge materials costs to user accounts including maintenance work orders. transport control module -- oriented to the transport fleet that hauls the workers who maintain the equipment and the tools and materials they require for their work. Components vary by type and scope of operation. Typical ones for large onshore/offshore operations provide the capability to schedule trips, match vehicles to trip load requirements and develop load lists. They may include a trip ticket data base and a system of tariffs and rules for charging users (for example, who pays for dead heading). budgeting and cost reporting module -- oriented to financial control of people, materials and transport operations. The work order data base, the maintenance contracts data base, the materials master stock ledger file and the transport trip ticket data base are subledgers in the financial accounting system. They are part and parcel of financial accounting and their structures must be correctly designed to accommodate this integration. This is where most existing logistics systems fail; adequate integration with financial accounting has not been achieved. management reporting module -- oriented to exception reporting of significant events and situations which merit management attention and correction. Pareto's law (the 80/20 principle) should be strictly followed. Various "hot lists" and "hit parades" should be produced. Detailed reporting should be scrupulously avoided. Most existing systems fail in this respect. They continue to emphasize bean counting. The significant few become lost in the trivial many. Finley (1985A) described a series of maintenance performance indicators and how an electronic spreadsheet can be used to process the data.
DATA BASE TECHNOLOGY Most existing logistics management systems are written in the COBOL language and are application specific. There is little flexibility for change. Integration is complex, difficult and sometimes, because of structural deficiencies, impossible. Today's approach is to use a DBMS (data base management system) to create subject data bases which are separate from application programs. The latter use the data in the data bases but do not destroy them. Each application program is stand-alone and independent of all other application programs. The management system is infinitely flexible for changes, enhancements and growth. On several occasions (Finley, 1984, 1985A, 1985B) we have said that all existing logistics management systems which are not built with data base technology are obsolete. We continue to hold that position.
SYSTEM DESIGN ERRORS Starting about 1979, several of the large multinational petroleum companies began converting their logistics management systems to subject data bases and separate application programs. We have had the opportunity to audit four of these projects. It is interesting that we found the same design errors in all four systems. In each situation the companies had to back track and correct the errors at a cost of $100,000 to $250,000. These errors were: ..
failure to recognize the difference between an equipment and the function it performs. There must be two modules in an equipment register. We call them: ..
FINLIST -- a data base containing the functional, process-related information. is the FIN (functional identification number) code.
..
SPINLIST -- a data base containing the equipment technical information. SPIN (specific plant identification number) code.
The key
The key is the
failure to recognize the difference between a job plan and a work order. Job plans used to be saved for reuse under a work order code number. These code numbers had no relationship to the content of the work. As a result, the plan could not be found for retrieval. trying to incorporate failure reporting within work order processing. This approach mixes equipment control with work control. They are two different things. Effective failure reporting can only be accomplished with a stand-alone failure reporting system.
FINLEY & HYIAND: Calm -- Computer Aided Logistics Management
67
failure to recognize Pareto's law. Rather than reporting only significant information, most systems incorporated saturation reporting. The result was a mass of virtually unusable data which discouraged potential users, hampered analysis and progressively undermined data gathering efforts. asking for too much feedback information. This approach focused attention on trivial information, thus compounding the problems of user confidence and serious analysis. trying to use the critical path method to schedule daily maintenance jobs. cannot be made to work.
This approach
These were errors common to all four companies. Additional errors were made by individual firms. For example, one company tried to build the material control system using the manufacturer's part number rather than a specific company commodity code. Given the lack of logical structure in this number and the frequency of number changes, the result was chaos in materials management.
CALM - COMPUTER AIDED LOGISTICS MANAGEMENT
Our company is developing CALM to be used in individual plants and operations. Examples of the latter include small to medium size petroleum refineries and petrochemical plants, NGL and LNG plants, offshore production platforms, drilling vessels. Basically, we are downsizing mainframe systems to fit on microcomputers. Hardware and software constraints limit CALM to operations of 1,000 to 3,000 pieces of equipment, 12,000 to 20,000 material articles and I00 to 250 repairmen. Only a few plants and operations exceed these limits. Today CALH is only partly finished.
We have:
done exhaustive research into coding requirements. of information (data) elements.
We have developed a comprehensive list
done extensive data modeling. We have determined which data elements should be in which data base modules and have specified primary and secondary keys. We have developed a Data Dictionary. developed a comprehensive data base strategy. We have identified which data bases will be owned by which organizational functions, which functions will share which data bases and where the data bases will be physically located. developed the initial data base modules. These include the FINLIST, SPINLIST, MATCAT (materials catalog), FAS (failure analysis system), RUNLOG, JBUDGET (a long-range work budgeting and planning tool) and LPIPLAN (a series of logistics performance indicator data bases). These modules are fully integrated. ..
developed application programs for advanced technical and cost analyses.
When finished, CALM will provide to small plants and operations the same powerful logistics management capabilities provided by the the main frame systems employed by the multinational giants. Specific modules already are in use in several plants and offshore production operations. CALM is evolutionary and can be implemented step by step. The first (and usually the most difficult) step is to get the equipment register and materials catalog in proper order. This may require 4 months for a small system to 12 months for a larger system.
MANAGEMENT BENEFITS The principal benefit management receives from CALM (or any other similar logistics management system) is not the bookkeeping capabilities (although, this is certainly an important benefit) but rather is the management discipline imposed by the integrated structure. Careful adherence to the philosophies embedded in CALM can lead to substantial reductions in direct logistics costs and to sharp increases in equipment availability. We regularly see cost reductions of 25 percent to 35 percent and reductions in equipment downtime of 35 percent to 50 percent. Scientific management of logistics is good business.
CALM provides the basis for this scientific
management.
REFERENCES Finley, H. F. (1985A).
Monitor Maintenance Performance with an Electronic Spreadsheet. Hydrocarbo n Processing, 64, 66-68.
Finley, H. F. (1985B).
Switch to Knowledge-based Maintenance and Reduce Costs. to Hydrocarbon Processing.
Finley,
H. F .
(1984).
HSS submitted
Maintenance Management Systems for the 1990's. Proceedings of the Energy Sources Technology Conference~ Plant Maintenance Workshop. New Orleans.
A MICROCOMPUTER BASED DESIGN OF A CELLULAR MANUFACTURING SYSTEM
S. A. Irani and S. K. Khator Department of Industrial and Management Systems Engineering University of South Florida Tampa, FL 33620
ABSTRACT This paper presents a new heuristic for machine-component cluster formation in Group Technology. The method eliminates user judgment and data suppression required by some methods in the literature. It reduces row and column manipulation to the determination of a particular sequence of listing of the machines. With this approach the problems of exception elimination and bottleneck machines are not severe. Test problems rated as "difficult" for the other methods are easily solved by this heuristic. KEYWORDS Machine-component cluster formation; Group Technology; Occupancy Value heuristic; Bottleneck Machine problem. STATEMENT OF THE PROBLEM Greene and Sadowski [I] describe several problems that will be faced by companies desirous of implementing group technology-based manufacturing cells. These include: a) Determining the exact number of cells to replace the existing system. b) Deciding which machine types will be assigned to the remainder cell, to be shared by all cells. c) Locating the cells to minimize the volume of intercellular material flow. d) Placing a limit on the maximum number of machines to be placed in any cell. e) Assigning the components among the cells so as to minimize the number of components that must visit more than one cell for processing. Machine-component matrix clustering algorithms can offer guidelines for the solution of these problems. UTILITY OF MACHINE-COMPONENT CLUSTER FORMATION METHODS These methods use part routing information to develop a machine-component matrix. By convention, the rows in the matrix will represent the machines and the columns will represent the components. However, in practice, the reverse representation is more convenient since the number of components will usually far exceed the number of machines. A machine-component matrix will be an array of Os and Is.
An entry A.. ij = I at the intersection of row "i" and column "j" will indicate that machine "i" processes component "j". If A.. = O, ij then there is no relationship between the machine and component. The clustering algorithms considered in this paper operate on the assumption that the machine-component array can be partitioned into matched groups of machines and components. These will appear as clusters along the diagonal of the matrix as shown below. This visual presentation of the independent sets of machines and components can provide useful guidelines for planning the cells. ~ C
I
M
c
~M
A
M = Machines C = Components
OR
A
A
68
IRANI & KHATOR: A Microcomputer Based D e s i g n o f C e l l u l a r M a n u f a c t u r i n g System
69
LITERATURE REVIEW The Rank Order Clustering algorithm [2,3] uses a positional weighting technique to alternately rearrange the rows and columns of the matrix in order of decreasing rank. A simplified version of this algorithm, the Direct Clustering algorithm [4], eliminates the sensitivity of the ranking procedure to the configuration of the initial matrix. Both these array-based methods are affected when several machines are common to one or more clusters. The authors describe such disperslon-causing entries in the matrix as either "exception operations" or "bottleneck machines." These algorithms fall to yield visible clusters when inherent dispersion of entries exists. The analyst needs to analyse intermediate matrix outputs to identify them. Fresh runs of the algorithms must be made with those entries suppressed. Such prior assumptions bias the solution. It is difficult for the analyst to perform visual identification. Further, an algorithm is expected to indicate the exceptions and bottleneck machines. These two algorithms require preliminary elimination of the entries that would improve the quality of the solutions they produce. The Matrix Transformation algorithm given by Chen and Chang [5] eliminates user Judgement in the selection of the matrix entries that should be eliminated. However, it requires the user to specify the machines and components to ignore during analysis by making them row and column border members, respectively. OBJECTIVE OF THE OCCUPANCY VALUE HEURISTIC The Occupancy Value method is a heuristic developed to eliminate the manual adjustment of intermediate resultsin the algorithms described in References [2], [3], [4] and [5]. It assumes that the analyst has no prior knowledge about "bottleneck machines" or "exception operations" in the initial matrix. The matrices which were used for the Rank Order Clustering and Direct Clustering algorithms were ideal for testing this new method. Results indicate that (a) the heuristic guarantees cluster formation when no machlne-sharlng exists in the initial matrix, (b) cluster identification is greatly facilitated even when limited machine-sharlng is inherent in the data. Further enhancements in this new heuristic will accommodate a larger number of bottleneck machines. But, while an algorithm for a larger number of bottleneck machines is worthwhile, it negates the basis for using Group Technology. AN APPROACH TO THE BOTTLENECK MACHINE PROBLEM A bottleneck machine creates high interaction between several clusters. The exception operation is essentially a bottleneck machine causing interaction between any pair of clusters through operations on fewer components. The large number of components using a bottleneck machine constitutes the problem in cluster formation. The problem complexity increases with the number of bottleneck machines. The previous algorithms concentrate on those machines which will need to be shared among the clusters. So prior identification becomes necessary to suppress them from further analysis in order for the algorithms to work. In contrast, the Occupancy Value method seeks to use the nonbottleneck machines in the input matrix. Theoretically, a minimum of two machines is required to define a cluster, regardless of any additional machines that may be shared. The clustering of components using the group of non-bottleneck machines they use will be sufficient. Operations on the bottleneck machines required by these components will appear dispersed outside the clusters. This will not affect the clarity of the clusters formed, as opposed to the initial outputs obtained by the Rank Order or Direct Clustering algorithms. Significant improvement in the clarity of clusters formed has resulted from this simplified clustering method. NOTATIONS USED
{Ck}:
kth cluster indiagonallzedmachlne-component matrix
{Mk}:
set of machines that constitute cluster {Ck}
{Kk}:
set of components that constitute cluster {Ck}
{Rj}:
set of machines contained in the route of the component number J
{Ms}:
superset of machines introduced into the diagonallzed machlne-component matrix till the last iteration
{Li}:
set of components that visit machine i for processing
n:
number of clusters constituted by the non-bottleneck machines (k = 1,2,...,n) in the input machine-component matrix.
70
PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING
THEORETICAL BASIS FOR THE OCCUPANCY VALUE METHOD The process of cluster formation wlth the OV method is mainly the ordering of all the machines In a machine superset {Ms}. This superset is a union of all machine sets representing the n clusters existing in the input data ({Ms} = {MI} U {M2} ... U {Mn}).
The entry of the components
associated with the machines in a particular cluster depends on the choice of machines entered into {Ms}. The machines in the route, {Rj}, of component j will be contained in the set {M k} if it occurs In {Kk}.
All the machines in {M k} may not occur in the route of a component J that belongs to {Kk}.
The routes of the components that belong to {K k} will be represented by several subsets of machines derived from {Mk}.
Hence, a three-step procedure for cluster formation can be
developed. Step I - Start by selecting a subset of machines that can be expected to belong only to a particular {Mk}. Step 2 - Select all components whose routes are completely contained in this subset only to enter
{Kk}. Step 3 - If {M k) is incomplete, return to step I to enlarge the previous subset of machines. This is preferably done by replacing {Mk} by {Mk}U{R j} where JE{Kk). The procedure terminates when all machines have been entered in {Ms}.
Implicitly, the routes of
all the components in the initial matrix can now be represented by this set of machines. THE SEED COMPONENT Machines must be entered into {Ms } to Ca) initiate and (b) continue cluster formation. a component whose machines are added to {Ms}.
A SEED is
This is preferable to random selection of
machines. A minimum of one component at least will enter the current cluster this way. Subsequent seeds will usually be components whose routes are partially contained in {Ms}. number of machines added by each seed to {M s } may vary.
The
This method assists In reducing cluster
formation to a one-dlmensional problem of ordering machines in {Ms}.
Currently, the computer
program has no provision for machine duplication when a NEW SEED uses machines introduced earlier into {MS}. The reason is that the clarity of the clusters formed around the non-bottleneck machines has proved sufficient on all test problems. CRITERION FOR SEED SELECTION Mj has been defined as the number of operations that remain to be performed on component J.
It
can also be interpreted as the number of machines in {Rj} that remain to be introduced into {Ms}. The SEED component is one whose route contains the least number of machines not contained in {Ms}. That is, it has the minimum value of Mj for all components in the usage llst {L I} of some machine "i". The minimum Mj value criterion for seed selection reduces combinatorial complexity and, hence, the possibility of incorrect machlne-component assignments among the clusters. Consider two components, one of whloh, A, uses only machine I ({R A} = {I}) whereas the other, B, uses machines ; and 2 ({R B} = {1,2}).
If SEED = A, then other components using machine ] only can be selected.
However, if SEED = B instead, then any component whose route is {I}, {2} or {1,2} or {2,1] can be selected. Ordering a large number of components to prevent cluster dispersion is more difficult. The minimum Mj criterion prevents the selection of any SEED with a large number of machines that have not been entered into {Ms}.
Control on the number of new machines entered limits the number
of part routings that can be developed from the new {Ms} formed. SEED SELECTION WITH THE CURRENT MACHINE RULE The minimum Mj criterion for SEED selection is not sufficient for ensuring cluster formation when
[ R s E E D } ~ { M k} instead of the case where {RSEED} = {Mk}. selection as SEED.
Several components can tie for
For example, both components I and 2 tie with Mj ~ 2, as shown below:
IRAIVI & KEATOR: A Microcomputer
(R,)
fl
U-$,1 - (A)
whereas
Based Design of CellularManufacturingSystem
(MS1 - e since
(R21 0
neither
71
machine E or F has been entered
into
It would be desirable to enter (R,) into (MS 1 as it will complete the machine set (MB). (MS). The computer progrm uses a Current Machine Rule which (a) retains the componentwith the lowest ~~ value ss the SEED and (b) ensures that this SEED is drawn from the usage list (Li) of the current
machine
“i”
entered
SEED - 1, corresponding seed has entered (MS).
into
(MS).
In this
to component 1, after
case,
of machineA, (LA),
the usage list
the last
Yields
machine,A , in the route of the Previous
SOLUTIONTO THE BOTTLENECK MACHINEPROBLEM Suppose
(Mk) and (Mb) are two sets
of machines
that
constitute
the Clusters
{Ok) and (CL)*
respectively: No bottleneck
Case 1: (Mk) n (ML) = 0. formation Case 2: (Mk)0
will
not pose
machine(s)
(ML1 * 0. Bottleneckmachine(s)
iscare)
(a)
values
of of
one of
not belonging
to
being
shared
the bottleneck (b) select analysis:
them as the next SEED.
components
Common to the tW0 ClUsters.
place
heuristic will perform two tasks: IC,), and eliminate them from further lists
iscare)
OluSte:
a problem.
The next (Ck) or
cluster
by the clusters.
The
machines in One Cluster, say some component from the usage formed will
be (CL)
{CL) would not have changed.
since Hence,
the Occupancy Value heuristic, the bottleneck machines and exception operations influence Clustering of the non-bottleneck machines. They will influence only in which the clusters appear along the diagonal. When a NEWSEED has been selected,
its
of
entered
the usage
lists
of
all
machines
route
is not previously
MNEWSEED+ OLD SEED becomes the next SEED.
Otherwise
ccmPonantsin the usage list
that
Of the machine
immediately into
NEWSEED
yielded
merged with
(MS) for
[MS).
an OLD SEED.
introduces
machines
the Mj with
do not the order
A scan
is made
If MOLDSEED < required by
it.
DESCRIPTION OF THE COMPUTER PROGRAM The OV heuristic has been coded in BASIC and run on a microcomputer. While the complete program has about 200 lines of code, the logic is contained in under 70 lines. The preliminary portion of the program accepts data for a run of the heuristic from the keyboard or an existing file on diskette. A facility for data correction prior to filing exists. The output portion of the program reprints the printouts of the Initial and Final Machinecomponent matrices. It recognizes the need to handle (a) a large number of machLnes and (h) number of components that exceeds the number of machines.
a
72
PROCEEDINGS OF THE 8TH ANNUAL CONFERENCE ON COMPUTERS AND INDUSTRIAL ENGINEERING
EXAMPLE PROBLEM The heuristic presented In the paper was tested using a rather difficult matrix used by King [3]. The results of this problem are shown below:
~(3( I N I T I A L MACHINE-COMPONENT HATRIX ~(~(
K3( FINAL MACHINE-COMPONENT MATRIX ~(3( HACH 1NE
HACHINE I
COMP
1234567899 I I I 1 I I I 1 1 1
I 2 3 4 5 6 7 8 ? 18 1I 12 13 14 15 16 17 18 19 28 21 22 23 24
I I I 1 1234
I I I I I
1 I
I I I I I
l I I I I I I I I
I
I I I I I I I I I I
I
1 1 l I
I 1
I
I I
1 I
1
C(~t'IP 19 17 28 I 2 23 18 6 7 8 24 21 3 4
1 ,4573120
I l I 1 1
l
I 1 I 1 3 2 6 8 9 4 ,
1 l 1 1 1 I
1
1
I1 1 11
22 18
II 11
14 16 5 12 9 II 13 15
I,I1 1
(~
k_J
= Exceptions
Ill IIII I
I I!
IIII
Two o p e r a t i o n s , corresponding t o the machine-component p a i r s 13-23 and 7-7, were the e x c e p t i o n s . The ROC a l g o r i t h m r e q u i r e d these e n t r i e s to be suppressed b e f o re i t produced the f i n a l s o l u t l o n . The OV heuristic produced the diagonalized matrix in a single scan. Component
19 was chosen as the SEED as it was the last component with minimum Mj = I.
entered {Ms}.
The usage list {L 4} constitutes
the only interactions
between {CI} and {C2}.
Machine 4
the components in {Cl}. Components 23 and 7 create Component 23 became the next seed, instead of
component 18 even though M23 = MI8 = I after {Ms} = {4,5,7}. Component 7 dld not qualify as seed since {{R7} N{Ms}}' ~¢ at this point. {R23}U{Ms} yielded {M s } = {4,5,7,13}.{L13} yields {C 2} since SEED selection proceeds by the Current Machine Rule (CMR). even though M10 = M14 = M16 = M22 = 2. SEED selection. be selected.
{C3} was SEEDed by component 24
Again, this is due to the sequential scan performed for
It causes the highest numbered component satisfying the minimum Mj criterion to
CMR easily formed {C3} and {C4} since both clusters are independent.
Results are
identical to those from the ROC algorithm. CONCLUSION This paper has described an efficient heuristic for machlne-component cluster formation in group technology. It demonstrates a method for reducing the problem of dlmensionallty. A method that can identify clusters of non-bottleneck machines helps the analyst concentrate on the allocation problem for the bottleneck machines only. Further experimentation Is in progress. REFERENCES Greene, T. J. and R. P. Sadowski (1980). Loading the Cellularly Divided Group Technology Manufacturing System. Fall IE Conf. Proe., pp 190-195. King, J. R. (1980). Machlne-component Group Formation in Production Flow Analysis: An Approach Using a Rank Order Clustering Algorithm. Int. J. of Prod. Res., Vol. 18, No. 2, pp. 213-232. King, J. R. (1980). Machlne-Component Group Formation in Group Technology. Omega, Vol. 8, No. 2, pp 193-199. Chan, H. M. and D. A. Milner (1985). Direct Clustering Algorithm for Group Formation In Cellular Manufacture. J. of Manuf. Sys., Vol. I, No. I, pp. 65-74. Chen, D. S. and J. Chang (1985). A Matrix Transformation Algorithm for System Decomposition and Classification. Comput. &. Ind. Engng., Vol. 9, Suppl. I, pp. 213-217.