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Modelling in Management Information Systems Design
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MODELLING IN MANAGEMENT INFORMATION SYSTEMS DESIGN Z. Strezova Scifllli/ic efl/In fvr .\lIS. Sofia. /JII/gllrla
Abstract. The use of modelling in MIS field is discussed. A general methodology for MIS design, based on the concept of alternative solutions is outlined. An optimal allocation model, used for MIS structures synthesis, is presented. ~odelling ac tivities in th e assess~ent of MIS stL~ctures are considered. Criteria of applicability of models are described. Applications of the developed methodology, models and program tools are pointed out. Keywords. Modelling; models; management information systems; manageme nt systems desi gn ; decentralized systems. I NT'10DUCTI ON
Eilon,1979; Van Gigch,1974) a p8licy conthat area has been outlined. This is a policy of app r oximations: approximate methods for system state des cription and for system structure synthesis, instead of e}:act methods, approxi"1ate cri teria, implicit models and algorithms instead of explicit ones, etc. Two other challeng ing policies were given in last decade in the systeo area. The first concerns the evolution of the use of mathematical modelling for management. The single imperative optimal solution was replac ed by the IF-THEN models and those with the interval pro gramming concept. The second policy is that of design by alt erna tives. cernin~
The use of formal models in the development of ManageMent Information Systems (~ns) has been at issue from the early seventies. A decade l a ter the old problems ~till exist and new ones arise.For better consideration of the diffic1l.1ties with the modelling in MIS field, some specificities of these systems and of the manage~ent process will be summarized. The MIS represent a class of lar7e scale systems whose goal is generation "of relevant information for the man~ement of a managed system, e.g. economic, industrial, administrative or social syste~. When a ~ IS is intended for a company, an industrial branch or a regional economy, its structure usually includes information and computer resources, distributed geographically, decentralized decision--making, distributed co~putations, computercommunication network. In such a case MIS is considered as a large scale system with decentralized structure. The manage~ent process which must be supported by a !'ITS in each particular case, is a process with ill-defined nature. Following Ackoff's statements, it can be considered as a management decision cycle, consisting of f our stages:decision-making, decision implementation, decision estimation and recommendation for change.
The present st ate of the "!IS desis-n is influenced by the policies and trends r'len tioned above. In this paper we discus some of the problems of MIS de si ~ n w ~ ich concern modelling activities. After outlining a gen eral me thodology for ":IS design, a n optimal allocation ~odel, used for ~J~ structure synth esis, is presented. A procedure of successive assess ment of MIS modelled structures is considered. Some non-formal criteria of models applicability are discussed. Applications of developed models and program tools in realworld MIS design are described. GENERAL METHODOLOGY FOR ~US
The design of complex MIS must follow the methodology of design of decentralized large scale systems. For the present, an adequate theory for treating the problems of this kind of systems is lacking. In a number of studies (e.g. B~llman,1974;
DESIGN
As was noted above, ~Us.s intended for an industrial branch or a regional economy are characterized by decentralized struGture. A detail of such a MIS structure is shown in Fig. 1. The MIS consists of two 501
Z. Strezova
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parts: information subsystem (ISS) and decision subsystem (DS S). The latter can be described as an entity of de cision-making units (JMU), s olving a set of decision tasks (:::JT), a, set of input variables x (de cision state variables), ~ set of input information v ariables v, a set of d ecision v a riables z, and a s et of feedback variables f ( objec t output).
The third stage of the '~I.S design consis1s in further assessment and selection of the chosen alternative structures. To the purpose, the use of a metamodel, performing sensitivity analysis on the simulated structures, is recommended. As a result a final set of feasible }ITS structures tFSS), {FSSJC{CSSJ is selected. Each one of these structures is a "card:idate" to be d esigned.
rhe ISS is d e s cribe d as an entity of inresources I R, a n entity of comp uter resour c es C? , a set of input vari ab les x, a set of feedback v a ri ab les f, a set of feedb a c k v a riable s b (decision state variables), a set of ou t pu t informa t ion v'lriables v ( conce rning dec i s ions in D5S) an~ a set of ou tnut v ar i ab les i (direct infor~ation t o t ~e ob ject) .
In fact, the methodology, outlined above, c ould be considered as one of MIS models design (MIN design), since it supposes activitie~ preceding the very system design.
for~ation
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~ eneral
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~ I S from a ' control viewpoint ( Strezova,
1978) a manaqement problem can be s ta ted . Its sol ution i s identifie d with so lvine the ":15 structure desien DToblem. Generally stated, this prob l em consis ts in definin ~ such structur e s of ~SS ~nd I SS pnd their ele~ents s o th at a s et of criteria of ~ I S functioning effi c iency be satisfied. In early studies (Strezova, 1781,1 Q 82) an a pproach to the design of systems under question wa s proposed. Acc ording to this approach, called approximate, the problem of a MIS structure desi gn is not stated as a n optimization problem, and the system structure is not modelled expli~ely. Instead, an architecture of models is de~ig ned concerning the structure and functl0ni ng of the MIS subsystems and of the system as a whole. Fi g . 2 shows the stages of such a proc ess of :.[IS des ign. All stages comprise modelling activities. The first stag e includes two phases: decision structures modelling and optimal allocation modelling. As a result of the first st~ge a set of possible system structures {PSs } are modelled, i.e. a set of alternatives, which will be then evaluated, is genera ted. Th e second stage represents a n ev a lua tion of the consequences of each alternat ive and a selection of a smaller set of MIS structures. The first phase of this stage consists in finding a decision model which d escribes the inputs and outputs of the structures rncxiellErl in the previous s tage • The decision model also supposes the measures for evaluating system structures. The next phase of the second stage is the design of simulation model by which all of the alternati:es, described in the terms of the decislon model will be simulated. Using the measures s~pposed in the decjsion model~ an . analysis of all simulated alt e rnatlves lS to be performed. As a result, a set of chosen system structures {CSS {CSS} C pSS}, will be selected.
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More details concerning the approach to MIS design by modelling can be found in the studies, 9.uoted above, and also in Sage and Thissen t 1980) .
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OPTPIA L ALLOCATION HODELLING IN MIS DESIGN In this section we consider in some detail one of the phases of the process of MIS structures modelling. This is the optimal allocation modelling wh05e goal is the distribution of a set of decision tasks among a set of computer resources. This is an important probleru, especially when a MIS has a loc a l computer network, as it is the case in most complex decentralized MIS. The problem in question also concerns the structure and the content of the information resources in MIS. The results of the allocation modelling determine to some extent the degree of decentralization of the 'US u nd er investi gation. Here we present the g eneral k ind of an optimal allocation model whose realizatio~ leads to modelling possible MI5 structures. The real-world situation which this mod el describes is as follows: There exists a set I of decision tasks TIT which must be solved by a set of computer resources C~ (co mp uters or computing complexes ) . Each DT can be solved according to one of the algorithms of the set ki' The effici ency of so lving the ith DT by the j·th CR, ac cording to the kith al g ori thm, is known. A resource mik is needed for solving the ~th DT , acco rding to the ki algorithm; each j·th CR is restricted to mj; the expected expense for the ith ~~ solved by the jth CR, according to the ki alg orithm, is eikj; the total expenses made for the j-th CR are Rj. The problem consists in distribution of th e set of DT among the set of CR so that the total efficiency of the MIS functionin g be maximal on g iven constra ints. The mathematical model of the stated problem is vf the following kind:
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ficult to identify' a common measure, adequate enough, to evaluate different alt~~ natives. Usually a number of measures is needed for this purpose. In onestage assessment procedures, even though they are iterative, the used input-output relationships in a quality of evaluating measures are insufficient for more precise comparison and estimation of alternatives. A better assessment is provided by a multistage procedure, in each stage of which a particular set of measures is used.
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Thus, the problem of optimal allocation of DT among a set of CR is stated as an integer linear programming problem. To obtain more appropriate results, the model (1)+ ( 6) must be experimented unde~ different ,ralues of variabl e s. So, sorn ' restrictions ; ". '~ he problem st u tement, due to difficul.ies with the quantification of some variables, could be relaxed. )V[ODELLING n: T!IE ASSESS" ' ElTT OF ms ST~UCTU\tBS Another activity in the MIS design process which requires modelling is that of the evaluation of alternative system structures. According to the approximate approach to MIS design, described briefly above, the evaluation process embodies several successive stages: design and use of decision model, design and use of simulation models, desirn and. use of metamodel. Below some reasons are pointed out in :favouring such a multistage evaluationlon procedure in MIS design.
In a number of studies (e.g. Van Gigch, 1974) the process of alternatives evaluation is considered as finding and implementation of decision model by which all needed assessments can be performed. To obtain such a decision model, detailed procedures are proposed, including activities as follows: finding the main variabl e s and parameters, defining functional relationships between variables, which must be validated by empirical data, studYing relationships among actual variables by statistical methods, estimation of the coefficients of the functional relationships. On the basis of all that a decision model, representing the situation under investi ga tion/can be built. This model will serve to study respollsiveness or sensitivity of outputs to inputs, to estim a te the cost of changes, and so on. Obviously, procedures for decision models desi~n. such as mentioned above, are pos s ible' only in rea l-world, well defined situations. In MIS cases, when the assessment process concerns modelled alterna tives, a range of difficulties exists. First of all, it is not possible to state explicit relationships among variables, validated by empirical data (SUCh data are lacking); secon~ it is not possible to identify an explicit system efficiency criterion (or criteria), on the basis of which alternative structures to be compared; third, the sensitivity analysis would not be adequate enough since not all outputs can be quantified and so on. Briefly, in MIS design cases~is impossible to find an explicit and adequate decision model by which the assessment of a set of modelled l·rIS structures to be performed. Appropriate results can be obtained by a multistage evaluating procedure. The stages of such a procedure were briefly described in the first section of the paper. Here some particularities of the models by which a structure assessment is made/are pointed out.
The decision model is used in the first stage of the evaluating procedure. It is an implicit model which describes variables, input-output relationships, permissible levels of outputs, etc. The simulation model performs further assessment. Since simulation can be an expensive technique, a design of simulation experiments is needed for specifying the conditions under In complex, ill-defined problems treat meant which the simulation runs will be executed. such as MIS modelling and design, is dif-
Modelling in Management Information Systems Design The third stage of the evaluating procedure includes the use of a meta model. As inputs the metamodel uses some of the input and output variables, described by the decision model and used through the simulations. The choice of these variables and the selection of combinations "variables-simulation runs " is an important task in constructing a metamodel. In the case of ~ns with decentralized structure, in each st age of the assessment procedure one can analyse the degree of decentralization of the modelled MIS structures. Three additional measures for this analysis can be ap plied, system operation, system developm ent and system management. These measures are interrelated and could be presented quantitatively (Champione, 1980). The system _operations can vary from completely centralized management, with all of its computing in a single location, to completely decentralized, with each DMU having it s own computer resource. Usually, the real-world MIS have a structure at s ome intermed iate level. Each modelled ~rrs struc ture, having all its elements and t~e int er ac tions between them deter mined, is c har a cterized by a given degree of d ecentralization. So, the d imension of system opera tions is defined. Following the quantitative r e lations bet ween the syst em dimensions, those of the system development and of the system management can be determined. The for ~ er concerns the design, prog ramming, testing, implement a tion a nd maintenance of the system. Some of the s e activities can be done centrally, ot he r loc a lly. Th e measures f or MIS decentralization could be very usefu l in the fin al choice of a MIS structure, left to the decisioJ'Po makers. APPLICABILITY OF IN HIS DESIGN
~IOD ELS
A number of questions concerning the quality of the d eveloped and u s ed models always arise s when modelling and simulations are included in the EIS design process. A model, or a system of models, must b e considered and j ud g ed from th: . following viewpoints: complexity, valldlty, accuracy and flexibility. The model complexity concerns the model size and is a result of the problem or situation that the model replicate. A more complex model requires a diffic~lt verific a tion. Besides, such a model Includes a large number of variable~, assumptions and relationships. As Ellon (1979) argues, the hopes that ~ize and compl exity are the key to provlding a better insight into the modelled system are often illusory. As it seems, the trend in MIS field is to build middle-
505
sized models and to run them successively under different assumptions and variable values. The model validity concerns the degree of "fit" between the model a nd " reality", i.e. the question needs to be answered if the model is a valid instrument for solving a given problem. The validity depends on factors such as: the appropriateness, the relevance of criteria and constraints, the time horizon, the assumptions regarding variables and the r e l a tionships between them, the sensitivity 0f the resul~ Each one of these factors ne eds an assesment when a model is considered as valid or not. A final phase of the validation process could be the economic feasibility, i.e. will the model ensure sufficient improvement in the modelled situation to justify its expense. In most cases, such a question can be hardly answered. The model accuracy is associated with the quality of the informa tion fed into it. E. g ., the historical data are often unsuitable for particul a r assumptions or particular models. The flexibility is a mode l quality of great i mport a nce in the field in question Each one of the ~odels, u sed in the ~rrs design stages, must b e adequate to situations described by v a rious sets of vari ables. Besides, the models must provide a set of results in given bounds, within which one could make choice. The set of cr i teria of mod e ls quality, pointed out above, determines, in gene r a ~ the ap plic ability and uSability of a particular model, or a system of models. Another set of criteria, stated more by the model builders, includes qualities such as non-triviality, power, elegance. As a rule, it is not possible to satisf y both sets of criteria. APPLIC ·' TI ONS The methodology and the rel a t ed t e chniqu~ outlined in t he previous sections, have been used i n tr ea ting a nUMber of proble ms in ~ I S field. So, the general procedure for ~ s design was applied in development of the information system of a large industrial branch. The first stage of the modelling process was performed by: a) discriminant analysis (concerning the decision structures modelling),and b) integer linear programming (concerning the optimal alloc a tion modelling). As a result, 12 possible MIS structures have been modelled. The second stage has included a design of decision model (in the terms of Naylor's methodology) for description of the possible system structures, and design and use of simulation models (using GPSS as a simulation tech-
506
Z. Strezova
nique). Six chosen ~'!IS structures have been selected through this stage. In the third stage a linear regression illod el,in a quality of metamodel,was designed and used to perform a sensitivity analysis on the simulated ~ IS structures. As a final result of the modelling process, t,~o feasible MIS structures were selected. As a software tool, supporting all modelling activities, the modular progra~ system FIMOS has been developed. In its current state FI~OS includes standard IBM proble ~ packages, a nu ~ber of particular programs and some non-auto mated procedures. Another application,using the '!lultistage modelling procedure in develop~ent of l a rg e scale system/i s under way. This is the information sys tem for man ag e~ent of regional economy. In this case the FI~OS is in use again, with minor modifications due to the specificities o f the mcdelled system. S ome of the progra:n modules of FInDS have b e en used in solvin[!' pHr ticul ar problems. Such a problen ,Ias the allocation of man a.g e me n t tas ks of 54 "D":U a mong a set of 28 regional and 9 dep,',rt mental computer centers. This case required a model d ecomposition because of the l arge size of the original model. Some cO!llputer experi ments, yieldine use·· ful results, have been carried out through the process of MI5 mode lling. A heuristic 0-1 algorithm for solving as si g nment problems was inv estiga ted (st rezova, 1 a8~).
The known ~et~ods and t ec~nia ues for sysynthes is and analysis a~e no~ aPD l~ c able in handline cOClp lex , lac'er e scal~ syste:ns, includinf\ "IS . Adva:lced met hodologies and tools for '1 odell i n~ and simula.tion Rre needed, b~sed on the new concepts in the syst ~~ area , such as desien by a ltern2.t ives and intery s l prosra'"ming. In -''IS field, in nart i c ul ar , the use of '1ultistage procedures for mo~ ellin~ and successive assessment of a lt e ~ n2t ive structures wo u l d be very prom ising . B~ e ~
The d ev elop ~ ent of '1 ode l l i ne a n :l si-'Ul ".t ion t oo ls for suppo rtinG t h~ d esi Gn of compl e x syRte~R is 2 challen 7' e fOT res e a rch. A nUCl!:;er of im!"ort2.nt probl sns h as to be treated, e.e .: d esi :n o f archi tectu r e of ," odels f') ~ s"ste'1 an2.1:.':oi:o, exp e ri Ments with '1o<1e l s O~ ~ odelled alterna tives, riesi l"n Cl,ne. choice of ''1o(;els f or
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n ellman, R.E. ( 1974).
Nowadays, however, larg e organizational, economic, administrative and other systems need a complex, computer-based information system, supporting their problems and decisions. To build such HIS on the basis of its first version in mind, without providing insi g ht into the system structure and functioning, would be a very expensive process with unknow~ outpu~. Obviously, modelling and simulatlon actlvities are pertinent to the development of complex MIS.
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Trans. Aut o!T!at . ::ontr •• ~_C -1 ~ , !~~ Cha.mpine, G.A. ( 1980 ) . Distr~d -:;-;c;puter Sys te ms . r';o ~'t h - Ho ll ;:, nc, ll,b:!. . Eilo!1, s . (1 97 :~ ). ~SPE?C-:S '?f Ua n Cl genl:? r.t, ~nd ed . Perg a~o n Press . "', aee , ;"" .P., ar.rl ',). ,'. . '.1 . Thissen (1 0 (>01 . Me~hodolog i es for syste'1 siMulation . In Cren, ~ hub , and Rot ': {Sri " " Si'1ulation Id t'1 "' iscrete "ocle ls: ' ~e of-the- .A .rt "{ieH . Ti nivc' ~si t. ..... n f r-~ t::n[?. . S tr ezova , ~ . (1 ~ 7 9 ~ . ~ont r ol p~ oble~s in :1 .:: n2.ge P.'ler! t
The past decade h~s witnessed an increasing interest in modelling and si ~ulation in design of various lare;c scale syste ,:ls , as power syste~s, econo mic a nd man a qe~ent systems, air a,nd urban traffic control systems, computer coc.ffiunication networks. In the manag e men t area the r esearch has iocussed more on development of models for particular management problems,than on the use of modellinR,' as a tool for 1,:15 design and implementation. One reason, perhaps, was the fact that most "'IS, ~it h smaller size, have been built by heurlStics and some practical knowledge. Other, more complex)approac~es have been cons idered sceptically.
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C()p~Tiglll
MODELLING IN MANAGEMENT INFORMATION SYSTEMS DESIGN Z. Strezova Scifllli/ic efl/In fvr .\lIS. Sofia. /JII/gllrla
Abstract. The use of modelling in MIS field is discussed. A general methodology for MIS design, based on the concept of alternative solutions is outlined. An optimal allocation model, used for MIS structures synthesis, is presented. ~odelling ac tivities in th e assess~ent of MIS stL~ctures are considered. Criteria of applicability of models are described. Applications of the developed methodology, models and program tools are pointed out. Keywords. Modelling; models; management information systems; manageme nt systems desi gn ; decentralized systems. I NT'10DUCTI ON
Eilon,1979; Van Gigch,1974) a p8licy conthat area has been outlined. This is a policy of app r oximations: approximate methods for system state des cription and for system structure synthesis, instead of e}:act methods, approxi"1ate cri teria, implicit models and algorithms instead of explicit ones, etc. Two other challeng ing policies were given in last decade in the systeo area. The first concerns the evolution of the use of mathematical modelling for management. The single imperative optimal solution was replac ed by the IF-THEN models and those with the interval pro gramming concept. The second policy is that of design by alt erna tives. cernin~
The use of formal models in the development of ManageMent Information Systems (~ns) has been at issue from the early seventies. A decade l a ter the old problems ~till exist and new ones arise.For better consideration of the diffic1l.1ties with the modelling in MIS field, some specificities of these systems and of the manage~ent process will be summarized. The MIS represent a class of lar7e scale systems whose goal is generation "of relevant information for the man~ement of a managed system, e.g. economic, industrial, administrative or social syste~. When a ~ IS is intended for a company, an industrial branch or a regional economy, its structure usually includes information and computer resources, distributed geographically, decentralized decision--making, distributed co~putations, computercommunication network. In such a case MIS is considered as a large scale system with decentralized structure. The manage~ent process which must be supported by a !'ITS in each particular case, is a process with ill-defined nature. Following Ackoff's statements, it can be considered as a management decision cycle, consisting of f our stages:decision-making, decision implementation, decision estimation and recommendation for change.
The present st ate of the "!IS desis-n is influenced by the policies and trends r'len tioned above. In this paper we discus some of the problems of MIS de si ~ n w ~ ich concern modelling activities. After outlining a gen eral me thodology for ":IS design, a n optimal allocation ~odel, used for ~J~ structure synth esis, is presented. A procedure of successive assess ment of MIS modelled structures is considered. Some non-formal criteria of models applicability are discussed. Applications of developed models and program tools in realworld MIS design are described. GENERAL METHODOLOGY FOR ~US
The design of complex MIS must follow the methodology of design of decentralized large scale systems. For the present, an adequate theory for treating the problems of this kind of systems is lacking. In a number of studies (e.g. B~llman,1974;
DESIGN
As was noted above, ~Us.s intended for an industrial branch or a regional economy are characterized by decentralized struGture. A detail of such a MIS structure is shown in Fig. 1. The MIS consists of two 501
Z. Strezova
502
parts: information subsystem (ISS) and decision subsystem (DS S). The latter can be described as an entity of de cision-making units (JMU), s olving a set of decision tasks (:::JT), a, set of input variables x (de cision state variables), ~ set of input information v ariables v, a set of d ecision v a riables z, and a s et of feedback variables f ( objec t output).
The third stage of the '~I.S design consis1s in further assessment and selection of the chosen alternative structures. To the purpose, the use of a metamodel, performing sensitivity analysis on the simulated structures, is recommended. As a result a final set of feasible }ITS structures tFSS), {FSSJC{CSSJ is selected. Each one of these structures is a "card:idate" to be d esigned.
rhe ISS is d e s cribe d as an entity of inresources I R, a n entity of comp uter resour c es C? , a set of input vari ab les x, a set of feedback v a ri ab les f, a set of feedb a c k v a riable s b (decision state variables), a set of ou t pu t informa t ion v'lriables v ( conce rning dec i s ions in D5S) an~ a set of ou tnut v ar i ab les i (direct infor~ation t o t ~e ob ject) .
In fact, the methodology, outlined above, c ould be considered as one of MIS models design (MIN design), since it supposes activitie~ preceding the very system design.
for~ation
~ons iderinR
t he
~ eneral
fu nctioni ng of a
~ I S from a ' control viewpoint ( Strezova,
1978) a manaqement problem can be s ta ted . Its sol ution i s identifie d with so lvine the ":15 structure desien DToblem. Generally stated, this prob l em consis ts in definin ~ such structur e s of ~SS ~nd I SS pnd their ele~ents s o th at a s et of criteria of ~ I S functioning effi c iency be satisfied. In early studies (Strezova, 1781,1 Q 82) an a pproach to the design of systems under question wa s proposed. Acc ording to this approach, called approximate, the problem of a MIS structure desi gn is not stated as a n optimization problem, and the system structure is not modelled expli~ely. Instead, an architecture of models is de~ig ned concerning the structure and functl0ni ng of the MIS subsystems and of the system as a whole. Fi g . 2 shows the stages of such a proc ess of :.[IS des ign. All stages comprise modelling activities. The first stag e includes two phases: decision structures modelling and optimal allocation modelling. As a result of the first st~ge a set of possible system structures {PSs } are modelled, i.e. a set of alternatives, which will be then evaluated, is genera ted. Th e second stage represents a n ev a lua tion of the consequences of each alternat ive and a selection of a smaller set of MIS structures. The first phase of this stage consists in finding a decision model which d escribes the inputs and outputs of the structures rncxiellErl in the previous s tage • The decision model also supposes the measures for evaluating system structures. The next phase of the second stage is the design of simulation model by which all of the alternati:es, described in the terms of the decislon model will be simulated. Using the measures s~pposed in the decjsion model~ an . analysis of all simulated alt e rnatlves lS to be performed. As a result, a set of chosen system structures {CSS {CSS} C pSS}, will be selected.
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More details concerning the approach to MIS design by modelling can be found in the studies, 9.uoted above, and also in Sage and Thissen t 1980) .
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OPTPIA L ALLOCATION HODELLING IN MIS DESIGN In this section we consider in some detail one of the phases of the process of MIS structures modelling. This is the optimal allocation modelling wh05e goal is the distribution of a set of decision tasks among a set of computer resources. This is an important probleru, especially when a MIS has a loc a l computer network, as it is the case in most complex decentralized MIS. The problem in question also concerns the structure and the content of the information resources in MIS. The results of the allocation modelling determine to some extent the degree of decentralization of the 'US u nd er investi gation. Here we present the g eneral k ind of an optimal allocation model whose realizatio~ leads to modelling possible MI5 structures. The real-world situation which this mod el describes is as follows: There exists a set I of decision tasks TIT which must be solved by a set of computer resources C~ (co mp uters or computing complexes ) . Each DT can be solved according to one of the algorithms of the set ki' The effici ency of so lving the ith DT by the j·th CR, ac cording to the kith al g ori thm, is known. A resource mik is needed for solving the ~th DT , acco rding to the ki algorithm; each j·th CR is restricted to mj; the expected expense for the ith ~~ solved by the jth CR, according to the ki alg orithm, is eikj; the total expenses made for the j-th CR are Rj. The problem consists in distribution of th e set of DT among the set of CR so that the total efficiency of the MIS functionin g be maximal on g iven constra ints. The mathematical model of the stated problem is vf the following kind:
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ficult to identify' a common measure, adequate enough, to evaluate different alt~~ natives. Usually a number of measures is needed for this purpose. In onestage assessment procedures, even though they are iterative, the used input-output relationships in a quality of evaluating measures are insufficient for more precise comparison and estimation of alternatives. A better assessment is provided by a multistage procedure, in each stage of which a particular set of measures is used.
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Thus, the problem of optimal allocation of DT among a set of CR is stated as an integer linear programming problem. To obtain more appropriate results, the model (1)+ ( 6) must be experimented unde~ different ,ralues of variabl e s. So, sorn ' restrictions ; ". '~ he problem st u tement, due to difficul.ies with the quantification of some variables, could be relaxed. )V[ODELLING n: T!IE ASSESS" ' ElTT OF ms ST~UCTU\tBS Another activity in the MIS design process which requires modelling is that of the evaluation of alternative system structures. According to the approximate approach to MIS design, described briefly above, the evaluation process embodies several successive stages: design and use of decision model, design and use of simulation models, desirn and. use of metamodel. Below some reasons are pointed out in :favouring such a multistage evaluationlon procedure in MIS design.
In a number of studies (e.g. Van Gigch, 1974) the process of alternatives evaluation is considered as finding and implementation of decision model by which all needed assessments can be performed. To obtain such a decision model, detailed procedures are proposed, including activities as follows: finding the main variabl e s and parameters, defining functional relationships between variables, which must be validated by empirical data, studYing relationships among actual variables by statistical methods, estimation of the coefficients of the functional relationships. On the basis of all that a decision model, representing the situation under investi ga tion/can be built. This model will serve to study respollsiveness or sensitivity of outputs to inputs, to estim a te the cost of changes, and so on. Obviously, procedures for decision models desi~n. such as mentioned above, are pos s ible' only in rea l-world, well defined situations. In MIS cases, when the assessment process concerns modelled alterna tives, a range of difficulties exists. First of all, it is not possible to state explicit relationships among variables, validated by empirical data (SUCh data are lacking); secon~ it is not possible to identify an explicit system efficiency criterion (or criteria), on the basis of which alternative structures to be compared; third, the sensitivity analysis would not be adequate enough since not all outputs can be quantified and so on. Briefly, in MIS design cases~is impossible to find an explicit and adequate decision model by which the assessment of a set of modelled l·rIS structures to be performed. Appropriate results can be obtained by a multistage evaluating procedure. The stages of such a procedure were briefly described in the first section of the paper. Here some particularities of the models by which a structure assessment is made/are pointed out.
The decision model is used in the first stage of the evaluating procedure. It is an implicit model which describes variables, input-output relationships, permissible levels of outputs, etc. The simulation model performs further assessment. Since simulation can be an expensive technique, a design of simulation experiments is needed for specifying the conditions under In complex, ill-defined problems treat meant which the simulation runs will be executed. such as MIS modelling and design, is dif-
Modelling in Management Information Systems Design The third stage of the evaluating procedure includes the use of a meta model. As inputs the metamodel uses some of the input and output variables, described by the decision model and used through the simulations. The choice of these variables and the selection of combinations "variables-simulation runs " is an important task in constructing a metamodel. In the case of ~ns with decentralized structure, in each st age of the assessment procedure one can analyse the degree of decentralization of the modelled MIS structures. Three additional measures for this analysis can be ap plied, system operation, system developm ent and system management. These measures are interrelated and could be presented quantitatively (Champione, 1980). The system _operations can vary from completely centralized management, with all of its computing in a single location, to completely decentralized, with each DMU having it s own computer resource. Usually, the real-world MIS have a structure at s ome intermed iate level. Each modelled ~rrs struc ture, having all its elements and t~e int er ac tions between them deter mined, is c har a cterized by a given degree of d ecentralization. So, the d imension of system opera tions is defined. Following the quantitative r e lations bet ween the syst em dimensions, those of the system development and of the system management can be determined. The for ~ er concerns the design, prog ramming, testing, implement a tion a nd maintenance of the system. Some of the s e activities can be done centrally, ot he r loc a lly. Th e measures f or MIS decentralization could be very usefu l in the fin al choice of a MIS structure, left to the decisioJ'Po makers. APPLICABILITY OF IN HIS DESIGN
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A number of questions concerning the quality of the d eveloped and u s ed models always arise s when modelling and simulations are included in the EIS design process. A model, or a system of models, must b e considered and j ud g ed from th: . following viewpoints: complexity, valldlty, accuracy and flexibility. The model complexity concerns the model size and is a result of the problem or situation that the model replicate. A more complex model requires a diffic~lt verific a tion. Besides, such a model Includes a large number of variable~, assumptions and relationships. As Ellon (1979) argues, the hopes that ~ize and compl exity are the key to provlding a better insight into the modelled system are often illusory. As it seems, the trend in MIS field is to build middle-
505
sized models and to run them successively under different assumptions and variable values. The model validity concerns the degree of "fit" between the model a nd " reality", i.e. the question needs to be answered if the model is a valid instrument for solving a given problem. The validity depends on factors such as: the appropriateness, the relevance of criteria and constraints, the time horizon, the assumptions regarding variables and the r e l a tionships between them, the sensitivity 0f the resul~ Each one of these factors ne eds an assesment when a model is considered as valid or not. A final phase of the validation process could be the economic feasibility, i.e. will the model ensure sufficient improvement in the modelled situation to justify its expense. In most cases, such a question can be hardly answered. The model accuracy is associated with the quality of the informa tion fed into it. E. g ., the historical data are often unsuitable for particul a r assumptions or particular models. The flexibility is a mode l quality of great i mport a nce in the field in question Each one of the ~odels, u sed in the ~rrs design stages, must b e adequate to situations described by v a rious sets of vari ables. Besides, the models must provide a set of results in given bounds, within which one could make choice. The set of cr i teria of mod e ls quality, pointed out above, determines, in gene r a ~ the ap plic ability and uSability of a particular model, or a system of models. Another set of criteria, stated more by the model builders, includes qualities such as non-triviality, power, elegance. As a rule, it is not possible to satisf y both sets of criteria. APPLIC ·' TI ONS The methodology and the rel a t ed t e chniqu~ outlined in t he previous sections, have been used i n tr ea ting a nUMber of proble ms in ~ I S field. So, the general procedure for ~ s design was applied in development of the information system of a large industrial branch. The first stage of the modelling process was performed by: a) discriminant analysis (concerning the decision structures modelling),and b) integer linear programming (concerning the optimal alloc a tion modelling). As a result, 12 possible MIS structures have been modelled. The second stage has included a design of decision model (in the terms of Naylor's methodology) for description of the possible system structures, and design and use of simulation models (using GPSS as a simulation tech-
506
Z. Strezova
nique). Six chosen ~'!IS structures have been selected through this stage. In the third stage a linear regression illod el,in a quality of metamodel,was designed and used to perform a sensitivity analysis on the simulated ~ IS structures. As a final result of the modelling process, t,~o feasible MIS structures were selected. As a software tool, supporting all modelling activities, the modular progra~ system FIMOS has been developed. In its current state FI~OS includes standard IBM proble ~ packages, a nu ~ber of particular programs and some non-auto mated procedures. Another application,using the '!lultistage modelling procedure in develop~ent of l a rg e scale system/i s under way. This is the information sys tem for man ag e~ent of regional economy. In this case the FI~OS is in use again, with minor modifications due to the specificities o f the mcdelled system. S ome of the progra:n modules of FInDS have b e en used in solvin[!' pHr ticul ar problems. Such a problen ,Ias the allocation of man a.g e me n t tas ks of 54 "D":U a mong a set of 28 regional and 9 dep,',rt mental computer centers. This case required a model d ecomposition because of the l arge size of the original model. Some cO!llputer experi ments, yieldine use·· ful results, have been carried out through the process of MI5 mode lling. A heuristic 0-1 algorithm for solving as si g nment problems was inv estiga ted (st rezova, 1 a8~).
The known ~et~ods and t ec~nia ues for sysynthes is and analysis a~e no~ aPD l~ c able in handline cOClp lex , lac'er e scal~ syste:ns, includinf\ "IS . Adva:lced met hodologies and tools for '1 odell i n~ and simula.tion Rre needed, b~sed on the new concepts in the syst ~~ area , such as desien by a ltern2.t ives and intery s l prosra'"ming. In -''IS field, in nart i c ul ar , the use of '1ultistage procedures for mo~ ellin~ and successive assessment of a lt e ~ n2t ive structures wo u l d be very prom ising . B~ e ~
The d ev elop ~ ent of '1 ode l l i ne a n :l si-'Ul ".t ion t oo ls for suppo rtinG t h~ d esi Gn of compl e x syRte~R is 2 challen 7' e fOT res e a rch. A nUCl!:;er of im!"ort2.nt probl sns h as to be treated, e.e .: d esi :n o f archi tectu r e of ," odels f') ~ s"ste'1 an2.1:.':oi:o, exp e ri Ments with '1o<1e l s O~ ~ odelled alterna tives, riesi l"n Cl,ne. choice of ''1o(;els f or
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Nowadays, however, larg e organizational, economic, administrative and other systems need a complex, computer-based information system, supporting their problems and decisions. To build such HIS on the basis of its first version in mind, without providing insi g ht into the system structure and functioning, would be a very expensive process with unknow~ outpu~. Obviously, modelling and simulatlon actlvities are pertinent to the development of complex MIS.
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Trans. Aut o!T!at . ::ontr •• ~_C -1 ~ , !~~ Cha.mpine, G.A. ( 1980 ) . Distr~d -:;-;c;puter Sys te ms . r';o ~'t h - Ho ll ;:, nc, ll,b:!. . Eilo!1, s . (1 97 :~ ). ~SPE?C-:S '?f Ua n Cl genl:? r.t, ~nd ed . Perg a~o n Press . "', aee , ;"" .P., ar.rl ',). ,'. . '.1 . Thissen (1 0 (>01 . Me~hodolog i es for syste'1 siMulation . In Cren, ~ hub , and Rot ': {Sri " " Si'1ulation Id t'1 "' iscrete "ocle ls: ' ~e of-the- .A .rt "{ieH . Ti nivc' ~si t. ..... n f r-~ t::n[?. . S tr ezova , ~ . (1 ~ 7 9 ~ . ~ont r ol p~ oble~s in :1 .:: n2.ge P.'ler! t
The past decade h~s witnessed an increasing interest in modelling and si ~ulation in design of various lare;c scale syste ,:ls , as power syste~s, econo mic a nd man a qe~ent systems, air a,nd urban traffic control systems, computer coc.ffiunication networks. In the manag e men t area the r esearch has iocussed more on development of models for particular management problems,than on the use of modellinR,' as a tool for 1,:15 design and implementation. One reason, perhaps, was the fact that most "'IS, ~it h smaller size, have been built by heurlStics and some practical knowledge. Other, more complex)approac~es have been cons idered sceptically.
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