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Management information systems structure, types and integration
Injom~ Sysrems Vol. 6. No. 4. pp. 143-254, 1981 F%inkd in Great Britain.
0306-+379181/040243-12SO2uIz.oo/0 @ 1981 Pergamon Press Ltd.
MANAGEMENT INFORMATIONSYSTEMS STRUCTURE, TYPES AND INTEGRATION OSCARBARROS Departamento
de Industrias, Fact&ad
de Ciencias Fisicas y Matematicas, Chile (Received 25 August
Universidad
de Chile, Sede Occidente,
1980)
Abstract-A framework for the definition of alternative general design patterns in MIS design is presented. This IS based on the identification of the general structure-general functions and their relationships-of a MIS, the definition of MIS types as instances of such structure, and the specification of various degrees of integration that can be achieved within and among types.
1.
design issue, i.e. the decision among alternatives of MIS integration within a given type or among types or both.
INTRODUCTION
When looking at the process of MIS design in practice, one finds that each problem is usually approached from scratch. In the best of the cases, an experienced analyst would look at similar systems designed in the past and try to “copy” what he thinks is relevant for the case at hand. There is little in terms of schemes that synthesize and codify experience and provide an analyst with precise design ideas applicable to his problem. In this paper we make an attempt to define a framework that clarifies alternative general design patterns available to an analyst. This has been developed by looking at the common design and structure patterns of many real systems known to the author. The concepts of functional decomposition and design [ I-31 have provided the basic unifying and ordering approach. The framework is based on the following ideas. We first identify the general structure of any MIS, i.e. the general functions a system may have and their relationships. Here MIS and function are defined in very general terms. so that they include management and Data Processing activities. The specific instances of the general MIS structure make possible the definition of basic MIS types. The degree of mechanization and automation (using the computer) in the implementation of the general functions is the criteria leading to these types. Such types provide design and structure alternatives from where to select when facing an specific problem. Then, different possible degrees of integration (joint design) among Data Processing and management activities within a MIS are established. This clarifies another
2. THE ORGANIZATION AND MIS STRUCTURE Our information processing view of the Organization distinguishes the Processes and the Management Information System (MIS). The Processes are the activities by means of which physical inputs-raw materials, money, capital goods and labor-are transformed into final goods or services. As such, they include the physical flows of materials, money, labor, capital and final goods or services. The Management Information System-in a very general sense-is the set of activities that regulate (manage, plan, control, decide about) the Processes (and in particular the flows). The input and output of these activities is information. The output information (policies, plans, programs, rules, instructions, etc.) is the mean that produces the regu1ation.t Further, we can distinguish an Enuironment, defined as everything outside the MIS that may affect it. Hence, according to our definition, MIS include management and information components and are oriented to the whole (all levels of the) Organization, Of course, specific instances of MIS may exist for specific levels and decisions, but they are all part, with different degrees of integration, of the overall Organization’s MIS. From a functional decomposition point of view,$ management and information components of a MIS can be divided into Management Functions (MF), or the specific decision! activities that have to be made to regulate the processes, and Data Processing Functions (DPF), or the gathering of data and its transformationfile, accumulation, calculation with, etc.-into information needed to make decisions. Further, MF can be partitioned-using Anthony’s planning and control levels I&into the functions of:
KJur ideas are related to Forrester’s and Beer’sIS] models of the Organization, which explicitly separate information flows from physical (materials, money, labor, capital and final goods or services) flows. *This is a hierarchical partition of activities as proposed in
(a) Generate strategic plans, or the generation of the highest level policies and plans that regulate the overall behavior of the Organization. (b) Perform tactical planning, i.e. the process of con-
[l-3,6,71. §The term decision is used in a very general sense, including activities such as coordination, organization, planning, control and the like. 243
244
0. BARROS
verting strategic objectives into detailed plans and obtainingand allocatingthe necessary resources to carry those plans on. (4 Control operalions, or assuring that the specific day-today tasks that implementtactical plans are carried out effectively and efficiently.
advertising,etc.). The models can be provided by (ii) or by a MF. (iv) Inform: provide the conclusionsderived from the runningof the modelson a routine, exception or as asked basis.
The MIS functions and other elements we have defined can be linked through the typical flows of information and physical flows that may exist among them, as shown (a) Basic DPE i.e. the simpler, common data gather- in the graphical model in Fig. 1. This model can be also ing, storing and transformation tasks performed in any considered as representing the general structuregeneral componentsand their relationships-of a MIS or Organization.These can be classifiedinto: the general functional architecture of a MIS. What distinguishesa specific MIS structure in prac(i) Obtain: get or collect the elementary data necessary to support-possibly through further transfor- tice is the way in which the different functions and informationflows are implemented-for a given problem mation-a given MF. (ii) Maintain: store, update, select and extract data at area-the degree of computer participationin their performance and the degree of integrationwith other areas. manualor computerizedfiles. Thus a class of structures is generated by means of (iii) Compute: perform transformations on datapossibly coming from (i) or (ii)-through simple opera- mechanizing (computerizing) the Basic and Analytic tions like sort, merge, compare, add, aggregate,multiply, DPF. Another class comes from the total or partial automation (computerization)of MF (of any level)4 Of etc. (iv) Provide: print or display data-coming from (i), course mechanizinga given DPF or automating a MF implies changingthe way things are done. For example, (ii) and (iii)-necessary to perform a given MF. mechanizinga given analytic DPF impliesthe possibility (b) Analytic DPF, or the more complex transfor- of supporting decisions (of MF) in ways not possible mations of data geared to get some meaning out of it. without the computer, such as statisticalanalysis of time The key of this type of DP is to model behavior through series to forecast the course of a given variable. In the data, usually by some type of. statistical processing. same way, the automation of a MF means developing models or decision rules to actually generate the Detailedfunctions involved are: (recommended)decision by the computer. On the other hand, a given set of DPF and MF can be (i) Accumulute: maintain (usually) aggregated and classifieddata as organized (ordered) histories (using in implementedfor a very specific problem area or for a most cases time as an index). In the mechanicalaspects, related set of areas giving raise to other structure class, this function is similar to Maintain,but its objective is dependingon the degree of integrationwe perform over completely different; i.e. it is oriented to discover pat- the functions. These structure classes-DPF mechanization, MF terns and relationships in and among data, while Maintain is used just to feed an specific simple computation. automation and degree of integration-can be also con(ii) Annlyre: perform transformation on accumulated sidered as design classes from where a specific design histories to bring out underlying patterns, relationships should be selected, among a number of alternatives and, in general modes of behavior, which can serve to available in each class, when developing a system in ascertain current or future events. Typical transfor- practice. Hence, the ideas above can be used to generate altermations are statistical-basedtime series estimations and predictions. Of course there is always an interaction of native specificstructures or designtypes-in the sense of this function with a corresponding MF that provides what functions are to be implemented,degree of comtentative models of behavior and finally evaluates the puter usage and degree of integration-for any MIS to be goodness of the derived models. developed. In what follows we will make precise these (iii) Calculate: run models that allow to quantify or design types. First-in the next point-we will define predict consequences of already performed or proposed MIS types in terms of MF level and degree of comMF acti0ns.t Models can be as simple as an accounting puterization as outlined in Fig. 2. Then-in point 4-we relationship (e.g. an explosion of a given product’s will specify the different degrees of integration that can assembly to quantify total number of components in a be achieved. production plan or an expression giving cost of goods 3. hiIs TYFW sold as a percent of gross sales) or as complex as an econometric model (e.g. relating sales to prices, GNP, 3.1 Traditional DP Traditional DP comes from the straightforward mechanization of the basic DPF. Such mechanization can be performed at several levels of sophisticationand tNot to be confused with models that are actually oriented to integration. It goes from modest payroll processing to produce a decision, which are part of the MF. sophisticated production control with on-line, real-time, $We adopt Zisman distinction between mechanization of supporting (DP) activities and automation of the supported MF[9]. distributedgatheringof data at the shop floor and on-line As to DPF, we can distinguishthe followingtypes:
Management information systems structure. types and integration
MANAGEMENT
INFORMATION
SYSTEM
. ANALYTIC
__*
GENERATE STRATEGIC PLANS
DP
INFGRM
4 ?
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1 CALCULATE
t t
ANALYZE I t
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.
J
c
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. PROVIDE L* CI .
i t r COMPUTE
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>
PROCESSES I
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246
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Sm*lnom*m Opmbl
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TACTICAL
Fig. 2. MIS types.
query facilities of such data. It can also range from a simple batch isolated application (e.g. invoice processing) to an integrated processing (e.g. order entry and processing) using Data Base Management Systems technology. It is obvious that the bulk of MIS we see in practice correspond to this concept: most accounting and finance processing, payroll and personnel processing, and production and inventory control are in this category. As an example of this type of system, we present a simplified salary processing application, in Fig. 3, where the basic DPF have been computerized. Many recent technical developments tend to facilitate and integrate Traditional DP. This is the case of Data Base technology, interactive and distributed processing and office mechanization toolsB]. Also, due that applications of this type are well defined, there has been an increasing tendency to package the computerized part of these MIS into generalized s0ftware.t 3.2 Automated operation The MIS of the Automated Operation type correspond to the partial or total computerization of some of the operational level MF. Such automation is based on tThis is advertized number of Publishers,
clear from the large number of offerings reported and in periodicals such as Computerworld and the large entries in surveys and evaluations done by Auerbach DATAPRO, Datamation and others.
model-derived or heuristic decision rules. Quite different degrees of sophistication are possible. We may have from practice-based rules for job-shop sequencing to complex vehicle routing and scheduling. Other typical examples for this Automated Operation MIS are computerized inventory reordering, order and credit authorization, production lot sizing and scheduling and distribution decisions. In Fig. 4 we show a simplified Inventory Control System with computerized reordering. The key to the existence of this type of MIS is the possibility of developing a good decision rule for the partial or total automation of the MF at hand, which depends on how structured-in the Simon’s sense[lO]is the decision. The use of these rules is made feasible, in many cases, by interactive facilities, since quick decisions for an ongoing operation are required, e.g. order and credit authorization. There has been some attempts to provide generalized model-based application packages for (semi) automated operation, specially in the production area-e.g. IBM’s IMPACT[i 1, 12]-but there is no indication-at least in the literature-that they are widely used. Today’s frontiers on automated operation is office i.e. the computerization of office automation, functions (91. 3.3 Computer supported decisions The main feature of the Computer Supported Decisions type of MIS is the computerization of the
Management
information
241
systems structure, types and in!egration
I
I
cm.
Tlrn.
Chuqr ad
BASIC OP
II
in work anus
force
Fig. 3. Traditional DP system (payrollprocessing).
Analytic DP functions. This is mainly oriented towards: (a) To forecast the future based on the historical data base of the Organization and other exogenous data. (b) To answer “what if” type of questions in terms of specific consequences for the Organization. This means building some sort of model of the behavior of the relevant state variables as a function of the control and exogenous variables for the problem at hand. For example, an econometric model relating sales to price, advertising, GNP, etc. The idea behind these models is to allow to simulate the future course of the Organization for a given set of conditions specified by decisions and exogenous variables. As such they do not actually generate decisions but predict the likely consequences of given actions.
One of the most relevant example of systems of this type is a Corporate (Financial) Planning System[13]. A simplified system of this type is shown in Fig. 5. The main idea of such system is to model the behavior of the market and of the production, financing and other costs that should be incurred to satisfy a given sales forecast. Model’s results are the sales forecast and the financial results, usually in the form of a pro-forma statement, for given scenarios of the economic environment, competitors actions, marketing policies and other exogenous variables. The model is usually made of a market econometric submodel and production and financial flows ones. It is clear that any simulation-based system-for tactical and strategic decisions-belongs to the class of
0. BARROS
248
MIS , PERFORM TACTICAL PLANNING IWMltOIY polkla \
l
GENERATE REORDERING RULES
+
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.
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SPECIFY INVENTORY
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,
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L
system (inventory control and reordering).
analytic DP type of systems, e.g. distribution simulation systems [ 141. Similarly, modeling packages of the statistical or simulation variety-that in some cases come in the same product, e.g. SIMPLAN[lS]-are tools for analytic DP. For example time series processors, such as IBM Trend Analysis/370; econometric packages, such as TSP[16]; and financial modeling systems such as IFPS [16]. Also requirements calculation systems such as Material Requirement Planning System [ 171and Capacity Planning Systems [ 181are of this type. Besides the tools just mentioned, Computer Supported Decisions type of MIS have been made possible by technological advances, such as Data Base technologywhich helps in organizing and storing the data for analytical DP-and interactive graphics processing, which facilitates the interface with the modelers and decision makers[l9]. In the future the challenge is in the in-
tegration of office automation technology-of the type experimented in DAISY [20]-and Computer Supported Decision Systems, since they are obviousiy complementary for tactical and strategic MF[ 191. 3.4 Model driven planning Besides computerizing analytic DPF, Model Driven Planning type of MIS partially automate tactical and strategic MF. This means actually generating high-level decisions by the computer through model or heuristicbased procedures. Decisions are then evaluated by decision makers and possibly modified or their assumptions reconsidered, leading to re-running the automated decision procedure. An example of this type of MIS is integrated Production Planning (considering its interaction with Sales). A simplified version of it is shown in Fig. 6. The main idea
Management
information
systems structure, types and integration
I I
249
ANALYTIC
DP
ENVIRONMENT
-._-_.__
BEHAVIOR
INTERNAL
AND
n., and .X1WMI GENERATETTRATEGIC
PLANS
-
BASIC
OP
PRODUCTION. SALES AND FINANCIAL PROCESSES
Fig. 5. Computer
suppled
decision system (system planning).
of this system is to use a model-usually an LP modelto generate a production plan-quantity to be manufactured of each of the Organization’s products in each period of a given horizon-such that forecasted sales (possibfy seasonal) are met, production capacity and other constraints are satisfied and net benefit (sales income less variable production costs) is maximized. We see that in this MIS, Analytic DPF do supporting
processing for the (decision) model, generating and formatting its constraints and objective function coefficients (based on sales forecast, production time for each product in each process, production variable costs, product prices. production capacities and inventory levels). Other cases of this type of MIS are Plant Location and Distribution System design.
250
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PERPOAM TACTICAL
PLANNING
06TAIN SALES. PRODUcTfoN AND COST DATA
PRooucTmN PROCESSES
Finr(aooOs
Fig. 6. Model driven planning MIS (production planning).
d TIE ~~~0~
OF MI!3
We now consider the problem of MIS integration. MIS integration can be achieved in two ways. Firstly, related activities can be integrated at a given MIS level (type). We call this ho~~ntal inte~ation. For example, Basic DP systems for related personnel activities-payroll, personnel history, personnel development, personnel
training, etc.-can be integrated in a unique Data-Base type of system. Secondly, different levels-Basic DP, Automated Operation, Computer Supported Decisions and Model Driven Planning-for the same type of activity can be combined in a given system. We call this vertical integration. For example an integrated production design,
Management information systems structure types and integration t OtWLlI MIS
l.q. d.. mminv mad to
‘1 Obtain for pmduction
d.zv
*..,
cmput*
data for P?oduC. WI”
control
L-
Fig. 7. Non integrated MIS
planning, scheduling and control system, where a single set of Basic and Analytic DP provides all the information needed for these functions. To represent and model this integration issue we propose a tree which shows the hierarchical partition or descomposition of the MIS functions3 Thus the basic MIS model presented in Fig. 1 can have several tree representations depending upon the degree of inte~ation we perform over its functions. Then, for example, an extreme case of non integration or fragmentation would be tFollowing the ideas of Simon[2], Emery[l] and structured design[3]. IS Vol. 6. No. 4-B
when each MF solves its Basic DP or Analytic DP problem inde~ndently of the others. This can be represented as in Fig. 7, where we partially show an overall (non-integrated) MIS for an Organization. Notice that in order to get to the specific MF, several partitions of the overall MIS are performed going through the functional areas and levels that exist in any Organization. Now the first type of inte~ation that can be pe~ormed is an horizontal one over the functions of a given level. For example we can take the production Basic DP functions and perform an integrated DP for production operation. This can be represented as in Fig. 8. Obviously we can combine this inregration with the
252
Fig.8. Horizontally integratedMIS.
jointly automationof some of the MF, havingin this case an integrated Basic DP and Automated Operation System. The next step would be to vertically integrate functions within a functional area. For example for the productionarea, we can have an integrated Basic DP ind Computer Supported Decisions System for production system design,planning,schedulingand control. This can be represented as shown in Fig. 9. We remark that this system could be also combined with Automated Operation and Model Driven Planning. Hence applying horizontal and vertical integration, many different types or structures of systems can be defined for any Organization.Also, we have many alternative integrationpaths that the Organizationcan follow beginningwith the inde~ndent Basic DP systems that
are usually developed when computer usage is initiated. The study of EDP growth stages[21,22]seems to show that integration is first performed at the Basic DP level for operational activities using Data Base technology. Then, in the later stages of EDP growth, emphasis is given to vertical integrationby performingAnalyticalDP over the basic data in trying to support tactical and strategic decisions. However there are many examples where Computer Supported Decisions Systems are deveIoped independently of Basic DP systems, as in Corporate Planning Models and SystemsI131and some Decision Support Systems[231. All this point to the great number of alternative designs we have for the overall MIS of a given Organization and alternative paths there are in the evofution that they would have over time.
Managementinformatjon systems structure, types and inte~a:ion
Fig. 9. Verticallyi~te~ated MIS.
[I] f. C. Emery: O~aniza~~~~ Planning and Control Systems.
Mac Miiian,New York(1969). [2] H. Simon: The Sciences of tkt ~~~~~4~. MIT Press, Cambridge,Mass.(1970). [3] W. P. Stevens, G. L. Myers and L. A. Constantine: Structured design. IBM Systems J. (2) (1974). [4] I. W, Forrester: Principles of Systems.Wright Allen Press, New York fl970). [5] S, Beer: Brain of the Finn. Allen Lane. The Peaguin Press, Baltimore,Maryland(19721. f6] 0. Barros, V. P&z and A. Ho&do: S~ctured logical design of information systems: a methodology,dowmentation and experience.Inform. Systems4(l) (1979). [7] 0. Barros: Sistemas de Informacih Adminisrrafiuos, 3rd Edn. Editorial Universitariade Chile (1980). @s]R. M. Anthony: ~lanaiag and Control Systems: a Framework for Analysis. Harvard University Press (1%5).
f9] G. R. Wagner and R. W, Clikeman: An iR~~uction to computer-assistedplanning using the IFPS. EXECUCON System Co. (1977). flO] H. Simon: Z?zeNew Sficnce of ~a~Qgern~~ Decisions. Harper & Row, New York (1%9). [ 111IBM: Retail IMPACT,applicationdescription(1965). 1121IBM: Wholesale IMPACT, advanced principles and irnw piementationreference manua! (1967). [13] T. H. Nayior and M. Skulls: A survey of users of corporate planning models. Maaagcmenf 5%. u(9) (1976). [l4] M. hi. Connors, C. &ray, C. J. Cuccaro,W. K. Green, D. W. Low and H. M. Markowitz: The dis~i~tion system simulator.Management Sci. lf@) (April 1972). [151T. H. Naylor: Corporate Planning Models. Addison-Wesley, Reading,Mass.(1979). (161TSP-Times Series Processor,Technical Papr No. 12.Harvard Institute of Economic Research, Harvard University (1973).
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[I?] IBM: COPICS, advanced function material requirements 124)0. Barros and A. Gomez: Decision support systems for planning (1979). economicpolicy analysis in a marketoriented environment. [t8] IBM: CAPOSS-E, capacity planning and operation Inform. Processing SO@X01. [ZS]A.-M. Geoffrion:A guide to-computer-assists methodsfor sequencingsystem-extended(1979). dis~bution systems pianning. .%~a ~un~geme~t Rev. [19] R. G. Cam: (Ed) Toois for bu~di~8 an EIS. E@ (Winter 1975). Analyzer 17(8)(1979). [20] 0. P. Buneman, H. L. Morganand M. D. Zisman: Display [26] A. M. Geoffrion,G. W. Graves and S. Lee: Strategicdistribution systemsplanning: a status report. A. C. Hax (Ed.) facilities for DSS support: the DAISY approach. DATA Studies in Operations Management. North Holland, AmBASE 8(3)(1977). [21] C.Gibson,R.Nolan.ManagingthefourstagesofEDPgrowth, sterdam(1978). Harvard Bushtess Review (January-February 1974). {27]A. C. Hax and W, A. Martin: Automatic generation of customizedm~el-used i~ormation systemsfor o~rations (221R. L. Nolan, ManagintJthe crises in data processing.Harmana~ment. DATA BASE f14)(1973). uunt BusiffessRev. (M~chAp~l 1979). [23] M. S. S. Scott-Morton: Management Lkcision Systems. (281M. D-Zisman: O&e automat& revolution or evolution?. Sloan Management Rev. (Spring 1978). HarvardUniversity Press (1971).
0306-+379181/040243-12SO2uIz.oo/0 @ 1981 Pergamon Press Ltd.
MANAGEMENT INFORMATIONSYSTEMS STRUCTURE, TYPES AND INTEGRATION OSCARBARROS Departamento
de Industrias, Fact&ad
de Ciencias Fisicas y Matematicas, Chile (Received 25 August
Universidad
de Chile, Sede Occidente,
1980)
Abstract-A framework for the definition of alternative general design patterns in MIS design is presented. This IS based on the identification of the general structure-general functions and their relationships-of a MIS, the definition of MIS types as instances of such structure, and the specification of various degrees of integration that can be achieved within and among types.
1.
design issue, i.e. the decision among alternatives of MIS integration within a given type or among types or both.
INTRODUCTION
When looking at the process of MIS design in practice, one finds that each problem is usually approached from scratch. In the best of the cases, an experienced analyst would look at similar systems designed in the past and try to “copy” what he thinks is relevant for the case at hand. There is little in terms of schemes that synthesize and codify experience and provide an analyst with precise design ideas applicable to his problem. In this paper we make an attempt to define a framework that clarifies alternative general design patterns available to an analyst. This has been developed by looking at the common design and structure patterns of many real systems known to the author. The concepts of functional decomposition and design [ I-31 have provided the basic unifying and ordering approach. The framework is based on the following ideas. We first identify the general structure of any MIS, i.e. the general functions a system may have and their relationships. Here MIS and function are defined in very general terms. so that they include management and Data Processing activities. The specific instances of the general MIS structure make possible the definition of basic MIS types. The degree of mechanization and automation (using the computer) in the implementation of the general functions is the criteria leading to these types. Such types provide design and structure alternatives from where to select when facing an specific problem. Then, different possible degrees of integration (joint design) among Data Processing and management activities within a MIS are established. This clarifies another
2. THE ORGANIZATION AND MIS STRUCTURE Our information processing view of the Organization distinguishes the Processes and the Management Information System (MIS). The Processes are the activities by means of which physical inputs-raw materials, money, capital goods and labor-are transformed into final goods or services. As such, they include the physical flows of materials, money, labor, capital and final goods or services. The Management Information System-in a very general sense-is the set of activities that regulate (manage, plan, control, decide about) the Processes (and in particular the flows). The input and output of these activities is information. The output information (policies, plans, programs, rules, instructions, etc.) is the mean that produces the regu1ation.t Further, we can distinguish an Enuironment, defined as everything outside the MIS that may affect it. Hence, according to our definition, MIS include management and information components and are oriented to the whole (all levels of the) Organization, Of course, specific instances of MIS may exist for specific levels and decisions, but they are all part, with different degrees of integration, of the overall Organization’s MIS. From a functional decomposition point of view,$ management and information components of a MIS can be divided into Management Functions (MF), or the specific decision! activities that have to be made to regulate the processes, and Data Processing Functions (DPF), or the gathering of data and its transformationfile, accumulation, calculation with, etc.-into information needed to make decisions. Further, MF can be partitioned-using Anthony’s planning and control levels I&into the functions of:
KJur ideas are related to Forrester’s and Beer’sIS] models of the Organization, which explicitly separate information flows from physical (materials, money, labor, capital and final goods or services) flows. *This is a hierarchical partition of activities as proposed in
(a) Generate strategic plans, or the generation of the highest level policies and plans that regulate the overall behavior of the Organization. (b) Perform tactical planning, i.e. the process of con-
[l-3,6,71. §The term decision is used in a very general sense, including activities such as coordination, organization, planning, control and the like. 243
244
0. BARROS
verting strategic objectives into detailed plans and obtainingand allocatingthe necessary resources to carry those plans on. (4 Control operalions, or assuring that the specific day-today tasks that implementtactical plans are carried out effectively and efficiently.
advertising,etc.). The models can be provided by (ii) or by a MF. (iv) Inform: provide the conclusionsderived from the runningof the modelson a routine, exception or as asked basis.
The MIS functions and other elements we have defined can be linked through the typical flows of information and physical flows that may exist among them, as shown (a) Basic DPE i.e. the simpler, common data gather- in the graphical model in Fig. 1. This model can be also ing, storing and transformation tasks performed in any considered as representing the general structuregeneral componentsand their relationships-of a MIS or Organization.These can be classifiedinto: the general functional architecture of a MIS. What distinguishesa specific MIS structure in prac(i) Obtain: get or collect the elementary data necessary to support-possibly through further transfor- tice is the way in which the different functions and informationflows are implemented-for a given problem mation-a given MF. (ii) Maintain: store, update, select and extract data at area-the degree of computer participationin their performance and the degree of integrationwith other areas. manualor computerizedfiles. Thus a class of structures is generated by means of (iii) Compute: perform transformations on datapossibly coming from (i) or (ii)-through simple opera- mechanizing (computerizing) the Basic and Analytic tions like sort, merge, compare, add, aggregate,multiply, DPF. Another class comes from the total or partial automation (computerization)of MF (of any level)4 Of etc. (iv) Provide: print or display data-coming from (i), course mechanizinga given DPF or automating a MF implies changingthe way things are done. For example, (ii) and (iii)-necessary to perform a given MF. mechanizinga given analytic DPF impliesthe possibility (b) Analytic DPF, or the more complex transfor- of supporting decisions (of MF) in ways not possible mations of data geared to get some meaning out of it. without the computer, such as statisticalanalysis of time The key of this type of DP is to model behavior through series to forecast the course of a given variable. In the data, usually by some type of. statistical processing. same way, the automation of a MF means developing models or decision rules to actually generate the Detailedfunctions involved are: (recommended)decision by the computer. On the other hand, a given set of DPF and MF can be (i) Accumulute: maintain (usually) aggregated and classifieddata as organized (ordered) histories (using in implementedfor a very specific problem area or for a most cases time as an index). In the mechanicalaspects, related set of areas giving raise to other structure class, this function is similar to Maintain,but its objective is dependingon the degree of integrationwe perform over completely different; i.e. it is oriented to discover pat- the functions. These structure classes-DPF mechanization, MF terns and relationships in and among data, while Maintain is used just to feed an specific simple computation. automation and degree of integration-can be also con(ii) Annlyre: perform transformation on accumulated sidered as design classes from where a specific design histories to bring out underlying patterns, relationships should be selected, among a number of alternatives and, in general modes of behavior, which can serve to available in each class, when developing a system in ascertain current or future events. Typical transfor- practice. Hence, the ideas above can be used to generate altermations are statistical-basedtime series estimations and predictions. Of course there is always an interaction of native specificstructures or designtypes-in the sense of this function with a corresponding MF that provides what functions are to be implemented,degree of comtentative models of behavior and finally evaluates the puter usage and degree of integration-for any MIS to be goodness of the derived models. developed. In what follows we will make precise these (iii) Calculate: run models that allow to quantify or design types. First-in the next point-we will define predict consequences of already performed or proposed MIS types in terms of MF level and degree of comMF acti0ns.t Models can be as simple as an accounting puterization as outlined in Fig. 2. Then-in point 4-we relationship (e.g. an explosion of a given product’s will specify the different degrees of integration that can assembly to quantify total number of components in a be achieved. production plan or an expression giving cost of goods 3. hiIs TYFW sold as a percent of gross sales) or as complex as an econometric model (e.g. relating sales to prices, GNP, 3.1 Traditional DP Traditional DP comes from the straightforward mechanization of the basic DPF. Such mechanization can be performed at several levels of sophisticationand tNot to be confused with models that are actually oriented to integration. It goes from modest payroll processing to produce a decision, which are part of the MF. sophisticated production control with on-line, real-time, $We adopt Zisman distinction between mechanization of supporting (DP) activities and automation of the supported MF[9]. distributedgatheringof data at the shop floor and on-line As to DPF, we can distinguishthe followingtypes:
Management information systems structure. types and integration
MANAGEMENT
INFORMATION
SYSTEM
. ANALYTIC
__*
GENERATE STRATEGIC PLANS
DP
INFGRM
4 ?
t I
1 CALCULATE
t t
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.
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. PROVIDE L* CI .
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PROCESSES I
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246
CDMFUTERIZATIDN
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MC LEVEL MF I~wtWl
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TACTICAL
Fig. 2. MIS types.
query facilities of such data. It can also range from a simple batch isolated application (e.g. invoice processing) to an integrated processing (e.g. order entry and processing) using Data Base Management Systems technology. It is obvious that the bulk of MIS we see in practice correspond to this concept: most accounting and finance processing, payroll and personnel processing, and production and inventory control are in this category. As an example of this type of system, we present a simplified salary processing application, in Fig. 3, where the basic DPF have been computerized. Many recent technical developments tend to facilitate and integrate Traditional DP. This is the case of Data Base technology, interactive and distributed processing and office mechanization toolsB]. Also, due that applications of this type are well defined, there has been an increasing tendency to package the computerized part of these MIS into generalized s0ftware.t 3.2 Automated operation The MIS of the Automated Operation type correspond to the partial or total computerization of some of the operational level MF. Such automation is based on tThis is advertized number of Publishers,
clear from the large number of offerings reported and in periodicals such as Computerworld and the large entries in surveys and evaluations done by Auerbach DATAPRO, Datamation and others.
model-derived or heuristic decision rules. Quite different degrees of sophistication are possible. We may have from practice-based rules for job-shop sequencing to complex vehicle routing and scheduling. Other typical examples for this Automated Operation MIS are computerized inventory reordering, order and credit authorization, production lot sizing and scheduling and distribution decisions. In Fig. 4 we show a simplified Inventory Control System with computerized reordering. The key to the existence of this type of MIS is the possibility of developing a good decision rule for the partial or total automation of the MF at hand, which depends on how structured-in the Simon’s sense[lO]is the decision. The use of these rules is made feasible, in many cases, by interactive facilities, since quick decisions for an ongoing operation are required, e.g. order and credit authorization. There has been some attempts to provide generalized model-based application packages for (semi) automated operation, specially in the production area-e.g. IBM’s IMPACT[i 1, 12]-but there is no indication-at least in the literature-that they are widely used. Today’s frontiers on automated operation is office i.e. the computerization of office automation, functions (91. 3.3 Computer supported decisions The main feature of the Computer Supported Decisions type of MIS is the computerization of the
Management
information
241
systems structure, types and in!egration
I
I
cm.
Tlrn.
Chuqr ad
BASIC OP
II
in work anus
force
Fig. 3. Traditional DP system (payrollprocessing).
Analytic DP functions. This is mainly oriented towards: (a) To forecast the future based on the historical data base of the Organization and other exogenous data. (b) To answer “what if” type of questions in terms of specific consequences for the Organization. This means building some sort of model of the behavior of the relevant state variables as a function of the control and exogenous variables for the problem at hand. For example, an econometric model relating sales to price, advertising, GNP, etc. The idea behind these models is to allow to simulate the future course of the Organization for a given set of conditions specified by decisions and exogenous variables. As such they do not actually generate decisions but predict the likely consequences of given actions.
One of the most relevant example of systems of this type is a Corporate (Financial) Planning System[13]. A simplified system of this type is shown in Fig. 5. The main idea of such system is to model the behavior of the market and of the production, financing and other costs that should be incurred to satisfy a given sales forecast. Model’s results are the sales forecast and the financial results, usually in the form of a pro-forma statement, for given scenarios of the economic environment, competitors actions, marketing policies and other exogenous variables. The model is usually made of a market econometric submodel and production and financial flows ones. It is clear that any simulation-based system-for tactical and strategic decisions-belongs to the class of
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MIS , PERFORM TACTICAL PLANNING IWMltOIY polkla \
l
GENERATE REORDERING RULES
+
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.
/ // COMPUTE INVENTORY STATUS
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,
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system (inventory control and reordering).
analytic DP type of systems, e.g. distribution simulation systems [ 141. Similarly, modeling packages of the statistical or simulation variety-that in some cases come in the same product, e.g. SIMPLAN[lS]-are tools for analytic DP. For example time series processors, such as IBM Trend Analysis/370; econometric packages, such as TSP[16]; and financial modeling systems such as IFPS [16]. Also requirements calculation systems such as Material Requirement Planning System [ 171and Capacity Planning Systems [ 181are of this type. Besides the tools just mentioned, Computer Supported Decisions type of MIS have been made possible by technological advances, such as Data Base technologywhich helps in organizing and storing the data for analytical DP-and interactive graphics processing, which facilitates the interface with the modelers and decision makers[l9]. In the future the challenge is in the in-
tegration of office automation technology-of the type experimented in DAISY [20]-and Computer Supported Decision Systems, since they are obviousiy complementary for tactical and strategic MF[ 191. 3.4 Model driven planning Besides computerizing analytic DPF, Model Driven Planning type of MIS partially automate tactical and strategic MF. This means actually generating high-level decisions by the computer through model or heuristicbased procedures. Decisions are then evaluated by decision makers and possibly modified or their assumptions reconsidered, leading to re-running the automated decision procedure. An example of this type of MIS is integrated Production Planning (considering its interaction with Sales). A simplified version of it is shown in Fig. 6. The main idea
Management
information
systems structure, types and integration
I I
249
ANALYTIC
DP
ENVIRONMENT
-._-_.__
BEHAVIOR
INTERNAL
AND
n., and .X1WMI GENERATETTRATEGIC
PLANS
-
BASIC
OP
PRODUCTION. SALES AND FINANCIAL PROCESSES
Fig. 5. Computer
suppled
decision system (system planning).
of this system is to use a model-usually an LP modelto generate a production plan-quantity to be manufactured of each of the Organization’s products in each period of a given horizon-such that forecasted sales (possibfy seasonal) are met, production capacity and other constraints are satisfied and net benefit (sales income less variable production costs) is maximized. We see that in this MIS, Analytic DPF do supporting
processing for the (decision) model, generating and formatting its constraints and objective function coefficients (based on sales forecast, production time for each product in each process, production variable costs, product prices. production capacities and inventory levels). Other cases of this type of MIS are Plant Location and Distribution System design.
250
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PERPOAM TACTICAL
PLANNING
06TAIN SALES. PRODUcTfoN AND COST DATA
PRooucTmN PROCESSES
Finr(aooOs
Fig. 6. Model driven planning MIS (production planning).
d TIE ~~~0~
OF MI!3
We now consider the problem of MIS integration. MIS integration can be achieved in two ways. Firstly, related activities can be integrated at a given MIS level (type). We call this ho~~ntal inte~ation. For example, Basic DP systems for related personnel activities-payroll, personnel history, personnel development, personnel
training, etc.-can be integrated in a unique Data-Base type of system. Secondly, different levels-Basic DP, Automated Operation, Computer Supported Decisions and Model Driven Planning-for the same type of activity can be combined in a given system. We call this vertical integration. For example an integrated production design,
Management information systems structure types and integration t OtWLlI MIS
l.q. d.. mminv mad to
‘1 Obtain for pmduction
d.zv
*..,
cmput*
data for P?oduC. WI”
control
L-
Fig. 7. Non integrated MIS
planning, scheduling and control system, where a single set of Basic and Analytic DP provides all the information needed for these functions. To represent and model this integration issue we propose a tree which shows the hierarchical partition or descomposition of the MIS functions3 Thus the basic MIS model presented in Fig. 1 can have several tree representations depending upon the degree of inte~ation we perform over its functions. Then, for example, an extreme case of non integration or fragmentation would be tFollowing the ideas of Simon[2], Emery[l] and structured design[3]. IS Vol. 6. No. 4-B
when each MF solves its Basic DP or Analytic DP problem inde~ndently of the others. This can be represented as in Fig. 7, where we partially show an overall (non-integrated) MIS for an Organization. Notice that in order to get to the specific MF, several partitions of the overall MIS are performed going through the functional areas and levels that exist in any Organization. Now the first type of inte~ation that can be pe~ormed is an horizontal one over the functions of a given level. For example we can take the production Basic DP functions and perform an integrated DP for production operation. This can be represented as in Fig. 8. Obviously we can combine this inregration with the
252
Fig.8. Horizontally integratedMIS.
jointly automationof some of the MF, havingin this case an integrated Basic DP and Automated Operation System. The next step would be to vertically integrate functions within a functional area. For example for the productionarea, we can have an integrated Basic DP ind Computer Supported Decisions System for production system design,planning,schedulingand control. This can be represented as shown in Fig. 9. We remark that this system could be also combined with Automated Operation and Model Driven Planning. Hence applying horizontal and vertical integration, many different types or structures of systems can be defined for any Organization.Also, we have many alternative integrationpaths that the Organizationcan follow beginningwith the inde~ndent Basic DP systems that
are usually developed when computer usage is initiated. The study of EDP growth stages[21,22]seems to show that integration is first performed at the Basic DP level for operational activities using Data Base technology. Then, in the later stages of EDP growth, emphasis is given to vertical integrationby performingAnalyticalDP over the basic data in trying to support tactical and strategic decisions. However there are many examples where Computer Supported Decisions Systems are deveIoped independently of Basic DP systems, as in Corporate Planning Models and SystemsI131and some Decision Support Systems[231. All this point to the great number of alternative designs we have for the overall MIS of a given Organization and alternative paths there are in the evofution that they would have over time.
Managementinformatjon systems structure, types and inte~a:ion
Fig. 9. Verticallyi~te~ated MIS.
[I] f. C. Emery: O~aniza~~~~ Planning and Control Systems.
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f9] G. R. Wagner and R. W, Clikeman: An iR~~uction to computer-assistedplanning using the IFPS. EXECUCON System Co. (1977). flO] H. Simon: Z?zeNew Sficnce of ~a~Qgern~~ Decisions. Harper & Row, New York (1%9). [ 111IBM: Retail IMPACT,applicationdescription(1965). 1121IBM: Wholesale IMPACT, advanced principles and irnw piementationreference manua! (1967). [13] T. H. Nayior and M. Skulls: A survey of users of corporate planning models. Maaagcmenf 5%. u(9) (1976). [l4] M. hi. Connors, C. &ray, C. J. Cuccaro,W. K. Green, D. W. Low and H. M. Markowitz: The dis~i~tion system simulator.Management Sci. lf@) (April 1972). [151T. H. Naylor: Corporate Planning Models. Addison-Wesley, Reading,Mass.(1979). (161TSP-Times Series Processor,Technical Papr No. 12.Harvard Institute of Economic Research, Harvard University (1973).
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[I?] IBM: COPICS, advanced function material requirements 124)0. Barros and A. Gomez: Decision support systems for planning (1979). economicpolicy analysis in a marketoriented environment. [t8] IBM: CAPOSS-E, capacity planning and operation Inform. Processing SO@X01. [ZS]A.-M. Geoffrion:A guide to-computer-assists methodsfor sequencingsystem-extended(1979). dis~bution systems pianning. .%~a ~un~geme~t Rev. [19] R. G. Cam: (Ed) Toois for bu~di~8 an EIS. E@ (Winter 1975). Analyzer 17(8)(1979). [20] 0. P. Buneman, H. L. Morganand M. D. Zisman: Display [26] A. M. Geoffrion,G. W. Graves and S. Lee: Strategicdistribution systemsplanning: a status report. A. C. Hax (Ed.) facilities for DSS support: the DAISY approach. DATA Studies in Operations Management. North Holland, AmBASE 8(3)(1977). [21] C.Gibson,R.Nolan.ManagingthefourstagesofEDPgrowth, sterdam(1978). Harvard Bushtess Review (January-February 1974). {27]A. C. Hax and W, A. Martin: Automatic generation of customizedm~el-used i~ormation systemsfor o~rations (221R. L. Nolan, ManagintJthe crises in data processing.Harmana~ment. DATA BASE f14)(1973). uunt BusiffessRev. (M~chAp~l 1979). [23] M. S. S. Scott-Morton: Management Lkcision Systems. (281M. D-Zisman: O&e automat& revolution or evolution?. Sloan Management Rev. (Spring 1978). HarvardUniversity Press (1971).