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A network of management support systems
OMEGA Int, J of Mgmt Sci.. Vol. 13, No. 4. pp. 263-276. 1985 Printed in Great Britain. All rights reser,,ed
0305-04.83:85 53.00 +0.00 Copyright ~ 1985 Pergamon Press Ltd
A Network of Management Support
Systems CHEN-HUA
CHUNG
University of Kentucky, USA (Receiced Nocember 1983: in recised form October 1984)
Based on the decision-making process that actually happens in most organizations, a management support system (MSS) or an integrated decision support systems (DSS) is defined as a network of subsystem interfaces, as opposed to management information system (MIS) being a network of information flows. Three modes for subsystem interfaces are discussed. To help the operationalization of the (computerized) support system, we also propose a design architecture which consists of the continuum of conceptual constructs, operational constructs and implementational constructs.
1. I N T R O D U C T I O N
RECENTLY, the concept of Management Support Systems (MSS) has emerged to provide an integrated view of the use of computers and related information technologies to support management. Scott Morton [58] presents an extensive review of the ongoing research in the MSS arena. Three categories of MSS are identified. They are: (1) Data Support Systems: to provide data and information for management; (2) Decision Support Systems: to provide modeling-oriented support for decision-making; and (3) Executive Support Systems: to provide data and analytical tools for supporting a broad range of managerial processes and decisions. The three systems are not mutually independent. They support and facilitate one another in aiding management. Scott Morton points out that MSS has been synonymous with DSS for a long time. Also, the research in DSS has been focused on a specific decision or a specific class of decisions. The facts that an organization is an integrated whole and that most decisions are related to one another have not been adequately addressed in the DSS or MSS literature. Incidentally, in recent years, the concept of networking has received a lot of attention in the research and development of computer applications. It can be expected that, like many other 263
computer and information technolo~es, the networking applications would have tremendous impacts upon business and management systems. Unfortunately, as Scott Morton points out, there is a surprising lack of research in the application of the networking concept to management or decision-making. As will be discussed later, the networking concept is consistent with t h e notion that organizational decision-making is an interrelated and integrated process. In this article, we define MSS as the support systems for an organization as a whole. And, for convenience, MSS will also be treated as a synonym of an 'integrated' DSS. That is, a D S S will not be limited to the support for a specific decision or a specific class of decisions. An MSS (or an integrated DSS) will be represented as a network of subsystem interfaces. Based upon this definition, we present an architecture for the development and implementation of MSS. The description of MSS as a network of subsystem interfaces is consistent with the decision-making and management processes that actually happen in most organizations. The conceptual framework also helps the distinction between MSS (DSS) and MIS (Management Information Systems). This distinction is certainly desirable since there is a seemingly everlasting controversy over whether DSS is a separate area from
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Management Science/Operational Research (MS/OR) and MIS [11, 54, 69]. Finally, the architecture presented here is something more than "conceptual'. As will be further discussed in detail, this architecture is an operationalistic blueprint that follows the continuum of transforming the conceptual constructs into operational constructs and the logical and physical development of databases. The paper is organized as follows: in Section 2, we describe organizational decision-making or management activities as a process of subsystem interfaces, in Section 3, the process is illustrated by three modes of subsystem interfaces; Section 4 presents the design architecture which converts the conceptual framework into operational constructs for the development and implementation of MSS; finally, Section 5 summarizes the study and points out the directions for future research.
2. THE PROCESS OF SUBSYSTEM INTERFACES The process of management usually involves a cycle of three interlocking stages of activities: planning, organizing, and control. Various decisions need to be made at each of these stages.
The decision-making or management process that actually happens in most organizations can be depicted in Fig. I. Vertically, as suggested by the 'Anthony Taxonomy' [6], corresponding to the hierarchy of organizational structure of top, middle and operating managers, managerial (i.e. decisionmaking) activities can be classified into strategic planning, tactical planning and operational control. Although staged, these activities are closely related to each other. Horizontally, an organization may consist of many functional areas such as production, marketing, finance, etc. Most decision-making processes would require interfunctional exchanges. In addition to the vertical and the horizontal interfaces, similar process also occurs along the time dimension. That is, the long range, intermediate range, and short range planning activities of an organization need to be correlated and coordinated. Each level of the decision-making hierarchy, each functional area, and each planning activity along the time dimension may be defined as a "subsystem' with respect to the whole organization. The management process, the decision-making process, or the organization itself then can be represented as a network of subsystem interfaces. The (computerized) sys-
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terns support developed for this network is called an MSS or an integrated DSS. The conversion of the above conceptual constructs into a more concrete (knowledge) representation of the actual system is the first step of operationalizing the MSS. To develop a single comprehensive representation model incorporating the complete vertical, horizontal, and temporal decision-making processes would be a formidable task. Even if developed, it may not be economically or technically feasible to implement the required computation or data processing. Therefore. it is desirable to decompose the complete system into several subsystems which hopefully will be consistent with the actual decision-making process prevailing in the organization. The concept and methodologies of decomposition has been well developed in operations research and management sciences. Methods like Dantzig-Wolfe decomposition [31, 32], Benders decomposition [9] and variations of these concepts are familiar to most management scientists. Although called "decomposition', most of these studies are oriented to solving largescale problems via decomposition instead of using the decomposition concept to capture the actual decision-making process of the organization. None the less, these decomposition methodologies do provide both a conceptual framework and the operational mechanisms for the process of subsystem interfaces. Examples of some interface mechanisms will be discussed in Section 3. With the decomposition concept, several relatively small models can be developed for individual subsystems. These models are then linked together or co-ordinated via some proper interface mechanisms. Conceptually, MIS can be defined as a network of information flows. Now, with MSS (DSS) defined as a network of subsystem (or model) interfaces, the distinction between MIS and MSS (DSS) becomes clear. The "identity' issue that arises from the debate over whether DSS is a separate area from MS/OR and MIS [11,54, 69] can be better understood, if not completely settled.
It should be noted that the term 'model" used here does not refer exclusivelyto mathematical models or mathematical programs. Any (knowledge) representational construct for the system is considered here as a "model'.
The concept that MSS is a network of subsystem interfaces is an improvement over the existing DSS frameworks such as those proposed by Sprague [61, 62] and Bonczek, Holsapple and Whinston (BHW) [12, 13]. The DSS framework by Sprague, as shown in Fig. 2(a), consists of three management subsystems: Data Base Management Software (DBMS), Model Base Management Software (MBMS), and Dialogue Generation Management and Software (DGMS). The purpose of introducing a model-base concept, as opposed to a data-base, is to emphasize the role of models in supporting decision-making. However, it should be noted that the model-base or model-pool concept does not explicitly address the fact that the organization as a whole is an integrated system and that models need to be co-ordinated together in aiding organizational decision-making. The framework presented in Fig. 1 serves the purpose of representing the above co-ordination process by defining MSS as a network of subsystem interfaces. On the other hand, to separate the model-base from the data-base may be misleading in some cases. Models need to be stored in data-base. Modelbase is actually a subset or a special kind of data-base. This is what should be kept in mind when one is trying to emphasize the role of models in aiding decision-making. The DSS
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Fig. 2(b). The structure of DSS (Source: [13]). Fig. 2. Conceptual frameworks for decision support systems. The BHW model of the DSS structure, as shown in Fig. 2(b), largely resembles the Sprague construct. The model-base in 2(a) is roughly equivalent to the knowledge subsystem in 2(b). However, the functions of a model-base would belong to the problem processing subsystem. Again, the BHW model does not explicitly address the fact that an organization is a whole system. Their model is used mostly for the DSS in individual functional areas. We believe that to define MSS (DSS) as a network of subsystem interfaces can better reflect the organizational decision-making process. The framework depicts an organization and/or an MSS (DSS) as an integrated system, yet still retains the flexibility of dealing with the cases where the DSS is to be designed for specific decisions. 3. THREE MODES OF SUBSYSTEM INTERFACES The interfaces or exchanges among subsystems may be realized in various fashions. For example, there may be formal or informal commuuication channels connecting these subsystems. As discussed in the previous section, the use of models to represent subsystems (i.e. the individual decision units) is one of the characteristics of DSS and MSS. In this section, we shall use three modes of interfaces that had been developed in management sciences to illustrate the process of subsystem interfaces. Certainly, these are not the only mechanisms applicable to facilitating the process of subsystem interfaces. However, they do enhance our understanding of what might be involved in interfacing the subsystems. The three modes of interfaces to be examined are (1) a goal-achieving/dual-pricing mechanism, (2) a rolling-horizon mechanism, and (3) a combination of the above two.
Interfaces via mechanism
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Ruefli [56] proposed a "generalized goal decomposition model" to represent decisionmaking in a three-level hierarchical organization. The three levels--Central Unit, Management Units and Operating Units-roughly corresponds to the 'Anthony Taxonomy' [6] discussed earlier. The central unit co-ordinates decision activity by generating goals (and/or selecting resource levels) for a subordinate level of management units. Each management unit will choose activity levels based on the goals and resource levels set by the central unit. It also determines values for the dual variables in its subunit problem and transmits these economic indicators to the central unit so that the latter can evaluate and improve its goal generating policies. On the other hand, the management units are also responsible for guiding the alternative generation activities of third level operating units. The operating units are then responsible for generating project proposals for their respective management units. Ruefli and Storbeck [57] further extend the model to represent nonpyramidal hierarchical decision problems. That is, two or more superordinate units are allowed to issue goal levels to a subordinate. Thus, complex organizational structures other than pure pyramidal hierarchies can be modelled. Krajewski [47] uses the concept of goal decomposition by Ruefli to interface the aggregate planning and master production scheduling problems. The procedure starts with solving the aggregate planning problem. Then a vector of goal stipulations is passed down to the master scheduling problem which optimizes its goal objective function given the goal stipulations. After the master scheduling problem is solved,
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the vector of dual prices associated with the goal constraints is passed back to the aggregate planning model to determine a new set of goal stipulations. The process is repeated until the goal deviations of the master scheduling solutions are at a minimum and no further revisions will yield a net decrease in the goal levels for the organization as a whole. King and Love [45] reported a successful real-world application of interface procedures similar to those described above. Winkofsky, et al. [71] presented a decision process model (DPM) for analysis of R & D resource allocation processes in hierarchical organizations. In all these applications, the key data transferred from a top level to a lower level are goal stipulations which are taken by the latter as constraints. After the lower level problem is solved, the data passed back to the top level are the dual prices associated with the corresponding goals. Thus, the two levels of planning (i.e. two subsystems) are interfaced via the goalachieving mechanism while the data needed to effect the interfaces are the goal stipulations and the dual prices. For any two levels of a hierarchical system, we can conceptually regard higher level management as the planning level while lower level management is the implementation level. Thus, the aforementioned subsystem interfaces or data flows or goal stipulations and dual prices can be applied to any two levels of a system hierarchy. 2 Other studies related to this interface mode can also be found in Baumol and Fabian [8], Burton et al. [16, 17, 18, 19], Christensen and Obel [23], Jennergren [41], Jennergren and Muller [42], Moore [53], and Ten Kate [67]. Particularly Sweeney, et al. [66] presented an extensive review of the modeling of (decomposed) organizational decision-making processes. Structurally, the interface mechanism for vertical subsystems can also be applied to horizontal interfaces. Various subsystems (i.e. different functional areas) may pose constraints for each other. This is similar to the goal-constraint relationship among any two levels of a hierarchical system. Also, there often exist some z In the discussion above, we have for convenience used the terminologies associated with a 'resources directive" model to illustrate the interface processes. It should be noted that the same logic can also be applied fo a 'price directive" model in hierarchical planning problems.
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common constraints to the various subsystems. These across-subsystem constraints can be used as a co-ordinating mechanism. This is what is usually called the 'multi-divisional problem'. The emphasis, however, will be placed upon the co-ordination mechanism that not only represents information flows but also facilitates interfaces or decision-making processes among subsystems. Related studies can be found in Abad [1], Abad and Sweeney [2], Damon and Schramm [29] and Welam [70]. Interfaces via a rolling-horizon mechanism
A rolling-horizon is a common practice in real world planning and problem-solving. As described by Baker [7], "the typical scenario (of a rolling-horizon strategy) is as follows: solve the model and implement only the first period's decisions; for the following period, update the model to reflect information collected in the interim, resolve the model, and again implement only the imminent decision pending subsequent model runs". That is, the practice of rolling horizons is to routinely update or revise the plans taking into consideration more reliable data as they become available. On the other hand, the rolling horizon practices simply reflect the continuity of the firm's business and activities. In rolling horizon practices, the scenario of subsystem interfaces would be as follows: the top level provides guidelines or constraints for lower levels; the lower levels solve their models and implement the first period's decision, then pass the implementation results back to the top level; as the horizon rolls ahead, the top level will establish new guidelines based on the previous performances and other updated information. Thus, the subsystem interfaces are carried out via the rolling-horizon practice. Most existing studies in the rolling horizon practice have been focused on the evaluation of the impacts of the rolling-horizon procedures upon the performance of various lot-sizing algorithms for production planning problems (e.g. [20, 21]). Chung and Krajewski [26, 27] use the rolling-horizon interface mechanism to investigate planning horizon effects in a hierarchical production planning setting. A combination of optimization and rollinghorizons
The two subsystem interface modes discussed
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above can be combined together. That is, every first-period decision at each subsystem can be optimized via the goal-achieving/dual-pricing mechanism before rolling the schedules ahead. However, this combined procedure may be computationally involved. Particularly. there may be quite a few subsystems in an organization. The choice of proper interface modes should be justified by the cost or other effectiveness criteria.
An example To further illustrate the process of subsystem interfaces, we examine the following hierachical production planning example taken from Chung and Krajewski [26, 27]. Figure 3 depicts the scenarios of the subsystem interfaces in a simplified hierarchical production planning process (Fig. 3(a)) involves aggregate planning,
/ongterm J_ capocity planning F
master production scheduling, and detailed operations planning and scheduling. In aggregate planning, management is concerned with determining the aggregate levels of production, inventory and workforce to respond to fluctuating demand in the future. The aggregate plan provides an overall guideline for master production scheduling which specifies the timing and sizing of the production of individual products. The master scheduler takes the aggregate decisions as targets and tries to achieve them as much as possible. The feedback of actual master scheduling performance (and/or dual prices associated with the goal constraints) provides information for modifying future aggregate plans. The master production schedule also serves as the base for 'requirements explosion' in detailed level scheduling. Mathematical programs are formulated to
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represent the decision-making at each individual subsystem. Figure 3(b) only shows the mathematical formulations for the problems. For detailed description of the formulations, see [26,27]). The interface procedure (Fig. 3(c)) beginswiththes°luti°n°ftheaggregateplan" The aggregate decisions are passed to the master scheduling model. Taking the aggregate decisions as goals (i.e. constraints), the master scheduling model is then solved. The information passed back to the aggregate planning model will depend upon the type of interface modes used. For example, if the dual-price mechanism is used, the dual prices (i.e. the opportunity costs) associated with the various goal constraints are passed back to the aggregate planning model. On the other hand. if the rolling-schedule mechanism is used, the actual performance of the first period master schedule is passed back as an input to the next aggregate
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Fig. 3(d). Fig. 3. Subsystem interfaces in a hierarchical production planning process. 3(a) The conceptual framework. 3(b) Representations via mathematical models. 3(c) Flow chart for the interface process. 3(d) The systems flow diagram.
planning session. Figure 3(d) depicts the interface process in the computer systems environment. The three modes of subsystem interfaces discussed above are only representative. Many other issues related to the interface process need to be addressed. For example, it may be necessary to investigate how the various interface modes affect the measure of integrativeness of the whole organization [39]; how will the interface process be affected by the (task) complexity and uncertainty within each subsystem [37, 52, 68]. These and many other factors can be crucial to the success of an MSS.
4. AN ARCHITECTURE FOR MSS DESIGN AND IMPLEMENTATION The design architecture for any system should consist of different levels of abstraction that can be conceived as a continuum from conceptual constructs, to operational constructs, and then to implementational constructs. That is, the architecture should provide a blue-print for converting the logical representations of the reality into the physical operations of the systems. Figure 4 presents such an architecture for the MSS (DSS) design and implementation.
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Long range plonnmg Intermediate range p l a n n i n g ~ Short range
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The conceptual construct of Fig. 1 is reproduced here as Fig. 4(a), which is a (knowledge) representation about the real world (i.e. an organization or a problem domain). In our case, an organization or an MSS is represented as a network of subsystem interfaces. Figure 4(b) translates the conceptual construct into a logical representation of the interrelationship among subsystems. A detailed exploration of this logical construct is presented in Fig. 5 where several examples of linkage among subsystems are shown. Both Figs 4(b) and 5 show that, in supporting decision-making, the MSS may consist of several models, one or more for each subsystem• Each model again may contain more than one module. These modules can be subroutines or partitioned data sets in a modular programming concept [43, 46]. They can also be groups of production rules in a 'production systems' approach to problem solving [ 5 5 ] 3 . The actual contents of the modules or the models will depend upon the actual problem domain and the representation methods used for the MSS. The terms "production rules" and 'production systems" are used here as the jargons from the Artificial Intelligence (AI) field. They do not refer to the terminologies in Production and Operations Management wh'ich is a functional area of some business organizations.
For example, if the (first order) predicate calculus is used for knowledge representation [22, 55], then the modules may contain various subsets of predicate formulations such as the 'well-formed formulas' (wff's). The linkage or interfaces between subsystems can take various forms. Figure 5(a) shows that
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Fig. 4(b).
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J Fig. 4(c).
Fig. 4(d).
Fig. 4. An architecture for MSS design and implementation. 4(a) Knowledge representation of an organization: a network of subsystem interfaces. 4(b) The logical construct of subsystems and models. 4(c) The logical construct of data model (the data relationship). 4(d) The physical data files and data bases.
two subsystems are linked together by a simultaneous decision model. Examples of such models can be found in [29, 70]. Figure 5(b) depicts the case where two models of two subsystems share a common module. For example, a production planning model and a marketing planning model may share a common demand forecasts module. Finally, Fig. 5(c) represents the case where two models interface with data inputs and outputs. The hierarchical production planning process discussed in the previous section (Fig. 3) is a typical example of this type of interface. Return to Fig. 4. The logical constructs of subsystems and models (Fig. 4(b)) will be further converted into a logical construct of data model. Although all the knowledge representations (of a problem, a model, or a subsystem) belong to the knowledge subsystem of a DSS (and/or MSS) [12, 13], the knowledge base is just a special type of data base. In other words, all knowledges should eventually be stored as groups of (related) data. Thus, the management of knowledge base can be reduced to the management of a data base with some special organization and manipulation methods. Conceptually, a knowledge base and a data base may be treated as two logical constructs belonging to different levels of abstraction (as shown in Fig. 4). However, operationally, the model base or knowledge base is embedded in the data base, This argument becomes obvious if one examines
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Fig. 5(c). Fig. 5. Examples of linkage among subsystems.
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(a)
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some of the recent developments in both the artificial intelligence (AI) and data base management fields. In the past, the issue of knowledge representation was addressed in the area of AI. Since a set of data is an abstract of reality, it is believed that some AI techniques are applicable to the management of data base. On the other hand, data base theories may also enhance the research in knowledge representation (and/or AI). For example, Bonczek and Whinston [15] suggest an approach to the integration of network data base management and problem resolution. Basically, an automated data retrieval process (of a data manipulation language) is considered as an inference process which can be governed by the use of axioms. On the other hand, axioms can also be used for the representation of knowledge. Thus, they suggest a mixed approach to storing knowledge. Some knowledge is directly represented in a network database, whereas other knowledge is represented in an axiomatic fashion [15]. Davis [33, 34] also demonstrated that the management of knowledge bases and data bases are closely related. % . . The knowledge base contains many data structures that indicate such things as which values belong with which attributes. It is useful to consider the terms data structure, extended data type, and knowledge representation as interchangeable" [33]. The last part of Fig. 4 is to convert the logical data models into physical data files or data bases. Some data base development and management architectures reported in the literature can be applied here. For example, Fig. 6(a) depicts the three-schema framework proposed by the ANSI/X3/SPARC Group [5]. The framework consists of the external schema, the con-
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Fig. 6(b). The four-schema concept. Fig. 6. Schema architecture for data bases.
ceptual schema and the internal schema. The purpose of the external schema is to provide the interface between the data base and the application program. Its function is roughly equivalent to that of the logical construct of subsystems and models shown in Fig. 4(b) or a subset of this logical construct. The interface between the data base and the application programs is indirect in the sense that the conceptual schema serves as buffer between the external schema and the internal schema. As defined by the ANSI/SPARC Group [5]: "A conceptual schema represents the enter-
prise's view of the structure it is attempting to model in the data base. This view is that which is informally invoked when there is a dispute between the user and the programmer over exactly what is meant by program specifications". Thus, the conceptual schema is to provide a universe of discourse. Ideally, it should serve as a reference framework not only for the design of data bases but also for the design of the information systems. Depending upon the individual
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cases, the conceptual schema may consist of Figs 4(a), 4(b) and 4(c). Figure 6(b) depicts the De e t al. [35] fourschema architecture. The conceptual model is now split up into two components--the enterprise schema and the canonical schema. Being responsible for the semantic mission, the enterprize schema provides an overall description of the data base at the conceptual model level. The canonical schema, on the other hand, is responsible for the internal-external schema mapping and therefore should provide an overall description of the data base more at the data model level. The purpose of splitting up the conceptual schema into two components is to enhance the communication among different parties involved in the system. With the same purpose in mind, we should find that Fig. 4 actually provides a more detailed continuum of different levels of abstraction. It is consistent with both the three-schema construct by ANSI and the four-schema model by the De et al. [35], yet it is more flexible in serving as a medium for communication or a universe of discourse. Depending upon the needs and roles of an individual party, the various stages depicted in Fig. 4 can be partitioned to cope with the situations. The construct can also be applied at the subsystem level. That is, an 'explosion' of the network (of subsystem interfaces) may be desirable. Thus, Fig. 4 represents a generalized model for data base design. It also provides a link between the problem processing and the development of data bases. Therefore, Fig. 4 is a viable architecture for the design and development of the network of subsystem interfaces--that is, the design and development of MSS (DSS). 5. CONCLUSIONS As pointed out by both Keen and Scott Morton [43] and Scott Morton [58], future research on MSS (DSS) will increasingly draw from artificial intelligence and data base management. However, no matter what techniques are employed, it should always be kept in mind that the research and development of these systems should be decision- or managementoriented [64]. In this study, we started with a conceptual framework of organizational decis,ion-making and management process. Based on this frame-
work, we defined MSS or an integrated DSS as a network of subsystem interfaces. Three modes of interfaces were used to illustrate the activation of the network. We further presented an architecture for the design and operationalization of MSS. The architecture is developed along the continuum of different levels of abstraction, translating the real world into conceptual constructs, knowledge representational constructs, logical data models, and finally the physical storage of data. To define MSS as a network of subsystem interfaces offers several promising features. First of all, it correctly describes the decisionmaking processes prevailing in most organizations. Secondly, the use of the 'network' concept emphasizes that organizations are an integrated system. This is an aspect commonly ignored by many DSS studies which had focused on only a specific decision or a specific class of decisions. Certainly, the framework is flexible enough to accommodate the case where the decision-making process involves only a subset of the network. Thirdly, the use of the 'subsystem' concept is consistent with the fact that human nature is in favor of decomposable systems. It is also consistent with the modularization concept in the state of art of computer programming, artificial intelligence, and data base management. Finally, the architecture presented in this study provides a logical process for the development and design of management support systems. Thus, we conclude that the framework proposed in this paper would be both useful and necessary for the future research and development in management support systems. However, it should be noted that a framework is only a general pool of constructs for understanding a domain. Usually, it is not tightly enough organized to constitute a predicative theory [4]. In addition to gaining empirical experiences with respect to the framework, it is desirable to use a 'metasystem' approach (such as the kind suggested by [44]) to evaluate and improve the current framework. REFERENCES 1. Abad PL (1982) An optimal control approach to marketing-production planning. Opl Control Appl. Meth. 3(1), 1-14. 2. Abad PL and Sweeney DJ (1982) Decentralized planning with an interdependent marketing-production system. Omega 10(4), 353-359.
Omega, Vol. 13, No. 4 3, Alter SL (1980) Decision Support Systems: Current Practice and Continuing Challenges. Addison-Wesley. Reading. Massachusetts. 4. Anderson JR (1983) The Architecture of Cognition. Harvard University Press. 5. ANSI:X3/SPARC (1975) Study Group on Database Management Systems. Interim Report. 6. Anthony RN (1965) Planning and Control Systems: A Framework for Analysis. Harvard University Grad. Sch. Bus. Admin., Studies in Mgrnt Control. 7. Baker KR (1977) An experimental study of the effectiveness of rolling schedules in production planning. Decis. Sci. 8(1), 19-27. 8. Baumol WJ and Fabian T (1964) Decomposition, pricing for decentralization and external economics, Mgmt Sci. 11(1), 1-32. 9. Benders JF (1962) Partitioning procedures for solving mixed variable programming problems. Numerische Mathematik 4(3), 238-252. 10. Bennett JL (1983) Building Decision Support Systems. Addison-Wesley, Reading, Massachusetts. 11. Blanning RW (1983) What is happening in DSS? Inter.faces. 13(5), 71-80. 12. Bonczek RH. Holsapple CW and Whinston AB (1980) Future directions for developing decision support systems. Decis. Sci. 11(4), 616-631. 13. Bonczek RH, Holsapple CW and Whinston AB (1981) Foundations of Decision Support Systems. Academic Press, New York. 14. Bonczek RH, Schell GP and Whinston AB (1983) Model knowledge and model representation in decision support systems. Paper presented at TIMS/ORSA Joint National Meeting, Orlando, Florida. 15. Bonczek RH and Whinston AB (1979) The Integration of Network Data Base Management and Problem Resolution. Inf. Syst. 4(2), 143-154. 16. Burton RM et al. (t974) The economics of decomposition: resource allocation vs transfer pricing. Decis. Sci. 5(3), 297-310. 17. Burton RM and Obel B (1977) The multilevel approach to organizational issues of the firm--a critical review. Omega 5(4), 395-414. 18. Burton RM and Obel B (1978) A comparative analysis of the price, quantity, and mixed approach for decentralized planning. Econ. Plann. 14(3), 129-140. 19. Burton RM and Obel B (1980) Efficiency of the price, budget, and mixed approach for decentralized planning. Mgmt Sci. 26(4), 401-4 17. 20. Carlson RC et al. (1982) The effectiveness of extending the horizon in rolling production schedules. Decis. Sci. 13(1), 129-146. 21. Chand S (1982) A note on dynamic lot sizing in rolling horizon environment. Decis. Sci. 13(1), 113-119. 22. Chang CL and Lee RCT (1973) Symbolic Logic and Mechanical Theorem Procing. Academic Press, New York. 23. Christensen JA and Obel B (1978) Simulations of decentralized planning in two Danish organizations using linear programming decomposition, Mgmt Sci. 24(15), 1658-1667. 24. Chung CH (1983) Implementation issues in problem processing of decision support systems. In Proceedings of the 16th Hawaii International Conference on Systems Science, Honolulu. Hawaii. 25. Chung CH (1983) The design of subsystem interfaces for decision support systems, Paper Presented at TIMS,:ORSA Joint National Meeting, Chicago. 26. Chung CH and Krajewski LJ (1982) Planning Horizons for hierarchical produ,:tion planning--An empirical
27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45, 46. 47. 48.
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analysis. In Proceedings of 14th National A IDS Meeting. San Francisco. Chung CH and Krajewski LJ (1984) Planning horizons for master production scheduling. J. Ops Mgmt 4(4), 389--406. Cyert RM and March JG (1963) A Behavioral Theory of the Firm. Prentice-Hall, Englewood Cliffs. New Jersey. Damon WW and Schramm R (1972) A simultaneous decision model for production, marketing and finance. Mgmt Sci. 19(2), 161-172, Dantzig GB (t963) Linear Programming and Extensions. Princeton University Press, New Jersey. Dantzig GB and Wolfe P (1960) Decomposition principles for linear programs. Ops Res. 8(1), 101-111. Dantzig GB and Wolfe P (1961) The decomposition algorithm for linear programming. Econometriea 29(4), 767-778. Davis R et al. (1977) Production rules as a representation for a knowledge-based consultation program. Artif. lntell. 8(1), 15-45. Davis R and Lenat DP (1982) Knowledge-Based Systems in Artificial Intelligence. McGraw-Hill, New York. De Pet al. (1981) Four-schema approach: an extended model for data architecture. Inf. Syst. 6(2), 117-124. Dirickx YMI and Jennergren LP (1979) Systems Analysis by Multilevel Methods with Applications to Eco nomics and Management. John Wiley, New York. Duncan RB (1972) Characteristics of organizational environments and perceived environmental uncertainty. Admve Sci. Q. 17(3), 313-327. Ein-Dor P and Segev E (1978) Organizational contex and the success of management information systems. Mgmt Sci. 24(10), 1064-1077. Galbraith J (1973) Designing Complex Organizations. Addison-Wesley. Reading, Massachusetts. Habermann AN and Perry DE (1983) ADAfor Experienced Programmers. Addison-Wesley, Reading, Massachusetts. Jennergren LP (1974) On the concept of coordinability in hierarchical system theory. Int. J. Sys. Sci. 5(3), 493--497. Jennergren LP and Muller W (1973) Simulation experiments of resource-allocation decisions in two-level organizations. Soc. Sci. Res. 2(4), 333-352. Keen PGW and Scott Morton MS (1978) Decision Support Systems: An Organizational Perspective. Addison-Wesley, New York. Kickert WJM and van Gigch JP (1979) A metasystem approach to organizational decision-making. Mgmt Sci. 25(12), 1217-I231. King RH and Love RR Jr (1980) Coordinating decisions for increased profit. Interfaces 10(6), 4-19. Knuth D (1972) The Art of Computer Programming, Vols 1-3, Addison-Wesley, Reading, Massachusetts. Krajewski LJ (1980) Goal programming for aggregate planning and master scheduling. Working Paper Series, The Ohio State University. Kunreuther HC and Morton TE (1973) Planning horizons for production smoothing with deterministic demand: I. All demand met from regular production. Mgmt Sci. 20(1), 110--125. Lippman S e t al. (1967) Algorithms for optimal production scheduling and employment smoothing. Ops Res. 15(6), 1011-1029. March JG and Simon HA (1958) Organizations. John Wiley, New York, Mesarovic MD et al. (1970) Theory of Hierarchical Multilevel Systems. Academic Press, New York.
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52. Mintzberg H (t979) The Structuring of Organizations. Prentice-Hall, Engtewood cliffs, New Jersey. 53. Moore JH (1979) Effects of alternate information structures in a decomposed organization, a laboratory experiment. Mgmt Sci. 25(5), 485---497. 54. Naylor TH (1982) Decision support systems or whatever happened to M.I.S.? Interfaces 12(4), 92-94. 55. Nilsson NJ (1980) Principles of Artificial Intelligence. Tioga, Palo Alto, California. 56. Ruefli T (1971) A generalized goal decomposition model. Mgmt Sci. 17(8), 505-518. 57. Ruefli T and Storbeck J (1984) Removing a selfimposed restriction in modelling hierarchical decision processes. Mgmt Sci. 30(3). 389-392. 58. Scott Morton MS (1983) State of the art of research in management support systems. Paper presented at the Colloquium on Information Systems. Harvard Business School, Cambridge, Massachusetts. 59. Sen A (1979) On the theory of logical database design for a decision support system. Ph.D. Thesis, Pennsylvania State University. 60. Simon HA (1957) Administratice Behacior. McMillan, New York. 61. Sprague RH (1980) A framework for the development of decision support systems. MIS Q. 4(4) 1-26. 62. Sprague RH and Carlson ED (1982) Building Effective Decision Support Systems. Prentice-Hall, Englewood Cliffs, New Jersey. 63. Sprague RH and Watson HJ (1975) MIS concepts-Parts I & II, J. Syst. Mgmt 26 (l&2).
64. Stabell CB (1975) Design and implementation of decision support systems: Some implications of a recent study. In The Implementation of Computer-Based AMs. (Edited by Keen PGW). The Center for Information Systems Research, MIT, Cambridge, Massachusetts. 65. Stabell CB (1983) A decision-oriented approach to building DSS. In Building Decision Support Systems (Edited by Bennett JL). Addison-Wesley, Reading, Massachusetts. 66. Sweeney DJ et al. (1978) Composition vs decomposition: Two approaches to modeling organizational decision processes. Mgmt Sci. 24(4), 1491-1499. 67. Ten Kate A (1972) Decomposition of linear programs by direct distribution. Econometrica 40(5), 883-898. 68. Van Dierdonck R and Miller JG (1980) Designing production planning and control systems. J. Ops Mgmt 1(1), 37-46. 69. Watson HJ and Hill MW (1983) Decision support systems or what didn't happen with MIS. Interfaces 13(5), 81-88. 70. Welam UP (1977) On a simultaneous decision model for marketing production and finance. Mgmt Sci. 23(9), 1005--1011. 71. Winkofsky EP et al. (1981) A decision process model of R & D resource allocation in hierarchical organizations. Mgmt Sci. 27(3), 268-283. FOR CORRESPONDENCE: Professor C-H Chung, Department of Management, College of Business and Economics, Universityof Kentucky, Lexington, KY40506, USA
ADDRESS
0305-04.83:85 53.00 +0.00 Copyright ~ 1985 Pergamon Press Ltd
A Network of Management Support
Systems CHEN-HUA
CHUNG
University of Kentucky, USA (Receiced Nocember 1983: in recised form October 1984)
Based on the decision-making process that actually happens in most organizations, a management support system (MSS) or an integrated decision support systems (DSS) is defined as a network of subsystem interfaces, as opposed to management information system (MIS) being a network of information flows. Three modes for subsystem interfaces are discussed. To help the operationalization of the (computerized) support system, we also propose a design architecture which consists of the continuum of conceptual constructs, operational constructs and implementational constructs.
1. I N T R O D U C T I O N
RECENTLY, the concept of Management Support Systems (MSS) has emerged to provide an integrated view of the use of computers and related information technologies to support management. Scott Morton [58] presents an extensive review of the ongoing research in the MSS arena. Three categories of MSS are identified. They are: (1) Data Support Systems: to provide data and information for management; (2) Decision Support Systems: to provide modeling-oriented support for decision-making; and (3) Executive Support Systems: to provide data and analytical tools for supporting a broad range of managerial processes and decisions. The three systems are not mutually independent. They support and facilitate one another in aiding management. Scott Morton points out that MSS has been synonymous with DSS for a long time. Also, the research in DSS has been focused on a specific decision or a specific class of decisions. The facts that an organization is an integrated whole and that most decisions are related to one another have not been adequately addressed in the DSS or MSS literature. Incidentally, in recent years, the concept of networking has received a lot of attention in the research and development of computer applications. It can be expected that, like many other 263
computer and information technolo~es, the networking applications would have tremendous impacts upon business and management systems. Unfortunately, as Scott Morton points out, there is a surprising lack of research in the application of the networking concept to management or decision-making. As will be discussed later, the networking concept is consistent with t h e notion that organizational decision-making is an interrelated and integrated process. In this article, we define MSS as the support systems for an organization as a whole. And, for convenience, MSS will also be treated as a synonym of an 'integrated' DSS. That is, a D S S will not be limited to the support for a specific decision or a specific class of decisions. An MSS (or an integrated DSS) will be represented as a network of subsystem interfaces. Based upon this definition, we present an architecture for the development and implementation of MSS. The description of MSS as a network of subsystem interfaces is consistent with the decision-making and management processes that actually happen in most organizations. The conceptual framework also helps the distinction between MSS (DSS) and MIS (Management Information Systems). This distinction is certainly desirable since there is a seemingly everlasting controversy over whether DSS is a separate area from
ChunT.--.~Ianagement Support Systems
264
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,
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Management Science/Operational Research (MS/OR) and MIS [11, 54, 69]. Finally, the architecture presented here is something more than "conceptual'. As will be further discussed in detail, this architecture is an operationalistic blueprint that follows the continuum of transforming the conceptual constructs into operational constructs and the logical and physical development of databases. The paper is organized as follows: in Section 2, we describe organizational decision-making or management activities as a process of subsystem interfaces, in Section 3, the process is illustrated by three modes of subsystem interfaces; Section 4 presents the design architecture which converts the conceptual framework into operational constructs for the development and implementation of MSS; finally, Section 5 summarizes the study and points out the directions for future research.
2. THE PROCESS OF SUBSYSTEM INTERFACES The process of management usually involves a cycle of three interlocking stages of activities: planning, organizing, and control. Various decisions need to be made at each of these stages.
The decision-making or management process that actually happens in most organizations can be depicted in Fig. I. Vertically, as suggested by the 'Anthony Taxonomy' [6], corresponding to the hierarchy of organizational structure of top, middle and operating managers, managerial (i.e. decisionmaking) activities can be classified into strategic planning, tactical planning and operational control. Although staged, these activities are closely related to each other. Horizontally, an organization may consist of many functional areas such as production, marketing, finance, etc. Most decision-making processes would require interfunctional exchanges. In addition to the vertical and the horizontal interfaces, similar process also occurs along the time dimension. That is, the long range, intermediate range, and short range planning activities of an organization need to be correlated and coordinated. Each level of the decision-making hierarchy, each functional area, and each planning activity along the time dimension may be defined as a "subsystem' with respect to the whole organization. The management process, the decision-making process, or the organization itself then can be represented as a network of subsystem interfaces. The (computerized) sys-
265
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terns support developed for this network is called an MSS or an integrated DSS. The conversion of the above conceptual constructs into a more concrete (knowledge) representation of the actual system is the first step of operationalizing the MSS. To develop a single comprehensive representation model incorporating the complete vertical, horizontal, and temporal decision-making processes would be a formidable task. Even if developed, it may not be economically or technically feasible to implement the required computation or data processing. Therefore. it is desirable to decompose the complete system into several subsystems which hopefully will be consistent with the actual decision-making process prevailing in the organization. The concept and methodologies of decomposition has been well developed in operations research and management sciences. Methods like Dantzig-Wolfe decomposition [31, 32], Benders decomposition [9] and variations of these concepts are familiar to most management scientists. Although called "decomposition', most of these studies are oriented to solving largescale problems via decomposition instead of using the decomposition concept to capture the actual decision-making process of the organization. None the less, these decomposition methodologies do provide both a conceptual framework and the operational mechanisms for the process of subsystem interfaces. Examples of some interface mechanisms will be discussed in Section 3. With the decomposition concept, several relatively small models can be developed for individual subsystems. These models are then linked together or co-ordinated via some proper interface mechanisms. Conceptually, MIS can be defined as a network of information flows. Now, with MSS (DSS) defined as a network of subsystem (or model) interfaces, the distinction between MIS and MSS (DSS) becomes clear. The "identity' issue that arises from the debate over whether DSS is a separate area from MS/OR and MIS [11,54, 69] can be better understood, if not completely settled.
It should be noted that the term 'model" used here does not refer exclusivelyto mathematical models or mathematical programs. Any (knowledge) representational construct for the system is considered here as a "model'.
The concept that MSS is a network of subsystem interfaces is an improvement over the existing DSS frameworks such as those proposed by Sprague [61, 62] and Bonczek, Holsapple and Whinston (BHW) [12, 13]. The DSS framework by Sprague, as shown in Fig. 2(a), consists of three management subsystems: Data Base Management Software (DBMS), Model Base Management Software (MBMS), and Dialogue Generation Management and Software (DGMS). The purpose of introducing a model-base concept, as opposed to a data-base, is to emphasize the role of models in supporting decision-making. However, it should be noted that the model-base or model-pool concept does not explicitly address the fact that the organization as a whole is an integrated system and that models need to be co-ordinated together in aiding organizational decision-making. The framework presented in Fig. 1 serves the purpose of representing the above co-ordination process by defining MSS as a network of subsystem interfaces. On the other hand, to separate the model-base from the data-base may be misleading in some cases. Models need to be stored in data-base. Modelbase is actually a subset or a special kind of data-base. This is what should be kept in mind when one is trying to emphasize the role of models in aiding decision-making. The DSS
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266
Chung--Management Support Systems
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Fig. 2(b). The structure of DSS (Source: [13]). Fig. 2. Conceptual frameworks for decision support systems. The BHW model of the DSS structure, as shown in Fig. 2(b), largely resembles the Sprague construct. The model-base in 2(a) is roughly equivalent to the knowledge subsystem in 2(b). However, the functions of a model-base would belong to the problem processing subsystem. Again, the BHW model does not explicitly address the fact that an organization is a whole system. Their model is used mostly for the DSS in individual functional areas. We believe that to define MSS (DSS) as a network of subsystem interfaces can better reflect the organizational decision-making process. The framework depicts an organization and/or an MSS (DSS) as an integrated system, yet still retains the flexibility of dealing with the cases where the DSS is to be designed for specific decisions. 3. THREE MODES OF SUBSYSTEM INTERFACES The interfaces or exchanges among subsystems may be realized in various fashions. For example, there may be formal or informal commuuication channels connecting these subsystems. As discussed in the previous section, the use of models to represent subsystems (i.e. the individual decision units) is one of the characteristics of DSS and MSS. In this section, we shall use three modes of interfaces that had been developed in management sciences to illustrate the process of subsystem interfaces. Certainly, these are not the only mechanisms applicable to facilitating the process of subsystem interfaces. However, they do enhance our understanding of what might be involved in interfacing the subsystems. The three modes of interfaces to be examined are (1) a goal-achieving/dual-pricing mechanism, (2) a rolling-horizon mechanism, and (3) a combination of the above two.
Interfaces via mechanism
a goal-achieving dual-pricing
Ruefli [56] proposed a "generalized goal decomposition model" to represent decisionmaking in a three-level hierarchical organization. The three levels--Central Unit, Management Units and Operating Units-roughly corresponds to the 'Anthony Taxonomy' [6] discussed earlier. The central unit co-ordinates decision activity by generating goals (and/or selecting resource levels) for a subordinate level of management units. Each management unit will choose activity levels based on the goals and resource levels set by the central unit. It also determines values for the dual variables in its subunit problem and transmits these economic indicators to the central unit so that the latter can evaluate and improve its goal generating policies. On the other hand, the management units are also responsible for guiding the alternative generation activities of third level operating units. The operating units are then responsible for generating project proposals for their respective management units. Ruefli and Storbeck [57] further extend the model to represent nonpyramidal hierarchical decision problems. That is, two or more superordinate units are allowed to issue goal levels to a subordinate. Thus, complex organizational structures other than pure pyramidal hierarchies can be modelled. Krajewski [47] uses the concept of goal decomposition by Ruefli to interface the aggregate planning and master production scheduling problems. The procedure starts with solving the aggregate planning problem. Then a vector of goal stipulations is passed down to the master scheduling problem which optimizes its goal objective function given the goal stipulations. After the master scheduling problem is solved,
Omega, Vol. 13, No. 4
the vector of dual prices associated with the goal constraints is passed back to the aggregate planning model to determine a new set of goal stipulations. The process is repeated until the goal deviations of the master scheduling solutions are at a minimum and no further revisions will yield a net decrease in the goal levels for the organization as a whole. King and Love [45] reported a successful real-world application of interface procedures similar to those described above. Winkofsky, et al. [71] presented a decision process model (DPM) for analysis of R & D resource allocation processes in hierarchical organizations. In all these applications, the key data transferred from a top level to a lower level are goal stipulations which are taken by the latter as constraints. After the lower level problem is solved, the data passed back to the top level are the dual prices associated with the corresponding goals. Thus, the two levels of planning (i.e. two subsystems) are interfaced via the goalachieving mechanism while the data needed to effect the interfaces are the goal stipulations and the dual prices. For any two levels of a hierarchical system, we can conceptually regard higher level management as the planning level while lower level management is the implementation level. Thus, the aforementioned subsystem interfaces or data flows or goal stipulations and dual prices can be applied to any two levels of a system hierarchy. 2 Other studies related to this interface mode can also be found in Baumol and Fabian [8], Burton et al. [16, 17, 18, 19], Christensen and Obel [23], Jennergren [41], Jennergren and Muller [42], Moore [53], and Ten Kate [67]. Particularly Sweeney, et al. [66] presented an extensive review of the modeling of (decomposed) organizational decision-making processes. Structurally, the interface mechanism for vertical subsystems can also be applied to horizontal interfaces. Various subsystems (i.e. different functional areas) may pose constraints for each other. This is similar to the goal-constraint relationship among any two levels of a hierarchical system. Also, there often exist some z In the discussion above, we have for convenience used the terminologies associated with a 'resources directive" model to illustrate the interface processes. It should be noted that the same logic can also be applied fo a 'price directive" model in hierarchical planning problems.
267
common constraints to the various subsystems. These across-subsystem constraints can be used as a co-ordinating mechanism. This is what is usually called the 'multi-divisional problem'. The emphasis, however, will be placed upon the co-ordination mechanism that not only represents information flows but also facilitates interfaces or decision-making processes among subsystems. Related studies can be found in Abad [1], Abad and Sweeney [2], Damon and Schramm [29] and Welam [70]. Interfaces via a rolling-horizon mechanism
A rolling-horizon is a common practice in real world planning and problem-solving. As described by Baker [7], "the typical scenario (of a rolling-horizon strategy) is as follows: solve the model and implement only the first period's decisions; for the following period, update the model to reflect information collected in the interim, resolve the model, and again implement only the imminent decision pending subsequent model runs". That is, the practice of rolling horizons is to routinely update or revise the plans taking into consideration more reliable data as they become available. On the other hand, the rolling horizon practices simply reflect the continuity of the firm's business and activities. In rolling horizon practices, the scenario of subsystem interfaces would be as follows: the top level provides guidelines or constraints for lower levels; the lower levels solve their models and implement the first period's decision, then pass the implementation results back to the top level; as the horizon rolls ahead, the top level will establish new guidelines based on the previous performances and other updated information. Thus, the subsystem interfaces are carried out via the rolling-horizon practice. Most existing studies in the rolling horizon practice have been focused on the evaluation of the impacts of the rolling-horizon procedures upon the performance of various lot-sizing algorithms for production planning problems (e.g. [20, 21]). Chung and Krajewski [26, 27] use the rolling-horizon interface mechanism to investigate planning horizon effects in a hierarchical production planning setting. A combination of optimization and rollinghorizons
The two subsystem interface modes discussed
Ctzung--J,Ianagernent Support .~vstems
268
above can be combined together. That is, every first-period decision at each subsystem can be optimized via the goal-achieving/dual-pricing mechanism before rolling the schedules ahead. However, this combined procedure may be computationally involved. Particularly. there may be quite a few subsystems in an organization. The choice of proper interface modes should be justified by the cost or other effectiveness criteria.
An example To further illustrate the process of subsystem interfaces, we examine the following hierachical production planning example taken from Chung and Krajewski [26, 27]. Figure 3 depicts the scenarios of the subsystem interfaces in a simplified hierarchical production planning process (Fig. 3(a)) involves aggregate planning,
/ongterm J_ capocity planning F
master production scheduling, and detailed operations planning and scheduling. In aggregate planning, management is concerned with determining the aggregate levels of production, inventory and workforce to respond to fluctuating demand in the future. The aggregate plan provides an overall guideline for master production scheduling which specifies the timing and sizing of the production of individual products. The master scheduler takes the aggregate decisions as targets and tries to achieve them as much as possible. The feedback of actual master scheduling performance (and/or dual prices associated with the goal constraints) provides information for modifying future aggregate plans. The master production schedule also serves as the base for 'requirements explosion' in detailed level scheduling. Mathematical programs are formulated to
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represent the decision-making at each individual subsystem. Figure 3(b) only shows the mathematical formulations for the problems. For detailed description of the formulations, see [26,27]). The interface procedure (Fig. 3(c)) beginswiththes°luti°n°ftheaggregateplan" The aggregate decisions are passed to the master scheduling model. Taking the aggregate decisions as goals (i.e. constraints), the master scheduling model is then solved. The information passed back to the aggregate planning model will depend upon the type of interface modes used. For example, if the dual-price mechanism is used, the dual prices (i.e. the opportunity costs) associated with the various goal constraints are passed back to the aggregate planning model. On the other hand. if the rolling-schedule mechanism is used, the actual performance of the first period master schedule is passed back as an input to the next aggregate
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P a s s back MPS results and/or
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Fig. 3(d). Fig. 3. Subsystem interfaces in a hierarchical production planning process. 3(a) The conceptual framework. 3(b) Representations via mathematical models. 3(c) Flow chart for the interface process. 3(d) The systems flow diagram.
planning session. Figure 3(d) depicts the interface process in the computer systems environment. The three modes of subsystem interfaces discussed above are only representative. Many other issues related to the interface process need to be addressed. For example, it may be necessary to investigate how the various interface modes affect the measure of integrativeness of the whole organization [39]; how will the interface process be affected by the (task) complexity and uncertainty within each subsystem [37, 52, 68]. These and many other factors can be crucial to the success of an MSS.
4. AN ARCHITECTURE FOR MSS DESIGN AND IMPLEMENTATION The design architecture for any system should consist of different levels of abstraction that can be conceived as a continuum from conceptual constructs, to operational constructs, and then to implementational constructs. That is, the architecture should provide a blue-print for converting the logical representations of the reality into the physical operations of the systems. Figure 4 presents such an architecture for the MSS (DSS) design and implementation.
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Long range plonnmg Intermediate range p l a n n i n g ~ Short range
Level 1
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Fig. 4(a).
The conceptual construct of Fig. 1 is reproduced here as Fig. 4(a), which is a (knowledge) representation about the real world (i.e. an organization or a problem domain). In our case, an organization or an MSS is represented as a network of subsystem interfaces. Figure 4(b) translates the conceptual construct into a logical representation of the interrelationship among subsystems. A detailed exploration of this logical construct is presented in Fig. 5 where several examples of linkage among subsystems are shown. Both Figs 4(b) and 5 show that, in supporting decision-making, the MSS may consist of several models, one or more for each subsystem• Each model again may contain more than one module. These modules can be subroutines or partitioned data sets in a modular programming concept [43, 46]. They can also be groups of production rules in a 'production systems' approach to problem solving [ 5 5 ] 3 . The actual contents of the modules or the models will depend upon the actual problem domain and the representation methods used for the MSS. The terms "production rules" and 'production systems" are used here as the jargons from the Artificial Intelligence (AI) field. They do not refer to the terminologies in Production and Operations Management wh'ich is a functional area of some business organizations.
For example, if the (first order) predicate calculus is used for knowledge representation [22, 55], then the modules may contain various subsets of predicate formulations such as the 'well-formed formulas' (wff's). The linkage or interfaces between subsystems can take various forms. Figure 5(a) shows that
The toTaJ system
Subsystem
Subsystem
Fig. 4(b).
272
Chung--Management Support Systems
J Fig. 4(c).
Fig. 4(d).
Fig. 4. An architecture for MSS design and implementation. 4(a) Knowledge representation of an organization: a network of subsystem interfaces. 4(b) The logical construct of subsystems and models. 4(c) The logical construct of data model (the data relationship). 4(d) The physical data files and data bases.
two subsystems are linked together by a simultaneous decision model. Examples of such models can be found in [29, 70]. Figure 5(b) depicts the case where two models of two subsystems share a common module. For example, a production planning model and a marketing planning model may share a common demand forecasts module. Finally, Fig. 5(c) represents the case where two models interface with data inputs and outputs. The hierarchical production planning process discussed in the previous section (Fig. 3) is a typical example of this type of interface. Return to Fig. 4. The logical constructs of subsystems and models (Fig. 4(b)) will be further converted into a logical construct of data model. Although all the knowledge representations (of a problem, a model, or a subsystem) belong to the knowledge subsystem of a DSS (and/or MSS) [12, 13], the knowledge base is just a special type of data base. In other words, all knowledges should eventually be stored as groups of (related) data. Thus, the management of knowledge base can be reduced to the management of a data base with some special organization and manipulation methods. Conceptually, a knowledge base and a data base may be treated as two logical constructs belonging to different levels of abstraction (as shown in Fig. 4). However, operationally, the model base or knowledge base is embedded in the data base, This argument becomes obvious if one examines
Subsystem I
© Subsystem 2
Fig. 5(a).
Subsystem 1
Fig. 5(b).
Subsystem 1
Subsystem 2
Fig. 5(c). Fig. 5. Examples of linkage among subsystems.
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(a)
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Fig. 6(a). ANSI~X3/SPARC three-schema architecture.
some of the recent developments in both the artificial intelligence (AI) and data base management fields. In the past, the issue of knowledge representation was addressed in the area of AI. Since a set of data is an abstract of reality, it is believed that some AI techniques are applicable to the management of data base. On the other hand, data base theories may also enhance the research in knowledge representation (and/or AI). For example, Bonczek and Whinston [15] suggest an approach to the integration of network data base management and problem resolution. Basically, an automated data retrieval process (of a data manipulation language) is considered as an inference process which can be governed by the use of axioms. On the other hand, axioms can also be used for the representation of knowledge. Thus, they suggest a mixed approach to storing knowledge. Some knowledge is directly represented in a network database, whereas other knowledge is represented in an axiomatic fashion [15]. Davis [33, 34] also demonstrated that the management of knowledge bases and data bases are closely related. % . . The knowledge base contains many data structures that indicate such things as which values belong with which attributes. It is useful to consider the terms data structure, extended data type, and knowledge representation as interchangeable" [33]. The last part of Fig. 4 is to convert the logical data models into physical data files or data bases. Some data base development and management architectures reported in the literature can be applied here. For example, Fig. 6(a) depicts the three-schema framework proposed by the ANSI/X3/SPARC Group [5]. The framework consists of the external schema, the con-
~
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Fig. 6(b). The four-schema concept. Fig. 6. Schema architecture for data bases.
ceptual schema and the internal schema. The purpose of the external schema is to provide the interface between the data base and the application program. Its function is roughly equivalent to that of the logical construct of subsystems and models shown in Fig. 4(b) or a subset of this logical construct. The interface between the data base and the application programs is indirect in the sense that the conceptual schema serves as buffer between the external schema and the internal schema. As defined by the ANSI/SPARC Group [5]: "A conceptual schema represents the enter-
prise's view of the structure it is attempting to model in the data base. This view is that which is informally invoked when there is a dispute between the user and the programmer over exactly what is meant by program specifications". Thus, the conceptual schema is to provide a universe of discourse. Ideally, it should serve as a reference framework not only for the design of data bases but also for the design of the information systems. Depending upon the individual
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cases, the conceptual schema may consist of Figs 4(a), 4(b) and 4(c). Figure 6(b) depicts the De e t al. [35] fourschema architecture. The conceptual model is now split up into two components--the enterprise schema and the canonical schema. Being responsible for the semantic mission, the enterprize schema provides an overall description of the data base at the conceptual model level. The canonical schema, on the other hand, is responsible for the internal-external schema mapping and therefore should provide an overall description of the data base more at the data model level. The purpose of splitting up the conceptual schema into two components is to enhance the communication among different parties involved in the system. With the same purpose in mind, we should find that Fig. 4 actually provides a more detailed continuum of different levels of abstraction. It is consistent with both the three-schema construct by ANSI and the four-schema model by the De et al. [35], yet it is more flexible in serving as a medium for communication or a universe of discourse. Depending upon the needs and roles of an individual party, the various stages depicted in Fig. 4 can be partitioned to cope with the situations. The construct can also be applied at the subsystem level. That is, an 'explosion' of the network (of subsystem interfaces) may be desirable. Thus, Fig. 4 represents a generalized model for data base design. It also provides a link between the problem processing and the development of data bases. Therefore, Fig. 4 is a viable architecture for the design and development of the network of subsystem interfaces--that is, the design and development of MSS (DSS). 5. CONCLUSIONS As pointed out by both Keen and Scott Morton [43] and Scott Morton [58], future research on MSS (DSS) will increasingly draw from artificial intelligence and data base management. However, no matter what techniques are employed, it should always be kept in mind that the research and development of these systems should be decision- or managementoriented [64]. In this study, we started with a conceptual framework of organizational decis,ion-making and management process. Based on this frame-
work, we defined MSS or an integrated DSS as a network of subsystem interfaces. Three modes of interfaces were used to illustrate the activation of the network. We further presented an architecture for the design and operationalization of MSS. The architecture is developed along the continuum of different levels of abstraction, translating the real world into conceptual constructs, knowledge representational constructs, logical data models, and finally the physical storage of data. To define MSS as a network of subsystem interfaces offers several promising features. First of all, it correctly describes the decisionmaking processes prevailing in most organizations. Secondly, the use of the 'network' concept emphasizes that organizations are an integrated system. This is an aspect commonly ignored by many DSS studies which had focused on only a specific decision or a specific class of decisions. Certainly, the framework is flexible enough to accommodate the case where the decision-making process involves only a subset of the network. Thirdly, the use of the 'subsystem' concept is consistent with the fact that human nature is in favor of decomposable systems. It is also consistent with the modularization concept in the state of art of computer programming, artificial intelligence, and data base management. Finally, the architecture presented in this study provides a logical process for the development and design of management support systems. Thus, we conclude that the framework proposed in this paper would be both useful and necessary for the future research and development in management support systems. However, it should be noted that a framework is only a general pool of constructs for understanding a domain. Usually, it is not tightly enough organized to constitute a predicative theory [4]. In addition to gaining empirical experiences with respect to the framework, it is desirable to use a 'metasystem' approach (such as the kind suggested by [44]) to evaluate and improve the current framework. REFERENCES 1. Abad PL (1982) An optimal control approach to marketing-production planning. Opl Control Appl. Meth. 3(1), 1-14. 2. Abad PL and Sweeney DJ (1982) Decentralized planning with an interdependent marketing-production system. Omega 10(4), 353-359.
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64. Stabell CB (1975) Design and implementation of decision support systems: Some implications of a recent study. In The Implementation of Computer-Based AMs. (Edited by Keen PGW). The Center for Information Systems Research, MIT, Cambridge, Massachusetts. 65. Stabell CB (1983) A decision-oriented approach to building DSS. In Building Decision Support Systems (Edited by Bennett JL). Addison-Wesley, Reading, Massachusetts. 66. Sweeney DJ et al. (1978) Composition vs decomposition: Two approaches to modeling organizational decision processes. Mgmt Sci. 24(4), 1491-1499. 67. Ten Kate A (1972) Decomposition of linear programs by direct distribution. Econometrica 40(5), 883-898. 68. Van Dierdonck R and Miller JG (1980) Designing production planning and control systems. J. Ops Mgmt 1(1), 37-46. 69. Watson HJ and Hill MW (1983) Decision support systems or what didn't happen with MIS. Interfaces 13(5), 81-88. 70. Welam UP (1977) On a simultaneous decision model for marketing production and finance. Mgmt Sci. 23(9), 1005--1011. 71. Winkofsky EP et al. (1981) A decision process model of R & D resource allocation in hierarchical organizations. Mgmt Sci. 27(3), 268-283. FOR CORRESPONDENCE: Professor C-H Chung, Department of Management, College of Business and Economics, Universityof Kentucky, Lexington, KY40506, USA
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