MODEL BASE MANAGEMENT

4.16 MODEL BASE MANAGEMENT

In theory, a model base management system (MBMS) is a software package with capabilities similar to those of a DBMS. There are dozens of commercial DBMS packages, but unfortunately there are no comprehensive model base management packages on the market. However, there are commonalities between the two, and thus ideas from DBMS have been applied in model management (see Tsai, 2001). Limited MBMS capabilities are provided by some spreadsheets and other model-based DSS tools and languages. There are no standardized MBMS for a number of reasons:

•• While there are standard model classes (like standard database structures: relational, hierarchical, network, object-oriented), there are far too many of them, and each is structured differently (e.g., linear programming vs. regression analysis).

•• Given a problem, several different classes of models and techniques may apply. Sometimes trial and error is the only way to determine which work best. •• Each model class may have several approaches for solving problems in the class, depending on problem structure, size, shape, and data. For example, any linear programming problem can be solved by the simplex method, but there is also the interior point method. Method specializations can work better than the standard methods if they match the model.

•• Every organization uses models somewhat differently. •• MBMS capabilities (e.g., selecting which model to use, how to solve it, and what

parameter values to use) require expertise and reasoning capabilities, which can be made available in expert systems and other artificial intelligence approaches.

Eom (1999) indicates that model management research includes several topics, such as model base structure and representation, the structured modeling approach, model base processing, model integration, and application of artificial intelligence to model integration,

CHAPTER 4 MODELING AND ANALYSIS

how to apply artificial intelligence to MBMS. Dolk (2000) discusses how model man- agement and data warehouses can and should be integrated. Wu (200) describes a model management system for test construction DSS. And Huh (2000) describes how collaborative model management can be done.

An effective model base management system makes the structural and algorithmic aspects of model organization and associated data-processing transparent to users of the MBMS (e.g., the P&G Web Chapter; and IMERYS Case Application 4.1) (Orman, 1998). Web capabilities are a must for an effective MBMS. The MBMS should also handle model integration (model-to-model integration, like a forecasting model feeding a planning model; data-to-model integration; and vice versa).

Some desirable MBMS capabilities include the following: " Control. The DSS user should be provided with a spectrum of control. The system

should support both fully automated and manual model selection, depending on which seems most helpful for an intended application. The user should also be able to use subjective information.

" Flexibility. The DSS user should be able to develop part of the solution using one approach and then be able to switch to another modeling approach if desired. " Feedback. The MBMS should provide sufficient feedback to enable the user to . know the state of the problem-solving process at any time.

" Interface. The DSS user should feel comfortable with the specific model from the MBMS in use. The user should not have to laboriously supply inputs.

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Redundancy reduction. Sharing models and eliminating redundant

storage.as in a data warehouse, can accomplish this. " Increased consistency. This can occur when decision-makers share the same model and data (designed into the IMERYS DSS).

To provide these capabilities, it appears that an MBMS design must allow the DSS user to

There are a number of additional requirements for these capabilities. For example, there must be appropriate communication and data changes among models that have been combined. In addition, there must be a standard method for analyzing and interpreting the results obtained from using a model. This can be accomplished in a number of ways (e.g., by OLAP or expert systems).

As a result of required e-commerce and Internet speeds, accurate models must be developed faster. Data must be ready to load them, and decisions based on solution results should be implemented quickly. We must use high-level modeling languages and tools in the modern business environment. Risk goes up because even the most successful models

PART /I DECISION SUPPORT SYSTEMS

deploy. Model petrification refers to an organization's loss of understanding of models after the development team leaves. As with any MIS, the understanding of models utilized in practice must be maintained to obtain the full benefits of them. Models, like any code, must

be documented and responsibility passed on. See Smith, Gunther, and Ratliff (2001). Model management is quickly moving to the Web in the ASP (application service provider) format. LogicTools (logic-tools. com), MultiSimplex (multisimplex.com) (watch

the online demo), and the Web-based Model Management System-MMM (meta-mmm.wiwi.hu-berlin.de) are three examples. Dotti et al. (2000) describe a Web architecture for metaheuristics.

The MBMS does directly influence the capability of a DSS to support decisionmaker. For example, in an experimental study, Chung (2000) determined that the adequacy of the modeling support provided by a MBMS influences the decision-maker's problem-solving performance and behavior. Decision-makers who receive adequate modeling support from MBMS outperformed those without such support. Also, the MBMS helped turn the decision-makers' perception of problem-solving from a number-crunching task into the development of solution strategies, consequently changing their decision-making behavior. This is important as OLAP and data mining tools attempt to improve decision-making (see the next chapter).