Operations Mng

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    Making Large-Scale Models Manageable: Modeling from an Operations Management Perspective

    Author(s): Frederic H. MurphySource: Operations Research, Vol. 41, No. 2 (Mar. - Apr., 1993), pp. 241-252Published by: INFORMSStable URL: http://www.jstor.org/stable/171775

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  • 8/3/2019 Operations Mng



    FREDERICH. MURPHYTempleUniversity, hiladelphia, ennsylvania

    (ReceivedJanuary1992;revisionreceivedDecember 1992;acceptedFebruary 993)Whilebuilding omplex models s an importantpartof operations esearch ractice,OR workers ave focused oo oftenon modeling's echnicalaspects nsteadof makingthe models manageable,hat is, designing hem around the wayspeople will operate them. The issues raisedfor complexmodelsare different rom those most widely discussed ordecisionsupport ystemsbecause he focus is on modelsthatrequirea staffto maintainandoperate hem and on howthe staff functions. Operationsmanagementprovidesthe tools for thinking about the operationsof large complexmodels; this paper examines some of these tools and shows how they relateto the design and operation of largecomplex models.

    Over the last two yearsInterfaces as publishedI some 30 articles describing the use of verycomplex models, and most of the Edelman Prizecompetition participants have used complex models.Similarly, the OR Practice section of OperationsResearch regularlypublishes articles involving com-plex models. Clearly, there is a role for these models,and, in fact, they are the bread and butter of ourprofession. This paper explores how we can makethem manageable.Managementscientists have finely honed technicaland craft skills, but they have written very little aboutmanaging complex models. Few academics have hadthe opportunity to manage them; they focus on thetechnical issues with which they are familiar, insteadof the organizationof the day-to-day operations.Prac-titioners tend to avoid writingabout their experiencesas managers; rather, they prefer discussing the tech-nical side of projects in which they are involved. Theproblem with ignoring operating ssues is that we havenot shared deas for improving the operations of com-plex models that involve groups of up to 30 profes-sionals or are diffused throughout an organizationandused by several small teams. Nevertheless, when deal-ing with complex models, the management aspectsare at least as criticalas the technical issues.The operations of large models tend to be chaotic

    and convoluted. They are chaotic because there aremany people doing different tasks with uncertaintimes to completion, such as updating dataand imple-menting new features, and they are convolutedbecause the tasks tend to be interrelated and requirea high degreeof coordination. At the same time, thestatus of the operationsis not completely observable.When operating models, highly skilled people spenda great deal of time doing routine tasksthat only theycan do, given the knowledge requiredto engagein thework.Those who have written about the subject includeGass (1987), Allen et al. (1992), Bennett (1989), andGoeller et al. (1985). Gass emphasizes issues of modelintegrity, assessment, and validation. Allen et al.address the staffing and general management issuesfor model development. Bennett focuses mostly ondata and results management. Goeller and his teamprovide an example of how to organize and use a suiteof models to evaluatea set of complex, interconnectedissues.In this paper I address a specific aspect of modelmanagement: How to design models and study theiroperationsto improve their operating characteristics?That is, can the tools of operationsmanagement (see,forexample, Weiss and Gershon 1992),which includeindustrial engineeringtechniques that weredeveloped

    Subject lassifications:Philosophy f modeling:managing argemodels. Production/scheduling:pplying perationsmanagement echniques o modeloperations.Areaofreview:OR FORUM.OperationsResearch 0030-364X/93/4102-0241$01.25Vol. 41, No. 2, March-April 993 241 ? 1993OperationsResearch ocietyof America

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    242 / MURPHYin an industrial setting, be brought to bear on thesemental, as opposed to physical, tasks? My conclusionis that some can be used directly and others, withmodification, can be of use as well.COMPLEXMODELS AND MODELMANAGEMENTTypically, we think of complex models in terms oftheir size, but size is not the only determinant. Asimple transportation problem with 100 sources anddestinations has 10,000 activities. It is large, but notcomplex, from a model management perspective.It iseasy to conceptualize, and, if costs are measured bydistance times some scale factor, it requires very fewdata elements that change. This model can beexpanded to make it complex throughseveralactions.A demand response can be added to price, multiplecommodities competing for capacity on links, multi-ple periods connected by inventories, and alternativeproduction processes.Simon (1981, p. 195) gives a useful definition ofcomplexity:"Acomplex systemis one made of a largenumber of parts that interact in a nonsimple way."What makes a model complex is structuralcomplex-ity, diverse sources and different kinds of data. Struc-tural complexity is a function of the number ofdifferentmodel components, the number of differentways these components can interact, the number ofdifferent types of variables and functions, and theexistence of spatial and time dimensions. The scopeand rangeof the problemenvironment also add to thecomplexity of the model. Managing operationsbecomes more difficult when the number of interac-tions between and among people and model compo-nents increases.This paper focuses on models that are used repeat-edly. This means the model-buildingteam can investin operational efficiencies, moving along a learningcurve. Given repeated use, one can affordto invest inan efficientproduction process.That a model is used repeatedlymeans it is used toanalyze issues that are under continuous re-evalua-tion. For ongoing concerns in the corporateenviron-ment, people usually understand he problem,and themodel usually functions as a productivity-enhancingtool through improved coordination or synthesis ofinformation, and usually, though not always,in oper-ations. Four of many examples areBlais,Lamont andRousseau(1990) in vehicle and manpower schedulingfor buses, Cohen et al. (1990) in multi-echelon inven-tory, Buchanan et al. (1990) in petroleum productrefining and distribution, and Schmitz, Armstrong

    and Little (1990) in comprehending data from bar-code scanners. The last paperis a marketingexample.Complex models are used regularly n public policydiscussions.Gass and Sisson (1975) survey the kindsof models used. Recent examplesinclude applicationsin forestry Hof and Baltic 1991, and Kent et al. 1991),electric power policy (Ford 1990), health care (Fetter1991), waste management (Baetz, Pas and Neebe1989), and water supply (Goeller et al. 1985). PIES,the ProjectIndependenceEvaluationSystem (Hogan1975), was used for studying energy policy from 1974to 1982, when it was replaced by the IntermediateFutureForecastingSystem (IFFS),which is still in use(see Murphy et al. 1988). These last two models areused to illustratesome of the points raisedhere.Public policy models become complex because, assoon as a model can address one question, otherrelated questions are asked, forcing its scope to beexpanded. A good test for when a complex model isneeded is if a small model raises more answerablequestions than it answers. Public policy modelsbecome especially complex when they address morethan just immediate impacts and take more of asystems view. For example, a change in oil marketsaffects other fuel markets and the macroeconomy,and the energymodels have to take this into account.Having a comprehensive model that provides a uni-form baseline is preferable to having individualmodels for each question that provide inconsistentconclusionsin theirareasof overlap. This is analogousto the coordinatingfunction of privatesector models.The emphasis in this paper is on the implicationsof model design and the implementation for ongoingmanagement. The production process is more thanalgo