DECISION SUPPORT SVSTEMS: TOOLS, EXPECTATIONS AND REALITIES INFORMATICA 3/87 UDK 519.86 Janez Grad and Milton A. Jenkins* Ekonomska fakulteta Borisa Kidriča, Ljubljana, Vugoslavia * School of Business, Indiana University, Bloomington, Indiana, USA In this paper we descrlbe and analyze Decision Support Systems (DSS) as a class of Information Systems. We first state a short definition of DSS, explain its relation to Management Information Svsteni, and the objeotlves of DSS. Aftervards, our major discussions are devoted to the following issues respeotively: (i) The iterative design for DSS (prototyping approach) as part of the broa- der aspeot of DSS building, where we consider cost and benefit impacts and the posaible problems and bottle-necks of this approach. (ii) The current oomputer and information technology that sup- ports DSS, e.g.: on-line oomputer sy3tems with the necessary software support for ioteraotive use, softkiare paokages for oomputer graphics, statistics, operations research, financial planning, etc, quick hit DSS programs,fourth generation languages, database management systems and so on. The advantages, disadvantages, and limitations in practioe are discussed. (iii) The current status and application of DSS for marketing analysi3, sales forecasting, financial planning, transportation, human resources management, use of graphics in decision iiiaking, etc. (iv) The future trends in DSS. We give a broad overvien of the expeoted development and use of DSS, in partioular the furt- her impact of technology and the neoes3ary changes in organizations and decision processes. Some methodologies and program paokages that were listed above were developed in the research programs carried out at the Craduate School of Business, Indiana University, Bloomington, Indiana, USA, and Ekonomska fakulteta Borisa Kidriča, Ljubljana, Yugoslavia. 1. INTRODUCTION In this paper we descrlbe and analyze Decision Support Systems (DSS) as a class of Informati­ on System3 (IS) that support deoision-making activities of managers and others who are in- volved in deoision-making prooedures and pro­ cesses, the technology of DSS and its applica­ tion, and the trends and future directlons of DSS and supporting teohnologies. We first discuss the relations betveen Elec­ tronic Data Processing (EDP), Management In­ formation Systems (MIS) and DSS. Differeot views about this issue are briefly stated and explained, taking into consideration the per- sonalities of various authors. The practition- ers and the theoreticians frequently view the problems differently. We then concentrate our attention on the variety of different teohno­ logies and their applications in DSS.'We de- tall more the most important teohnologies in DSS, emphasizing those on which some reasonab- le amount of research has been conducted uithin the high sohools the.authors belong to. For instance, data base management, analytical raethods, oomputer graphics, spreadsheets, pro- totyping and fourth-generation languages C^GLs). In conclusion we summarize the general views and believes on the future trends and developments in DSS and DSS technology. Short- term trends up to 1990, and long-term trends, after 1990, are briefly discussed. We believe these forthcoming technological and organiza- tional changes in DSS will have a significant impact on future high school curriculum and pedagogy in the MIS area. 2. DECISION SUPPORT SYSTEMS VERSUS MANAGEMENT INFORMATION SYSTEMS AND THE OBJECTIVES OF DECISION SUPPORT SISTEMS Withih the steady advanoement of oomputer-based IS in organizations a new stage has been reac- hed where the term DSS has been introduced. Different explanations of DSS have been provi- ded. Some view DSS as another step in the natu- ral evolutionary advanoement of information technology and its use in the organizational context, following EDP and MIS. Others view DSS as an important subjeot of MIS or just a type of system that has been developed and used for several years already but has only reoently been uniquely defined. Some olaim that the term has been introduced merely to attract people but it does not deflne anything partiou­ lar new in the fleld of oomputer-based IS. We discuss briefly these issues in the following lines, state some definitions on DSS and in this way define the subject which we present and analyze in this paper. Hany vague, either restriotive or very broad, definitions of DSS vere put forward in the 1970s. They didnit help to clarify which of the above stated explanations were appropriate 19 and which were not. The first definitlon of DSS as an interactive oomputer-based system that helps decision makers utilize data and modela to aolve unstructured problema was later exten- ded to ali the sy3tems that oontribute to deci­ sion makihg. Some exaraple3 of complex systems were also examined. More promising than the de- finitional approaoh or the example approaoh vas the "characteristics" approaoh of DSS [2, 11] that assooiated with DSS the follouing characteristics: - they are aimed at the less well struotured, underspecified problems that upper-level managera faoe - they combine the use of models of analytic techniques with traditional data acoess and retrieval functiona - they focua on featurea that make thera eaay to use by noncomputer people in an interac­ tive mode - they eraphasize flexibility and adaptability to aocommodate ohanges in the 'environment and the decision-making approaoh of the user In short, DSS should support managers in their decision-making aotivities. The ideas and re- search resulted in the form of programmed pac- kages for building DSS, first used on raainfra- mes and later on personal computers. Based on the previoua developraent and research, Sprague and Carlson [21] gave the following definitlon of DSS: "Computer-baaed 3ystems that help decision makera confront ill-structured problems thro- ugh direot interaotion with data and analysls models". Aooording to them a good DSS should have the folloving three capabilities: - it should be easy to use and should support the interaotion with nonteohnical users - it should have access to a wide variety of data, and - it should provide analysis and modeling in a variety of ways In order to make a clear disti the terras MIS and DSS we must in our analysi3. There are two on what MIS and DSS should be. the theoretioal view, is the v cians who are concerned about theoretical baokgrounds of DSS oalled connotational, view is era' view, who make conclusion tions on the basis of their ex creating and using some partic DSS. It is largely the practit port the view that MIS is an a EDP and that DSS is a further MIS. nction betvieen go a bit further general views The first view, iew of academl- and who develop The seoond, so the praotition- s and defini- periences in ular MIS and ioners who sup- dvanoement of advancement of Connotational view on EDP-MIS-DSS differentla- tes the three terms on the following basic cha- raoteristica: - EDP emphaslzes optimum data prooessing with the outputs aimed and used at the operatio- nal level - MIS emphasizea integrated Information acgui- sition environment based on DBMS with the outputs aimed at the taotical level - DSS emphasizes user decision-making real- -time Information acquisition environment aimed at top managers and executive decision makers The view is partlally supported by oase studies, but it is nevertheless inapprophiate as far as the future developraent of DSS is concerned. Decision-making is not the exclusive domain of the top le*el management. Decision-making must be diatributed across aH three funotional levela. The theoretical view pro^eeds from and is ba­ sed on the folloving objeotives of the IS funotion: - improving performanoe in order to get the right Information to the right person at the right tirne - users involved in IS are knouledge workers, auch as managers, professionals, and other employees, who also are responsible for furt­ her developraent of Information technology - the paradigm of IS ia a goal-seeking organi- zation This view places DSS among other major techno- logy subsystem3 of IS which are interacting with each other and other application systeras and whioh are supporting users on ali vertical levels of management, not only at the very top level. Other major technology sub3ystems are (i) the system to support oomraunication needs and (ii) the struotured reporting system. This standpoint is defended by a general merging of Information teohnology, operations research, statistics and management science approaches in the form of interactive modeling [21] . DSS is acoepted as evolutionary advancement in the systems diraension of a three-dimensional IS model which evolved from the two-dimensional model of MIS. DSS requirea new strategies in design of the Information systems teohnology and its Interactive usage, from those used in MIS. Hackathorn and Keen [1 O] interpret DSS in term of the number of people that participate in the decision-making prooess as - independent decision-making where a deoision- maker makes decisions. This approaoh requires personal support - sequential interdependent decision-making vhere a decision-maker makes part of a deci­ sion, vihioh is then paased on to someone else. The approaoh requires organizational support - pooled interdependent decision-making uhere several decision-makers negotiate and inter- aot in order to make a decision. This requi- res group support Experie nologie deliver somethl ted at mation became (instea ganizat compute Nowaday of DSS nce show3 s in IS u Frequen ng qulte the very diverted well-3tru d of elec ions) and rs (inste users s as define tha sual tiy diff begi from otur tron wor ad o till d ab t new approaoh ly promise mor the real contr erent from wha nning. MIS and their promise ed reporting s io nervoua sys d prooessing o f a paperless believe in th ove. DSS CONCEPT, TECHNOLOGY OF DSS, CATIONS OF DSS es and tech- e than they ibution ia t was expec- offlce auto- 3 [22lahd ystems tem for or- n personal Office). e promise AND APPLI- Traditlonal data prooessing 3y3teras (suoh as payroll, inventory, alrline and similar reser- vation systeras) sinoe the early 1950s have typically taken the form of predefined reports. Infomation is produced by either aggregating or disaggregating data within the system. The 20 informatlon is static and rigid and any question that had not been inoluded in the process would demand a dlfficult and tlme-oonsuraing procedure, including new programa and changes to the data structures. Traditlonal EDP and MIS are not "user-frlendljt" (require programmers), their technologjr, whioh the programmers use, is rlgld, hard to change and demands a lot of programraer tlme in order to produce results. Ouestions to the aystem must be predeflned and not some unusual requests from the hlgh-level deelsion makers. DSS commonly oopes with problema that are not structured, i.e., no prooedures for their so- lutlon are known. Such problems-decisions, are for example: plannlng the amount of orga- nizational expendlture, deoidlng whether to Introduce a neK product or program, such as a new airllne, or a new technology procedure. Declslon-making Involves multiple crlteria and results in a number of trade-offs which are analyzed and modified iteratively by the deoi- sion-maker possesslng great experlence C 73. It requlre3 an interaotlve computer 3ystem wlth ali the neces3ary support software and a oonsiderable amount of data which the deoision- maker ušes while explorlng the problem. The syst«m must support the decision-maker by sug- gesting Solutions and the possible oonsequen- ces of acoepting a particular solutlon. Withln the 3ul>sequent steps of the iterative process the deoislon-maker may baok-traok, modify, reflne and introduce more sophistlcation into the solution several times until, he flnds a satisfaotory final solution. Thus, in this way, a DSS beoomes a tool for building a model or oreating a solution of the future state of the business, based upon sets of assumptions and relationships supplied by managers and other users. DSSs need subsystems with data and algorithms that a decision-maker can use. These subsy- stems can sometimes act independently but they can also be inte rated. Examples of such ap- plioatlon are: (1) to retrieve a sifflple data itero whlle processing an order for a particu­ lar commodity, (2) to generate a report of ali the flrm's forelgn customers In the last year, (3) to use mathematical prograraning algorithra in order to perform allocation plan­ nlng, (t) to perform a formal statistical analy3is to find the correlation betneen dif- ferent variables, (5) the use of models, uhere an expert DSS system has been created, based on the past many years' experlenoes and deoislon-making prooedures of some expert. The preaent decision-maker gets help by uslng this model when he has to make a deel­ sion, The DSS approaoh is a user-friendly approach. Users acqulre informatlon from a DSS nithout the help from a programmer. They do this by uslng a query language and/or a request gene­ rator. Instead of procedural languages, like COBOL, FORTRAN, etc, DSSs use the fourth- generatlon languages. In tCLs one statement is equivalent of many statements in a proce­ dural language. These languages also use sy- stem prompts and help oommands in order to make them easler to use and understand. In the follovflng paragraphs we discuss the technology necesaary for implementlng the DSS concept. We restrict ourselves mainly to the computer software and techniques used In prob- lem-aolving. One of the most iraportant raethodologies is a DATA BASE MANAGEMENT. The DSS user (manager) frequently retrieves data items from a DB randomly, produces reports frora a DBMS or creates and manipulates more eomplex logleal data structures for a sy3tem which allows him to produce raodels involving data of certain properties. Present DBMS are stili part of the aoftnare, although in the future they wHl probably beoome part of hardvare or firmnare. They are also a.vailable on microoomputers and personal oomputers, not only on large and po- werful mainframea. Software produots with im- beded DBMs such as FOCUS, RAMIS II, N0MAD2, EXPRESS, GADS, EIS, REGIS, GMIS, etc. are avallable on the market. They are expenslve to buy (but prices are hopefully declining) and they require large amounts of oomputlng resour- ces [7, 21, 222. Therefore, initially many of these systems were belng used in independent domputing oompanies or in coraputers manufacturers Effičient use of this technology is based largely on the adequate supporting documenta- tion, such as: - data flow dlagrams, which present a graphlc model of processing, the storage of data and the movement of data - data dictionary, Hhioh oontains the terms and their definltons - process descriptions, where each process bubble must be deseribed in sufficient detail [18] Hhen building the DB management part of DSS we must choose one or more basio data structures, i.e., a method of representing and retrievlng data in a computer. In addltion to the four well-known data models used in MIS — the re- oord model (flat file), the hierarchio model, the netvfork model, and the relational model, one more model, the rule model, is belng uaed in DSS environment 121^ . This model is oommon in artlficial intelllgenoe systems and in so- called "knouledge-based" DSS. It apecifles produotion rules and enables making inferences based on the data. The rule model describes data by a set of rules, i.e., a set of data definltions. The ehoice of model should not be based on the representation of the data, but on the operations and integrlty oonstralnts. Among the ANALYTICAL METHODS that DSS needs for analyais and modellng are statistical pro- cedures, data projectlon or slmulation and optimizing models. Statistical packages, such as SPSS and IDA are belng used In many univer- sities and firms ali around the uorld. The In­ teractive Financial Plannlng System (IFPS) Is an example of the flnanoial planning modelling languages for data projectlon and slmulation. It can be used on VAX and some other minicom- puters, and malnframes. IFPS has been taught and used in the Graduate School of Business, Indiana University, Bloomington, Indlana, USA as an effičient programming tool for several years, and it has been widely adopted in the USA, in both industry and univeraltles. Its impetus oame from the desire to model rlsk in a way whlch oould easlly be understood by executives. IFPS haa a self-contalned non- prooedural language whioh is easy to underst­ and and use [ 8^. The loglc and output of IFPS resemble those of apreadsheet paokagea. A number of third-party computer software pac­ kages compatible with IFPS are available; For instance: SENTRlf for data entry in a form com­ patible Hith IFPS; DATASPAN for converting data bases In arbltrary format to a form usable by IFPS; GRAPHICS for presenting IFPS reaults on oolor graphica di3plays; and OPTI- MUM for flnding optlmal solutions of IFPS mo­ dels by linear, nonlinear, or integer program­ ming. Optimizing models are usually based upon mathematical programming algorithraa. A group of reaearchers at Ekonomska fakulteta Borisa Kidriča, Ljubljana, Yugoslavia has pur- sued this field of researeh for many years, 21 developed several program procedures in Ope- rations Research (linear and dvnamio program- ming) and published numerous researoh papers Cl, 5, 19]. Part of this softnare development waa supported by Intertrade, Ljubljana, the IBM repreaentative in Yugoslavia, the lugoslav Computer manufacturers firm Iskra-Delta, LJubljana and the 3oftware house Iskra-CAOP, LJubljana. INTERACTIVE COMPUTER CAPABILITIES must be available in DSS environment. The followlng approachea of DSS computer Implementation.are now possible: - DSS 30ftware on a large-scale general com­ puter whloh users acoess from terminals in an Interactive mode - DSS softHare on a dedlcated minicoraputer, Hhere users are involved in different DSS applicationa simultaneoualy through their terminala. Here the special DSS softvare doea not 3low down other Jobs being run on the mainframe. A disadvantage of this ap- proach is that the required central data must often be transmitted to the minicora­ puter through magnetic tape, eto. which makes the system slov* - the use of of smaller because of Serious dis . softnare th; and minicom secondary s traction an mlcrocomput in the čase Some DSS (now tion System3 employing bul deoision-maki system, the e is studied an that behaves 3y3tems have oil'explorati various busin king commerci eto.). personal computers to host DSS size. This approach is popular easy acces3ibllity for the user. advantages are far leas powerful n is available on mainframes puters and relatively small torage oapacity. Also the abs- d loading of central data on ers are even more awkward than of the minicomputers being labeled Exeoutive Informa- (EIS)) help decision-makers by It-in EXPERT SYSTEMS -- expert'3 ng procedures. To build suoh a xpert's decision-making process d a computer program ia written similarly to the expert. Expert been used in medical diagnosis, on, computer chip design, and in ess applications (auditing, ma- al loans, financial planning, An especially important technology for busi-^ ness probleo-solving and decision-making is COMPUTER GRAPHICS. It helps managers to acguire visual representations of data, rela- tionships and summaries for Information aoti- vities are not based on predefined processes or procedures. Graphics allows the users to view or search the data in new and creative way3 in the context of their particular prob­ lema or goals. Several graphics forms can be generated by computers, such as texts, tirne series charts, bar charts, motion graphics, scatter diagrams, maps, hierarohy charts, seguence charts, etc. They can be used in DSS In many different ways such as: reports, pre- sentations, management traoking of performan- ce, analysis, planning and scheduling, com- mand and control, for design, engineering and prodiction drawings. Graphics can also be used in coraputer-aided design, computer-aided manufacturlng, teleoonferencing and videotex systems. The benefits of computer graphics over manual graphics are in oosts and tirne. Formats, scales and colors can be tested in' order to obtain the best comprehenaion of the information. Objections to computer graphics are that high resolution graphics are stili very expensive; sometimes the graphs are of low quality, and require skilled and experien- ced users who can produce good graphics. Some of these objections vili become irrelevant with the new computer graphics products. The Operations and Systems Management department at the Graduate School of Business, Indiana Uni- versity, Bloomington, Indiana has made in the last decade extensive research on the effects of different forms of computer graphics presenta- tions, their complexity and color of presentation on the human decision-maker, see for example [93. This line of research is a part of a broader pro­ gram of research, called PRIMIS - Program of Re­ search for Investigating MIS, which focuses on the user-system interface of DSS. A theory of graphics information presentation has been forma- lized: Performance with a given information pre­ sentation is a function of question difficulty, information complexity, the form of presentation and color. Another widely accepted DSS teehnology are SPREAD- SHEETŠ. They are self-documenting systems with explanatory Internal documentation and prompts that help the user to prooeed his dialog with the computer from one step of the problem solving procedure to another. Their main advantages are that the user gets the data in a table form on video soreen and the relationships betveen data series in a form of report. The user may test the impact of some particular data item or group of data items and/or relations aoong them on the model'3 output. He can do this by ineraotively entering different values for data items, tempo- rarily changing the algorithm and analyzlng the oomputed results. This "uhat if" oapability is present in many spreadsheet packages on the market. These spreadsheet packages are aimed at problem-solving and model-development in fields of financial planning (amortization, depreclati- on, lease-versus-buy, discounted oash flows and net present value), real-estate investments (fi- nanaing alternatives,impact on taxea, payoffs, cash flows), busineaa reoord-keeping and accoun- ting, budgeting and statiatics. Deapite their popularity, theae apreadaheeta have many inherent Keakneaaea, such aa the difflculty of specifying ali data requirements a priori, data-model depen- dence, limitation of the relations that represent the model and model's complexity by the spreads- heefs table format, user'3 session cannot be recorded and little flexibility in report uriting featurea. It is estimated that 20 to 30 percent of the users will become dissatisfled with spreads- heets and wlll ask for more ponerful tools L22l. Spreadsheets appear to be most useful for smaller problema. The poaaible solution to the existing variety of many different spreadsheet 3ystems which the users have to learn, would be integra- ted packages that uill conbine spreadsheets, word Processing, data management, graphics, data Communications, and other resources. SVMPHOlflf is a (not too successful) example of this trend in the software market. SYMPH0NY expands the capa- bilities of LOTUS 1, 2, 3- QUICK HIT DSS 1163 is a term that explalns a special procedure used more and more in DSS. It stands for a rather simple DSS prototype which the decision-maker creates and processes before he decides vhether to build a full DSS or not, Three types of quiok hit DSS include: - reporting DSS - short analysis programa - DSS generators Reporting DSS is a very frequently used form of decision support which includes simple data manipulations (selecting, summarizing, and listing data from files, some other arithmetic operations on theae data, presentation of trenda and variances, by means of computer graphics) in order to meet some information needs of decision- maker. Short Analy3is Programa are uaed for analyzing data. They need small amount of data and are usually written by deciaion-makers themselves 22 In BASIC or some other high-level programming language. Functions that help the declslon- maker to make deolsions Include projeotion of oosts, income, and proflts; allooation of fixed oosts among produotsj project manage- ment; graphing of some activity output figu- res, etc... Exaraples of short analysis pro­ grama are atudied In [1, 6, 163. Deeision Support Sy3tem Generators are pro- ducts which Include languages, interfaoes, and other faoilities that help to frame up specific DSS. DSS generator can be used to build more specific DSS within a olass of applications. In recent year3 users are gene- rally interested in DSS generators and fourth- generation languages and not much in the other two types of quick hit DSS. These quiok hit approaches are not appropria- te in areas suoh as forecaating or allooation of resourcea where a deep understanding of aophistlcated methoda, tebhnlques and the whole appllcation area is neces3ary and the final modela cannot be replaced by aome ap- proximationa of them in order to make some starting deeision C33- PFOTOTYPING has been defined in many different ways: as a philoaophy, a methodology and a procedure. Klthin each of theae clasaea of definition are further differenoes. For exam- ple, aa a methodology for the development of Information ay3tem3 two definitlons are very evident: the "rapid prototyping" approaoh from Computer scienoe and the "prototyping methodology (PM)" from MIS. The PM Dslia moat appropriate in disoussing DSS and is broad enough to encompaas the various more limited definitions found in the DSS litera­ ture. Under the PM the prooess for building an operational prototype is described and this operational prototype raay be used in various waya, e.g., atand alone, with life- scale methodologies, as pilots or prototypes and as "throwaway" prograras. Senn desoribes prototyping as one of the seven aotivities within the system development llfe cyole['20] : preliminary inveatigation, determination of requirementa, development of prototype aya- tem, design of 3y3tem, development of soft- ware, system3 testing, and implementation. This approach ia used when we cannot define ali the featurea of the 3y3tera in advanoe, due to the lack of experience or Information, or when we face high-cost and high-riak situations. In auch caaea an inexpensive small-soale version of the softvare is pre- pared in order to provide some preliminary Information about the environraent in uhlch the ay3tem ia going to work. The prototype ia a simple worklng sy3tem that captures the essence of the real sy3tem it represents, and it may be refined and redone aeveral times uithin the iterative prooess, in order to find the optimum solution for the defined problem. Prototyping approach needs a 3oftware that enables a guick and simple building of a Korking system. Conventional programming languages and methods, or more adequate soft- ware products, like program generators can be used for this purpose. The erophaais is on trying out ideaa and providlng assumptions about requirement, not on system efficiency or completeness L20l • According to [l2l, the ideal softvare prototyping environment has four components: - a 1GL or other development tool to allow quick creation of the prototype - well-raanaged data resources for easy access to corporate data - a user who has a problem, uho has consldered the idea of uslng the new tool, uho knows his or her funotional area well, and who seeka assiatanoe from data processing - a prototype builder — an Information sy3tem3 professional Hho is versed in uaing the varioua development toola and understands the organiza- tiona's data resources The same author also auggeata an ideal team size for prototyping -- one user and one builder. Larger teams impose more uncertainty into the problem definition and solving procedure, need more tirne for coordination, etc... Proper anavers to the key output que3tion3 represent an important issue of prototyplng. These questions are: - who will receive the output — vhat is its planned use - hov much detail is needed is the output needed, and - by what method Hhen and how often There are three main usea for aoftware prototy- pea [22]: - to olarify uaer requirementa. Host users cannot fully describe their current requirements and their future needs. By building, using, and changing a prototype, users can make further declsions about the sy3tem they want to verify the type can show tem how the ne eiency and its the 3y3tem ina to create a fi may become par peoially, when very often, it the production easier feasi the e sy3 cost dequa nal 3 t of the is u vers bility of desi nd users of a tem viould oper 3. The end uae te and stop or y3tem. Part of the production syateB ia expe sefully to use ion. This make gn. A proto- designed 3ys- ate, its effi- rs may find change it the prototype version. Es- cted to change MCL also for 3 future use d with prototyping tičdted~ software ng brings forward o a sy3tem, based on requirement3 and a needed syatem. This eling of reguireraen pecifications based nefficienoies and e For this reason, ctive when used to the eatablished information 3y3tem are (i) toola and nly the user quiok does not ts and on proto- rrors of proto- enhance, analysis develop- The problems relate the need for sophis (ii) that prototypi physical aspects of demands of physical realization of the support loglcal mod Solutions. System s type3 may inherit i the original system typing is most effe rather than replace process in computer ment[l8;). According to [i 3"! the commereial FOURTH-GENERA- TION LANGUAGES (UGLs) have provided a significant contribution tonards making the concept of proto- typing practloable as a methodology for sy3tem design and development. They have also helped to create a new information prooesslng environment, referred to as "End-user Computing", despite their primary objective — to speed up develop­ ment and maintenance by professional programmers. It is claimed that the productivity in applica­ tions development when using MGLs is 5 to 10 times over that when using third-generation languages, particularly C0B0L[17]. The early the end of were devel known gene have beoom Further, a uage Inter lity, that or previos (electronl devel the oped ral-p e ava vari faoes coul ly de o spr opment of tG 196OS when t As the resu urpose MGLs ilable: RAMI ety of other ulth eleme d be used wi veloped MGLs eadsheets - Ls started ime-sharin It, three softuare p S, FOCUS, usei—frie nts of non th the exi have bee LOTUS, bus towards g and DBMS most well- roducts and NOMAD. ndly lang- procedura- sting DBMS, h developed iness 23 modeling - IFPS, business graphios, statiatics, etc). Anothep signiflcant event, related to the '4GLs"evolution was the development of IBM'3 DB2, uhioh is a member of a faraily of relatio- nal DBMS produots from IBM, ali supportlng a comnon relatlonal language called SQL (Struc- tured Query Language). IBM announeed its entry into the l)GL market in mid 1986, Cross Sy3tem Product (CSP). The features of MCLs that coraprise the funotio- nailty that is inoluded in fourth-generation 3oftware tools are several [22], like: DBMS, data dictionary (DD), non-procedural language, Interactive query faoilitles, report genera­ tor, aelection and sorting, soreen formatter, word prooessor or text editor, graphlcs, data analy3i3 and modeling tools, programming in- terfaoe, softuare development library, backup and recovery, links to other DBMS, records and flle maintenanoe, etc. The heart of a IGL is a DBMS, which can manipulate formatted data re­ cords, as well as unformatted text and graphios data. Just as important as the DBMS is the DD, for storing the data definitions used by the i*CL. In contrast to 3GLs, UCLs eraploy an English-like 3yntax, and are eventually non- procedural in nature — they allOH statements to ocour in the logioal order that a user would think, rather than imposing a aeguence reguired by the computer. Besides an underlying comm.ind language, many IGLs provide a variety of interfaoes that help end-users in using them. As far as the results (output) are con- cerned, some IGLs generate only slngle prog­ rama (oode generators), and produce an inter- mediate step code in 3GL, usually in COBOL, while others generate coraplete Integrated ap- plications (application generators), and do not produce any 3GL intermedlate step code (FOCUS, RAMIS II, N0MAD2, . ..).• The functions performed by tGLs vary greatly from product to product. Some tGL products have highly fooused but limited functionality, oriented tonards speclfic applications, like decision-support/modeling tools suoh as IFPS; graphies generators suoh as Tell-A-Graf; query and report-generating tools suoh as DATATRIEVE, and INTELLECT. Some «GLs are more powerful and comprehensive in terms of their functional oapabilities, and represent more nearly integrated software sy3tems rather than "programming languages" as the terra is oommon- ly understood [131- First thoughts, that the emergence of IGLs means the demise of COBOL have been revised when i)GL-related softuare (analyzers, genera­ tors, and programmer uorkbenches) began emer- ged. It is now believed that COBOL will con- tinue to be the dominant language of business Into the twenty-first century. The MGL market has not roatured yet. The current effort to­ nards 5CL hardware impose a questlon uhether MGL softuare Mili mature at ali or simply blend into 5GL softuare. 14. THE FUTURE TRENDS IN DSS AND DSS TECHNOLOGY The short-term trends, from 1986 to 1990, will be an extension of today happenings, vith a substantial inoreasa in personal and organiza- tional use of computer teohnology, as hlgh as 70 to 90 percent inorease in computer Proces­ sing power per year. In less than five years, for instance, there are 8 million users of personal computers with an inorease by more than 30 percent annually. But even more impor­ tant than the above stated advanoements are the trends in the change in the application of Information teohnology to the point uhere users no longer face technical intermediaries betveen technology and its application. The follouing major trends can be expected [223: - personal computer-based DSS will continue to grow, vith spreadsheets and other creati- vity supportlng packages taklng more and more functions in analysis and declslon making - grouth in distributed DSS, with close linka- ges betvieen mainframe DSS languages and gene­ rators and the PC-based faoilitles - group DSS approach, suRTirted by looal area networks and group Communications services, like electronlc mail, wlll become much more oommon - DSS products will incorporate products (tools and teohnique3> of artificial intelligence, instead of the statistical and management science models of the past. "Iritelligent DSS" will assimilate expert systems, knowled- ge representation, natural language query, voice and pattern recognition, etc..., and will be able to "suggest, learn, and under- stand" tasks and problema More user friendllness is expected from the computer technology, such as dialog support hardware (light pens, touch screens), high- resolution graphlcs, speeeh recognition and synthesis, raenus, windows, etc... It has been proven, for exarople, that for data manipulation the users strongly preferred volee over keying, because they could continually look at the screen while they dictated operations. It is also believed that expert 3ystems, as part of DSS, will be used more than they are nov. Many experts stili argue on vhat is and is not an expert system. For this reason, some authors t22'3 prefer to speak only about praotical "expert-like" systems. These are systems, that oapture logic of the application problem by means of a small number or even hundreds of IF...THEN...rules, and can be programmed in any high-level language, such as COBOL, FORTRAN, APL, BASIC, LISP and PROLOG or be expressed in a decision table form. Vhat really matters is that they must help users in making better decisions. Future trends vili shou no major changes in basic hardvare teohnologles, though speeds and oapaoities vili be improving 3teadily at about 10 to 20 percent per year rate, physical rate vili be dirainishing, vhile the product life- cycle vili remain at about three years. In some areas of applications,for example in transacti- ons prooessing systems, the trends to replace procedural languages vith more poverful tools vili be very slov. In the 1990s the personal computer is going to become a management support facility (MSF). It vili be videly used in organizations ali around the vorld because of its technological attrlbutes and lov cost. Many decision-makers vili have MSF both in the offioe and at home. MSF vili have memorv sizes of 10-15 megabytes, and sooondary disk storage of up to 250 mega- byte3. Very u3er-friendly l4GLs, vhich integrate oomputing, modeling, data management, and text processing vili be available on ali devices. More interesting functions of the technology vili be related to the deciaion-support funct- ion: the mainframe databases that are neces- sary in an organization'3 transactions proces­ sing system vili be created and comblned vith large sub-databases at MSF. Certain system3 vili be developed to vork vith their local databases in a problem-finding mode vhich vili help the manager in decislon-making. One mode of this type of operation is the use of expert systems in order to locate and solve problems. These problems can be categorized as: dealing vith 24 a erises, evaluating the overall effect of a change, balanclng the use of resources, deci- slons that must be inade on resource replaoment or acqulsltlon, and trylng to foreoast the future. Todajfs largest expert sy3teins involve thousands of loglcal rules and thousands of objects to which the rules apply. The goals for the 19903are the expert sy3teins wlth tens of thousands of inference rules and up to 100 mlllion objects C22]. Technologlcal and organlzatlonal changes vili also cause changes in the process of manage- ment. Three posslble manager groups are fore- seen: - Information syste(n3 managers, who are res- ponslble for the creation, maintenance, and developoeht of the over-all informatlon sy3tems and Its resources; - user managers, who use the centralized In­ formation resources, and create, develop and use their personal and funotlonal area Information 3y3tems; - senior managers (executive management) who pursue the Information systems and resource allocatlon policies. The impllcations drawn frora the application of DSS across ali forms of enterprise has great importanoe to higher education. Univer- sities must produce Information llterate as well as Computer literate graduates who can function in the environment of the modern organization. There is, therefore, a need to provlde a general course in informatlon sys- tems that would be taken by ali students re- gardless of their major discipline. We need to produce intelligent users of IS. niversities offering a re even greater. 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