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PRODUCTION PLANNING & CONTROL, 1999, VOL. 10, NO. 5, 414–425 An expert system for the selection of production planning and control software packages I. P. TATSIOPOULOS and N. D. MEKRAS Keywords expert systems, logic programming (Prolog) pro- duction planning and control (PPC) software, production systems Abstract. This paper presents a rule-based expert system that can be used for the selection of a suitable production planning and control (PPC) software package to be applied in a manu- facturing rm. A production system’s typology and a compact PPC software reference model are included in the knowledge base which has been created. The inferences made are based on rules that relate a semantic network of PPC software features with a semantic network of production systems’ attributes. The results given by the expert system include the module architec- ture and the set of features of the PPC software package, which are applicable in a certain manufacturing setting. 1. Introduction Production managers and consulting experts nd it diæ cult to make the right choice for the appropriate production planning and control (PPC) software system that should be applied in a speci c manufacturing rm, because of the diå erent types of production systems and the lack of suæ cient standardization of PPC com- mercial software packages. These packages may be either autonomous systems or part of large integrated ERP (enterprise resource planning) systems. Relatively few eå orts have been reported in the literature on this speci c subject of evaluating and selecting PPC software packages. Early frameworks for structuring the above problem are put forward by Wortmann (1984) and Tatsiopoulos (1989, 1990), while the basic pioneer- ing eå ort in this eld that produced results of signi cant industrial relevance is the methodology and software system BAPSY of the TH Aachen (Hoå and Virnich 1986, Hackstein 1990, Hackstein and Virnich 1991, Paegert and Vogeler 1996) . This system uses multicriteria and value analysis methods to compare the PPC software packages of the German market. A Authors: I. P. Tatsiopoulos and N. D. Mekras, National Technical University of Athens, Mechanical Enginnering Department, Industrial Management and OR Section, 15780 Zogra ´fos, Athens, Greece. I lias P. Tatsiopoulosis an associate professor in production and logis- tics at the Industrial Management and OR Section of the National Technical University of Athens (NTUA). He is also a member of the Greek Government Committee for Purchasing. He has been active for several years as a professional production engineer in both industrial and consulting rms, and he served as a lecturer in management information systems at the Economic University of Athens. He studied Mechanical and Industrial Engineering at NTUA (1978) and followed post- graduate studies at the TH Aachen (Germany) and the University of Lancaster (UK) under a NATO grant. He holds a PhD (1983) in Operational Research from the University of Lancaster. He has been a member of the Senate of NTUA and Vice-Chairman of the Greek Institute for Production Management (HMA). Nicholas D. Mekras is a research scientist at the National Technical University of Athens (NTUA). He received his Diploma as a Mechanical and Industrial Engineer from NTUA, and his PhD from the Sector of Industrial Management and Operations Research of the same University in 1993. His main research interests come from the eld of applications of arti cial intelligence in production systems, including knowledge-based systems and arti cial neural net- works. He has participated in several EU projects, and he has given several courses, concerning production management and applications of information technology in production systems. Production Planning & Control ISSN 0953–7287 print/ISSN 1366–5871 online Ñ 1999 Taylor & Francis Ltd http://www.tandf.co.uk/JNLS/ppc.htm http://www.taylorandfrancis.com/JNLS/ppc.htm

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PRODUCTION PLANNING & CONTROL, 1999, VOL. 10, NO. 5, 414–425

An expert system for the selection of productionplanning and control software packages

I. P. TATSIOPOULOS and N. D. MEKRAS

Keywords expert systems, logic programming ( Prolog) pro-duction planning and control ( PPC) software, productionsystems

Abstract. This paper presents a rule-based expert system thatcan be used for the selection of a suitable production planningand control ( PPC) software package to be applied in a manu-facturing � rm. A production system’s typology and a compactPPC software reference model are included in the knowledgebase which has been created. The inferences made are based onrules that relate a semantic network of PPC software featureswith a semantic network of production systems’ attributes. Theresults given by the expert system include the module architec-ture and the set of features of the PPC software package, whichare applicable in a certain manufacturing setting.

1. Introduction

Production managers and consulting experts � nd itdiæ cult to make the right choice for the appropriate

production planning and control ( PPC) software systemthat should be applied in a speci� c manufacturing � rm,because of the di å erent types of production systemsand the lack of suæ cient standardization of PPC com-mercial software packages. These packages may beeither autonomous systems or part of large integratedERP ( enterprise resource planning) systems. Relativelyfew e å orts have been reported in the literature onthis speci� c subject of evaluating and selecting PPCsoftware packages. Early frameworks for structuring theabove problem are put forward by Wortmann ( 1984)and Tatsiopoulos ( 1989, 1990) , while the basic pioneer-ing e å ort in this � eld that produced results of signi� cantindustrial relevance is the methodology and softwaresystem BAPSY of the TH Aachen ( Ho å and Virnich1986, Hackstein 1990, Hackstein and Virnich 1991,Paegert and Vogeler 1996) . This system usesmulticriteria and value analysis methods to comparethe PPC software packages of the German market. A

Authors: I . P. Tatsiopoulos and N. D. Mekras, National Technical University of Athens,Mechanical Enginnering Department, Industrial Management and OR Section, 15780Zografos, Athens, Greece. Ilias P. Tatsiopoulosis an associate professor in production and logis-tics at the Industrial Management and OR Section of the National Technical University of Athens(NTUA) . He is also a member of the Greek Government Committee for Purchasing. He has beenactive for several years as a professional production engineer in both industrial and consulting� rms, and he served as a lecturer in management information systems at the Economic Universityof Athens. He studied Mechanical and Industrial Engineering at NTUA ( 1978) and followed post-graduate studies at the TH Aachen (Germany) and the University of Lancaster ( UK) under aNATO grant. He holds a PhD (1983) in Operational Research from the University of Lancaster.He has been a member of the Senate of NTUA and Vice-Chairman of the Greek Institute forProduction Management ( HMA) .

Nicholas D. Mekras is a research scientist at the National Technical University of Athens(NTUA) . He received his Diploma as a Mechanical and Industrial Engineer from NTUA, andhis PhD from the Sector of Industrial Management and Operations Research of the sameUniversity in 1993. His main research interests come from the � eld of applications of arti� cialintelligence in production systems, including knowledge-based systems and arti� cial neural net-works. He has participated in several EU projects, and he has given several courses, concerningproduction management and applications of information technology in production systems.

Production Planning & Control ISSN 0953–7287 print/ISSN 1366–5871 online Ñ 1999 Taylor & Francis Ltdhttp://www.tandf.co.uk/JNLS/ppc.htm

http://www.taylorandfrancis.com/JNLS/ppc.htm

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similar methodology which is less theoretically sound butmore practically oriented is the Geitner ( 1993) PPC mar-ket analysis database system. As an extensionof the research work on BAPSY, a recent paper byLuczak et al. ( 1998) advocates a fuzzy-set approach tothe evaluation of PPC systems. Sof tware Finder( Manufacturingsystems 1997) is a database rankingERP systems in the American market and selectingtheir suitability for speci� c industrial sectors ( e.g. chemi-cals, electronics, etc.) and/or speci� c operations systemstypes ( e.g. assembly to order, make to order, etc.) . Fromthe operations research world ( Fandel 1993) comes amathematically oriented approach to evaluate PPCsystems.

Another tool that has been developed under the Espritproject 5424 ( CIMple) is the software system FTM ( fasttrack modelling, Shemwell et al. 1992) , that helps the userto determine his functional requirements for PPC soft-ware and compare them with functions of existing PPCsoftware packages. Especially for the PPC software pack-age selection problem of SMEs, a recent software toolcalled SMEST ( Little et al. 1998) has been developedthat includes systems analysis, gap analysis and require-ments speci� cations modules.

From the consultants’ world come catalogues of soft-ware features, e.g. ELCO ( European LogisticsConsultants Association 1989) against which the suitabil-ity of a PPC software package for the needs of a speci� cmanufacturing � rm are assessed. This approach uses asizeable scoresheet ( requirements list) of over 500 criteria( ‘the longer, the better’) which forms part of the requestfor bidding sent to various software vendors ( ‘the more,the better’) . This process-oriented method is criticized byDe Heij ( 1995) as being maximalistic in nature and lead-ing to a useless surplus of software functionalities thatonly cause excessive costs to the speci� c user. De Heijadvocates a data-oriented approach to software selection,where the data models of software packages, forming thebasis of all functionality and being more stable over time,are compared to ideal reference data models. The prob-lem with this approach is that the software vendors areusually not willing to reveal details about their package’sunderlying data structures.

An interesting di å erent approach reported byWunderli ( 1988) forms ‘scenarios’ in two phases: ( i) a‘shakeout’ phase where a gross selection of packagestakes place; and ( ii) a ‘squeezeout’ phase where an ex-ample business process is chosen to be handled by allcandidate packages in a benchmarking mode. However,these case demonstrations are very intensive, especiallyfor the end-user representatives involved. Although casedemonstrations are seen as the best way for assessment,the results are still not satisfactory. Nevertheless, nobetter alternatives are available. The disadvantages are:

( i) the demonstrations are far too short to get a completeand clear insight; ( ii) the evaluation of the demonstra-tions is still very subjective and arbitrary; ( iii) the evalua-tion often says more about the quality and preparationperformed by the supplier who is demonstrating the case;and ( iv) the demonstrations are very time consuming.

The reported relevant e å orts in the literature concern-ing AI techniques in connection to problems of ‘selection’and ‘con� guration’ are the TWAICE system of Nixdorf( Mensel and Michel 1985, Krallmann 1986) which con-� gured the COMET integrated manufacturing softwarepackage to the customer needs, and Mertens et al. ( 1993)who developed a system to regulate and con� gure theparameters of the MRP system by SAP AG. A morerecent professional e å ort that turned into a commercialconsulting software product for the implementation ofSAP R/3 is the LIVE-Kit tool of Siemens/Nixdorf( 1998) as an evolution of the TWAICE system. Similarimplementation guides in the form of software productsare also available from the big ERP vendors themselves,e.g. SAP Business Engineer 4.0, ARIS Prof. Scheer( 1995) and DEM ( Dynamic Enterprise Modelling) ofBAAN. Although these software con� guration methodsand tools do not directly refer to the problem of PPCsoftware selection, they are based on the same commonbasis, which is the determination of the speci� c businessrequirements. In the case of selection, these requirementsare used for comparing the o å ered software packages( coverage analysis) , while in the case of software con� g-uration they are used for determining the customizatione å ort ( gap analysis) .

In this paper, a method based on expert system tech-niques ( Keller 1987, Meyer 1990) that can be used forthe selection of PPC software packages is presented.Using this method, expert knowledge concerning produc-tion systems and PPC software is gathered and classi� ed.Also, logical rules are used to relate characteristics of theproduction systems with the functions of PPC software.For the realization of the method, an expert system hasbeen constructed that gives as inference results therequired PPC software functions and the structure ofthe whole software package that these functions belongto. Explanation procedures have been embodied in theexpert system that shows the way in which the resultshave been obtained.

Semantic networks and rules are the basic knowledgerepresentation structures that Frost ( 1986) and Schneiderand Karagiannis ( 1988) used for the creation of theexpert system’s knowledge base. The programming lan-guage that has been used for the construction of thewhole expert system is Prolog, due to its capabilities forthe creation and processing of the knowledge representa-tion structures we mentioned before ( Malpas 1987, Rowe1988) .

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2. Knowledge base

The knowledge base consists of two semantic networksthat represent a production systems typology and a gen-eric PPC software package description in the form of amultiple reference model. Entities of the two semanticnetworks are linked by logical rules that express relationsbetween the production systems typology and the soft-ware modules with their features included in the PPCsoftware package. These rules are necessary for the infer-encing process because they drive the expert system to theconclusions concerning the suitability of the PPC soft-ware features for a certain manufacturing setting.

2.1. Production systems typology

Many authors have proposed classi� cations of produc-tion systems, e.g. Schmenner ( 1981) , Kettner et al. ( 1984)and Schmitt et al. ( 1985) . Their common characteristic isthat they adopt a narrow view of the physical productionsystem, dealing only with functional characteristics, e.g.shop layout and process � ow classi� cations, which are notenough to determine production management require-ments. Factors like management objectives and measuresof performance, main problem areas, special techno-logical processes and speci� c environmental constraints,which are connected to the various industrial sectors andsubsectors ( e.g. clothing industry, metals industry, etc.)are equally important.

The proposed typology in this study is of a twofoldnature, a functional typology and a typology based onindustrial sectors as classi� ed in public statistics services

( � gure 2) . For the � rst one, a system of classes of par-ameters and values has its origin on the Schomburg( 1980) typology that formed the basis of the BAPSYsystem, a methodology and software package of the THAachen that evaluates and selects production manage-ment software packages. The same typology has beenused by Scheer ( 1995) in his CIM reference architecture.The typology proposed here has signi� cant enlargementsand departures from the Schomburg model, which wasrestricted to mechanical assembly products in a singleplant environment. Moreover, the taxonomy proposedhere is extended and built using a logic programmingsoftware tool ( Prolog) . This software tool facilitates theconnection of the functional taxonomy ( FUNCTIONALCHARACTERISTIC, PRODUCTION TYPE) to the

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Figure 1. Knowledge base of production systems taxonomy.

Figure 2. Public classi� cation of industrial sectors and sub-sectors.

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industrial sectors taxonomy and all relative classes, i.e.PROBLEM AREA, OBJECTIVE, EXTERNALFACTOR and SIZE RANGE as seen in � gure 1.

For the creation of the production systems functionaltypology, a set of nine characteristics has been used( � gure 3) . These characteristics express the functionalsetting of production systems and are used to representtheir structure in the knowledge base.

According to the CIMOSA cube terminology, partialmodels of production systems can be built based on thosenine typological characteristics. Speci� c combinations oftheir instances give production system types. A particularmodel concerns the requirements of a speci� c company,which may have some di å erences from the partial modelof the production system type that the company belongsto. The above-described stepwise instantiation is greatlyhelped by the adoption of AI techniques supportinginheritance.

The typological knowledge base built using Prologincludes descriptions ( in terms of basic characteristics)of the following.

( i) Production systems found in sectors of the manu-facturing industry ( e.g. electronics, clothing,metals, oil and gas, automotive, etc.) .

( ii) Basic types of production systems in two directionswith subtypes for each one. The product-oriented

basic types ( ETO = Engineer to order, MTO =make to order, ATO = assembly to order, MTS= make to stock) which mainly in� uence thematerial management PPC modules, and the pro-cess-oriented basic types ( discrete, repetitive, pro-cess, one of a kind) which mainly in� uence theshop � oor control and process management PPCmodules.

The above form the production systems typologywhich is described and classi� ed in one of the two seman-tic networks of the knowledge base. In � gure 4, an ex-ample is given for the discrete manufacturing type in theelectronics industry.

For each of the above subcategories or combinations ofthem, a set of functional characteristics is de� ned thatgives the structure of the production system that theuser wants to describe.

2.2. A compact PPC software package ref erence model

In order to decide on the selection problem of PPCsoftware packages, the production systems typology andthe generic PPC software knowledge bases have to becombined and inferenced. For the creation of the genericPPC software package knowledge base, a second seman-

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Figure 3. Typology of production systems.

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tic network has been developed to represent the structureof its modules.

For the determination of PPC requirements of a spe-ci� c manufacturing � rm wanting to select a softwarepackage, a ‘normative’ approach ( Davis and Olson1985) using a generic description of an ‘ideal’ genericPPC software package ( reference model) will be followedfor the following reasons

� I t is quite common that the proposed informationsystem embodied in the production managementgeneric software package may be fundamentally dif-ferent from existing patterns on the factory ( in itscontent, form, complexity, etc.) , so that anchoringon an existing information system or existing obser-vations of information needs, will not yield a com-plete and correct set of ‘ideal’ requirements.

� Further on, manufacturing � rms prefer to take thechance for reengineering their business processes byadopting a «best practice» reference model embo-died in a modern ERP system.

� In practice, there is not usually enough time at thisstage to proceed to a detailed analysis of the com-pany’s current information processing and organi-zational procedures.

� A database of existing commercial packages wouldnot be practical to maintain given the continuous

release of updated versions and new products in this� eld, so that the practical implementation anddevelopment of the database content would be animmense e å ort.

Most of the available commercial software modules forproduction management belong to large integratedpackages of ERP software, which even though they useslightly di å erent names for their modules, it can be saidthat the terminology of most of the modules o å ered isfairly standard as, e.g. MRP for materials managementof MPS for master production scheduling.

This generic PPC software package description is ana-lysed in modules, main menu functions and features. Theabove analysis is based on the MRPII design philosophy,and is performed by superimposing software functionsand features found in the worldwide mostly-used com-mercial PPC software packages, as well as the PPC lit-erature. The main modules of this package are shown in� gure 5.

This study adopts the opinion that a detailed DataDictionary for building a complete reference model of aPPC software package, i.e. a dictionary including mod-ules, programs, functions, databases, � les ( tables) , datagroups ( user view, query, report) and data elements isquite impractical for the purpose of evaluating andselecting PPC packages due to their immense size and

418 I. P. Tatsiopoulos and N . D. Mekras

Figure 4. An example of the discrete manufacturing type in the electronics industry.

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complexity. Instead, from the level of module down-wards, the notion of ‘main menu functions ( MMFs) ’and ‘Features ( FTRs) ’ with their corresponding instancesis used for the description of a compact reference model.This will serve as a requirements catalogue to be sent tothe PPC software vendors for tendering.

The compact PPC reference models developed in thisstudy follow the CIMOSA concept of generic and partialmodels. The partial models belong to two di å erentgroups, which are the industry settings and industry sol-utions. The industry settings correspond to the typologyof production systems, i.e. MTS, MTO, ATO, ETO ordiscrete, repetitive, process, one of a kind reference mod-els. The industry solutions are reference models that cor-respond to industrial sectors ( e.g. automotive,telecommunications, etc.) . Based on our past project � nd-ings, we have already constructed and stored in theknowledge base the industry solutions for the clothing

( Tatsiopoulos et al. 1998) , electronics ( Tatsiopoulos et al.1997) , metals ( aluminium and steel, REALMS EspritProject) , and oil and gas industrial sectors.

2.2.1. Modules, main menu functions and features

The concept of module feature is characteristic in theworld of commercial software being of a rather verbalnature. Usually it represents some sort of functionalityand how it is performed. Its analytical description anddocumentation would require extensive data � ow dia-grams and � owcharts, which is not practical for our pur-poses. Therefore, the ‘feature’ is described by a shortphrase and a set of values corresponding to alternativemethods of performing its function. In this study, anattempt is undertaken to codify, standardize and storein a knowledge base all known PPC software features

Expert system for selection of PPC sof tware packag es 419

Figure 5. Module and main menu functions of PPC software packages.

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and their alternative values. In terms of the requirementsof the production management system, � rst we are inter-ested in the very existence of a speci� c feature and then inits value, i.e. the method used. The features are groupedinto ‘main menu functions’, i.e. the � rst level of function-ality usually seen in the main menu of standard modulesin commercial PPC packages. At this high level of func-tionality, there are no signi� cant di å erences betweenwell-known PPC packages.

Two examples of software features of the MRP( material requirements planning) and SFC ( shop � oorcontrol) modules are listed below.

MODULE: SFC (shop � oor control)Menu function: shop_orders_release ( SFC.a)

FeaturesSFC.a.1 material_allocation_&_dispositionSFC.a.2 workload_controlSFC.a.3 release_of_order_speci � c_bill_of_materialsSFC.a.4 release_of_order_speci � c_process_plansSFC.a.5 printing_of_shop_order_documents

Menu function: work_assignment_in_work_centre( SFC.x)

FeaturesSFC.x.1 creation_of_job_listsSFC.x.2 determination_of_job_prioritiesSFC.x.3 listing_of_time_critical_jobsSFC.x.4 assignment_of_jobs_to_machinesSFC.x.6 input/output_control_of_work_centre_load

MODULE: MRP ( material requirements planning)

Menu function: pegging_of_requirements (MRP.y)FeaturesMRP.y.1 single_level_peggingMRP.y.2 full_level_explosive_peggingMRP.y.3 full_level_implosive_peggingMRP.y.4 MRP_by_contract

Menu function: lot_sizing (MRP.z)FeaturesMRP.z.1 by_� xed_order_quantityMRP.z.2 by_economic_order_quantityMRP.z.3 lot_for_lotMRP.z.4 by_� xed_period_requirementsMRP.z.5 by_least_unit_cost_( LUC)MRP.z.6 by_Wagner–Whitin_algorithm

3. Inference rules for linking PPC software withproduction systems

The third part of the knowledge base consists of a set oflogical rules that link the functions of the PPC softwarewith the production system characteristics. These rules

express the need for the existence of certain features whena set of characteristics is present in a production system.

There is a broad spectrum of ‘suitability’ of a particu-lar software module feature from very good to very bad.For this reason, there is often a temptation to use quan-titative methods, e.g. value analysis, which specify thedecision on a numerical scale, e.g. from 1 to 100.However, the factors talked about in this study are gen-eral guidelines, often not very well speci� ed, and in areal-world situation they may not be known with greataccuracy. This suggests that a 0–100 decision may be ameaningless breakdown given the fuzziness of the factorsthat go into making the decision. A better choice wouldbe to limit ourselves to a three-way decision: critical,desired, not necessary. For simplicity reasons, the follow-ing rules reduce the problem into a two-way decision forthe PPC software module features, i.e. to be included ornot in the requirements catalogue that will be sent to thesoftware vendors for tendering.

The conditions of the rules are joined with ‘conjunc-tion ( AND) ’ or ‘disjunction ( OR) ’ logical relationships,giving in this way the possibility to create any kind ofcondition combinations of all the classes of the produc-tion systems functional characteristics. Only for the SFCmodule, has a total of 435 rules been de� ned that arenecessary for all of its main menu functions and features.

The following lists examples of rules that as a resulthave the required features of the SFC and MRPmodules.

420 I. P. Tatsiopoulos and N . D. Mekras

RULE 1 (SFC)Menu function: shop_orders_release ( SFC.d)Feature: release_of_order_speci� c_bill_of_materialsConditions (disjunction)( 1) 1.1. Products to customers’ speci� cations( 2) OR 1.2. Own catalogued products with non-

standard options

RULE 2 (SFC)Menu function: work_assignment_in_work_centres( SFC.x)Feature: assignment_of_jobs_to_machines (SFC.x.4)Conditions (conjunction)( 1) 6.2. Batch production( 2) AND 7.1 Job-shop

RULE 3Menu function: pegging_of_requirementsFeature: full_level_implosive_pegging ( MRP.y.3)Conditions (conjunction)( 1) 4.1. Make to order ( MTO)( 2) AND 5.1. Vertical production

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4. Inferencing process and results

After the creation of the two knowledge bases and theintroduction of the rules that relate them, the expertsystem can activate a procedure of questions to the userfor the description of his production system. The answers,coming from the user, are based on both the existingknowledge of the production systems taxonomy and onextra queries concerning functional characteristics notincluded in the already classi� ed production systems. Inthis way, any exceptions and special characteristics ofproduction are considered before the inference pro-cedures are activated. A set of starting menus and ques-tions helps the user to give a � rst description of hisproduction system. The questions that follow depend onthe answers given previously. For example, if the userde� nes his system as repetitive type, then the questionsthat follow concern its subcategories and its special char-acteristics ( e.g. repetitive production of standard prod-ucts based on customer orders) .

The existing rules in the knowledge base are used bythe inference engine, and drive the expert system to thosemenu functions and features that are appropriate foreach manufacturing setting. For each selected feature,the explanation mechanism shows how it was selectedand the rules that were responsible for this selection.The explanation process is necessary to make the useful-ness of each selected software feature clear. The resultsthat can be obtained concern sectors and subsectors ofmanufacturing industry, types of production systems andtheir specializations, or structures of production settingsfound in speci� c industries.

The results include the whole architecture of the mod-ules and main menu functions that contain the selectedfeatures. In this way, modules and main menu functionsthat contain software features that are not needed areexcluded from the proposed solutions. The � nal outcomeof the expert system is the required features catalogue( RFC) document ( � gure 6) that accompanies the requestfor proposals ( RFP) sent to PPC software package ven-dors. The whole software selection process is depicted in� gure 7 and described in section 5.

5. Overall framework of the PPC softwareselection process

The expert system presented in this paper addresses theproblem of evaluating PPC commercial softwarepackages on the basis of purely technical criteriaexpressed in the form of a PPC required features cata-logue ( RFC) . This catalogue forms the basis of therequest for proposals ( RFP) document sent to softwarevendors. However, this is only part of the whole softwareselection process, as depicted in � gure 7.

The � rst step in this process is to initialize a preselec-tion process by preparing a request for interest ( RFI)document to be sent to PPC software vendors. The RFIincludes a short pro� le of the user company and a ques-tionnaire concerning the basic strategic criteria that willbe used for preselection. These criteria have to do withthe vendor reliability and support services, the request fora speci� c reference model ( e.g. assembly to order or pro-cess industry) , the conformity of the PPC software to theexisting IT infrastructure of the user company, and thesize range of the user company in terms of number ofemployees and sales turnover. In case a particular indus-try solution is required ( e.g. oil and gas) then usually thepreselection process is bypassed as very few vendors as yethave such industry solutions. For example, the oil and gassolution is only o å ered by SAP, ORACLE andJDEdwards.

The preselection process is based on the declaration ofinterest ( DOI) document sent by the vendors, whichincludes the answers to the strategic criteria question-naire, a vendor’s company pro� le, and the detailed pros-pectus with the functional description and features of thesoftware package. The value analysis method is used toproduce scores for every vendor, and � nally the � rst threeor � ve are preselected.

The preparation of the request for proposals ( RFP)document is up to now the main application area of theexpert system presented in this paper. The RFP, which issent to the preselected software vendors, contains severalparts, i.e. a legal part, a technical part and a � nancialterms part. The technical part mainly consists of therequired features catalogue ( RFC) , which is producedwith the help of the expert system.

The next step after receiving the vendor’s proposals isthe technical evaluation of the o å ered software packagesin order to produce the PPC software package pro� les.This consists of the coverage analysis to determine thedegree of covering the company’s requirements by thecandidate packages’ oå ered features. This is achievedby experienced internal or external consultants, and isnot solely based on the answers of the software vendors,as software vendors usually assume the explanation ofrequirements most favourable to them. Here we must

Expert system for selection of PPC sof tware packag es 421

RULE 4Menu function: lot_sizing ( MRP.z)Feature: lot_for_lot ( MRP.z.3)Conditions (conjunction)( 1) 6.1. Repetitive production( 2) AND 7.4. Flow shop( 3) AND 1.4. Standard products without options

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take into account that the coverage analysis is performedon the basis of the � rm’s particular ‘ideal’ referencemodel, as compared to the generic or partial PPC refer-ence model, and the features of every o å ered softwarepackage.

Next to the technical evaluation, a � nancial evaluationtakes place, which calculates non-recurring costs( licences, customization, training, consulting) and recur-ring costs ( maintenance, new releases) of the oå ered soft-ware solutions. On the basis of this cost analysis and thepreviously performed technical evaluation, a � nal com-bined evaluation of packages takes place that leads to theselected solution.

6. Conclusions and further research

A rule-based expert system has been developed whosesemantic networks describe the type of production systemthrough the representation of a production systems typol-ogy – which leads to a company typological pro� le – andcompact reference model of generic and partial PPC soft-ware packages. These two semantic networks are linkedthrough the inference rules of the expert system to pro-duce the required features catalogue as a part of therequest for proposals ( RFP) document.

In addition to the evaluation of pure technical fea-tures, the consultancy work that supports the selection

422 I. P. Tatsiopoulos and N . D. Mekras

Figure 6. Extract of the required features catalogue (RFC) .

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Expert system for selection of PPC sof tware packag es 423

Figure 7. The overall process of selecting a PPC software package.

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of PPC software systems is equally concerned with aplethora of human, cultural, � nancial, educational, tech-nological, marketing, strategic and other possible factors,mainly of a qualitative nature. A central factor judgingthe suitability of PPC software is the required change –and the associated change management e å ort – either ofthe software package or the organization, or both ofthem. A framework for facing this problem has beenpresented by Tatsiopoulos ( 1989, 1994) . A lot of researchwork has been performed recently in this area, which hasled to the appearence of commercial workbenches andsoftware tools, e.g. R/3-LIVE by Siemens/Nixdorf orBusiness Engineer 4.0 workbenches by SAP AG. This isdue to the great interest of the large ERP vendors inreducing the time and costs connected to the implemen-tation projects.

However, the ERP vendors do not have the same inter-est in the selection process, therefore very few workbenchtools exist to support this extremely important task asseen from the user companies point of view. The researchobjective of our team in NTUA is to develop an indus-trial engineer’s workbench to support all steps of the PPCsoftware selection process seen in � gure 7.

Two other fruitful areas of further research are: ( i) theselection of APS ( advanced planning systems) softwarepackages; and ( ii) the development of industry solutionsreference models. APS have recently appeared in thelogistics and production management software arenaas supplementary or sometimes competitive productsto the PPC modules of large ERP systems( Manufacturingsystems 1998, Wortmann 1998) . Sincethe share of manufacturing in current ERP packages isfairly limited, they have not been able to develop all thedecision support and optimization functionality requiredby modern world class manufacturers. This vacuum isclosed by APS ( e.g. I2 or ILOG) that o å er extendedlogistics planning or shop � oor scheduling functionality.The selection process of these systems either as standaloneor in connection to ERP systems is an interesting researchproblem.

As far as ( ii) is concerned, we have already mentionedthat our team has only developed partial reference mod-els for the clothing, aluminium, electronics and oil indus-tries. There are many more industrial sectors that needreference models. The trend in ERP systems( Manufacturingsystems 1998) is clearly to follow thispath of vertical industry solutions because they need farless implementation time and e å ort and are more easilyaccepted by the customers.

The method and expert system developed can be valu-able to those who plan to buy/develop and implement aPPC software package in a manufacturing � rm. Suchevaluation and selection decisions are far too complexto be faced either by quantitative multicriteria methods

or simple guessing. AI technology techniques have beenapplied for the creation and processing of a knowledgebase required for the systematization of the softwareselection process. The knowledge base can be updatedwhen new managerial principles must be included, newsoftware functions and features are o å ered by softwarevendors or new inference rules must be added.

The results of the method can be used as a guide forconsulting experts, production managers and PPC soft-ware developers, helping them to determine the appro-priate software for a certain type of manufacturingsetting. The developed expert system is also expected tobe a signi� cant educational tool for those who want toapply new managerial principles and new productionplanning methods in several production environments.

References

Davis, G. B., and Olson, M. H., 1985, Management InformationSystems (New York: McGraw Hill) .

De Heij, J. C. J., 1995, The use of data models for assessing,standard logistics software. Computers in Industry, 25, No. 2.

ELCO (European Logistics Consultants) , 1989, ComparingPPC Software Packag es ( Amsterdam: Elsevier) .

Fandel, G., 1993, Activity analysis of production planning andcontrol systems. In I . Pappas, and I. Tatsiopoulos ( eds)Advances in Production Manag ement Systems, IFIP Transactions( New York: North-Holland ) .

Frost, A., 1986, Introduction to Knowledg e Based Systems (W.Collins & Sons Ltd.: London) .

Geitner, U., 1993, PPS Marktuebersicht. FB/IE, REFA ,December.

Hackstein, R. 1990, ( ed.) , Auswahl, Einfuehrung undUeberpruefung von PPS-Systemen ( Koln: Verlag TUVRheinland) .

Hackstein, R., and Virnich, M., 1991, Selecting standardsystems of production and inventory control ( PIC) with theaid of util. Value analysis ( BAPSY model) . In InformationT echnology f or Organisational Systems H. J. Bullinger et al.( eds) , (Amsterdam: Elsevier) .

Hoff, H., and Virnich, M., 1986, Wie man ein PPS-Systemrichtig auswaelt und einfuert. io Manag ement-Zeitschrift, 55, 9( 1. Teil) ; 55, 10 ( 2. Teil) .

Keller, R., 1987, Expert System Technology ( New York:Prentice-Hall) .

Kettner, H., Schmidt, J., and Greim, H. R., 1984, Leitfadender systematischen Fabrikplanung , ( Muenchen: Carl Hanser) .

Krallmann, H., Expertensysteme fuer die computerintegrierteFertigung. FB/IE, 35, 100–106.

Little, D. et al., 1998, Supporting SME information systemsdevelopment using a structured method and tool for packageselection. In Bititci and Carrie ( eds) Strategic Management of theManufacturing Value Chain, ( Boston: IFIP) .

Luczak, H., Nicolai, H., and Kees, A., 1998, PPC-systems:reengineering or replacement? Venus: a fuzzy-decision-toolhelps to evaluate outdated production planning and controlsystems. Production Planning and Control, 9, 448–456.

Malpas, J., 1987, Prolog: A Relational Languag e and its Applications( Englewood Cli å s, NJ:Prentice-Hall) .

424 I. P. Tatsiopoulos and N . D. Mekras

Page 12: An expert system for the selection of production planning ... Engineering/2… · ductionplanningandcontrol(PPC)software,production ... Thispaperpresentsarule-basedexpertsystemthat

Manufacturingsystems, 1997, Special reports, next genera-tion ERP supplement. Manufacturing Systems J ournal,October, ( www.manufacturingsystems.com) .

Mensel, G., and Michel, J., 1985, Moeglichkeiten desEinsatzes wissenbasierter Systeme in der Fertigung, ZwF,80, 495–500.

Mertens, P. et al., 1993, Tools to regulate the parameters ofMRP systems. In I. Pappas and I . Tatsiopoulos ( eds)Advances in Production Manag ement Systems, IFIP Transactions,( New York: North-Holland) .

Meyer, W., 1990, Expert Systems in Factory Manag ement-K nowledg e based CIM ( Chichester, UK: Ellis Horwood) .

Paegert, Ch., and Vogeler, Ch., 1996, Produktionsplanungund -steuerung 1996 – aktuelles Marktangebot undEntwicklungstrends bei Standard-PPS-Systemen. FB/IE, 45,53–66.

REALMS Espirit Project, 1997, WP1 – Speci� cation of sol-utions, ELVAL – GRAI/LAP – IFAB – SNI – NTUA –BSW.

Rowe, C. N., 1988, Arti�cial Intelligence through Prolog ,( Englewood Cli å s, NJ: Prentice-Hall International) .

SAP Business Engineer 4.0, 1998, SAP AG, Walldorf.Scheer, A.-W., 1995, Wirtschaftsinf ormatik. Referenzmodelle fuer

industrielle Geschaef tsprozess, ( Berlin: Springer) .Schmenner, R.G., 1981, Production/Operations Management,

SRA, Chicago.Schmitt, T. G., Klastorin, T., and Shtub, A., 1985,

Production classi� cation system: concepts, models and stra-tegies. International J ournal of Production Research, 23, 563–578.

Schomburg, E., 1980, Entwicklung eines betriebstypologischenInstrumentariums zur systematischen Ermittlung derAnforderungen an EDV-gestuetzte Produktionsplanungs-und-steuerungssysteme in Maschinenbau. Dissertation, THAachen.

Shemwell, A., Shepherd, N., and Wood, A., 1992,Consultancy tools – A å ordable CIME solutions for SMEs

( FTM) . Kewill Group Consultancy Services, CIMpleESPIRIT II project 5424.

SIEMENS/NIXDORF R/3-LIVE Kit Structure, 1998, SBS.Schneider, H., and Karagiannis, D., 1988, In intelligent

knowledge bases of CAD environments: The hybrid systemKANON. N ATO ASI Series, Vol. 1. ( F49, pp. 161–196,( Berlin: Springer) .

Tatsiopoulos, I. P., 1989, A systematization of knowledge forthe selection and implementation of materials managementsoftware. In J . Browne ( ed) K nowledge-based ProductionManagement Systems, ( Amsterdam: Elsevier) .

Tatsiopoulos, I. P., 1990, Requirements analysis of produc-tion management software systems. Computer IntegratedManufacturing Systems, 3.

Tatsiopoulos, I. P., 1994, An architecture of automated con-sultancy tools for the implementation of production planningand control software. In A. Rolstadas, ( ed.) BenchmarkingWorkshop, Trondheim ( London: Chapman & Hall) .

Tatsiopoulos, I. P., Pappas, I. A., and Ponis, S., 1998,Clothing industry 2000: The archetype of the extended enter-prise. In Schoensleben and Buechel ( eds) Organizing theExtended Enterprise ( IFP) .

Tatsiopoulos, I. P., Theoharis, I., and Avramopoulos, I.,1997, A reference data model for production control in theelectronics industry. Computers in Industry, 34, 221–231.

Wortmann, J. C., 1984, How to select a standard softwarepackage for production/inventory control. In G.Doumeingts and Calter W. A., ( eds) Advances in ProductionManagement Systems ( New York: North-Holland) .

Wortmann, J. C., 1998, Evolution of ERP systems. In Bititciand Carrie ( eds) Strategic Supply Chain Manag ement ( IFIP) ,( New York: Kluwer) .

Wunderli, M., 1988, Eine neue Methode der PPS-Evaluation.OUTPUT , 9403 Goldach, Nr. 4.

Expert system for selection of PPC sof tware packag es 425