Abstract Directions for an ERP-based DSS

Directions for an ERP-based DSSStanislaw Stanek*Henryk Sroka**Zbigniew Twardowski***Katowice University of EconomicsKatowice, Poland*Email: stanek@**Email: sroka@ae.katowice.pl***Email: twardy@.plAbstractERP packages are not sufficient from a decision making point of view (cf. e.g. Adam 2001) . Organizations that have implemented such systems are now facing the challenge of incorporating new resources and experiences for decision support purposes. The paper presents the findings of research on the construction of a hybrid decision support system consisting of three components: analyzer, simulator, and communicator (Stanek, Sroka Twardowski 2003) within an organization which already had a functioning ERP system. The differences between the idea of ERP and the DSS concept are discussed. Attention is brought to additional benefits and opportunities arising from the combination of these two.KeywordsERP, DSS, architecture for hybrid systems components integration, business models, interface agent, expert systems1. INTRODUCTION.Many of the observations made several years ago on the relationships between Enterprise Resource Planning (ERP) systems and decision support systems (DSS) remain fundamentally true and just as relevant today as they were at the time. One such comment came from Adam (2001):…But ERP packages are not sufficient from a decision making point of view. Theyconstitute vast repositories of data that provide a perfect basis for decision making, butbased on empirical research carried out recently, it seems that the reporting capabilities ofmany of the ERP packages available is not sufficient for the organizations that implementthem. Despite vendors’ claims that their software includes leading-edge reportingcapabilities, many organizations find themselves purchasing additional software to fullyexploit the large volumes of data contained in their newly-acquired systems. In one case westudied, managers initially tried to make use of the functionality provided by their ERPpackage, but became disillusioned with the lack of flexibility of the reporting tools and theexcessive time needed for staff to become fluent in developing additional reports oramending existing ones.Recent research reveals many cases of successful system implementations, by different providers and in different application areas, on top of existing technologies, where combining a DSS with a previously implemented information system has resulted in increased automation of processes within the organization. The research aim of this paper is to contribute to the evolution of the concept of a hybrid system within which are seamlessly integrated an operational and an analytical component (cf. Turban 1993; Chamoni, Gluchowski 1999). Within such a hybrid system, the operational component, such as e.g. an Enterprise Resource Planning (ERP) system, may be looked upon as a data source supplying a body of data which are then processed by the decision support system (DSS). The paper attempts to face the challenge of integrating these two components into a homogeneous whole in such a way as to achieve the effect of synergy (Verandat 1996).It is assumed that each of the two components already represents a degree of integration. Our hybrid solution is based on an original DSS architecture where the decision support system itself consists of three components: an analyzer, a simulator and a communicator. One of the ways in which an existing ERP system may be combined with such a DSS is by feeding the ERP data into the analyzer component. The original architecture is thus enhanced by mounting it on top of an ERP system in a way which would justify relabelling its analyticalDecision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004 component as an ERP-fed analyzer. In the following chapters, the resulting system architecture is not only discussed theoretically but also practically illustrated with a case study showing how a hybrid ERP-fed information system works at three operating levels. Given the overall complexity of the solution, the case study will focus on the interaction of the ERP and the expert system technology.2. THE RESEARCH MODEL.An analysis of the development dynamics of the computer-based decision support concept and application has led us to adopting the following assumptions or theses:•Integrating different information technologies within a hybrid approach creates new opportunities such as: strengthening the best qualities of each component technology while at the same time minimizing its weaknesses; producing entirely new qualities, as is the case in e.g. combining quantitative and qualitative technologies within a decision support system; extending the autonomy of the computer system by entrusting it with a wider range of tasks in the human-computer dialog; creating tools that suit the needs of both systematic and intuitive decision makers (cf. also Lenard et al. 1995).•Decision makers need more than information – they need understanding at many levels of abstraction (Briggs et al. 2002).In view of the above and other considerations, research (Stanek, Sroka, Twardowski 2003) was initiated on the A-S-C architecture where the decision support systems consists of the following three components (see Fig. 1):Figure 1: The architecture of the Web-based decision support system under discussion (Stanek, Sroka, Twardowski 2003).•Analyzer (A) – operating in continuous mode, scanning the environment for emergent patters which may indicate that a decision making situation occurs and which therefore are relevant from the viewpoint of tasks that the decision maker performs (traceable to earlier research of the early warning concept),•Simulator (S) – the discrete element whose operation relies on the current understanding of the relationships between the patterns identified (the decision maker’s wisdom is part and parcel of the system), and which is capable of supporting ad hoc decisions (traceable to the findings of previous research on simulation and modeling),•Communicator (C) – the component which is a repository of meta-knowledge of the subject area, as well as of the system which supports the user’s creativity in utilizing support systems within the subject area (traceable to prior research on communication in strategy games and on interface agents).Directions for an ERP-based DSS The Analyzer is activated via the Communicator, where the analyzer’s basic functionality is defined through control parameters. Input data, depending on the required degree of generalization of observation (operational or strategic level) come from1:•transaction systems – directly from the data bases and/or from alarms generated by procedures which monitor changes taking place in the data bases – the so-called triggering subsystem (cf. Bassiliades, Vlahavas 2000);•data warehouses – where pre-defined quantitative data aggregates stored within OLAP cubes, as well as qualitative data, allow the generation of warning signals relating to long-term monitoring of strategic goal performance.A special data-in access into the Analyzer is reserved for the findings of consultation with the Simulator, where results of sensitivity analysis of parameters being monitored are used to produce a conclusive assessment of dangers and to suggest corrective or preventive measures (a “what if” analysis).Figure 2: The architecture of an ERP-fed analyzer.The architecture of the Analyzer (see. Fig. 2) is based on an analytical platform which can be described as an integrated application development environment utilizing OLAP technologies and expert knowledge bases. An essential element of the subsystem architecture is an expert system processing knowledge in the form of fuzzy rules. The expert system carries out the following three functions:•diagnosis – assessment of the company’s condition based on a set of observable symptoms;identification of weak warning signals at the strategic level (e.g. those from the environment), •construction – producing more detailed reports for users corresponding with the diagnosis performed, •control – it controls applications by monitoring the user’s activities and triggering actions required in the current context of analysis (customization of report generation, execution of data transformation scripts).The data output from the Analyzer (which is based on the double loop principle) can be then input into the Communicator and the Simulator. However, it must be born in mind that the Analyzer’s primary products are warning signals and reports identifying the endangered areas of the processes being monitored. The form in which reports are delivered is suited to the user’s current needs; the critical information is conveyed in brief conclusions produced by the inference rules activated and in explanations provided by the expert system in the form of tables and presentation graphics (see Fig. 3).1 For more information on the architecture of the Analyzer component, see Stanek, Sroka, Twardowski (2003)Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004Figure 3: An excerpt from a diagnostic report on the company’s financial condition.3. BUSINESS PROCESS MODELING – A CASE STUDY.The proposed approach is used for the purpose of identifying and diagnosing the warning signals at either operational or strategic management level. At the operational level, monitoring is, in our approach, focused on the short-term budgeting process – in domains depending on the specific needs of the company’s decision support system (cf. Reichmann 1977) – and financial analysis in such areas as: current financial liquidity, profitability, and long-term debt (Brealey, Myers 1996; Bernstein 1993; Fridson 1995). Control is exercised by comparing the values captured against reference models, simulating the effect of deviations identified (owing to the Simulator facility), issuing warnings of threats, and offering multi-variant suggestions for corrective measures. At the strategic level, on the other hand, the company’s competitive position is analyzed, in future-state terms, and risk relating to the performance of current market strategy is continuously assessed against reference strategies. The monitoring capability relies on two information sources: information on the enterprise’s internal resources, which is retrieved from the MRP/ERP systems, and the information from theDirections for an ERP-based DSS environment concerning factors which affect the level of risk related to the company’s market position. Assessment of the long-term market trends is performed in quantitative terms and based on expected values estimated by subjective probabilities of experts’ opinions. The quantification of risk factors is accomplished by the fuzzification of linguistic variables via a membership function.The implementation approach for the proposed solutions is founded on a three-dimensional perception of information within a business enterprise, the first dimension being the classical functional areas such as material supply, production and sales, and the second dimension being information stored in the accounting systems (OLTP), e.g. income, costs, cash flows, assets, capitals, etc. The third dimension is determined by the time frame in which particular events occur. The events are thus identified as short-term, medium-term, and long-term.The first level comprises detailed information which is elementary at the level of accounting systems. This level constitutes the main data source for the data warehouse and the OLAP cubes. The second level is made up of reports carrying management information. The third and last level is composed of a set of specially selected synthetic indicators, chiefly for use by the top management function. All these dimensions within an information system must form an integrated environment of interrelated and easily identifiable objects and relations (a prerequisite for interactive data mining).The Southern Power Corporation is the greatest national power producer and, at the same time, one of the biggest business organizations in Poland. The Corporation has a 18% share of the domestic power generation market, while its share of the local heat generation market comes up to 16%. The proposed solutions were to be implemented in the area of controlling, encompassing a controlling model, where the strategic and the operational areas were isolated, as well as, due to its sectoral significance, financial controlling. The model was implemented in a multi-dimensional analytical environment. Its optimum functionality is achieved through a three-layer information system development platform (see Fig. 4) where the three layers are identified as follows:•basic layer – integrated transaction systems of the MRP/ERP class – IFS Applications v. 2003 (ORACLE data base); implementation performed by IFS POLAND consultants,•analytical layer – information technology solutions of the OLAP and data warehouse class – the OPTIMA CONTROLLING software application supplied by CONSORG Sp. z o.o. (MS SQL SERVER and MS OLAP development platform),•publication layer (corporate portal) – which provides publishing capabilities and user access to synthetic (cumulative) reports via an intranet and/or an extranet facility.Figure 4: The three-layer analytical platform for corporate information system development as implemented in PKE S.A – case study.Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 20043.1. Operational levelThe principal application of solutions proposed in the Southern Power Corporation (PKE S.A) is in providing operational support for planning and monitoring of the profit capacity of operating budgets. The procedure presented, being an element of a controlling information system, is employed for assessing the operating budgets which constitute profit centers, where the general budget is the aggregate Corporation budget consisting of five primary budgets. The application aspect discussed below encompasses a short-term planning perspective for selected management areas, where the primary budgets are aggregations, or sum-totals, of the component sub-budgets. The budget preparation procedure consists in cost and income planning for the subordinate budgets (of the n-th degree) with a view on their impact on the performance of the higher level budgets, the primary budgets and, consequently, on the general Corporation budget. Within each primary budget, a model was developed for the purpose of cause-and-effect analysis of deviations from plan (the primary budgets are mostly attached to responsibility centers). Analysis is performed on individual products, in terms of how changes in their price, sales volume and sales structure will affect the operating profit and the cash flow. Similarly, variable costs are analyzed for each of the Corporation’s primary activity areas, in order to study the effect of changes in consumption and price (relative to plan). In this way we arrive at a multi-level estimate of deviations from plan indicative of the impact that each factor (across such dimensions as: products, responsibility centers, income, invariable costs, variable costs) will have on the bottom-line result. Depending on volatility shown by the parameters on which the plan and the targets are based, successive versions of the budgets are built. One, or several (e.g. an optimistic scenario, a pessimistic scenario and a realistic scenario), of these may be then adopted as a reference for the purpose of performance monitoring and used in establishing the reasons for deviations. A synthetic result for each operating budget being a profit center is the cover margin account. The budget period is one year, however, for the sake of plan performance monitoring and early warning of threats to the achievement of targets, the annual budget is split into quarterly and monthly budgets. The IBA expert system (identified with the Analyzer in the proposed hybrid architecture) is activated by alarms generated by the triggering subsystem of the IFS Applications transaction system. Among the symptoms which fire alarms there are e.g. advice of the worsening balance of current receivables relative to short-term liabilities, and a drop in sales to major customers. Based on these is produced a set of most likely hypotheses indicating an increased risk of financial liquidity loss. A domain model of financial liquidity management is used assuming a relationship between sales, working capital, asset liquidity and demand for short-term debt. For each area under observation, the hypotheses are validated in terms of impact that the deviations of the parameters may have on dangers affecting the company. In doing so, reference is made to plan, which means that planned values are adopted as reference values for the validation procedure. Each parameter of the model is described by three coordinates across the plan-performance-deviation paradigm: present value, short-term change dynamics, and the historical trend over a number of periods. For each parameter is estimated the membership function whose shape resembles that of Gaussian functions (Pi curves) (cf. Cox 1995).Directions for an ERP-based DSS 3.2. Strategic level – monitoring of the competitive positionFigure 5: Poland’s power plant monitoring in a benchmarking layout – published via EIP.Assessment of risk related to the company’s competitive position constitutes one of the aspects of strategic planning. The role of early warning models is to assess and validate warning signals in all areas being monitored – those coming from the competitive environment as well as those originating within the enterprise (Fig. 5). An important application of such models, combining support for projections and financial monitoring within PKE S.A., is found in economic and financial simulations. Simulation is understood to mean the examination of the model’s sensitivity to changes in parameters input – that is, it consists in answering “what if” questions. By using the model in such a way, we gain the possibility to identify the most sensitive areas under observation, which stands for a better risk assessment and a better informed choice of strategy realization option.Monitoring of this sort can be regarded from two different angles: firstly, in the context of business risk fluctuations, and secondly, from the viewpoint of business strategy performance. It is possible to monitor all areas of the enterprise or just the so-called critical success factors, which will be specific to each industry sector. What is being tracked here is the sensitivity (risk level) of the solution to changes of parameters originating e.g. from the environment. For each simulation variant tested, a summary of strengths and weaknesses is produced. Warning signals generated at the strategic level are enhanced with additional explanations, e.g. in the form of a strengths and weaknesses tree (Fig. 6). Any deviation from expected values will activate, as is the case at the operational level, the Simulator, where a sensitivity analysis is performed, followed by an assessment of the ultimate impact on risk levels in the areas affected.Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference 2004Figure 6: A diagnostic report from the assessment of a company’s strategic position (an excerpt).4. CONCLUSIONS.Standardization introduced through the implementation of enterprise software and resulting from standardizing the processes and artifacts does have global benefits, but it, at the same time, sacrifices local customized capabilities. The desirable level of customization can be achieved by due care and diligence in implementation (e.g. Koch et al. 1999 emphasize that it takes an average of 8 months after the new ERP system is installed to see any benefits) and owing to the development of add-on elements by so-called niche companies. Support systems are built on a different philosophy – as tools addressing specific problems (e.g. prototyping) and dedicated to different actors. A distinctive feature of a support system user is the ability to learn quickly according to the double loop learning pattern (cf. Dutton 1993; Stanek, Sroka 2000).Decision support philosophy coincides with business process automation philosophy as a logical sequence of changes in technology, attitudes, processes, strategy and organization culture. The implementation of an ERP system changes the way organizations do business and the way people carry out their work. Three major motivations for implementing ERP systems were identified by Koch et al. (1999): to integrate financial data, to standardize manufacturing processes, and to standardize HR information. However, ERP technology is not sufficient to support decision making in organizations.New information technologies are making it possible to enhance the traditional DSS components: data, modeling and dialog. The research presented aimed to verify the applicability of the Analyzer-Simulator-Communicator architecture of a hybrid ERP-fed information system. We believed that integrating different information technologies within a single IT solution could yield an opportunity to strengthen the best qualities of each component technology while at the same time producing an added value arising from the concerted actions of them all.On the whole, developing the decision support system on top of an existing ERP system has proved to be a sound idea. In our instance, it resulted in an easier development process. Moreover, it confirmed the functionality of the proposed A-S-C architecture. By enabling organizational standardization, eliminating information asymmetries and providing on-line and real-time information – an effect which can be expected of most ERP implementations (c.f. O’Leary 2000) – the ERP system facilitated the development of the data subsystem for the DSS. The decision support system, on the other hand – owing to its Analyzer componentDirections for an ERP-based DSS founded on knowledge server technology – allows an ongoing process analysis and performs on-line filtering of incoming data.The ERP system discussed in the case study was specifically addressed to the financial director and the finance function. Combined with a decision support system whose architecture follows the proposed A-S-C model, it proved capable of supporting a finance executive in the performance of his/her most demanding tasks: controlling, simulating and forecasting.One of the greatest advantages of the proposed A-S-C architecture seems to be in that it is an open one and, as such, it will allow a host of modifications without undermining the general model. There are reasons to believe that its further elaboration will be a rewarding task.REFERENCESAdam, F. (2001) ERP and its Impact on Decision Making, Journal of Decision Systems, Volume 10, No. 1. Bassiliades N., I. Vlahavas, I. (2000) Active Knowledge-Base Systems, in C.T. Leondies (Ed), Knowledge-Base Systems, Techniques and Applications, vol. I., New York: Academic Press.Bernstein, L.(1993) Financial Statement Analysis. Theory, Application and Interpretation, New York: Irwin McGraw-Hill.Brealey, R.A., Myers S.C. 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(1993) Decision Support and Expert Systems, Macmillan Publishing.COPYRIGHTStanislaw Stanek, Henryk Sroka, Zbigniew Twardowski © 2004. The authors grant a non exclusive licence to publish this document in full in the DSS2004 Conference Proceedings. This document may be published on the World Wide Web, CD ROM, in printed form, and on mirror sites on the World Wide Web. The authors assign to educational institutions a non exclusive licence to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. Any other usage is prohibited without the express permission of the authors.。