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Compositional enterprise modeling and decision support

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Abstract

In this article, we present a conceptual model and a web-based architecture for implementing an enterprise-wide modeling system for decision support. It describes a framework and develops a method that is aimed at effectively organizing, integrating, and reusing knowledge and model components from various sources across an organization in order to provide better knowledge access to decision makers. Given a query, the compositional modeling system responds with automatically building models for the described scenario by searching the organizational knowledge base and retrieving and integrating relevant knowledge components. We conclude with outlining an illustrative business example that demonstrates how our enterprise modeling system can be applied as useful tool for operational and strategic decision-making.

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Notes

  1. Qualitative simulation (QSIM), originally developed by (Kuipers 1986), is perhaps the most widely known qualitative reasoning system. It consists of the QSIM modeling language and the QSIM solver. Besides allowing the representation of qualitative arithmetic constraints, QSIM particularly features the qualitative representation of monotonically increasing (M+ constraints) and monotonically decreasing (M− constraints) functional relationships.

  2. Actually one can also define so called corresponding values which are, again in qualitative terms, specific points of an M+ relationship. Thus we could specify a corresponding value (0, (0,inf)), meaning f(0) > 0. That is we would restrict the class M further to M = {g|g(0) > 0,g′ > 0}, or in other words, even without any promotional expenditure we would still expect some sales.

  3. Rules-constraint-reasoning (RCR) is a semi-qualitative reasoning system introduced by Kiang et al (1994) and Hinkkanen et al. (1995). It uses an interval-based representation to hybridize qualitative and quantitative information. Kuipers and Berleant (1988), Williams (1991), and Berleant and Kuipers (1998), present alternative hybrid modeling languages.

References

  • Alavi M, Leidner DE (2001) Knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly 25(1):107–136

    Article  Google Scholar 

  • Annand V, Manz CC, Glick WH (1998) An organizational memory approach to information management. Acad Manage Rev 23:796–809

    Article  Google Scholar 

  • Ba S, Lang K, Whinston AB (1997) Enterprise decision support using intranet technologies. Decis Supp Syst 20(2):99–134

    Article  Google Scholar 

  • Barkhi R, Rolland E, Butler J, Fan W (2005) Decision support system induced guidance for model formulation and solution. Decis Supp Syst 40:269–281

    Article  Google Scholar 

  • Bell D (1973) The coming of post-industrial society: a venture in social forecasting. Basic Books, New York

    Google Scholar 

  • Berleant D, Kuipers B (1998) Qualitative and quantitative simulation: bridging the gap. Artif Intel 95(2):215–255

    Article  Google Scholar 

  • Blake MB, Gomaa H (2005) Agent-oriented compositional approaches to services-based cross-organizational workflow. Decis Supp Syst 40:31–50

    Article  Google Scholar 

  • Bonczek RH, Holsapple CW, Whinston AB (1981a) Foundations of decision support systems. Academic, New York

    Google Scholar 

  • Bonczek RH, Holsapple CW, Whinston AB (1981b) A generalized decision support system using predicate calculus and network data base management. Oper Res 29(2):263–281

    Article  Google Scholar 

  • Brown JS, Duguid P (1998) Organizing knowledge. California Manage Rev 40(3):90–111

    Google Scholar 

  • Castells M (2000) The rise of the network society, 2nd edn. Blackwell, Oxford

    Google Scholar 

  • Conlon SJ, Conlon JR, James TL (2004) The economics of natural language interfaces: natural language processing technology as a scarce resource. Decis Supp Syst 38(1):141–159

    Article  Google Scholar 

  • Davenport T, Jarvenpaa S, Beers M (1996) Improving knowledge work processes. Sloan Manage Rev 37(4):53–65

    Google Scholar 

  • Davenport T, De Long D, Beers M (1998) Successful knowledge management projects. Sloan Manage Rev 39(2):43–57

    Google Scholar 

  • Davenport T, Prusak L (1998) Working knowledge. Harvard Business School Press, Boston

    Google Scholar 

  • David PA, Foray D (2002) Introduction to the economy of the knowledge society. Int Social Sci J 54(171):9–24

    Article  Google Scholar 

  • Davis, E (1990) Representations of Commonsense Knowledge, Morgan Kaufman, San Mateo, CA.

  • de Kleer J (1986) An assumptions-based TMS. Artif Intel 28:127–162

    Article  Google Scholar 

  • Dolk DR, Kotteman JE (1993) Model integration and a theory of models. Decis Supp Syst 9(1):51–63

    Article  Google Scholar 

  • Drucker P (1968) The age of discontinuity: guidelines to our changing society. Harper & Row, New York

    Google Scholar 

  • Earl M (2001) Knowledge management strategies: towards a taxonomy. J Manage Inf Syst 18(1):215–233

    Google Scholar 

  • Fahey L, Prusak L (1998) The eleven deadliest sins of knowledge management. California Manage Rev 40(3):265–276

    Google Scholar 

  • Falkenhainer B, Forbus KD (1991) Compositional modeling: finding the right model for the job. Artif Intel 51:95–144

    Article  Google Scholar 

  • Friedman TL (2006) The world is flat: a brief history of the twenty-first century, 2nd edn. Farrar, Strauss and Giroux, New York

    Google Scholar 

  • Gray PH, Meister DB (2003) Fragmentation and integration in knowledge management research. Inf Technol People 16(3):259–265

    Article  Google Scholar 

  • Grover V, Davenport T (2001) General perspectives on knowledge management: fostering a research agenda. J Manage Inform Syst 18(1):5–21

    Google Scholar 

  • Hinkkanen A, Lang KR, Whinston AB (1995) On the usage of qualitative reasoning as an approach towards enterprise modeling. Ann Oper Res 55:101–137

    Article  Google Scholar 

  • Hinkkanen A, Lang KR, Whinston AB (2003) A set-theoretical foundation of qualitative reasoning and its application to the modeling of economics and business management problems. Inf Syst Frontiers 5(4):379–399

    Article  Google Scholar 

  • Holsapple C, Jones KG, Singh M (2007) Linking knowledge to competitiveness: knowledge chain evidence and extensions. In: Jennex M (ed) Knowledge management in modern organizations. IGP, London, pp 51–76

    Google Scholar 

  • Holsapple C, Whinston AB (1987) Knowledge-based organizations. Inf Soc 5(2):77–90

    Article  Google Scholar 

  • Holsapple C, Whinston AB (1996) Decision support systems: a knowledge-based approach. West Publishing Company, St. Paul

    Google Scholar 

  • Ipe M (2003) Knowledge sharing in organizations: a conceptual framework. Hum Resour Dev Rev 2(4):337–359

    Article  Google Scholar 

  • Kaplan RS, Norton DP (1992) The balanced scorecard—measures that drive performance. Harvard Bus Rev 70(1):71–79

    Google Scholar 

  • Kalfoglou Y, Menzies T, Althoff KD, Motta E (2000) Meta-knowledge in knowledge in systems design: panacea or undelivered promise? Knowl Eng Rev 15(4):381–404

    Article  Google Scholar 

  • Kiang MY, Hinkkanen A, Whinston AB (1994) Reasoning in qualitatively defined systems using interval-based difference equations. IEEE T Syst Man Cybern 25:1110–1120

    Article  Google Scholar 

  • Kuipers B (1986) Qualitative simulation. Artif Intel 29:289–338

    Article  Google Scholar 

  • Kuipers B (1994) Qualitative reasoning: modeling and simulation with incomplete knowledge. MIT, Cambridge

    Google Scholar 

  • Kuipers B, Berleant D (1988) Using incomplete quantitative knowledge in qualitative reasoning. In: Proceedings AAAI-88. St. Paul, pp 324–329

  • Kwan MM, Balasubramanian P (2003) KnowledgeScope: managing knowledge in context. Decis Supp Syst 35:467–486

    Article  Google Scholar 

  • Kwok SH, Gao S (2004) Knowledge sharing community in P2P networks: a study of motivational perspective. J Knowl Manage 8(1):94–102

    Article  Google Scholar 

  • Kwok SH, Gao S (2002) Decentralized knowledge Reuse with peer-to-peer technology. In: Proceedings of the first workshop on e-Business. Barcelona, pp 136–146

  • Lee YC (2004) A knowledge management scheme for meta-data: an information structure graph. Decis Supp Syst 36:341–354

    Article  Google Scholar 

  • Madhusudan T, Uttamsingh N (2006) A declarative approach to composing web services in dynamic environments. Decis Supp Syst 41:325–357

    Article  Google Scholar 

  • Markus L (2001) Toward a theory of knowledge reuse: types of knowledge reuse situations and factors in Reuse success. J Manage Syst 18(1):57–93

    Google Scholar 

  • Marshall C, Prusak L, Shpillberg D (1996) Financial risk and the need for superior knowledge management. California Manage Rev 38(3):77–101

    Google Scholar 

  • Monge PR (1990) Theoretical and analytical issues in studying organizational processes. Org Sci 1:406–430

    Article  Google Scholar 

  • Moitra D, Ganesh J (2005) Web services and flexible business processes: towards the adaptive enterprise. Inf Manage 42:921–933

    Article  Google Scholar 

  • Muhanna WA, Pick RA (1994) Meta-modeling concepts and tools for model management: a systems approach. Manage Sci 40(9):1093–113

    Article  Google Scholar 

  • Nonaka I (1991) The knowledge creation company. Harvard Bus Rev, Cambridge, pp 96–104

  • Papows J (1998) Enterprise.com—market leadership in the information age. Perseus Books, Reading

    Google Scholar 

  • Ponniah P (2001) Data warehousing fundamentals: a comprehensive guide for IT professionals. Wiley, New York

    Book  Google Scholar 

  • Romer P (1990) Human capital and growth: theory and evidence. Carnegie-Rochester Conf Ser Publ Policy 32:251–286

    Article  Google Scholar 

  • Ruggles R (1998) The state of the notion: knowledge management in practice. California Manage Rev 40(3):80–89

    Google Scholar 

  • Scheer AW (1997) Business process engineering: reference models for industrial enterprises, 2nd edn. Springer, New York

    Google Scholar 

  • Sen A (2004) Metadata management: past, present and future. Decis Supp Syst 37:151–173

    Article  Google Scholar 

  • Szulanski G (1996) Exploring internal stickiness: impediments to the transfer of best practice within the firm. Strat Manage J 17:27–43

    Google Scholar 

  • Stein EW, Zwass V (1995) Actualizing organizational memory with information systems. Inf Syst Res 6(2):85–117

    Article  Google Scholar 

  • Toffler A (1990) Powershift: knowledge, wealth and violence at the edge of 21st century. Bantam Books, New York

    Google Scholar 

  • Walsh JP, Ungson GR (1991) Organizational memory. Acad Manage Rev 16(1):57–91

    Article  Google Scholar 

  • Weick KE (1989) Theory construction as disciplined imagination. Acad Manage Rev 14(4):516–32

    Article  Google Scholar 

  • Wijnhoven F (1999) Development scenarios for organizational memory systems. J Manage Inf Syst 16(1):121–146

    Google Scholar 

  • Williams BC (1991) A theory of interactions: unifying qualitative and quantitative algebraic reasoning. Artif Intell 51:39–94

    Article  Google Scholar 

  • Wolstenholme EF (1999) Qualitative vs. quantitative modeling: the evolving balance. J Oper Res Soc 50:422–428

    Google Scholar 

  • Zhuge H (2003) Component-based workflow systems development. Decis Supp Syst 35:517–536

    Article  Google Scholar 

  • zur Muehlen M, Nickerson JV, Swanson KD (2005) Developing web services choreography standards—the case of REST vs. SOAP. Decis Supp Syst 40:9–29

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, CT, USA

    Sulin Ba

  2. Department of Computer Information Systems, Zicklin School of Business, Baruch College, City University of New York, New York, NY, USA

    Karl R. Lang

  3. McCombs School of Business, The University of Texas, Austin, TX, USA

    Andrew B. Whinston

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  1. Sulin Ba
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  2. Karl R. Lang
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  3. Andrew B. Whinston
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Correspondence to Karl R. Lang.

Additional information

This article is part of the “Handbook on Decision Support Systems” edited by Frada Burstein and Clyde Holsapple W (2008) Springer.

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Ba, S., Lang, K.R. & Whinston, A.B. Compositional enterprise modeling and decision support. ISeB 6, 137–160 (2008). https://doi.org/10.1007/s10257-007-0068-7

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