Multifaceted Modelling of Complex Business Enterprises

We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.

[1]  R. Turvey,et al.  Cost-Benefit Analysis: A Survey , 1965 .

[2]  Yongtang Shi,et al.  Extremality of degree-based graph entropies , 2014, Inf. Sci..

[3]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[4]  Henry Montgomery,et al.  Decision Rules and the Search for a Dominance Structure: Towards a Process Model of Decision Making* , 1983 .

[5]  François Bousquet,et al.  Multi-agent simulations and ecosystem management: a review , 2004 .

[6]  José A. Gámez,et al.  Domains of competence of the semi-naive Bayesian network classifiers , 2014, Inf. Sci..

[7]  C. Fornell,et al.  The American Customer Satisfaction Index: Nature, Purpose, and Findings , 1996 .

[8]  Ana Sánchez,et al.  Addressing imperfect maintenance modelling uncertainty in unavailability and cost based optimization , 2009, Reliab. Eng. Syst. Saf..

[9]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[10]  Evangelos Grigoroudis,et al.  Measuring Customer Satisfaction Using a Collective Preference Disaggregation Model , 1998, J. Glob. Optim..

[11]  Craig Boutilier,et al.  The Influence of Influence Diagrams on Artificial Intelligence , 2005, Decis. Anal..

[12]  Michael Pidd,et al.  A conceptual framework for understanding business processes and business process modelling , 2000, Inf. Syst. J..

[13]  Claudio De Stefano,et al.  Using bayesian networks for selecting classifiers in GP ensembles , 2011, Inf. Sci..

[14]  Enrique J. Gómez,et al.  Analysis and Specification of Telemedical Systems Using Modelling and Simulation: the MOSAIK-M Approach , 2005, MIE.

[15]  T. Söderqvist,et al.  Ecological-economic analysis of wetlands; scientific integration for management and policy , 2000 .

[16]  Fredrik Nilsson,et al.  On complex adaptive systems and agent-based modelling for improving decision-making in manufacturing and logistics settings: Experiences from a packaging company , 2006 .

[17]  Sandra Johnson,et al.  An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation. , 2010, Marine environmental research.

[18]  Evangelos A. Kiountouzis,et al.  The use of business process modelling in information systems security analysis and design , 2000, Inf. Manag. Comput. Secur..

[19]  L. Kyriakides,et al.  Critical analysis of the current approaches to modelling educational effectiveness: The importance of establishing a dynamic model , 2006 .

[20]  M. Ramachandran,et al.  Application of multi-criteria decision making to sustainable energy planning--A review , 2004 .

[21]  William J. Kettinger,et al.  Business Process Change: A Study of Methodologies, Techniques, and Tools , 1997, MIS Q..

[22]  Markus Schwaninger,et al.  Combining multicriteria decision aid and system dynamics for the control of socio-economic processes. An iterative real-time procedure , 1998, Eur. J. Oper. Res..

[23]  Kerrie Mengersen,et al.  Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models. , 2009, Ecology.

[24]  K. Kristensen,et al.  Customer satisfaction measurement at Post Denmark: Results of application of the European Customer Satisfaction Index Methodology , 2000 .

[25]  Paloma Main,et al.  Sensitivity to evidence in Gaussian Bayesian networks using mutual information , 2014, Inf. Sci..

[26]  Enrique J. Gómez,et al.  HIS modelling and simulation based cost-benefit analysis of a telemedical system for closed-loop diabetes therapy , 2007, Int. J. Medical Informatics.

[27]  Prakash P. Shenoy,et al.  A causal mapping approach to constructing Bayesian networks , 2004, Decis. Support Syst..

[28]  Peter Hackl,et al.  On structural equation modelling for customer satisfaction measurement , 2000 .

[29]  J. A. Díaz-García,et al.  SENSITIVITY ANALYSIS IN LINEAR REGRESSION , 2022 .

[30]  Lazaros G. Papageorgiou,et al.  A combined optimization and agent-based approach to supply chain modelling and performance assessment , 2001 .

[31]  László Monostori,et al.  Agent-based systems for manufacturing , 2006 .

[32]  O. Svenson.  Process descriptions of decision making. , 1979 .

[33]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[34]  Jayanath Ananda,et al.  A critical review of multi-criteria decision making methods with special reference to forest management and planning , 2009 .

[35]  Kevin D. Barber,et al.  Business-process modelling and simulation for manufacturing management: A practical way forward , 2003, Bus. Process. Manag. J..

[36]  N A Ashford,et al.  ALTERNATIVES TO COST‐BENEFIT ANALYSIS IN REGULATORY DECISIONS , 1981, Annals of the New York Academy of Sciences.

[37]  Kerrie L. Mengersen,et al.  A proposed validation framework for expert elicited Bayesian Networks , 2013, Expert Syst. Appl..

[38]  George M. Giaglis,et al.  Simulation for intra- and inter-organisational business process modelling , 1997, Informatica.

[39]  Timothy W. Finin,et al.  A negotiation-based Multi-agent System for Supply Chain Management , 1999 .

[40]  John B. Wong,et al.  The Influence of Influence Diagrams in Medicine , 2005, Decis. Anal..

[41]  Prakash P. Shenoy,et al.  A Bayesian network approach to making inferences in causal maps , 2001, Eur. J. Oper. Res..

[42]  Luca Bertolini,et al.  Why Cost Benefit Analysis is perceived as a problematic tool for assessment of transport plans: A process perspective , 2012 .

[43]  Rosanna Garcia.  Uses of Agent-Based Modeling in Innovation/New Product Development Research , 2005 .

[44]  John Psarras,et al.  Equity portfolio management within the MCDM frame: a literature review , 2009 .

[45]  Weiming Shen,et al.  Applications of agent-based systems in intelligent manufacturing: An updated review , 2006, Adv. Eng. Informatics.

[46]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[47]  Pekka Korhonen,et al.  Multiple criteria decision support - A review , 1992 .

[48]  M. Hare,et al.  Further towards a taxonomy of agent-based simulation models in environmental management , 2004, Math. Comput. Simul..

[49]  Bill Curtis,et al.  Process modeling , 1992, CACM.

[50]  Rainer Sommer,et al.  Comparison and evaluation of business process modelling and management tools , 2007, Int. J. Serv. Stand..

[51]  J. Elíasson.  A cost–benefit analysis of the Stockholm congestion charging system , 2009 .

[52]  Jayanta K. Ghosh,et al.  Bayesian Networks and Decision Graphs, 2nd Edition by Finn V. Jensen, Thomas D. Nielsen , 2008 .

[53]  Michael Windle,et al.  The science of prevention: methodological advances from alcohol and substance abuse research. , 1997 .

[54]  Michael J. Earl,et al.  How new is business process redesign , 1994 .

[55]  Matthias Dehmer,et al.  The Discrimination Power of Molecular Identification Numbers Revisited , 2013 .

[56]  Thomas H. Davenport,et al.  The New Industrial Engineering: Information Technology and Business Process Redesign , 2011 .

[57]  Enrique F. Castillo,et al.  A hierarchical optimization problem: Estimating traffic flow using Gamma random variables in a Bayesian context , 2014, Comput. Oper. Res..

[58]  Jason R. W. Merrick.  Bayesian Simulation and Decision Analysis: An Expository Survey , 2009, Decis. Anal..

[59]  Jayashankar M. Swaminathan,et al.  Modeling Supply Chain Dynamics: A Multiagent Approach , 1998 .

[60]  Matthias Dehmer,et al.  On Sphere-Regular Graphs and the Extremality of Information-Theoretic Network Measures , 2013 .

[61]  Qiang Shen,et al.  Learning Bayesian networks: approaches and issues , 2011, The Knowledge Engineering Review.

[62]  E Taniguchi,et al.  EFFECTS AND PROFITABILITY OF CONSTRUCTING THE NEW UNDERGROUND FREIGHT TRANSPORT SYSTEM , 1999 .

[63]  Ming Lim,et al.  A multi-agent based manufacturing control strategy for responsive manufacturing , 2003 .

[64]  Ron S. Kenett,et al.  Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis , 2011 .

[65]  George M. Giaglis,et al.  Dynamic Modeling to Assess the Business Value of Electronic Commerce , 1999, Int. J. Electron. Commer..

[66]  Sebastiaan N. Jonkman,et al.  Integrated Hydrodynamic and Economic Modelling of Flood Damage in The Netherlands , 2008 .

[67]  S. Kauffman.  At Home in the Universe: The Search for the Laws of Self-Organization and Complexity , 1995 .

[68]  John W. Graham,et al.  Analysis With Missing Data in Prevention Research , 1997 .

[69]  Ronald D. Anderson,et al.  A Bayesian Network Estimation of the Service-Profit Chain for Transport Service Satisfaction , 2004, Decis. Sci..

[70]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[71]  Thomas W. Lauer,et al.  Optimizing airline passenger prescreening systems with Bayesian decision models , 2012, Comput. Oper. Res..

[72]  Shueh-Chin Ting,et al.  The asymmetrical and non-linear effects of store quality attributes on customer satisfaction , 2002 .

[73]  R. Brent.  Applied Cost-benefit Analysis , 1996 .

[74]  Theodor J. Stewart,et al.  Integrated object-oriented framework for MCDM and DSS modelling , 2004, Decis. Support Syst..

[75]  Kevin D. Barber,et al.  Modelling the strategy management process: An initial BPM approach , 2004, Bus. Process. Manag. J..

[76]  Ross D. Shachter,et al.  Influence Diagrams for Team Decision Analysis , 2005, Decis. Anal..

[77]  Ron S. Kenett,et al.  Bayesian networks of customer satisfaction survey data , 2009 .

[78]  A. Gustafsson,et al.  The Evolution and Future of National Customer Satisfaction Index Models , 2001 .

[79]  Joakim Wikner.  Dynamic Modelling and Analysis of Information Flows in Production-Inventory and Supply Chain Systems , 1994 .