QPRC 2003 IBM TWRC Cross-Enterprise Data Analysis

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QPRC 2003 IBM TWRC Cross-Enterprise Data Analysis

  1. 1. Cross-Enterprise Data Analysis Methodologies, Challenges, Opportunities James Ding Michael G. DeGroote School of Business McMaster University May 23, 2003
  2. 2. Presentation Agenda <ul><li>Enterprise Decision-Making </li></ul><ul><li>After-ERP Solutions </li></ul><ul><li>Modeling and Analysis Tools </li></ul><ul><ul><li>Adaptive System Design (ASD) </li></ul></ul><ul><ul><li>Global Development Modeling (GDM) </li></ul></ul><ul><ul><li>Differential Integration System (DIS) </li></ul></ul><ul><li>Case Studies </li></ul><ul><ul><li>Quality-related Information Sharing under JIT Operation </li></ul></ul><ul><ul><li>DW/DM Integration for Massive Data Processing </li></ul></ul><ul><ul><li>Cross-Enterprise BPR for Supply Chain Design </li></ul></ul><ul><li>Concluding Remarks </li></ul>
  3. 3. Enterprise Decision-Making: Framework EIS DSS ERP MANAGEMENT INFORMATION SYSTEM Strategy Planning Operation
  4. 4. Enterprise Decision-Making: Contents MANAGEMENT Global Strategy Competitive Priorities Process, Quality, Technology Capacity, Location, Layout SOP, MRP, CRP, DRP, IM/WM Transaction-based Decision <ul><li>Time Horizon </li></ul><ul><li>Life Cycle </li></ul>Strategy Planning Operation
  5. 5. After-ERP Solutions: Extension View Prior-ERP Solutions ERP Solutions After-ERP Solutions Technology - Data Warehouse/Data Mining - Intelligent Portal - Integration (BOB, Plug-in/Add-on) - E-Business Operation (Function) <ul><li>SCM </li></ul><ul><li>CRM/SFA </li></ul><ul><li>APS </li></ul><ul><li>LIS </li></ul><ul><li>WMS </li></ul>Strategy (Organization) <ul><li>Cross-Enterprise BPR </li></ul><ul><li>Out-Sourcing </li></ul><ul><li>Virtual Firm </li></ul><ul><li>Learning Organization </li></ul>
  6. 6. After-ERP Solutions: Value Chain View Supplier Manufacturer Distributor Retailer Customer Strategy Planning Operation After-ERP Solutions Chopra (2001), Porter (1985) APS WMS CRM SFA Demand Management LIS ERP MES Catalogue Management Supply Chain Management
  7. 7. After-ERP Solutions: Wave View SAP AG (2003) Mainframe C/S Structure Internet ERP SRP Prior-ERP Solutions After-ERP Solutions <ul><li>Intelligent ERP </li></ul><ul><li>ERP II </li></ul><ul><li>XRP </li></ul><ul><li>Mental Data </li></ul><ul><li>Relationship </li></ul>Transactional Analytic Inferential Technology: Data: System:
  8. 8. Modeling and Analysis: Framework DIS GDM ASD MANAGEMENT MODELING TOOLS Strategy Planning Operation
  9. 9. Modeling and Analysis: ASD <ul><li>Tools for decision-making under complex systems </li></ul><ul><ul><li>Bayesian Statistics (Thomas Bayes, 1821) </li></ul></ul><ul><ul><li>Information Theory (Shannon, 1948) </li></ul></ul><ul><ul><li>Cybernetics (Wiener, 1950) </li></ul></ul><ul><ul><li>System Dynamics (Forrester, 1961) </li></ul></ul><ul><ul><li>Game Theory </li></ul></ul><ul><ul><li>… … … … </li></ul></ul><ul><li>Adaptive System Design (ASD) Approach </li></ul><ul><ul><li>Simulation is a useful tool for complex systems but can not explore values of latent variables </li></ul></ul><ul><ul><li>Prediction with updated training data can lead to graduate exploration of values of latent variables </li></ul></ul>
  10. 10. Modeling and Analysis: GDM <ul><li>Global Development Modeling (GDM) is all about Optimization under Different Decision Spaces with Different Information </li></ul><ul><ul><li>Define A, B, C as decision spaces, </li></ul></ul><ul><ul><li>under the decision space X. </li></ul></ul><ul><ul><li>Step I, Generalization: Derive the optimal function from A to C. </li></ul></ul><ul><ul><li>Step II, Specialization: Derive the optimal function from C to B. </li></ul></ul><ul><li>Managerial Interpretation and Pitfalls in the Implementation </li></ul><ul><ul><li>Approach I, Strategic Solution </li></ul></ul><ul><ul><li>Approach II, Myopic Solution </li></ul></ul>as the decision function A B C A B A C A B B
  11. 11. Modeling and Analysis: DIS KI BI SI II PI OI Differential Integration System (DIS) is all about Effective Integration KI: Knowledge Integration BI: Business Integration SI: System Integration PI: Process Integration II: Information Integration OI: Organization Integration Strategy Planning Operation
  12. 12. Modeling and Analysis: DIS DIS is a Powerful Tool to Achieve Strategic Fit under Complex Systems Strategic Fit Chopra (2001) Certainty Information Uncertainty KI BI SI
  13. 13. Case Study: Information Sharing <ul><li>Bullwhip Effect: Good Thing or Bad Thing </li></ul><ul><ul><li>Academic Research Focuses on Evaluations of Information Sharing </li></ul></ul><ul><ul><li>Practitioners are in need of Methodologies to Guide Practices </li></ul></ul><ul><li>Information Sharing in Practice </li></ul><ul><ul><li>Data Model Integration </li></ul></ul><ul><ul><li>Truncated Quality Data Mixed Distribution Model </li></ul></ul><ul><ul><li>Censored Quality Data PH Regression Model </li></ul></ul><ul><ul><li>Complete Quality Data Log-Linear Regression Model </li></ul></ul><ul><li>Case Study: Intl. Pump Mfg. Company </li></ul><ul><ul><li>Scrap-Processing for the Optimal Order Quantity under ATO </li></ul></ul><ul><ul><li>Global JIT/SCM/TQM Strategy in Emerging Economy Area </li></ul></ul>Push for Complete Data Pull for Implementation
  14. 14. Case Study: DW/DM Integration Utilization of the Accounting Transaction Data for Standard Costing ERP ERP DW DM COA COA F/S COA Consolidated Algorithms Entry Entry Entry Entry Actual Costing System Standard Costing System Classification
  15. 15. Case Study: DW/DM Integration <ul><ul><li>Business Scenario </li></ul></ul><ul><ul><ul><li>Semi-mechanical, semi-chemical industry, Bill of Materials (Recipe) is decided by latent variables, eg. temperature, location, hardness, etc. </li></ul></ul></ul><ul><ul><ul><li>Repeated experiments are needed for each individual order to decide the BOM, hence generate massive technical data in the computer. </li></ul></ul></ul><ul><ul><li>DW/DM Integration Solution </li></ul></ul><ul><ul><ul><li>Utilization of the existing experimental data in the Data Warehouse to construct the function curve for each known product series </li></ul></ul></ul><ul><ul><ul><li>Utilization of the existing function curves to construct the critical points of the finite experiment design for unknown product series </li></ul></ul></ul><ul><ul><ul><li>Utilization of the DM algorithm to predict the function curve for unknown product series based on results from the finite experiment design </li></ul></ul></ul><ul><ul><li>What is the Advantage of DW for DM ? </li></ul></ul>Automatic BOM Generation System with Finite Experiment Design
  16. 16. Case Study: Cross-Enterprise BPR <ul><li>Strategic Planning is Important for the Supply Chain Design, even before the ERP Implementation </li></ul><ul><ul><li>SWOT Analysis helps to leverage Priorities of SCM during the ERP Implementation </li></ul></ul><ul><ul><li>Cross-Enterprise Business Process Re-engineering (BPR) helps to smooth the move to SCM and E-Business after the ERP Implementation </li></ul></ul><ul><li>Comparison of Three Companies in Asia </li></ul><ul><ul><li>Company ERP SCM Focus Integration </li></ul></ul><ul><ul><li>BASF R/2, R/3 Resource COSMOS System </li></ul></ul><ul><ul><li>Kodak R/3 DC Network Sales/Mfg. </li></ul></ul><ul><ul><li>GM R/3 Supply Network Supply/Mfg. </li></ul></ul>
  17. 17. Concluding Remarks: Conclusion <ul><li>Challenges in Cross-Enterprise Data Analysis </li></ul><ul><ul><li>Increasing Qualitative Data </li></ul></ul><ul><ul><li>Increasing Uncertainty in Data Resource </li></ul></ul><ul><ul><li>Involvement of Relationships </li></ul></ul><ul><ul><li>Involvement of Mental Data and Latent Variables </li></ul></ul><ul><li>Approaches for Cross-Enterprise Data Analysis </li></ul><ul><ul><li>Utilization of the Dynamic/Adaptive Mechanism </li></ul></ul><ul><ul><li>Utilization of the Power of Integration for the Existing Methods </li></ul></ul><ul><ul><li>Utilization of the Differential Strategy for the Integration </li></ul></ul><ul><li>Challenges are just Opportunities </li></ul><ul><ul><li>New Tools to Quantify the Qualitative Data </li></ul></ul><ul><ul><li>New Methodologies to Make Prediction under Latent Variables </li></ul></ul>
  18. 18. Concluding Remarks: Further Information <ul><li>To request working papers or further information, contact: </li></ul><ul><li>James Ding </li></ul><ul><li>MGD A210, School of Business, McMaster University </li></ul><ul><li>Hamilton, Ontario, Canada </li></ul><ul><li>Tel.: (905) 525-9140 X 26181 (Voice) </li></ul><ul><li>Fax: (905) 521-8995 </li></ul><ul><li>E-Mail: [email_address] </li></ul>

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