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Intelligent Software Development Dr Don Perugini PowerPoint Presentation
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Intelligent Software Development Dr Don Perugini
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  1. Consumer Forecasting Micro-Simulation for Strategic Planning and Environmental Policy Intelligent Software Development Dr Don Perugini

  2. What is common to these questions? • How can we manage demand for water to delay $Billions in infrastructure costs? • How can we more accurately forecast demand/sales to save $millions in revenue? • How can we assess the impact and budget for new concessions or rebates? • How can we quantify and isolate the effectiveness of different strategies to improve business or policy outcomes? • How can we increase sales and customer retention? • How can we determine when and where to place a new product or franchise? • The answers are dependent on the decisions of millions of different individual consumers/people

  3. What Influences Decision Making? The real world is complex, dynamic, distributed, heterogeneous, & human-centric Social Demographics, psychology, preferences, goals, culture, reasoning, marketing and market research, communications and perception, social interactions, ... Economic Price, financial position, expendable income, discretionary and nondiscretionary expenditure, financial costs/benefits, elasticity, ... Environmental (physical/engineering) Products, consumption/efficiency, age, size, functionality, weather, ... Political Policies, regulation, constraints, programs, legal, ... Millions of individual consumers with different characteristics and circumstances making different decisions in a constantly changing world ...

  4. The Challenge We need to better understand What influences consumer decision making What is the significance/effectiveness of the influence Then we can Invest in better strategies Improve our investments and efforts Maximise our ROI ... and ultimately produce better policy or business outcomes We need to accurately predict and influence consumer decision making

  5. Spreadsheets a 2D tool (rows and columns) to tackle an multi-dimensional problem Limited scalability Difficult to run many iterations and incorporate time based influences Incorporating complex logic can become difficult and “unmaintainable” “Hit the wall” – and start again….. “Econometric Only” and mathematical models Limited in addressing these non-linear, dynamic, human-centric problems Limited detail, unrealistic assumptions about decision making, … Data mining and statistics Observes what people do, no insight into “why” they do it The past may not be a good predictor of the future Current limitations

  6. Introducing SimulAIt SimulAIt is a new, unique and proven product New approach – beyond econometrics Initially developed in the Defence Industry to better understand the response of enemy troops to different allied strategies SimulAIt incorporates numerous innovative technologies Agent-based modelling & Artificial Intelligence Scalable micro-simulation Multi-dimensional database & human cognition reasoning engine Advances in computing capability now enables these technologies to be cost-effectively applied to mainstream Government and business problems

  7. What is SimulAIt? Business tool to help you accurately predict and explore options to influence mass-consumer behaviour and decision making Simulates a complete population (e.g. city, state, national levels) – via individual consumers and their decision making Powered by Census data and population dynamics Capability to model reactions and behaviours of individual consumers to new strategies, policies, products, prices and competitive behaviour Handles different types of data to incorporate many decision making factors Models are configured to the specific business problem for better results Enables execution of many “what-if” scenarios to isolate individual strategies and explore options to influence consumer behaviour Answers a broad range of questions

  8. Proven approach Results of a joint project with Central Highlands Water and Department of Sustainability and Environment (2010) Over 95% Accuracy!

  9. Technology theory – ants…

  10. Consumers are rule-based • Consumer behaviour can also be described by rules which are driven by a consumer's demographic characteristics, situation, and preferences • Mass-consumer behaviour can be predicted by observing the emergent behaviour of millions of individual consumers

  11. SimulAIt Platform • SimulAIt consumer modelling and forecasting platform uses agent-based modelling to help you understand and predict mass-consumer behaviour • Agents represent consumers and their prescribed rules to simulate decisions and behaviours of different consumers • Rules are constructed by integrating different types of data to better understand how different consumers make decisions under different circumstances • Millions of consumers can be simulated using SimulAIt, allowing you to predict and explore options to influence mass-consumer behaviour

  12. SimulAIt Overview Input Execution of Multiple Scenarios Output • Configuration • Scenarios • Trends • Assumptions • Parameters/variables • Results • Demand Forecasts • Product uptake • Revenue forecasts • Scenario comparisons • Drill-down by dimensions (regions, demographics, time) Dynamic multi-dimensional database Models • Data: • Market research & social data • Econometric & statistical data • Engineering and environmental data • Customer data (billing, purchases....) SimulAIt Engine ABS & Population Dynamics

  13. SimulAIt Outputs Type of outputs Consumption Revenue Output formats Graphs, GIS, tables, spreadsheets Raw results Slice and dice, drill down, filter..... Region Time Product / Product Group Demographic type

  14. Tables and raw data

  15. SimulAIt GIS Output

  16. Customers Awards Customers Cleantech Excellence Award 2009 Runner Up 2009

  17. CHW/DSE – Bounce-back • Strategic business problems addressed • Bounce-back from easing restrictions • Pricing impact on consumption • Impact of product uptake • Business case to regulators • Demographic analysis • Involved different departments • Forecasting, marketing, strategy, finance • Accuracy in validation over 95% • Over 99% accuracy in forecasting demand bounce-back and behaviour maintenance Ballarat validation Bendigo validation

  18. DSE Rebates Objective Identify a mix of products and prices for the water rebates program that maximises efficiency and keeps within the program budget Simulation Incorporated consumer preference and affordability, and product age, failure and price Simulated product uptake and efficiency with different prices and consumer types Simulated 2 million households, 4.5 million consumers Outcomes Cost/benefit (triple bottom line) analysis of different product mixes and prices Accurate predictions of product up-take and budget spend Analysis to assist with targeted marketing of the rebate program

  19. Adelaide Water Pricing • Simulated 0.5 million households • Different responses of 400 demographic types to water price • Identified a more effective and fairer pricing policy… • that increases revenue

  20. Questions?

  21. Intelligent Software Development Pty Ltd Innovation House Mawson Lakes Boulevard Technology Park, Mawson Lakes South Australia, 5095 Phone: +61 8 8343 8455 Fax: +61 8 8260 8100