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Presenting large scale forecast results in an intuitive way

Presenting large scale forecast results in an intuitive way. An industry case study. David Edison. Moore Stephens Consulting Limited. Part of Moore Stephens LLP: Founded in London in 1907 Specialist expertise in Business Intelligence and forecasting

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Presenting large scale forecast results in an intuitive way

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  1. Presenting large scale forecast results in an intuitive way An industry case study. David Edison

  2. Moore Stephens Consulting Limited • Part of Moore Stephens LLP: • Founded in London in 1907 • Specialist expertise in Business Intelligence and forecasting • 19,300 staff in 621 locations in 95 countries • Global turnover last year $1,844M • Part of Moore Stephens International, offering accounting and consulting services to all industries in all locations • Microsoft Gold Partner • Ten years of modelling using @RISK

  3. General Forecasting Issues • Presenting statistics to people who don’t understand statistics • Providing all projection results, rather than just the selected few • Single-use datasets and models

  4. Project Overview • Major tobacco manufacturer • Projection of manufacturer and competitor sales • Automated, low level forecasting system • Facility for ad hoc forecasting • Integration with existing IT systems • NOT a ‘black box’

  5. Forecast Requirements • Forecasts to be made monthly, automatically • For next 60 days, 24 months, 5 years • For each of 50+ geographical regions • For each of 300+ tobacco products • i.e. over 1 Million variables forecast each month • Generic model, indicating confidence in results

  6. User Interaction • NOT a ‘black box’ – models to be built in Excel • Users to be able to see and influence models • Ability to add subjective assumptions and influence results • Users to be able to experiment (ad hoc modelling) • @RISK is optimal solution

  7. Data Sourcing • Objective forecasts driven by past data • Automatically sourced from existing systems (cubes) • Explanatory variables investigated included: • Various economic data • Tourism • Price: absolute and relative to market average • Various time data / day of the week / month of the year • Days until next / since previous trading day • Promotions, marketing campaign data

  8. Specific modelling functionality • Allow users to enter explanatory variable assumptions • Allow selected historic data to be ignored • Make anticipated proportional adjustments • Enter expected ultimate results • Apply subjective weightings throughout, effectively allowing for anywhere from fully objective to fully subjective forecasting • All at the very lowest level • Switching of variables

  9. Proof of Forecasting Concept • Generic forecasting required to be more accurate than existing, manual forecasts • Model proven using multiple regression analysis (StatTools) • Blind forecasts retrospectively made on 100 key datasets: an average of half the error term of previous projections = project approval • Agreed that @RISK was best and only tool for forecasts • All controlled by Excel, @RISK and StatTools VBA

  10. Presentation of Results • Did not just want a lever arch file or series of Excel spreadsheets • Specific high level key exhibits required (Excel) • Flexible, freestyle analysis required, both of past and forecast data (Business Intelligence) • What IS Business Intelligence?

  11. Data In Your Existing Systems Data presented in your BI system Before Business Intelligence After Business Intelligence What is Business Intelligence? “The ability to easily report, analyse, interrogate and manipulate any aspect of your business performance, when you want, how you want, where you want, irrespective of underlying systems technology”

  12. Sourcing of Data / Return of Results • Business Intelligence • Cubes (automated direct querying) • ProClarity (briefing books)

  13. Business Intelligence: Additional Functionality • Fully web deployable • Integrates fully with IT environment • Not an IT tool – it’s for business performance, from analysts to executives • Fits any business model • One version of the truth • Provides the speed and agility to make the difference

  14. What could we have done differently and why did it work? • RDK • @RISK & StatTools – a powerful, flexible combination • Users have complete control over assumptions and understand the models • Users are able to experiment with the ad hoc model, meaning they understand the impact their assumptions will have on the automated system

  15. Questions? David Edison david.edison@moorestephens.com +44 (0)20 7334 9191

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