6 forecasting best practices for smart businesses | anaplan and bettervu

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  • 6 forecasting best practices for smart businesses

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    www.BetterVu.com

    PARTNERBETTERVU

    25+ Years of experience in Financial Performance ManagementMultiple industries with service focus

    Frequent speaker and innovation leaderBased in Toronto, Canada

    MITCH MAX

    Todays speaker

    mmax@BetterVu.com

    1-844-423-8788 x201

    @Better_Vu

  • 3

    www.BetterVu.com

    BetterVu designs and implements innovative

    solutions that link sales, operational and

    financial applications to create

    transformational change.

    Anaplan is our canvas to bring Best

    Practices in Performance Management from

    vision to reality.

    www.BetterVu.com

  • 4

    www.BetterVu.com

    WHATS NEXT?

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    www.BetterVu.com

    Evolution of Forecasting

    Driver-based, data-driven Updated at least monthly Sales and Operations focus Rolling forecast, looks into

    next fiscal year

    Manual Submission and Consolidation

    1 or 2x per year Finance-focused Validate ability to meet Budget

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    www.BetterVu.com

    TRACK AND IMPROVE FORECAST ACCURACY

    IDENTIFY A STRONG SET OF DRIVERS

    INTEGRATE FORECASTING

    INTO MANAGEMENT

    PRACTICES

    BUILD A CONTINUOUS

    FORECAST

    AUTOMATE AND

    OVERRIDE

    BUILD COLLABORATIVE FORECASTING PROCESSES

    6 Best Practices

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    www.BetterVu.com

    #1 IDENTIFY THE RIGHT DRIVERS

    Focus on Predictive drivers search for causation and linkage not all measures are drivers

    Determine / test for sensitivity and impact to identify the most relevant

    Set the right level of granularity Time Product / Service Geography

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    Driver example wholesale sporting goodsONE: IDENTIFY A STRONG SET OF DRIVERS FOR YOUR BUSINESS OR FUNCTION

    2 Data-Driven Forecasting // 6 Best Practices to Efficiently Drive Timely and Accurate Decision Making

    In this example, Revenue is the product of Sales Volume and

    Average Price. Macro-level adjustments may be made at this level,

    but it is best to drill down to a lower driver - in this example sales

    volume is driven by the number of stores, sales per store, and the

    number of promotions. Each of these drivers is further driven by

    lower level components. By modelling each of the components

    and their relationship and sensitivity based on historical trends, a

    robust and reliable forecast can be constructed based on changes

    in any of the underlying components. Often the granularity helps

    to identify those drivers which are the most impactful historically

    - assuming that historical trends will continue into the future - so

    that forecast effort can be more tightly focused.

    Its important to note that having a simple list of drivers is not

    sufficient; we strongly recommend that each organization

    understand its drivers in chain fashion, as shown above. This

    allows metrics to be used in multiple chains while understanding

    how each metric contributes to overall success.

    Driver Example - Wholesale Sporting Goods

    Revenue

    # of Stores

    Sales/Store

    # Promotions

    eCommPrice

    WS Price

    Discounting

    Retention

    New

    Sales PerCustomer

    Current

    New Stores

    Store Size

    Sales perSquare Foot

    CustomerTraffic

    Effectiveness

    Price Changes

    Store Size

    Sales perSquare Foot

    Average Price

    SalesVolume

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    www.BetterVu.com

    #2 build a continuous process

    Use driver data to project future outcomes Build on linkages and trends Adjust for likely variations Avoid fiscal timeframes

    Target a quick process (1-2 days)

    Move from fiscal cycle to on-demand Constantly updated Alerts trigger review and action

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    www.BetterVu.com

    Example forecast process

    98

    104

    108

    100

    105

    110 110

    115

    120

    130

    132

    108 108

    110

    115 115 115

    120

    108109

    112

    118

    121

    128

    132

    Y1Q1 Y1Q2 Y1Q3 Y1Q4 Y2Q1 Y2Q2 Y2Q3 Y2Q4 Y3Q1

    Actuals

    Plan

    Base Forecast

    Rev Forecast

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    #3 automate and override

    Create statistical projections that are auto-tuned to each driver

    Allow for human override or capture of field data Use statistical data for high volume, low value

    items Gather local expertise for high value items Manual input may be required if low data volumes

    Identify accuracy of auto-forecasts and improve over time

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    www.BetterVu.com

    #4 collaborate across the organization

    Create shared assumptions

    Link all parts of the business and update in real time

    Leverage collaborative tools

    Build supportive culture

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    www.BetterVu.com

    #5 track and improve accuracy

    Continuous Improvement is key to forecasting Improvement drives reliability and usefulness Under and Over can be equally wrong

    Measure accuracy regularly and compare: Accuracy by forecasting team Accuracy by forecasting subject (product, customer) Accuracy by forecasting timeframe

    Track and celebrate improvement over time Dont incentivize forecast accuracy

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    www.BetterVu.com

    #6 integrate forecasting into management practices

    Make forecasting a core part of your management operating model

    Demand and Supply Planning Monthly Operating Review

    Change the dialog from what to so what and do what

    Ultimately, effort will shift from budgeting and variance analysis to trends and forecasting

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    THANKS FOR WATCHINGDownload our White Paper @ www.BetterVu.com

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    ANY QUESTIONS?

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