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    Variance Analysis

    Variance AnalysisDuring the budgetary process, an important step is the comparing of attained results with initially budgeted results in order to understand the sources of such variances. This step is important for managers who require explanations in relation to the informal contract that they have signed with their subordinates. This exercise also improves managers’ knowledge of the organisation and allows them to anticipating the costs.

    Actual vs. Budget Module allows the comparison between the overall model and the actual data and the removal of variances specific to the client’s needs. By creating a flexible budget and variance analysis, the module’s objective is to provide managers with explanations of the differences between budgetary data and actual data. The Actual vs. Budget Module calculates a flexible budget that will be used to put the original budget and actual activity volume in perspective, thus allowing managers to analyse the variances on a comparative basis with the reality of a given period.

    The Actual vs. Budget Module was designed to allow companies to apply their actual production costs with a minimum amount of information. Therefore, a company that has only one actual activity volume and one actual trial balance as basic information, will still be able to generate a flexible budget as well as variance analysis. There is no argument that the more actual information a company has, the more precise the variance analysis will be.

    Business applications

    Actual vs. Budget
    • Accelerates the processing time and increases time available for variance analysis
    • Ensures conformity and consistency of variance calculations between periods
    • Increases the quality of variance analysis by making available a high number of simple and complex variances such as volume and composition variances
    • Centralizes the calculation of variances and deploys standard results to managers, which increases the time available for comprehension and analysis
    • Tests the model’s predictive value
    • Improves both the model and management thanks to the thoroughness with which data is collected and the way different data is compared to one another
    • Allows updating the business model for the next budget year
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