![]() That’s the problem here – it’s subjective until your organisation defines how it is to be measured. Feel free to write a brief note and send it to someone who cares. I want to calculate a periodic growth rate objectively. This might not always be the case, but the concept remains the same even if the periodicity is not annual. It appears to go through a cycle once every 12 months. We need to identify the cyclicality of the data. Now who would like to present that projection to their senior management team? You extrapolate linearly, you get a straight line. Ladies and gentleman, you may have heard of hockeystick projections well, let me now present you with the swordfish. Similarly, it’s preferable to leave constant blank in the TREND function. We only specify the constant if we want to force c in the equation (not common – it will usually be left blank). Here, time is our independent variable ( x) and sales is our dependent variable ( y). the equation of a straight line ( ? is the gradient of the line and c is the y-intercept).īefore you disregard linear regression, bear in mind many non-linear relationships can become linear ones by taking logarithms of the variables, for example. ![]() TREND(known_y’s,known_x’s,new_x’s,) projects assuming that there is a relationship between two sets of variables x (independent variable – here, the dates) and y (dependent variable – the sales), through a formula y = ?x + c, i.e. There are several functions that can help us here, with one of the simplest being TREND. I want to extrapolate it until the end of 2020 (I want 2020 foresight!). I have data from September 2012 to July 2017. Now let’s be honest, anyone who has historical data looking this perfect should be referred to the auditors, but hey, this is for illustration purposes. Using my attached Excel file (below), let’s take a look at ways Excel can do this for you. I wanted to set the scene for having a simple mechanical approach for budgeting. There is no need to disagreements or confrontation: both parties may work together as a team. Furthermore, operational managers can review the trend and state where future numbers are wrong and all they need to do is explain the variation, i.e. This way, analysts may prepare this data in moments without feeling emotionally attached to the outputs. This needs to be a mechanical, objective process. By this, I mean something that can be constructed simply such that if anyone follows the same process they will get the same figures. There is a need for objective forecasting. The art of war / budgeting: don’t you just love it? Managers sometimes feel accountants / analysts request forecasts from scratch: do they have either the time or skills to prepare a zero-based budget, whereas the analysts feel if they spend time preparing the base budget it is then torn apart by their operational counterpart. Meanwhile, back on Planet Earth, often we find managers and analysts in a state of “co-operational flux” (a new term I have just invented). If you believe that you’re probably a Board Director… ![]() That goes without saying and is never a problem in reality as the two sides work beautifully together, skipping off into the sunset hand in hand after a productive day in the office, understanding each other’s needs and issues. Operational managers and analysts need to work together to improve forecasting. Similarly, most analysts do have these skills but may not be close enough to the front line to be able to understand operations sufficiently. It’s the operational managers at the coalface who have the best understanding of likely demand and expected costs, yet many do not have the necessary tools or financial skills to forecast to the level that senior management requires. Let’s face an ugly truth in the world of finance / accounting.
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