Over 65% of executives, financiers and investors are never confident about their subordinates’ business plan forecasts. Most of these forecasts are meant to protect their interests (subordinates) rather than give a clearer picture to the investors and/or top management of where the company is heading.
Most times, these figures can be misrepresentative. A company might project sales of up to Ugx. 100,000,000 in ten years on which it borrows to expand works. This figure might fall shot by a significant figure and might lead to unanticipated liabilities that could be detrimental to the growth of your organisation. Moreover, these are decisions made on intuition.
There are techniques your executives can use to increase confidence in these sales forecasts and most of these can be done with tools that are at your fingertips. I am not saying expensive off shelf software will not do a good job. In fact, they will do a great job.
Nevertheless, Microsoft Excel will do a job similar to those, yet all your employees already know most of the basics of this platform. What they do not know is how to use the tools of this powerful platform. They need to be empowered with the skills to make much more reliable forecasts. The amount you will spend on training them these techniques is just a prick of the amount you spend on maintaining expensive packages.
Some of the confidence implanting tools include; hedonic models, stress testing, regression analysis and smoothing. These are tools very expensive off shelf software can manipulate and so can Ms Excel efficiently.
These techniques have saved a certain Ugandan Telecom company over UgX. 1.2 billion In fabricated forecasts, which could have gone wrong had they not learned the skills. You never know, they could have closed shop.
With #beyondEXCEL Productivity package, our experts will empower your executives with skills in business intelligence and predictive modelling in Microsoft Excel, which will save you a great deal of unanticipated risks, protect your top line and unearth inconsistencies in your data.
