Forecasting for a better future

Published: 05 July 2013
"The best laid plans of mice and men often go awry", said the Bard over 400 years ago and we haven't devised any way of looking into the future in the interim. We have, however, advanced exponentially in our ability to forecast what will happen within often startlingly narrow margins of accuracy. This week's newsletter looks at the science and the simplicity of forecasting in the business world.

It is practically impossible to know what is going to happen tomorrow. And yet, as much as the future is unknown, one can make the reasonable assumption that it will be influenced by what is happening today - what is happening right now. Conditions are always changing - it is quite safe to expect the unexpected - the uncertain, the volatile and the unplanned are bound to arise eventually.

While one of the most powerful ideas is that decisions in themselves create certainty, the importance of forecasting should never be forgotten. Forecasting is a technique that we use consciously or unconsciously to predict what will happen and what the likelihood is of specific events. Forecasting weighs the risks, with the aim to model the overall impact of decisions and known eventualities in a specific role or function. As such, forecasts form the basis of sales planning, production and inventory planning, manpower planning, financial planning and budgeting, research and development planning, acquisition and merger analysis and strategic planning.

With it being so important to know whether a business will survive and be profitable, one would expect that brilliant techniques and systems, by which we can predict the future, would abound. It would be great if there was a big data repository from which all the variables in the world could be drawn and used to support predictions regarding, for example, how many people would like Product A vs. Product B in Market X. In fact, in this ideal world, the connections between these countless variables would also be totally clear.

So if one then decides, for example, to put more money into advertising, net profit generated directly by that advertising would be easy to identify. Variables such as the different prices offered by the suppliers, an unstable labour market, government policy, fluctuations in the financial markets, and countless others would be known. It would be lovely if all of this complexity could be analysed and built into a micro-model for decisions.

Now, although this ultimate Utopian system does not yet exist, forecasting has come a long way. Some of the top minds in the world have developed advanced micro-modelling techniques, based on advanced artificial intelligence, that have this level of complexity built into them.

But these are not the types of calculations that an entry-level programme like MS Excel is going to be able to do for you. Unfortunately, most data and analytical models are just not advanced enough to bring this type of modelling into practical use. The executive or manager is expected to simply be aware of the macro and micro factors that drive the business model of the enterprise and to be able to predict what will happen to the business given a specific set of variables.

The millions of bits of information flowing in from all sources add to our understanding of what will happen and influence, either intuitively or quantitatively, the direction of decision making. This information shapes management's perception of what the best next step is and, as such, may fundamentally shape the outlook of the business.

So how do we, in practical terms, go about forecasting?

Step 1: Decide the time period of forecasting

A forecast should be made for a period that is practical. Let's say that you are forecasting for something that changes often - then, at best, you will be able to make a short-term forecast that has some level of accuracy. Updating such a forecast more regularly is important as it is likely to change often.

So you have to decide on what the period to which the forecast will refer and how regularly it will be updated.

Step 2: Break up the period of forecasting into shorter time windows

If you are forecasting events a year away, you might break your forecast up into shorter periods of months. If you are forecasting for a month, then break it up into days.

It may be useful to break up a year into days if you understand your process well but this might require you to introduce more variables, which will complicate your forecast.

Let's build a spreadsheet to record the various time intervals. Column 1 would thus look at Period 1, Column 2 at Period 2, etc.

Step 3: Map the inputs, process and outputs

If you understand the process or business model of a specific flow in your business, then you can look at the inputs, the process that they need to go through and the outputs.

These become the rows of your spreadsheet.

So, the idea is that you look at how many people there are, how much these people can sell, how much of what they sell is produced, how much needs to be ordered, how long delivery time will be (if the delivery time will cause significant delays), how much existing stock is in the inventory, how much of what is in the inventory can be sold, etc. This last variable may come full circle and have a bearing on the number of people that are available. This is the nature of forecasting.

Step 4: Build the micro model

The micro model is how you get to the results and is usually captured in the equations you put into your spreadsheet. It is the tool by which you map out the links between the various parts of the process.

How the processes impact each other can become a matter of some gut feel and experimentation. Sometimes it is as easy as choosing between a plus and a minus and whether or not to multiply the figure by the inflation rate.

Sometimes, however, it is more complicated and requires statistical analysis or investigation into the existence of theoretical models in this knowledge area that explain how something works. It may also be possible to use your experience or a clear process definition to get to the equation that drives it all.

We all know, for example, that Sales Price = Cost Price X Mark-Up, but your taxes may be different, and you may have commissions and other factors that go into your cost price and a million other factors that go into mark-up. You only have to build the micro-model to the level of complexity that is required. It can be as simple as putting the cost price and the mark-up into your spreadsheet or infinitely more complex, as the number of variables demands.

Step 5: Test the micro model

The test of the micro model is to observe these variables in the real world. If your forecast works for historical inputs and is robust enough to cater for what happened in the past, it is usually a good indicator of future trends. Many people forget to model their micro models on the past and then find themselves trying to explain why things went wrong and they blame their models. This is largely where forecasting fails.

If no past data exist, it may be beneficial to do parameter tests. What if a parameter was this, what is the outcome? This type of testing, although possibly expensive, may refine forecasting models and give a very clear indication of what will happen, given specific conditions.

Step 6: Move from model to execution

A forecast is only useful if it gets implemented. It is recommended that forecasts are shared, discussed and regularly updated. Some successful sales organisations forecast weekly and production businesses automate the process to the level where they can forecast daily, weekly and further ahead.

Once a forecast is established and proven, it becomes a tool for monitoring variance. If the actual is compared to the forecast, the variance starts explaining deviations that occurred. By understanding the re-occurrences, it allows us to either eliminate them, or to build them into the forecast and understand their impact on the bottom- and top-lines.

Step 7: Break the mould

A forecast, as a tool, aids in perfecting the outcome of a process - but it also needs to become a source of innovation. When we know what we budgeted and what actually happened, it allows us to look for innovation and improvement. It may require us to rethink the business model fundamentally and to come up with interesting and new ways to do things. This process of breaking the mould should translate into new measures and disassembling old measures. So forecasts, while useful for showing us what the future holds, must also be used to keep the business competitive and on the path to innovation.

Whether you rely on intuition or highly scientific forecasting, you have no choice but to constantly be aware of what is happening in the present in order to try to predict the future. A view on the future is necessary to allow businesses to embed strategy and achieve prosperity and sustainability. Being better at forecasting and making it a daily discipline will help businesses to drive a view of the future that is practical and that is built on innovation. 
- Regenesys


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