11 Mar 5 fundamentals of business forecasting
Business forecasting is an essential part of running a successful, sustainable business. Being able to predict (with as much certainty as possible) and prepare for whatever is coming around the next corner ensures that your business has the maximum chance of success. Without forecasting, you leave a great deal more to chance. Here we take a look at five fundamentals of business forecasting.
1. Forecasting is essential to sustainable success
“Without a rigorous set of projections, says Rodney Schwartz, CEO of ClearlySo, “a strategy is just a bunch of words”.
To run a successful business you need to match demand and supply. In order to understand and prepare for future demand, businesses must create forecasts.
Demand forecasting – the process of estimating the future demand of a product in terms of a unit or monetary value – is a fundamental part of supply chain management.
If you run a seasonal business, understanding the peaks and troughs of previous demand and incorporating them into your current business forecast allows your business to better manage its inventory. With an informed forecast, you can assess what amounts of stock should be maintained, what raw materials are likely to be required, and also what workforce you’ll need to fulfil orders.
Forecasting helps you to fully understand expected costs, revenue and profits, which in turn impacts process management across the entire business.
In terms of workforce management, it has a significant impact on staff recruitment and HR activity. And business forecasting also informs product strategy. Analysing and predicting potential future growth in demand, cash flow, sales and profits helps identify the right time for new product development and launch.
It also helps a business to adapt its overall cash flow strategy in line with predicted outcomes and growth aspirations. Understanding the most likely outcome for sales, revenues and profits helps ensure that any borrowing or repayment plans are scheduled for optimum cost-effectiveness, maximum opportunism and minimum risk.
2. Your business forecast should mirror your business plan
Business forecasting is concerned with understanding what could realistically happen based upon your historical performance.
Business plans include the growth aspirations of the business, and are arranged around a set of goals. They describe what the business wants to achieve, based on a set of assumptions. They provide the vision for the business, and shape all decisions moving forwards.
Having a business plan with clear targets is key to developing a relevant business forecast. Your business plan should inform your business forecast methods, assumptions and relevant data points.
Your forecast findings should then help to inform your business plans.
Therefore, it is crucial that business forecasts are continually reviewed and reassessed to maintain accuracy and alignment to your goals. Continual analysis of business performance against forecasts and regular reviewing/refreshing ensures that the forecast remains current and a useful management tool.
This helps to inform a robust and relevant business forecast, and in turn, a sustainable business plan. In short, you must keep your forecast methods and assumptions as fluid as possible, and closely linked to any alterations in your business plan.
3. Business forecasting methods and processes
The basic process of forecasting is essentially the same, whatever the methods employed:
A problem is selected – e.g. ‘what will our sales look like in October next year?’
Relevant data points are chosen – what variables and how to collect them
Assumptions are made to simplify the process and cut down on time and data
A forecasting model is chosen that is suitable for the above points
The data is analysed and the forecast is drawn up
The forecast is verified through comparison to actual events and performance
There are two main methods of forecasting: qualitative and quantitative.
Quantitative forecasting is concerned with data.
Businesses that have been established use primarily historical data of their own performance, combined with market and other macroeconomic factors. The analysis and extrapolation of this historical data to provide an indication of future performance or demand is known as Time Series Analysis. This is the most common type of business forecasting and forms a large part of many business’ approach. Due to its reflective nature, this is only generally useful for existing products and services.
Historical trend analysis looks for stable, upward or downward trends and patterns in historical data, including industry changes, and technological, cultural, and political developments.
With these methods, the more historical data there is, the better. This makes them less useful for businesses and markets that are relatively newly established.
If your business is newer, or has less historical data to work with, then either the ‘naive approach’ to data – that assumes the upcoming period demand will be the same as current – can be more useful.
Another option for those businesses with a lack of historical data can be the ‘moving averages’ approach. With this approach, the forecaster averages out the last 3 months, then uses that as the forecast for the next month. This can be particularly useful if there is very little trend data to work with.
Qualitative business forecasting models are generally used for short-term predictions, or for when data is scarce – for example, when a new product is first introduced to market. They consist of the following approaches:
- Expert opinions – what do experienced executives think will happen
- Delphi method – conducting surveys, interviews, and phone calls to a panel of experts (outside the business) from multiple areas, and try to reach a consensus.
- Talking to your sales force and asking them to predict sales based on performance and other variables
- Market surveys – Asking customers their opinions, preferences, etc. to gauge demand.
Qualitative business forecasting can come up against limitations due to its reliance on subjective opinion rather than concrete, measurable data points and trends. For example, salespeople are likely to overestimate how much they will sell. Problems can also arise if there is a lack of consensus among the experts polled.
Which leads onto the following…
4. There will always be limitations with forecasting
Due to the nature of forecasting, the goal is not to be able to create a 100% accurate prediction of future performance and events. It’s simply to formulate the best guess or estimate based upon the available relevant information.
Aiming to paint as realistic and informed a picture of how the next week, month, year, and even decade will play out, however, comes up against inherent limitations.
Due to the historical nature of the data used in qualitative forecasting methods, it is always old. If your data is not used regularly, the quality of it can decay. Errors go undetected, and inconsistencies go unnoticed. It must be used or checked regularly to ensure the data is robust enough to provide useable analysis. Solid, fresh data with more assumptions applied is better than old, rarely used data.
Forecasts, as with any predictions, are often biased to some degree. This is difficult to eliminate as the set of assumptions (which data points or factors to use, and how to weight them etc) will likely always add bias to the results.
Forecasting is, by it’s nature, never totally accurate, and always evolving. If your forecast does prove to be correct (or highly accurate), it’s important not to assume that this was due to your brilliant forecasting methodologies and sound logic. A correct forecast does not prove your forecast method is correct – it could have been down to sheer good luck. Always check and reassess your methods.
Robust, informed forecasting is always an iterative process. The more iterations and the better it is attached to real costs and coherent assumptions – the better the forecast will be.
Forecasting generally assumes overall economic stability and no significant changes in the industry or market. However, there is no guarantee that conditions in the past will carry over into the future. This makes historical data and trend analysis limited as a stand alone method for future predictions.
External unexpected events (think of the subprime mortgage meltdown) can instantly undermine assumptions and render a forecast irrelevant. It is impossible to factor in completely unexpected events. Therefore, there needs to be flexibility built into any business forecast.
5. Keep it simple where possible
A well constructed forecast should be simple to understand and provide information relevant to the strategy of the business. They should also be easy to adjust. The more simple the methodology used, the easier it is to understand, analyse, and figure out why, should anything go wrong. If a method is too complex, it can obscure key assumptions and reasons for failure.
When it comes to cash flow forecasting, Rodney Schwartz, CEO at ClearlySo advises: “The simpler the better – one of the best is just a projection of the bank balance”.
Finding a simple, flexible cash flow solution is key to maintaining the agility and financial robustness essential to sustainable growth. Unexpected events – either positive or negative – can be managed with more confidence, knowing that your working capital fund is backed up by an easy to access, cost effective and flexible finance facility.
Pay4’s simple and flexible supplier payment facility provides the working capital bridge essential for funding opportunities, bridging gaps in cash flow and managing the unexpected. However your forecast plays out, you’ll have the cash available to make the most of it.