Business Forecasting: Making it Work for Your Business

January 5, 2020
Posted in: Uncategorized

 

If you want your small business to thrive and remain healthy through all markets, you must engage in business forecasting. This multidimensional approach to accounting will help you predict revenue, potential growth, cash flow, and other economic factors. Business forecasting isn’t a perfect science as there are limitations and a somewhat steep learning curve. However, with the information contained in this article and widely available forecasting tools, even a novice entrepreneur should be able to effectively predict key business developments.

 

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What is Business Forecasting?

 
Business forecasting is defined as an estimate or prediction of future business developments such as sales, expenses, cash flow, and profit.  Past and present economic, social, and financial information is analyzed to predict future events. These predictions could have a bearing on company policies as well as future financial, production and marketing operations.

Drastic shifts in economic activity and the dire effects these fluctuations can have on profit margins, make it essential for entrepreneurs to include forecasting in business planning. After the Great Depression of the 1930s, business forecasts became more widespread as business owners sought to avoid future economic catastrophes. As a result, a large number of consulting firms were established to provide forecasting guidance to governments and businesses.

Forecasting is an invaluable tool for business owners to anticipate economic trends and ready themselves to either benefit from these trends or offset them. For example, if business owners expect an economic downturn, they can cut back on hiring, production quotas, and inventories. On the other hand, if an economic boom is anticipated, the owners can take steps to prepare themselves to benefit from it. These steps could include increasing inventory levels and increasing their workforces. Sound business forecasting helps business owners and managers effectively adapt to changes in the economy. 

As the Great Depression taught us, the economy can experience drastic shifts with dire consequences. Businesses large and small can fall prey to these shifts so it’s prudent for businesses of all sizes to make business forecasting an essential part of their financial management systems. Had the businesses affected by the Great Depression engaged in forecasting, they would have been able to estimate the impact that this massive economic downturn would have on their businesses. 

How frequently should a forecast be performed? At a minimum, businesses should forecast a year ahead. Annual averages are preferred by business planners because sudden changes in the economic climate can lead to inaccuracies in quarterly forecasts. For example, a sudden growth spurt during the first half of 1984 skewed business forecasts for the remainder of the year. With the surge in cash flow, businesses rapidly added to inventories, plant, and equipment only to experience a sharp downturn in the second half of the year. Government spending and the demand for credit (and interest rates) increased. If businesses had foreseen the short-term trend, this could have been avoided. 

Again, business forecasting isn’t a perfect science and because business cycles aren’t repetitious, business owners are wise to employ other variables like experience, sound judgment, and instinct in addition to historical data. Even the most experienced forecasters can miss the mark when predicting business developments but forecasting is still better than not attempting to predict and better understand the nature and causes of economic shifts. By forecasting, small business owners and executives can use the estimates to create short and long-term plans. For instance, if a sales forecast shows a substantial increase in sales from one year to the next, the business owner can be prepared for it by hiring more staff, increasing inventory levels, and adjusting production quotas to meet the higher demand. If the sales forecast shows a substantial decrease in sales, the owner can take steps to minimize the impact such as reducing the workforce or inventory levels.

 

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The Difference Between a Business Forecast and a Budget

 
The difference between a budget and a forecast is that the budget is a plan that a business owner or management creates to determine how they want to grow the business. It doesn’t predict what will happen. Instead, it represents management’s plan for what they want to happen. On the other hand, forecasting estimates the future financial progress of the business. Business owners and management teams use historical data and growth rates to forecast what the business’s financials will look like in the future.

With budgeting, historical data is also used along with information about current finances to plan for the future. Budgets are generally set on an annual basis with progress checks throughout the year to ensure that the business is on track. 

Forecasting serves as somewhat of a real-time update to the budget progress. It helps to ensure that you stay on track of your budget and that you can promptly respond to your business’ changing needs. For example, a forecast can show that conditions are favorable for you to pick up more work and generate more revenue than was shown in your budget. This will allow you to take action to handle the additional demand such as hiring more staff to grow your business accordingly. 

A thorough forecast also takes into account other factors that aren’t generally included in a budget. Those factors include changes in the economy or stock market, major events, news articles, and trends. If you had a shoe store, for example, and you noticed the UGG boot obsession starting to take hold, your forecast would help you decide how to react. You could make the assumption that sales will increase and then increase your inventory of UGG boots to handle the anticipated increase in demand. 

Although they’re two totally distinct financial processes, a business forecast and a budget are equally important to a business’s success. Inaccuracies in either process could make a small business owner cash poor, let employees go, or worse yet, close its doors. 

 

The Importance of a Business Forecast

 
As mentioned above, if you only create a budget to estimate financial and business growth, you’re not taking changes in the economic or social climate into consideration. You could end up wasting time budgeting for financial and business growth that will never actually happen. With a forecast, your predictions are grounded in reality as past financial growth is used to predict future growth. 

A forecast also allows you to react to changes in a way that a budget doesn’t. For example, if you have forecasted your business growth based on retaining a large client, and that client decides to engage another business to provide the services, you can quickly modify your forecast to reflect the change or anticipated loss. 

 

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Conducting the Business Forecast

 
Business forecasting begins with a survey of the industry or industries in which the business operates. The forecasting analyst then determines the degree to which the business’s share of each market may vary during the forecasting period. Modern business forecasting implements computers and special programs that are designed to model the economic future. Over a period of thirty years, Nobel Prize winner Lawrence Klein of the Wharton School of Business constructed various forecasting systems that are based on macroeconomic models.  Two of Klein’s systems, the econometric unit of the Chase Manhattan Bank and the Data Resources Inc. (DRI) model, are among the most widely used forecasting systems of today.

Forecasting programs are often composed of and run as a system of mathematical equations. Early programs were composed of a dozen or so equations whereas today’s programs contain anywhere from a few hundred to upwards of 10,000 variables that are used to generate a forecast.  In addition to using these forecasting systems, forecasters also take certain external factors into account such as population, government spending, taxation, and monetary policy. These factors are examined to determine how each will influence future trends and economic developments.

Forecasters may also conduct what’s called a naïve forecast. This technique uses the present period’s actual growth rate (without adjusting it for causal factors) to predict the next period’s growth rate, assuming it’ll be the same.  A forecasting model is then created for a future week, month, year, or decade. This “quick and dirty” forecast should be used only for comparison to forecasts generated using more sophisticated or scientific techniques. The naïve approach is the most cost-effective and is only used for time series data, where forecasts are made that are equal to the last observed value. This approach is useful in industries where past patterns aren’t likely to be replicated in the future. In these instances, the most recently observed value is likely to provide the most relevant information. 

Business trends are forecasted on three levels: at the national level, at the industry or market level, and at the individual company level. Combining forecasts for one, two, or three years ahead has become popular as well as including quarterly forecasts that span the same timeline. Depending on the industry, some businesses require weekly or monthly forecasts whereas other industries like life insurance, public utilities, and long-term construction or manufacturing use forecasts that look ahead two or three decades to make decisions.  

 

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Business Forecasting Methods

 
There are several different forecasting methods that a business can employ to predict everything from sales growth to the state of the economy as a whole. All of these methods will generally be based on one of two encompassing techniques. Business forecasting can either be based on the qualitative method or quantitative method.

 

Qualitative Method

The qualitative method of forecasting doesn’t depend on statistical data but instead depends on the forecaster’s intuition and experience or information (opinions) provided by market experts or well-informed investors to predict future events. This method is thought of as subjective or expert-driven and for this reason, qualitative forecasting models have limitations. Without historical data to analyze, it can be challenging to make a viable prediction using this method.  The qualitative method is best used for short-term predictions where the scope of the forecast is limited or when there is no historical data available such as in the case of new product launches. 

 

Examples of qualitative forecasting methods:

  • Market research – If a large number of people are polled to get their input regarding a specific product or service to predict how many of them would likely buy or use it once it’s launched, qualitative forecasting is used.
  • Delphi Method – In this method, field experts are asked for their general opinions and those opinions are compiled into a forecast.

 

Quantitative Method

Unlike the qualitative method, the quantitative method focuses solely on statistical data to predict the most likely outcome a business might experience. It relies on repeated patterns in order to make assumptions. The quantitative method doesn’t take your intuition or the opinions of experts or well-informed investors into consideration. This method is number-centric and uses such variables as sales figures, the gross domestic product, and housing prices as the basis for making long-term predictions (measured in months or years).  

 

Examples of quantitative forecasting methods:

  • Indicator Approach – The Indicator Approach relies on the relationship between certain economic indicators, like gross domestic product and unemployment rates, remaining relatively unchanged over a period of time. The relationships between these economic indicators as well as the indicators that are leading are tracked and used to estimate the performance of the lagging indicators.  
  • Econometric Modeling – This method is more mathematically rigorous than the Indicator Approach. Instead of assuming that the relationships between economic indicators stay the same, econometric modeling tests datasets over time to see if there is consistency and how strong or significant the relationships between datasets are. This method is sometimes used to create custom indicators for more accurate forecasting results. Econometric models are more often used in academic settings in order to evaluate economic policies.
  • Time Series Methods – There is a collection of different forecasting methodologies that rely on historical data to predict future events.  The difference between these Time Series methodologies lies in subtle details like giving recent data more weight than older data. Forecasters hope to use historical data to obtain a better-than-average prediction about future events. The Time Series methods are the most common forms of business forecasting because they’re more economical to conduct and the results are generally as reliable as those produced by more expensive methods.

 

Choosing the Best Forecasting Method

By combining one of the qualitative methods with one of the quantitative forecasting methods, forecasters can obtain the best estimation of what will occur in the forecasted period. It’s also recommended that at least two, ideally three, forecasts be created. The different forecast versions should reflect (1) the best possible outcome, (2) the worst possible outcome, and (3) the satisfactory outcome.  Actual performance in the forecasted period is then compared against the three versions.

 

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The Forecasting Process

 
Although there are various methodologies that can be practically employed in business forecasting, all forecasts follow the same basic process on a conceptual level:

 

  1. Develop a basis – Before you can start forecasting, a problem or data point is chosen. This can be something like “what will our sales growth be for the next fiscal year?” or “will people be interested in buying Product X?” You would then develop a system to assess the current economic climate. This includes your industry and its present position, as well as the popular products within the industry in order to best predict the problem or data point that you’ve chosen.
  2. Choose theoretical variables and an ideal data set – In this step the forecaster identifies the relevant variables to include in the forecasting process and decides how to collect the needed data.
  3. Make assumptions – To reduce the time and data needed to complete a forecast, the forecaster makes explicit assumptions to simplify the process.
  4. Choose a forecasting method – The forecaster selects the forecasting method that best fits the data collected, selected variables, and assumptions.
  5. Analyze the collected data – Using the chosen forecasting method, the collected data is analyzed and a forecast is created from the analysis. The likelihood that certain events will take place in the business’s industry is estimated. 
  6. Verification of forecast – The forecaster must compare the forecast to actual results in order to tweak the forecasting process if necessary. Any problems are identified and action is taken to correct them so that future results are more reliable.

 

The first step in conducting a forecast involves the systematic investigation of economic conditions and industry position. The results of this investigation are used to predict future sales and general business operations. The general economic forecast is considered the primary step in the forecasting process.

The second step involves predicting conditions and future events within the industry. The information and data collected during investigation are used to predict future business operations. Certain assumptions are made in order to make quantitative estimates for future scale of operations. 

Once completed, forecasts are compared with actual results to ascertain if there are deviations. Any deviations between forecasts and actual performance are evaluated in order to improve the forecasting process and quality of future forecasts.  

 

Business Forecasting Data Sources

 
The first step in any statistical analysis is the collection of data. In order to conduct a forecast, you need data to analyze and interpret. The results of your forecast will only be as good as the data you put into it so you need to ensure that you’re collecting relevant data. Before starting the collection process, ask yourself the following questions: 

  • Why is data being collected?
  • What kind of data should be collected?
  • When should data be collected?
  • From where will data be collected?
  • Who will collect the data?
  • How will data be collected?

The answers to these questions will help shape your data collection efforts. Before starting the process, it’s important to have an idea of your objective for data collection, the type of data you’ll need, and how data will be collected. Once you have a plan in place, data can be collected from a variety of primary and secondary sources depending upon your available time and resources. 

 

Primary Sources

Data collected using primary sources is considered to be first-hand data and is collected personally by the business owner (or person assigned to the task) or through the use of reporting tools. If needed data isn’t available through reporting tools, it must be sourced through interviews, questionnaires, or observations. 

This method of data collection is costly and time-consuming and is generally used in cases where secondary data isn’t available.

 

Secondary Sources

Secondary sources include data that has already been published or collected by others. The data is considered to be “secondhand” and the collection process involves more of a compilation of data.

Sources of secondary data:

  • Official reports from government agencies
  • Financial statements from banks or other financial institutions
  • Annual reports of companies
  • Journals, newspapers, magazines, and other periodicals
  • Publications of financial institutions

Although secondary data is more readily available and therefore less expensive to collect, it’s still considered second hand data.  As such, it can be less reliable or relevant than data collected from primary sources so care needs to be taken when using secondary data.

 

 
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The Best Business Forecasting Tools

 
Strategic planning requires accurate data and data collected from primary sources is considered to be more reliable as it’s “first-hand”. One way to obtain first-hand data is through the use of reporting tools like forecasting software. Quality forecasting software won’t just give you projections. It will help you understand those projections, and how to make strategic decisions from them. To find and analyze first-hand data to be used in the forecasting process, here are some highly-rated tools to try: 

 

Demand Works

Demand Works provides demand and supply planning solutions that facilitate improvements in forecast accuracy, coordination, and asset utilization. Their forecasting software for businesses is entirely browser-based so it can be run from servers, the cloud, or your desktop. Demand Works’ demand planning software is specifically for inventory optimization, finite capacity planning, and sales and operation (S&OP) planning. Solutions are available in six languages and used by hundreds of global manufacturers and distributors.

 

QuickBooks

QuickBooks by Intuit has been providing accounting solutions to businesses since 1983. Its robust platform allows you to generate trends reports and forecasting reports that help in the financial planning process. 

The Forecast Overview Report lets you know what your business’s monthly forecasted income and expenses are for a particular time period. The Forecast vs. Actual Report provides you with the budget-to-actual revenues and expenses or account balances compared to the forecasted or projected amounts.

 

Tableau

Tableau has been recognized as a superb software solution for business forecasting.  Forecasting in Tableau uses a technique known as exponential smoothing. Forecast algorithms try to find a regular pattern in measures that can be continued into the future. Tableau automatically selects the best of up to eight models, the best being the one that generates the highest quality forecast. This helps business owners and managers get the most accurate picture of several different business areas so that they can strategize around the data.

 

Why You Should Consider Forecasting Software

 
There are various models and methodologies used to generate forecasts. Whatever model or methodology a business owner decides to employ, forecasting software can make the process go much smoother and often results in much more accurate results. 

Here are some benefits of using forecasting software:

 

  • Easy-to-run reports – One of the best features of forecasting software is the ease with which it generates reports. Accurate and practical projections are available with a few clicks. 
  • Alerts to stay on track – Automated updates provide you with insight to help you stay on track. Alerts give you a warning if it looks like things aren’t going according to plan. This helps you get in front of any issues before they get out of hand.
  • Business analysis – Quality forecasting software will give you a breakdown of your business’s performance to help you plan, streamline, and improve as needed. This helps you allocate limited resources most effectively.
  • Inventory management – Forecasting software can help you compare potential demand to the cost of storing unsold inventory. This ensures you won’t have too much inventory on hand, and that you won’t experience shortages either.

 

Limitations of Business Forecasting

 
While business forecasting is a valuable tool that businesses can use to gain insight into what the future holds, some argue it’s a waste of time and resources, yielding little in return.  It’s an irrefutable fact that a forecaster can use a variety of methodologies, carefully follow the steps, and still end up with a flawed forecast. Besides possible errors in calculations and the biased views of the people conducting the process, all of the variables that can impact future events can never be fully managed. 

Add to that the element of guesswork in the forecasting process and the argument against forecasting as a viable business tool is strengthened.

Here are other problems with relying on forecasts:

 

  • Historical data is all that the forecaster has at their disposal so the data is always going to be old. There’s no guarantee that the conditions that existed in the past will exist in the future.

 

  • Forecasting doesn’t allow for factoring in unique or unexpected events or externalities.  Assumptions made during the forecasting process can be faulty and black swan events are impossible to predict and have become more common as dependence on forecasts has increased. 

 

  • Management can potentially become dependent on historical data and trends instead of focusing on what the business is doing currently because forecasts can’t integrate their own impact. This means the results of a forecast can’t be used as a variable in the forecast.

 

If business owners can accept that business forecasting isn’t an exact science and that they won’t have a clear, unobscured view of the future, the importance of forecasting can be realized. It’s not possible to create forecasts with spot-on precision but any insight into likely future trends that a business can gain is better than no insight at all. For a business in a highly competitive industry, that can be significant.

Business forecasting is a formidable tool for organizations looking to gain an advantage over competitors. To get the most out of the process, business owners or management should make use of statistical and econometric models to create forecasts and then apply experience, skill, and objective judgment to evaluate the forecasts. 

 

 

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Reducing Forecasting Errors

 
Forecasting errors can distort a prediction’s validity and cause short-term issues to become catastrophic. To minimize forecast errors, the key is to determine what causes them and then take steps to mitigate them.

Causes of forecasting errors:

 

  • No methodical process – Forecasting is a highly methodical process so it’s imperative that a methodical forecasting process is used to make predictions about the future.
  • Ignoring reality – Planning around business goals instead of reality is characteristic of the budgeting process but shouldn’t be practiced in the creation of forecasts.
  • Assuming that forecasting software is always right – Forecasting software is a useful tool for collecting and analyzing data, as well as predicting outcomes. However, it has limitations so it shouldn’t be completely relied upon.
  • Not taking forecasts seriously – If there are inaccuracies in the forecasts, these need to be evaluated so that they don’t continue to happen.
  • Not communicating with other departments – Departments need to regularly communicate to ensure that any interdepartmental changes which may affect the business (and forecast results) are accounted for.
  • No one is held accountable for input inclusion – Someone in the organization needs to be responsible for integrating the various inputs and ensuring that they’re included in the forecast. They also need to determine if any assumptions were incorrect if there are forecast errors.

 

Ways to reduce forecasting errors:

  • Use quality forecasting software – Catastrophic forecasting errors are often made because businesses fail to use quality software to make predictions. The right software will provide unlimited forecasting scenarios and be easy to use with uncomplicated input requirements. It will also offer suggestions for improving business performance.
  • Clean up bad data – When using forecasting software, quality data needs to be input to obtain the best results. Inaccurate data is one of the primary causes of forecasting errors.
  • Modify the forecast timing – Consider generating forecasts as soon as possible. Forecasts that are created closer to the outcome date are generally much more accurate.
  • Modify the forecast granularity – Use the largest possible data set since more accurate results are achieved when a larger data set is used. For example, input product category data into forecasting software instead of the smaller product brand data set.
  • Consider changing forecasting method – It may be necessary to go beyond assessing problems with the data and instead determine if the current forecasting method is to blame for inaccuracies.

 

Final Thoughts

 
Whether it’s predicting sales or determining if you’ll need to grow your workforce, forecasting will help you assess where your business is and where it may be headed.  Appropriately used, forecasting will help you to plan ahead of your business’s needs and increase its chances of staying healthy in all economic climates. 

Forecasting, however, isn’t an exact science and there is the potential for errors and bias. The negatives aside, the right forecasting techniques can lead to reliable forecasts for your business. To get the best results, decide on your goals and this will lead you to the best forecast tools and techniques to plan a strategy to accomplish those goals.

 

 

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