What to do to avoid the deemed dividend trap.
Research tax-efficient structures to facilitate real estate investing.
Using the Income Tax Act to avoid tax.
Utilizing the butterlfy.
Navigating through the delicate nature of non-arms length transactions.
Establishing A Forecasting Framework
The exercise of creating, measuring and implementing short- and long-term strategic initiatives brings with it substantial accountability and due diligence requirements. Staff and their families tacitly rely on the ability of management to keep the business competitive; vendors have possibly entrusted a bulk of their resources, processes and forecasts to your continued business with them, and other arms-length stakeholders exist with various other financial or non-financial expectations which are directly tied to your continued success.
So the path of creating and testing short- and long-term strategy needs to be carefully constructed to facilitate accurate assessments of influencing variables. At some point in the strategic formulation process, the strategic options and their resource demands need to be forecasted to:
- Determine if strategic initiatives meet profitability goals
- Calculate resource needs, both quantitative and qualitative
- Assess the strategy’s ability to position the business appropriately in its participating markets.
To sufficiently acquire answers to these 3 points, the forecasting exercise needs to be standardized. Following we describe the main components that go into the forecasting process. Testing all strategic initiatives using this process – with sufficient data already acquired – will enable the business to make viable and confident decisions regarding the feasibility of these initiatives.
THE FORECASTING PROCESS
- Company and Industry Analysis
The first step in the forecasting process involves determining the true operational earnings of the business, as well as benchmark industry comparisons against which the business’s current operations can be assessed.
Determining operational earnings includes performing ratio analysis and recognizing the value drivers of the company. This in turn will provide an objective understanding of how the company is performing and what attributes influence its performance.
Some attributes, such as economic variables and interest rates, are beyond the company’s ability to control. Nevertheless, when we can determine those components that can be controlled by the company, we can take steps to arrange and manage them to realize corporate goals.
We then compare the company’s performance – given its utilization of resources and investment – against industry standards. The result of this comparison will tell us where we need to change in order to improve performance and enhance the company’s competitive position.
- Economic Analysis
What is the future economic outlook of the market(s) in which the company operates? Are preliminary goals and objectives realistic given current economic conditions?
The existing economic climate needs to be synchronized to the company’s objectives. If the company intends on executing a cash flow strategy where a return on assets can only be achieved by utilizing a borrowing rate that is well below what institutions are providing, then there’s a separation between economic reality and economic variables underlying the strategy.
So a strict analysis, utilizing econometric methods, becomes vitally important to assess the forecast and , therefore, the viability of the strategic option. When we can confidently assert that the economic climate in the operating market can facilitate the goals and objectives of the company, then there is synchronization.
- Building the Forecast
When we have normalized earnings, and a concrete understanding of the company’s position within its industry, accompanied by an awareness of what’s possible in the industry and the economic environment in which it operates, we now have sufficient information upon which to determine:
- The scope of the forecast
- The appropriate forecasting model
Various econometric models exist to satisfy the measurement and forecast of variables influencing a company’s operational performance. Linear models, such as time series, and regression, or non-linear such as ARIMA and Exponential Smoothing, exist to provide fit to the economic and operational elements of each particular company.
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Refinements in practice as well as academic pursuit has provided us with these enhanced forecasting methods that result in increasingly (though of course not absolutely) accurate estimations of business revenues and their associated components.
For example, ARIMA methods of forecasting allow us to incorporate seasonal influences, such as summer weather or rising wheat prices, into the forecast. Such influences, however, have ramifications all the way through business processes such as production, distribution, and supply chains.
It becomes vitally important, therefore, to utilize the best forecasting model for the business that recognizes all relevant infuences on the variable that is being forecasted, and it’s here that practitioners spend a substantial amount of time testing the forecasting model and applying it against accuracy benchmarks such as t-tests.
- Applying the Forecast
So how does this all come together? How do we go from an equation to an accurate assessment profitability and of what we need to spend to get resources in place to manufacture or sell our forecasted product?
The whole exercise of putting a forecast together begins with asking “What are we forecasting” (the forecasted variable?) Then, based on that answer, we need to sufficiently determine all the components that go into the production, distribution, and delivery of the forecasted product (ie: fixed and variable costs in the operational budget).
Hopefully, we’ve identified the extent of the causal relationship between the forecasted variable and the costs that are associated with its’ ultimate delivery. Progressing from that, we expect that the forecasted sales from the product are comfortably greater that the cumulative costs budgeted to deliver it (or, in other words, that our target margins are reached).
For example, a tool manufacturing facility needs to forecast demand for its newly patented screwdriver. In so doing, it will also have to budget possible increases in manufacturing space (will additional warehouse space be required to build x number of tools?), human resources and benefits, and increased distribution costs.
So the forecasting exercise and the budgeting exercise really become 2 interlocking components of unified task. I need to forecast sales, but anticipating sales alone provides no value if I don’t also know the marginal costs involved in realizing those sales.
When we understand that the forecast is first applied to the forecasted variable, -revenues for example- and that all components relevant to the ultimate delivery of revenues are then budgeted, we can have confidence that we are estimating all parts of the operating cycle and can apply the forecasting model (or equation) by taking what we’ve identified as influencing drivers of sales and inserting them into the model to forecast revenues.
- What-if analysis
As a final due diligence step in the exercise, we engage in what-if analysis to ensure that the model performs as expected. Here we plug various numbers into the equation, expecting the model to perform in certain way. If the model performs unexpectedly, then additional investigation is required to see if the results are due to the inferiority of the model or due to input error.
Ultimately, the purpose of the forecasting exercise is to provide more clarity on the viability of strategic options. Whether the forecast is applied to operational costs, revenues, or just determining the added value of an another manufacturing warehouse, all forecasts should contribute to the managers ability to make clear, confident, and consensus-driven decisions regarding the future of the company.
Nicholas Kilpatrick is a partner at the accounting firm of Burgess Kilpatrick. He leads the firm’s consulting and strategy practice and works with companies to enhance their Analytics, Forecasting, and Data Optimization functions. The practice’s focus includes quantitative forecasting, corporate and unit strategy and planning. Please visit our website at www.burgesskilparick.com or on Facebook at www.facebook.com/Burgess Kilpatrick for more information on our firm.