FPAC Part 2 Chapter 05 Analyzing Information and Giving Feedback.pdf

FPAC Exam Prep Platform FPAC Part 2: Chapter 5 Analyzing Information and Giving Feedback Topic 1: Perform Variance Analysis and Reporting………………………..…...….…….. 5-2 Topic 2: Perform Competitive Analysis……………………………………....…………... 5-24 Topic 3: Provide Feedback and Revise Documentation………………….…...….…...

FPAC Exam Prep Platform FPAC Part 2: Chapter 5 Analyzing Information and Giving Feedback Topic 1: Perform Variance Analysis and Reporting………………………..…...….…….. 5-2 Topic 2: Perform Competitive Analysis……………………………………....…………... 5-24 Topic 3: Provide Feedback and Revise Documentation………………….…...….…..... 5-34 ©2019. Association for Financial Professionals Table of Contents 5-1 FPAC Exam Prep Platform Topic 1: Perform Variance Analysis and Reporting Variance Analysis: Introduction Variance analysis can be done for either planning or analysis purposes, but it is primarily an analysis task because it provides information on the organization’s current state. For planning, variance analysis can be used to improve future projections because they can show specifically where the assumptions differed. The need to periodically reevaluate results is at the heart of variance analysis. ©2019. Association for Financial Professionals Topic 1 5-2 FPAC Exam Prep Platform Exhibit II.A.5-1 – Developing Model and Recommendations as PDCA Cycles While there are many types of variances, those discussed here start with static-budget variances, which can be split into sales-volume and flexible-budget variances. Sales-volume variances can be further split into sales-mix and sales-quantity variances, and flexible-budget variances can be further split into price and efficiency variances. Additional splitting can be done from there, and specific business units could be analyzed in detail, such as evaluating marketing efforts, selling expenses, administrative expenses or capital expenses. ©2019. Association for Financial Professionals Topic 1 5-3 FPAC Exam Prep Platform Purpose and Uses of Variance Analysis Variance is the quantified difference between actual results and expected or projected (planned, forecasted, budgeted) results. The actual amount less the projected amount equals the basic variance. While variances can be positive or negative values, they are often recorded as absolute values (no plus or minus sign) and then labeled favorable (F) when the variance helps the organization (reduces costs or increases revenue) relative to what was projected and unfavorable (U) when it harms the organization relative to what was projected. Another way to record variances is simply by performing the raw subtraction and retaining the positive and negative values. Note that when summing variances in this way, it is still important to differentiate which variances are favorable to the organization and which are unfavorable. (Both a positive cost variance and a negative revenue variance will be unfavorable.) A third way to record variances is a help/(hurt) framework, where favorable variances are always shown as positive and unfavorable variances are always shown as negative. In this method, revenue overruns are always shown as positive, while expense overruns are always shown as negative. The uses of variance analysis include the analysis of root causes and interrelationships and management by exception, as discussed next. Analysis of Root Causes and Interrelationships Variances are split into subcomponents to separate out the root causes of variance so that: Interrelationships can be understood Problems can be tracked and fixed without creating other unintended consequences Successes can be replicated Variances are related to the various aspects of the organization’s cost structure, so it is necessary to understand this cost structure to understand the interrelationships between variances. One favorable variance could be creating several unfavorable variances, and the net effect may not be favorable. For example, labor efficiencies may be due to workers rejecting too many parts instead of reworking some. See Part I, Domain B, for a further discussion of cost accounting concepts. When variances are found and their root causes and interrelationships are determined to the degree possible, responses include determining whether anyone at the organization can control the variance and, if so, determining who is responsible for addressing the variance. This responsibility sometimes falls to the FP&A function when the variance is related to their planning and analysis activities. ©2019. Association for Financial Professionals Topic 1 5-4 FPAC Exam Prep Platform Management by Exception Variances enable management by exception, which involves focusing management attention on reviewing just exceptional problems or benefits. This is accomplished by highlighting both favorable and unfavorable variances that meet a materiality threshold for review. Materiality is a monetary or unit value for favorable or unfavorable variances that, if exceeded, triggers management by exception review. Materiality is set for each cost component of each item separately based on a percentage deviation from the budgeted or standard cost (defined later) or by item importance. Revenue variances have similar materiality thresholds. Exception review involves determining the root cause of the variance, determining if it is controllable, uncontrollable or partly controllable, and assigning wholly or partly controllable issues to a responsible party so they can be analyzed and remedied. Unfavorable variances need to be eliminated if possible. Favorable variances need to be studied to see if they are causing quality problems (e.g., a favorable material price variance could arise from cheap, low-quality materials), are creating other unfavorable variances when the variances are interrelated, or are practices to be emulated. When analyzing variances, it is important to treat variances similarly to how outliers are examined for their information value: A variance is examined for its information value rather than treating it as conclusive evidence of a good or bad result. Often a variance represents a bit of both due to the interrelationship of many favorable and unfavorable variances. Variance in Static and Flexible Budgets Variance can be calculated for a static budget and a flexible budget. Each is discussed next, but the terms are best defined together: A static budget is a master budget that is fixed at a level of sales in units that was determined when the budget was finalized. Most master budgets are static. A flexible budget is a master budget that uses the actual sales in units to determine other budgeted revenues and costs as if the sales target in units had been exactly correct. Flexible budgets cannot be calculated until actual sales units are known, so they are most often used to allow variance analysis to compare apples to apples. Calculating Variances in Static Budgets Variance in static budgets is simply the actual results less budgeted results for each line item. Exhibit II.A.5-2 on the next page shows a static-budget variance analysis for the mining company case study for the Panama mine division’s 2014 budget. Note that variances are recorded in absolute values but are always calculated as follows: Actual Results - Static Budget = Static Budget Variances ©2019. Association for Financial Professionals Topic 1 5-5 FPAC Exam Prep Platform Exhibit II.A.5-2– Static-Budget Variances Are Difficult to Interpret (Mine Case Study Example) Mining Company Case Study The Panama mine is purchased in 2013, and infrastructure development begins while the exploration company completes its geological survey. When done, the organization is pleasantly surprised that the proven copper reserves are right at the level of expectations and also that there is significant gold at the site, enough to extract about 1 MT of gold per year, which is worth about $52 million per MT. It builds this into the budget, as shown below. (Note that the company has no direct materials as its products are mined.) At the end of 2014, the FP&A professional evaluates the first-year results against the original (static) budget. The budget model can be downloaded within the Exam Prep Platform in Chapter 5, topic 1. Exhibit II.A.5-2 –Explained The formula bar shows that revenue drivers (units sold and price per unit) and revenue measures (revenues, contribution margin and operating income) are neither favorable nor unfavorable when equal, favorable when actual results are greater than the static budget, and unfavorable when the opposite is true. Costs (variable costs and fixed overhead) are the opposite: favorable when actual results are less than the static budget and unfavorable when the opposite is true (the operator is changed from greater than to less than [ < ]). The problems with analyzing variances using a static budget are that the variances do not give a real indication of why they exist and some variances could be hidden. This is because the favorable variance in the number of units sold is part of the variance for all variable costs and thus also contribution margin and operating income. Exhibit II.A.5-3 shows the input data used to generate this budget variance analysis. (Some cells such as H11 and those similarly formatted ©2019. Association for Financial Professionals Topic 1 5-6 FPAC Exam Prep Platform are inputs derived from other direct inputs on the page.) Note that direct labor per unit of copper has a favorable variance of $31 per unit ($978 − $1,009), but the static-budget variance shows an unfavorable variance of $1.5 million for copper (cell W7 of the prior exhibit). This is caused by more units being mined and sold than budgeted for, meaning more labor was used overall than budgeted. Thus the favorable labor variance per unit for copper is completely absorbed. Exhibit II.A.5-3 – Static-Budget Variance Input Data (Mine Case Study Example) Exhibit II.A.5-3 –Explained Similarly, the impact of other variances per unit cannot be separated from the sales-volume variance. Only the fixed overhead is accurately depicted because it is not dependent on units sold—so long as units sold remains within the relevant range of what can be produced without expanding mining capacity, etc. To make variance analysis more informative, it is often necessary to convert a static budget into a flexible budget. Calculating Variances in Flexible Budgets A flexible budget can help separate out the portion of each variance caused by sales-volume variance and thus allow other causes of variance to be detected. The remaining variances are called flexible-budget variances. Flexible-budget variances plus sales-volume variances equal the static-budget variances. Exhibit II.A.5-4 shows how the inputs can be designed for a flexible budget. It simply references the units sold from the actual results inputs and references the static budget amounts for all other amounts. ©2019. Association for Financial Professionals Topic 1 5-7 FPAC Exam Prep Platform Exhibit II.A.5-5 – Inputs for Flexible Budget Analysis (Mining Case Study Example) Exhibit II.A.5-5 on the next page shows actual results, the flexible budget and the static budget. The second part of the exhibit shows how static- budget variances can be split into two different components: flexible- budget variances and sales-volume variances. The basic formulas follow: Flexible Budget Variances = Actual Results - Flexible Budget Sales Volume Variances = Flexible Budget - Static Budget There are two ways to calculate the static-budget (total) variances, and calculating both can be a good way to perform an error check: Actual Results – Static Budget = Static-Budget (Total) Variances Flexible-Budget Variances + Sales-Volume Variances = Static-Budget (Total) Variances ©2019. Association for Financial Professionals Topic 1 5-8 FPAC Exam Prep Platform Note that for the second method to work correctly, the values for the variances must retain their plus or minus signs (not absolute) until after the variances are summed, and then the absolute value should be reported as the result. The exhibit shows only the first way. Sales-volume variances and flexible-budget variances can each be broken down further and studied to determine root causes, as discussed next. Sales-Volume Variances Sales-volume variances arise whenever sales goals or sales quotas in the budget were not met or were exceeded. Since sales volume directly relates to an organization’s profitability, studying the causes of sales- volume variances are a type of profitability analysis. Exhibit II.A.5-6 lists some potential root causes for unfavorable sales-volume variances, whether they are likely to be controllable, partly controllable or uncontrollable, and the party who is likely to be held responsible for correcting the situation if it is at all controllable. ©2019. Association for Financial Professionals Topic 1 5-9 FPAC Exam Prep Platform Exhibit II.A.5-6 – Potential Root Causes of Unfavorable Sales-Volume Variance Potential Cause Control Responsible Party Sales-mix variance: Products or Partly divisions sell in a different mix than Marketing/sales manager controllable budgeted (addressed in detail later). Planning variance: Economic or None, but FP&A may verify industry-wide conditions are worse than Uncontrollable whether assumptions could budget assumptions. have been improved. Competitor actions: For example, Partly competitors’ lower prices and the Marketing/sales manager controllable organization did not also lower prices. Adaptation to demand patterns: Slow Controllable Marketing manager to adapt to customer preferences. Production/procurement Poor quality: Inferior goods sell poorly. Controllable manager Poor/insufficient marketing: Controllable Marketing manager Customers not properly motivated. Poor/erroneous quantitative forecast Fully or partly FP&A or budget: Forecast error exists. controllable Bias in expert judgment: Sales or Fully or partly FP&A and marketing/sales marketing bias goes unchallenged. controllable manager Sales Volume Variances: Continued The marketing manager and the sales manager are often the ultimate parties held responsible and accountable for most controllable or partly controllable causes of sales-volume variances. This is because the marketing manager sets the range of prices for the goods/services (which affects demand), controls marketing expenditures, and the sales manager is ultimately responsible for the sales budget and the demand forecast upon which it is based. If the FP&A function is involved in gathering data and assumptions and producing and/or running the model for the demand forecast and/or sales budget, some sales-volume variance duties may be the responsibility of the FP&A function. When this is the case, FP&A professionals may be involved in analyzing the root cause of the variance, determining whether the organization can control it, and accepting responsibility for issues under the control of the FP&A function. For example, an incorrect price used in a budget is a controllable error. Sales variances attributed to marketing or salesperson bias that was not adequately challenged and adjusted by FP&A professionals through negotiation are also controllable. Quantitative forecasting that needs improvement might be partly controllable. However, when demand is highly unpredictable, this type of forecasting error is usually uncontrollable. ©2019. Association for Financial Professionals Topic 1 5-10 FPAC Exam Prep Platform A planning variance occurs because a budget’s economic or industry- specific assumptions do not play out as anticipated. When the FP&A function was involved in setting these assumptions, FP&A professionals may need to establish whether planning could have been improved, if there are errors in plans or assumptions that are controllable, or if all of this variance is due to unforeseeable economic or industry conditions. When deemed uncontrollable, no one is held responsible for the planning variance. Just as the static-budget variance was able to be split into sales-volume and flexible-budget components that summed to the total static-budget variance, the sales-volume variance can be split into two components that sum: a sales-mix variance and a sales-quantity variance. These are discussed next. Sales-Mix Variance A sales mix refers to an organization’s mix of products or mix of sales divisions such as wholesale and retail. When more than one product/ division exists, part of the sales-volume variance can be attributed to when the mix differs from what was budgeted. Since each product or division usually has a different contribution margin (revenues less variable costs) per unit, changing the mix from what was budgeted can result in increased or decreased profitability. A sales mix is budgeted by percentage of sales of each type of product/service or, at a higher level, by percentage of each product/service division. Each element of the mix will also have a budgeted contribution margin per unit. The sales-mix variance is calculated as follows: Sales-Mix Variance = Actual Units Sold (All Products) * (Actual Sales Mix % – Budgeted Sales Mix %) * Budgeted Contribution Margin/Unit Exhibit II.A.5-7 shows the preliminary calculations made to assemble all of the necessary information for a sales-mix variance calculation. Note that for the mine example, the percentages have to go out to multiple decimal places because gold represents a very small portion of the sales mix in terms of a common unit of measure, metric tons (MT). In most cases, a less precise percentage calculation (e.g., 65 percent/35 percent) will suffice. ©2019. Association for Financial Professionals Topic 1 5-11 FPAC Exam Prep Platform Exhibit II.A.5-7 – Preliminary Calculations for Sales-Mix Variance (Mine Case Study Example) Exhibit II.A.5-8 – Sales-Mix Variance (Mine Case Study Example) The preliminary calculations pull data from the prior budget inputs (see Exhibit II.A.7-4) and lists them in the order needed to calculate the contribution margin and mix. The above information might also make a good informative report. The sales mix percentage in column M is calculated by dividing the item’s sales volume in units by the total sales volume in units. Exhibit II.A.7-8 shows how to calculate the sales-mix variance. Note that the mining company had quite a profitability boom from an unexpectedly large amount of gold recovered from mining operations, while copper had a small unfavorable variance as a result of the tiny difference in sales mix. ©2019. Association for Financial Professionals Topic 1 5-12 FPAC Exam Prep Platform Sales-Quantity Variance The remainder of the sales-volume variance is attributable to the sales- quantity variance. The sales quantity variance is calculated as follows: Sales-Quantity Variance (for one item in mix) = (Actual Sales Volume for All Units – Budgeted Sales Volume for All Units) * Budgeted Sales Mix % * Budgeted Contribution Margin/Unit The total sales-quantity variance is then the sum of the above calculations performed separately for each item in the sales mix. Exhibit II.A.7-9 on the next page shows how this is calculated for the mine case study. The sales-mix variance is pulled in from the prior schedule, and the total equals the total sales-volume variance from Exhibit II.A.7-5, except that it is not in millions. Since this calculation is based on the overall sales-volume variance, all sales-quantity variances will be in the same direction as the sales-volume variance (either favorable or unfavorable). Note in the formula bar how the total variance is calculated so that the absolute value (ABS) is taken only after the individual calculations are summed. This becomes necessary when the same formula is used for later variances that could have a sign change. Exhibit II.A.5-9 – Sales-Quantity Variance (Mine Case Study Example) ©2019. Association for Financial Professionals Topic 1 5-13 FPAC Exam Prep Platform Exhibit II.A.5-10 – Quantity and Price for Budgeted (or Standard) and Actual Revenue/Cost Flexible-Budget Variances Exhibit II.A.5.10 shows how there are two components of flexible- budgeted revenue for items sold and two components of flexible- budgeted cost for direct materials, direct labor or variable overhead: quantity (or volume) and price (rate or spending). Actual revenues or costs are also calculated using these components, but they use actual quantities and actual prices. Variances in quantity are called efficiency variances, and variances in price are called price variances (sometimes rate for labor and spending for variable overhead). Note that variable overhead costs are allocated on the basis of a cost driver, which is a measurable aspect of an operation that is selected as a reasonable basis for allocating overhead among various business units or products. Direct-labor hours are a commonly selected cost driver. Fixed overhead is calculated as part of the total static-budget variance. Flexible-budget variances include sales/revenue variances and cost variances. Each is discussed next. Flexible-Budget Sales/Revenue Variance: Selling Price While the sales-volume variances of sales-mix variance and sales- quantity variances were static- budget variances, the final component of sales/revenue variances is a flexible budget variance. This variance is called selling-price variance (sometimes sales-price variance). Selling- price variance is calculated as follows: Selling-Price Variance = (Actual Selling Price – Budgeted Selling Price) * Actual Units Sold Exhibit II.A.7-11 shows how the selling-price variance is calculated for the mine case study. Note that these variances are also shown in cells K5, M5 and O5 of Exhibit II.A.7-5, except that they are in whole dollars below. ©2019. Association for Financial Professionals Topic 1 5-14 FPAC Exam Prep Platform Exhibit II.A.5-11 – Selling-Price Variance Calculations (Mine Case Study Example) Variances: Continued Selling-price variances might be caused by differences in market demand and a resulting need to lower prices or opportunity to raise prices. Other reasons might include a different inflation rate than anticipated, changes in unit quality affecting prices, or changes in salesperson compensation incentives or controls on how they can set discounts. Flexible-Budget Cost Variances When breaking down flexible-budget cost variances into their components, organizations often set standards, which are carefully set benchmark values for input unit volumes, prices and costs since the actual amounts cannot be known until actual values are available. There are several standards that can be set. A standard input is the benchmark input quantity required to produce one output unit. A standard price is the price the organization expects to pay for one input unit. A standard cost is the benchmark cost of an output unit of a product/service or of a component of this cost in terms of direct materials, direct labor, or fixed and variable overhead. These standards are carefully determined based on assumptions about cost structures and demand and production volumes over a period of time. Standard inputs, prices and costs are how much something should cost, etc., and may be based on time and motion studies, historical actual costs and/or financial projections. Standards are set for each input to a product/service, and any of these quantities or prices could have variances. Note that a budgeted cost is a broader term than a standard cost. Some organizations set budgeted input quantities, prices and costs without relying on standard costs or perhaps overriding some standard costs to account for certain economic assumptions. Other organizations set standards in their budget models and automatically apply them. In the latter case, “budgeted” and “standard” can be used interchangeably. While the text continues to use the terms “budgeted” input, price, or cost in this topic, “standard” can often be used in its place. ©2019. Association for Financial Professionals Topic 1 5-15 FPAC Exam Prep Platform Price and Efficiency Variances for Direct Materials/Labor The flexible-budget variances calculated for direct materials and direct labor can be further divided into price and efficiency variances. Price Variance = (Actual Price of Input – Budgeted Price of Input) * Actual Quantity of Input Efficiency Variance = (Actual Quantity of Input – Flexible-Budgeted Quantity of Input) * Budgeted Price of Input A price (rate) variance reflects when the price paid for direct material inputs (raw materials) or direct labor inputs (average hourly rate) differs from the budget. Favorable variances could be caused by good negotiating, switching suppliers or the use of lower-quality materials or labor. Other possibilities include that the budgeted or standard prices were unrealistic or that prices are based on supply and demand and are uncontrollable or only partly controllable (e.g., hedging or supplier/ labor contracts). An efficiency variance reflects whether total resource input quantities (raw materials or labor hours in total) are used efficiently (favorable) or inefficiently (unfavorable) relative to the total budget input quantities. Unfavorable variances may reflect that too much scrap is being produced or workers are being less productive than expected, perhaps because they are less skilled than intended, in which case there may be a favorable price variance that reflects this. Failing to maintain equipment could also reduce efficiency. In other cases, budgets or standards were set too tightly and are unrealistic. Returning to the case study example, variances from Exhibit II.A.5-5 show that the flexible-budget direct labor variance in total was $0.4M U. For copper this was $0.8M F, and for gold this was $1.2M U. Exhibit II.A.5-4 lists some flexible-budget and actual labor rates per unit; however, the information needed is the average hourly labor rate per direct labor hour and the total number of direct labor hours used. If there were direct materials, the necessary information would likewise be prices for raw materials and total quantities of those raw materials for the period in question (e.g., 10,000 meters of fabric @ $2/meter). Exhibit II.A.5-12 pulls data from the budget input and budget tabs and places it in the order needed to calculate the direct labor variances. Calculations are as follows: Direct Labor Hours (DLH)/Unit (hours needed to produce one unit or MT) * Average DLH Rate/Unit = Direct Labor Cost per Unit DLH/Unit * Actual Units = Total Hours Direct Labor Cost/Unit * Actual Units = Total Costs ©2019. Association for Financial Professionals Topic 1 5-16 FPAC Exam Prep Platform Exhibit II.A.5-12 – Direct Labor Price and Efficiency Variance Calculations (Mine Case Study Example) Exhibit II.A.5-12: Continued Note that gold mining has a very large number of DLH/unit because it is actually extracted primarily as part of the copper mining process and not as a separate activity. Since so few units are produced, the total number of hours for the period are allocated to very few units. Using the information from the prior exhibit, the price (rate) variance can be calculated as shown in Exhibit II.A.5-13. Note that the calculation uses the total hours for the period and not the hours per unit. Exhibit II.A.5-13 – Direct Labor Price (Rate) Variance (Mine Case Study Example) Exhibit II.A.5-13: Continued The price or rate variance shows how much impact the difference in the average cost of labor per hour or the price of individual material inputs affects the flexible budget. The other part of the flexible-budget variance for direct materials or direct labor is the efficiency variance. Exhibit II.A.5-14 shows how this is calculated for the mine case study. Note that the total DL price variance is included from the prior schedule and that the sum of the DL efficiency and price variances equal the total DL flexible-budget variance from Exhibit II.A.5-5 when converted to millions. ©2019. Association for Financial Professionals Topic 1 5-17 FPAC Exam Prep Platform Exhibit II.A.5-14 – Direct Labor Efficiency Variance (Mine Case Study Example) Direct Material Variances Direct material variances are calculated using the same formulae and methods, except that a product could have quantity and price variances for each component of its bill of materials, potentially many hundreds of ingredients or components. Therefore, these analyses can get quite detailed and usually are aggregated to summary levels to help explain which components or major subcomponents contribute most strongly to the variances. Material variances are pulled out for exception review. Efficiency and Spending Variance for Variable Overhead While fixed overhead variance is simply determined as the actual fixed overhead less the budgeted fixed overhead and cannot be broken down further (unless one gets into cost allocations per business unit), variable overhead (VOH) can be split into spending and efficiency variances. The spending variance is basically the same as the price variance, except applied to the VOH/cost driver unit. The variance formulae follow: VOH Spending Variance = (Actual VOH/Cost-Driver Unit – Budgeted VOH/Cost-Driver Unit) * Actual Cost-Driver Quantity VOH Efficiency Variance = (Actual VOH Cost-Driver Quantity – Flexible-Budgeted VOH Cost-Driver Quantity) * Budgeted VOH/Unit of Cost Driver ©2019. Association for Financial Professionals Topic 1 5-18 FPAC Exam Prep Platform Exhibit II.A.5-15 shows how data can be pulled from the budget inputs and budget to assemble the information needed to calculate the variable overhead variances. The calculations are as follows: Actual Units * VOH Cost Driver/Unit (DLH/Unit) = Total Cost Driver (Direct-Labor Hours or DLH) Total Costs/Total Cost Driver (DLH) = VOH Cost/DLH Total Costs/Actual Units = VOH Cost/Unit Exhibit II.A.5-15 – Variable Overhead Calculations (Mine Case Study Example) Exhibit II.A.5-16 – Variable Overhead Spending Variance (Mine Case Study Example) Exhibit II.A.5-16 shows how the variable overhead spending variance is calculated for the mine case study. ©2019. Association for Financial Professionals Topic 1 5-19 FPAC Exam Prep Platform Exhibit II.A.5-17 – Variable Overhead Efficiency Variance (Mine Case Study Example) Favorable spending variances indicate that the amount spent on variable overhead items such as electricity or waste-water treatment was less than expected. For example, if the kilowatt/hour rate for electricity was less, it could reduce the variable overhead cost per cost driver unit. Exhibit II.A.5-17 shows how the variable overhead efficiency variance is calculated. Again, the spending variance from the prior schedule is added and the sum equals the total VOH flexible- budget variance from Exhibit II.A.5-5 budget when converted to millions. Favorable Variances vs Unfavorable Variances A favorable efficiency variance means that fewer cost driver units were needed to produce the products/services than expected, perhaps because of more efficient labor in this example than expected. (If the cost driver were machine hours, perhaps the machines were more efficient than expected.) Variances could alternately mean that there were problems with the cost driver: Labor hours might have been greater or less than normal due to a variety of factors such as a special order, or the selected cost driver may not correlate well to the variable costs. Summary of Variances Exhibit II.A.5-18 on the next page shows how the variances all interrelate and add up to larger summary levels. Note that there are more types of variance analysis that can be done and that this is just a representative set. Variance Reporting Many of the budgets and schedules shown in this section can be used directly for variance reporting. In addition to reporting summary information, variance reporting should highlight exceptions for special review. These exceptions should be material (high financial impact) and controllable (management can do something about them). ©2019. Association for Financial Professionals Topic 1 5-20 FPAC Exam Prep Platform Exhibit II.A.5-18 – Summary of Variances (Mine Case Study Example) Variance Analysis and Useful Charts Variance reporting should also highlight the interrelationships between different variances and explain the root cause of the interrelated variances when possible so as to provide recommendations that do not cause new problems in other areas. Another important component of variance analysis is to move beyond just explaining what happened to making conclusions and recommendations regarding how to make improvements and identifying the person in the best position to make those improvements. Real business value is achieved when the impact of making the improvement or not making it is spelled out explicitly in financial terms and the type of change that needs to be made can be given a cost/benefit justification. ©2019. Association for Financial Professionals Topic 1 5-21 FPAC Exam Prep Platform Variance reporting can make use of charts to emphasize these interrelationships as well as the relative size of particular variances. One type of custom chart that is useful for variance analysis is called a waterfall chart. A waterfall chart is a stacked column chart that has hidden columns. Exhibit II.A.5-19 is an example of a waterfall chart for the mine case study showing variances from budgeted revenue. Exhibit II.A.5-19 – Waterfall Chart (Stacked Column with Hidden Columns) (Mine Case Study Example) ©2019. Association for Financial Professionals Topic 1 5-22 FPAC Exam Prep Platform Exhibit II.A.5-19: Explained The waterfall chart can be created by setting up the data table as shown in the exhibit. The actual values for each revenue amount and each variance are listed in the Values row but use positive and negative values rather than absolute values. The Hidden row is for those column stacks that will be set to be invisible once the chart is created; the revenue values for the first and last columns are thus set to zero. The intervening variance columns in this row are the cumulative total of the prior column’s Values cell plus Hidden cell, plus the Values cell for the current column but only if it is a negative number, which will reduce the starting point for that column’s visible stack. For example, the formula in cell D3 is =C$3+C$2+IF(D$2>0,0,D$2), which reduces the cumulative total by the negative value in cell D2. The Base Values row is simply the starting and ending revenue values that should appear; they are copied by reference from the Values row. The values in between are set to zero. The final two rows test the Values row and list all positive values for variances in the favorable column and all negative values in the unfavorable column (the labels would be the opposite if charting costs rather than revenues, etc.), except that the first and last rows are set to zero because they are not variances in this example. The variance columns for these rows can be automated by using a MAX function for the favorable row, such as =MAX(C$2,0) for cell C5, which returns the positive variance, or a MIN function for the unfavorable row =ABS(MIN(D$2,0)) for cell D6 in the unfavorable row. Note that while the Values row is set to use negative numbers for purposes of calculating the cumulative revenue, an absolute value for the negative variances is used in the actual chart data, so the function returns the absolute value for all negative revenue variances. To create the chart in Excel, select just the rows highlighted in the exhibit (the range B3 to E6 in this example), omitting all row and column labels for now and also omitting the Values row, which is used to calculate the cumulative sums. On the Insert tab, select Column, 2-D Column, Stacked Column. On the chart that is created, select the horizontal axis label and, on the pop-up menu (right-click for PC), choose Select Data. On this menu, select Edit for the Horizontal (Category) Axis Labels, and select the range for the labels (B1 to E1 in this example). Finally, on the chart, select a bar stack representing data from the Hidden row and, on the pop-up menu, select Format Data Series... For the Fill and Border Color choices, select No fill and No line respectively. Case Study Mining Company Case Study While the mine is very profitable this year, the company cannot count on larger-than-normal gold extraction each year, so the internal report focuses on how they can control their unfavorable cost variances. The Panama Mine was purchased using long-term debt, and part of the loan covenant requires that the organization submit detailed annual reports on the mine’s status showing variances and other revised projections. This report highlights the mine’s profitability but lists cost controls that management has decided to implement. ©2019. Association for Financial Professionals Topic 1 5-23 FPAC Exam Prep Platform Topic 2: Perform Competitive Analysis Competitive Analysis: Introduction Performance is a relative term. An organization may be exceeding its revenue goals and have net positive variances, but if its competitors are doing even better or the performance trends are diverging over time to the organization’s disadvantage, then the results may indicate that the organization is not doing well enough. Performance measurements do not mean anything when studied in isolation. Studying the organization’s own results over time is one method of providing a relative reference point, but even establishing a positive trend is relative. If, for example, the number of back orders for products is falling, is this good enough or would looking at the competition show that they have a much lower level of back orders or their trends are improving faster? Competitive analysis might also confirm when the organization’s successes are keeping pace with the competition, when trends show that a gap is narrowing, or when results are truly exceptional and need to be replicated in other business units. Competitive analysis requires judgment skills to select the proper organizations for comparison, to interpret available external data and assumptions properly, and to interpret the organization’s own data properly. Selecting the wrong reference organizations or reference points between organizations can have serious repercussions to the quality of the analysis and to the decisions made using that analysis. Competitive analysis also requires interpretation and judgment because organizations will often have performance indicators that contradict one another relative to the organization (e.g., a higher ratio than the organization in one area but a lower ratio in another). Thus it requires judgment calls to determine why differences exist and to determine which factors or performance indicators should be weighted more heavily and which need to be discounted as irrelevant to the analysis. Like the prior variance analysis topic, there could be many reasons for performance gaps or diverging trends, from competitive innovation that should be studied to the organization’s internal goals and budgets being set at easy, unchallenging levels. Unlike variance analysis, the root cause of a difference may not always be easy to discern since the reasons for competitor choices are not always easy to discover. ©2019. Association for Financial Professionals Topic 2 5-24 FPAC Exam Prep Platform Purpose of Competitive Analysis The purpose of competitive analysis is to allow the organization to monitor its current state or projected course relative to the competition and thus correct course in midstream or improve future decision making. Therefore, the best types of competitive analysis will focus on the “freshest” data and leading indicators that have predictive value and can be used to steer current decision making. Often the organization will have a set of value drivers and key performance indicators that are of the most information value to its business model and industry. These are typically the drivers and metrics of choice when they can be gathered or imputed for the reference organizations. FP&A professionals may sometimes need to settle for less fresh data or use lagging indicators when the desired data on reference organizations are unavailable. When comparing the organization to reference organizations, industry averages or industry leaders, there are several types of relative measurements that can be made for any given metric: Measurement variance Measurement variance measures how much higher or lower the key metric is in absolute value at the point the measurement is being taken. Determining a relative difference may require using common-size analysis and making other adjustments as discussed later. Trend comparison A trend comparison is also vital, because it can show if the gaps are increasing or decreasing and the projected rate of change. Metric interrelationships Metric interrelationships involve a holistic study of multiple metrics to determine what trade-offs each organization is making to improve certain metrics and ratios and to judge whether those trade-offs are worth making. For example, if the organization decides to reduce its back orders because competitors have better results, it may have to accept the cost of higher inventory levels. If a competitor has both low back orders and low inventory, this may be an outlier that can be studied and emulated. However, when sets of metrics as a whole are worse than industry averages, this is a red flag. In fact, problems in any of these areas relative to the competitive reference are red flags or exceptions that may require immediate management attention depending on the severity of the problem and the importance of the metric to the organization’s strategy and goals. ©2019. Association for Financial Professionals Topic 2 5-25 FPAC Exam Prep Platform Prerequisites for Competitive Analysis Competitive analysis requires having information about competitors. This usually means publicly available information, such as financial statements, annual reports, press releases and marketing information, or it could take the form of financial ratios and research purchased from a research firm. Information on future plans of competitors is not likely to be publicly available, but there may be ways to obtain it. Annual reports often contain high-level descriptions of strategic plans. For more difficult-to-gather information, the process may include calling the competitor’s sales force, suppliers, distributors or subcontractors. This may or may not produce useful results. The key to using externally gathered information is to determine how to make the information as comparable as possible to the organization’s own information. Therefore, the processes of gathering information and using experience and judgment to get the information in comparable shape may take the bulk of the time required for competitive analysis. Preparation may involve common-size analysis (defined later) to make much larger or smaller organizations easier to compare. It may also involve making certain accounting adjustments to recast data as if the organizations used the same accounting methods (e.g., inventory valuation or depreciation methods) or to remove “window dressing” techniques that are frequently used to make statements appear stronger than they actually are. Seasonality, inflation and other elements may also need to be adjusted. Specifics on making adjustments such as these are addressed in Part II, Domain B, of these materials. When possible, selecting organizations that have similar locations, demographic trends and economic conditions allows FP&A professionals to isolate the competitive analysis to as few variables as possible so just the remaining differentiators can be isolated and studied. For example, a common purpose of competitive analysis is to isolate the effect of management performance so that management actions can be judged against a relative benchmark. Competitive analysis also requires having the correct information on the organization itself. The accounting department will supply the necessary information, such as data from quarterly close totals. However, it is the FP&A function’s responsibility to get these data in a useful and comparable format for analysis and to perform the competitive analyses. Dimensions of Competitive Analysis Competitive analysis can focus on one or more dimensions: Overall competitive landscape Product positioning analysis Management performance analysis Strategic analysis ©2019. Association for Financial Professionals Topic 2 5-26 FPAC Exam Prep Platform Each of these dimensions can be further specialized. For example, for strategic analysis, the analysis could focus on the other organizations’ mergers and acquisitions, the different markets they are entering, understanding their strategy, or how well they are doing at executing their strategy. Competitive analysis is often done at a strategic level to determine the relative success of the organization’s strategies against the competition’s strategies. When the competition is using a more efficient or effective strategy, FP&A professionals need to provide decision makers with the necessary unbiased information that shows when organizational strategies are less effective than what others are doing. Types of Competitive Analysis Competitive analysis can take several forms that have subtle differences, but there is overlap between the types and multiple methods might be employed for an analysis. Types include benchmarking, peer group analysis and ratio analysis. Each of these types is discussed next. Benchmarking Benchmarking is a very common business improvement tool in which the performance of one entity is compared with that of another entity (or multiple entities) for the purpose of goal setting. Benchmarking can use internal or external entities for comparison, as discussed in Part I, Domain A; only external comparisons are discussed here. External benchmarking can compare the best practices in a given specialty, such as accounts receivable, from any industry or the best practices of specific competitors or a peer group. Benchmarking may also make comparisons against industry averages or industry best practices. The key to benchmarking success is systematic and continuous measurement against a point of reference to provide ongoing feedback. Benchmarking follows a Plan, Do, Check, Act (PDCA) process to ensure that analysis conclusions are acted upon. Using industry averages has its strengths and limitations. Industry averages allow organizations to determine when they are operating in certain areas well above or below the averages so that these exceptions or red flags can be reviewed. Some of the differences from industry averages will be justified based on the organization’s business model and so on, while others are problems that need to be addressed. Limitations of industry averages include that averages are not best practices and are therefore not necessarily challenging goals. Some organizations prefer to compare themselves to industry leaders instead. Another issue is that the industry as a whole may be in trouble, such as the auto industry during the Great Recession of 2008. ©2019. Association for Financial Professionals Topic 2 5-27 FPAC Exam Prep Platform Peer group analysis A peer group is a carefully selected, small set of organizations that are similar in relative size and line of business. Direct competitors for market share are often included in a peer group, but it is not a requirement. Peer groups are sometimes called benchmark companies. (The terms overlap.) Peer group analysis is similar to benchmarking, except that it involves systematically and continuously tracking a select set of organizations that share many things in common. Organization size can be made relative for some types of analysis, but a peer group made of similar-sized organizations can reduce some of the comparability issues that remain even when this is done. Organizational size issues that are difficult to compensate for include the impact of the organization’s purchasing power and economies of scale and quality and depth of management talent as well as negatives such as level of bureaucracy or reduced relevance of old historical costs due to inflation. Line of business relates to similarity of industry, operating cycle, types of customers, types of risks, debt structures, capital requirements and so on. Making comparisons of metrics and ratios of organizations in very different lines of business can be misleading. For example, high financial leverage in one industry may be a necessary price of entry, while the same amount of debt to equity in a different industry might be deemed excessively risky. Another method of qualifying peers is by determining those organizations that use the same key performance indicators. If they use the same revenue drivers or cost drivers, they are inherently more comparable and the key information used for comparison may be much easier to obtain. However, when selecting a set of peers, narrowing down the list too tightly can lead to an insufficient number of reference points for comparison. Valid conclusions could not be drawn against just one peer, for example, because there is no way to know if that peer is doing well enough itself and any inferences would have no corroboration. A good set of peers will have a number of similar organizations but a few outliers at either end of each criteria range (e.g., larger, slightly different industry). Recall that outliers often have strong information value. However, it is still best to avoid making comparisons of organizations in widely different sectors of the economy such as mining versus retail or financial versus utilities. When an organization is a highly diversified conglomerate, it may be best to select peers by business unit rather than for the entire organization. Each business unit can then be compared to pure players in that industry that have a size similar to that of the business unit. For example, an organization could compare its paper mill business unit to several other paper mills. Peer groups can also be defined specific to the type of analysis being performed, for example, if analysis is of credit quality, selecting relatively similar organizations in similar industries all in the same credit rating peer group (e.g., all ranked AA by Standard & Poor’s). ©2019. Association for Financial Professionals Topic 2 5-28 FPAC Exam Prep Platform Ratio analysis Ratio analysis involves forming ratios by dividing one financial value by another to determine their relationship and then comparing various interrelated ratios against standards or rules of thumb, change over time or relative benchmark and/or peer group ratios. Ratios make financial information more comparable, because instead of trying to determine if revenue of $1 million for one firm is better or worse than revenue of $10 million for another, the basic relationship between the values is determined and the resulting ratios can be directly compared. Ratio analysis is often a big part of either benchmarking or peer group analysis because financial ratios are publicly available from rating agencies or professional analysts or can be directly calculated from the organization’s financial statements and other public information. The ratios selected depend on the purpose of the analysis, but within any given area, there will be ratios with strong information value and ratios that have less meaning. For example, when studying future earnings potential or free cash flow, ratios that are based primarily on book values (accounting values) are more subject to manipulation or may be based on historical costs that are no longer relevant due to inflation. Ratios based on actual cash flows or the potential to generate future cash flows will be more meaningful. Using other, more exotic ratios to make choices more palatable may simply be a form of rationalization to justify risky choices, as was discovered by many organizations during the Great Recession. In addition to what is outlined in this Domain, some ways of making ratio analysis more reliable are mentioned here: Smooth out fluctuations or temporary accounting shifts by taking an average of an organization’s ratios over several periods. Exclude extraordinary or other unlikely-to-recur events and other outliers (or include them if deemed improperly excluded) and run the ratio analysis again. Study trends in ratios over time and their rate of change after adjusting for seasonality or other cyclical factors. Use statistically validated analysis models to create composite ratios that have specific weightings for each ratio in the model. Models exist that have proven statistical relationships between financial ratios and other events such as organizational default risk. Create common-size financial statements (discussed below) to help interpret underlying changes in ratio trends. Use of techniques such as these can help when attempting to rank organizations from best to worst in terms of ratios. They can also help resolve situations in which ratios conflict with one another (e.g., Company A has a high fixed charge coverage ratio but poor financial leverage while Company B has the opposite situation). ©2019. Association for Financial Professionals Topic 2 5-29 FPAC Exam Prep Platform When unexplained differences persist, FP&A professionals need to do more research, talk to colleagues or persons at that organization, or at least indicate in reports and recommendations what questions remain unanswered. In other words, FP&A professionals need to use a form of reasonableness testing when interpreting ratios. They also need to step back and determine how a particular ratio should correlate to the end product. For example, if it is credit quality, how does that ratio typically relate to default risk? Ratios plus other gathered information should provide the necessary clues to arrive at a reasonable, defensible conclusion. Tools for Competitive Analysis Tools used for any type of competitive analysis might include trend analysis, common-size analysis and percentage-change analysis: Trend analysis Trend analysis examines how a KPI or ratio changes over time. Trends are examined relative to the same trends for reference organizations or industry averages to show how they are diverging or remaining steady over time. A line graph can be charted and statistical trends can be added automatically as shown in Exhibit II.A.5-20 on the next page. Common-size analysis Common-size analysis expresses all values as a percentage of a total value to remove the confusing effect of organizational size when making comparisons. The most common example is to set revenues (sales) at 100 percent and all other income statement items as a relative percentage and to set total assets and total liabilities and equity at 100 percent and all other balance sheet items as a relative percentage. Percentage-change analysis Percentage-change analysis calculates the percentage change from period to period for financial statement elements or other comparative data. The basic formula follows: New Value − Old Value New Value Percentage Change = or −1 Old Value Old Value ©2019. Association for Financial Professionals Topic 2 5-30 FPAC Exam Prep Platform Exhibit II.A.7-20 – Trend Analysis (Mine Case Study Example) Selecting the Most Appropriate Type of Competitive Analysis and Tools When determining what type of competitive analysis to perform and what tools to select, start with the end product and the audience. The end product or business question will dictate the types of analysis that will be useful to decision makers. Then start gathering data and assumptions. When gaps exist in external data, it may be necessary to turn to one of the different types of analysis or to use proxies and assumptions. For example, if an organization’s KPIs cannot be gathered for competitors, it may be necessary to select some proxies such as financial ratios that are available and can alternately satisfy the end product. Another option is to make some assumptions, for example, to impute the desired KPI values by studying the reference organization and deriving the needed data using known interrelationships. A third option is to find alternative sources for the desired information, if they can be found. This may require making phone calls and having conversations with the competitor’s salespersons or their suppliers, or polling their customers, or it could involve purchasing information from a research group. Developing Reports and Recommendations As in prior chapters, the results of analysis need to be validated and corroborated to the extent possible, recommendations need to be submitted to decision makers for review, feedback and changes, and a final report needs to be presented. The results of competitive analysis can be presented in formal conclusions and recommendations, but the information can also be used to regularly update decision-maker- specific dashboards with key metrics from the organization and competitors so that daily decision making can be improved. ©2019. Association for Financial Professionals Topic 2 5-31 FPAC Exam Prep Platform Competitive analysis is useful only if it is actually used to improve decision making. Just as described in the earlier topics on making conclusions and recommendations, the metrics and the organizations chosen for analysis must be relevant to the decision maker or the analysis is at risk of not being used at all. One aspect that makes a measurement relevant to a decision maker is the ability for that decision maker to influence what is being measured. Analysis that focuses on elements controllable by that decision maker and provides realistic, actionable recommendations will be considered value added. Finally, competitive analysis can also be used by the FP&A function to improve its own planning and analysis processes. This is discussed further in the final topic of this chapter. Case Study Mining Company Case Study Copper Mines Company performs competitive analysis against industry averages and a peer group of six organizations primarily involved in mining. One new organization added is a Panama mining company. While many of the organizations are more diversified than Copper Mines, and not all extract copper, all have significant mining operations and report metric tons of ore that they extract along with variable costs and revenues associated with those extraction processes. Extracting unprocessed ore is the most resource-intensive mining task for Copper Mines and for its industry and peer group. The analysis performed below shows that Copper Mines is slightly below revenue per MT targets, even including their gold boom. Also, their labor costs are below the peer group average but above the industry average, and they are below average in contribution margins. However, the biggest red flag is their variable overhead per MT of ore. It is the second highest and is a full dollar over the peer group average and the industry average per MT of ore. This is the area that the FP&A professional will highlight for further analysis and improvement recommendations. ©2019. Association for Financial Professionals Topic 2 5-32 FPAC Exam Prep Platform Mining Company Case Study ©2019. Association for Financial Professionals Topic 2 5-33 FPAC Exam Prep Platform Topic 3: Provide Feedback and Revise Documentation Providing Feedback The analyses discussed in this chapter relate directly back to the Plan, Do, Check, Act (PDCA) cycle introduced at the beginning of this domain. Without measurement relative to the right internal or external reference points, the organization’s current state will not be properly understood and the need for improvement will not be clear. FP&A professionals are financial professionals, so their strength is in quantifying the risks of not making changes as well as the benefits of making improvements in financial terms. Analysis forms the Check part of PDCA, and the conclusions and recommendations that result from analyses such as those described in this chapter will pay dividends to the organization only if the advice is given full weight by decision makers. Therefore, it is an FP&A professional’s responsibility to make feedback pertinent and compelling. FP&A professionals need to present bad news promptly and clearly spell out the consequences of various options. They also need to transform a set of numbers into a compelling, realistic and credible story that explains the overall context and the problems or business questions and provides a feasible path to make improvements. Often, lessons learned from planning or analysis projects can be used to improve the FP&A function itself. This may take the form of informally sharing modeling tips or successes with colleagues or more formal post- project audits that involve reviewing a model and the recommendations against actual results to determine what areas can be improved. Communicating these results to other FP&A professionals can improve the efficiency of the FP&A function and help it be perceived as an increasingly value-added source of insight for the organization. Taking the time to perform these steps will not be easy. Professionals are always pulled on to the next task. However, the value of the effort is that it can make future tasks more routine and less like emergencies. ©2019. Association for Financial Professionals Topic 3 5-34 FPAC Exam Prep Platform Revise Documentation and Models The importance of documenting as you go has been stated numerous times. However, when a task is complete and a model is no longer immediately needed, it is important to go through documentation used for planning or analysis models or other documentation. The purpose of this update is to improve how the documentation is presented so that another FP&A professional can understand what is going on without needing any inside information (such as information only inside your head). Even if you will be the only person who will use the model again, clarifying notes about functionality now will make the model or other documentation more understandable after extended time has passed. Models that are self-explanatory due to good documentation and have strong potential for reuse can be added to a shared model library. A shared model library is a repository for models that satisfy several criteria: They are likely to be needed again, they were built using best practices, and they contain good documentation. A library of models can be a big help, not only for re-performing the same planning or analysis task but also as a place to look for portions of models that can be used in new models. Many calculations are generically applicable to any model, such as for taxation, depreciation, or amortization. A good pro forma financial statement model is also often easily reusable. Reviewing a model library might also help when posed with a modeling issue whose solution is elusive; an innovative method may have been implemented in the past by another contributor. Case Study Mining Company Case Study The FP&A professional for Copper Mines Company updates the documentation for the Panama mine purchase analysis model and the Panama mine annual budget variance analysis model. He uses comments fields to explain how the formulas and calculations work, specifically focusing on any exceptions to best practices that might have been made due to necessity or because following a particular rule too rigidly would have been an overall detriment to the model’s usefulness. For example, for the budget variance model, the calculations that determine if a variance is favorable or unfavorable have a formula change within the column to account for when a variance is revenue- or expense-related. He also adds some formatting to those cells to indicate when the change occurs. Finally, he adds both models to the shared model library for future use. ©2019. Association for Financial Professionals Topic 3 5-35