Now, let's turn to our GnG Landscapes setting, and let's assume that we're focused on computing variances for materials costs related to the production of decorative clay pots. We have standard information and actual information pertaining to a given accounting period. The standard information suggests that we are supposed to purchase each pound of material input at $2 per pound. We're supposed to use 10 pounds per pot that we produce. And we're expected to produce 400 pots in a week's time. What actually happened after a given week, was that we spent $1.50 per pound. We ended up using more pounds per pot at 12, and we produced more pots than we had expected at 500. So let's use our framework to calculate the materials cost variances for this setting. So let's use our framework to calculate these variances. Let's start with the static budget. To compute the static budget amount, we used all standard information. Per the given information, we are expected to spend $2 per pound. We are expected to use 10 pounds per output unit. And for the given week in question, our plan production was 400 output units. That means per our static budget, we would've expected materials costs to be $8,000. Now the static budget is relatively constraining. It assumes we produce what we had planned to produce. But one of the first things that we oftentimes lose control over is how many units we need to produce in a given time period. Perhaps our customer demand fluctuated in some way, and we expected more sales than we had originally budgeted. So therefore we had to increase production. And of course a similar explanation might be the case for why we produced less than we had planned. So let's compute what the flexible budget suggests is our costs. Again, the flexible budget is how much we should have spent given what we actually produced. So, we would leave everything in the static budget unchanged with the exception of the number of units that we produced. Per the actual information, instead of the 400 that we had planned, we actually produced 500. So given that we actually produced 500, we should have spent 2 pounds for each of the material inputs, and we should have used 10 pounds per each of those 500 units. So our expected cost, given what we actually produced, is $10,000. Further isolating different explanations for the overall difference between actual and budgeted costs, we can calculate the next column. We label that, somewhat arbitrarily, the standard price column. In that column, the only thing standard is the price. Everything else is actual information. So the price per input that we had calculated as a standard was the $2. But what we actually did, instead of using 10 lbs per pot, we actually used 12 lbs. And we did that for each of the 500 pots, That we ended up producing. So multiplying the standard price times the actual input times the actual output yields $12,000. And finally, the actual cost that we really did incur is calculated using all of the actual information that we learned, at the end of the accounting period. Instead of $2 per input, we had actually spent $1.50. We actually used 12 pounds per pot, and we actually produced 500 output units, or pots. That total dollar amount is $9,000. Now using this framework we can calculate the amount of each individual variance. Our spending variance is the difference between what we actually spent and our standard price column, or $3,000. The difference between the standard price and flexible budget column, referred to as our efficiency variance, is $2,000. And the difference between the flexible budget and the static budget is $2,000. Now earlier we talked about categorizing these different variances. Specifically we could categorize each variance as favorable or unfavorable. When we categorize, we think about the relation between what we actually spent and what we had expected to spend. So looking at each individual variance, we would classify them as favorable or unfavorable. Let's look at the activity variance. The activity variance is the difference between the flex budget and the static budget. Looking at the static budget, we have a standard piece of information, a standard piece of information, and a standard piece of information. In the flexible column we also have standard information. That's the price and the input. But we have an actual piece of information in the form of our output. So relative to the static budget column, the flexible budget column is more actual than that static budget column. And in this case the actual costs that we had incurred are greater than what we had expected to incur, primarily because we produced more units than we thought we were going to. Now thinking about variances, we said that a favorable variance was when actual net income was great than standard net income, or budgeted net income. And it's unfavorable when actual net income is less than expected net income. In this case, the more actual costs are higher than the more standard costs, which means that the actual net income, given this change alone, is less than what we had expected it to be. So we would classify this variance as unfavorable. Now let's look at the efficiency variance. That $2000 is the difference between the standard price column and the flex budget column. As we just mentioned, in the flex budget column, we have a standard piece of information, a standard piece of information, and an actual piece of information. In the standard price column, we have a standard piece of information and two actual. So between the two columns, the standard price column has the more actual information. And the flex budget column has the more standard information. The actual costs are higher than the standard costs, which means that our actual net income is lower than what we expected it to be, at least compared to between these two columns. So this variance would be classified also as unfavorable. Finally let's look at the spending variance. That amount of $3,000 is driven by the fact that we spent less per input unit than we had expected to spend. Looking at the actual column, of course, each of those individual pieces of information is actual. So our actual column represents our more actual piece of information compared to the standard price column. Our costs are lower when we think about the actual information, 9,000 is less than 12,000. And so therefore our actual net income would be higher than expected at least comparing these two columns. So unlike the previous two variances, we would classify this variance as favorable. Just like we did in our previous example, let's start with our static budget. For labor, our static budget suggests that we were going to spend $10, Per hour on average for our direct labor. Each pot that we produced required three hours. And we expected to produce 400 pots. Our static budget for direct labor costs at the beginning of the period was $12,000. Focusing on the flexible budget, we'll leave everything the same except for produced output. The amount that we expect to spend for each hour remains the same at the standard of $10. The standard input per output is 3 hours per unit of output, or per pot. What's different between the flex and the static budget is the output. We move from the standard output to the actual output, or 500 pots for the week. So in the flex budget column, our total is $15,000. Again, this is what our expected cost would be given what we actually produced. The next column is our standard price. Moving from the flex column into that new column, we leave everything the same, Except for our usage of the input. In that case, we were told that we actually used 2.5 hours per pot. Multiplying the standard price times the actual input times the actual output yields a total of 12,500. And finally, our actual column is all actual information. We actually ended up paying our employees $12 per hour, using 2.5 hours per pot, and as we have said, we actually produced 500 pots. These three components multiplied together yield total actual costs of $15,000. Now looking at each of our individual variances, the spending variance, Is the difference between the actual column and the standard price column, or in this case $2,500. The difference between the standard price and the flex column also $2,500. And the difference between the flex budget and the static budget, Is $3,000. Again, the first variance is the spending variance. The second variance is the efficiency variance. And the third is the activity variance. One final step, we need to classify each of these variances as favorable or unfavorable. In terms of the activity variance, the flex budget total is higher than the static budget total, which means that our more actual costs were greater than our more standard costs, which means that our more actual net income was less than what we had expected it to be. So, we would classify that variance as unfavorable. The story is the opposite in the efficiency variance. In the efficiency variance, the standard price column has more actual pieces of information. So therefore, it's represented our actual costs. And the flexible, at least relative to the standard price column, has less actual information. So that represents our standard costs. In this case, our actual costs were less than our standard costs, which means that our actual net income, at least in comparing these two columns, is more than what we expected it to be. So we classify that variance as favorable. And for similar logic but in the opposite direction, we would classify the spending variance as unfavorable. And as a shortcut, you can take a look at what our actual spending was relative to what we expected it to be. We expected it to be $10 per hour, but we ended up paying $12 per hour. This means that our actual costs were higher than what we expected them to be, which means that our actual net income was less than what we expected it to be, resulting in an unfavorable variance. Now let's do one more example. And let's focus on fixed costs. With respect to fixed costs, we have standard information. And that is that we expect to spend, on a weekly basis, $4,000. Now for many different reasons, firms might choose to break down these fixed costs into their individual components. Let's suppose that GnG Landscapes uses machine hours as the basis by which they spread about fixed costs. So you can calculate an actual price, an input and an output. And so, that $4,000 per week, given what all of our expectations are, might be broken down into a $5 per machine hour amount, 2 machine hours per pot, and 400 pots per week. On the actual side we can calculate each of those individual components or ultimately what they total out to be, and that is actual spending of $6,000. Now let's calculate our fixed cost variances. As we mentioned before the variable cost framework doesn't necessarily apply to the fixed cost side of the world. The reason is that fixed costs don't truly fluctuate with the usage of inputs or the actual production. We broke those fixed costs down into the individual components just for information purposes or for other decision making. But really, attributing a difference between what we actually spent and what we expected to spend to those individual components is not logical. Again, fixed costs don't fluctuate with volume. If they did, they wouldn't be classified as fixed costs. So ultimately for calculating variances for fixed costs, in a relatively simple world we just have two dollar amounts, the actual, And the budget. And because we don't need to isolate any individual components, we can just look at the totals. $6,000 was what we actually spent. And $4,000 was what we had expected to spend. The only real difference between actual and budgeted fixed cost is that the price was different between what we expected it to be and what we actually paid. So the variance is $2,000. And because it's all attributable to price, we classify it as a spending variance. We do, however, use the same categorization scheme of favorability and apply that to the fixed cost just like we did for variable costs. And in this case our more actual piece of information suggests that our costs were higher than what we budgeted or expected them to be. So we would classify this variance as unfavorable. So now what do we do with this information? Some important points to make here, and that is, what do organizations do to interpret and investigate variances? Well first of all, as I mentioned before, variance analysis allows for management by exception. So oftentimes organizations will identify different thresholds. If a variance is above a particular threshold, it might require further investigation. But if it's below that threshold, or actual and standard performance was close enough, then we don't need to investigate that particular variance. Now, let's suppose that the GnG's owners and managers are investigating variances at the end of the year. And they might look at each of the variances that we've computed. Let's focus on direct materials and direct labor for this purpose. The activity variance for both materials and labor was driven by higher than planned production. So what you might learn from this is to ask the following questions. First off, why did we produce more than we had planned? Did orders increase? Perhaps we need to adjust the standard going forward. As you can see, what I'm doing in this interpretation is not necessarily providing answers, but providing questions that we would ask, based on what we saw in our variance analysis. With respect to materials, we had a favorable spending variance. Questions that we might ask is, did the purchasing department negotiate a discount? Or perhaps, did we buy lower quality materials? Cost us less, but might cost us more in the future. Or again, do we need to adjust the standard? Have prices changed for our materials out there in the marketplace, and our old standard is no longer in effect? With respect to the efficiency variance, we saw in unfavorable amount there. Why did we use more input units than we had planned, anything to do with the material quality? Did we have more scrap or rework that was necessary because we purchased lower quality units? Again, do we need to adjust the standard? Is what we use as a standard obsolete? On the labor side we can ask similar questions. In contrast to the materials spending variance, we actually saw an unfavorable labor spending variable. Wages actually that we paid were 20% higher than what we had planned them to be. We had budgeted $10, and we ended up paying $12, why? Did we have more overtime hours requiring higher rates of pay for those particular hours which bumped up the average pay across all employees? Did we have more experienced labor that requires higher wages? Do we need to adjust the standard? Is $10 just an obsolete estimate of what our expected payment to our employees is? And one more labor variance, and that was the efficiency variance. That was favorable. Did we have more experienced labor? Kind of makes sense, given that we ended up paying them more. But perhaps this greater experience led them to be more efficient with the time that they spent per unit produced. Are there implications for quality? Did some of our employees take shortcuts and not put as much time into producing each unit compared to what expect them to? And yet again, do we need to adjust the standard?