Standard costing I: setting standards and direct-cost variances

A standard cost is a carefully predetermined cost of one unit of output: how much of each input should be used and what each unit of input should cost. ACCT-102 lesson 11 introduced flexible budgets and the basic price and efficiency variances for direct materials and direct labor. This lesson and the next two develop standard costing in more depth. Here we look at how standards are set, at the choice between ideal and attainable standards, at when to measure the materials price variance, and at mix and yield variances, which split the efficiency variance when several inputs can be substituted for one another. Lesson 9 covers overhead variances and the journal entries of a standard cost system, and lesson 10 turns to sales variances.

Why companies use standard costs

Standard costs serve several purposes at once. They provide benchmarks for evaluating performance, so that managers can focus on significant departures from plan. They simplify bookkeeping, because inventory can be carried at standard cost and the differences recorded in variance accounts. They support pricing and bidding, because the standard cost of a new order can be estimated quickly from the standard bills of materials and routings. And they make budgeting easier, because the budget for any level of output is simply standard cost per unit multiplied by planned units.

Standard costing is sometimes described as old-fashioned, suited to labor-intensive mass production rather than to automated, flexible or service operations. There is some truth in the criticism: in a plant where direct labor is 5 percent of cost, detailed labor variances matter little, and monthly variance reports arrive too late to guide shop-floor decisions that are made by the hour. But surveys continue to find standard costing in wide use, especially in process industries, food, chemicals and metals, where materials dominate and yields matter. The tool has adapted: modern systems compute variances daily, focus on materials and yields rather than labor, and pair the financial variances with non-financial measures such as scrap rates and first-pass yield.

Setting standards

A standard has a quantity part and a price part. For direct materials, the standard quantity per unit comes from engineering specifications (the bill of materials), adjusted for unavoidable waste, evaporation or shrinkage. The standard price is the expected purchase cost per unit of material, including freight and handling and net of expected discounts. For direct labor, the standard hours per unit come from time-and-motion studies or historical data (the routing), adjusted for unavoidable breaks, set-ups and rejects. The standard rate is the expected wage rate including payroll taxes and benefits where the company treats them as part of direct labor.

Setting standards is a joint effort. Purchasing proposes materials prices, engineering proposes quantities, production supervisors and human resources advise on labor hours and rates, and accounting assembles the standard cost card. The participation matters: managers are more likely to accept standards they helped set, and they bring knowledge the accountant does not have.

Ideal versus attainable standards

Ideal standards assume perfect operating conditions: no waste, no breakdowns, no idle time, every worker at peak efficiency. Currently attainable standards allow for normal waste, normal machine downtime and normal worker fatigue; they are demanding but achievable by a skilled, motivated team. Most companies use currently attainable standards. Ideal standards produce unfavorable variances every month, which become routine and are ignored, and they can discourage employees who see the target as impossible. Attainable standards produce variances that mean something: a favorable variance signals genuinely good performance, and an unfavorable variance signals a problem worth investigating.

Companies that pursue continuous improvement sometimes use a third approach: standards that tighten each period, built into kaizen budgets, so that the target moves as the process improves. Whatever the approach, standards should be reviewed at least annually and whenever a significant change in materials, methods or prices occurs. Stale standards, set years ago and never updated, are one of the commonest reasons standard costing loses credibility.

The basic variances revisited

For each direct input, the total flexible-budget variance (actual cost minus the standard cost allowed for the actual output) splits into a price variance and an efficiency (quantity) variance. Using AQ for actual quantity, AP for actual price, SQ for the standard quantity allowed for actual output and SP for the standard price:

A positive result means actual cost exceeded standard and the variance is unfavorable (U); a negative result is favorable (F). In October Halden produced 1,800 S-100 housings. Purchasing bought and the foundry used 15,600 kg of cast iron at $1.25 per kg and 4,200 kg of nickel alloy at $5.80 per kg. The assembly line worked 760 senior technician hours at $43 and 1,100 junior technician hours at $25.50.

# Direct materials and direct-hour price and efficiency variances, October (illustrative)
output = 1_800
materials = {   # name: (std qty per unit, std price, actual qty, actual price)
    "cast iron":    (8, 1.20, 15_600, 1.25),
    "nickel alloy": (2, 6.00, 4_200, 5.80),
}
crew = {
    "senior": (0.4, 42.0, 760, 43.0),
    "junior": (0.6, 26.0, 1_100, 25.5),
}

def variances(inputs, label):
    tp = te = 0
    for name, (sq_u, sp, aq, ap) in inputs.items():
        sq = sq_u * output
        price = (ap - sp) * aq
        eff = (aq - sq) * sp
        tp += price; te += eff
        print(f"{label} {name:<13} SQ {sq:>7,.0f}  price {price:>+8,.0f}  efficiency {eff:>+8,.0f}")
    print(f"{label} total price {tp:+,.0f}   total efficiency {te:+,.0f}   (+ = U, - = F)")

variances(materials, "DM")
variances(crew, "DL")

DM cast iron     SQ  14,400  price     +780  efficiency   +1,440
DM nickel alloy  SQ   3,600  price     -840  efficiency   +3,600
DM total price -60   total efficiency +5,040   (+ = U, - = F)
DL senior        SQ     720  price     +760  efficiency   +1,680
DL junior        SQ   1,080  price     -550  efficiency     +520
DL total price +210   total efficiency +2,200   (+ = U, - = F)

For materials, the price variances nearly offset each other: cast iron cost $780 more than standard, while nickel alloy cost $840 less, for a net $60 favorable. The efficiency variances, however, total $5,040 unfavorable, because the foundry used 19,800 kg of alloy in total against a standard of 18,000 kg for the castings produced. For labor, the rate variance is a modest $210 unfavorable (seniors paid $1 above standard, juniors $0.50 below), but the efficiency variance is $2,200 unfavorable because the line used 1,860 hours against a standard of 1,800.

When to record the materials price variance

The materials price variance can be measured when materials are purchased or when they are used. Measuring it at purchase is generally preferred. It gives purchasing managers timely feedback on the decisions they control, it allows materials inventory to be carried at standard price (which simplifies record-keeping), and it avoids the delay that occurs when materials sit in inventory for months before use. Measuring it at use defers the variance until the materials enter production, which can hide purchasing problems for a long time.

In October Halden bought exactly what it used, so the timing does not matter. In November, purchasing took advantage of a volume discount and bought 30,000 kg of cast iron at $1.14 per kg, of which only 14,500 kg was used. Measured at purchase, the price variance is (1.14 − 1.20) × 30,000 = $1,800 favorable, reported in November; measured at use, only (1.14 − 1.20) × 14,500 = $870 favorable would be reported in November, with the rest appearing as the iron is used. The large purchase also raises a question the price variance alone does not answer: did the discount justify the cost of holding 15,500 kg of extra inventory? A favorable price variance can hide carrying costs, storage space and the risk of obsolescence.

Mix and yield variances

When a product uses several inputs that can be substituted for one another within limits, the efficiency variance can be split further. Halden's foundry blends cast iron and nickel alloy in a standard ratio of 80:20 by weight. The metallurgist can vary the blend somewhat: more nickel alloy improves corrosion resistance and reduces cracking, but nickel alloy costs five times as much as iron. The efficiency variance then has two causes. The mix variance measures the effect of using a different blend from standard. The yield variance measures the effect of using more or less total input than standard for the output achieved.

To separate them, compute the actual total quantity of all inputs used, apply the standard mix to it, and compare. The mix variance compares the actual quantities with the actual total at standard mix, both valued at standard prices. The yield variance compares the actual total at standard mix with the standard quantities allowed for actual output, valued at standard prices.

Here m_i is input i's standard share of the mix. Together the mix and yield variances add up to the total efficiency variance.

# Mix and yield variances for the foundry blend and the assembly crew (illustrative)
output = 1_800

def mix_yield(inputs, label):
    total_actual = sum(aq for _, _, aq in inputs.values())
    std_total_per_unit = sum(q for q, _, _ in inputs.values())
    mix_v = yield_v = 0
    for name, (q_u, sp, aq) in inputs.items():
        share = q_u / std_total_per_unit
        at_std_mix = total_actual * share
        sq = q_u * output
        mix_v += sp * (aq - at_std_mix)
        yield_v += sp * (at_std_mix - sq)
        print(f"{label} {name:<13} actual {aq:>7,}  actual total at std mix {at_std_mix:>8,.0f}  SQ {sq:>7,.0f}")
    print(f"{label} mix variance {mix_v:+,.0f}   yield variance {yield_v:+,.0f}   "
          f"sum {mix_v + yield_v:+,.0f}   (+ = U, - = F)")

mix_yield({"cast iron": (8, 1.20, 15_600), "nickel alloy": (2, 6.00, 4_200)}, "DM")
mix_yield({"senior": (0.4, 42.0, 760), "junior": (0.6, 26.0, 1_100)}, "DL")

DM cast iron     actual  15,600  actual total at std mix   15,840  SQ  14,400
DM nickel alloy  actual   4,200  actual total at std mix    3,960  SQ   3,600
DM mix variance +1,152   yield variance +3,888   sum +5,040   (+ = U, - = F)
DL senior        actual     760  actual total at std mix      744  SQ     720
DL junior        actual   1,100  actual total at std mix    1,116  SQ   1,080
DL mix variance +256   yield variance +1,944   sum +2,200   (+ = U, - = F)

For the foundry, the actual blend used 4,200 kg of nickel alloy out of 19,800 kg, about 21.2 percent instead of the standard 20 percent. At the standard mix, the 19,800 kg would have contained 15,840 kg of iron and 3,960 kg of nickel alloy. Using more of the expensive input produced a mix variance of $1,152 unfavorable. The yield variance, $3,888 unfavorable, shows the larger problem: the foundry needed 19,800 kg of input to produce castings that should have needed 18,000 kg, about 10 percent more than standard. The materials efficiency variance of $5,040 is the sum of the two.

For the assembly line, the labor mix variance is $256 unfavorable (slightly more senior hours than the 40:60 standard), and the yield variance, $1,944 unfavorable, shows that the crew needed 60 more hours than standard in total for the pumps produced.

Interpreting mix and yield together

Mix and yield variances often move in opposite directions, and interpreting them requires talking to the people who make the decisions. The metallurgist's explanation in October was that a batch of cast iron from a new supplier had higher sulfur content, which caused cracking, so she raised the nickel share to compensate. That decision cost $1,152 in mix. It did not prevent the yield problem: many castings still cracked and had to be remelted, which consumed extra metal. The purchasing manager, meanwhile, had a slightly unfavorable price variance on iron because the new supplier's price was higher than standard as well. The variances together point to the new iron supplier as the root cause, which the separate price, mix and yield figures alone could not show.

A favorable mix variance is not automatically good news either. If the foundry had cut the nickel share to 15 percent to save money, it would have reported a favorable mix variance, but more castings might have cracked, producing an unfavorable yield variance and, worse, more pumps failing in the field. Mix decisions should be judged by their effect on total cost and quality, not by the mix variance alone.

Labor variances in a modern plant

Labor efficiency variances assume labor hours vary with output. In many plants they do not: assembly crews are scheduled for full shifts, and if demand falls, workers are reassigned to cleaning, training or maintenance rather than sent home. In that setting, an unfavorable labor efficiency variance may simply reflect a slow month rather than poor productivity, and pressing supervisors to eliminate it can encourage overproduction to keep workers busy, building inventory nobody ordered. Lean plants often replace labor efficiency variances with measures such as units per labor hour on the bottleneck, on-time delivery and first-pass yield, and treat direct labor as a fixed cost of capacity.

The labor mix variance remains useful where crews combine workers of different skill levels and pay rates. Halden's assembly standard of 40 percent senior and 60 percent junior hours reflects the work's skill requirements. A crew that relies too heavily on seniors costs more; one that relies too heavily on juniors may produce more defects and rework, which would show up later as spoilage costs and warranty claims.

Deciding which variances to investigate

Variances are signals, not verdicts. Managers cannot investigate every variance, so they use management by exception, investigating those that are large in absolute terms, large relative to standard, recurring, or trending in one direction. Statistical process control charts can help distinguish random fluctuations from genuine shifts. The cost of an investigation should be weighed against the expected benefit of correcting a problem: a variance of $500 in a month is unlikely to justify a week of engineering time, unless it is the first sign of a trend that will grow.