The Solution Below The Materials Price Variance Is Computed

The Solution Below The Materials Price Variance Is Computed Suppose we analyze the manufacturing variances for Becton Labs, Inc., focusing on materials, labor, and variable overhead costs related to the production of Fludex. These variances offer insight into cost control and operational efficiency, which are essential for managerial decision-making. Specifically, the analysis involves calculating the materials price and quantity variances, labor rate and efficiency variances, and variable overhead rate and efficiency variances, based on actual versus standard costs, quantities, and hours. Furthermore, interpreting these variances allows us to make recommendations regarding supplier contracts and labor strategies.

The first variance analysis pertains to direct materials purchased during November, where 12,000 ounces of materials were bought at a total cost of $225,000. The standard cost assigns a price of $20.00 per ounce, with a standard usage of 9,375 ounces for the 3,750 units produced. The remaining inventory at month-end consisted of 2,500 ounces. The computed variances help us evaluate the purchasing efficiency: the material price variance and the quantity variance.

For the materials price variance, the calculation compares the actual purchase price to the standard price, multiplied by the actual quantity purchased. The actual price per ounce is obtained by dividing total cost by total quantity: $225,000 / 12,000 ounces = $18.75 per ounce. The price variance formula is: AQ (AP – SP). With 12,000 ounces purchased, the variance becomes: Materials Price Variance = 12,000 ounces × ($18.75 – $20.00) = 12,000 × (–$1.25) = –$15,000 The negative sign signifies a favorable variance of $15,000, indicating that the materials were purchased at a cost lower than the standard price. This favorable outcome suggests that the supplier’s terms are advantageous, and the company could consider entering into a long-term contract with the supplier to lock in this lower price, provided that other quality and delivery considerations are met.

Next, the materials quantity variance assesses whether the actual amount of materials used in production aligns with standard expectations. The standard quantity allowed for the output is 9,375 ounces, based on 3,750 units at 2.5 ounces per unit. The variance calculation is: Materials Quantity Variance = SP × (AQ – SQ) = $20 × (9,500 ounces – 9,375 ounces) = $20 × 125 = $2,500 Since the actual quantity used (9,500 ounces) exceeds the standard (9,375 ounces), the variance is

unfavorable by $2,500, indicating higher consumption than planned. However, given that the excess usage is only 125 ounces, and the favorable price variance offsets some concerns, the company’s decision to sign a long-term contract should weigh other factors such as supplier reliability and material quality.

Moving to labor variances, the analysis involves actual hours worked, actual rate paid, standard hours allowed, and standard rate. During November, 5,600 hours were worked at an average rate of $12 per hour, whereas the standard rate is $12.50 per hour. The standard hours allowed based on production output is 5,250 hours. The variances are calculated as follows: Labor Rate Variance = AH × (AR – SR) = 5,600 × ($12 – $12.50) = 5,600 × (–$0.50) = –$2,800 This favorable variance indicates that labor was paid at a lower rate than standard, contributing to cost savings. In terms of efficiency, the variance is: Labor Efficiency Variance = SR × (AH – SH) = $12.50 × (5,600 – 5,250) = $12.50 × 350 = $4,375 Since actual hours exceeded standard hours, the efficiency variance is unfavorable, reflecting less productive labor utilization than expected. Although the company saved on wages per hour, the increased hours led to higher costs, making the overall labor expenditure higher than planned.

Given the new labor mix experimentation—reducing senior technicians and increasing assistants—there is a trade-off. While reducing senior staff may lower hourly wages, the increased labor hours and potential decline in productivity cause higher total labor costs. Hence, the company should reevaluate whether the cost savings from personnel substitution outweigh the productivity and quality impact, and whether this new mix warrants continued use.

Finally, the analysis extends to variable manufacturing overhead, which is allocated based on direct labor-hours. The actual variable overhead incurred was $18,200 for 5,600 hours, with standard variable overhead costs based on 5,250 hours. The standard rate per hour for overhead is derived as: Variable Overhead Rate = Total overhead / Standard hours = $19,600 / 5,250 hours ≈ $3.73 per hour (assuming standard cost per hour) However, the data suggests a standard rate of $3.50 per hour, allowing consistency with previous calculations. The variances are as follows: Variable Overhead Rate Variance = AH × (AR – SR) = 5,600 × ($3.25 – $3.50) = 5,600 × (–$0.25) =

–$1,400 and Variable Overhead Efficiency Variance = SR × (AH – SH) = $3.50 × (5,600 – 5,250) = $3.50 × 350 = $1,225 Since actual hours exceed standard hours, both the variable overhead rate and efficiency variances are unfavorable, indicating higher overhead costs, possibly due to increased usage or inefficiencies in production. Notably, there is a direct link between labor efficiency and overhead efficiency: increased labor hours typically lead to higher overhead costs, reinforcing the importance of efficient labor utilization.

In conclusion, the variances highlight critical insights for managerial decision-making. Favorable price variances for materials and labor rate variances contribute positively, but unfavorable quantity and efficiency variances can offset these gains. The decision to sign long-term supplier contracts should consider stability, quality, and the potential for cost savings, while labor strategies must balance wages and productivity to optimize overall costs. Continuous monitoring and analysis of variances enable companies like Becton Labs to refine their operational strategies and improve financial performance.

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