Introduction
In the realm of cost analysis and management, variance analysis plays a crucial role in evaluating how actual costs deviate from standard costs. This case study focuses on Becton Labs, Incorporated, a chemical compound producer, specifically examining the production of Fludex. This compound involves various cost elements, such as direct materials, direct labor, and variable manufacturing overhead. By dissecting the variances and their implications, Becton Labs can make informed decisions about its cost management strategies. This analysis provides insights into whether the company’s procurement practices, labor mix adjustments, and overhead management are effective or require modifications.
1. Direct Materials: a. Price Variance: Price Variance = (Actual Price – Standard Price) * Actual Quantity Price Variance = ($361,800 – ($28.00 * 13,500)) = $361,800 – $378,000 = -$16,200 (unfavorable)
b. Quantity Variance: Quantity Variance = (Actual Quantity – Standard Quantity) * Standard Price Quantity Variance = (13,500 – 2,900) * $28.00 = 10,600 * $28.00 = $296,800 (favorable)
In this case, since the quantity variance is favorable, it suggests that the company used fewer materials than expected, leading to cost savings.
- Direct Labor: a. Rate Variance: Rate Variance = (Actual Rate – Standard Rate) * Actual Hours Rate Variance = ($11.50 – $13.00) * 21 * 140 = -$3,045 (unfavorable)
b. Efficiency Variance: Efficiency Variance = (Actual Hours – Standard Hours) * Standard Rate Efficiency Variance = (21 * 140 – 4,200 * 0.50) * $13.00 = 5,880 * $13.00 = $76,440 (favorable)
In this case, the efficiency variance is favorable, indicating that the company used fewer labor hours than expected to produce the given output.
Regarding the labor mix experiment, it’s important to compute the variances based on the actual mix used during November and compare them to the standard mix. If the new mix is significantly more cost-effective and does not negatively impact the quality of the product, the company might consider continuing with the new mix.
- Variable Overhead: a. Rate Variance: Rate Variance = (Actual Rate – Standard Rate) * Actual Hours Rate Variance = ($4,400 – ($3.60 * 4,200 * 0.50)) = $4,400 – $3,780 = $620 (unfavorable)
b. Efficiency Variance: Efficiency Variance = (Actual Hours – Standard Hours) * Standard Rate Efficiency Variance = (4,200 * 0.50 – 4,200 * 0.50) * $3.60 = 0
The efficiency variance for variable overhead is zero, indicating that the actual hours matched the standard hours for the production level.
In summary, for each variance, unfavorable variances indicate that the actual costs were higher than the standard costs, while favorable variances indicate that the actual costs were lower. For the recommendations:
- Direct Materials:
- The company should investigate the reasons for the unfavorable price variance and consider negotiating better terms with the supplier.
- The favorable quantity variance suggests efficient usage of materials.
- Direct Labor:
- The company should look into the reasons for the unfavorable rate variance and consider evaluating the technicians’ pay rates.
- The favorable efficiency variance indicates efficient use of labor hours.
- Variable Overhead:
- The company should analyze the reasons for the unfavorable rate variance and assess the variable manufacturing overhead costs.
- The zero efficiency variance for variable overhead suggests that the actual hours matched the standard hours.
As for the labor mix experiment, the company should conduct a thorough analysis of the cost savings and potential impacts on product quality before making a decision to continue with the new labor mix.
References
Smith, A. L., & Johnson, R. W. (2021). Variance Analysis in Cost Management: A Comprehensive Review. Journal of Managerial Accounting, 37(3), 45-63.
Brown, K. J., & Davis, M. R. (2022). Evaluating Supplier Contracts: A Strategic Approach. Strategic Finance, 104(8), 28-35.
Anderson, E. F., & Turner, L. B. (2020). Labor Mix Strategies and Cost Efficiency: A Comparative Study. International Journal of Production Economics, 185, 198-213.
