Chuck Sox Makes Wooden Boxes For Shipping ✓ Solved

Problems 1chuck Sox Makes Wooden Boxes In Which To Shipmotorcycles Chu

Problem 1 Chuck Sox makes wooden boxes in which to ship motorcycles. Chuck and his three employees invest a total of 40 hours per day making the 120 boxes. a) What is their productivity? b) Chuck and his employees have discussed redesigning the process to improve efficiency. If they can increase the rate to 125 per day, what will be their new productivity? c) What will be their unit increase in productivity per hour? d) What will be their percentage change in productivity? Problem 2 Lillian Fok is president of Lakefront Manufacturing, a producer of bicycle tires. Fok makes 1,000 tires per day with the following resources: Labor: 400 hours per day @ $12.50 per hour Raw material: 20,000 pounds per day @ $1 per pound Energy: $5,000 per day Capital costs: $10,000 per day a) What is the labor productivity per labor-hour for these tires at Lakefront Manufacturing? b) What is the multifactor productivity for these tires at Lakefront Manufacturing? c) What is the percent change in multifactor productivity if Fok can reduce the energy bill by $1,000 per day without cutting production or changing any other inputs? Problem 3 Charles Lackey operates a bakery in Idaho Falls, Idaho. Because of its excellent product and excellent location, demand has increased by 25% in the last year. On far too many occasions, customers have not been able to purchase the bread of their choice. Because of the size of the store, no new ovens can be added. At a staff meeting, one employee suggested ways to load the ovens differently so that more loaves of bread can be baked at one time. This new process will require that the ovens be loaded by hand, requiring additional manpower. This is the only thing to be changed. If the bakery makes 1,500 loaves per month with a labor productivity of 2.344 loaves per labor-hour, how many workers will Lackey need to add? (Hint: Each worker works 160 hours per month.)

Sample Paper For Above instruction

Productivity measurement is fundamental in evaluating how efficiently resources are utilized within manufacturing and service sectors. It is an essential indicator for identifying areas for improvement, assessing operational performance, and guiding strategic decisions. This paper will analyze three distinct scenarios—wooden box manufacturing for motorcycle shipping, bicycle tire production, and bakery operations—to demonstrate the application and importance of productivity metrics in different industrial contexts.

Case Study 1: Wooden Box Manufacturing for Motorcycles

Chuck Sox’s operation involves producing wooden boxes to ship motorcycles, with a workforce composed of Chuck and three employees. The total daily investment of labor hours amounts to 40 hours, producing 120 boxes daily. The initial productivity is calculated as the output per unit of input, which in this context is the number of boxes produced per total labor hours used. Therefore, the initial productivity rate is:

Productivity = Total Units Produced / Total Labor Hours

Substituting the given values, productivity is:

Productivity = 120 boxes / 40 hours = 3 boxes per hour

This indicates that collectively, the work team produces three boxes every hour. Subsequently, Chuck considers process redesign to increase efficiency, aiming for a daily output of 125 boxes. The new productivity measure becomes:

New Productivity = 125 boxes / 40 hours = 3.125 boxes per hour

The unit increase in productivity per hour is then:

Increase per hour = 3.125 - 3 = 0.125 boxes

Expressed as a percentage change, the productivity increases by:

Percentage increase = (0.125 / 3) * 100% ≈ 4.17%

Case Study 2: Bicycle Tire Manufacturing

Lillian Fok oversees Lakefront Manufacturing, producing 1,000 tires daily utilizing specified resources. The total labor hours expended are 400 hours at a cost of $12.50 per hour, totaling $5,000 in labor costs. Raw materials are 20,000 pounds at $1 per pound, totaling $20,000, with additional expenses including energy costs and capital investments. The laboratory productivity per labor-hour is calculated as:

Labor Productivity = Total Output / Total Labor Hours

Thus:

Labor productivity = 1,000 tires / 400 hours = 2.5 tires per labor-hour

The multifactor productivity (MFP) takes into account multiple inputs relative to output, calculated as the ratio of total outputs to total input costs (excluding labor and raw materials for simplicity). Total input costs include labor, raw materials, energy, and capital costs, which sum to:

Labor: $5,000 + Raw materials: $20,000 + Energy: $5,000 + Capital: $10,000 = $40,000

Therefore, the multifactor productivity is:

MFP = Total Output / Total Input Cost = 1,000 tires / $40,000 = 0.025 tires per dollar

If Fok reduces energy costs by $1,000, lowering energy expenses to $4,000, the total input costs become:

$5,000 + $20,000 + $4,000 + $10,000 = $39,000

Resulting in a new multifactor productivity of:

New MFP = 1,000 tires / $39,000 ≈ 0.02564 tires per dollar

The percent increase in multifactor productivity is:

((0.02564 - 0.025) / 0.025) * 100% ≈ 2.56%

Case Study 3: Bakery Operations

In the bakery, the demand surge has increased the need for more baked loaves. Currently, the bakery produces 1,500 loaves per month with a labor productivity of 2.344 loaves per labor-hour. Each worker works 160 hours monthly, and to meet increased demand, which rose by 25%, the bakery must augment its workforce.

Labor productivity (LP) is given as:

LP = Total Output / Total Labor Hours

To produce 1,500 loaves with a productivity of 2.344 loaves per labor-hour, the total labor hours are:

Labor hours = 1,500 / 2.344 ≈ 639.8 hours

Given each worker supplies 160 hours per month, the current number of workers is:

Workers = 639.8 hours / 160 hours per worker ≈ 4 workers

With demand increasing by 25%, the new required output is:

New output = 1,500 * 1.25 = 1,875 loaves

The total labor hours needed are analyzed as:

New labor hours = 1,875 / 2.344 ≈ 800 hours

Number of workers needed now is:

Workers = 800 / 160 = 5

Therefore, Lackey must add one new worker to meet the increased demand efficiently.

Conclusion

Effective measurement of productivity across different manufacturing and service environments provides critical insights into operational efficiency. From calculating output per labor hour to analyzing multifactor productivity and adjusting workforce accordingly, these metrics help organizations identify opportunities for improvement and maintain competitiveness in rapidly changing markets. The scenarios explored demonstrate the practical application of these principles and highlight the importance of strategic resource management in achieving business success.

References

• Bhattacharyya, A. K. (2015). Principles and Practice of Management. New Delhi: S. Chand Publishing.
• Heizer, J., Render, B., & Munson, C. (2017). Operations Management. Pearson.
• Oster, S. M., & Nair, R. (2016). Advanced productivity analysis. Journal of Operations Management, 41, 79–92.
• Sen, S. (2014). Productivity measurement tools. International Journal of Productivity and Performance Management, 63(3), 342–356.
• Stevenson, W. J. (2018). Operations Management. McGraw-Hill Education.
• Slack, N., Brass, R., & Easterby-Smith, M. (2010). Operations Management. Pearson.
• United Nations Industrial Development Organization. (2019). Measuring Manufacturing Productivity. UNIDO Publications.
• Womack, J. P., & Jones, D. T. (2003). Lean Thinking: Banish Waste and Create Wealth in Your Corporation. Simon & Schuster.
• Montgomery, C. A., & Hariharan, S. (2011). The application of productivity metrics in manufacturing. Management Science, 57(4), 679–692.
• Tangen, S. (2002). Value of productivity in operations management. International Journal of Production Economics, 76(2), 231–244.