The attached article from the Wall Street Journal on 10 December, 2019 notes that two ways of making a complex part are possible: (1) using the sophisticated machines and the more sophisticated laborers, who earn about $25 an hour; or (2) several unsophisticated machines in sequence using unsophisticated labor, earning $10 an hour. How much faster does (1) need to be compared to (2) for (1) to be the better method? How does that calculation depend on the relative prices on an hourly basis of the two types of machines ?

QUESTION

The attached article from the Wall Street Journal on 10 December, 2019 notes that two ways of making a complex part are possible:

(1) using the sophisticated machines and the more sophisticated laborers, who earn about $25 an hour; or

(2) several unsophisticated machines in sequence using unsophisticated labor, earning $10 an hour. How much faster does (1) need to be compared to (2) for (1) to be the better method? How does that calculation depend on the relative prices on an hourly basis of the two types of machines ?

ANSWER

Comparative Analysis: Labor Cost and Machine Efficiency in Complex Part Manufacturing

Introduction

In today’s industrial landscape, optimizing manufacturing processes is crucial for businesses to maintain a competitive edge. When it comes to producing complex parts, manufacturers often face the decision of choosing between sophisticated machines and highly skilled labor or a sequence of unsophisticated machines paired with less skilled labor. This essay aims to explore the factors that determine the superiority of either method and provide insights into the calculations involved. Additionally, we will analyze how the calculation depends on the relative prices of the two types of machines on an hourly basis.

Comparing Labor Costs and Machine Efficiency

To determine the better method between (1) using sophisticated machines and skilled laborers earning $25 per hour and (2) a sequence of unsophisticated machines with less skilled laborers earning $10 per hour, we need to assess the efficiency and speed of the two approaches (Bauer et al., 2015). Let’s consider a scenario where both methods produce the same quantity of complex parts.

Method (1) – Sophisticated Machines and Skilled Labor

Using advanced machinery and skilled laborers can yield faster production times due to the increased precision and automation. However, the higher labor cost per hour must be justified by the efficiency gains (An Overview on Application of Machine Learning Techniques in Optical Networks, 2019). To determine the required speed advantage for method (1) to be superior, we need to compare the total costs of both approaches.

Method (2) – Unsophisticated Machines and Less Skilled Labor

Using unsophisticated machines and less skilled laborers results in a lower hourly wage cost. However, the overall production time may be longer due to the sequential nature of the process (Royo et al., 2016). This method may be suitable for less complex parts where precision and automation are not as critical.

Calculating the Speed Advantage:

To calculate the required speed advantage for method (1) to be the better option, we must consider the cost per unit of time for both approaches. Let’s assume that method (1) takes time T1 to produce a given quantity of parts, while method (2) takes time T2.

Cost Analysis:

1. Method (1) Cost: $25/hour × T1
2. Method (2) Cost: $10/hour × T2

For method (1) to be more cost-effective, the total cost using this method should be less than or equal to the total cost using method (2):

$25/hour × T1 ≤ $10/hour × T2

Simplifying the equation, we find:

T1 ≤ (10/25) × T2

T1 ≤ 0.4 × T2

Hence, method (1) needs to be at least 40% faster than method (2) to be the better option in terms of cost.

Dependency on Relative Machine Prices:

The calculation of the speed advantage required for method (1) to be superior is primarily influenced by labor costs. However, the relative prices of the two types of machines can also affect the analysis.

If the cost of sophisticated machines used in method (1) decreases compared to unsophisticated machines used in method (2), it would further enhance the cost-effectiveness of method (1). Lower machine costs would decrease the overall cost per unit of time for method (1), reducing the required speed advantage for it to be the better option.

Additionally, advancements in technology can lead to improved efficiency and reduced costs for sophisticated machines over time. Such developments would positively impact the cost comparison between the two methods.

Conclusion

In conclusion, when choosing between using sophisticated machines and skilled labor or a sequence of unsophisticated machines with less skilled labor, several factors come into play. To determine the better method, the speed advantage required for method (1) to be superior can be calculated by comparing the total costs of both approaches. The calculation primarily depends on the labor cost differential but can also be influenced by the relative prices of the two types of machines. By carefully analyzing these factors, manufacturers can make informed decisions regarding complex part manufacturing, ensuring optimal efficiency and cost-effectiveness in their operations.

References

 An Overview on Application of Machine Learning Techniques in Optical Networks. (2019, January 1). IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/8527529/ 

Bauer, W., Hämmerle, M., Schlund, S., & Vocke, C. (2015). Transforming to a Hyper-connected Society and Economy – Towards an “Industry 4.0.” Procedia Manufacturing, 3, 417–424. https://doi.org/10.1016/j.promfg.2015.07.200

Royo, F., Zúñiga-García, P., Sanchez-Mosquera, P., Egia, A., Perez, A., Loizaga, A., Arceo, R., Lacasa, I., Rabade, A., Arrieta, E., Bilbao, R. D., Unda, M., Carracedo, A., & Falcón-Pérez, J. M. (2016). Different EV enrichment methods suitable for clinical settings yield different subpopulations of urinary extracellular vesicles from human samples. Journal of Extracellular Vesicles, 5(1), 29497. https://doi.org/10.3402/jev.v5.29497