Question 1: Electric Manufacturer Of High Voltage Switches
Question 1td Electric Manufactures High Voltage Switches And Other E
Question #1 T&D electric manufactures high-voltage switches and other equipment for electric utilities. One line staffed by three workers assembles a particular type of switch, with each worker exclusively responsible for a specific set of components. The assembly line uses a conveyor allowing queues before each worker. The bottleneck is at the middle station, with a processing rate of 11 switches per hour. Raw processing time per switch is 15 minutes. Management is considering cross-training workers to improve efficiency.
Question #2 Floor-On, Ltd., operates a line producing self-adhesive tiles. The line consists of single-machine stations, nearly balanced, with a bottleneck rate of 2200 cases per 16-hour workday and a raw process time of 30 minutes per batch. The line averages 1500 cases daily, with an average cycle time of 5 hours.
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Paper For Above instruction
Introduction
Manufacturing lines are critical components of industrial productivity, and their efficiency depends on balancing throughput, work-in-progress (WIP), and cycle times. Analyzing bottlenecks, WIP levels, and potential improvements enables organizations to optimize operations. This paper examines two distinct manufacturing scenarios, focusing on calculating critical WIP, cycle time, throughput capacities, and potential optimization strategies.
Analysis of T&D Electric’s High-Voltage Switch Line
T&D Electric’s assembly line comprises three workers, with a clear bottleneck at the middle station operating at 11 switches per hour.
a) Critical WIP Level
The critical WIP — representing the maximum number of items that can be effectively managed without causing delays — is largely dictated by the bottleneck capacity. Using Little’s Law:
\[
\text{WIP} = \text{Throughput} \times \text{Cycle Time}
\]
Given the bottleneck rate \( R_b = 11 \ \text{switches per hour} \), the maximum throughput for the line cannot exceed this rate in equilibrium. Assuming the line is working optimally, the critical WIP must at least match the capacity of the bottleneck:
\[
\text{Critical WIP} = \frac{\text{Bottleneck rate} \times \text{Cycle Time}}{1}
\]
The raw processing time per switch is 15 minutes (0.25 hours). Therefore, the minimum cycle time associated with the bottleneck is:
\[
\text{Cycle Time} = \frac{1}{\text{Throughput}} \times \text{time per switch}
\]
But as the maximum throughput is constrained by the bottleneck, the minimal cycle time per item at capacity is approximately:
\[
\text{Cycle Time} = \frac{1}{11} \ \text{hours} \approx 0.0909 \text{ hours} \ (\sim 5.45 \text{ minutes})
\]
Thus, the critical WIP, considering the throughput limit, is:
\[
\text{Critical WIP} = 11 \ \text{switches/hour} \times 0.0909 \ \text{hours} \approx 1 \text{ switch}
\]
This suggests that at minimum, approximately 1 unit of work is needed to keep the bottleneck occupied. However, considering the entire line and potential queues, a slightly higher WIP level (around 2-3 units) would be necessary to prevent idle time at upstream stations and ensure smooth flow.
b) Current Throughput and Average WIP-based Cycle Time
With an observed throughput of 10.5 switches/hour and an average WIP of 7 jobs, the average cycle time (time a job spends on the line) can be estimated using Little’s Law:
\[
\text{Cycle Time} = \frac{\text{WIP}}{\text{Throughput}} = \frac{7}{10.5} \text{ hours} \approx 0.6667 \text{ hours} \approx 40 \text{ minutes}
\]
This indicates that, on average, each switch spends approximately 40 minutes on the line, accumulating in WIP buildup before completion.
c) Practical Worst-Case Throughput
The worst-case throughput defines the maximum capacity if disruptions or inefficient scenarios occur. Since the bottleneck rate is 11 switches/hour, the practical worst-case throughput aligns with this maximum, assuming no additional delays or breakdowns.
Hence, the practical worst-case throughput is approximately 11 switches per hour, matching the bottleneck capacity.
d) Possibility for Process Improvements
Given the current bottleneck rate, there is limited scope for throughput increases unless the bottleneck rate improves. Cross-training workers and implementing a flexible labor system can reduce idle time and help balance line workloads. For example, if workers can switch roles or assist at the bottleneck station during high demand, it may effectively increase throughput marginally.
Further improvements could involve investing in automation or upgrading bottleneck equipment to surpass the current capacity of 11 switches per hour. Additionally, reducing cycle times or increasing overall WIP intelligently could elevate line utilization, yet such measures must carefully balance WIP levels to prevent excessive delays or inventory costs.
Analysis of Floor-On Ltd.’s Tile Production Line
Floor-On Ltd.’s line features nearly balanced single-machine stations with an estimated bottleneck rate of 2200 cases per 16 hours, and an average of 1500 cases produced daily. The raw process takes 30 minutes per batch, and the average cycle time is 5 hours.
a) Estimating Average WIP Level
Applying Little’s Law, the average WIP can be calculated:
\[
\text{WIP} = \text{Throughput} \times \text{Cycle Time}
\]
Given the average throughput of 1500 cases per day:
\[
\text{Cycle Time} = 5 \text{ hours}
\]
\[
\text{Daily production} = 1500 \ \text{cases}
\]
The average daily throughput is close to the bottleneck capacity since the line is almost balanced, suggesting the throughput is approximately 2200 cases per 16-hour day. To estimate WIP:
\[
\text{WIP} = 2200 \ \text{cases} \times \frac{5 \text{ hours}}{16 \text{ hours}} \approx 687.5 \text{ cases}
\]
This indicates an average WIP of approximately 688 cases in the system.
b) Comparing Actual and Worst-Case Throughput
The actual average throughput of 1500 cases per day falls below the maximum of 2200 cases, indicating some inefficiencies or downtime. The ratio of actual to maximum capacity is:
\[
\frac{1500}{2200} \approx 68\%
\]
The practical worst case throughput aligns with the bottleneck rate of 2200 cases per day; thus, there exists an opportunity to improve throughput closer to this maximum by addressing process inefficiencies.
c) Effect of Increasing Bottleneck Capacity
If the bottleneck capacity increases while WIP remains constant, the throughput of the line can be increased correspondingly, assuming no other constraints emerge. For instance, boosting the bottleneck rate to 2500 cases/day or higher would allow the line to process more cases, potentially leading to higher overall production without necessarily increasing WIP, thus improving efficiency and reducing cycle times.
d) Workforce Reassignment Post-Process Improvement
Initially, ten workers manage ten machines; after automation reduces the need to operate all ten, only five workers are required to sustain the improved process. The remaining five workers should be redeployed within the organization to other operational areas or preventive maintenance teams. They could also be assigned to quality control, inventory management, or auxiliary tasks that support manufacturing processes, thus optimizing resource utilization without layoffs.
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Conclusion
The analysis demonstrates the importance of understanding bottleneck capacities, WIP levels, and process times to enhance manufacturing efficiency. In both scenarios, calculated metrics such as WIP, cycle time, and throughput inform strategic decision-making. Implementing cross-training at T&D Electric and capacity improvements at Floor-On Ltd. show potential avenues for productivity enhancement. Moreover, resource reallocation post-automation underscores the importance of flexible labor management in manufacturing environments. Continuous monitoring, targeted investments, and process adjustments are vital for sustaining operational excellence.
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