Case 3 Line Balancing Quality Technology Qt Inc Was F 259885

Case 3 Line Balancingquality Technology Qt Inc Was Founded By T

Case 3 -- Line Balancing Quality Technology (QT), Inc. was founded by two first-year college students initially to produce a knockoff real estate board game similar to Monopoly. The company's growth has led to a complex manufacturing process involving multiple steps, each with specific tasks assigned to different stations. The questions revolve around identifying the process strategies used, calculating line cycle time and efficiency, determining capacity, applying task assignment methods, and analyzing process improvements.

Paper For Above instruction

This paper analyzes the manufacturing process of Quality Technology (QT), Inc., focusing on the process strategies employed, cycle time, efficiency, capacity, task assignment, and process improvement through line balancing. The core intent is to understand how QT's operations are structured and how they can be optimized for productivity and efficiency.

Process Strategy Identification

QT's process employs a combination of job shop and continuous flow processing strategies, characteristic of a mixed-process system. The initial design phase is a job shop process, given the custom nature and variable design time depending on client input. Post design approval, the manufacturing segment adopts a flow-process strategy for printing, cutting, and assembly, which involves standard, repetitive tasks executed in sequence. The printing department exemplifies a batch process, producing multiple units, while the cutting and assembly stations follow a line process approach, emphasizing high-volume, repetitive manufacturing. The line's evolution was unplanned but resembles assembly line manufacturing, allowing for specialization of tasks and potential for line balancing and efficiency improvements.

Cycle Time and Line Efficiency

The cycle time of a line is determined by the workstation with the longest task time, serving as the bottleneck. From Table 1, the task with the maximum duration is station 15, which takes 60 seconds (for placing money and wrapping). However, a detailed review indicates the majority of the tasks are considerably shorter, with the initial tasks being 10-45 seconds. The critical observation is that task 15 appears to be the longest at 60 seconds, setting the cycle time at 60 seconds assuming no additional delays.

Calculating the efficiency involves dividing the sum of task times by the product of cycle time and number of stations. The total task time must be summed over all tasks, and then divided by (cycle time × number of stations) to find the efficiency percentage.

Estimating total task time based on Table 1 yields approximately 610 seconds of work (add all task times). With a cycle time of 60 seconds (from the longest task), the theoretical maximum number of units per cycle is one per cycle, i.e., 1 unit every 60 seconds. Thus, line efficiency is (Total task time / (Cycle time × Number of stations)) × 100%, which approximates to (610 / (60 × 19)) × 100% ≈ 53.5%. This suggests that nearly half of the capacity is utilized effectively, with potential improvements to reduce idle time and balance workloads.

Capacity Calculations

Given an operating shift of 8 hours less two 15-minute breaks, the net working time per shift is 7.5 hours or 450 minutes. Converting to seconds, 450 × 60 = 27,000 seconds. With a cycle time of 60 seconds per unit, the maximum capacity per day is 27,000 / 60 = 450 units. Assuming the process functions continuously at maximum throughput, the annual capacity considering 200 operating days is:

• Annual Capacity = Daily Capacity × Operating Days = 450 × 200 = 90,000 units.

Thus, QT’s annual manufacturing capacity is approximately 90,000 units, assuming continuous operation at the calculated cycle time and no significant downtime or inefficiencies.

Task Assignment Using the "Greatest Number of Following Tasks" Approach

The "greatest number of following tasks" method assigns tasks to workstations by prioritizing tasks with the highest number of subsequent tasks, thus ensuring critical steps follow closely and potential bottlenecks are minimized. Using Table 1, tasks must be arranged to optimize workflow, with precedence constraints maintained.

For example:

• Task 15 (placing instructions in box) has 18 subsequent tasks, so it should be assigned to the last station or a station close to the end of the process.
• Tasks like 1 (getting box bottom) and 2 (counting money) have multiple follow-up tasks and should be assigned accordingly.

Creating a detailed task assignment plan involves plotting tasks in a sequence that respects order and minimizes idle time, but the core principle is to assign tasks with the most following tasks earlier or later, depending on their dependencies and duration.

Process Improvement and Efficiency Gains

Applying line balancing techniques, such as reassigning tasks to match workstation capacities and smoothing workload distribution, can improve efficiency. For instance, combining shorter tasks or decoupling complex tasks into smaller processes reduces bottlenecks. Introducing parallel stations where feasible, especially for repetitive tasks like printing or cutting, may also improve throughput.

By reorganizing tasks according to the "greatest number of following tasks" approach and balancing workload, the efficiency can potentially be increased from the current estimate (~53.5%) to above 70%, leading to higher capacity, reduced cycle times, and better utilization of resources.

Conclusion

In summary, QT employs a mixed-process manufacturing strategy combining job shop and flow-line elements. The current line cycle time is approximately 60 seconds, with an efficiency around half of optimal. Its capacity is about 90,000 units annually, assuming 200 working days. Effective task assignment and line balancing, guided by task precedence and workload analysis, can significantly enhance productivity. Future improvements should focus on reassigning tasks to minimize idle time, balancing workloads across stations, and streamlining the overall process for higher efficiency and capacity.

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