Assess Multistep Processes Using Various Parameters ✓ Solved

Assess Multistep Processes Utilizing A Variety Of Parameters Such As B

Assess multistep processes utilizing a variety of parameters such as bottleneck, capacity, utilization, flow rate, rework, and cycle time. The case involves Aquatica, which manufactures underwater camera housings for divers. The production process begins with a solid rectangular block of aluminum, which is drilled using a CNC machine to create the metal frame of the housing. Each housing type requires 15 minutes of drilling on the CNC machine, which also involves a 30-minute setup time before machining begins. After drilling, the frame undergoes chemical treatments in a series of baths, a stage with ample capacity and thus not a primary bottleneck. The final assembly involves manual attachment of buttons and components, taking 120 minutes per housing, performed by one of six trained workers.

The company produces five different housing types with varying demands expressed in houses per hour: D7000 (0.4), 5DS Mark III (0.5), AN5n (0.6), D300 (0.7), and T2i (0.8). The production process faces multiple considerations, including how to optimize throughput given the setup times and variability in demand. A key management recommendation is reducing product variety to improve utilization. To analyze the impact of this strategy, the assignment involves recalculating utilization percentages after removing two housing types from the product mix, considering the process constraints, and assessing which combinations yield the highest utilization. Additionally, the analysis explores whether the optimal combination can be predicted without detailed calculations, based on qualitative reasoning about demand and process bottlenecks.

Further, the discussion extends to exploring process improvement strategies and supply chain enhancements that could benefit Aquatica. The concept from Cachon and Terwiesch (2017) is explained—specifically, how fast processing times can sometimes compensate for the costs incurred from setup times, with relevance to Aquatica’s manufacturing cycle and capacity planning. The overall goal is to provide comprehensive insights into multistep process analysis, bottleneck identification, capacity utilization, and strategic improvements for manufacturing efficiency and customer service responsiveness.

Sample Paper For Above instruction

Analyzing multistep manufacturing processes requires a comprehensive understanding of various parameters such as bottlenecks, capacity, utilization, flow rate, rework, and cycle time. In the context of Aquatica, a manufacturer of underwater camera housings, these parameters play a critical role in optimizing production efficiency amid product diversity and process constraints.

Process Overview and Critical Parameters

The manufacturing process begins with a solid aluminum block, which is drilled to form the housing's metal frame using a CNC machine. This step is critical because it determines the initial throughput capacity. The CNC machine requires a setup time of 30 minutes for each production run and 15 minutes to drill each type of housing. Since there are five different housing types, switching between types incurs additional idle and setup times, which affect overall utilization.

Following drilling, the frames undergo chemical treatments in baths with ample capacity, indicating that this step is unlikely to serve as a constraint. The final assembly involves manual attachment of components, performed by six workers, each dedicating 120 minutes per housing. This step becomes the primary bottleneck, especially as demand increases, because it requires significant labor time and is limited by worker availability.

Impact of Product Variety and Demand on Capacity Utilization

The demand rates for the various housing types influence the utilization of the limiting resources, particularly the assembly stage. Higher demand for certain types increases the workload for workers, pushing the process closer to capacity limits. Conversely, introducing too many varieties can create frequent setup changes, increasing downtime and reducing overall throughput.

To analyze the effects of product variety reduction, specific combinations of housing types are examined by removing two varieties, which simplifies the production schedule, reduces changeover times, and improves resource utilization.

Scenario Analysis: Removing Housing Types and Calculating Utilization

Assuming the total demand is calculated based on demand rates, the process capacity is estimated based on the bottleneck—the manual assembly stage. The assembly stage's total available capacity per hour is 6 workers × 60 minutes = 360 minutes per hour.

For each housing type, the demand per hour is derived, and the total processing time required is calculated. For example, D7000 with a demand rate of 0.4 houses/hour translates to 0.4 houses × 120 minutes = 48 minutes per hour needed for assembly (assuming demand is evenly spread). For combinations with fewer types, the aggregate demand and processing time are summed, and utilization is computed as:

Utilization = (Total processing time required per hour) / (Available assembly time per hour) × 100%

By testing different combinations—such as removing the two lowest demand types, or two that create the most changeover complexity—the resulting utilization percentages can be tabulated and graphically represented. The combination with the highest utilization indicates the most efficient simplification of the product mix.

Results and Interpretation

The highest utilization occurs when D7000 and 5DS are removed, with approximately 70%. This reduction simplifies the product mix, moderates changeover times, and maximizes resource capacity utilization.

Notably, this outcome could be anticipated qualitatively by recognizing that removing demand-heavy or frequently changing products can streamline operations and reduce idle time. Still, precise calculations provide definitive conclusions and are necessary for strategic decision-making.

Additional Supply Chain Strategies for Improvement

Beyond product variety reduction, Aquatica could adopt several strategies to enhance throughput and responsiveness. Implementing flexible manufacturing systems could allow quick adjustments to changing demands, reducing changeover times. Lean manufacturing techniques, such as just-in-time (JIT) inventory, could decrease excess work-in-progress and improve flow. Enhancing supplier relationships for quick procurement of materials can reduce delays caused by rework or component shortages.

Moreover, investing in faster or additional CNC machines could decrease setup times, thereby increasing throughput. Introducing automation in manual assembly processes might also reduce labor bottlenecks, increase accuracy, and speed up production cycles.

Supply chain resilience can be bolstered through diversified sourcing and dynamic scheduling algorithms, which adapt to demand fluctuations in real time, maintaining high service levels despite variability. These strategies collectively contribute to better capacity utilization, lower lead times, and improved customer satisfaction.

Relevance of Cachon and Terwiesch’s Concept

As Cachon and Terwiesch (2017) state, “The advantage of a fast processing time can outweigh the disadvantage of the setup.” Applied to Aquatica’s process, this means that investing in technologies or methodologies that reduce processing times, such as more efficient CNC machines or automation, can compensate for or even negate the negative impact of extended setup times. For instance, if faster machining reduces cycle times, the overall throughput might increase enough to justify higher setup costs, especially if setups can be performed concurrently or outside peak hours.

Furthermore, process improvements that focus on reducing rework and delays enhance agility, allowing the company to respond swiftly to demand changes without sacrificing quality or delivery times. This dynamic balance underscores the importance of strategic process investments that favor speed and flexibility over initial cost savings, ultimately leading to a more competitive manufacturing system capable of serving customers more effectively.

References

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