Technical Feature
collection technologies help monitor machine states and inventory levels, providing crucial insights into production efficiency. However, interpreting this data accurately requires sophisticated analysis to pinpoint performance bottlenecks and their root causes.
Adapting to real- time challenges An effective bottleneck analysis is not a static event. Bottlenecks can shift due to various factors like machine slowdowns, downtime, or
and changeovers. In the end, Operation 20 provides a capacity of 1,800 parts per minute.
● In our example, Operation 30 provides a capacity of 2,100 parts per minute with 1 machine, so nothing further is needed.
● Finally, Operation 40 is made up of 2 machines for a combined capacity of 1,900 parts per minute.
guides the selection and deployment of equipment at each operation. However, not all machines operate at the same speed. Some operations may require multiple machines to meet production goals, while others may run faster than needed.
Let’s look at an example
for consideration: ● Let’s say that the machine at Operation 10 runs at 1,000 parts per minute. This operation might require two machines running to reach 1,500 parts per minute. Now, at full capacity, this operation can produce 2000 parts per minute.
● Assume that Operation 20 uses machines that produce 300 parts per minute. We need 5 of these machines reach our target, but will add a 6th machine to account for downtime
In this example, Operation 20 becomes the built-in or designed bottleneck – its capacity sets the average throughput for the entire line. The red, dashed line indicates our average production capacity. Some might be tempted to consider this maximum capacity, when in reality, max capacity may be higher for short periods of time when faster operations have inventory available to process.
Managing inventory and downtime Beyond machine speed, inventory management plays a critical role in bottleneck analysis. Each operation requires a buffer of raw materials and work-in-process (WIP) inventory to account for downtime, changeovers, or unexpected delays. This inventory acts as a buffer to ensure continuous flow throughout the production process.
Challenges and solutions Accurately sizing inventory levels and monitoring machine performance are ongoing challenges. Sensors and data
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mastering bottleneck analysis is a crucial step towards achieving leaner and more efficient manufacturing processes.
In essence,
changes in production schedules. Visibility into real-time production with continuous monitoring and analysis allow manufacturers to adapt as bottlenecks move, ensuring optimal performance and efficiency.
Conclusion Bottleneck analysis is not just about identifying constraints but also about understanding the dynamics of production flow. By focusing on the bottleneck, manufacturers can prioritize improvements that have the most significant impact on overall throughput. This approach not only enhances operational efficiency but also supports informed decision-making in production planning and resource allocation.
In essence, mastering bottleneck analysis is a crucial step towards achieving leaner and more efficient manufacturing processes. It empowers manufacturers to navigate complexities, minimize downtime, and ultimately, maximize productivity.
For manufacturers looking to optimise their operations or delve deeper into real-time bottleneck analysis, products like Flexware Innovation’s Acumence software are significant cost-savers over the course of hours, weeks, months, and years. Acumence has been providing production insights for nearly 20 years and is found in 22 countries across the globe.
Learn more about Acumence and request a demo by visiting
www.acumence.com.
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