Plastic Injection Molding Process Optimization

Improving manufacturing efficiency, reducing inventory, reducing waste, and maintaining product quality should all be carefully tracked, but the most critical performance measure for plastics processors is mold cycle time.

Key performance indicators (KPIs) provide insight to quickly respond to changing conditions and immediate threats, or to implement subtle adjustments to keep business trends in a healthy direction. While there are many important areas to track, one metric is undoubtedly the most critical and should be at the top of the list for any plastics processor.

A strong case can be made for collecting and analyzing metrics related to improving efficiency or responsiveness, reducing inventory, reducing waste, and of course maintaining product quality. Each of these areas is very important to the health of a company and should be carefully tracked. But for plastics manufacturers, the most critical measure of performance is mold cycle time. Cycle time is the total time it takes to mold one shot of plastic—from the time the plastic is injected into the mold until the next plastic shot begins.

Let’s establish from the outset that cycle time is variable and is affected by several factors—mold design, cavity (part) configuration, molding material. These and other factors will affect the time it takes to inject plastic into the mold, cool the plastic, open the mold, eject the part, and reclose the mold. Likewise, the time required for the material to cool depends on the characteristics of the cooling lines designed into the mold and the thickness of the molded material (part size and geometry).

Plastic injection molding manufacturers typically measure mold cycle time after the fact. This is of little help in supporting operational improvements. Best Ways to Determine Cycle Time While a base (theoretical) cycle time is designed into the process and mold, the operator and operating conditions will have some influence on the actual cycle time. Most companies only measure mold cycle time indirectly after the fact. This is often determined simply by dividing the total time required to produce a series of products by the number of parts (cycles) produced. While this may be useful for accounting and engineering purposes, this information is of little help in supporting work management and operational improvements.

The best option is to measure, analyze, and display cycle time continuously during a given shift. In this way, management can keep an eye on the actual performance of the molder’s central resources and ensure that the facility is operating at full capacity in real time.

The design of the mold and the process itself determines the theoretical operating cycle time. This can be considered the best or optimal overall cycle. Of course, while running below the theoretical optimum is less productive and inefficient, running above the design cycle is not necessarily a good thing either. For example, if the cycle time should be 11 seconds, but the job runs at 10 seconds, the product coming off the machine may look fine, but could have hidden defects that cause it to fail downstream quality checks. In this case, continuous cycle time monitoring would trigger instant alerts to quickly check product quality and take corrective action to prevent the production of more soon-to-be scrapped parts.

Every second counts. . . Really
Operator actions, equipment condition and maintenance, and environmental conditions all affect mold cycle time. Early detection of any inhibitors allows for a quick response to corrections and increased output.

A well-maintained machine will be faster and more reliable, resulting in more consistent performance. Likewise, the operator’s experience and expertise can have a significant impact on the mold cycle and productivity of the machine, and ultimately the factory. There must be a system in place to continually track historical cycle times, manage maintenance schedules, and determine what the cycle time for each machine should be to improve overall productivity and profitability. More importantly, the data from such a system allows for better planning and scheduling.

Let’s assume a mold is designed with a cycle time of 10 seconds. To maintain his/her duties and produce good parts, an inexperienced operator might run the machine at an 11-second cycle. You might be wondering: What difference does one second really make? An extra second of cycle time means a 10% reduction in productivity. The cumulative effect means it takes 10% more time to complete a given job and the overall output of the machine per shift is 10% less. Returning the machine to design speed would yield an 11% improvement over the reduced speed (divide 9 seconds by 10 to get the percentage improvement), while increasing efficiency, productivity, and reducing costs.

Optimizing Machine Selection
Tracking mold performance (cycle time) by machine also provides an opportunity to better manage the factory and achieve optimal output. A given mold may run effectively at a 10-second cycle on one machine, but 8 seconds on another.

All other things being equal, it may be more advantageous to run the job on the second machine. Considering the jobs to be processed and their specific run rates, color variations, priorities, maintenance schedules, operator availability, and other factors, alternate assignments may be beneficial. Without good performance information for the mold, machine, and operator, these decisions are difficult at best.

A relatively small investment in data collection can significantly improve production efficiency.

Return on Investment Another measurement opportunity exists with constant checking of cycle time compared to packaging efficiency. Automated shop floor input from the machine, directly compared to barcode scans of the items being packaged, can keep a running count of job progress and quality (rejects). This provides continuous real-time data for planning when the current job can be shipped and when the next job can start. In addition, this provides the added benefit of more precise tracking of raw material usage.

In most cases, simple machine monitoring sensors integrated or retrofitted into machine controls can be used to provide data collection and analysis capabilities in planning and management systems. A relatively small investment in data collection provides input that produces better scheduling, more efficient operations and factory management, and overall efficiency gains.

Understand the warning signs
Indicators are all around us. That console light lets us know when the car needs service. The smoke alarm chirps when the battery is low. The bank may send a text message when the funds are exhausted.

While there are no flashing “low productivity” alarms for manufacturers, there are clear signs of underperformance and trouble if you know where to look and how to prioritize their importance. For plastics processors, mold cycle time is the most critical measure of performance.