Still chasing the dream to achieving optimum manufacturing flow?
We have arrived at the 4th and final installment of the series of achieving optimum manufacturing flow.
The previous posts covered Process Engineering, production planning (Scheduling) and material management in the factory and how we can optimize each of the tasks?
In this blog post, we focus on SMT and the shopfloor.
Bottleneck management in SMT
Overall Equipment Effectiveness (OEE) is a metric that includes throughput, availability and quality. It is a metric that allows organizations to view performance of assets in the factory.
- Availability = operating time/planned production time
- Throughput (run rate) = (total pieces/operating time) / planned cycle time
- Quality = TPY = (good pieces / total pieces)
Based on an Aberdeen Group study, top performing organizations are focused on OEE. Although one would think that improvement in performance might come with increased cost, the information in Table 1 shows that for the top 20% they are able to reduce annual maintenance cost by 8% and overachieve on their Return on Asset (RoA) by 11%. It is clear that in order to realize these benefits, software tools such as Mentor’s MSS suite and optimized processes need to be employed.
Since statistically over 80% of components are placed with automated SMT equipment, we move our focus on those specific assets. There are 3 main elements to optimizing process performance in SMT equipment, and ensuring high OEE:
1) Ensuring maximum availability of assets by minimizing downtime
2) Ensuring high throughput by ensuring optimal machine efficiency
3) Ensuring high quality by monitoring placement quality real-time.
Downtime at an SMT machine typically occurs due to material shortage. It is not common that SMT equipment is down due to mechanical failures. One of the most painful bottlenecks in production lines is parts exhaust or parts out. What happens then? Typically the SMT Machine alerts the operator, then operator runs to a sub-store or stock area, pulls the material (or worse orders the material and waits), while the machine sits idle. A software system that has a SCADA level direct interface to the SMT equipment, which is monitoring every placement and ensuring adequate parts are available in real-time can also monitor when material will be exhausted from a feeder.
This predictive functionality like what is available in Mentor’s MSS vManage product, would alert the line operator that they must be ready with kitted material, so as to minimize the downtime of the machine. Having a real-time predictive parts exhaust monitor, and regular preventative maintenance programs should ensure high SMT uptime.
Throughput and machine efficiency should also be monitored to ensure that production rates are monitored against planned rates. Production schedules are based on planned rates of production through the assets. If the planned rates are unrealistically lower, then production schedules will always be behind. The key to knowing is having visibility to the actual values. Actual production rates, many times calculated with the CPH (components per hour) value, should be visible using a real-time production monitoring system.
A production monitoring system will allow identification of throughput values, target vs. actual analysis, and alert related production personnel when the rates are unacceptably low. Typically excessive machine errors due to intermittent component picks lengthen total placement time of a specific board, adversely affecting throughput. Nozzle errors due to vacuum errors or unclean nozzles are one of the causes. Placement identification errors are another cause of a reduction in throughput. This is usually on NPI runs, or production part replacement with alternate parts defined in the BOM (bill of materials). When an alternate part is used, there is risk that a different component vendor may be used which provides the same functionality chip. However, that chip may physically be a little different in height, lead-length or body width. AVL Validation tools available in Mentor’s vPlan application can remove this risk.
Finally, ensuring that the quality of the placements is monitored in real-time so excessive pick-rejects are caught before too many parts are rejected. This would allow line operators to stop a machine before component attrition gets too high, further adding to production costs, and troubleshoot the reason for the high errors.
We have ensured high availability and we have analyzed how to ensure good throughput. Now we must monitor quality. Ensuring high quality is done by integrating real-time quality statistics to the OEE metric. Typically an in-process quality step is added at the end of the SMT line to collect this information. This can be a visual inspection station where a quality inspector is reviewing each board, or an automated inspection system such as an AOI (automated optical inspection) machine. The Throughput Yield (TPY), also called First Pass Yield, is collected. A simple approach to TPY can be employed by calculating the number of acceptable units divided by the total number of units produced thus far. In the real-time calculation reworked units are normally not considered, but in the final TPY calculation, they should. Mentor’s vCheck system performs real-time calculations of TPY and can feed this information to an OEE monitor in real-time, thus providing the organization a true OEE in a real-time dashboard. With this visibility of the OEE metric, production and quality engineers are better equipped to analyze and act to optimize process performance.
On our journey through production planning and scheduling, process engineering, material management and into the SMT area of the shop floor, we’ve seen how systems can dramatically affect manufacturing flow. Optimizing material flow reduces non-value added tasks, reduces potential areas of production bottlenecks and stoppages, increases the utilization and efficiency of assets, and helps schedule more efficiently with real-time production information, while ensuring material is available where it is needed, when it is needed. Addressing these areas and bottlenecks can bring you increased manufacturing velocity, leading to an optimized manufacturing factory.
Still think achieving optimum manufacturing flow is a dream?
Looking forward to your comments either here or in our Community site!