Add examples of common manufacturing bottlenecks

[programmablemanufacturing]

Many manufacturing AI projects start with broad goals such as “improve quality,” “reduce cost,” or “increase throughput.”

This issue is for collecting common bottleneck types that can help translate broad pain points into more structured manufacturing problems.

Possible categories include:

• scrap
• yield loss
• defect formation
• process drift
• long qualification cycles
• slow parameter tuning
• inspection bottlenecks
• inconsistent operator decisions
• machine-to-machine variation
• material batch variability

Useful contributions could include:

• additional bottleneck categories
• short public or generalized examples
• suggested grouping of bottlenecks by quality, cost, throughput, or qualification
• comments on which bottlenecks are most common in different manufacturing domains