The Money You Can Save, Just by Looking at "Suspect" Cycle Times

Updated: Nov 19, 2020

Aberrant Cycle Times, Process Changes, Part Quality, and Costs

There is simply immense – yet often understated or outright unrecognized (and thus untapped) -- potential for cost-savings inherent in aberrant cycle time patterns. Where cycle times deviate from optimal parameters, factory-floor operators could be employing atypical production processes, which could harm part quality.

Defective, “scrapped” parts aren’t just a waste of labor and material costs, they’re a waste of mold life and investment. It is because of these sheer cost-inefficiencies that we at eMoldino analyze cycle times; we want to pinpoint when quality-affecting process changes occur, and how often. This lets us show OEMs the extent of their wasted production costs.

“Suspect Cycle Times” Might Betray Undesirable Process Changes

When we’re talking part quality, we’re usually talking consistency. Consistent quality is ensured if the processes used for manufacturing are consistent – and the most surefire signs of consistent processes are stable, well-controlled cycle times, mold temperature, pressure, and other such variables, each ideally kept within their “optimal parameters”.

So if cycle times are inconsistent, or run beyond their ideal parameters? This might be an indication of unexplained and unjustified process changes, which are sure to result in defective, scrapped parts.

Thus, it’s imperative for OEMs and suppliers to find out when their cycle times have deviated too far from their optimal parameters. Once these “suspect” cycle time periods have been located, quality control personnel can concentrate their efforts on inspecting the parts that were produced during those suspect periods (as those parts have the highest chance of being defective).

Furthermore, in our experience, the range of these “suspect periods of production” can sometimes turn out to be enormous. For example: if, during a 12-month period, 40% of a mold’s recorded cycle times were found to have deviated from stable parameters, and some 100,000 shots were tracked during the same 6-month period, that would be a minimum of 40,000 parts that are “suspect”. This figure multiplies depending on the number of active cavities in the mold; a mold with four cavities will have 160,000 suspect parts; sixteen cavities 640,000, and so on.

80,000 potentially defective, scrap parts. Multiply that with the underlying labor and material costs of making just one part alongside the number of active cavities, and you can start to see just how large the potential is for wasted spending. The implications for cost-optimizations are staggering.

This is how we visualize cost-optimization opportunities for each of our clients. We monitor cycle times for any deviances, which indicate process changes, which estimate defective quality parts, which ultimately demonstrate the extent of the cost-inefficiencies. Suspect-quality parts need to be swiftly inspected and processes better-managed in order to lower the potential for spending waste. Our management/monitoring system provides these applications – a system of automated alerts and real-time KPIs that monitor for “suspect” activity and alert all relevant stakeholders when they occur.


With all that said, suspect cycle times on their own don’t necessarily equal process changes. Cycle time changes could be explained by different machines requiring different cycle times for optimum output; perhaps those bizarre cycle times you’re seeing are due to harmless blank shots or warm-up shots.

Cycle time analysis shows periods where process changes might have happened; thus, cycle time analysis shows a range of parts whose quality could have been affected. The emphasis is on probability; deviant cycle times indicate a higher chance of poor quality, but the association isn’t necessarily absolute.

This isn’t to say that cycle time analysis is entirely useless! As explained above, cycle times can viscerally illustrate the sheer amount of potentially wasted costs, while directing QA/QC inspections to the exact time and location where “suspect parts” were produced. As a means of overall visualization and concentrating resources for quality control, cycle times are still useful… but admittedly, the specificity of cycle time-based estimates leaves something to be desired.

Triangulating Defective Parts via Temperature & Pressure

So cycle times on their own can’t actually tell if processes have changed, and accordingly, if suspect parts are actually defective. At best, cycle times can provide rough estimates of suspect production. How then does one go about specifying these estimates?

Cycle times aren’t the only indicators of process changes. Temperature, pressure, injection speeds – all of these are affected by processes. At eMoldino, we make it a point to use not just cycle times, but a combination of cycle times, mold temperature, and injection pressure to pinpoint exactly when significant process changes have occurred and triangulate defective parts production.

The cloud-based system we service to OEMs and suppliers gathers all relevant datasets – cycle times, temperature, injection times – and compares them with their optimal parameters. Through AI and machine learning capabilities, we try to calculate reliable, real-time scrap rate estimates that managers and engineers can use to actively benchmark performance.

A clear, real-time scrap rate helps our clients quantify their performance and cost-savings. If one lowers scrap rate, for instance, then costs have been saved by the amount of parts that weren’t scrapped. Another way of looking at cost optimizations is in terms of maximizing mold lifespan (and the investment that went with it). Assuming the average mold costs $600,000 to create, a 1% reduction of the mold’s reject rate throughout its entire lifespan would equate to $6,000 of saved investment.

Real Cost-Savings through Real-Time Quality

eMoldino aims to provide these cost savings opportunities through our real-time management/monitoring system. Our whole solution revolves around tracking cycle times, temperature, and injection times straight from the molds using our IoT hardware devices. Our analytics platform is how we estimate our clients’ live scrap rates; the system is where we associate potential quality deficiencies with costs, and alert OEMs and suppliers alike of activity that could potentially harm anyone’s bottom line.

Interested in getting to know more about our system’s quality modules? So too were the companies undertaking our free-of-charge pilot program. See for yourself your production data coming in in real-time; the degree of your “suspect” activity; and the massive opportunity for cost savings that had thus far been hidden behind the lack of real-time visibility.

Enjoy the fruits of our dedicated, IoT-enabled B2B manufacturing and supply chain solution, completely free, on a limited basis, after asking about our pilot program. Schedule a time for our experts to call you; feel free to ask in-person about the lessons we’ve learned and our deliverable use cases.

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