John Martz — New Contributor

When facing yet another field issue with a high price tag, my Chief Technical Officer asked me, “How do we get more predictive so we can identify and prevent these failures from occurring in the future?”  Similarly, I had a friend who was trying to optimize a key customer feature of a future product.  He ran robustness experimentation considering over 40 noise and control factors that the team had brainstormed.  And yet, when field trials started, the device had several failures of unknown cause.  Of the more than 40 factors that they had considered during brainstorming, they missed the noise factor that was triggering these failures.  I’ll turn to you and ask the same question:  How do we get better at predicting future failures and preventing them from occurring?  If we had infinite knowledge, we could see these failures before they occurred.

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We can learn more from our failures than we can from our successes.  This isn’t just a good rule for us to live by personally but is applicable to the products that we design and manufacture.  In my experience, the all-important step that is short-changed in “learning from our failures” is “learning”—not just getting to the root cause in order to solve the problem but to understanding the gaps that exist in our current design and manufacturing requirements and practices that allowed the failure to spill into the field.   Identifying these gaps is the key to future failure prevention.  In this installment, I’m going to highlight how Weibull Analysis (a reliability tool when used in the time domain) of module field failures can be used to identify the holes in our design or manufacturing requirements or processes.

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Robustness:  That Often Overlooked Yet Powerful Discipline and Toolset.  Most companies have a Quality organization, and some have a Reliability organization.  But rarely will you find a company with a Robustness organization or even a single Robustness Engineer.  And yet, of these three disciplines, Robustness can have the largest impact on your customer satisfaction because Robustness as a discipline and toolset addresses:  

• a wider range of field issues than reliability and quality
• those issues that occur during the “design life”

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Fear is that hidden factor present in every organization, preventing leadership from achieving the growth they desire.  In the 2 previous articles, I brought up the types of employee fears and how that fear can negatively impact a given organization.  Additionally, I brought up the importance of assessing these fears and understanding how they’re specifically affecting your organization.  In this third and final article in this series, I’ll explore what leadership can do to mitigate these fears towards cultivating a culture of trust and empowerment.

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In the previous article, I shared how employee fears can negatively influence the work culture of your organization, stifling innovation, hampering collaboration, limiting growth and preventing continuous improvement.  In this, the second article of a 3-part series, I’ll share the elements of an effective tool to evaluate your organization for employee fears and the impact of those fears.

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In any organization, effective leadership is crucial to driving success and achieving goals. However, one often overlooked aspect of leadership is the role fear plays in an organization. Fear and its negative effects are present in every organization but rarely identified or measured. And when employees are afraid, they are less likely to take risks, share ideas, or give clear, honest assessments of risk. In this first article of a 3-part series, I’ll share the general effects that fear can have on your organization.  And in the follow-up articles, I’ll share what can be done to identify, measure and mitigate these fears.  In the end, employee fears stifles innovation, hampers collaboration, limits growth and prevents continuous improvement as detailed below.

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The first article in The Power of Design of Experiments Article Series

In the world of research and development, time and money are precious commodities.  DOE is the one statistical tool to save both time and money through its efficiency and efficacy.  By carefully planning and executing experiments, DOE not only saves time and money but also leads to optimized designs and improved outcomes.

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