ARTICLES BY BEN LOCWIN

• Advanced Computing In Pharma & Medtech: How Cognitive Biases Can Cost You Millions

  In their race to leverage sophisticated computing technologies (particularly artificial intelligence, robotics, augmented/virtual reality, and IoT), life science companies face a hidden, insidious challenge — avoiding the cognitive biases that can cause them to unwittingly exacerbate already business-threatening cyber risks.

• FDA’s Drug Shortages Root Cause Report: Does It Miss The Mark?

  At the behest of Congress, an inter-agency Drug Shortage Task Force, led by the FDA, published the "Drug Shortages: Root Causes and Potential Solutions" report. The very first root causes listed in the report are “economic forces,” but there's a lot more to the story.

• Use These 7 Tools For Breakthrough Quality And Performance In 30 Days

  In this sixth installment on the selection and use of quality improvement tools in your organization, Ben Locwin wraps up the series with a treatise on the 7 Tools of Quality.

• “Work Harder” — And Other Ways To Completely Miss The Mark On Quality

  Why does “Quality” seem like such a slippery, elusive term? The answer is easy: It’s because the people who claim to know it really don’t. And everyone seems to go along with the rhetoric.

• Solving Problems More Effectively Than Sherlock Holmes: The Contradiction Matrix

  This is the fourth article in a five-part series on better investigation and problem-solving methods and principles in the life sciences. In writing this one, I’ve been thinking quite a bit about Sherlock Holmes. Not only his exquisite methods, but also flaws in the metacognition and metaphilosophy about how the fictitious detective underwent his work.

• Fault Tree Analysis: Uncovering The Root Causes Of More Complex Problems

  A refreshed approach to fault tree analysis can be an incredibly helpful addition to your problem-solving armamentarium. Having the confidence to begin using it — or begin using it more often — is the first step toward getting more comfortable with it and mastering its effects and nuances.

• When To Use A Fishbone Diagram … And Why You Should Do It More Often Than You Think

  This article provides an in-depth and comprehensive explation one of the most broadly applicable and durable root cause analysis tools to investigate the quality of your manufacturing processes: the fishbone diagram.

• How To (Better) Identify And Analyze Manufacturing Trends In Your GxP Organization

  This is the first in a five-part article series, Identifying And Resolving Errors, Defects, And Problems Within Your Organization. This article will enhance your understanding and prime you for visual detection of real trends in your organization by looking at a couple of examples.

• Not Just Another Article About Pharma Employees At Work: What’s Your Differentiator?

  How often do you hear pharma companies (your company?) pride themselves with trite phrases like “our people are our differentiating factor.” Sounds good. Yet the reality in the trenches is far from this panacea — most industry talent reports refer to disgruntled staff who are poorly managed and organized into factions that are out for their own discrete success.

• Big Data Vs. Small Data: What’s The Proper Prescription For You?

  What can playoff beards, infographics, and emojis teach us about Big Data's role in the biopharmaceutical industry? This article shares some amusing examples to explain what Big Data and small data are, build your fluency with the benefits and risks of each, and provide recommendations to improve our data-rich future.

• “Human Error” Deviations: How You Can Stop Creating (Most Of) Them

  In the time it takes you to read this article, the industry will generate (conservatively) about 115 new process deviations, and about 65 of them will be misclassified as having something related to “human error” as either the principal causal factor or one of the proximal causal factors.

• I Don’t Think The Word “Quality” Means What You Think It Means

  The word “quality” is bandied about with reckless abandon across the healthcare, clinical practice, pharmaceutical, and biotech industries. Everyone thinks they understand quality at a deep level, just like everyone thinks they are “better than average” drivers.