How to Improve Quality Assurance Processes

I scanned 2.8 GB of QMS documents in a week. Then, shared this uncomfortable truth with the QA director: More documentation ≠ a better quality management system. Most companies drown in paperwork that doesn’t actually improve quality. Want to fix that? Start here: 1️⃣ Kill redundant SOPs. Do you really need three SOPs saying the same thing Nope. Review processes and cut the fluff. 2️⃣ Make SOPs usable. Stop writing mini-novels. Focus on clarity and actionability—less “legalese,” more “get it done.” 3️⃣ Use risk-based thinking. Not everything needs a procedure. Focus on the high-impact stuff that actually matters. 4️⃣ Streamline approvals. Empower the right people to finalize documents. Endless review cycles = wasted time. 5️⃣ Go digital. Why deal with stacks of paper when workflows can live in a streamlined system? Go for a lightweight eQMS and automate what you can. The goal isn’t more documents—it’s lean, effective processes that drive compliance AND improvement. QA people have a great opportunity here to be creative, which is quite underrated. What would you add?

One of the most impactful changes I've seen in quality happens when you implement one specific process: a 30-minute QA-Dev sync meeting for each feature before coding begins to discuss the implementation and testing strategy. When I first bring this up with a client, I get predictable objections: Developers don’t want to "waste" their time. Leadership doesn’t want to "lose" development time. Testing is necessary anyway, so why discuss it? Our QA doesn’t couldn't possibly understand code. The reality is that the impact of effective testing can be remarkably hard for an organization to see. When it goes smoothly, nothing happens — no fires to put out, no production issues. As a result, meetings like this can be difficult for leadership to measure or justify with a clear metric. What confuses me personally is why most engineering leaders say they understand the testing pyramid, yet they often break it in two, essentially creating two separate pyramids. Instead, you should have a collaborative session where QA and Dev discuss the entire testing pyramid — from unit tests to integration and end-to-end tests — to ensure comprehensive and efficient coverage. Talking through what constitutes effective unit and integration tests dramatically affects manual and end-to-end testing. Additionally, I'm continually impressed by how a QA who doesn’t "understand" full-stack development can still call out issues like missing validations, test cases, and edge cases in a method. QA/Devs should also evaluate whether any refactoring is needed, identify potential impacts on existing functionality, and clarify ambiguous requirements early. The outcome is a clear test plan, agreement on automated and manual checks, and a shared understanding that reduces late-stage bugs and improves overall product quality. #quality #testing #software

DMAIC is a 5-step approach for making improvements. One of the steps most commonly skipped over steps is... Control. Control ensures the improvements will last in the long run, and not revert back to the "old way." Too often, teams are seeing improvements in their project... but ready to move on, usually to start a new project. If you don't implement the Control phase effectively, the problems might come back, and the team will be frustrated, and leadership will (rightfully) question if the project was even a success. Here are the activities I recommend in this phase, in order to ensure long-term sustainment of your improvements: ✅ Create a system and/or process to monitor the results using charts and graphs ✅ Update any documentation of the processes (work instructions and procedures) ✅ Update any training of the processes (for current and future staff) ✅ Create a response or mitigation plan in case there is a drop in performance ✅ Add any visual controls or job aids that can be posted in the work area ✅ Review incentives to encourage new improvements to be used or followed ✅ Identify contact names and resources if employee has questions about the new improvements ✅ Schedule process audits or reviews for the next 30, 60 and 90 days ✅ Ensure improvements have been labeled, have a clear storage space, and have someone assigned to reorder them or perform maintenance on them (if applicable) ✅ Ensure clear ownership of the improvements have taken place by the process owners Not all of these will be applicable to your project, but if you implement a few of these, you're sustainment and chance of long-term success will greatly improve! If you'd like to learn more, check out Episode 74 of my podcast, where I go into details on each of these recommendations. https://lnkd.in/ex-XqTzT

If all you do is sort 'Good' from 'Bad' you will continue making "Good & Bad." Something more is needed. Your Red-Green Dashboard May Be Limiting You. When a retail buyer, a hospital unit manager, or an IT operations lead reacts to every “red” number, they are practicing what Donald Wheeler calls “judging outcomes.” The attached table, "Two Interpretations of Variation" provides effective alternative perspectives, avoiding the judgment instincts that so often backfire. “Judging Outcomes” is a low-yield strategy. Retail – Daily “shrink” numbers jump from 0.8 % to 1.3 %. The loss-prevention team fires off emails, yet the spike is just common-cause noise in store traffic. Over-reaction wastes labor and morale. Healthcare – A surgical ward toggles between “green” and “yellow” on its “Falls Dashboard.” Each color change triggers new in-service training, annoying nurses while masking a single special-cause event (a new floor wax). Software Ops – Error counts breach a budgeted limit of 100 per week. Executives demand weekend code freezes, delaying vital updates. A control chart would have shown the system is stable—and that real improvement requires design changes, not heroics. What “Improving the Process” Looks Like. ☑️ Plot a process behavior chart (e.g., X-bar & mR chart) for the last 20–30 data points. ☑️ Ask Wheeler’s three questions: ❓ Is the process predictable [i.e. shows only common cause variation]? ❓ If not, which signals point to special causes? ❓ If stable, is the level of performance good enough for the customer? ☑️ Act on causes, not outcomes. In an automotive paint shop, a single point beyond the upper control limit led to a search for the special cause: a clogged nozzle. One fix prevented thousands of defects. ☑️ Embed learning. Deming’s PDSA cycles turn each signal into a learn-then-improve experiment, building knowledge that survives staff turnover. For Leaders ➡️ Red-green scorecards answer yesterday’s question; control charts answer tomorrow’s. ➡️ Treat every data point as a story about the system, not a grade for the people. ➡️ Move away from judging outcomes to seeking process insights thus converting wasted fire-fighting energy into lasting system improvements 

Process variability can often go unnoticed... (until it impacts quality and compliance) This is why your validation strategy needs to be data-driven 🖥️ In a world where FDA expectations are clear on a science- and data-driven approach, validation strategies need more than manual checks. That’s why building a validation strategy rooted and grounded in data is no longer optional; it’s essential. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆 𝗱𝗮𝘁𝗮 𝗶𝘀 𝘁𝗵𝗲 𝗸𝗲𝘆: 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 & 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗩𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: ☑️ No waiting for audits or surprises. ☑️ Data helps us catch even the smallest variabilities. ☑️ Real-time monitoring offers a clear view of process stability. ☑️ It reveals shifts in storage conditions or equipment performance. ☑️ Imagine being alerted when a unit drifts by even a fraction of a degree. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗳𝗼𝗿 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: ☑️ It gives companies a proactive edge. ☑️ Data integration in validation creates this. ☑️ The FDA expects a science-based, preventive approach. ☑️ This means they can fine-tune storage, processing, and equipment use for consistency. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁 🌍 In a recent project, a biotech company partnered with a client to monitor ultra-low freezers at each stage of storage. They found subtle inconsistencies that could have affected sample integrity. But with 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗱𝗮𝘁𝗮 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴, they could quickly adjust, meeting stringent compliance requirements and safeguarding product quality. 𝗪𝗜𝗡! Implementing a data-driven validation process can be the difference between catching an issue in real-time and facing costly product recalls or compliance setbacks. 💬 What steps are you taking to make your validation strategy more data-driven? #TemperatureMapping #Validation #QualityAssurance #LifeSciences #DataDriven #Compliance #GLP #GMP #FDA #Ellab

The best systems need the least management. Yet we keep adding steps, checkpoints, and approvals. I used to believe great companies were built on comprehensive processes. My first startup had detailed procedures for everything — each sales interaction, support ticket, and feature release followed a precise playbook. As we scaled, our process documentation grew faster than our revenue. Team velocity slowed. Innovation suffered. Talented people spent more time following protocols than solving problems. The turning point came when we rebuilt our approach around outcomes instead of activities: 1️⃣ We replaced activity metrics ("number of calls made") with outcome metrics ("deals progressed") 2️⃣ We stopped documenting how tasks should be done and started defining what success looked like 3️⃣ We built automated guardrails instead of manual checkpoints 4️⃣ We focused quality control on system inputs and outputs, not every step in between The results were transformative. Teams moved faster. Quality improved. People stayed energized. Business process exists to manage risk and ensure quality—both valid concerns. But most companies implement these controls at the tactical level when they belong at the systems level. Think of it like this: You can micromanage a road trip by dictating every turn, or you can set a destination, provide a reliable vehicle with good brakes, and trust the driver to navigate. The difference is critical. Tactical processes control behaviors while systems-level thinking shapes environments. Some practical shifts to consider: 1️⃣ Replace decision chains with clear boundaries and after-action reviews 2️⃣ Substitute detailed instructions with clear success criteria 3️⃣ Trade activity monitoring for outcome measurement 4️⃣ Swap manual checks for automated testing 5️⃣ Replace rigid workflows with principles and guardrails Design systems that make quality inevitable, not processes that make errors impossible. Operational excellence is fundamentally about outcome clarity, not process quantity. #startups #founders #growth #ai

“It’s just one bad test result.” Words that have sunk more batches than contamination ever did. ⸻ Friday night. The production line was running at full capacity, with mounting pressure to release orders on time, then: QC testing FAILED. The supervisor says “hold the batch”. The manager says “rerun it”. Retest passes. Relief. Monday shipment. Three months later: same failure. This time in a distributed lot. Recall. Investigation. Devastation. ⸻ 📜 Regulatory reality FDA’s Out-of-Specification (OOS) guidance, EU GMP Chapter 6, and MHRA’s OOS/OOT framework, all say the same: 🔹Investigate first. Invalidate only with proof. Retests, resampling, and averaging aren’t shortcuts, they’re controlled exceptions. And yet… citations under 21 CFR 211.192 keep piling up. ⸻ 5 phrases that kill OOS integrity, and what to say instead: 1️⃣ Don’t say: “Let’s retest and see.” Do say: “We’ll investigate the original result first.” 🎯 Without a hypothesis, retesting is testing into compliance, a top FDA observation. 2️⃣ Don’t say: “We can average this with other results.” Do say: “We’ll report every individual result to QA/QP.” 🎯 Averaging can bury a fail, and your credibility. 3️⃣ Don’t say: “It’s just an outlier.” Do say: “We’ll use outlier analysis only with documented cause.” 🎯 Stats can’t erase valid data. Regulators want evidence, not probability. 4️⃣ Don’t say: “OOT is still in spec, so no action.” Do say: “We’ll investigate OOTs as early warnings.” 🎯 EMA/MHRA see OOT trends as prevention, not paperwork. 5️⃣ Don’t say: “The rest of the data is fine, release it.” Do say: “We’ll hold release until investigation is complete.” 🎯 Every regulator forbids release with unresolved OOS. ⸻ Bottom line: An OOS isn’t a nuisance. It’s a flare in the night , a warning that demands light, not a blanket. Your turn: What’s the smallest OOS you’ve seen become the biggest problem? ⸻ ♻️ Repost to help your teams stay ahead. 📬 Subscribe to The Beacon Brief — monthly, free: https://lnkd.in/gNXeXDzH

If I were starting a new role as a Process Engineer, here's exactly what I’d do to understand the flow: 🔍 1. Walk the process — and don’t rush. I’d spend hours just watching. No stopwatch. No clipboard. Just observe. Patterns reveal themselves when you’re patient. 👂 2. Talk to the operators — and really listen. I'd ask: “What’s the most annoying part of your day?” “What slows you down?” “What do you wish was different?” And then I’d shut up and listen. ❓ 3. Ask tons of questions. Even the obvious ones. Especially the obvious ones. “Why do we do it this way?” “What happens if we skip this step?” Curiosity uncovers the real process—not the one on the SOP. 📷 4. Sketch the flow. No fancy software. Just a pen and paper. I'd map what I see, not what’s supposed to happen. Because often, those two aren’t the same. 🧩 5. Connect the dots — from raw material to finished product. I’d try to understand: Where are the delays? Where’s the rework? Where are people waiting on machines — or worse, machines waiting on people? Why bother doing all this? 📌 Because if I jump to solutions too fast, I’ll miss the root cause. 📌 If I rely on reports, I’ll miss the reality. And if I don’t listen to the people in it every day, I’ll never earn their trust. Understanding the flow takes time. But that’s how you find the friction. And friction is where real improvement begins. ✨ DM me if you’re stuck in a messy flow and want a second pair of eyes. Let’s find the friction — and fix it. #ProcessEngineering #ContinuousImprovement #processflowoptimization

🚨 Attention Long-Term Care Administrators! 🏥📊 Let's talk QAPI (Quality Assurance & Performance Improvement)! 🧐 These two are often mixed up, but they're NOT the same. Here’s what you need to know: 🔑 Key Points for Success in QAPI: 1️⃣ PIPs (Performance Improvement Plans): Your facility should have 3 to 5 PIPs max! 📝 Not 8, not 10—3 to 5 is the sweet spot! Too many can overwhelm your team. 2️⃣ Root Cause Analysis: Always dig deep into the root cause of issues! Use tools like Fishbone diagrams or Five Whys to get to the core. 🔍 3️⃣ PIP Timeline: PIPs should NOT last forever. Aim for 3 months max to fix the issue and remove the PIP from your QAPI plan. 4️⃣ Measure Effectiveness: Every month, check if you're seeing improvement. No trend? Time to revisit your interventions. 💡 5️⃣ Be Proactive, Not Reactive: Focus on issues that lead to citations, like falls, abuse, or missed contracts! 📑 Remember, even in a 5-star facility, there’s always room for improvement! 🌱 Use your CASPER report or do a walk-through to identify areas that need attention. QAPI isn’t just paperwork—it’s about making lasting, positive changes in care. 💪 👉 Need help with your QAPI process? Reach out—I’m here to support you every step of the way! 🙌 https://www.rfr.bz/l614d7a