Process Optimization Tactics

Treating Quality as something you tolerate is a mindset that often leads to overlooked margin improvement opportunities. Here’s how we turn Quality into an engine for margin, speed, knowledge, and value, without adding bureaucracy or unnecessary processes. 1) Skip-Lot Testing -Write the math: tie sampling frequency to risk (severity × occurrence × detection). -Earn the skip: only suppliers with clean history + validated methods get reduced sampling. -Automate the gate: if COA/spec/lot history meet criteria→ release; if not → hold and escalate. -Result: 20–40% test spend reduction while maintaining (or improving) release confidence. Speed improvements are generally proportional to cost reduction in this specific area. 2) Supplier Stratification (treat A like A, C like C) -Tier by evidence: A/B/C based on capability, audit outcomes, complaint rate, and change control discipline. -Align controls: A-tier = lighter incoming checks + longer review cadence. C-tier = tighter sampling + more frequent system reviews. -Price the risk: negotiate with data like chargebacks, deviations, change management, prevention/appraisal costs, etc. -Result: Fewer surprises, faster throughput, better pricing with the vendors who deserve it, and a happier team. 3) Evidence-Driven Claims (marketing that survives discovery and diligence) -Start with the file: substantiation matrix mapping each claim → source (study, spec, method, dose). -Dose matters: an ingredient study ≠ your formula. If your dose/form changes, the claim changes. -Tier the risk: green (low), yellow (moderate with qualified language), red (don’t touch). -Result: Lower legal exposure, cleaner copy approvals, and no $50k “learning experiences.” 4) Release Authority Lives in Quality (and nowhere else) -Quality dispositions; Supply Chain assembles the package. Decentralizes process command is your best friend. -Exceptions ≠ process: deviations trigger CAPA, not folklore or an easily spooked manager. -Result: Predictable cycle times and fewer “heroics” to get product out the door. 5) Measure Prevention, Not Drama -KPIs that matter: first-pass release rate, days-to-disposition, right-first-time docs, effective CAPA closure, supplier tier migration. -Cost of Quality: track prevention + appraisal vs internal/external failures. Spend where you can save money. -Result: P&L improvements and a happier executive team. It's not easy to set up these systems, but with the right leaders in the right places, you can build a culture of quality inside a team that only moves fast. We've helped brands and manufacturers of all shapes and sizes de-risk their businesses simply by eliminating unnecessary work, decentralizing Quality management, right-sizing teams, and supporting quick decision-making. If you want to learn how to begin making this transformation happen in your own business, you know where to find us.

𝗛𝗼𝘄 𝘁𝗼 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗪𝗮𝘀𝘁𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 I often hear leaders say, "We need to optimize our workflow with digital tools." But here's what usually happens: They buy a fancy new tool. Spend weeks setting it up. Train the team. And then... Nothing changes. Why? Because they didn't solve the real problem. Here's how to actually optimize your workflow: 1. Map out your current process What steps do you take? Where are the bottlenecks? What takes the most time? 2. Identify the root causes Is it a people problem? A process problem? Or a technology problem? 3. Set clear goals What does "optimized" look like? How will you measure success? 4. Choose the right tool Look for one that solves your specific problems Not just the one with the coolest features 5. Implement in phases Start small Get quick wins Build momentum 6. Measure and adjust Track your progress Be ready to change course if needed I've seen teams cut their workflow time in half using this approach. Without spending a fortune on new tech. The key? Focus on the problem, not the solution. What's holding your team back from peak efficiency?

Most logistics consultants skip this step when optimizing small parcel services. It's the reason your ops are stuck at 80% efficiency.👇 Here's the truth: data is king in logistics optimization. But not just any data. The right data. The step most consultants miss? Comprehensive carrier performance analysis. They focus on rates, but ignore: - Actual transit times vs. promised - Damage rates by route and carrier - Exception handling efficiency - Claims resolution speed Without this intel, you're flying blind. Your optimization efforts hit a ceiling. You can't improve what you don't measure. How to fix it: 1. Implement detailed tracking for every shipment 2. Analyze patterns over 3-6 months 3. Identify weak points in your carrier mix 4. Negotiate based on real performance, not just rates 5. Continuously monitor and adjust Result? Happier customers, lower costs, smoother operations. The difference between good and great logistics is hidden in the details most overlook. Master these details, and watch your logistics transform. Optimize smarter, not harder. #LogisticsOptimization #DataDriven #CarrierPerformance #EfficiencyBoost #SupplyChainManagement #ParcelDelivery #OperationalExcellence #PerformanceAnalysis #ShipmentTracking #ContinuousImprovement

Operational Excellence: 2025 Strategies for Manufacturing Leaders Manufacturing leaders aiming for transformative 2025 goals must integrate advanced methodologies like Predetermined Motion Time Systems (PMTS) and industrial engineering principles. These proven frameworks, coupled with digital tools, enable superior efficiency, quality, and sustainability. Here’s how to align operations with industry best practices: 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗯𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Utilize digital twins and predictive maintenance alongside time study techniques from PMTS to monitor and optimize operations with precision. Key Metrics: Enhanced Overall Equipment Effectiveness (OEE), reduced unplanned downtime, and faster issue resolution. 𝗟𝗲𝗮𝗻 & 𝗔𝗴𝗶𝗹𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝘄𝗶𝘁𝗵 𝗮 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗘𝗱𝗴𝗲 Apply lean principles, guided by industrial engineering insights, to identify and eliminate waste. Use PMTS to standardize and optimize manual tasks, ensuring balanced workflows. Key Metrics: Increased throughput, shorter cycle times, and better work content balance. 𝙌𝙪𝙖𝙡𝙞𝙩𝙮 𝘾𝙤𝙣𝙩𝙧𝙤𝙡 𝙬𝙞𝙩𝙝 𝙍𝙞𝙨𝙠 𝙈𝙞𝙩𝙞𝙜𝙖𝙩𝙞𝙤𝙣 𝙏𝙚𝙘𝙝𝙣𝙞𝙦𝙪𝙚𝙨 Integrate Advanced Product Quality Planning (APQP) and Process FMEA for robust quality assurance. PMTS can streamline quality inspections by standardizing operator tasks. Key Metrics: Reduced defect rates, improved First Pass Yield (FPY), and enhanced supplier compliance. 𝙀𝙧𝙜𝙤𝙣𝙤𝙢𝙞𝙘𝙨 𝙖𝙣𝙙 𝙒𝙤𝙧𝙠𝙛𝙤𝙧𝙘𝙚 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙖𝙩𝙞𝙤𝙣 Use PMTS to analyze and redesign workstations, improving ergonomic efficiency and reducing operator fatigue. Combine this with immersive training programs for new workflows and tools. Key Metrics: Lower Lost Time Injury Frequency Rates (LTIFR), increased training participation, and better ergonomic compliance scores. 𝙎𝙪𝙨𝙩𝙖𝙞𝙣𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙖𝙣𝙙 𝘾𝙤𝙨𝙩 𝙍𝙚𝙙𝙪𝙘𝙩𝙞𝙤𝙣 𝙬𝙞𝙩𝙝 𝙋𝙧𝙤𝙘𝙚𝙨𝙨 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙖𝙩𝙞𝙤𝙣 Apply industrial engineering methods like value-stream mapping and PMTS to reduce waste and energy use. Key Metrics: Decreased carbon footprint, material waste reduction, and cost savings from energy-efficient practices. 𝙎𝙚𝙖𝙢𝙡𝙚𝙨𝙨 𝙉𝙚𝙬 𝙋𝙧𝙤𝙙𝙪𝙘𝙩 𝙄𝙣𝙩𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙤𝙣 (𝙉𝙋𝙄) Use PMTS and discrete event simulations to plan and validate new product workflows, minimizing disruptions and ensuring efficient line balancing. Key Metrics: Faster time-to-market, improved pre-launch efficiency, and fewer launch delays. 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙞𝙣𝙜 𝙎𝙪𝙥𝙥𝙡𝙮 𝘾𝙝𝙖𝙞𝙣 𝙖𝙣𝙙 𝙇𝙤𝙜𝙞𝙨𝙩𝙞𝙘𝙨 Apply Kanban, JIT, and simulation-driven logistics planning to streamline material flow and inventory management. PMTS ensures operator tasks are aligned with logistics processes. Key Metrics: Higher on-time delivery rates, reduced inventory holding costs, and streamlined in-plant logistics.

KEY SIX SIGMA TOOLS VS. THEIR PURPOSES: DMAIC (Define, Measure, Analyze, Improve, Control) – A structured problem-solving approach for process improvement. DMADV (Define, Measure, Analyze, Design, Verify) – Used for designing new processes/products with Six Sigma quality. SIPOC Diagram – Identifies Suppliers, Inputs, Process, Outputs, and Customers to understand process scope. Process Mapping – Provides a visual representation of workflows to identify inefficiencies and improvement areas. Pareto Chart – Prioritizes problems using the 80/20 rule, focusing on major issues first. Fishbone Diagram (Ishikawa) – Categorizes potential root causes of problems for root cause analysis. 5 Whys – A simple method to identify root causes by repeatedly asking "Why?" Failure Mode and Effects Analysis (FMEA) – Identifies potential failures and their impact, allowing preventive actions. Control Charts – Monitors process stability and variations over time using statistical control methods. Histogram – Displays data distribution to analyze process performance and variations. Regression Analysis – Determines relationships between variables to optimize process outcomes. Gage R&R (Repeatability & Reproducibility) – Evaluates measurement system accuracy to ensure reliable data collection. Design of Experiments (DOE) – A statistical approach to optimize process settings and analyze factors affecting performance. Value Stream Mapping (VSM) – Identifies waste in processes and highlights opportunities for Lean improvement. Poka-Yoke (Error Proofing) – Prevents defects by designing foolproof mechanisms into processes.

I’ve worked in data engineering for more than 10 years, across different technologies, and one thing remains constant—certain optimization techniques are universally effective. Here are the top five that consistently deliver results: 1️⃣ Divide and Conquer: Break down data engineering tasks into multiple parallel, non-conflicting threads to boost throughput. This is especially useful in data ingestion and processing. 2️⃣ Incremental Ingestion: Instead of reprocessing everything, focus only on new or modified records. This approach significantly improves efficiency and reduces costs. 3️⃣ Staging Data: Whether using temp tables, Spark cache, or breaking down transformations into manageable stages, caching intermediate results helps the optimization engine work smarter. 4️⃣ Partitioning Large Tables/Files: Proper partitioning makes data retrieval and querying faster. It’s a game-changer for scaling efficiently. 5️⃣ Indexing & Statistics Updates: In databases, indexes speed up searches while keeping table statistics updated. The same concept applies to big data file formats—triggering an OPTIMIZE command on Delta tables ensures efficient query performance. 🚀 These fundamental principles remain true regardless of the tech stack. What other optimization techniques do you swear by? Let’s discuss in the comments! 👇

Have you ever tried to 'optimize' a machining operation based on 'machinability' data? How useful were these generic 'feeds and speeds'? One of the first lessons I learned as a young machinability consultant and engineer at TechSolve in Cinncinati OH was that optimal process paramters (tool material, geometry, coating, feeds, speeds, coolant, etc.) depend strongly on the specifics of a given operation, including workpiece material, geometry, and the cost structure of the specific job. Most importantly, I also quickly learned that the primary purpose of a machining process is to generate reliable and maximal profit. Therefore, an optimum process is one that is as robust and repeatable as possible, providing 'in spec' parts at the maximum profitability and throughput. The goal of machinability studies should be to generate necessary relationships and data, most importantly progressive tool-wear as a function of cutting time and the impact of tool-wear and feeds/speeds on product quality (dimensions, surface integrity, etc.). We need this information and its variability to model wear progression and the onset of unacceptable workpiece quality for data-driven process optimization. When optimizing, we are not simply trying to maximize metal removal rate and push tool-life to its maximum extent, but our optimization has to be constrained by the statistical variability of tool-wear and associated workpiece quality. While machinability standards such as ISO 8688-2:1989 or controlled/locked aerospace procedures suggest arbitrary end of tool-life criteria such as 0.3 mm maximum flank wear (~0.012"), the end-of-life criterion should always be intelligently defined based on workpiece quality; It does not matter that the tool can keep on cutting when we cannot sell the resulting workpiece and thus generate a profit. I have found that experienced machinists and engineers inherently know this and will consequently limit tool-life to relatively low values to avoid scrapping the workpiece. This practice makes a lot of sense, especially when detailed tool-wear and associated workpiece quality data are not available. Nevertheless, the benefits of even basic tool-wear analysis and quality-constrained process paramter optimization can be substantial. With relatively limited effort, profitability and throughput can often be improved anywhere from 20% for well estbalished (reasoanbly pre-optimized) processes and I have personally helped implement improvements as high as 20x greater process performance in particularly difficult-to-machine alloys and complex operations. The ROI for data-driven optimization depends on the cost metrics of each operation, but can be quite substantial in many cases. I personally feel that we should teach this advanced approach more broadly, particularly to experienced machinists and engineers, as well as the next generation of young professionals entering the field. Figure credit: https://lnkd.in/e5qQrtYM