Engineering Challenges In Manufacturing

Lean Root Cause Analysis (RCA) is a structured approach used in Lean thinking to identify the fundamental reason for a problem rather than just treating its symptoms. The goal is to eliminate the true cause to prevent recurrence, supporting continuous improvement and operational excellence. Core Concepts of Lean Root Cause Analysis: Problem Definition: Clearly state the problem in observable and measurable terms: what, where, when, and how big. Data Collection: Gather facts, not opinions, use visual management, process data, and real-time observation (go to the Gemba). Root Cause Identification: Several tools are used here: 5 Whys: Repeatedly ask “Why?” (usually 5 times) until the true cause is found. Fishbone Diagram (Ishikawa): Categorizes possible causes (e.g., Methods, Machines, Materials, Manpower, Measurement, Mother Nature). Fault Tree Analysis or Why-Why Trees in complex situations. Countermeasure Development: Develop solutions that directly address the root cause and not just symptoms. Implementation and Follow-up: Apply countermeasures and track their effectiveness using visual controls, KPIs, or A3 thinking. Example Using 5 Whys: Problem: A machine stopped on the packaging line. Why 1: Because the motor overheated. Why 2: Because it wasn't lubricated. Why 3: Because the preventive maintenance wasn’t performed. Why 4: Because the schedule was not followed. Why 5: Because the technician wasn’t trained in PM procedures. Root Cause: Lack of technician training. Countermeasure: Implement a structured PM training program and audit compliance. Benefits of Lean RCA Prevents recurrence of problems Involves cross functional collaboration Promotes learning culture Reduces waste (Muda) caused by rework and defects

Meet anyone in manufacturing, and for their top two concerns, you'll hear about:   1. Supply Chain Disruptions: Challenges related to inventory and supply chain management. 2. Operating Costs: Navigating economic headwinds and operational inefficiency.   Our clients in the manufacturing sector work in a fast-paced world where maintaining operational efficiency is crucial. One of our clients faced significant challenges with their Clean-In-Place (CIP) process, which directly impacted their quality check procedures. Frequent unplanned downtimes due to equipment failures were hampering productivity and throughput, highlighting the need for a more proactive maintenance approach. They needed real-time insights to make informed preventive maintenance decisions! To address their challenges, our team developed and implemented an AI-based predictive maintenance solution for the CIP equipment. Leveraging data analytics and machine learning, this solution integrated critical datasets from batch processes, sensors, and maintenance records.   By empowering our client with real-time insights through anomaly detection and a risk scoring system, we enabled them to make informed preventive maintenance decisions. This proactive approach not only improved their operational efficiency but also set a new standard for maintenance practices in the manufacturing industry.   Our client went from reactive and corrective maintenance to predictive maintenance! Would love to hear from the network on what you are seeing in this area. If you have a story, let us talk.

How Butterflies help us to transform Sewage Sludge into Next-Gen 3D Printing Materials Every year, millions of dry metric tons of sewage sludge, an organic-rich byproduct of wastewater treatment, pose a huge disposal challenge and environmental burden. Traditionally destined for incineration, landfills, or limited agricultural use, this overlooked resource is now getting a second life through innovative material science! We developed a method to harness hydrothermal processing (HTP) to convert wet sewage sludge into hydrochar, carbonaceous solid that can be further activated. Unlike typical biomass, sewage sludge contains unique metallic and metalloid dopants. These impurities lead to surprising outcomes during thermal activation: instead of the expected boost in carbon content and improved graphitic ordering, the process actually decreases carbon ordering, creating a distinct material structure with its own set of properties. When incorporated into 3D printing resins, this hydrochar acts as a sustainable filler. Initially, it may compromise stiffness and hardness due to limited resin-filler adhesion. However, by adopting nature-inspired gyroid geometries, designs reminiscent of butterfly wings and bird feathers, the composite’s toughness and elongation can not only be recovered but enhanced! This integration of bio-inspired architecture overcomes inherent material weaknesses and paves the way for eco-friendly prototypes, packaging, and beyond. 1️⃣ Diverting millions of tons of sludge from landfills and incineration reduces greenhouse gas emissions and pollutant dispersion. 2️⃣ Incorporating waste-derived hydrochar in 3D printing reduces reliance on raw synthetic materials, promoting a circular economy and sustainable manufacturing. 3️⃣ The synergy between material science and bio-inspired design opens new horizons for advanced composites with tailored properties through innovative design. This fusion of waste valorization, unconventional chemistry, and cutting-edge design showcases a transformative path toward sustainable manufacturing. Read more details in the paper (open access): Sabrina Shen, Branden Spitzer, Damian Stefaniuk, Shengfei Zhou, Admir Masic, Markus J. Buehler, Communications Engineering, Vol. 4, 52 (2025), https://lnkd.in/eBeESHJY

𝐀𝐈 𝐚𝐧𝐝 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐢𝐳𝐢𝐧𝐠 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠: 𝐖𝐡𝐚𝐭'𝐬 𝐍𝐞𝐰?  In today's rapidly evolving manufacturing landscape, AI and automation are at the forefront of transformative change. Recent studies highlight the increasing adoption of AI technologies within the industry, underscoring both opportunities and challenges. 👉𝐀𝐈 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭𝐬 𝐢𝐧 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 • AI is transforming the sector, with investment in generative AI expected to spike, adding $4.4 billion in revenue from 2026 to 2029 • 70% of manufacturers now use generative AI for discrete processes, particularly in computer-aided design (CAD), significantly boosting productivity • AI-powered predictive maintenance is reducing downtime, with companies like Pepsi and Colgate leveraging this technology to detect machinery problems early 👉𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧𝐬 • Collaborative robots (cobots) are gaining traction, with BMW and Ford utilizing them for tasks like welding and quality control • Amazon has deployed over 750,000 robots in its fulfillment centers, including the new Sequoia system that processes orders up to 25% faster • AI-driven "smart manufacturing" enables more precise process design and problem diagnosis through digital twin technology 👉𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐧 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 • AI is enabling "lights-out" factories, where production can continue 24/7 with minimal human intervention • Machine learning models are optimizing supply chains, enhancing resilience to volatility • AI-powered quality control systems are improving product consistency and reducing defects 👉𝐊𝐞𝐲 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 • The global AI in manufacturing market is projected to reach $20.5 billion by 2029 • 85% of manufacturers have invested or plan to invest in AI/ML for robotics this year • Manufacturers using AI report a 69% increase in efficiency and 61% improvement in productivity 👉𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐢𝐧 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 • Talent Gap: There's a shortage of experienced data scientists and AI engineers in the manufacturing sector • Data Quality and Privacy: Ensuring clean, accurate, and unbiased data while maintaining privacy and security is crucial • Technology Infrastructure: Integrating AI with legacy systems and ensuring interoperability between different technologies can be complex • Cultural Resistance: Overcoming employee concerns about job security and adapting to new AI-driven processes can be challenging • Ethical Considerations: Ensuring fairness, transparency, and accountability in AI decision-making processes is essential As AI and automation continue to evolve, they're reshaping the manufacturing landscape. How is your company leveraging these technologies to stay competitive? 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: https://lnkd.in/ge3TGArE https://lnkd.in/gc276FhK #AI #DigitalTransformation #GenerativeAI #GenAI #Innovation  #ThoughtLeadership #NiteshRastogiInsights 

As headhunters, we are witnessing how leaders in the manufacturing industry are thriving in their decision-making under pressure by implementing the following recommendations: Embrace IoT for Predictive Maintenance: Implementing the Internet of Things (IoT) in manufacturing operations, as seen with General Electric, enables predictive maintenance, reducing downtime and enhancing efficiency. Utilize AI for Quality Control: Adopting Artificial Intelligence (AI) for tasks like quality control, like BMW's use of AI for assembly line analysis, leads to more accurate and faster decision-making processes. Leverage Big Data for Supply Chain Optimization: Companies like Cisco Systems demonstrate how big data can optimize supply chain management, allowing manufacturers to respond swiftly to changes and disruptions. Incorporate 3D Printing for Rapid Prototyping: Utilizing 3D printing technology, as Ford does, speeds up the prototyping process, enabling quicker decision-making and reducing time to market. Use Digital Twins for Testing and Simulation: As Siemens does, implementing digital twins for product and process simulation can significantly enhance decision-making efficiency and accuracy. Implement Real-Time Dashboards for Operational Insight: Integrating real-time dashboards, like Tesla, offers immediate operational insights, aiding faster and more informed decision-making. Adapt JIT Philosophy for SMEs: Small and Medium Enterprises (SMEs) should consider adopting Just-In-Time (JIT) strategies with adjustments for scale, as demonstrated by ABC Manufacturing, to enhance efficiency and responsiveness. Build Robust Local Supplier Networks: Like ABC Manufacturing, SMEs can benefit from developing strong local supplier relationships to reduce dependency and increase supply chain resilience. Adopt Flexible Production Strategies: Incorporating flexible production strategies allows companies to respond rapidly to market changes, a crucial aspect for SMEs in JIT implementation. Commit to Continuous Improvement and Feedback: As practiced by ABC Manufacturing, regular process reviews and incorporating feedback are essential for adapting and refining strategies and ensuring continuous improvement in decision-making processes. The following article provides a holistic approach to leaders’ decision-making under pressure in the manufacturing sector, emphasizing the importance of digital integration, agility, and strategic partnerships in navigating modern manufacturing challenges. #decisionmaking #topnotchfinders #sanfordrose

How to tackle legacy system modernization at scale: How Booking(.)com tackled a legacy API that had gotten completely out of hand: The situation: A 14-year-old API in their Perl monolith had grown from handling simple app updates to managing 21 different features across 7 teams. Instead of a quick migration to Java, the team took a thoughtful approach to breaking down this complex system. Key insights from their successful modernization: 1. Map before you migrate. The team created visual diagrams to understand how 1,500 lines of code connected to various parts of their system. 2. Know your stakeholders. Using repository history, they identified every team dependent on the API and included them in the planning process. 3. Split strategically. They separated the system into focused services based on functionality and platform requirements, making it more maintainable. 4. Test thoroughly. When they encountered unexpected issues with marketing metrics, they used A/B testing to identify and fix problems without disrupting service. The biggest lesson? Modernizing legacy systems isn't just rushing to new technology. It's about understanding what you have and carefully restructuring it into something better. Follow Pratik Daga for daily informative posts on software engineering.

Because inventory causes exponential pain with multiple warehouses... This infographics shows how to manage inventory in this context: ➡️ Centralize Inventory Visibility ↳ Issue: not knowing inventory levels across locations can lead to overstock in one warehouse and stockouts in another ↳ Action: Implement an inventory management system/ ERP that shows real-time inventory positions for all warehouses in one snapshot ➡️ Classify Products and Prioritize ↳ Why: Not all SKUs deserve the same treatment; some are high-value, others are seasonal ↳ Action: Use ABC analysis to rank products by focusing on A-items for tighter control ➡️ Define Replenishment Rules by Warehouse ↳ Why: Different warehouses cater to different regions or demand patterns. One-size-fits-all reorder points (ROP) won’t cut it ↳ Action: Tailor ROP, safety stock, and min-max levels by location. Consider lead times from central distribution centers or suppliers for each site ➡️ Breakdown Forecast by Warehouse ↳ Why: Each warehouse faces unique market dynamics ↳ Action: Generate warehouse-level forecasts, combining local sales trends with broader S&OP inputs ➡️ Plan Transfers Strategically ↳ Why: Sometimes it’s of lower cost or faster to transfer stock than reordering from suppliers ↳ Action: Set up a transfer framework; regularly review surplus vs. deficit at each location. Automate triggers for transfer orders when it’s cost-effective. ➡️ Monitor KPIs Proactively ↳ Why: Multi-warehouse complexity can hide inefficiencies when not tracking the right metrics ↳ Action: Track fill rate, inventory turnover, stock aging, and transfer costs at each site. ➡️ Plan Direct Dispatches & Save Costs ↳ Why: Dispatch directly from the plant to save logistics costs ↳ Action: Prepare daily dispatch plans targeting direct replenishment from the plant and use these warehouses for milk runs for distributors Any others to add?

In the rapidly evolving sector of manufactured homes, navigating the complex challenge of selecting the optimal production layout is crucial, especially when technological advancements significantly impact decision-making. The core of this dilemma revolves around choosing between the flexibility of cell layouts and the efficiency of assembly line methodologies. The adaptability of cell layouts makes them suitable for low-volume, high-variety production, offering the customization needed to meet customer preferences. However, this approach might lead to resource underutilization in scenarios where demand fluctuates. On the other hand, assembly line methodologies excel in reducing the cost per unit through speed and efficiency in high-volume, low-variety production but may lack the necessary flexibility, requiring significant reconfiguration efforts for product changes. A hybrid model that combines the cell layout's flexibility for initial customization stages with the assembly line's efficiency for standardized processes could be the optimal solution. The importance of considering factory layout during the concept and product roadmap development stages cannot be overstated, as it ensures the seamless integration of product design and manufacturing capabilities. Early layout considerations can preemptively address potential production challenges, aligning product development with manufacturing realities and allowing for the dynamic adjustment of the factory layout as products evolve. Perhaps, the key to navigating these challenges lies in the early engagement of Product Lifecycle Management (PLM), which can define the future course of manufacturing, ensuring that the production strategy is as dynamic and adaptable as the products it aims to create ?

🔧 Base Movement in Rotating Equipment – Part 57 is live! In this video, I dive into the critical issue of structural resonance and excessive excitation in pump systems – a hidden but significant contributor to mechanical failures. 🔍 Key Highlights: How vibration problems lead to equipment breakdowns. A real-world case study showcasing experimental analysis to diagnose vibration issues. Redesigning the base plate to mitigate resonance and enhance structural integrity. 💡 Solutions that Delivered Results: Replacing the base plate for greater stability. Realigning pipes to minimize vibration pathways. The result? Significant vibration reduction and long-term equipment reliability. This episode is packed with insights for engineers and maintenance teams looking to prevent mechanical failures caused by resonance. 👉 Watch now to see how simple design changes can make a huge impact on operational performance. https://lnkd.in/gyzijMgN #Engineering #ConditionMonitoring #RotatingEquipment #VibrationAnalysis #ReliabilityEngineering

Most manufacturers treat symptoms, not causes. They fix the machine. Retrain the operator. Blame the supplier. Then wonder why problems keep coming back. Root cause analysis isn't about finding someone to blame. It's about finding the system failure that allowed the problem. Here's your toolkit for different scenarios: WHEN EQUIPMENT FAILS UNEXPECTEDLY: → 5 Whys Analysis - Simple questioning technique → Fishbone Diagram - Visual mapping of contributing factors   → Fault Tree Analysis - Logical breakdown of failure sequences → Timeline Analysis - Chronological review of events WHEN QUALITY ISSUES ARISE: → Statistical Analysis - Data-driven investigation → Process Mapping - Visual workflow analysis → Design of Experiments - Systematic testing of variables → Mistake Proofing Review - Error prevention assessment → Supplier Analysis - Investigation of incoming materials WHEN SAFETY INCIDENTS OCCUR: → Incident Reconstruction - Detailed event recreation → Policy Review - Analysis of existing protocols → Human Factors Analysis - Training and procedural review → Witness Interviews - Structured personnel discussions → Equipment Inspection - Thorough machinery examination → Corrective Action Planning - Systematic prevention measures The method matters less than the mindset. Are you asking "Who made the mistake?" Or "What system allowed this mistake to happen?" One question leads to blame. The other leads to solutions. Your choice determines whether problems disappear permanently. Or just hide until next time. Which root cause analysis method does your team use most often?