Process Improvement Methods

Louder for the people at the back 🎤 Many organisations today seem to have shifted from being institutions that develop great talent to those that primarily seek ready-made talent. This trend overlooks the immense value of individuals who, despite lacking experience, possess a great attitude, commitment, and a team-oriented mindset. These qualities often outweigh the drawbacks of hiring experienced individuals with a fixed and toxic mindset. The best organisations attract talent with their best years ahead of them, focusing on potential rather than past achievements. Let’s be clear this is more about mindset and willingness to learn and unlearn as apposed to age. To realise the incredible potential return, organisations must commit to creating an environment where continuous development is possible. This requires a multi-faceted approach: 1. Robust Training Programmes: Employers should invest in comprehensive training programmes that equip employees with the necessary skills for their roles. This includes on-the-job training, mentorship programmes, online courses, and workshops. 2. Redefining Hiring Criteria: Organisations should revise their hiring criteria to focus more on candidates’ potential and willingness to learn rather than solely on prior experience or formal qualifications. Behavioural interviews, aptitude tests, and probationary periods can help assess a candidate's ability to learn and adapt. 3. Partnerships with Educational Institutions: Companies can collaborate with educational institutions to design curricula that align with industry needs. Apprenticeship programmes, internships, and cooperative education can bridge the gap between academic learning and practical job skills. 4. Lifelong Learning Culture: Encouraging a culture of lifelong learning within organisations is crucial. Employers should provide ongoing education opportunities and support for professional development. This includes continuous skills assessment and access to resources for upskilling and reskilling. 5. Inclusive Recruitment Practices: Employers should implement inclusive recruitment practices that remove biases and barriers. Blind recruitment, diversity quotas, and targeted outreach programmes can help ensure that diverse candidates are given a fair chance. By implementing these measures, organisations can develop a workforce that is adaptable, innovative, and resilient, ensuring sustainable success and growth.

Our research center in Princeton has become a magnet for healthcare AI expertise. Every time I catch up with Dorin Comaniciu and the team there, conversations quickly move from what’s possible to what really matters in healthcare delivery. Take for instance, our work on what we call the Operational Twin, an advisory service. It starts with creating a virtual representation of a clinical department, reflecting how patients, staff, and equipment interact in everyday operations so that different scenarios can be explored more safely and at scale. By simulating billions of scenarios representing dynamic conditions, AI agents learn how operational decisions shape outcomes. They can begin to anticipate bottlenecks and understand the long-term impact of short-term choices. The goal is more efficient planning of patient schedules, staffing, and equipment use, aligning daily decisions with broader clinical and organizational priorities. This becomes even more relevant as clinical innovations accelerate workflows. Faster scanning technologies such as Deep Resolve can shorten patient timeslots and an Operational Twin can help organizations adapt by optimizing schedules and resources to fully realize gains in speed and throughput. At its core, this work is about creating clarity in complex systems so that action becomes more precise and more purposeful. We see a similar principle in clinical innovation. With photon counting CT, we can visualize the heart in extraordinary detail, including structures inside the left ventricle that were previously difficult to see clearly. That deeper insight is captured by a Foundation Model that could help physicians guide ablation therapies with greater precision and confidence, especially when combined with live ultrasound to support real-time decision making in the procedure room. In both cases, whether in clinical imaging or in operations, the ambition is the same: better insight leading to better decisions at the moments that matter most for patients. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴/𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴 𝘢𝘯𝘥/𝘰𝘳 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨𝘴 𝘮𝘦𝘯𝘵𝘪𝘰𝘯𝘦𝘥 𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘺𝘦𝘵 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦 𝘪𝘯 𝘢𝘭𝘭 𝘤𝘰𝘶𝘯𝘵𝘳𝘪𝘦𝘴. 𝘐𝘧 𝘵𝘩𝘦𝘴𝘦 𝘴𝘦𝘳𝘷𝘪𝘤𝘦𝘴 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘮𝘢𝘳𝘬𝘦𝘵𝘦𝘥 𝘪𝘯 𝘤𝘦𝘳𝘵𝘢𝘪𝘯 𝘤𝘰𝘶𝘯𝘵𝘳𝘪𝘦𝘴 𝘧𝘰𝘳 𝘭𝘦𝘨𝘢𝘭 𝘰𝘳 𝘰𝘵𝘩𝘦𝘳 𝘳𝘦𝘢𝘴𝘰𝘯𝘴, 𝘵𝘩𝘦 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨𝘴 𝘤𝘢𝘯𝘯𝘰𝘵 𝘣𝘦 𝘨𝘶𝘢𝘳𝘢𝘯𝘵𝘦𝘦𝘥. 𝘍𝘰𝘳 𝘮𝘰𝘳𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯, 𝘱𝘭𝘦𝘢𝘴𝘦 𝘤𝘰𝘯𝘵𝘢𝘤𝘵 𝘺𝘰𝘶𝘳 𝘭𝘰𝘤𝘢𝘭 𝘚𝘪𝘦𝘮𝘦𝘯𝘴 𝘏𝘦𝘢𝘭𝘵𝘩𝘪𝘯𝘦𝘦𝘳𝘴 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘷𝘦.

Technology Is the Connector — and Pharmacy Leaders Are Using It to Expand Access Connected Care is no longer just a concept—it’s happening now, powered by technology and led by pharmacy teams. Becker's Healthcare recently highlighted how health systems across the U.S. are leveraging digital tools to bring care closer to patients. From virtual prescribing to automated prior authorizations, from centralized dispensing hubs to mobile infusion units—these innovations are breaking traditional barriers in access. Here’s how technology is driving impact: - Digital platforms like Intermountain’s On-Demand service allow patients to access contraception, naloxone, and more—without a physical visit. - Telepharmacy models are enabling medication therapy management in rural and underserved communities. - EHR integration is optimizing medication safety and coordination, especially for chronic diseases. - Mobile and same-day delivery solutions are transforming the last mile of care, bringing meds directly to doorsteps. - Data-driven workflows are embedding pharmacists into specialty care to fast-track therapy and reduce wait times. What we’re seeing is more than operational change—it’s the evolution of care delivery. When tech tools are built around patient needs and used by empowered clinical teams, we achieve what Connected Care truly stands for: continuity, personalization, and equity. It’s time we stop thinking of pharmacy as a silo—and start recognizing it as a digital front door to better health. Link to article: https://lnkd.in/e5ZDG_se #ConnectedCare #DigitalHealth #PharmacyTech #Telepharmacy #HealthEquity #HealthcareInnovation #BeckersHealthcare

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

Manufacturing Efficiency is More Than Numbers…It’s Transformational Science that Delivers Value. In my experience of deploying continuous process improvement, I’ve seen one truth repeat itself: small changes in cycle time create massive changes in organizational success. Consider a real-world example from a Fortune 500 distribution center. The facility struggled with a 12-hour lead time from order receipt to shipping. When we applied Manufacturing Cycle Time (MCT) and Manufacturing Cycle Efficiency (MCE) analysis, the data revealed that only 35 percent of production time was true value-added work. The rest was waiting, unnecessary movement, or inefficient scheduling. Through Lean tools like value stream mapping, Kaizen events, and standard work design, we cut average lead time from 12 hours to 8 hours. That 4-hour reduction meant faster customer fulfillment, increased throughput capacity, and a remarkable financial impact, more than 3.2 million dollars in annualized savings through reduced overtime, lower inventory holding costs, and fewer expedited shipments. The return on investment went far beyond financials. Employees who once felt pressured by bottlenecks were now empowered to work in a smoother, more predictable system. Morale increased as they could focus on craftsmanship and problem-solving rather than firefighting. When people feel their contributions directly improve performance, you build a culture of ownership and innovation. I have led these transformations across industries, from aerospace to government services and the outcomes are consistent. The combination of measuring cycle efficiency and acting on it with Lean methods delivers scalable success. Organizations gain profitability, employees gain pride, and customers gain trust. Continuous improvement is not just about efficiency metrics. It is about unlocking hidden capacity, protecting margins, and most importantly, enabling people to thrive in environments designed for excellence. That is the real power of Lean.🔋

Designing a smarter retirement for batteries: The Digital Battery Passport In ABC Australia’s TV show Utopia, actor Rob Sitch quips: “Every element in the process is intelligent, but as a whole it is ridiculous.” That could describe today’s lithium-ion battery recycling system. It is a patchwork of innovation trapped in systems that forget the big picture. The challenge is not innovation but integration. Across mining, refining, manufacturing, regulation, and recycling, progress happens in isolation. The result is a technically brilliant but disconnected ecosystem. The fix is digitisation and collaboration, connecting engineers, data scientists, policymakers, and industry through digital tools such as the Digital Battery Passport. At RMIT University, we are developing Australia’s first Digital Battery Passport, a secure blockchain based record that traces each cell’s chemistry, performance, ownership, and event history. Our work is funded by the Australian Government’s AEA Ignite initiative and has more recently received further support through its Quad Clean Energy Supply Chain Diversification Program. Think of the passport as a digital birth certificate and retirement plan for batteries, dynamically adapting to supply chain changes, national priorities, and sustainability targets. Now, I might not speak fluent Python, but I do speak battery. I understand how cells are built, how they age, and why they fail. I also understand what the shift from nickel and cobalt rich chemistries toward the good old lithium iron phosphate (LFP) cells means for the recycling world: safer, cheaper batteries with far fewer high value metals, challenging traditional recycling economics. Our AI models interpret unstructured multi modal battery data, from factory test sheets to in-field performance logs, automating workflows to reduce human intervention. They predict health, classify materials, and flag optimal pathways for reuse or recovery. In the rapidly growing second hand Electric Vehicle market, this capability is invaluable: a digital battery certificate generated from passport data verifies the health and history of a used vehicle’s battery, giving both buyers and regulators confidence in its safety and remaining life. Looking ahead, we are extending these capabilities to recycling plants, where AI will fine-tune process parameters in real time and optimise material recovery with minimal waste. By giving every cell a digital identity, we can turn what would be waste into a data-rich asset, making the global battery supply chain cleaner, fairer, and more resilient. As Rob Sitch might say, it’s about making the whole as intelligent as its parts. Giving batteries a second life and a digital identity, isn’t just recycling. It’s reimagining responsibility in the age of electrification. Interested in collaboration? Get in touch: https://lnkd.in/gM-tktFV

A lot has changed in the last couple of years. LiDAR for biomass measurement is now a real option for carbon projects today (tech and efficiency advances). I believe this will become the 'new standard' for the highest quality nature-based carbon projects in the next few years 🌳. Most projects in the Voluntary Carbon Market still rely on traditional approaches—manual measurements of tree diameter using a tape measure and generalised allometric equations. These methods were, for many years, the only viable option. They are low-cost, relatively simple to implement, and have contributed significantly to the growth of the forest carbon sector. While low cost, these approaches suffer limitations with precision, accuracy validation, and auditability. And as expectations for scientific integrity rise, their limitations—particularly around uncertainty and bias—should no longer be overlooked. As seen in the amazing work conducted by Sylvera, these methods can under- or over-estimate carbon by 1.5x to 2.2x. In many cases, these errors have not been appropriately reflected in project-level credit deductions. For a market whose core unit is a ton of CO₂, accurate measurement of biomass is critical. The tools now exist. The bar is rising. And it's time for a new generation credits underpinned by LiDAR-backed biomass measurements. At Revalue, we’re investing to demonstrate what is possible and get ahead of what is coming. 🌍 In Ruvuma Wilderness, Africa’s largest community-led project, we worked with Carbon Tanzania to: - Capture 19 billion data points, from canopy to understory - Scan trees at <7mm resolution - Pair under canopy (TLS) LiDAR scanning with larger area drone-based (ALS) LiDAR We are now creating a new “ground truth” that does not require allometric equations. Next, we fuse this with aerial (drone) LiDAR and high-quality geospatial data (via our partner Chloris Geospatial), integrating it with species-specific data. We’re using these measurements as part of creating auditable, scientifically-rigorous baselines for carbon projects. If we want scientifically-rigorous credits, we need scientifically-rigorous measurement. #CarbonMarkets #NatureTech #CarbonCredits #Biodiversity #ClimateAction #NatureBasedSolutions #ClimateTech #RegenerativeFinance #VoluntaryCarbonMarkets #ESG #NetZero #ClimateInnovation #CarbonRemoval #EnvironmentalFinance Nicolas L., Alexandra Ponomarenko, Charlotte Wheeler, PhD, Gabriel Cardoso Carrero, Carolina Ramirez Mendez, Dimas Maulana Ichsan

As more companies embrace A/B testing, the bottleneck is no longer running experiments—it’s ensuring those experiments lead to trustworthy decisions. In this tech blog, the data science team at Booking.com explains how they scaled experimentation quality across the organization. Rather than enforcing rigid rules, they chose to preserve team autonomy while building the supporting systems needed to encourage better experimentation practices. The team’s solution followed a simple but thoughtful progression: process, metric, then tool. They first invested in community initiatives like Experiment Ambassadors and peer experiment reviews to build a shared experimentation culture. They then introduced an Experimentation Quality framework that evaluated every experiment across three dimensions—Design, Execution, and Decision—making experimentation quality measurable and easier to improve. Finally, they embedded those standards directly into their internal experimentation platform through features such as quality checks, power-calculation guidance, and stronger defaults that naturally guided teams toward better decisions. The goal was to maintain flexibility while making good experimentation practices easier to follow. This work highlights an important lesson: improving experimentation at scale requires more than statistical knowledge or individual discipline. Sustainable improvement comes from combining strong organizational processes, meaningful quality metrics, and tooling that reinforces good practices into the everyday workflow. When these pieces work together, teams can make more reliable decisions. #DataScience #MachineLearning #Experimentation #ABTesting #Analytics #SystemDesign #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gg8eX3Yv 

Hyperautomation moves companies from task automation to smarter workflows. With RPA, AI, process mining, analytics, and emerging AI agents, teams can reduce manual work and coordinate operations with greater consistency. Hyperautomation extends automation from isolated tasks to broader operating models: - RPA reduces repetitive work and helps employees focus on activities with higher value. - AI improves automation by supporting analysis and decision quality. - Process mining shows where workflows slow down or create unnecessary effort. - Advanced analytics help teams measure performance and refine operations over time. - AI agents may become the next step when workflows require coordination across multiple systems. - Wider automation requires stronger governance so quality and control remain clear. Hyperautomation creates value when processes are redesigned before technology is added, with people, data, and governance aligned around measurable outcomes. #Hyperautomation #RPA #AI

Want to become a strong Technical Project Manager in RPA and AI? Let me share 3 things based on my experience. 1-Get your hands dirty with real bots Managing automation projects is not just about timelines and stakeholders ,it’s about understanding the process logic. If you’ve never designed or configured a bot yourself (even a small one), you’re missing a big piece of the picture. Once you build and break a few workflows in UiPath or Automation Anywhere, you start thinking differently , like an automation architect and not just a project lead. 2-Use proven delivery frameworks and templates Every RPA project follows similar stages ,discovery, design, development, UAT, deployment, and support. Yet, many teams still start from scratch every time. Having standard templates (PDD, SDD, test cases, hypercare checklist) and a delivery playbook can cut your project cycle time by 30–40%. 3-Leverage AI and analytics to manage smarter AI can now help you manage automation projects more efficiently , not just technically, but operationally. Use AI to write better documentation. Tools like ChatGPT or Copilot can help you draft PDDs, summarize process maps, or create test case outlines from your discovery notes. Analyze logs automatically. Instead of manually reviewing Orchestrator logs, use AI-powered log analyzers (like UiPath Insights, Power BI with AI visuals, or ElasticSearch dashboards) to detect recurring exceptions, long-running jobs, or unattended downtime. Automate your project tracking. Use AI to summarize daily stand-ups, extract action items, or even update Jira or Azure DevOps tasks automatically. Measure business impact continuously. Combine RPA data (execution time, volume, error rate) with business metrics (cost saved, hours returned) to build ROI dashboards that update weekly. What else you can add? Sarah Ghanem