Automated Picking Systems

Every AI pilot should come with a risk map. 𝗕𝗲𝗰𝗼𝗺𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗔𝗜 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟭 𝗺𝗶𝗻𝘂𝘁𝗲 𝗮 𝗱𝗮𝘆. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘀𝗺𝗮𝗿𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝗲𝗮𝗱. 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 𝗻𝗼𝘄 → aiforleaders.com __________ Here’s the map to check before rollout: AI failures rarely come from one bad model. They usually come from a known risk that nobody named early enough. The AI Risk Periodic Table breaks the problem into: → 50 specific risks → 5 core categories → 1 clearer way to audit enterprise AI systems 1. Data risks Bad data breaks good models fast. Look for: → Bias, privacy exposure, data leakage, and weak consent controls → Data drift when inputs change but the system keeps acting confident → Embedded secrets where private credentials or sensitive context sit inside data Check: → Review source, consent, quality, and lineage before any AI workflow goes live 2. Model risks This is the category most teams already know. Look for: → Hallucination, overfitting, underfitting, and model drift → Explainability gaps when nobody can explain why an answer was produced → Metric blindness when dashboards look fine but real users get bad outputs Check: → Test model outputs against real business cases, not just clean benchmark examples 3. Agent risks This is the category getting underestimated in 2026. Look for: → Autonomy risk when agents make too many decisions alone → Tool misuse when the wrong API, file, or workflow gets triggered → Loop failure when agents repeat bad actions without stopping Check: → Set tool permissions, approval points, rollback rules, and memory limits upfront 4. Security and ops risks AI creates new weak points in the operating system. Look for: → Prompt injection, API abuse, token theft, and data exfiltration → Deployment risk when unstable workflows reach employees or customers → Cost overrun when usage scales faster than the budget owner expects Check: → Track logs, latency, token usage, failed actions, and integration errors weekly 5. Governance risks This is where AI risk becomes company risk. Look for: → Decision ownership gaps when nobody knows who approved the output → Audit gaps where actions cannot be traced later → Regulatory risk when legal requirements are assumed instead of checked Check: → Assign a human owner for every AI system before it touches real decisions The mistake is only watching for hallucinations. That is one box on the table. The better move is building a risk register before rollout. Name the risks. Assign the owners. Track the logs. Close the audit gaps. Set human oversight. Monitor the costs. AI is not risky because it is new. It is risky because teams deploy it without a map. Which risk would cause the most damage in your business right now?

Robot safety isn't optional. ⚠️ The person in this video walked away. The Reality: - 41 robot-related deaths in US workplaces over 26 years (1992-2017) - 77 serious injuries reported to OSHA (2015-2022) - Most fatalities happen during maintenance - unjamming, cleaning, troubleshooting The numbers are low. But anything above zero is unacceptable. Best Practices to Prevent This: 1. Physical Barriers 🚧 Light curtains, safety fences, and guards. If a human enters the zone, the robot stops. 2. Lockout/Tagout 🔒 Power down and lock the robot during maintenance. Most deaths happen when someone thinks "I'll just quickly fix this." 3. Speed & Force Limiting ⚡ Collaborative robots should operate at reduced speed around humans. Impact force limits matter. 4. Training 👷 Every person near a robot needs to understand the danger zones and emergency stops. 5. Risk Assessment 📋 Map every scenario where human-robot interaction occurs. Design safety systems accordingly. The Bottom Line: That 99.998% uptime means nothing if someone gets injured or dies.

Tired of Upgrade Headaches? How GitOps & Terraform Keep Our Investing Infrastructure Always Current. In the world of investing engineering, where speed, accuracy, and reliability are paramount, keeping infrastructure and applications in a perpetually good upgrade state is a critical challenge. Manual processes for upgrades can introduce risk, downtime, and significant overhead. That's why we're deeply invested in GitOps principles combined with the power of Terraform for our upgrade management strategy. This approach has been transformative for us, enabling: 1. Declarative Infrastructure & Applications: Define your desired state in Git for both infrastructure (with Terraform) and application configurations. 2. Automated Rollouts & Rollbacks: Changes pushed to Git trigger automated deployments, making upgrades predictable and rollbacks simple. 3. Continuous Reconciliation: GitOps agents constantly monitor the live state against the declared state in Git, automatically resolving deviations and ensuring your systems are always at their intended version. 4. Improved Security & Compliance: Every infrastructure and application change is a pull request, providing an audit trail and enabling peer review before deployment. 5. Reduced Technical Debt: Proactive upgrade management keeps our tech stack current, minimizing the accumulation of costly technical debt. For any organization striving for true operational excellence and resilient systems in the investing space, adopting GitOps with Terraform isn't just an advantage – it's quickly becoming a necessity. How are you tackling upgrades in your environment? Let's discuss! #InvestingEngineering #GitOps #Terraform #InfrastructureAutomation #DevOpsPractices #ContinuousDelivery #CloudInfrastructure #SiteReliability

🌍Global Standards Certifications for BESS Container-Based Solutions🔋 As Battery Energy Storage Systems become critical to modern power infrastructure, compliance with international standards ensures safety, performance, and interoperability across components from cells to containerized systems. Here’s a breakdown of key standards at each level with snapshot🔻: 1️⃣ Cell / Module Level: ✅ IEC 62619 and IEC 63056 ensure safety and performance for industrial lithium-ion cells. ✅ UL 1642 and UN 38.3 verify safety and transport compliance of lithium cells. ✅ RoHS and REACH (NPS) ensure environmental and chemical safety. ✅ IEC 60529 governs ingress protection (IP rating) against dust and water. ✅ IEC 60730-1 applies for safety of electrical controls, often embedded in smart modules. ✅ IEC 60332-1-2 addresses flame retardancy for wires and components. ✅ UN 3480 ensures proper sea and road transport labeling and packaging. ✅ UL 9540A helps assess fire propagation behavior of individual cells. 2️⃣ Pack / Rack Level: ⚡️ IEC 62619, IEC 63056, and UL 1973 provide safety and performance compliance for energy storage packs and systems. ⚡️ IEC 62485-5 focuses on installation safety in battery systems. ⚡️ IEC 61000-6-2, 61000-6-4, and 61000-4-36 ensure electromagnetic compatibility (EMC). ⚡️ IEC 62477-1 offers safety guidelines for power electronic converters in racks. ⚡️ RoHS, REACH, and UN 38.3 apply at this level as well. ⚡️ UL 9540A evaluates thermal runaway propagation between cells in modules/racks. 3️⃣ Container / System Level: 🧿 IEC 62933-2-1 and IEC TS 62933-5-1 / UL 9540 ensure complete system safety and performance. 🧿 IEC 62040-1 covers general safety for uninterruptible power systems. 🧿 NFPA 855, NFPA 69, and NFPA 68 provide fire protection, explosion prevention, and ventilation design standards. 🧿 UN 1364 and UN 3536 regulate transport and hazard labeling for large systems. 🧿 IEC 60529 (IP ratings) and IEC 62485-5 address protection and operational safety. 🧿 UL 1973, UL 9540A, RoHS, and REACH also remain applicable. Compliance with these standards builds trust, ensures grid compatibility, and supports the global transition to sustainable energy. #BESS #BatteryStorage #EnergyStorage #IECStandards #ULStandards #FireSafety #SustainableEnergy #RenewableIntegration #CleanTech #GridModernization #ESS #Electromobility #EnergyTransition #SmartGrid #GreenEnergy #SafetyFirst

Automation is more than just clicking a button While automation tools can simulate human actions, they don't possess human instincts to react to various situations. Understanding the limitations of automation is crucial to avoid blaming the tool for our own scripting shortcomings. 📌 Encountering Unexpected Errors: Automation tools cannot handle scenarios like intuitively handling error messages or auto-resuming test cases after failure. Testers must investigate execution reports, refer to screenshots or logs, and provide precise instructions to handle unexpected errors effectively. 📌 Test Data Management: Automation testing relies heavily on test data. Ensuring the availability and accuracy of test data is vital for reliable testing. Testers must consider how the automation script interacts with the test data, whether it retrieves data from databases, files, or APIs. Additionally, generating test data dynamically can enhance test coverage and provide realistic scenarios. 📌 Dynamic Elements and Timing: Web applications often contain dynamic elements that change over time, such as advertisements or real-time data. Testers need to use techniques like dynamic locators or wait to handle these dynamic elements effectively. Timing issues, such as synchronization problems between application responses and script execution, can also impact test results and require careful consideration. 📌 Maintenance and Adaptability: Automation scripts need regular maintenance to stay up-to-date with application changes. As the application evolves, UI elements, workflows, or data structures might change, causing scripts to fail. Testers should establish a process for script maintenance and ensure scripts are adaptable to accommodate future changes. 📌 Test Coverage and Risk Assessment: Automation testing should not aim for 100% test coverage in all scenarios. Testers should perform risk assessments and prioritize critical functionalities or high-risk areas for automation. Balancing automation and manual testing is crucial for achieving comprehensive test coverage. 📌 Test Environment Replication: Replicating the test environment ensures that the automation scripts run accurately and produce reliable results. Testers should pay attention to factors such as hardware, software versions, configurations, and network conditions to create a robust and representative test environment. 📌 Continuous Integration and Continuous Testing: Integrating automation testing into a continuous integration and continuous delivery (CI/CD) pipeline can accelerate the software development lifecycle. Automation scripts can be triggered automatically after each code commit, providing faster feedback on the application's stability and quality. Let's go beyond just clicking a button and embrace automation testing as a strategic tool for software quality and efficiency. #automationtesting #automation #testautomation #softwaredevelopment #softwaretesting #softwareengineering #testing

𝗘𝘃𝗲𝗿 𝗳𝗲𝗹𝘁 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂𝗿 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗽𝗮𝘁𝗵𝘄𝗮𝘆 𝗶𝘀 𝗮 𝗯𝗶𝘁 𝘁𝗲𝗻𝘂𝗼𝘂𝘀? If so, you're not alone. When it comes to bringing a medical device to market, the journey can feel anything but straightforward. Here are some actionable steps to make your regulatory path less tenuous and more secure: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 → Different regions have different requirements. → For instance, the FDA in the U.S. and the MHRA in the UK have unique criteria. → Knowing the specifics can save you from surprises later on. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗞𝗲𝘆 → Maintain thorough and organised documentation. → This includes everything from design history files to risk management reports. → Trust me, when an auditor/inspector comes knocking, you'll be thankful for your meticulous records. 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 → Misclassifying your device can lead to major setbacks. → Ensure you understand whether your device falls under Class I, II, or III. → This will dictate the level of regulatory scrutiny your product will face. 𝗘𝗻𝗴𝗮𝗴𝗲 𝗘𝗮𝗿𝗹𝘆 𝘄𝗶𝘁𝗵 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝗕𝗼𝗱𝗶𝗲𝘀 → Don't wait until the last minute to interact with regulatory authorities. → Early engagement can provide critical insights and help you avoid common pitfalls. → For example, presubmission meetings with the FDA can be invaluable. 𝗦𝘁𝗮𝘆 𝗨𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 → Regulatory standards are constantly evolving. → Subscribe to industry newsletters and join relevant forums. → Being proactive can often mean the difference between compliance and costly delays. 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗤𝗠𝗦) → A robust QMS is not just a regulatory requirement; it’s a business asset. → Implementing standards like ISO 13485 can streamline your processes and improve product quality. 𝗛𝗶𝗿𝗲 𝗼𝗿 𝗖𝗼𝗻𝘀𝘂𝗹𝘁 𝘄𝗶𝘁𝗵 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 → Don’t hesitate to bring in external expertise. → Regulatory consultants can provide specialised knowledge and help navigate complex requirements. → This can be particularly useful for SMEs with limited in-house resources. Conduct Thorough Testing and Validation → Ensure that all necessary tests are conducted and well documented. → This includes biocompatibility, electrical safety, and performance testing. → Proper validation can prevent last minute hitches during the approval process. Plan for PostMarket Surveillance → Regulatory compliance doesn’t end at market entry. Remember, the regulatory journey might seem tenuous, but with the right approach, you can navigate it successfully.

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗦𝘁𝗿𝗼𝗻𝗴 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺 (𝗜𝗠𝗦) 𝗳𝗼𝗿 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗖𝗮𝗻𝗻𝗮𝗯𝗶𝘀 𝗖𝘂𝗹𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 In the medical cannabis industry, compliance and quality assurance are crucial. Creating a robust Integrated Management System (IMS) allows cultivators and processors to integrate standards such as Good Agricultural and Collection Practices (GACP) for cultivation and EU Good Manufacturing Practice (GMP) for processing. This unified approach supports regulatory compliance, operational efficiency, and ultimately, product quality and patient safety. Developing an IMS begins with a clear definition of scope, outlining which standards the system will integrate. This often includes ISO 9001 for Quality Management, ISO 14001 for Environmental Management, ISO 45001 for Occupational Health and Safety, and ISO 27001 for information security. An effective IMS encompasses every stage, from cultivation and processing to packaging and distribution. With core policies in place, a strong documentation system is essential. This involves developing detailed Standard Operating Procedures (SOPs) and Work Instructions (WIs) covering each critical area. Key documents include quality manuals, batch records, and deviation logs, which help maintain high standards and meet audit requirements. Training is central to any IMS. Staff must be competent in everything from contamination prevention to documentation practices. Competency assessments further ensure that personnel consistently follow protocols, reducing risks and improving compliance. Risk management is another cornerstone of an effective IMS. Identifying and controlling risks associated with product quality, environmental impact, and worker safety ensures processes remain robust and safe. For medical cannabis, this involves rigorous contamination controls, sustainability measures, and comprehensive safety protocols. Regular audits provide vital checks and balances. Through scheduled internal reviews and third-party audits, businesses can verify IMS effectiveness, addressing any gaps through corrective and preventive actions (CAPA). Management reviews help organisations to adapt and improve, making IMS a dynamic system, always aligned with current standards. Finally, maintaining regulatory compliance is essential. Certification under GACP and EU GMP standards ensures medical cannabis operations meet stringent quality and safety requirements. By leveraging technology for efficiency, traceability, & reporting, companies can streamline IMS operations and ensure accurate records for audits. Building a strong IMS requires a commitment to quality, compliance, & ongoing improvement. It’s a structured approach that fosters resilience, operational excellence, & trust within the medical cannabis industry. https://lnkd.in/evksacRg #MedicalCannabis #IMS #QualityManagement #GACP #EUGMP #RegulatoryCompliance #OperationalExcellence

A company rushed AI into production, then realized nobody owned the risks. The model was live. The dashboards looked good. The launch was celebrated. But basic questions had no answers. Who monitors drift? Who handles harmful outputs? Who approves high-risk use cases? Who responds when something breaks? This is where many AI programs struggle. They focus on deployment and ignore governance. Shipping AI is one milestone. Managing AI responsibly is the real operating model. Here is a cheatsheet on AI risk management frameworks. 1. NIST AI RMF A practical framework for identifying, measuring, managing, and governing AI risks across the lifecycle. 2. ISO 42001 A global standard for building structured AI management systems and internal controls. 3. EU AI Act Risk Tiers A regulatory model that classifies AI by risk level and applies stricter rules where impact is higher. 4. FAIR Risk Model Helps quantify financial exposure from threats, failures, and vulnerabilities tied to AI systems. 5. AI Red Teaming Adversarial testing used to uncover jailbreaks, prompt injection, bias, and unsafe behaviors. 6. Model Cards Clear documentation covering intended use, limitations, metrics, and known risks of a model. 7. AI Governance Board Cross-functional ownership across legal, security, product, compliance, and leadership teams. 8. AI Incident Response A defined process to detect, contain, investigate, and recover from AI failures quickly. 9. Continuous Monitoring Tracks drift, abuse, quality drops, data issues, and operational signals after launch. 10. AI Risk Register A living system for logging risks, owners, severity, actions, and review dates. The biggest AI risk is often not the model. It is unclear ownership around the model. Who owns AI risk in most companies today: nobody, everyone, or the wrong team? Follow Vaibhav Aggarwal for more such insights!!

The CXOs scaling AI fastest aren’t removing humans from the loop. They’re getting precise about which loop humans belong in. Only one in five companies has a mature governance model for autonomous AI agents. (Deloitte, 2026) The core question: Where does the machine stop and where must the human begin? 1/ Start with the risk question If this AI decision is wrong, what breaks and can it be undone? Use two axes: → Reversibility → Blast radius A formatting mistake is not the same as a flawed lending decision. 2/ Low risk: automate fully, monitor passively Use for reversible, low-cost workflows: → Report generation → Scheduling → Routine ticket triage Human role: → Sampling → Anomaly alerts → Drift monitoring Gartner projects 15% of day-to-day work decisions will be made autonomously by agentic AI by 2028. 3/ Medium risk: automate execution, require review Use when workflows are useful to automate, but too consequential to leave unsupervised: → Customer communications → Contract drafting → Marketing personalization Human role: → Approval gates → Exception handling → Override authority Organizations need approval matrices, approved tools, logged outputs, and rollback procedures. (McKinsey, 2025) 4/ High risk: human-led, AI-assisted Use when decisions carry legal, financial, regulatory, or safety consequences: → Regulatory filings → Lending decisions → Clinical recommendations → Legal outputs Human role: → Decision ownership → Formal sign-off → Auditability High-risk AI systems require human oversight, risk management, and conformity controls. 5/ The cost of failure is asymmetric → Under-supervising high-impact workflows creates liability. → The issue is whether the organization can catch, correct, and explain an AI mistake. Enterprise leaders cite inaccurate or unreliable AI outputs as a major risk in AI-enabled delivery. (HFS Research, 2024) 6/ Speed vs. safety is a false trade-off Good governance shows where AI can move faster. A risk-tiering model helps organizations: → Automate low-risk work → Add review where needed → Preserve judgment for high-risk decisions → Create audit trails early More than 40% of agentic AI projects may be canceled by 2027 due to cost, unclear value, or inadequate risk controls. (Gartner, 2025) 7/ Build the oversight matrix first Simple model: → Low risk: AI executes, humans monitor → Medium risk: AI recommends, humans approve → High risk: AI assists, humans own the decision Organizations must define where humans stay in control, how decisions are audited, and what records are retained. (Deloitte, 2026) The question is no longer whether humans belong in the loop. It is whether you have decided: → Which loop → At what point → With what authority → And why Save for future reference.

Managing Projects in a Fast-Moving Beverage and Packaging Industry In the beverage and packaging industry, speed is not an advantage. It is the entry ticket. New SKUs are launched quarterly. Consumer preferences shift overnight. Retailers demand shorter lead times. Margins are constantly under pressure. And sustainability targets are tightening. In this environment, managing automation and robotics projects is not about installing machines. It is about enabling operational strategy. As a Key Account Manager (PM) in Automation and Robotics, working closely with high-volume beverage manufacturers, I have seen firsthand that project success depends on five critical dimensions: 1. Understanding the Production Economics Before a single machine is commissioned, we ask: * What is the current OEE? * What is the cost of downtime per hour? * Where are micro-stoppages occurring? * What is the impact of SKU proliferation on changeover time? In many beverage plants, even a 1% improvement in line efficiency can translate into millions annually. Projects must be justified not by technical sophistication, but by economic impact. 2. Engineering Around Downtime Constraints Unlike greenfield projects, most beverage automation upgrades happen in brownfield environments. Lines are live. Production schedules are tight. Peak seasons cannot be disrupted. This means: * Phased installations * Night or weekend cutovers * Simulation before deployment * Risk-mitigated commissioning plans Precision in planning is as critical as precision in robotics. 3. Managing Integration Complexity Modern packaging lines are ecosystems — fillers, cappers, labellers, vision systems, palletizers, and warehouse automation. Add legacy systems into the mix, and integration becomes the real project. The success of automation lies not in isolated performance, but in how seamlessly systems communicate across PLCs, MES, and ERP layers. Poor integration is often the hidden cause of instability. 4. Balancing Speed with Standardization Market demands tempt organizations to customize every solution. But without standardization, scalability becomes impossible. Strong project governance ensures: * Modular automation design * Replicable system architecture * Standard operating procedures * Clear KPI ownership post-handover Automation must be scalable, not just functional. 5. Aligning Stakeholders Beyond Engineering In beverage and packaging, a project touches multiple stakeholders: * Operations * Maintenance * Supply chain * Finance * Quality * Sustainability teams True project leadership involves aligning commercial goals, technical feasibility, and operational readiness — long before go-live. The pace of the beverage and packaging sector will only accelerate. The question is not whether to automate, but how to execute automation strategically. For leaders in manufacturing: Are your automation projects designed for immediate output or long-term competitive advantage?