Intelligent Scheduling Systems

What happens when you aim industrial AI at production scheduling but treat it like every other engineering problem? We built a multi-agent AI system that achieved a 21% increase in profit. Here’s how: 1. Make the goals explicit Production scheduling is a complex process with numerous trade-offs. Highest demand or most efficient run? Overtime or on-time delivery? We spelled out the real goals and KPIs so the agent system knew exactly which knot it had to untangle. 2. Capture expertise through machine teaching Machine teaching breaks the job into bite-size skills. An engineer shows the system why a decision works, not just what happened in the data. Rather than rely purely on data, machine teaching transfers deep human expertise into the system - digitizing decades of experience and knowledge, crucial as expert operators retire. 3. Structuring the Multi-Agent System The multi-agent system was designed to mimic human decision-making: Sensors: Gather real-time data on production status, resources, and external market conditions. Skills: Modular units responsible for specific actions, such as forecasting demand, optimizing scheduling, or adapting to sudden changes. Each skill can evolve on its own, giving the plant the same modular flexibility you expect from any well-engineered system. 4. Establishing a Performance Benchmark Good engineering demands clear benchmarks. We ran a standard optimization-based system as our baseline. This allowed us to objectively measure whether our AI agents delivered measurable improvements. 5. Rigorous Testing & Iteration Engineering thrives on iteration. We created and tested 13 agent system designs, continuously iterating based on performance data. Each iteration leveraged insights from the previous, systematically improving performance until we identified the optimal solution. --- By treating AI as an engineered system (modular, explainable, and configurable) it demonstrates significant potential results: ✅ 21% higher profit margins ✅ Improved adaptability to rapidly changing market conditions ✅ Preservation and amplification of valuable human expertise Full breakdown of the build and tests is below.👇 #ProductionScheduling #IndustrialAI #MachineTeaching #SmartManufacturing

𝗖𝗮𝗻 𝗮 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗧𝗼𝗼𝗹 𝗥𝗲𝗱𝘂𝗰𝗲 𝗕𝘂𝗿𝗻𝗼𝘂𝘁 𝗮𝗻𝗱 𝗪𝗮𝗶𝘁 𝗧𝗶𝗺𝗲𝘀 𝗮𝘁 𝗢𝗻𝗰𝗲? Orlando Health thought their infusion clinics were running at full capacity. Turns out, they were just poorly scheduled. After implementing Epic’s infusion scheduling template generator, everything changed. 𝗧𝗵𝗲 𝗕𝗲𝗳𝗼𝗿𝗲 → Patients waited up to a week for an appointment → Nurses overwhelmed during midday peaks → 6-minute average scheduling calls → High turnover, overbooked chairs 𝗧𝗵𝗲 𝗔𝗳𝘁𝗲𝗿 → 32% drop in patient wait times → 50% increase in nurse satisfaction → 200 monthly care hours recovered → Appointments offered within 24 hours The difference? Smarter scheduling built around actual staffing, capacity, and patient needs not guesswork. 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗗𝗶𝗱? → Used Epic’s system to auto-build templates based on data → Shifted scheduling conversations to system-recommended slots → Consolidated appointment info onto one screen → Automatically rebalanced unclaimed appointments overnight 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗦𝗵𝗶𝗳𝘁? This wasn’t about more chairs or overtime. It was about reducing chaos through system logic and giving nurses and patients a better experience. 𝗬𝗢𝗨𝗥 𝗧𝗔𝗞𝗘? → Is your clinic really full or just misaligned? → Would automated scheduling free up care hours in your workflow? → Could smarter workflows reduce nurse turnover without increasing cost? #EpicSystems #DigitalHealth #InfusionCare #PatientExperience #ClinicalWorkflows #NurseRetention #SmartScheduling #OrlandoHealth #HealthTech #OncologyCare #EpicShare #TechlingHealthcare

Glad to share our recent work published in collaboration with Emirates Health Services, addressing one of the important challenges in Healthcare. No-show appointments are a persistent challenge for healthcare providers, contributing to inefficient resource allocation, longer wait times, and revenue loss. But what if Artificial Intelligence (AI) could change that? By leveraging AI-driven solutions, healthcare organizations can: Predict no-show patterns: Analyze historical patient data to identify trends and factors contributing to missed appointments. Enable proactive engagement: Send personalized reminders and offer rescheduling options to high-risk patients through their preferred communication channels. Optimize scheduling: Dynamically adjust schedules to fill potential gaps, ensuring minimal downtime and better resource utilization. Improve patient convenience: Implement smart appointment bookings that align with patient preferences and availability, reducing the likelihood of cancellations. The result? A significant reduction in no-show rates, improved patient satisfaction, and enhanced operational efficiency. AI is no longer a "nice-to-have"—it's becoming a game-changer in improving care delivery. Healthcare providers who adopt intelligent scheduling and resource management are taking a critical step toward building a more resilient, patient-centered system. How is your organization addressing no-shows? Let’s collaborate and exchange ideas! #AIinHealthcare #HealthInformatics #PatientEngagement #OperationalExcellence #FutureOfHealth Bashar Balish, MD, CHCIO, CDH-E, CIO Alaa Adel (AJ) Sadaf Naqvi Neema Preman Oottumadathil Read further at following link.

𝗬𝗼𝘂𝗿 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝗱𝗼𝗻’𝘁 𝗰𝗮𝗿𝗲 𝙬𝙝𝙚𝙣 𝘁𝗵𝗲𝘆 𝗿𝘂𝗻. 𝗕𝘂𝘁 𝘁𝗵𝗲 𝗽𝗹𝗮𝗻𝗲𝘁 𝗱𝗼𝗲𝘀. Most factories still run on fixed schedules, even when the grid is at its dirtiest. But here’s the twist: AI can now schedule your production around real-time grid data, so you consume more renewable energy without changing what you produce. Imagine: ✅ Running energy-heavy tasks during solar peaks  ✅ Delaying non-urgent loads during fossil-fueled hours  ✅ Letting AI replan on the fly when the grid shifts It’s not a future concept, it’s happening now.  And manufacturers using smart scheduling are already cutting emissions by 10–15%, plus saving on energy costs. ♻️ This is the low-hanging fruit of sustainable ops; invisible, automated, and measurable. So, the real question is:  𝗪𝗵𝘆 𝗿𝘂𝗻 𝗯𝗹𝗶𝗻𝗱 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂𝗿 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲 𝗰𝗮𝗿𝗯𝗼𝗻-𝗮𝘄𝗮𝗿𝗲? #Sustainability #AI #SmartFactory #CarbonReduction #EnergyEfficiency #GreenManufacturing ------------------------     ✅ Follow me on LinkedIn at https://lnkd.in/gU6M_RtF to stay connected with my latest posts.   ✅ Subscribe to my newsletter “𝑫𝒆𝒎𝒚𝒔𝒕𝒊𝒇𝒚 𝑫𝒂𝒕𝒂 𝒂𝒏𝒅 𝑨𝑰” https://lnkd.in/gF4aaZpG to stay connected with my latest articles.   ✅ Please 𝐋𝐢𝐤𝐞, Repost, 𝐅𝐨𝐥𝐥𝐨𝐰, 𝐂𝐨𝐦𝐦𝐞𝐧𝐭, 𝐒𝐚𝐯𝐞 if you find this post insightful.  ✅ Please click the 🔔icon under my profile for notifications! 

Scheduling Hourly Workers Doesn’t Have to Be a Headache 😵💫 😱 Crazy idea… right?! As someone who’s spent years in HR and retail, I know first-hand that managers spend hours adjusting schedules, trying to balance business needs and employee preferences. And let’s be honest, no matter how much effort goes into it, someone is always unhappy. Unhappy is the opposite of what I, or any HR leader, want to hear. This is where I see an opportunity for AI. Just like you can ask AI to help you research and prepare for a job interview, AI can take all the inputs and requirements needed to create the “perfect schedule” off of the manager’s plate and produce a first draft quickly and accurately. Managers can spend less time creating, some time reviewing and editing the schedule if needed, and more time coaching their team, interacting with people, and training to get better. Legion AI is a great example of this: ✅ 96% of schedules match employee preferences and business needs ✅ 50% reduction in time spent on scheduling effort ✅ Increased employee engagement & retention When employees get schedules that work for them, they show up more engaged—and that directly impacts business success. #HR #Retail #WorkforceManagement #EmployeeExperience #AI #Scheduling