Intelligent Workforce Scheduling Solutions

10^30000 scheduling combinations. 50 hours per week in Excel. If you've lived inside traditional WFM tools, you know this headache. Assembled's new AI-powered Schedule Generation does it in minutes. Here's the breakdown: 1,000 agents. 5 shifts each. 8 hours per shift. That's 5,000 shifts to schedule. Each shift needs: One productive event (chat, email, or phone). Two breaks. One lunch. One meeting. Discretize 8 hours into 15-minute blocks and you get 32 options. For non-productive events alone: 32 × 31 × 30 × 29 / 2 = 431,520 combinations per shift. Multiply by 3 productive event options. 1,294,560 combinations per shift. Now do that for 5,000 shifts. (10^6)^5000 = 10^30000. That's a number with 30,000 digits. At 2,000 digits per page, it takes 15 pages just to write it out. The “nurse scheduling” problem is a classic NP-hard problem. This is what workforce managers are solving with spreadsheets. Assembled's AI-powered Schedule Generation feature handles this in minutes. Agent needs Thursday off for a doctor's appointment? Old way: Submit request. Wait for approval. Hope it doesn't conflict. Assembled's way: Integer linear programming for coverage optimization. Constraint programming for breaks, lunches, and labor law compliance. Decomposition to break 34,000 weekly shifts into 50 parallel subproblems. 2 hours becomes 10 minutes. Agents can also browse available swaps directly in the system. AI ensures swaps follow your rules: Matching skills Queue compatibility Channel requirements. Our schedule Layers prevent coverage gaps entirely. It has three intelligent layers: Productive work Meetings/breaks Time off. When a training cancels, productive work surfaces automatically underneath. One global payments company told us: "This replaces our hideous spreadsheet where we export schedules just to flag compliance issues. Programming rules directly in is chef's kiss." AI handles 10^30000 combinations. Managers can now handle strategy. Kudos to the team on this big, NP-hard launch. Antony Phillips, Claire D., Jack Gleeson, Malfy Das, Nicole Pan, Zach Clark, Chancie(Qianshi) Zheng, Charlie Rotholtz, David Patou, Devon Berger, Todd Bergman, Dan Hertz

Workforce Management (WFM) is poised for significant advancements, leveraging technological innovations to revolutionize the workplace. In the near future, several key developments are expected to reshape WFM strategies: 1. **AI-Driven Forecasting and Scheduling:** Artificial Intelligence (AI) will play a pivotal role in refining forecasting accuracy. Machine learning algorithms will analyze historical data, market trends, and various parameters to predict staffing needs more accurately. Dynamic scheduling, empowered by AI, will adapt in real-time to meet demand fluctuations, ensuring optimal resource allocation. 2. **Enhanced Remote Work Capabilities:** With the rising prominence of remote work, WFM tools will prioritize features tailored to distributed teams. This includes virtual collaboration platforms integrated into WFM systems, advanced remote monitoring capabilities, and algorithms to equitably distribute workloads between on-site and remote employees while ensuring seamless communication. 3. **Predictive Analytics for Employee Engagement:** WFM will delve deeper into predictive analytics to anticipate employee engagement levels. By analyzing performance metrics, sentiment analysis, and other indicators, these systems will help managers identify potential issues affecting morale, allowing proactive interventions to boost engagement and retention. 4. **Personalized Employee Experience:** Future WFM systems will focus on delivering tailored experiences to individual employees. Customized scheduling preferences, career development plans, and targeted feedback mechanisms will enhance employee satisfaction and productivity by catering to their unique needs and work styles. 5. **Integration of Internet of Things (IoT):** IoT devices will be integrated into WFM to provide real-time data on employee activities and environmental factors impacting productivity. Smart sensors and wearables will offer insights into employee well-being, optimizing work conditions and schedules accordingly. 6. **Adaptive Learning and Development:** WFM platforms will incorporate adaptive learning algorithms to identify skill gaps and recommend personalized training programs. Continuous learning approaches will ensure employees remain equipped with the necessary skills for evolving job roles. 7. **Ethical AI and Bias Mitigation:** There will be a heightened emphasis on developing ethical AI models within WFM to mitigate biases. Ensuring fairness and equity in decision-making processes and opportunities for all employees will be a significant focus. These innovations collectively represent a shift towards more intelligent, adaptive, and employee-centric WFM systems, empowering organizations to optimize their workforce strategies and navigate the complexities of the modern workplace efficiently. #wfm #innovation #efficiency

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

AI scheduling isn't magic. It's math, automation, and speed, working together. Here's what the data says: 👇 The average home services business loses 30% of after-hours calls. Technicians waste 30-40% of their day driving inefficient routes. And only 15-25% of leads convert — because response time is too slow. That's the problem AI scheduling solves. Here's how it actually works: 📞 Step 1: AI answers the call (24/7). No voicemail. No missed leads. AI picks up instantly, even at 2 am. 🔍 Step 2: It qualifies the job AI asks diagnostic questions: "Is your AC blowing warm air or not turning on at all?" Then, it determines urgency: emergency vs. routine. 📅 Step 3: It books the appointment AI checks technician availability, skills, location, and even parts inventory. Then schedules the best slot — no back-and-forth. 🚐 Step 4: It optimizes the route AI assigns jobs based on location and reduces drive time by 25-35%. 📲 Step 5: It keeps the customer updated with a confirmation SMS with tech name, photo, and ETA. Real-time tracking link. Auto-updates if anything changes. Result? 80% fewer "where are you?" calls. 🔁 Step 6: It syncs everything. Appointments flow directly into your CRM and calendar. No double-entry. No errors. The results speak for themselves: ✅ 40-70% more appointments booked ✅ 25-35% fewer no-shows ✅ 10-20 hours/week saved on admin ✅ Handle more jobs without hiring more staff One HVAC company using AI scheduling went from 145 to 204 after-hours bookings, with a 90% booking rate. A plumbing company reduced response time by 40% and increased appointments by 25% in just 3 months. This isn't about replacing your team. It's about removing the bottlenecks that slow them down. AI handles the busywork. Your techs handle the craftsmanship. This is exactly the kind of automation we build for home services businesses at Makarios. Systems that save time, book more jobs, and run in the background, without adding more work to your plate. Have you tried AI scheduling in your business? What's been your experience, game changer or overhyped? I'd love to hear what's working (or not).

Most waited feature: SAP brings shift scheduling home — no integration, no sync, no guesswork. The latest SAP SuccessFactors Workforce Scheduling capability marks a major milestone in time management transformation. For the first time, HR leaders can: 1) Create weekly and monthly shift plans within SuccessFactors 2) Align staffing with real-time production demand 3) Auto-match employees based on skills, certifications, and availability 4) Sync shifts seamlessly with Time Tracking and Payroll No more juggling third-party tools. No more data sync delays. Also roadmap defined, SAP announced upcoming extensions: 1) Smart Shift Proposals: AI-generated schedules (based on demand, skills, holidays). 2) Joule Integration: Conversational “Create next week’s schedule for Plant 01” capability. 3) Integration to Payroll Costs in Business Data Cloud. Everything — from demand to shift to pay — now flows within one intelligent platform. It’s efficient, compliant, and AI-ready. This is how HR and Operations finally converge under a single source of truth: SAP SuccessFactors. #SAP #SuccessFactors #WorkforceScheduling #AIinHR #SAPTimeTracking #DigitalHR #ShiftPlanning #HXM #RetailWorkforce #ManufacturingWorkforce #LogisticsWorkforce #healthcareWorkforce

🚀 Just witnessed a contact center breakthrough that's actually worth noting - Amazon Connect dropped multi-skill forecasting!🚀 We have treated (human) agents like interchangeable with others for years. Your tri-lingual superstar gets the same scheduling priority as someone who barely handles basic English calls. Meanwhile, French customers wait 5 minutes while English-only agents sit idle. What changed: ✅AI now segments demand by actual skills (language, expertise, technical know-how) ✅Agents get matched to their strengths, not just availability ✅Real-time deployment based on demand patterns ✅SLAs actually get met without overstaffing I watched a demo where tri-lingual agents seamlessly flexed between French peak periods and English overflow. Without manual intervention & without missing targets. This is intelligent workforce deployment. #WorkforceIntelligence. The math is simple: Right agent + Right customer + Right time = Better CX and lower costs. #ContactCenter #WorkforceManagement #AI #CX #AmazonConnect What is your biggest scheduling headache? Curious how others are tackling multi-skilled teams 👇 Tom J. Kevan Mah Chris Lee Randy Woo Matt Pope Matt Taylor Connie Watkins Christopher Hernandez, MBA Sunil Menon Jitender Singh Asher Bramwell Hannah Bloking Kishore Korimilli Ayesha Borker Mo Miah Ankur Taunk Naveen Narayan Yati Barakin Vikas Prasad Cheryl Yu Sergio Klarreich James Huddleston

🆕 Shift planning & scheduling just got smarter in Dynamics 365 Contact Center 📅 Smart shift planning is no longer a luxury - it's essential. One of the latest updates to Dynamics 365 Contact Center brings some much-needed maturity to workforce management in modern service environments. Microsoft has just rolled out shift planning and scheduling capabilities to their native CCaaS solution, designed to align teams with real-world demand - something many platforms still struggle to get right. Here’s why I believe this matters: 🔹 Shift templates help standardise coverage and reduce planning overhead 🔹 Capacity-aligned staffing means fewer coverage gaps and burnout 🔹 Skills-based assignments = better routing and faster resolution 🔹 Built-in break distribution and structured activities support agent wellbeing 🔹 Bulk scheduling automates the grunt work, while manual tweaks give you agility when you need it most 🔹 Global shift calendars ensure timezone precision across distributed teams In short: better planning = better service, happier teams, and fewer fire drills 🔥 🔍 Link to full article by Gopal Yuvaraj, Product Manager at Microsoft: https://lnkd.in/eDkDHqcM #dynamics365 #contactcenter #customerservice

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

𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥, 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐢𝐧𝐠 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐢𝐧 𝐎𝐫𝐝𝐞𝐫-𝐃𝐫𝐢𝐯𝐞𝐧, 𝐇𝐢𝐠𝐡-𝐕𝐚𝐫𝐢𝐞𝐭𝐲 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐉𝐨𝐛 𝐒𝐡𝐨𝐩𝐬: I noticed underutilization of powerful, scientific scheduling software in many job shops. Some people are happy if the software quickly and effortlessly generates a meaningful schedule for each resource on each day. Such software can offer far more benefits if it is used as intended by its vendor. A job shop scheduler can do the following using a powerful scheduling software. 1. Creating regularly unambiguous job lists (schedules) for individual resources on shopfloor over short intervals like a shift, a day or a week 2. Visualizing the progress and completion times of jobs over the planning period 3. Visualizing the daily resource utilization and temporary bottleneck formations over the planning period 4. Finding right times for releasing material to shop floor for jobs 5. Predicting the impact on KPIs of mitigating temporary bottlenecks by increasing resource capacities over some time intervals 6. Predicting the completion time of a tentative customer order without any priority given the committed customer orders 7. Predicting the impact of expediting a job on the progress and completion times of other jobs 8. Predicting the impact of potential material delays on the progress and completion times of other jobs. 9. Predicting the impact of a large machine breakdown on the progress and completion times of other jobs 10. Predicting the impact of an addition or removal of a worker the progress and completion times of other jobs 11. Assessing the impact of optional vendor operations on the progress and completion times of other jobs 12. Etc. With the availability of powerful, scientific scheduling software, it is relatively very simple and easy for schedulers to create a rational, comprehensive, plant-level, detailed production schedule. However, the scheduler must be intelligent enough to perform required what-if analysis as shown in the above tasks 5 to 11. Any powerful job shop scheduling software like Schedlyzer Lite will support fast and reliable what-if analysis of production schedule in complex job shops. Of course, such software needs reliable production data which does not have to be perfect. I would be happy to demonstrate over web how to perform the above tasks easily so that schedulers can get more value from powerful scheduling software which duly considers uncontrollable variation in the system. #jobshops #hmlv #mto #eto #productionscheduling #productionplanning #schedulingsoftware