Workflow Automation in Supply Chain Management

Athena Voice is now rolling out to customers! Since the Olympus platform is mobile-friendly, we imagine more work will get done on the go. Early adopters are using voice to control the platform across applications: Capabilities 1. Search and filter across your entire Drive to find that exact document... even when the document is a video, audio, ppt, or docx file. Quickly locate any file by speaking the file name or keywords. 2. Pulling data into Sheets and prepping a template for analysis. Effortlessly import data and set up templates for comprehensive analysis, including pivot tables and charts. 3. Writing the first version of a Jupyter Notebook. Generate initial code snippets and markdown cells to kickstart your data science projects. 4. Drafting a document in Reports. Create detailed reports with text, tables, and charts through simple voice commands. Workflow: Supply Chain Use Case for an Enterprise Imagine a supply chain manager at a large consumer products company leveraging Athena Voice to streamline their daily tasks: The manager starts by using Athena Voice to search and filter across their entire Drive to find a specific document related to a recent supplier audit. Whether the document is a video, audio, pptx, or docx file, Athena Voice quickly locates it. Next, they use voice commands to pull the latest shipment data into Sheets. Athena Voice assists in prepping a template for analysis. With the data ready, the manager instructs Athena Voice to create a Jupyter Notebook. The first version includes code snippets for advanced analytics, such as predictive modeling for demand forecasting and optimization algorithms for inventory management. Finally, the manager uses Athena Voice to draft a weekly supply chain performance report in Reports. The report includes key metrics such as inventory levels, shipment delays, and supplier performance, all compiled through simple voice commands.... they may have even completed this entire workflow on their subway ride to work! With Athena Voice, the supply chain manager can efficiently manage their tasks, make data-driven decisions, and ensure smooth operations, all while on the go.

Three more ways AI can enhance the Supply Chain: Improved Warehouse Efficiency AI can enhance warehouse efficiency by organizing racking and designing layouts. By evaluating the quantities of materials transported through warehouse aisles, machine learning models can suggest floor layouts that accelerate access and reduce travel time of inventory—from receiving to racks to packing and shipping stations. They can also plan optimal routes for workers and robots to shuttle inventory more quickly, further boosting fulfillment rates. Additionally, AI-enabled forecasting systems analyze demand signals from marketing, production lines, and point-of-sale systems to help manufacturers balance inventory against carrying costs, thereby optimizing warehouse capacity. More Accurate Inventory Management AI-powered forecasting systems can analyze inventory information shared by downstream customers to assess their demand. If the system identifies a decrease in customer demand, it adjusts the manufacturer’s demand forecasts accordingly. Manufacturers and supply chain managers are increasingly deploying computer vision systems—installing cameras on supply chain infrastructure, racks, vehicles, and even drones—to track goods in real time and monitor warehouse storage capacity. AI records these workflows in inventory ledgers and automates the process of creating, updating, and extracting information from inventory documentation. Optimized Operations Through Simulations Supply chain managers can utilize AI-powered simulations to gain insights into the operations of complex global logistics networks and identify opportunities for improvement. They are increasingly employing AI alongside digital twins—graphical 3D representations of physical objects and processes, such as assembled goods or factory production lines. Operations planners can simulate various methods and approaches on digital twins—for example, how much output would increase if they added capacity at point A versus point B—and evaluate results without disrupting real-world operations. When AI selects the models and manages the workflows, these simulations become more precise than those conducted with traditional computing methods. This application of AI assists engineers and production managers in assessing the impacts of redesigning products, replacing parts, or installing new machines on the factory floor. In addition to 3D digital twins, AI and machine learning can also aid in creating 2D visual models of external processes, allowing planners and operations managers to evaluate the potential impact of changing suppliers, redirecting shipping and distribution routes, or relocating storage and distribution hubs.

AI Roadmap for BOM Optimization ! Optimizing a Bill of Materials (BOM) is no longer a manual, time-consuming task. With AI-powered workflows, companies can streamline data ingestion, eliminate duplicates, enrich records, and continuously monitor supply chain risks. This roadmap shows how AI, automation, and PLM/ERP integration work together to drive efficiency, cost savings, and smarter sourcing decisions. AI not just make BOM management faster - it makes it intelligent. From duplicate detection and automated merging to cost forecasting and scenario simulations, each step builds toward a fully optimized, future-ready supply chain. 🔹 Step 1: BOM Data Ingestion & Consolidation Collect data from ERP, PLM, and spreadsheets into unified pipelines. 🔹 Step 2: Data Cleansing & Standardization Normalize part numbers, units, and metadata for consistency. 🔹 Step 3: Duplicate Part Detection Use NLP embeddings to detect and flag duplicate parts. 🔹 Step 4: Automated Merging of Duplicates Merge duplicates dynamically with automated workflows. 🔹 Step 5: BOM Enrichment with External Data Add supplier, lead time, and compliance data for smarter records. 🔹 Step 6: Intelligent Sourcing Recommendations Suggest alternate suppliers/components based on cost, risk, and availability. 🔹 Step 7: Cost Forecasting Models Predict BOM costs under different sourcing and demand scenarios. 🔹 Step 8: Scenario Simulations Run “what-if” analyses on risks, delays, or compliance changes. 🔹 Step 9: Real-Time Agent-Driven BOM Validation Agents monitor compliance, quality, and risks continuously. 🔹 Step 10: Automated Alerts & Notifications Trigger instant alerts for shortages, compliance breaches, or cost spikes. 🔹 Step 11: Integration with ERP/PLM Systems Feed optimized BOM data back into ERP/PLM systems seamlessly. 🔹 Step 12: Continuous Learning & Improvement Retrain AI models with new supplier and cost data for ongoing optimization. AI is redefining BOM management. Save this roadmap and start building an intelligent, automated BOM system for your business today! For a deep dive into PLM, MES, or CAD and to elevate your understanding of PLM, connect with us at PLMCOACH and Follow Anup Karumanchi for more such information. #plmcoach #plm #teamcenter #siemens #3dexperience #3ds #dassaultsystemes #training #windchill #ptc #training #plmtraining #architecture #mis #delmia #apriso #mes

Gen AI is about to revolutionize Supply Chain In a recent Gartner survey, 36% of leaders see Gen AI contributing to at least a 15% company productivity improvement and 56% see at least an 11% improvement over the next two years. These gains may be no truer then in Supply Chain Management. Gen AI, particularly large language models (LLMs), is already beginning to transform supply chain management by automating data analysis, enabling rapid scenario planning, and improving decision-making efficiency. A recent study in Harvard Business Review, "How Generative AI Improves Supply Chain Management," shows that by integrating LLMs, companies can reduce reliance on data scientists, accelerate insights, and optimize processes like inventory planning, demand forecasting, and procurement. 💡 Key Use Cases of Generative AI in Supply Chain (so far) Data Discovery and Insights: ➡️Querying supply chain data in plain language for immediate insights, e.g., inventory levels, cost optimization, and trend analysis. ➡️ Automating demand-drift analysis, reducing analysis time from weeks to minutes. Scenario Planning: ➡️ Simulating "what-if" scenarios such as cost implications of factory closures or transportation changes. ➡️ Complementing existing mathematical models for customized planning adjustments. Interactive Planning: ➡️ Updating supply chain models dynamically in response to real-time disruptions, e.g., natural disasters. ➡️ Enhancing decision-making by integrating up-to-date business conditions. Contract Enforcement and Optimization: ➡️ Identifying opportunities in complex supplier agreements, leading to procurement savings. Workforce Automation and Collaboration: ➡️ Automating routine tasks like contract generation while enabling strategic roles for human planners. 🚀 Potential for the Future End-to-End Decision Support: ➡️ Full integration of generative AI into supply chain systems to support complex decision-making scenarios like inventory allocation and production planning. Enhanced Collaboration: ➡️ Breaking down silos between functions such as trade planning, demand forecasting, and financial operations, creating a closed-loop system. Workforce Transformation: ➡️ Shifting human roles from operational tasks to value-added activities like strategic planning and supplier relationship management. Increased Automation: ➡️ Automating significant supply chain processes, including planning, execution, and forecasting, while maintaining adaptability to changes. Generative AI holds the promise of revolutionizing supply chain management. But first, companies must tackle the challenges of (1) accessible, clean, organized data, (2) challenges of adoption, (3) workforce training, and (4) system validation. How is your company currently leveraging Gen AI to improve your supply chain? What are additional use cases you are seeing? #supplychainmanagment #supplychain #digitaltransformation #genAI #genrativeAI

🚀 The Evolution of Supply Chain Automation: From Single-Use to an AI-Powered Ecosystem Robotics and automation in the supply chain are no longer just isolated tools—they now form a dynamic, interconnected ecosystem. To stay ahead, businesses must move beyond single-use automation and adopt AI-driven orchestration to optimize how these systems work together. At Kenco Group, we’re leading this transformation by working with strategic partners like GreyOrange and KPI Solutions to develop advanced orchestration software. This technology ensures that autonomous mobile robots (AMRs), robotic picking systems, and AI-driven logistics platforms operate seamlessly, intelligently, and efficiently across the entire supply chain. 📦 Why AI-powered orchestration is essential: ✅ Optimized Robotics Coordination – Ensures different automation systems work in sync ✅ Real-Time Decision Making – AI adapts to demand fluctuations and disruptions ✅ Increased Efficiency & Resilience – Maximizes throughput while reducing errors The future isn’t just about deploying more robots—it’s about intelligently managing automation to drive supply chain agility. With AI at the core, companies can unlock the full potential of robotics and automation to stay competitive in an ever-changing logistics landscape. Excited about where supply chain automation is headed? Let’s discuss! 👇 #AI #Automation #SupplyChain #Robotics #Logistics #SmartSupplyChain #Industry40 #DigitalTransformation #GreyOrange #KPI #Kenco

Managing 100+ suppliers? That’s 100+ potential Post PO risks for procurement. Manufacturing procurement teams I’ve spoken to spend up to 70% of their day coordinating with suppliers. Things slip through the cracks. It happens all the time when you only have messy email threads and spreadsheets to manage your orders after they have been issued. → Endless back-and-forth with suppliers. → Order acknowledgements get missed. → Documents don’t come in on time. → Parts deliveries get pushed back. → Your order is not fulfilled on time. You then face the cost of production switching, rescheduling and downtime. Automated Post PO workflows can eliminate most of this risk for procurement teams. Things like order confirmations, supplier follow-ups, and chasing documents are mundane, repetitive tasks. Once automated, it can free up hours of time for procurement to focus on more strategic activities. This is how you get your orders fulfilled on time without having to chase endless updates.