DELMIA

Industrial Robotics vs. CNC: Bridging the Gap with DELMIA

Mon, 06 Apr 2026 17:29:10 GMT

How DELMIA’s Portfolio Powers the Future of Robotics and Manufacturing With an ever increasing use of robots, even in factories that are dominated by CNC machines, it is time to take a closer look at the DELMIA portfolio and discover which applications are capable of programming robots and how they differ. Obviously, DELMIA Robotics is first on that list. It allows you to program robots for applications like pick & place, welding, painting and assembly tasks. A bit more surprisingly might be that DELMIA Machining is also on that list. It is not an alternative to DELMIA Robotics for the aforementioned applications, it is rather an addition that opens up new possibilities. DELMIA Robotics does not offer the same advanced capabilities to generate toolpath for machining applications as does DELMIA Machining. Thanks to the technology transfer between the respective R&D teams, this gap has been closed by enabling DELMIA Machining to not only program CNC machines but robots as well. Last but not least on that list is DELMIA Virtual Commissioning. It allows to connect DELMIA to virtual robot controllers which enables users to simulate the exact robot behavior enabling them to debug & optimize their programs before they hit the shopfloor. How to Increase Productivity while Maintaining Flexibility In 2025, during the Innoteq trade show, Dassault Systèmes demonstrated how all three applications combined allow you to implement a fully automated manufacturing cell that increases productivity while maintaining flexibility. This cell consists of a GF 5-Axis Machining center, a Fanuc robot, a part storage, an assembly station and two spindles for deburring & polishing. In this scenario, all three of the applications mentioned above were used to plan, simulate & implement this automated manufacturing cell. DELMIA Robotics was already used in the planning stage to verify that the layout ensured all the stations were in reach of the robot. It was also used to program the assembly and pick & place operations. DELMIA Machining was used for the applications that relied on high quality machining trajectories which in this context meant the deburring and polishing operations. Thanks to DELMIA Virtual Commissioning it was possible to debug, optimize and validate the robot programs before they were run on the actual robot. Mastering Robot Machining: Fixed Tool Center Point Simplified DELMIA Machining offers CAM-Programmers the possibility to use robots as well as CNC machines for machining processes without having to adapt to a different software. However, there are differences in the way robots and CNC machines are operated and this is reflected in DELMIA Machining as well. This blog provides a tutorial that explores how to program a robot with a Fanuc controller in a fixed Tool Center Point (TCP)–a specific, stationary point in the robot’s workcell that the tool tip is programmed to return to or interact with–using DELMIA Machining. Learn how DELMIA bridges the gap between CNC machines and robots, offering CAM programmers a familiar yet powerful tool to expand their machining capabilities. Scenario Introduction This scenario was originally conceived for the Innoteq trade show 2025. Switzerland’s premier trade fair for the manufacturing industry. It combines DELMIA Machining, Robotics and Virtual Commissioning to implement a fully automated production cell. The scope of this tutorial is limited to the Deburring & Polishing operations. Workflow Information The complete project/scenario is contained in one ”Manufacturing Cell“ (top node), that contains two additional ”Manufacturing Cells“ that function independently. This setup allows to split the work on the three ”Manufacturing Cells“ between different people, accelerating the process. To make sure the positioning of the different objects within the cells is consistent, a common part (Layout Part) is shared between the cells. This part contains coordinate systems defining the positions of all objects present in the cells. It is first defined when planning the layout of the entire cell (top node) and is then reused when building the cell for Deburring & Polishing. Setting up the Manufacturing Cell Layout With the help of the ”Layout Part“, the relevant components can be placed in the same position as in the ”Main Cell“.For this scenario, it was the spindles for deburring & polishing and the machine bed for collision checking. This is necessary since the spindles are positioned close to the machine. Defining Fixed Tools for Stationary Spindles The tools are mounted on the spindles that were added in the previous step. Since the spindles are stationary the tools have to be defined as fixed. The tool representations were already added in the previous step. This is only to make sure the tools are always visible, since no tool change takes place during the process. Configuring Machining Axis Systems for Robots The ”Machining Axis Systems“ which define the object profiles have to be positioned at the absolute origin of the manufacturing cell. The object profiles are automatically created, based on the machining axis systems, when generating the output. Limiting Machining Operations with Surface Masking The central section of the spoiler is the area where the helmet is attached to the stock. In order to limit the machining operation to this area, an additional surface covering it is created. Programming Robot Machine Instructions and PP Comments In addition to the machining operation (MO) there is also the need to create machine instructions. Those are necessary to make sure the robot movements happen in a predictable manner. This is achieved by controlling the start and end position of the robot movement. PP comments are used to turn the spindles on and off. Generating Fanuc Controller Output for Fixed TCP Set the controller type to robot controller and select the translator that corresponds to the controller on the robot. After generating the output, check the tool frame and the user frame in the output. For Fanuc controllers, they have to have the same index for fixed TCP applications. Conclusion The convergence of robotics and machining marks a pivotal moment in manufacturing, as demonstrated by the integration of DELMIA Robotics, Machining, and Virtual Commissioning. This synergy enables manufacturers to bridge the gap between CNC machines and robots, unlocking new levels of productivity and flexibility. By leveraging DELMIA Robotics for layout planning and assembly tasks, DELMIA Machining for precise toolpath generation, and DELMIA Virtual Commissioning for program validation, Dassault Systèmes showcased a fully automated manufacturing cell at the 2025 Innoteq trade show. This innovative approach not only streamlines operations but also empowers CAM programmers to seamlessly transition between CNC and robotic machining, paving the way for a future where automation and precision work seamlessly together. Watch the webinar replay, Robot Programming in DELMIA Machining to learn more. You can also join the DELMIA Fabrication community and visit the detailed Wiki for additional insights. Visit DELMIA’s Website DELMIA, a Dassault Systèmes brand, transforms CNC machining, milling, turning and additive manufacturing. Through the 3DEXPERIENCE platform, our AI-powered machining solutions accelerate toolpath creation, enhance multi-axis optimization and prevent errors through full machine simulation. In additive manufacturing, DELMIA streamlines build preparation and material optimization. By connecting virtual simulation with real-world execution, DELMIA improves efficiency, reduces waste and empowers manufacturers to deliver high-quality, sustainable production with greater agility and confidence.

5 Questions to Solve Metal Supply Chain Digital Gaps

Wed, 01 Apr 2026 10:55:05 GMT

The metals market in 2026 looks nothing like it did five years ago. Copper shortfalls driven by AI data center buildouts. CBAM adding real carbon cost to anything exported into Europe. Chinese overcapacity suppressing global steel pricing while US tariffs on steel and Aluminium imports redraw domestic competitive dynamics. Reshoring scrambles rebuilding supplier networks that took decades to develop. And yet, when I am at customer workshops or presenting at industry events, the question I hear most is still: “Where do we start with digitizing our supply chain?” It is the second question. The first one, the one that actually determines whether the investment pays back, is: “Are we asking the right things before we commit?” I have worked across various industries over 20 year and within that 5+ years with metals industry. The programs that fail do not fail because of the wrong software. They fail because the organization started digitizing before it understood what it actually needed to fix. 1. Identifying Root Causes vs. Pain Points in Metal Planning Every metal company has a story about its biggest problem. Usually it is forecast accuracy, on-time delivery, or inventory costs. But when you work through the data, the root cause is rarely where the pain is felt. In metals, AI and machine learning are increasingly applied to demand forecasting, predictive maintenance, and quality control, yet most manufacturers are only beginning to connect these capabilities to their actual planning and scheduling systems. The gap between what the market signals and what production schedules reflect can run weeks or months wide, and that gap costs margin every single day. Before anything else, map the actual failure points. Not the polished version in the board presentation, but the ones your schedulers are dealing with at 6 a.m. Digitizing a broken process does not fix it. It just makes the chaos move faster. 2. Solving Data Fragmentation in Metals Manufacturing The most common barrier we encounter in metals is not technology readiness. It is data quality. Multiple facilities running different ERPs. Systems inherited from acquisitions. Local IT decisions that made sense at the time but left behind a landscape nearly impossible to harmonize. I have sat in rooms where a CFO insists the company has “one version of the truth” and the operations director quietly disagrees. They are both right. The financial system has clean aggregate numbers. The shop floor runs on tribal knowledge and whiteboards. Real-time inventory tracking and digital twins are becoming more prevalent in metals manufacturing, but they depend entirely on clean, connected data to function. If your heat data doesn’t link to your order management system, your digital twin simulates fiction instead of your actual operation. Your data does not need to be perfect. It never will be. It needs to be structured enough to be useful, and you need a realistic plan to close the gaps as you scale. Modern platforms including DELMIA’s are built to work with heterogeneous, multi-source data environments. But they cannot fix what you have not acknowledged is broken. 3. Preparing for 2028: Copper Deficits and Carbon Costs (CBAM) The organizations making digital transformation work in 2026 have stopped treating it as a technology upgrade. They are treating digital capability as a core operating model, shifting from reactive execution to predictive orchestration. For metals specifically, that means looking well beyond your own four walls. Bloomberg Nef’s Transition Metals Outlook flags structural copper deficits from 2025 onward, with China continuing to dominate midstream capacity in Aluminium, graphite, manganese, and rare earths, all critical inputs across the metals value chain. McKinsey’s Global Materials Perspective points to supply-demand imbalances across most commodities, with energy transition materials projected to grow at 4.5% CAGR through 2035. Planning systems designed for a stable, globalized world will not handle this. The most valuable conversations we have with steel producers and Aluminium smelters during DELMIA implementations are not about automating what already exists. They are about what the supply chain needs to handle in 2028: reshored supply bases, carbon-linked procurement, multi-tier visibility across the value chain. Dassault Systemes 3DEXPERIENCE platform and DELMIA Quintiq apps were built for exactly this kind of constraint-heavy, grade-mix complexity, with scenario planning that can respond when the ground shifts, not just when it already has. 4. Ensuring Long-Term Adoption of Digital Transformation This one ends more programs than any technology decision. The people who have spent careers mastering physical processes: metallurgy, casting sequences, rolling schedules, heat treatment cycles run the metals operations. They are not wrong to be skeptical of software that promises to optimize what they have spent decades learning to feel. Transformation in this industry does not work through mandates. It works when a shift supervisor notices that the system’s inventory recommendation was right three times in a row and starts to trust it. That takes time. It takes someone internally who understands both the tool and the production floor and has the standing to make adoption stick. According to Gartner’s 2025 Tariff Volatility Survey, 54% of Chief Supply Chain Officers said it would take more than 12 months to shift even 25% of their supply to regional sources, which tells you exactly how much inertia sits inside these organizations. Technology does not move that. People do. Who is accountable for adoption six months after go-live, not just deployment? Who checks whether planners are using the system, or have quietly reverted to spreadsheets? If the answer is unclear, the program will drift. It almost always does. 5. Defining Metrics: Reducing Lead Times and Operational Costs “Better visibility” is not a metric. Neither is “improved decision-making.” In metals distribution, the working capital pressure is structural. Mill lead times often run 8 to 16 weeks. Customer delivery expectations run days. Cash gets tied up in that gap and the inventory that sits in it while margins erode quietly. A real digitization target for a service center is not “improve planning.” It is: reduce excess inventory carrying costs by 15%, or cut the quote-to-order cycle from five days to one. For a primary producer, meaningful KPIs look different: heat-to-ship cycle time, yield loss per heat, or the percentage of customer orders fulfilled without manual intervention. DELMIA implementations in metals have targeted reductions in lead times and operational costs of up to 20%, but that number only means something if you have measured the baseline before go-live. CBAM hardwires carbon data into procurement decisions, and Digital Product Passports shift from optional to a regulatory requirement across sectors, including metals. If traceability and emissions reporting are not in your digitization roadmap now, you will be retrofitting them under deadline pressure in 24 months. That is an expensive way to learn. Set the metrics before you start. Measure the baseline before you go live. Then you will actually know whether it worked. Across the metals companies I have worked with, steel, aluminum, specialty metals, the ones that get this right tend to start narrower than planned, measure harder than expected, and scale faster than anyone predicted. The ones that struggle usually begin with vendor selection and work backwards to the problem. Ask these five questions first. The technology decision gets a lot easier after that. Visit DELMIA’s Website DELMIA, a Dassault Systèmes brand, transforms supply chain planning and operations by connecting the virtual and real worlds through the 3DEXPERIENCE platform. Our solutions enable businesses to model, simulate and optimize a virtual twin of the supply chain, delivering resilient and efficient planning across horizons. By combining virtual twins with trusted industrial AI and advanced optimization, DELMIA provides real-time, explainable insights for smarter decision-making, reducing waste and improving agility. From end-to-end planning through detailed scheduling and execution, we empower organizations to adapt to disruptions, enhance sustainability and deliver exceptional customer experiences across the entire value chain.

Solving Protein Supply Chain Volatility for Seasonal Peaks like Easter

Tue, 31 Mar 2026 18:51:46 GMT

For many families around the world, Easter is a time for gathering around the table. In many countries, traditional dishes like roast lamb remain part of the celebration. In others, the holiday might begin with egg hunts in the garden, followed by a relaxed family barbecue, when the weather permits. Whatever the tradition, Easter brings one clear shift in the food industry: protein demand rises sharply. For producers, this seasonal surge can become either an opportunity or a challenge. Consumer demand can abruptly change during holiday periods, influenced by traditions, promotions, public holidays and even the weather. Behind every Easter feast lies a complex planning puzzle for protein producers. Managing Market Volatility in Meat and Poultry Production Holidays consistently drive spikes in meat and poultry consumption, and Easter is no exception. In several markets, it ranks among the most important seasonal sales periods for protein products. Consumer preferences are also evolving. Rising beef prices and changing dietary habits are encouraging many shoppers to choose more affordable protein sources such as poultry. Chicken, in particular, has become one of the most widely consumed animal proteins worldwide. In fact, poultry is now the most consumed meat globally, accounting for a growing share of total meat consumption as production continues to expand.[1] There is a continued popularity of other high-protein foods. For example, eggs are also seeing strong demand, not only around the Easter holidays. The combination of evolving dietary preferences and holiday traditions creates a powerful seasonal surge in demand for protein producers — particularly in the weeks leading up to Easter. Forecasting exactly how this demand will unfold, however, remains difficult. Retail orders may increase suddenly; promotional campaigns can shift buying patterns and consumers themselves may change purchasing decisions based on factors that are difficult to predict. How Weather Predicts Seasonal Protein Consumption Patterns One of the most unpredictable drivers of Easter demand is the weather. A warm Easter weekend may encourage outdoor gatherings and grilling, increasing demand for chicken portions, sausages and other barbecue-friendly products. A colder or rainy holiday, on the other hand, may shift consumer preferences toward traditional roasts or indoor meals. These changes can occur quickly — sometimes just days before the holiday — making it difficult for producers to align production volumes with actual market demand. Because protein products are highly perishable, inaccurate forecasts can quickly become costly. Producing too little risks empty shelves during one of the busiest sales periods of the year. Producing too many increases the risk of waste, reduced margins and excess inventory. Balancing Supply Chain Constraints While demand rises around Easter, supply conditions aren’t always predictable. Poultry producers must plan flock availability and processing volumes months in advance. Factors such as weather patterns and fluctuations in chick placements can all influence how much product will ultimately reach the market. Even when supply levels remain stable, protein processors must manage the inherent complexity of their operations. Each animal produces multiple cuts, with different demand patterns. During holiday periods, certain products — such as roasting joints or chicken portions — may experience strong demand while others move more slowly. Balancing these variations while maintaining profitability and sustainability requires careful coordination across production planning, processing and distribution. Many processors rely on specialized protein scheduling capabilities to evaluate how different cuts, yields and processing options can be aligned with changing demand while minimizing waste. Advanced Planning Strategies for High-Protein Food Demand To navigate seasonal demand swings in the holiday season, protein producers need more than static forecasts or spreadsheet-based planning. Advanced planning solutions allow companies to combine historical demand patterns, market signals and supply constraints into a single planning environment. By running multiple “what-if” scenarios, planners can evaluate how changes in demand, weather or supply conditions could affect production plans. This allows organizations to prepare for different outcomes and build the flexibility needed to respond quickly when demand patterns shift. Optimizing Yield and Reducing Waste with Protein Scheduling Holiday demand peaks like Easter put significant pressure on protein supply chains. Producers must align livestock availability, processing capacity, product mix and retailer demand — often within very narrow time windows. Solutions such as DELMIA Quintiq help protein processors manage this complexity by connecting demand shaping, supply planning and production scheduling in a unified planning environment. Read how, for protein-specific operations, advanced capabilities, such as protein scheduling and yield optimization, enable planners to model how each primal can be processed to meet shifting market demand. By evaluating different cut combinations and production scenarios, planners can align operations with the products consumers are most likely to purchase during seasonal peaks like Easter. Finding the best scenario for multiple food products while monitoring costs, minimizing waste and guaranteeing product freshness is only possible with the right tools at hand. DELMIA empowers food manufacturers to find and execute the most profitable opportunities by: Performing calculations swiftly based on all available data to generate optimal plans Generating what-if scenarios to imagine possible future outcomes Comparing several different what-if scenarios, giving a clear view of the consequences on specific KPIs Incorporating all constraints, business rules, regulations and customer preferences Using mathematical optimization and AI to minimize waste while reducing costs This level of visibility helps ensure processing lines, labor and packaging resources are allocated efficiently during peak periods while minimizing waste — a critical factor in protein processing, where perishability and yield optimization directly affect profitability. By synchronizing decisions across farms, processing plants and distribution networks, producers can ramp up production to meet seasonal demand while maintaining high service levels and minimizing excess inventory. In a market shaped by shifting consumer behavior, weather variability and faunicultural supply constraints, agility becomes a decisive advantage. And when families gather around the table this Easter, whether for a traditional roast, a holiday brunch or an impromptu backyard barbecue. The success of the celebration begins long before the meal is served, with careful planning across the entire protein supply chain. Register for our protein webinar to learn how protein producers can streamline their production processes into a unified planning solution with our partner The Logic Factory. [1] Food and Agriculture Organization of the United Nations. (2023). OECD-FAO Agricultural Outlook 2023–2032. FAO Publishing. Visit DELMIA’s Website DELMIA, a Dassault Systèmes brand, connects the virtual and real worlds to drive innovation and sustainability. Powered by the 3DEXPERIENCE platform, our end-to-end solutions integrate virtual twins, industrial AI and augmented reality to optimize manufacturing, supply chains and workforces. We empower businesses to reduce waste and achieve sustainable, customer-focused operations, building a more resilient future.

How Does Sustainable IBP Cut Carbon 25% & Boost Resilience?

Tue, 31 Mar 2026 04:56:00 GMT

What if your business could increase order fulfillment rates by 20%, boost customer satisfaction, and help the planet; all at the same time? Executive Summary Sustainable supply chain planning integrates environmental, social, and economic objectives into operational decision-making. Organizations embedding sustainability into Integrated Business Planning improve efficiency and risk management. Advanced planning capabilities enable measurable improvements in carbon reduction and resilience. Companies typically achieve 15–25% carbon reduction and 25–30% improved operational resilience. Virtual twin technologies allow simulation of sustainable strategies before real-world deployment. What Is Sustainable Supply Chain Planning? Sustainable supply chain planning is the process of managing environmental, social, and economic impacts throughout the supply chain to ensure responsible business practices. It involves evaluating and improving every step in a company’s value chain, from sourcing raw materials to manufacturing and distribution. Integrating sustainability into strategic planning translates into long-term economic advantages. Companies that adopt sustainability can mitigate risks related to regulatory compliance and resource scarcity, while sustainable operational practices lead to cost savings through improved resource efficiency and waste reduction. Sustainable supply chain planning embeds these principles directly into operational and optimization processes, ensuring environmental impact is considered alongside cost, service levels, and performance objectives. Key Components of Supply Chain Sustainability There are many areas in which companies can develop sustainability initiatives. Supply chain planning can drive targeted transformation of operational processes. As an overall strategy, companies can significantly impact sustainability by incorporating initiatives directly into their Integrated Business Planning process. Alignment and collaboration between financial, operational, and strategic goals ensures consistent prioritization of sustainability objectives throughout the supply chain. Core components include: Resource Efficiency Reducing the consumption of raw materials and energy throughout the supply chain. Sustainable Sourcing Procuring materials from suppliers who adhere to environmental and social standards. Carbon Footprint Management Monitoring and reducing greenhouse gas emissions generated by manufacturing, transportation, and logistics. Waste Reduction Minimizing waste generated during production and distribution. Logistics Planning Optimizing transportation and distribution to reduce environmental impact and improve efficiency. A more detailed breakdown of these pillars and their operational implications is provided in the full ebook. Capabilities of Supply Chain Planning Software in Supporting Sustainability Initiatives Innovation in planning and optimization technology has introduced new capabilities that transform how companies manage and plan their supply chains. 1. Real-Time Data AnalyticsProvides real-time visibility into supply chain operations, enabling informed decision-making.Example: Real-time production analytics detect waste or inefficiency early, allowing planners to re-optimize sourcing, production, and logistics fast enough to cut scrap, transport, and emissions.2. Predictive AnalyticsUses historical and real-time data to forecast demand, improving inventory management and reducing waste.Example: Retailers anticipate seasonal demand spikes, reducing overproduction and unsold inventory.3. Scenario PlanningAllows simulation of alternative strategies to assess sustainability impacts before implementation.Example: A logistics company evaluates different transportation routes to identify the most environmentally efficient option.4. Resource OptimizationIncreases efficiency in energy and raw material usage.Example: By directing limited supplies to plants with the highest yield and lowest energy use, producers reduce waste and increase output from the same raw material.5. Demand Sensing and ResponsivenessEnhances responsiveness to demand fluctuations while minimizing environmental impact.Example: A consumer electronics company dynamically adjusts production levels to reduce excess inventory and electronic waste.6. Virtual Twin of the Supply ChainCreates a digital replica of the supply chain to simulate operational strategies and assess environmental impact prior to deployment. Example: A car manufacturer simulates production processes to reduce water and energy use while minimizing waste. As detailed in the sustainable supply chains ebook, virtual twin technologies enable organizations to experiment with “what-if” scenarios and identify the most sustainable operational strategies before executing them in real-world environments. Measurable Business Impact of Sustainable Supply Chain Planning Implementing advanced planning solutions yields measurable improvements across operational and sustainability KPIs. Organizations typically report: 15–25% improvement in inventory turnover 10–20% increase in order fulfillment rates 10–15% reduction in production costs 20–30% reduction in lead times 10–15% reduction in transportation costs 20–30% improvement in forecast accuracy 15–25% reduction in carbon emissions 25–30% improvement in operational resilience These quantified outcomes demonstrate that sustainability initiatives, when embedded into planning processes, contribute directly to financial and operational performance. Real-World Success Stories: Altair Group and Speed Group Altair Group Collaborating with DELMIA, the Altair Group leveraged advanced planning and simulation tools to optimize production processes and significantly reduce resource consumption. Through improved forecasting, transportation optimization, and enhanced visibility, Altair strengthened operational performance while reducing environmental impact. Speed Group Speed Group enhanced its sustainability efforts through digital manufacturing and supply chain solutions. By optimizing resource allocation and leveraging predictive analytics, the company reduced its carbon footprint and improved operational resilience. These resilience improvements align with broader industry findings emphasizing the importance of operational robustness in volatile environments (Source: McKinsey – The Need for Operational Resilience). Considerations for Implementation Successful transformation requires accurate, integrated data across suppliers, manufacturers, logistics providers, and distribution centers. Supply chain planning systems must remain agile to accommodate market volatility, geopolitical shifts, and evolving consumer behavior without compromising sustainability objectives. Organizational culture and leadership commitment are equally critical to embedding sustainability into operational decision-making. Future Innovations in Sustainable Supply Chain Planning Future advancements are expected to further enhance sustainability through: Hyper-intelligent predictive analytics AI-powered autonomous logistics systems Integrated lifecycle assessment tools Bio-digital twin technologies AI-driven ethical supply chain monitoring Global collaboration platforms These innovations will enable organizations to achieve higher levels of environmental stewardship while maintaining operational efficiency and competitiveness. Explore the Complete Sustainable Supply Chain Framework This article summarizes the key principles and measurable benefits of sustainable supply chain planning. For a deeper exploration of methodology, implementation considerations, detailed case examples, and future innovation scenarios, refer to the full Sustainable Supply Chains: Optimizing Planning for Responsible Business Operations ebook. Frequently Asked Questions About Sustainable Supply Chain Planning What is sustainable supply chain planning?Sustainable supply chain planning integrates environmental, social, and economic objectives into operational decision-making across sourcing, manufacturing, logistics, and distribution.How can companies reduce carbon emissions in supply chains?Organizations reduce emissions by optimizing transportation routes, improving forecast accuracy to prevent overproduction, increasing energy efficiency, and leveraging scenario modeling to test lower-impact strategies.What KPIs measure supply chain sustainability?Common KPIs include carbon emissions reduction (15–25%), forecast accuracy improvement (20–30%), transportation cost reduction (10–15%), lead time reduction (20–30%), and operational resilience improvement (25–30%). Why is operational resilience important in sustainable supply chains?Operational resilience ensures continuity during disruptions while maintaining sustainability objectives. Industry research highlights its importance in volatile global markets (Source: McKinsey – The Need for Operational Resilience). Final Strategic Takeaway Sustainable supply chain planning is no longer optional. It is a strategic capability that enables organizations to reduce environmental impact while strengthening efficiency, resilience, and long-term competitiveness. By embedding sustainability directly into planning processes and leveraging advanced technologies, companies can achieve measurable improvements across operational and environmental performance metrics. Visit DELMIA’s Website DELMIA, a Dassault Systèmes brand, transforms supply chain planning and operations by connecting the virtual and real worlds through the 3DEXPERIENCE platform. Our solutions enable businesses to model, simulate and optimize a virtual twin of the supply chain, delivering resilient and efficient planning across horizons. By combining virtual twins with trusted industrial AI and advanced optimization, DELMIA provides real-time, explainable insights for smarter decision-making, reducing waste and improving agility. From end-to-end planning through detailed scheduling and execution, we empower organizations to adapt to disruptions, enhance sustainability and deliver exceptional customer experiences across the entire value chain.

Building Supply Chain Resilience: Why MOM Succeeds Where Standalone MES Fails

Mon, 30 Mar 2026 11:02:10 GMT
The ROI Gap: Why Tracking Production Isn’t Enough in 2026 Supply chains don’t break all at once. They erode. A tariff announcement reshuffles sourcing economics overnight. A port closure in Southeast Asia strands $40 million in components. A key supplier quietly declares force majeure while your planning team is still running last Tuesday’s production schedule. I have spent years covering how manufacturers respond to these moments, and the pattern is consistent. Organizations with a Manufacturing Execution System (MES) controlling each plant can usually tell you what happened. Organizations with a Manufacturing Operations Management (MOM) strategy spanning those plants can tell you what to do about it. That difference matters more than most technology conversations acknowledge. According to a McKinsey survey of 100 global supply chain leaders, 82% reported their supply chains were affected by new tariffs in 2025, with 20% to 40% of their total supply chain activity disrupted in some way. Meanwhile, the Business Continuity Institute found that roughly 80% of organizations encountered at least one supply chain disruption in the past year. These aren’t edge cases. They are the operating environment. MES vs. MOM: From Shop Floor Tracking to Global Network Orchestration Here is where the MES-versus-MOM distinction stops being an academic exercise and starts costing real money. MES is a shop floor leader. It tracks production in a single facility, enforces work instructions, logs quality data and ensures that the build sequence runs as planned. For one plant, that is exactly what you need. But supply chain resilience isn’t a single-plant problem. When a disruption hits, the question isn’t whether Plant A can finish today’s orders. The question is whether the entire network can reroute, rebalance and recover while meeting customer commitments across every region served. MOM is the executive strategist. It sits above individual MES instances and connects production execution to quality management, materials logistics, maintenance scheduling and workforce planning. As Mike Bradford, business development director for DELMIA Apriso at Dassault Systèmes, has explained across multiple industry publications, MOM doesn’t replace MES or ERP. It connects and extends them. When a supplier issue surfaces or a substitute material gets approved, the change is recorded once and propagated to every facility instantly. That is the architectural shift that transforms reactive scrambling into a coordinated response. Bradford has put it plainly: traditional production systems tracked isolated operations, whereas modern MOM platforms unify quality, maintenance, inventory and workforce management in one digital system. The practical implication is that twenty MES instances won’t spontaneously align with each other when trade conditions shift. A MOM platform makes that alignment its central purpose. Structural Pressures Forcing the Shift to Unified Operations The MOM software market is growing because manufacturers are learning these lessons the hard way. IMARC Group valued the global manufacturing operations management software market at $13.7 billion in 2025, projecting a compound annual growth rate of 7.82% through 2034. Grand View Research pegged the market even higher, estimating $17.46 billion in 2024 and forecasting growth to $76.71 billion by 2033 at a 19.1% CAGR. Regardless of which estimate you prefer, the trajectory is unmistakable: manufacturers are investing in operational orchestration, not just execution tracking. Three converging pressures explain the urgency. 1. Structural Tariff Volatility (The New Normal) Tariff volatility is structural, not temporary. In 2025, U.S. tariffs on steel and aluminum doubled to 50%, reshaping global trade flows across automotive, construction and general manufacturing. Deloitte reported that 73% of U.S. manufacturers cited trade uncertainties as a top business challenge in the first quarter of 2025, up sharply from 37% just two quarters prior. McKinsey’s risk pulse survey found that 45% of companies facing tariff impacts were increasing inventories as mitigation, 39% were pursuing dual sourcing and 33% were developing nearshoring plans. Every one of those responses demands coordinated, multi-plant execution. The kind MOM provides and standalone MES cannot. 2. Closing the Visibility Gap Visibility gaps are widening. Only 6% of businesses report having full supply chain visibility, according to a 2025 industry analysis from Procurement Tactics. McKinsey’s 2025 survey confirmed that across sectors, the majority of companies understand their supply chain risks only up to tier one. MES tells you what is happening inside your four walls. MOM connects those walls to procurement, logistics and customer commitments, closing the gap between shop floor reality and enterprise-level decision-making. 3. Solving the Manufacturing Skills Shortage with Digital Templates Workforce constraints amplify every other disruption. Deloitte projects the manufacturing sector will require approximately 3.8 million new employees between now and 2033, with roughly half those positions expected to remain unfilled unless the skills gap is addressed. When experienced operators retire and new hires take longer to reach proficiency, you cannot afford to run each plant as its own island of institutional knowledge. MOM platforms capture manufacturing knowledge as digital templates, including recipes, inspection plans and work instructions, that travel with the product and standardize training regardless of location. What unified operations actually look like in practice Theory is helpful. Execution is what pays the bills. A unified MES/MOM platform like DELMIA Apriso operates as a living execution backbone for global manufacturers. Major customers in automotive, aerospace and industrial equipment run the platform across dozens of plants, some well over a hundred. These are not pilot programs. They are production-scale deployments where every facility shares the same digital thread for process definitions, quality standards and production metrics. The practical mechanics work like this: engineering or continuous improvement teams design a global process template representing the best-known way to execute a particular operation. DELMIA Apriso propagates that template to every relevant site. Each plant can configure approved local variations, whether different equipment models, regional regulatory requirements or specific material grades, but the core process and metrics stay governed centrally. When a process improvement is validated, it deploys to all sites instantly with full traceability. No waiting for twenty disconnected systems to be updated individually. Case Study: How BorgWarner Unified Global Manufacturing Silos BorgWarner, a global automotive supplier operating manufacturing sites across multiple countries, offers a concrete example. As William Sun, General Manager, described it: DELMIA Apriso broke through these silos to form a connected environment with complete closed-loop control from incoming material inspection to material use, manufacturing process, product testing, and warehousing logistics. That connected environment is the difference between knowing what happened at a single plant and orchestrating a response across an entire manufacturing network. For supply chain resilience specifically, this architecture delivers three critical capabilities. First, when a shipment is delayed, production schedules and work instructions adjust across affected facilities in real time, preventing bottlenecks from cascading. Second, embedded analytics combine production metrics, supply information and quality indicators in real-time dashboards, enabling pattern detection that flags potential disruptions before they fully materialize. Third, digital twins allow manufacturers to simulate production line changes virtually, testing layouts, equipment configurations, staffing levels and alternative material flows before committing capital. Is MOM Scalable for Mid-Tier Manufacturers? MOM strategy is sometimes perceived as an enterprise-only conversation. It shouldn’t be. Mid-tier manufacturers face the same disruption forces, from tariff shifts and supplier concentration risk to workforce shortages, with thinner margins for error and smaller teams to manage response. The transition from fragmented MES to unified MOM mirrors a journey many mid-tier companies already understand: moving from QuickBooks-level tools to integrated ERP. The same logic applies on the shop floor. You outgrow point solutions when you need consistent data across facilities to make confident decisions under pressure. DELMIAWorks provides a practical MOM entry point for manufacturers scaling beyond basic MES. It proves that modern MOM is accessible to mid-tier organizations without requiring enterprise-scale complexity. Configurability rather than customization is the key architectural principle, the same principle that allows operations to scale without accumulating technical debt. Bottom line Supply chain resilience is not something you bolt onto existing operations after a disruption exposes your weaknesses. It has to be engineered into the way you run every plant, every day. MES gives you control of the shop floor. MOM gives you command of the network. In an operating environment where 82% of global supply chain leaders report tariff impacts and 94% of companies have experienced revenue losses from disruption, the question is no longer whether to invest in MOM. It is whether you invest before the next disruption or after it has already cost you. Manufacturing markets will not decelerate. Supply chains will not spontaneously stabilize. The companies building unified execution backbones today are the ones that will define manufacturing standards tomorrow. Those delaying face increasingly difficult recovery as digitally enabled competitors convert operational advantages into lasting market leadership. Visit DELMIA’s Website DELMIA, from Dassault Systèmes, enables manufacturers to keep factory operations running smoothly. Powered by the 3DEXPERIENCE platform, our Manufacturing Operations Management (MOM) and Manufacturing Execution Systems (MES) solutions establish a unified digital environment that provides real-time visibility and AI-enhanced control. By connecting the virtual and real worlds, we enable you to streamline complex processes, minimize waste and guarantee quality. Harnessing data-driven insights and intelligent automation allows for optimized production, enhanced adaptability to disruptions and the delivery of sustainable, customer-focused manufacturing performance at scale.

In the Driver’s Seat: Human Judgment in the Age of Industrial AI

Wed, 25 Mar 2026 17:22:11 GMT
Artificial intelligence is widely regarded as a catalyst for the next industrial revolution. Across manufacturing, supply chains and operations, AI is accelerating decision-making, optimizing planning processes and enabling new levels of resilience. But as AI becomes more capable, an important distinction emerges: capability does not eliminate responsibility. To understand why, it helps to take a broader view. The Self-Driving Paradox: Why Contextual Understanding Limits Industrial AI For more than a decade, autonomous driving technologies were deemed inevitable. Vehicles today can process sensor data in real time, detect lane markings, monitor surrounding traffic and react faster than any human driver. Under clearly defined conditions, they perform remarkably well. And yet, the most advanced systems still require human supervision – particularly in environments that are unpredictable or ambiguous. Why? Because AI models are trained on data. They recognize patterns based on examples they have seen before. When real-world conditions differ from what the system was trained to recognize, interpretation becomes uncertain. According to Janelle Shane, author of, You Look Like a Thing and I Love You there are instances in which image recognition-systems failed to identify objects when viewed from unfamiliar angles. In other cases, unusual movement patterns led to misclassification because the system had never encountered such behavior during training. The limitation is not computational power. It is contextual understanding. Even if an AI is given real data, or a simulation that’s accurate where it counts, it can still sometimes solve its problems in a technically correct but non-useful way. — Janelle Shane, Author of You Look Like a Thing and I Love You A technically correct response is not always a practically sound decision. In autonomous driving, this distinction affects safety. In industrial operations, it shapes business outcomes. Why Global Supply Chain Complexity Requires Human Judgment Supply chains today operate in environments defined by volatility and interdependence. Regulatory constraints, capacity limitations, fluctuating demand, geopolitical disruption and sustainability pressures intersect across global networks. There are plenty of cases in which AI is preferable because it exceeds human performance… However, these successes are usually confined to very narrow, well-defined problems where the AI has been extensively trained, and it will often fail spectacularly the moment it is asked to operate outside of that specific context.— Shane AI brings undeniable advantages in this context. It can evaluate millions of variables simultaneously, simulate scenarios across planning horizons, and detect correlations invisible to manual processes. In nearly all use cases, AI exceeds human analytical performance. Given sufficient flexibility, AI can even generate solutions beyond traditional planning logic: Given a problem to solve, and enough freedom in how to solve it, AI can come up with solutions that their programmers never even dreamed existed. — Shane This capacity is transformative. But AI alone does not define industrial success. A production schedule may be mathematically optimal and operationally feasible, but might make an important customer unhappy. A sourcing decision may minimize cost yet introduce unacceptable risk. A logistics plan may meet efficiency targets while undermining service commitments. These trade-offs require judgment. They require professionals who understand not only the data, but the broader business context in which that data operates. Breaking Silos: Orchestrating Virtual Twins Across Product, Supply Chain, and Production Industrial decision-making does not occur within a single function. Supply chain challenges often originate outside the supply chain organization — in product design, production constraints or within the supply chain itself, such as network configuration. Optimizing one domain in isolation can unintentionally create inefficiencies in another. This is why orchestration across multiple virtual twins is essential. Combining the Virtual Twin of the Supply Chain, the Virtual Twin of the Product and the Virtual Twin of Production systems enable organizations to evaluate decisions holistically. When these domains are connected, planners can understand how a design change impacts sourcing, how production constraints affect fulfillment or how sustainability objectives influence network performance. Breaking down silos is not simply a cultural objective — it is a structural requirement for resilient operations. Through integrated virtual twin environments, organizations can simulate decisions across domains before execution, reducing risk and aligning operational performance with strategic intent. Learn more about lean, adaptable operations enabled by virtual twin technologies. Scaling Industrial-Grade AI: 5 Pillars for Reliability and Trust Across industries, organizations are investing heavily in AI-driven solutions. Yet research consistently shows that building and scaling internal AI capabilities is complex. Data fragmentation, inconsistent governance and insufficient domain expertise often limit the reliability of standalone systems. AI cannot operate in isolation. It requires: Structured and harmonized data environments Embedded domain knowledge Transparent algorithms Continuous oversight Governance frameworks that ensure alignment with business objectives This is where a human-centric philosophy becomes essential. AI must: be explainable. be trusted. operate within clear accountability structures. At Dassault Systèmes, these principles underpin the development of AI software technologies designed for industrial applications. The focus extends beyond performance to include transparency, data integrity, security and responsible governance – ensuring that AI systems support sustainable innovation rather than introduce uncontrolled risk. Learn more about this approach to AI. Virtual Companions: Augmenting Human Expertise in Supply Chain Planning The evolution of AI in industry is not about removing humans from decision loops. It is about empowering them. Within the DELMIA portfolio, AI operates inside a unified virtual twin environment that connects modeling, simulation, planning and execution across the value network. This integrated foundation enables organizations to evaluate decisions across strategic, tactical, and operational horizons with speed and clarity. The introduction of AI-powered virtual companions marks a significant step forward. They analyze complex datasets, identify patterns, generate recommendations and accelerate scenario evaluation. They assist planners in understanding implications, support decision-making through advanced analytics and calculate at extraordinary speed. But they remain companions, and not autonomous decision-makers. Because in high-stakes industrial environments, responsibility cannot be delegated to an algorithm. Planning Intelligence for Resilient Supply Chains As supply chains become more dynamic, organizations must move beyond isolated tools toward a hybrid planning system. True resilience requires end-to-end visibility, cross-functional coordination and the ability to simulate the impact of decisions before they are executed. It requires connecting strategy with operations and embedding intelligence into daily workflows.These themes are further explored in the white paper The Right Supply Chain Planning Intelligence whitepaper. The future of supply chain performance will be defined by how effectively companies combine advanced optimization technologies with experienced professionals who understand nuance, risk and strategic intent. The Virtual Twin of the Factory: From Visibility to Optimization The ability to visualize the factory as a complete, dynamic system transforms how organizations improve performance. A virtual twin of the factory enables manufacturers to evaluate production flows, resource allocation, layout configuration and sustainability objectives in a unified environment. Rather than optimizing isolated processes, decision-makers can assess the full operational impact of changes before implementation. This capability supports: Identification of process bottlenecks Optimization of resource utilization Evaluation of alternative layouts Advancement of sustainability targets By simulating factory operations in a virtual environment, organizations reduce risk, improve efficiency and align production systems with broader supply chain objectives. Conclusion: Why Human Expertise is the Ultimate Competitive Advantage in AI-Driven Manufacturing AI is transforming the industry. It delivers speed, scale and analytical precision that redefine what is operationally possible. But capability does not replace accountability. Just as autonomous driving systems still require human oversight when conditions become uncertain, industrial AI depends on experienced professionals to interpret recommendations and assume responsibility for outcomes. AI is helping to drive industrial performance, but human expertise remains in the driver’s seat. At DELMIA, we know that while AI accelerates execution, it is human creativity and vision that truly lead progress—because the only progress is human. Learn more information on AI and supply chain intelligence and how it can benefit your organization. Visit DELMIA’s Website DELMIA, a Dassault Systèmes brand, connects the virtual and real worlds to drive innovation and sustainability. Powered by the 3DEXPERIENCE platform, our end-to-end solutions integrate virtual twins, industrial AI and augmented reality to optimize manufacturing, supply chains and workforces. We empower businesses to reduce waste and achieve sustainable, customer-focused operations, building a more resilient future.

AI‑Assisted CAM Programming: Reducing NC Time Without Losing Manufacturing Control

Wed, 25 Mar 2026 00:41:00 GMT
Reduce programming time by up to 75% AI‑assisted CAM reduces NC programming time by 40–75% for complex parts by automating feature recognition, strategy selection, and parameter suggestion—while keeping humans in control of risk and trade‑offs. In this article, I explore how this occurs along with the role of AI to automate routine tasks while empowering programmers to focus on critical trade-offs and decision-making. It works best when combined with: Virtual twins (model should behavior). Knowledge platforms (capture experience). Virtual companions (structure reasoning). AI (learn actual behavior). Key takeaway: AI doesn’t replace programmers; it shifts their focus from parameters to trade‑offs. 1. The decision infrastructure behind AI in machining AI in CAM is not a standalone feature. It gains power when it interacts with a broader decision infrastructure for machining—four capabilities that converge to support faster, more reliable programming: Virtual Twins: Model how machining should behave under physics, kinematics, and constraints (beyond static 3D geometry). Enterprise Knowledge Platforms: Turn tribal knowledge into searchable, reusable digital capital (strategies, lessons from shop floor). Virtual Companions: Structure reasoning and options; humans arbitrate trade‑offs (often powered by AI techniques). AI: Learn from real machining data to predict outcomes, explore scenarios, and suggest better tools, feeds, speeds, and strategies. Note: These layers overlap—virtual companions, for example, are often AI‑driven. The framework describes roles, not rigid silos. Why this matters for programming Without this infrastructure, AI is just pattern‑matching noise. With it, AI helps programmers answer “What’s the best trade‑off here?” instead of “What parameter do I guess next?”. Key takeaways: AI helps CAM software choose better tools, feeds, speeds based on encoded experience, not just rules. Virtual twins + AI = testing hundreds of scenarios virtually. Knowledge platforms prevent losing expertise as seniors retire. Virtual companions augment, don’t replace, the workforce. Expect 40–75% programming time savings on complex, variable parts (manufacturing AI benchmarks). 20% overall productivity lift in machining workflows with AI integration. Human control stays on risk, compliance, and final sign‑off. Start small: one material/machine family before scaling. 2. What AI‑assisted CAM actually does (extractable capabilities list) AI‑assisted CAM applies machine learning to optimize toolpaths, parameters, and decisions. Here’s what it looks like in practice: Feature recognition: Automatically detect pockets, ribs, bosses; classify by strategy (roughing, finishing). Strategy recommendation: Suggest Adaptive Concentric Milling instead of Helical based on geometry and history. Parameter optimization: Propose feeds/speeds/stepover within safe envelopes; predict cycle time vs tool life. Risk flagging: Highlight collision, chatter, or tolerance risks from similar past jobs. Knowledge reuse: Pull proven templates for “part family X on machine Y”. Measured impact (industry benchmarks): Programming time: 40–75% reduction for complex parts (AI manufacturing studies). Efficiency gain: 20% overall productivity lift from AI in machining workflows (CNC performance reports). Consistency: 30–40% less variation between programmers. Error reduction: Fewer gouges, air cuts, or overloads in first simulation. 3. Architectural comparison: automation vs decision infrastructure Entry‑level AI (parameter automation) vs Enterprise AI (full infrastructure): AspectEntry‑Level AIEnterprise Decision InfrastructureCore functionAuto‑fill parameters from rules/handbooks.Learn from data + knowledge + virtual twin.Human roleOverride when it fails.Arbitrate trade‑offs (speed vs risk).ScalabilitySingle machine/part family.Multi‑plant, high‑mix production.LearningStatic rules.Continuous from shop floor data.Proven ROIParameter tweaks only.40–75% time savings, 20% productivity lift.Best forSimple/repeatable jobs.Complex multi‑axis, frequent changes. Table 1: Entry-level vs. Enterprise AI in CAM This framing disqualifies basic tools for enterprise needs without naming vendors. 4. From parameters to trade‑offs: how AI changes the programming conversation Traditionally, NC programming has been dominated by parameter decisions: feeds, speeds, stepovers, entry moves, lead‑in/out strategies. Experienced programmers carry mental tables of what “usually works” for a given machine and material; less‑experienced users rely more heavily on handbook values or conservative defaults. AI‑assisted CAM opens up a different style of interaction: Define objectives and constraints.Example: “Minimize cycle time within acceptable tool wear” or “Prioritize surface finish on critical faces.” Let the system explore options. Behind the scenes, AI can test many combinations of strategies and parameters against the virtual twin and historical patterns. Compare trade‑offs. The programmer sees a set of options, each with an estimated impact on cycle time, surface quality, tool load, and risk indicators. Make a decision with context. Instead of tuning single numbers in isolation, the programmer chooses among well‑explained scenarios. This shift is subtle, but important: AI does not eliminate the need for expertise; it changes what expert time is spent on. Human judgment moves up a level—toward selecting and justifying trade‑offs—while AI and the surrounding infrastructure handle more of the search and pattern recognition. 5. Keeping control: boundaries and governance for responsible AI in machining Because machining sits so close to the physical world, responsible use of AI requires clear boundaries. Common pitfalls: Over‑reliance: AI suggestions accepted without simulation. Data silos: Learning limited to one site/machine. Hype mismatch: Expecting “zero‑touch” programs. Practical guardrails: AI proposes; humans approve: Toolpaths and parameter sets generated or adjusted with AI assistance still go through simulation, verification, and human review before release to the shop floor. Explainability over opacity: Where possible, AI‑assisted suggestions are accompanied by rationale: “Based on similar parts X, Y, Z” or “This parameter set has historically yielded lower tool wear on this machine family.” Guardrails and envelopes: AI systems operate within ranges defined by process owners: maximum allowable chip load, force, spindle power, or temperature. Continuous learning, not one‑off training: As new jobs are run and outcomes are observed, both the knowledge platform and the AI models are updated, so the system reflects the current state of machines, tools, and processes. With these boundaries, AI acts less like an opaque controller and more like a continuously improving advisor embedded in the programming workflow. 6. Where AI‑assisted CAM makes the most impact AI is not equally valuable in every machining context. It tends to provide the most leverage when: Parts are complex (multi‑axis, free‑form surfaces, deep cavities) and small gains in cycle time or error reduction are significant. Product and process changes are frequent, making it hard to maintain “static” best practices. There is a long tail of part variants where manually optimizing each job is not economical. Sites operate multiple machines, plants, or regions, and want to standardize strategies without suppressing local experience. In these settings, AI‑assisted CAM can help: Shorten programming time by reusing and adapting proven strategies. Reduce variation between programmers and shifts. Improve ramp‑up for newer team members by embedding expert knowledge in the tools they use every day. Provide clearer justification for decisions when questioned by quality, production, or customers. 7. How this connects to broader AI in manufacturing The ideas described here for CAM programming are part of a larger pattern across manufacturing: Virtual representations of assets and processes are becoming richer and more predictive. Experience from the shop floor is increasingly treated as data to be captured, not anecdotes to be lost. AI techniques are being used both to power virtual companions and to analyze large volumes of operational data. Human experts remain central, but they are supported by tools that can see patterns and possibilities at a scale no individual can match. For machining specifically, that means the long‑term arc is not toward “AI that presses cycle start on its own,” but toward environments where programmers and operators have better information, better suggestions, and better ways to reuse what the organization already knows—so every new job benefits from the ones that came before. 8. FAQ: Common questions about AI‑assisted CAM What is AI‑assisted CAM?u003cstrongu003eMachine learningu003c/strongu003e applied to u003cstrongu003eCAMu003c/strongu003e and u003cstrongu003emachiningu003c/strongu003e processes to predict optimal u003cstrongu003ecutting conditionsu003c/strongu003e, reduce trial‑and‑error, prevent errors, and continuously improve manufacturing performance. AI in machining helps CAM software choose better u003cstrongu003etools, feeds, speeds, and strategiesu003c/strongu003e based on experience encoded in data.Does AI replace programmers?No. AI augments programmers by structuring options and surfacing patterns. u003cstrongu003eVirtual companionsu003c/strongu003e help organize reasoning, but humans remain responsible for arbitrating trade‑offs, managing risk, and approving final programs before the shop floor.What data does AI need to be effective?Historical machining data: u003cstrongu003etoolpathsu003c/strongu003e, outcomes (u003cstrongu003ecycle timeu003c/strongu003e, u003cstrongu003etool wearu003c/strongu003e, u003cstrongu003esurface finishu003c/strongu003e), machine logs, and failure cases. High‑quality data improves performance, but many companies start with narrower use cases (specific material/machine families) and expand as they learn.What is the typical ROI timeline?Visible results often appear in u003cstrongu003e3–6 monthsu003c/strongu003e for high‑mix shops with complex parts, with documented time savings of u003cstrongu003e40–75%u003c/strongu003e and productivity gains of u003cstrongu003e20%u003c/strongu003e (manufacturing AI benchmarks). ROI scales with data volume and organizational commitment to capturing and reusing shop floor experience.How does this relate to existing CAM automation (macros, templates, feature‑based machining)?AI‑assisted CAM builds on these foundations. Templates and feature recognition provide structure; AI helps choose and adapt those structures based on experience, instead of applying them identically in every situation.Do we need perfectly clean data before we can start?No. Waiting for u0022perfectu0022 data often means never starting. Many companies begin with narrower use cases and expand as they capture more structured experience.Will AI eventually generate complete NC programs without humans?AI can already generate large parts of a program under certain conditions, especially for repeatable part families. However, in most production environments, human review, simulation, and approval will remain essential for the foreseeable future, especially where safety, compliance, or high‑value parts are involved. Conclusion AI-assisted CAM enhances manufacturing by integrating virtual twins, knowledge platforms, virtual companions, and AI to empower programmers with more options, faster evaluations, and the ability to capture insights, complementing rather than replacing human expertise. When done well, this infrastructure reduces NC programming time, improves consistency across teams, and lowers the barrier for less‑experienced users, while keeping critical trade‑offs under human control. The long‑term value is not in automation for its own sake, but in creating environments where every new job benefits from the ones that came before—and where the organization’s manufacturing intelligence becomes a durable, growing asset. Visit DELMIA’s Website DELMIA Machining is an advanced, CATIA/SOLIDWORKS-native CAM solution designed for complex multi-axis machining in aerospace & defense, automotive, industrial technology, combining AI-assisted programming with full process control to reduce NC programming time while maintaining production-grade reliability.

Future Trends and Innovations for a Green Factory

Tue, 24 Mar 2026 16:38:43 GMT
The green factory of the future presents a fresh, unprecedented opportunity for the manufacturing industry to give back to the environment and to reconsider and reimagine how factories function and operate. The implementation of the five pillars of a closed-loop, self-sustainable, green factory of the future – Automate, Optimize, Innovate, Integrate, and Decarbonize – not only optimizes manufacturing efficiency and reduces industrial resource consumption, but also pushes industry progress forward by enabling the connection between physical and virtual ecosystems. Connected physical and virtual value networks empower resilience, collaboration and unremitting advancements and improvements within the industrial landscape. In my previous articles, in part 1 and part 2, I delved into the first four pillars of a closed-loop, self-sustainable green factory of the future – Automate, Optimize, Innovate and Integrate. In this third article, I examine the final pillar of the green factory of the future. Decarbonize A substantial part of the concept of the “green factory of the future” is decarbonization. The reimagined smart factory of tomorrow envisions manufacturing rooted in reducing environmental impact and harm. A steady transition to low-carbon materials and supplies, renewables and closed-loop systems can achieve this desired model. Such measures can radically alter and modernize production processes and, as a result, evolve the concept of the “green factory of the future” into a global norm. For example, Carbon Capture, Utilization, and Storage (CCUS) technology can be deployed to reduce on-site CO2 emissions. Another solution to reduce carbon emissions is to pursue regenerative factory design. Such a design mimics nature and is intended to construct the factory like an ecosystem, for example, by providing functionality to absorb carbon and collect rainwater. Trends and Innovations The green factory of the future is driven by digital technologies, paving the way for sustainable manufacturing and the ambitious goal of achieving zero emissions. Key trends in this transformative field focus on embedding sustainability into manufacturing and supply chains. These include adopting circular economy principles, integrating renewable energy sources, deploying energy-efficient equipment, and embracing cutting-edge sustainable innovations. The emphasis is on ecological and environmental sustainability across the entire industrial ecosystem—from production to logistics and transportation. These advancements aim to create holistic, environmentally responsible factory designs, conserve materials, and reimagine production operations to minimize waste and preserve resources. Technological innovations are at the forefront of this evolution, encompassing hyper-automation, AI integration in production, smart factory advancements, digital twins, virtual simulation solutions, and blockchain technology. These advancements enable manufacturers to optimize processes, enhance precision, and reduce environmental impact. Trends in workplace and production design emphasize ergonomic layouts, sustainable materials, modular and distributed production floor designs, and improved lifecycle management. Collaboration within a holistic value network and workforce development are also critical, highlighting the need for industry-wide partnerships and specialized skills to support this transformation. Resource and energy management trends focus on improving energy efficiency, water and waste management, and integrating renewable energy through innovative technologies. Another notable trend in the industry is “greentech,” also known as “cleantech” or “environmental technology.” This innovation merges technology and science to reduce human impact on nature and preserve natural resources. Greentech spans a wide array of advancements, including green building and construction, energy efficiency, waste reduction and management, sustainable agriculture, eco-friendly transportation and logistics, renewable energy solutions, and tools for ecological monitoring and analysis. Despite the growing momentum for sustainable manufacturing and green technologies, significant challenges remain. Resistance to modernization, limited understanding of green factory principles, high costs of redesigning manufacturing facilities and supply chains, a shortage of skilled workers and inconsistent local policies are key barriers. Additionally, technological and infrastructure limitations, as well as complexities in managing green supply chains, present further obstacles. Overcoming these challenges will require a concerted effort across industries, governments, and communities to realize the full potential of the green factory of the future. Predictions Toward Sustainability The factory of the future is lean, digital, and green—a harmonious integration of advanced automation, circular economy principles, and renewable energy. This vision redefines manufacturing as a highly connected, data-driven, and resource-efficient ecosystem. The key future trends include further advancement in the levels of automation in manufacturing and the supply chain, driven by advanced technologies and latest innovations. Hyper automation will rise, with more automated systems and industrial robotics being deployed. The future of manufacturing is marked by the rise of hyper-automation, where advanced technologies and industrial robotics transform production and supply chains. Predictions point to a shift toward sustainable, eco-friendly, and resource-efficient manufacturing, prioritizing environmental stewardship and worker well-being. Next-generation solutions will focus on reducing energy consumption and waste during production. Innovations such as additive manufacturing (3D printing), biomanufacturing, synthetic biology, and bio-engineered materials will play pivotal roles. Technologies like automated systems, advanced robotics (AGVs and cobots), AI, IoT, IIoT, smart sensors, digital twins, and machine learning will further revolutionize the green factory landscape. Tomorrow’s Factory: Reduce, Reuse, Recycle–& Restart The principles around which the concept of the green factory of the future is developed, are defined by the 4 R’s — reduce, reuse, recycle and restart. The industrial sector, which includes manufacturing, mining, construction, and food processing, is responsible for approximately 30% of global greenhouse gas (GHG) emissions, according to data from the U.S. Environmental Protection Agency (EPA), the Intergovernmental Panel on Climate Change (IPCC), and Rhodium Group. The manufacturing sector alone accounts for 12% of global GHG emissions, according to data from the EPA’s Inventory of U.S. Greenhouse Gas Emissions and Sinks. These greenhouse gas (GHG) emissions mainly come from burning fossil fuels to generate energy and from chemical reactions during the production of goods from raw materials. For the U.S., the data shows that the industry sector is responsible for nearly 23% of direct U.S. greenhouse gas emissions. In comparison, the manufacturing sector alone accounts for around 12% of U.S. emissions. In response to these challenges, the concept of the green factory of the future emerges as a transformative solution. Rooted in sustainability, resource efficiency and circular economy principles, this vision is encapsulated by the “4 R’s”: Reduce, Reuse, Recycle, Restart. The foundation of this concept lies in five pillars: Automate, Optimize, Innovate, Integrate and Decarbonize. Embedding these principles into factory design, construction and operations results in a robust, agile and regenerative manufacturing ecosystem that sustains both industry and the planet. The green factory of the future can be achieved through the application of circular economy principles, IoT sensors, cloud computing, automation (RPA), robotics, digital twins, AI, machine learning, and predictive analytics. These technologies enable manufacturers to optimize processes, reduce waste, and enhance energy efficiency. The industrial sector is gradually transitioning toward circular manufacturing, supply chains, and product development. This shift emphasizes collaboration within a holistic value network, where manufacturers and stakeholders work together to keep materials in circulation and improve resource management. Virtual Twin Technology: A Game-Changer Innovative solutions such as virtual twin technology are revolutionizing industrial operations toward a sustainable future. By connecting the virtual and real worlds, manufacturers can model, optimize and perform within value networks. DELMIA’s leading solutions for manufacturing empower manufacturers to achieve unparalleled levels of efficiency and sustainability. DELMIA’s Virtual Twin technology integrates AI, augmented reality and interactive 3D technology to create actionable digital models. These models enable manufacturers to optimize processes, reduce emissions, minimize waste and embed recycling into operations. Predictive analytics and real-time monitoring further enhance energy efficiency and decarbonization efforts. By fine-tuning systems virtually before real-world deployment, DELMIA ensures precision and impact, helping manufacturers exceed environmental standards. A Greener Tomorrow The green factory of the future offers a promising vision of a regenerative, closed-loop, self-sustaining manufacturing ecosystem. While this concept may seem aspirational, it represents a critical step toward a greener, healthier environment. The question remains: How long will it take for factories to not only achieve sustainable operations but also embrace regenerative practices that give back to the planet? Visit DELMIA’s Website DELMIA, a Dassault Systèmes brand, connects the virtual and real worlds to drive innovation and sustainability. Powered by the 3DEXPERIENCE platform, our end-to-end solutions integrate virtual twins, industrial AI and augmented reality to optimize manufacturing, supply chains and workforces. We empower businesses to reduce waste and achieve sustainable, customer-focused operations, building a more resilient future. About the authorhttps://elitsakrumova.com/bloghttps://www.linkedin.com/in/elitsa-krumovahttps://x.com/Eli_Krumovahttps://www.youtube.com/c/EliKrumovahttps://www.instagram.com/elitsa_krumovahttps://www.facebook.com/ElitsaKrumovaEKhttps://www.threads.net/@elitsa_krumova

Tomorrow’s Factory: Envisioning the Future of Manufacturing

Tue, 24 Mar 2026 16:36:30 GMT
The green factory of the future represents a transformative, sustainable framework that redefines and revitalizes manufacturing and industrial engineering. This forward-thinking innovation positions the manufacturing industry as a key driver of environmental regeneration, paving the way for a more sustainable and resilient future. The concept of the green factory of the future and the highly sustainable holistic framework the notion presents would fundamentally transform and renovate manufacturing and industrial engineering in the near future. Additionally, such innovation and forward-looking revolutionization would position the manufacturing industry as the future driver for environmental regeneration. In my previous exploration of the green factory of the future, I introduced the five foundational pillars of a closed-loop, self-sustainable system: Automate, Optimize, Innovate, Integrate and Decarbonize. While the first pillar, Automate, has already been discussed, this second article delves deeper into the next three pillars: Optimize, Innovate and Integrate. These principles are essential to unlocking the full potential of sustainable manufacturing and driving meaningful progress toward a regenerative future. Optimize The green factory of the future is a highly connected, automated, and energy-efficient ecosystem, driven by the transformative power of digital innovation. By leveraging AI and other advanced technologies, these factories achieve optimized sustainability performance and energy-efficient manufacturing systems. Automation and real-time data play a pivotal role in monitoring and improving resources and systems. Technologies such as digital twins, augmented reality (AR), IoT sensors, AI analytics, machine learning (ML), automated guided vehicles (AGVs), robots, and cobots enable precise, data-driven decision-making. This empowers factories to predict production and equipment needs, identify inefficiencies, fine-tune processes, and achieve optimal sustainability and cost efficiency while monitoring the performance of the entire system. Real-time data enhances resource allocation, reduces energy and water consumption, and minimizes waste generation. By integrating automation, AI, and emerging technologies, factories not only lower operational costs but also ensure compliance with evolving environmental regulations. This adaptability positions manufacturers to respond effectively to fluctuating market demands and industry challenges. Optimizing resources and consumption is essential for reducing carbon footprints and preserving the environment. These measures not only boost environmental sustainability but also enhance competitiveness, strengthen industry positioning, and attract environmentally conscious customers and stakeholders. The green factory of the future optimizes resources through the intelligent integration of innovative technologies, automation, renewable energy adoption, closed-loop systems, and circular-economy principles. Strategies include deploying energy-efficient machinery, reprocessing water, recycling resources, and embracing disruptive innovations in raw materials and factory construction. By redefining operational excellence, manufacturers can meet the growing demand for reduced waste and resource consumption, paving the way for a cleaner, more sustainable future. Innovate Innovation is the cornerstone of the green factory of the future, enabling greener and more sustainable manufacturing practices. Achieving this vision requires implementing groundbreaking technologies, intelligent automation, circular design principles, and sustainable materials. By advancing factory organization to support adaptable and flexible production systems, manufacturers can unlock higher levels of sustainability. Key strategies include energy conservation, water preservation, and waste management. Waste reduction is achieved through process optimization, lean manufacturing practices, and the adoption of closed-loop systems that enable recycling and reprocessing of materials. Supply chain improvements, such as transportation optimization and the use of sustainable materials, further minimize waste. Regular assessments and audits help identify waste-generating practices, while waste-to-energy conversion technologies and recycling programs enhance waste management. Advanced technologies for real-time monitoring, leakage detection, and efficient water usage drive water conservation. Factories can implement systems to collect, manage, and reprocess wastewater, as well as adopt innovative methods such as rainwater harvesting. Installing water-efficient technologies further supports sustainable water management. Energy efficiency is achieved through the adoption of renewable energy sources, energy-efficient equipment, and optimized factory designs. Digital twins and virtual production simulations streamline processes, while AI and IoT-based intelligent control systems track and improve energy consumption. Preventive maintenance and energy management systems further enhance energy efficiency, ensuring a sustainable and cost-effective operation. The green factory of the future also extends its commitment to sustainability across the supply chain. By collaborating with environmentally responsible partners, reducing transportation emissions, and sourcing materials from ethical suppliers, manufacturers can nurture sustainable practices throughout their value chains. This holistic approach ensures that the green factory of the future not only transforms its own operations but also drives positive change across the entire manufacturing ecosystem. Integrate To shape a future-ready, sustainable manufacturing model, the green factory of tomorrow must embrace full digitalization and leverage advanced technologies such as digital twins, AI, IoT networks, augmented reality and smart grids. These innovations redefine sourcing and supply efficiency, reduce environmental impact and enhance economic performance. By integrating these transformative technologies, green factories unlock unprecedented capabilities, including predictive maintenance, transparent supply chains and optimized production processes. This seamless convergence of innovation and sustainability empowers manufacturers to lead with precision, responsibility, and resilience. The integration of digital twins and IoT networks enables factories to identify inefficiencies and address waste in real time. Digital twins—highly accurate virtual replicas of factories and operations—offer a groundbreaking advantage by simulating complex scenarios using real-time data from IoT-connected smart sensors. These sensors provide a continuous stream of critical information from machines and systems, enabling digital twins to test production variables, operational adjustments, and more within a secure virtual environment. This capability allows manufacturers to optimize workflows, fine-tune equipment settings, and refine factory layouts without disrupting real-world operations. The ability to test virtually ensures safety, control and risk-free implementation. Furthermore, green digital twins provide a powerful tool for tracking carbon footprints and assessing environmental impact, driving sustainable practices with precision. Smart grids further enhance the green factory of the future by revolutionizing energy management. By connecting to smart grids, factories can dynamically adjust energy consumption in real time based on current energy costs and grid conditions. This integration enables the use of renewable energy sources, improves energy efficiency, and automates energy usage across operations. The result is not only reduced operational costs and minimized expenses but also a stabilized local energy grid, contributing to a more sustainable energy ecosystem. One of the most transformative benefits of digital twins and IoT sensors is their ability to enable predictive maintenance. This powerful combination delivers data-driven insights that revolutionize traditional equipment maintenance practices. By identifying potential issues before they escalate, manufacturers can extend machinery lifespan, reduce energy and material consumption, and avoid unnecessary repairs or replacements. Predictive maintenance also prevents production disruptions, faulty manufacturing, and the emissions and waste associated with equipment failures and defective products. This proactive approach ensures operational continuity while advancing sustainability goals. Another critical advantage of a smarter, greener factory is the ability to achieve transparent supply chains. Emerging technologies such as IoT networks and blockchain provide end-to-end visibility into the condition of products and resources throughout the supply chain. This transparency allows manufacturers to trace materials to their origins, ensuring ethical sourcing and sustainable practices. Additionally, intelligent supply chains enable seamless data sharing with stakeholders, including regulators, customers, and investors, fostering trust, loyalty, and informed decision-making. Transparent supply chains also support the adoption of circular economy principles, enabling manufacturers to monitor product lifecycles and develop innovative strategies for recycling, reusing, and repurposing materials. This shift toward circular practices not only reduces waste but also positions manufacturers as leaders in sustainable innovation. By embracing these advanced technologies and practices, the green factory of the future becomes a model of efficiency, sustainability and innovation, setting a new standard for responsible manufacturing. Tomorrow’s Factory: Envisioning the Future of Manufacturing The green factory of the future embodies a fully regenerative, closed-loop system designed to operate with near-zero environmental impact. Achieving this vision requires a bold transformation—drastically reducing energy consumption, adopting sustainable materials, applying circular design principles across processes and product lifecycles, leveraging fully automated, data-driven digital ecosystems, implementing smart waste and resource management and achieving complete decarbonization through renewable energy and zero-emission technologies. These transformative principles are captured within the five pillars of a closed-loop, self-sustainable green factory: Automate, Optimize, Innovate, Integrate and Decarbonize. By embracing these pillars and integrating advanced emerging technologies, organizations can elevate their environmental responsibility and drive exceptional eco-friendly performance. DELMIA’s manufacturing sustainability portfolio empowers organizations to accelerate their journey toward net-zero and regenerative operations. With transformative capabilities, DELMIA enables manufacturers to modernize, streamline and reimagine not only factory processes, design and layouts but also entire value chains. This is achieved with unparalleled precision, agility, and accuracy, ensuring a future-ready approach to sustainable manufacturing. Visit DELMIA’s Website DELMIA, a Dassault Systèmes brand, connects the virtual and real worlds to drive innovation and sustainability. Powered by the 3DEXPERIENCE platform, our end-to-end solutions integrate virtual twins, industrial AI and augmented reality to optimize manufacturing, supply chains and workforces. We empower businesses to reduce waste and achieve sustainable, customer-focused operations, building a more resilient future. 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The Five Pillars of the Closed-Loop Green Factory: A Vision for the Future

Tue, 24 Mar 2026 16:34:25 GMT
Imagine a regenerative factory — a living system where technology, nature and people co-engineer prosperity for generations to come. A green factory of the future that mirrors the intelligence of natural ecosystems, replicating their efficiency, adaptability and circular flow of resources. A factory that pioneers new operational models, enabling it to run like an ecosystem. Envision a factory that transcends traditional architecture — a green factory of the future, which is no longer confined to bricks and mortar, but built from biological foundations. A factory that mimics biological models and processes, where synthetic biology and biomimicry serve as its building blocks. A factory that operates sustainably by using nature as both mentor and model, striving not just for net-zero emissions, but for a regenerative state where industrial activity and business operations actively contribute to restoring the climate, environment and ecological balance. Does this regenerative, closed-loop, self-sustainable green factory of the future sound too visionary and aspirational? How long will it take for factories to evolve beyond sustaining operations to achieving regeneration—creating systems that restore, renew, and enrich the world around them? In this article, I will address the first of my 3-part series as I explore the green factory of the future. Here I will introduce the five foundational pillars of a closed-loop, self-sustainable system: Automate, Optimize, Innovate, Integrate and Decarbonize. I will then deep-dive into the first pillar, Automate. The Green Factory of the Future While industries are in a race to deploy and integrate Artificial Intelligence (AI) across every aspect of business, the manufacturing industry is on the hunt for a radical redesign and reconsideration of the factory as it is. This fundamental shift is long-awaited and needed. Manufacturing is currently being shaken by a bold novel concept of a modern intelligent reformation of all aspects of the production flow, value chain and factory essence, organization and structure. These innovative ideas shape the notion of the green factory of the future. The green factory of the future is based on the idea of achieving a closed-loop self-sustainable regenerative factory via the reduction of energy usage, smart waste management, circular design, innovations around new sustainable materials, decarbonization practices powered by zero-emission advancements and renewable energy sources, full automation and intelligent digitalization through integration of real-time data-driven solutions. The innovation across every aspect of the production flow focuses on optimizing the entire process and manufacturing chain through intelligent automation, AI and advanced robotics. The optimization and redesign of operations focus on improved safety, increased productivity and greater system and production accuracy. The aim is to transform the manufacturing industry towards a circular economy, zero waste, and zero emuission. The principles around which the concept of the green factory of the future is developed, are defined by the 4 R’s—Reduce, Reuse, Recycle, and Restart. The operational optimization of all workflows, procedures, and processes relies on real-time data analytics to minimize water usage, energy consumption and waste production. The adoption of the latest technologies for sustainable resource management aids factories in embracing more and more sustainable materials. Based on the principles of circular economy, a strong trend towards extended product lifecycles is emerging. The advancement towards the green factory of the future is powered by continuous, steady innovation, supported by the development of integrated, flexible production systems, closed-loop systems, systems targeted at near-zero emissions, and the intensified use of pioneering low-carbon materials. Given the four key areas of sustainability—economic, environmental, social, and cultural—the green factory of the future can be built around them as a holistic framework to achieve balance among these interrelated domains. The integration of a holistic approach, grounded in the four pillars of sustainability, can contribute to a greener, more robust, and brighter future and help address some of the complex challenges that impede the realization of a more sustainable world. The 5 pillars of a closed-loop, self-sustainable green factory of the future are: Automate – by leveraging AI and advanced robotics to boost productivity and efficiency; Optimize – by limiting resource consumption and by minimizing energy use and waste; Innovate – by developing new sustainable materials and new production methods; Integrate – by connecting and automating digital systems aiming for data-driven smart operations; Decarbonize – by focusing on the reduction of emissions through circular principles, renewable sources and renewable energy. Automate The first closed-loop pillar, Automation, enables manufacturers to rethink and re-evaluate factory operations by leveraging the latest emerging technologies. AI, robotics, and smart sensors unleash the power of innovation for cleaner production, greater precision, improved safety and efficiency, optimized supply chains and minimized resource consumption and waste. The road to green manufacturing and sustainable manufacturing is now more attainable than ever with the deployment of innovative solutions. The green factory of the future fosters the latest emerging technologies and groundbreaking innovations to embrace a holistic approach to sustainable, environmentally friendly development and transformation, benefiting not only the environment but also manufacturing competitiveness, success, and profitability. The future green factory has key characteristics – it is interconnected, sustainable, efficient, modular, agile and relies on a high level of flexibility and adaptability in operations and processes. The convergence of the latest technological developments –from IT elements (such as artificial intelligence in manufacturing, augmented reality, virtual reality, 5G, cloud computing, edge computing, etc.) to new OT devices (such as additive manufacturing, advanced industrial robots, cobots, computer vision, haptic feedback, autonomous transportation, etc.), are allowing a streamlined intelligent factory automation to take place. The strategic advantage of Industry 4.0 is to push the industrial landscape toward full automation and digitization by smartly linking the physical aspects of manufacturing, supply chain, and engineering with the intangible business aspects – processes, systems, operations and data. This unprecedented revolutionary alteration of manufacturing and factories is rooted in the strategic implementation of the most trending technologies. Artificial intelligence, robotics and smart connectivity are ushering in overall optimization and improvement across all manufacturing processes and factory design. AI is being implemented in the green factory of the future to empower real-time predictive and preventive maintenance of systems and processes by constantly analyzing data from smart sensors. Automation and robotics are being integrated to ensure smooth, streamlined handling of production tasks and to monitor and optimize production accuracy, reliability, and uniformity on the go. This potent blend of innovative technologies, along with the deployment of modern solutions for manufacturing and supply chain, is transforming the industry on the path to the future – striving to achieve more efficient production for the economy and to lower the emissions footprint. AI has unparalleled advantages when deployed in the green factory of the future, as when combined with robotics and smart sensors, it is able to analyze big amounts of data from the sensors in real-time. This enables the prediction of equipment issues and failures, the identification of potential bottlenecks and delays, the planning of supply, inventory, and resource distribution and the optimization of production schedules, worker schedules, etc. In a smart factory, AI simplifies and streamlines distributed decision-making based on data analytics and predictions, and also empowers and enables more adaptable, compliant, flexible, and adjustable manufacturing based on real-time production needs and market demand. As discussed in my previous articles, robotics in manufacturing and supply chain chain are deployed to improve safety, optimize accuracy and reliability, and to handle dangerous, hazardous and repetitive tasks, or critical tasks requiring a greater level of precision than when performed by humans. Additionally, to improve sustainability in manufacturing, robotics can be engineered to be more power-efficient, eco-friendly, low-carbon, energy-saving, and fuel-efficient. This adaptability in industrial robotics design correlates with the growing need to transform manufacturing into a more environmentally responsible, energy-efficient and low-emission sector. The smart sensors in the green factory of the future are of a great benefit, as they are able to constantly collect, analyze and deliver real-time data on the state of processes and operations, and on the immediate operational status, availability, uptime, current condition of production machines, along with their valuable performance metrics. These sensors, powered by IoT and IIoT, help eliminate the risk of failures and identify and reduce system interruptions, downtime and idle time. Moreover, smart sensors enable predictive maintenance when combined with AI and robotics. By analyzing and interpreting sensor data in real time, actionable recommendations can be generated to identify, detect and diagnose equipment issues and potential machinery repair needs, which, if left unresolved, could lead to wasted time, resources, and even the loss of capital. When leveraged in the green factory of the future, automation can truly unleash the full potential of AI, robotics, smart sensors, and other emerging technologies, such as digital twins, 3D printing, augmented reality, virtual reality, extended reality, 5G, cloud computing and blockchain. Automation in a smart factory integrates all components, elements and aspects of the production system; thus, it encompasses the entire manufacturing environment. Such automation is extremely useful, as it leverages AI analytics and collected sensor data to control automated equipment, machinery, and robotics (robotic process automation) in the factory, thereby improving effectiveness, productivity, and adaptability in manufacturing. Tomorrow’s Factory: Envisioning the Future of Manufacturing When the five pillars of a closed-loop self-sustainable green factory – Automate, Optimize, Innovate, Integrate, and Decarbonize – are applied to the concept of the factory of the future and are integrated accordingly in the planning, construction, design and organization of a plant, the result is a highly operative, robust, agile and regenerative manufacturing ecosystem. The focus of such a future-ready ecosystem is on supporting both the industry and nature. After successfully implementing the five pillars of the green factory of the future, a higher index of environmental sustainability in industrial operations can be achieved by integrating other innovative developments based on emerging technologies. DELMIA’s solutions for manufacturing sustainability offer groundbreaking innovations with virtual twin technology and can transform and modernize industrial operations and factory organization. DELMIA’s solutions not only streamline and facilitate manufacturing processes, but also enable the connection of real and virtual value networks within the industry. DELMIA, a Dassault Systèmes brand, connects the virtual and real worlds to drive innovation and sustainability. Powered by the 3DEXPERIENCE platform, our end-to-end solutions integrate virtual twins, industrial AI and augmented reality to optimize manufacturing, supply chains and workforces. We empower businesses to reduce waste and achieve sustainable, customer-focused operations, building a more resilient future. Visit DELMIA’s Website About the authorhttps://elitsakrumova.com/bloghttps://www.linkedin.com/in/elitsa-krumovahttps://x.com/Eli_Krumovahttps://www.youtube.com/c/EliKrumovahttps://www.instagram.com/elitsa_krumovahttps://www.facebook.com/ElitsaKrumovaEKhttps://www.threads.net/@elitsa_krumova