AI
scanning the existing facility. AI can then be used to identify specific robots in the point cloud and replace them with precise 3D engineering models of those robots.
4. AI-DRIVEN INVENTORY MANAGEMENT Effective inventory management is a delicate balance. Too much stock ties up capital and increases storage costs, while too little risks production delays and lost sales. AI is revolutionising this area by providing highly accurate demand forecasting. Machine learning algorithms analyse historical sales data, market trends, supply-chain disruptions and even weather patterns to predict future demand with remarkable precision.
This allows companies to optimise their stock levels and supply plans, ensuring they have the right parts and products at the right time. AI-powered systems can also automate the reordering process, triggering purchase orders based on a combination of stock levels, production activity and demand plans. The result is a leaner, more responsive supply chain that minimises waste, reduces carrying costs and improves cash flow.
5. ENERGY CONSUMPTION OPTIMISATION
Energy is one of the largest operational costs for many manufacturers. AI offers a powerful tool for reducing this expense and improving sustainability. By analysing data from plant-wide sensors, AI systems can build a comprehensive model of a facility’s energy usage. This model can identify equipment, processes and times of day that consume the most power. With this insight, machine learning algorithms can recommend or even automate adjustments to optimise energy consumption. This could involve shutting down non-essential machinery during periods of low production, adjusting HVAC systems based on real-time occupancy and weather conditions, or optimising machine settings to run at their most energy-efficient levels.
These incremental improvements can lead to substantial cost savings and a significant reduction in a company’s carbon footprint – combining material, process and equipment data to suggest ways to reduce energy costs, optimise labour and improve reliability across the operation as a whole.
THE SIZE OF THE OPPORTUNITY The market reflects how quickly this shift is happening. According to MarketsandMarkets, the value of AI in manufacturing is projected to reach roughly $20 billion within the next few years, growing at a pace well above the broader industrial software market. That growth is being driven less by novelty and more by measurable results: predictive maintenance programmes
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consistently reduce unplanned downtime, and quality-inspection systems built on computer vision routinely catch defects that human inspectors miss on a fast-moving line.
Despite that momentum, adoption still varies significantly by company size and sector. Larger manufacturers with more mature data infrastructure tend to move faster, while smaller manufacturers are more likely to start with a single, narrowly scoped use case, often predictive maintenance or inventory forecasting, before expanding further. Even with that adoption lag in parts of the industry, the broader trend is unmistakable: manufacturing as a whole is likely to continue embracing artificial intelligence, not retreat from it.
DELMIA
www.3ds.com/products/delmia
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