AI technologies can make cost forecasting more accurate by looking at past data, market patterns, and real-time variables to make predictions about costs. AI can use machine learning to objectively analyze how resources are being used and find ways to make production processes more efficient. Using AI-powered digital twins—virtual models of industrial systems—to mimic and improve production in real time is a new approach that cuts down on waste and lowers the cost of production overall.

dear Qasim Habeeb Nashid

In my opinion, AI technologies can significantly enhance cost forecasting and objective measurement by leveraging advanced data analytics, machine learning, and predictive modeling to analyze historical data, identify patterns, and predict future costs with greater accuracy. By automating data collection and processing, AI reduces human error and bias, enabling more accurate budgeting and resource allocation. In addition, AI-based optimization algorithms can identify inefficiencies in manufacturing processes, suggest cost-saving measures, and streamline operations, ultimately reducing manufacturing costs while maintaining or improving quality. Real-time monitoring and adaptive learning further enable continuous improvement and ensure that cost forecasts and measurements remain aligned with dynamic market conditions and operational realities. Of course, digital twins can also play a big role in reducing production costs. I suggest you read my articles to get a better perspective.

Hello Qasim Habeeb Nashid

Great questions — they cut to the heart of how AI can drive smarter, leaner operations.

AI can significantly improve cost forecasting accuracy and objective measurement by leveraging historical data, real-time inputs, and predictive modeling. Unlike traditional methods that often rely on static assumptions, AI can detect patterns, seasonality, and anomalies in financial and operational data — helping businesses anticipate costs with much greater precision.

When it comes to cost reduction, AI contributes in three key ways:

🔹 Elimination: Identifies redundant processes or underused resources using process mining or anomaly detection.

🔹 Consolidation: Recommends ways to streamline vendors, suppliers, or production lines based on usage trends.

🔹 Human Replacement/Support: Automates repetitive or rule-based tasks, freeing up human talent for higher-value activities (e.g., chatbots, robotic process automation).

AI also helps manage production volume fluctuations without distorting cost allocation by dynamically adjusting cost models in real time. For example, machine learning can reallocate fixed and variable costs more accurately based on actual output, demand shifts, or utilization — avoiding the common pitfalls of over/under-costing during production swings.

In short, AI transforms cost management from reactive to proactive — improving visibility, reducing waste, and supporting agile decision-making.

BRgds,

Anil