Michael Amori is CEO and co-founder of Virtuality. Data scientist and entrepreneur with a background in finance and physics.
Accurate demand forecasting is the key to effective inventory, cost management and efficient preparation. Knowing how much stock to keep is essential: too much stock (whether of consumer products or machine parts) leads to wasted resources and increased costs, while too little stock can lead to unexpected downtime and increased risk.
AI-driven demand forecasting has become a powerful way for executives to predict inventory needs, especially in a data-rich landscape that has become too complex for traditional models to analyze.
The power of precision
While it may seem like forecasting demand has become easier in a world that is full of data, the reality is that more information does not mean more information. Modern organizations don’t just collect more data; they also collect more complex data on social media trends, climate change, and economic shifts that traditional business intelligence tools cannot easily (or quickly) analyze for insights.
AI, however, brings a new level of accuracy to demand forecasting by leveraging advanced analytics, machine learning, and large data sets to predict demand with unprecedented accuracy. AI-powered forecasting tools integrate massive amounts of data from multiple sources, then analyze it all to provide business leaders with actionable insights. AI models look for patterns, trends, and correlations that humans or conventional statistical methods might miss, ensuring that leaders can make strategic decisions based on accurate, unified data.
AI-powered forecasting tools help executives anticipate demand more accurately, even in the face of volatility, seasonal variations, or unexpected disruptions. For example, a platform can indicate whether parts need to be ordered early due to an early heat wave or raise an alert when parts are not available for a machine that will break down before its next scheduled maintenance.
AI to the rescue in maintenance, manufacturing and beyond
There are several ways businesses can benefit from AI-driven demand forecasting:
Know what your mission requires
Consider a program that uses AI to forecast demand for different product areas. AI can analyze data from various locations, incorporate historical data, and then predict which assets are likely to be in demand in specific regions or during certain periods. With this information, managers can adjust inventory levels to ensure high-demand items are well-stocked while reducing excess inventory of low-demand parts.
Keep the human informed
AI predicts demand with greater accuracy by analyzing historical data as well as other external data sources such as weather conditions and other events. But great AI also keeps valuable human experience in the loop and takes into account the information from the manager. By combining data and subject matter expertise, AI provides strategic and transparent recommendations.
Reduce waste and costs
Manufacturers must balance the need for efficient production schedules with fluctuating demand. AI can help mitigate this risk by providing more accurate predictions. By analyzing historical production data, part orders, failure trends and external factors, AI models can predict the parts needed, optimal production schedules and the most efficient inventory levels.
Improve relationships with suppliers
By providing suppliers with accurate demand forecasts, manufacturers and maintainers can negotiate better terms, optimize order quantities and reduce the risk of stockouts. With accurate forecasting, manufacturers can coordinate with suppliers to ensure on-time delivery of specific components, avoiding costly production delays. This not only helps maintain consistent production schedules, but also improves overall supply chain resilience.
Tips for Implementing Intelligent AI
For organizations considering adopting scalable AI for demand forecasting, it is essential to approach implementation strategically. Here are three ways to do this:
1. Choose your tools wisely
Selecting the right AI platform requires a thorough assessment of its capabilities. Dive into AI engine specifics with potential vendors. Does it use machine learning, natural language processing, or other advanced techniques? How accurate are its predictions and recommendations?
Find out if the platform has a strong, user-friendly UX and transparent explainable AI (XAI) capabilities. XAI is a human-interpretable framework, enabling maintenance professionals to understand and trust the insights and recommendations created by their AI models. This increases the likelihood that decision-makers will feel more comfortable using the platform to develop their business intelligence – another way to encourage keeping humans in your technology loop.
2. Plan a phased approach
Introducing AI into daily operations often represents a fundamental shift in the way teams approach their work. For many, this change may be met with skepticism or resistance, especially if AI solutions challenge long-standing practices or appear to threaten job security.
A phased implementation approach, for example with a small pilot project, can help demonstrate the value of the AI platform and build stakeholder trust over time, particularly if the AI system incorporates XAI. By implementing AI in a controlled environment, teams can see its benefits firsthand without feeling overwhelmed. As trust grows, extending AI to broader operations will feel like a natural progression rather than a disruptive change.
3. Create the right training programs
For AI and business intelligence to be effective, employees must be trained to use them correctly. Using the tool in the context of real forecast scenarios is essential to avoid remaining a theoretical construct. Through a combination of hands-on training sessions, e-learning, and interactive demonstrations, users can learn how to implement the software’s recommended actions and use them regularly. Offering certifications or recognition for completing training is another great way to motivate everyone to commit to the solution.
Ongoing support is also essential. Establishing a dedicated change management team to monitor adoption, address challenges, and provide ongoing training ensures your teams are well-equipped to leverage AI effectively.
Ready for anything
AI-driven demand forecasting is more than just a tool: it’s a competitive advantage that ultimately increases mission readiness. By leveraging AI, manufacturers and maintainers can predict demand and ensure they are prepared for whatever the mission throws at them.
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