AI for Demand and Supply Forecasting

Demand and supply forecasting tests your company’s ability to predict inventory demand. Too much inventory gathers dust on shelves, while too little means stockouts ‌and disappointed customers. Meanwhile, traditional forecasting methods are showing their limits, forcing businesses to look for new technologies and tools.

Forward-thinking businesses are leveraging the power of AI for inventory planning. The technology brings unparalleled scope, speed, and precision to analyzing data sets, uncovering patterns, and anticipating trends.

Read on to learn how businesses can get started with AI for inventory management and planning, review common inventory planning challenges, and get tips for improving forecasting results and inventory levels.

The Role of AI in Forecasting Demand and Supply

AI in demand and supply forecasting offers a transformative approach for predicting future demand and inventory needs. AI tools use machine learning and data analytics to process large amounts of data, look for patterns, and make informed scenario-based predictions. This approach eliminates manual data analysis and can offer deeper and wider insights than traditional forecasting methods.

AI-powered tools can help supply chain and operations managers adjust forecasts based on changing market conditions, analyze the impact of promotional activities on demand, and even predict potential supply chain disruptions. 

Companies looking to incorporate AI into their forecasting systems have two main options: Use existing tools while adding AI capabilities or incorporate new AI-powered systems. Let’s look briefly at each method.

Integrating AI Into Existing Tools

Many companies start their journey into AI-enhanced forecasting by integrating AI capabilities into their existing supply chain and inventory planning system. Sometimes, these systems bolt on AI capabilities; other times, companies integrate third-party AI analytics tools. This approach can be the least disruptive, as supply chain functions keep existing systems while improving their forecasting accuracy and efficiency. 

 

The AI tool Microsoft Copilot, for example, integrates with tools such as Power BI and Excel. Users can use conversational language to query data and generate insights. Copilot enables businesses to analyze historical sales data, assess market trends, and improve demand planning directly within their familiar Excel spreadsheets and Power BI dashboards. 

Adopting New AI Systems

Some legacy systems can’t be upgraded to use AI capabilities. Other times, companies choose to install new AI-powered systems rather than trying to update old systems or grapple with the integration challenges of third-party tools. 

 

With new systems, you’re getting software designed specifically for use cases including forecasting demand and supply. These systems offer advanced features such as predictive analytics, scenario modeling, and real-time data processing. This pathway can be the best option for companies looking to stay on the cutting edge and fully embrace AI’s potential for forecasting.

Common Demand and Supply Forecasting Challenges

Traditional forecasting methods often fall short in accurately predicting demand and supply patterns, even before your business considers AI-powered alternatives. Let’s explore some of the common challenges faced in demand and supply forecasting.

Data Quality and Availability

Everything in supply chain forecasting and inventory planning starts with data. Do you have the data you need? Is it accessible? Is the data high quality, accurate, and up to date? 

 

There are a few common challenges with data. Historical data, while valuable, can often be incomplete, outdated, or inaccurate. Or, companies might have the data they need but struggle to organize it in one place. Meanwhile, without AI’s sophisticated data-processing capabilities, data isn’t as clean or integrated as it should be, which reduces the effectiveness of data analysis. This leads to less accurate demand forecasts and assessments of supply conditions.

Seasonality and Trends

Recognizing and adjusting for seasonal variances and emerging market trends is crucial, yet outdated sales forecasting methods often lack the agility to do so. Manual analysis and static forecasting models can’t incorporate new information or readily adjust to changes. These supply chain teams struggle to anticipate and react to fluctuations in demand and supply.

Demand Volatility

Market demands are as volatile as ever, with rapidly changing consumer preferences and external factors. Traditional forecasting methods don’t always adapt to these changes, and the result is excess inventory or stockouts. This inability to respond in real time can frustrate customers and escalate financial pressures.

Supply Chain Complexity

Modern supply chains are complex, multilayered, and often global, with a myriad of challenges in securing suppliers and managing lead times. Many companies struggle to track their inventory as it moves through the supply chain.

 

All of this makes forecasting difficult — more so with traditional methods that don’t account for these variables. Misaligned production schedules and inventory levels lead to poor results, inefficiencies, and higher costs.

Lack of Adaptability

A variety of tech challenges can hamper supply chain functions. These include legacy systems that can’t handle modern forecasting techniques, data silos that impede collaboration, and organizational resistance to adopting new technologies or methods. This lack of adaptability means companies are less likely to invest in AI or implement it effectively.

4 Benefits of Unified Planning, Analytics, and AI 

To solve common forecasting challenges, organizations are combining data and AI-powered tools in a single environment. With unified planning, analytics, and AI, you can consolidate data sources and provide a single source of truth, enabling more accurate, efficient, and data-driven decisions. Here are four benefits of this approach.

Enhanced Data Integrity and Insights 

A unified approach provides consistent, complete data for AI algorithms to detect patterns that might elude human analysis without substantial time investment. These patterns encompass a variety of elements, including reorder points, stock levels, and price points. AI tools consider historical trends and external information to furnish a comprehensive picture of present and past performance.

Tailored Analysis for Greater Relevance

One of the most powerful aspects of AI for supply chains is its ability to tailor analysis to specific industries for greater relevance and application. Using Copilot in Power BI, for example, users can interact with data intuitively, blending the human element with machine precision to refine forecasting strategies. The use of regression analysis and evolutionary computation can identify overlooked trends, further enriching decision-making.

Comprehensive View for Improved Decision-Making

Unified planning, analytics, and AI provides a holistic view of the business landscape by combining internal and external data sources. This approach improves forecasting intelligence and inventory control by giving businesses a wider view of conditions — both now and as they change. They can respond accordingly with inventory planning strategies that are resilient and responsive.

Improved Efficiency and Responsiveness

With a single source of truth and AI-powered planning and analysis, teams improve their analytical powers while spending less manual time and effort. Decisions are made more quickly and with greater accuracy, while real-time changes can be dealt with more swiftly by analyzing the latest data. This increased efficiency optimizes inventory planning and improves operational performance, helping businesses stay agile and competitive.

5 Tips for Forecasting Demand and Supply With AI

While adopting AI for demand and supply forecasting offers many long-term advantages, success with AI isn’t automatic. Here are some tips to help you refine your approach and continue to improve your forecasting results.

Prioritize Data Quality and Consistency

The foundation of effective AI-driven forecasting is high-quality, consistent data. Before deploying AI tools, focus on aggregating, cleansing, and ensuring data consistency across all sources. This step is crucial for enabling algorithms to generate meaningful and accurate insights. Historical data, in particular, holds immense value for AI models, but it requires validation and structuring to ensure its reliability for predictive analysis.

Integrate External Data for Comprehensive Analysis

AI models benefit from the inclusion of public data to better understand demand and supply dynamics, such as economic indicators or weather patterns. Make sure to incorporate this outside data without exposing sensitive or proprietary internal data.

Embrace Continuous Learning and Adaptation

Supply chain teams that operate in a culture of learning and flexibility will fare better at implementing AI, especially as the technology continues to evolve. Stay in the habit of learning by regularly updating data sources to ensure accurate forecasts and reporting. Make sure to then continuously update and refine AI models with the new data. This helps your models deliver accurate and relevant insights so you can adapt to any changes in the market or consumer behavior.

Balance Automation With Human Oversight

Human oversight remains indispensable when using AI tools for demand and supply chain forecasting. Regularly review AI-generated forecasts to validate their accuracy and applicability. Make sure to audit algorithms and their results not just for quality, but also for legal obligations and ethical norms.

Practice Robust Data Security and Governance

Understand where and how your data is stored, used, and shared within AI processes, particularly when incorporating external data sources. Adopting secure, closed network solutions like Acterys can help protect sensitive information while still benefiting from AI-driven insights.

Connect Acterys, Copilot and Power BI for AI-Powered Forecasting

One of the most important steps in optimizing demand and supply forecasting is providing accurate, reliable data for AI models. Acterys simplifies the process of consolidating each of your data sources and enhances your planning, reporting, and analytics with a single AI-powered solution. The platform provides a bridge between your data and the insights you need to drive the business forward.

 

Acterys doesn’t just streamline data consolidation and analysis; it also introduces write-back capabilities with Power BI and Excel. This means changes and adjustments made in your forecasts and planning can be directly written back into your central database. This real-time synchronization ensures that all stakeholders are working with the most current data for maximum collaboration and decision-making accuracy. Turn your forecasts into actionable insights so you can make strategic decisions with confidence across your operations.

 

Ready to transform your approach to demand and supply forecasting with the power of AI? Start your journey towards optimized inventory planning today by signing up for a free trial of Acterys.