The good news for retail supply chains is conditions have largely normalized after several years of pandemic-related disruptions, followed by a massive distortion of shopper behavior caused by government stimulus and surging inflation. While the unprecedented nature of recent circumstances created an extremely challenging environment for accurate demand forecasting, the counter-effect was that demand forecasting gained C-suite relevance as a critical capability for future success and sales growth.
Given this reality, it naturally follows that there would be great interest in how retailers can achieve improvement in accurate demand forecasting. Several areas stand out as worthy opportunities to quickly create value. What follows are seven effective strategies for leveraging AI to improve the accuracy of demand forecasting, inventory replenishment, and overall supply chain management efficiency:
- Leverage the power of predictive analytics: This is the low-hanging fruit of the demand forecasting world, but if retailers aren’t already doing so, they should immediately begin using AI algorithms to analyze historical data and identify patterns, trends, and seasonality in customer demand. By doing so, retailers can predict future demand more accurately and enable better inventory planning to ensure in-store inventory levels align with shopper demand to grow sales.
- Put machine learning algorithms to work: As a subset of AI, machine learning algorithms are the perfect application for the data-rich supply chain environment. Retailers can employ ML algorithms to continuously learn from new data, thus improving forecasting models and demand planning over time. The beauty of the algorithms is they adapt to changing market conditions, evolving customer behaviors, and multiple other variables to enhance the accuracy of demand forecasting in a way that non-AI-powered solutions can’t.
- Real-time data integration: The world is awash in data and retailers should be taking advantage of as many sources as possible to improve supply chain efficiency. For example, unify and integrate real-time data from various sources such as point-of-sale systems, social media, online marketplaces, weather forecasts, market research, supplier partners, and third party sources to create a powerful cumulative effect. By analyzing this up-to-date information, AI models can generate more accurate demand forecasts and enable proactive inventory replenishment that aligns with retailers’ in-stock goals and service levels.
- Demand sensing: Forecast accuracy benefits greatly when retailers use AI-powered demand sensing techniques to capture real-time demand signals and market insights. For example, monitoring consumer behavior, competitor activities, economic indicators, and other data sources for specific categories helps retailers make better-informed decisions and respond more quickly to changes in demand.
- Collaborative forecasting: Not necessarily a new concept, but enabling collaboration and data sharing between suppliers, retailers, and other partners in the supply chain is essential for efficiency and forecast accuracy. AI algorithms play a key role and can facilitate the integration of multiple forecasts, identify discrepancies, and generate a consensus forecast that reflects collective intelligence. It is a great way to reduce forecasting errors and improve inventory management.
- Optimization and simulation: Scenario planning is essential in the modern supply chain, a lesson retailers learned well over the past few years. It’s why retailers should use AI-driven optimization and simulation models to fine-tune supply chain processes and improve decision-making. These models can optimize inventory levels, production schedules, transportation routes, and other variables to minimize costs, improve service levels, and enhance overall supply chain efficiency.
- Increased visibility of shelf conditions: AI-powered computer vision combined with optical character recognition is helping retailers see what is really going on in stores and creating a new type of Store Intelligence. Gaining an accurate view of shelf conditions enables retailers to solve on-shelf availability issues and gain a clearer picture of true inventory levels to inform demand forecasts.
Implementing some or all of these strategies is a surefire way retailers can improve the accuracy of their demand forecasts. Doing so is an effective means to build a rock-solid foundation to support broader supply chain efficiency and customer satisfaction goals which depend on the availability of product inventory.
To learn more about how AI-powered demand forecasting can improve financial performance speak with a supply chain expert today!