How AI-powered WMS enhances demand forecasting for better production planning

As supply chains become more complex, accurate demand forecasting is crucial for aligning production with customer needs. Traditional methods often fail amidst market dynamics, leading to errors such as overproduction and inventory shortages.

AI-powered warehouse management systems can improve forecasting by analyzing real-time data, leading to smarter demand predictions and more efficient production planning.

The challenges of traditional demand forecasting

For decades, businesses have relied on historical data and basic predictive models to forecast demand. While this approach worked in more predictable markets, it presents bigger limitations now that adaptability is more important than ever. Key challenges include:

• Static data reliance: Traditional systems use static, often outdated data, leading to forecasts that don’t account for sudden shifts in demand.
• Limited visibility across supply chains: Many businesses struggle to have an end-to-end view of their supply chains, leading to forecasting errors that impact production.
• Underestimating market volatility: External factors like economic trends, shifts in consumer behavior, or even weather patterns can disrupt forecasts built on historical models.
• Poor data integration: Without a seamless link between departments, forecasting errors increase, resulting in production inefficiencies and costly misalignments between inventory and actual demand.

For businesses looking to improve their production planning, the gaps in traditional forecasting tools are costly. Late adjustments to production schedules can lead to excess inventory, stockouts, or missed deadlines, each resulting in financial losses and strained supplier relationships.

AI-powered WMS: A new approach to demand forecasting

AI-driven warehousing software offers a potential solution by enhancing demand forecasting through continuous analysis of real-time data. These systems go beyond traditional forecasting methods in several key ways:

• Real-time data processing: These systems integrate data from multiple sources, including historical sales, market trends, and external factors, analyzing it in real-time to provide more accurate forecasts.
• Predictive analytics: AI can model complex scenarios and predict demand fluctuations before they occur, helping businesses adjust production schedules proactively.
• Dynamic adjustment: Continuous learning and adjustments make these solutions adept at managing the unpredictability of real-world markets. This flexibility helps businesses maintain optimal production levels, reducing overproduction or underproduction risks.
• Holistic visibility: AI-powered WMS offers an integrated view of the entire supply chain, ensuring that demand forecasts are informed by every aspect of the production process, from procurement to delivery.

For example, Blue Yonder (pictured above) combines statistical methods, machine learning, and AI to create accurate demand forecasts.

The platform analyzes hundreds of signals (internal and external) to provide clear, data-driven predictions for better business decisions.

How AI-enhanced demand forecasting enhances production planning

Once a business has accurate demand forecasts, production planning becomes far more efficient. Here’s how AI-driven WMS facilitates better production outcomes:

• Improved production scheduling: With more accurate demand forecasting, production schedules can be adjusted to reflect real-time conditions. This minimizes overproduction and underproduction, helping businesses operate efficiently while meeting customer demand.
• Reduced inventory costs: Integrating AI-driven WMS reduces inventory holding costs by accurately forecasting demand, preventing stockouts and excess inventory. McKinsey reports that early adopters achieved a 15% reduction in logistics costs, a 35% decrease in inventory levels, and a 65% improvement in service levels.
• Enhanced resource allocation: AI-enhanced systems also provide insights into optimal resource use; analyzing production capacity, labor availability, and material lead times allows businesses to allocate resources more effectively and keep every part of the production chain working efficiently.
• Decreased lead times: With predictive analytics, AI-driven WMS systems can anticipate potential supply chain disruptions and suggest alternative suppliers or production shifts to keep operations running smoothly. This reduces lead times and improves the overall responsiveness of production to market demand.
• Continuous learning and adaptation: These systems continuously learn from new data, enabling them to refine their predictions over time. As production cycles continue, the WMS becomes better at forecasting demand, leading to even more efficient production planning.

Another strong example is Kinaxis Maestro, whose exception resolution feature precisely determines the impact of changes on your plans, supply chain, and overall business. As a result, it enables data-driven decision-making in the face of disruptions.

Real-world use cases of AI in demand forecasting and production planning

Amazon's learnings from a two-year demand spike

During peak sales events like Prime Day and Black Friday, Amazon employs AI-driven forecasting tools to anticipate demand surges.

Their WMS integrates real-time data from customer behavior, previous sales patterns, and online search trends, enabling them to scale warehouse and delivery operations accordingly (insights that proved invaluable throughout the pandemic's demand spikes).

Walmart's AI-optimized stock levels

Walmart employs AI extensively to optimize inventory levels, especially during high-demand periods like the holiday season.

The company analyzes both in-store and online sales data to strategically stock and replenish popular products.

AI also supports their logistics, improving supply chain efficiency and helping the retailer avoid costly stockouts.

Verusen's approach to inventory optimization

With an investment from BMW i Ventures, Verusen took a cutting-edge, AI-driven approach to inventory optimization, addressing the prevalent issue of 'dirty data' that undermines traditional inventory management solutions.

The integration of advanced data cleansing techniques to remove duplicates and errors enhances the accuracy of its predictive models for inventory forecasting.

Verusen reported an average of $10 million of verified savings opportunities for manufacturing clients including Georgia Pacific and AB InBev within just a few months of deployment.

Ocado's smart platform

The UK-based online grocery retailer Ocado utilizes AI in its proprietary platform to control inventory management and demand forecasting.

Ocado's combination of AI with robotic picking systems optimizes order fulfillment and streamlines its entire supply chain, resulting in faster deliveries and more accurate stock levels.

The role of integration with existing systems

It’s important to note that AI-driven WMS does not always operate in isolation.

Many businesses already rely on existing ERP or SCM systems to manage their production and supply chains. The power of AI-enhanced WMS lies in its ability to integrate seamlessly with these systems, providing:

• Unified data streams: Data is pulled from multiple sources and integrated with existing ERP or SCM systems to provide a comprehensive view of the production process.
• Improved interdepartmental collaboration: Real-time data integration across departments allows for faster communication and better alignment between production, procurement, and inventory management.
• Actionable insights across the business: AI-driven WMS delivers actionable insights that inform not just warehouse management but broader business decisions, such as pricing strategies or marketing efforts based on predicted demand.

For medium to enterprise-scale businesses, SAP EWM stands out for its seamless integration with S/4HANA. 

SAP EWM uses AI and ML to connect systems, assets, and people across supply chains and businesses. This real-time data sharing between WMS and ERP provides not only greater visibility, but flexibility to boot.

A full breakdown of SAP EWM's system architecture and integration options is available here.

Potential challenges and limitations

While AI WMS solutions are a significant upgrade to traditional demand forecasting methods, it’s not a perfect solution (yet). Potential challenges include:

• Data quality issues: AI systems are only as good as the data they process. Incomplete or inaccurate data can lead to flawed forecasts, highlighting the need for strong data handling practices.
• Complex implementation: For some businesses (particularly SMBS), integrating AI-enhanced WMS into existing systems can be a complex and costly process, requiring significant technical expertise.
• Dependence on technology: While AI enhances forecasting, it’s crucial to maintain a balance between automated insights and human oversight, especially when dealing with nuanced or highly volatile markets.

AI-powered WMS as part of a broader solution

While AI-driven WMS offers considerable improvements in demand forecasting and production planning, it’s not always a standalone fix.

Businesses should view these systems as part of a broader strategy to optimize operations. Integrating WMS with other technologies such as ERP, CRM, and SCM platforms enhances the system's ability to make informed decisions, improving both accuracy and efficiency across the supply chain.

In summary

AI warehousing marks a significant leap forward for demand forecasting and production planning. Businesses can now use real-time data, predictive analytics, and dynamic forecasting to make more informed production decisions.

However, it’s important to recognize that AI is just one tool in a larger toolkit. The true value of AI-driven WMS is unlocked when it is integrated with existing systems and guided by strong human oversight.