TrueCommerce Unveils ReplenishAI™ to Boost Demand Forecasting Accuracy

⇨ ReplenishAI by TrueCommerce revolutionizes supply chain management by utilizing AI algorithms to analyze historical data, dramatically reducing the time required to identify demand patterns from weeks or months to just hours.

⇨ The solution integrates seamlessly with TrueCommerce's VMI system, providing businesses with superior demand forecasting and replenishment optimization, leading to improved inventory management and reduced human error.

⇨ Recent SAPinsider research highlights the growing importance of AI in supply chain operations, with 79% of companies either implementing or planning to implement AI to enhance their supply chain efficiency and adaptability.

SAP Solution Build Partner, TrueCommerce, a global provider of supply chain and trading partner connectivity, integration and omnichannel solutions, recently announced the launch of ReplenishAI. This industry-first, artificial intelligence (AI)-powered solution leverages AI algorithms to analyze historical sales and generate demand pattern clusters, equipping businesses with clearer insights into optimal replenishment strategies with unprecedented accuracy.

Identifying products that follow promotional or seasonal sales patterns is a very manual and time-consuming process—sometimes taking weeks or months. ReplenishAI dramatically shortens this timeline to hours, efficiently reviewing tens of thousands of items, identifying key demand trends, and predicting when inventory spikes will occur. The AI-driven data is seamlessly integrated into TrueCommerce’s VMI solution, delivering superior demand planning and replenishment optimization. For added confidence, the models are validated against historical data to ensure accuracy and reliability.

Recent SAPinsider research shows a continuing focus on mitigating the rising complexities and risks within supply chains, with 53% of companies noting that increasing complexities and risks is the top driver of supply chain transformation projects. In the recent research report, Building Resilient and Agile Supply Chains via Data, Analytics and Automation, companies also recognized the significance of AI/ML in supply chain operations, with 43% of respondents highlighting its increasing prominence on the corporate agenda. External pressures also influence the need to enhance data, analytics, and automation capabilities. Evolving customer expectations and demand (37%) and the pressure to run more sustainable supply chains (36%) indicate a growing societal and environmental consciousness among supply chain leaders.

As it relates to AI in supply chain operations, SAPinsider research indicates that 79% of companies are currently using or implementing AI in supply chain, plan to implement AI within 24 months or are evaluating AI for supply chain. Leaders in the SAP ecosystem are quickly moving past the hype to leverage AI to drive tangible benefits in areas like demand forecasting, inventory optimization, route optimization, logistics planning, and predictive maintenance.

The ReplenishAI solution leverages AI to analyze a company’s entire product portfolio, automatically grouping items into clusters based on their unique demand patterns. Some key processes of the solution include:

• Data Standardization: Takes product-level demand data over time and standardizes it, ensuring consistency across different units of measure, whether the product is tracked by case, pallet, or other metrics.
• Demand Smoothing: Eliminates disruptive data spikes, making it easier to produce generalized, actionable insights for replenishment.
• AI-Powered Clustering: Applies sophisticated algorithms to group products into distinct profiles based on yearly demand trends, unlocking more accurate, efficient replenishment strategies.

“Driving innovation that addresses our customer needs is a strategic priority of our team, and ReplenishAI delivers on this strategy,” said TrueCommerce’s Senior Vice President and General Manager of VMI solutions, Lee Kimball. “This AI-driven solution reduces human effort and error while optimizing inventory levels throughout the seasons. With ReplenishAI, maximizing sales and minimizing residual inventory in support of seasonal and promotional demand becomes a reality for our customers. We’re excited to be leading the charge as the first mover in this space, demonstrating how advanced VMI technology can deliver even greater efficiency and impact than ever before.”

What this means for SAPinsiders

Participate in SAPinsider research. SAPinsider will be publishing a research report in December on AI in the Supply Chain that will explore the key drivers causing companies to adopt AI in supply chain, the primary strategies companies are deploying to operationalize AI in supply chain, the critical underlying capabilities that must be in place in order for AI to deliver durable outcomes, and the leading technology tools and providers companies are using to embed AI into supply chain processes and systems. Take the survey today and let your voice be heard!

TrueCommerce worth a look as boon to agility and responsiveness. TrueCommerce’s order routing solutions enable SAP customers to respond dynamically to changes in demand and supply conditions. This flexibility helps reduce lead times, optimize logistics costs, and adapt to sudden shifts in consumer demand. SAP customers using TrueCommerce can implement omni-channel fulfillment strategies, allowing them to fulfill orders from the most efficient locations and delivery methods, whether online, in-store, or through third-party logistics providers.

Use AI to continuously adjust demand forecasts in real-time. AI systems like ReplenishAI can update forecasts in real time based on new data inputs, such as shifts in market conditions, supply chain disruptions, or competitor actions. This enables companies to adjust their supply chain and production strategies quickly in response to changing demand signals. AI-powered demand forecasting models are self-improving; they learn from new data and continuously optimize their predictions. As more data accumulates, the algorithms refine their predictions, minimizing errors over time.