Noodle.ai Reinvents Supply Chain Planning with New Math

⇨ Noodle.ai is revolutionizing supply chain planning by integrating probabilistic models using advanced AI and machine learning techniques, moving beyond traditional deterministic approaches to embrace uncertainty and improve demand forecasting accuracy.

⇨ The company's innovative features, such as 'featurization' and 'Ensembler', allow for more sophisticated forecasting methods that better handle various demand patterns, aiming for a minimum of 10% improvement in forecast accuracy and additional gains in inventory and cost reduction.

⇨ There is a growing trend among companies to implement AI in supply chain processes, with 79% of organizations currently utilizing or planning to utilize AI, highlighting the importance of strategically focusing on areas that can benefit the most from AI-driven efficiencies.

Supply chain planning refers to the process of coordinating the flow of goods, services, and information from suppliers to end consumers. The goal is to ensure that supply meets demand efficiently, with minimal waste, cost, and delays. Supply chain planning encompasses various activities that help businesses anticipate demand, optimize inventory, and manage production, transportation, and distribution.

Historically, planning models have been based on deterministic math. Deterministic math refers to mathematical systems or models in which outcomes are Precisely determined by the inputs or initial conditions, with no randomness involved. In deterministic systems, given the same input, the system will always produce the same output. These models are predictable and can be described with exact equations or algorithms. There is no element of chance or uncertainty in deterministic models; everything operates according to a set of known principles.

Through advanced data science and AI/ML techniques, San Francisco-based Noodle.ai is bringing probabilistic planning to supply chain organizations, embracing uncertainty in their planning models, and enabling companies to maximize profits and minimize waste.

Traditional demand forecasting models have been based on historical orders and shipments alone, while Noodle.ai’s solution incorporates data and signals, in addition to historical orders and shipments, under a capability known as featurization. The solution allows planners to appropriately consider smooth, intermittent, erratic, and lumpy demand intervals. Further, forecasts in recent years have traditionally only used basic auto-regressive models, while Noodle.ai have developed what they simply call forecasters, which rely on a library of advanced AI, ML, probabilistic, and deep learning models.

Supply chain planners in charge of vast, complex operations have been building forecasts for many years using a single algorithm, often selected through best-fit. Noodle.ai has devised a way to build blended forecasts, improving every prediction with every model in the library, under a capability known as Ensembler. Another limitation of historical forecasting methods is that forecasts have been limited to simple aggregation and disaggregation rules. Whereas Noodle.ai has introduced a capability known as Reconciler, which allows for the identification of accuracy and bias reduction across product, geography, and time hierarchies.

Noodle.ai claims it can help supply chain organizations achieve at least a 10% improvement in their demand forecast accuracy. In addition, companies stand to make gains in inventory reduction, fill rate improvement, and supply chain cost reduction, according to Noodle.ai.

What this means for SAPinsiders

Participate in SAPinsider research. AI is transforming the landscape of supply chain management by introducing new capabilities that enhance efficiency, prediction accuracy, and operational resilience. Related 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. 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!

Sharpen Your Supply Chain AI Focus. Supply chain operations encompass a vast array of functions, systems and business processes, a target-rich environment for AI. When it comes to proving out the value of AI, it is important to pick your battles. Focus in on the pockets of waste and inefficiency that stand to benefit the most from the advanced decision-improving capabilities of AI. This is what Noodle.ai has done by zeroing in on the opportunities to innovate supply chain planning and forecasting.

Mind your Metrics. Like with any investment, when testing the potential impact of AI in your supply chain it’s important to tackle problems worth solving, per the point above, but also to have an idea of what success looks like. What is a meaningful progression across which metrics, like forecast accuracy, inventory reduction, fill rate, or cost, even on a small scale under the scope of a proof of concept? Document and communicate these metrics with internal sponsors and external technology partners like Noodle.ai so you have an objective basis for either continuing to invest or changing course.