How AI in Supply Chain Brings Value to Your Business
Supply chain leaders are looking to get a firmer grasp on their supply chain planning. And what better way to start than artificial intelligence (AI)?
Over the last few buzzword-laden years, we’ve seen many shiny new tools generate excitement – and confusion – around the potential of AI. Technologies are being designed from the ground up to use AI capabilities to improve forecasts and reveal insights faster than any human could. And even ChatGPT is making it easy for anyone to create content – even complete articles and white papers – to answer virtually every question people can imagine.
When it comes to supply chain planning, it may be hard to believe that these current capabilities can bring any real value. But it is setting the foundation for a new paradigm that could cut 70% of weekly planning time, 15%–30% fewer forecast errors, and impressively improved inventory outcomes.
From science project to supply chain enabler
According to Gartner, AI is expected to permeate every technology-driven innovation and every strategic decision in the coming year. And AI’s decision-making precision and speed couldn’t come at a better time for supply chain planners.
To keep up with current market dynamics, planners need to modernize their traditional supply chain strategies with forecasts considering market signals and demand drivers that shift faster and more frequently.
For example, integrating real-time customer behavior, economic changes, environmental changes, and ongoing geopolitical events into forecasts allows companies to predict and adapt to evolving scenarios with more agility. AI-enabled analytics fill critical gaps inherent in conventional models, addressing base demand, promotional lift, causal forecasts, and user insights within a unified solution – significantly enhancing forecast accuracy.
Artificial intelligence has many other valuable applications in the supply chain, including:
- Continuous improvement through real time visibility, actionable business intelligence, and automated data analysis
- Enhanced monitoring precision of order availability and real-time status
- Early warning of upstream delays to trigger contingency planning or alternative sourcing
- Faster identification of declining product popularity and end-of-life cycles through stock-level analysis
- Optimized pricing strategies with comparison analysis of product prices, supply chain costs, and retail profit margins
- Fine-tuned demand, replenishment, and supply planning through analysis of commodity prices and weather patterns
While the list of potential improvements is extensive, getting data from numerous internal and external sources to leverage AI meaningfully is a significant challenge for most supply chain organizations. And once collected, the data often requires some cleansing and standardization.
How AI matures supply chain planning capabilities
The convergence of increased data availability and technology advancements makes now the right time to embrace an AI-powered supply chain. Luckily, several capabilities are available today to automate supply chain processes and augment your supply chain team’s decision-making.
Optimized forecast algorithm selection helps ensure that forecast accuracy is optimized throughout a product’s lifecycle. It automatically blends multiple algorithms whenever new data is added to demand history and compares the accuracy of every forecasted item against every available forecast algorithm, ultimately choosing which group of algorithms that minimizes forecast error.
Demand outlier adjustment automatically detects anomalous demand history data points and provides a mechanism to fix or explain the outliers. This eliminates the time and effort needed by the demand planner to manually identify and account for anomalies due to stockouts, competitors’ promotional programs, unplanned disruptions, or non-repeating events. Addressing this “bad data” helps improve forecast accuracy by ensuring the data used by the models is as clean as possible.
Demand sensing from unstructured data leverages pattern recognition and natural language processing to read and analyze big data to recognize complex relationships and provide data insights. The capability keeps up with every shift in consumer preference and behavior by automatically analyzing terabytes of unstructured data in a matter of minutes to determine buyer sentiment and quickly predict an impact on short- and long-term demand.
Probabilistic demand and supply simulations understand variability in demand and supply capacity at the record level. In contrast to single-valued forecasts, these capabilities build a range of possible demand and supply forecasts and create randomized forecasts, which are used in n-tiered, supply-constrained digital twin simulations to predict supply chain resilience. Incorporating product-level revenue and profit data into these Monte Carlo—type simulations empower planners to assess the risks of meeting volumetric and financial targets.
Automatic data cleansing and parameter population recognizes incomplete or inaccurate supply chain data and either automatically applies the correct data or alerts the appropriate data manager to take corrective action. Advanced solutions that automatically cleanse data and populate supply chain parameters ensure timely and accurate data is available for supply chain planning operations.
Scenario selection augmentation uses advanced cognitive capabilities to develop new insights and augment a planner’s ability to make fast, well-informed decisions. It autonomously searches for the best solutions for disruptions and opportunities and can provide the planner with the best alternatives to accelerate decision-making.
Product lifecycle profile optimization improves item-level forecast accuracy through attribute-based modeling techniques for creating demand profiles, assigning them to new items, continually assessing their accuracy, and revising them. It learns from previous product introductions to optimize the profile shape and volume for new product launches.
Where foresight meets responsiveness
Embracing AI in supply chain operations doesn’t merely streamline processes. It unlocks a realm of possibilities – from continuous improvement through automated data analysis to fine-tuned demand planning and resilient simulations. And as AI matures, its potential to reshape the supply chain landscape becomes increasingly tangible, promising a future of unparalleled efficiency and foresight.
At Logility, this juncture in supply chain planning is not just a technological advancement but also a pivotal opportunity for supply chain leaders. Enhanced AI capabilities increasingly empower teams to resolve issues immediately with real-time alerts and instant insights, intelligent scoring and economic prioritization, and the best inventory policies for each SKU.