Supply chains are becoming more complex. In the search for optimization and automation, more and more businesses are using artificial intelligence (AI) and machine learning (ML) to help manage supply chain operations. This technology can be applied throughout the supply chain, from demand forecasting to inventory optimization to vendor management.
One application of AI is to remove the guesswork from supply and demand forecasting, while ML can be leveraged for pricing and inventory planning using historical data. To predict stock-outs at every level, capture demand trends, and improve supplier lead times, implement the technology that drives better decisions.
Improving Supply Chain Efficiencies with Artificial Intelligence and Machine Learning Capabilities
Forecasting demand complexity with the massive increase in available data we see today requires a machine learning solution as it’s impossible to scale traditional forecasting methods to handle the data-heavy, SKU-level analysis that’s needed. Here are three initiatives to get your ML program moving in the right direction.
Effective inventory control, combined with the ability to predict customer behavior, can make the difference between success and failure. ML offers the ability to quickly react, extract new insights, and make smart inventory allocations.
Applying machine learning to demand forecasting brings transformative results that include improved accuracy and transparency, data analysis that provides deeper insights, and the ability to quickly respond to market conditions, among other must-haves. Here’s why you can’t ignore this forecasting solution.