9 Ways Machine Learning Can Help Your Supply Chain
Organizations are always interested in new ways to increase efficiencies in their supply chains, and machine learning (ML) might be a valuable tool for them to consider adopting.
ML is a subset of artificial intelligence that allows an algorithm, software or a system to learn and adjust without being specially programmed to do so, explained Mitul Makadia in a recent article in ITProPortal. “ML models, based on algorithms, are great at analyzing trends, spotting anomalies and deriving predictive insights within massive data sets,” Makadia wrote. “These powerful functionalities make it an ideal solution to address some of the main challenges of the supply chain industry.”
Supply chain applications for ML include inventory management, safety and quality, management of scarce resources and supplier relationship management. “Integrating machine learning in supply chain management can help automate several mundane tasks and allow the enterprises to focus on more strategic and impactful business activities,” wrote Makadia, the founder of Maruti TechLabs.
He outlined 9 supply chain uses for ML:
- Predictive analytics for demand forecasting
- Automated quality inspections for robust management
- Real-time visibility to improve customer experience
- Streamlining production planning
- Cost and response time reduction
- Warehouse management
- Reduction in forecast errors
- Advanced last-mile tracking
- Fraud prevention
“Innovative technologies like machine learning make it easier to deal with challenges of volatility and forecasting demand accurately in global supply chains,” Makadia said. “Gartner predicts that at least 50% of global companies in supply chain operations would be using AI and ML-related transformational technologies by 2023. This is a testament to the growing popularity of machine learning in the supply chain industry.”