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9 Ways Machine Learning Can Improve Supply Chain Management

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Efficiency and cost-effectiveness are the biggest challenges facing supply chain management today. Businesses are constantly striving to reduce costs, enhance profit margins, and provide exceptional customer service. In such a competitive market, disruptive technologies like Machine Learning (ML) and Artificial Intelligence (AI) have opened up exciting opportunities for companies. Are you grabbing these opportunities? Artificial Intelligence and Machine Learning have recently become buzzwords across different verticals, but what do they actually mean for modern supply chain management?


7 Ways Machine Learning can Solve Supply Chain Challenges

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In the supply chain industry, rising customer expectations have given rise to larger product ranges, more complex logistics, and shamelessly fast lead times. All of this has led to soaring costs throughout the supply chain network. And minimizing the effect of these factors manually at each individual level is again a recipe for magnified operational costs. This is where Machine Learning in Supply Chain can help breathe a sigh of relief! Integrating machine learning in supply chain management can help automate a number of mundane tasks and allow the enterprises to focus on more strategic and impactful business activities.


10 Ways Machine Learning Can Transform Supply Chain Management

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To begin, using machine learning in supply chain management may aid in the automation of a variety of routine operations, allowing businesses to focus on more strategic and significant business activities. Supply chain managers may use sophisticated machine learning tools to optimize inventories and locate the best suppliers to keep their business operating smoothly. ML has piqued the interest of a growing number of organizations, owing to its numerous benefits, including the ability to fully leverage the massive volumes of data generated by warehousing, transportation systems, and industrial logistics. It may also assist businesses in developing a complete machine intelligence-powered supply chain model to reduce risks, increase insights, and improve performance, all of which are critical components of a globally competitive supply chain. Machine learning has a lot of applications in the supply chain because it is such a data-driven business.


8 Ways machine learning can improve supply chain planning

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An efficient supply chain planning is the fundamental block for building a successful and well-organized supply chain mechanism. Many businesses are unable to achieve the desired operational excellence due to manual operative approaches, lack of visibility and poor supply chain planning. This restricts brands from creating synchronized, smooth and responsive supply chains. The most crucial activity in supply chain management is planning. Supply chain planning is the process of accurately planning a product flow from raw material sourcing to reaching the final consumer.


How to improve supply chains with machine learning: 10 proven ways

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Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionising supply chain management in the process. Machine learning algorithms and the models they're based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon's Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyses 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions.