Machine learning will be used for the supply chain management for procurement solutions to manage the entire business to ensure to supply of a product with success mode to single & multiple suppliers.
Today, amid shifting supply chain market dynamics, changing ways of working, increasingly volatile demand, businesses are wondering how to make their supply chain less vulnerable to disruption. Machine learning holds the answer to many well-known as well as emerging supply chain challenges.
Machine learning applications in the supply chain are revolutionizing the way retailers and suppliers work. As a branch of artificial intelligence, machine learning uses data to train a computer model so it can adjust to conditions without being programmed to do so. This way, the machine can teach itself over time, improving the accuracy of its own algorithms. There are a number of machine learning methodologies used in the supply chain.
Machine Learning is a type of AI that permits an algorithm or a software program to research and alter without explicit programming. It educates itself over time in ways that could enhance its operations.
Machine Learning typically makes use of observations or statistics. Patterns within these statistics, blended with anticipated and actual outcomes, are analysed via machine learning to improve the functions of technology. This cycle repeats, and the increase in information continuously refines the technology.
1. Predictive Analytics
Predictive analytics techniques allow organizations to identify patterns and trends hidden in their data to understand market trends, identify demand, and establish appropriate pricing strategies. A study by the Council of Supply Chain Management Professionals revealed that 93% of shippers and 98% of third-party logistics firms feel like data-driven decision-making is crucial to supply chain activities, and 71% of them believe that big data improves quality and performance. The predictive Analytics technique has the advantage of enabling real-time decisions based on statistical estimates of future outcomes. It has the potential to enhance strategic thinking and overall performance.
2. Inventory management
Inventory management is extremely crucial for supply chain management as it allows enterprises to deal and adjust for any unexpected shortages. No supply chain firm would want to halt their company’s production while they launch a hunt to find another supplier. Similarly, they wouldn’t want to overstock as that starts affecting the profits.
Inventory management in supply chain is largely about striking a balance between timing the purchase orders to keep the operations going smoothly while not overstocking the items they won’t need or use.
Automation and the pace of technology have changed not only the way businesses operate but also nearly every aspect of daily life. From autonomous vehicles to robots in factories, it is one of the biggest advances of the human race yet.
It can also be used in helping to manage supply chain operations and make them more efficient. Companies can use automation such as robotics, artificial intelligence (AI), and automated sorters with “put and pick to light” that use Radio Frequency Identification (RFID) technology. They can also use drone deliveries to speed up their operations and maximise efficiency.
4. Streamlining Production Planning
Machine learning can play an instrumental role in optimising the complexity of production plans. Machine learning models and techniques can be used to train sophisticated algorithms on the already available production data in a way which helps in identification of possible areas of inefficiency and waste.
Further, the use of machine learning in supply chain in creating a more adaptable environment to effectively deal with any sort of disruption is noteworthy.
5. Dynamic inventory management
Inventory management is a problematic area for many businesses, particularly for smaller ones and start-ups. However, by making it more dynamic, they can increase their overall efficiency when it comes to managing supply chains. To reduce waste, smart and data-driven Sales and Operations Planning can be implemented. Businesses could also use a Just In Time process or Vendor Managed Inventory to further increase the efficiency of their operations and supply chain management.
6. Reducing Cost and Response Time
An increasing number of B2C companies are leveraging machine learning techniques to trigger automated responses and handle demand-to-supply imbalances, thus minimizing the costs and improving customer experience. The ability of machine learning algorithms to analyse and learn from real-time data and historic delivery records helps supply chain managers to optimize the route for their fleet of vehicles leading to reduced driving time, cost-saving and enhanced productivity. Further, by improving connectivity with various logistics service providers and integrating freight and warehousing processes, administrative and operational costs in the supply chain can be reduced.
Innovative technologies like machine learning makes it easier to deal with challenges of volatility and forecasting demand accurately in global supply chains. Its 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 supply chain industry.