5 Ways Machine Learning Can Transform Supply Chain Management

5 Ways Machine Learning Can Transform Supply Chain Management

Machine learning is changing the future of supply chain management. Increasing costs, Revenue losses, Bad customer service, and reducing profits are all By-product of operational inefficiencies. For the Supply chain business to survive in today’s competitive and complex market, Machine Learning (ML) and Artificial Intelligence (AI) are considered as the most promising technologies available.

Gartner says by 2023, at least 50% of large global companies will be using AI, advanced analytics and IoT in supply chain operations.

The ability for the system to analyse the data, learn, and improve automatically from experience, without any programming is done through Machine learning. The future of supply chain technologies will be highly automated and highly responsive. Machine learning is used to identify missing, rogue, or duplicate data points and uses history and historical actions to correct the data.

Why and How does Machine Learning is ideally suited to transform supply chain management?

The answer is such that Machine Learning algorithms can be best used to effect for detecting patterns and predictive insights. By doing so, the supply chain companies can forecast error rates, reduce costs, improve demand planning productivity, and increase on-time shipments.

Here are 5 ways that a Supply Chain Management can be transformed by Machine Learning Technology

Here are 5 ways that a Supply Chain Management can be transformed by Machine Learning Technology:

1. Predictive Analytics
2. Reducing Cost and Response Time
3. Improve Customer Experience
4. Scheduling Maintenance
5. Fraud Prevention

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.

According to a Statista survey, visibility is a significant organizational challenge for 21% of supply chain professionals

2. 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.

3. Improve Customer Experience
Machine learning techniques can be used to enhance the customer experience by improving supply chain visibility and achieve faster delivery commitments. The historical data from various sources are analysed by Machine learning models along the supply value chain. Machine learning techniques, including a combination of deep analytics, IoT, and real-time monitoring, can be used to improve supply chain visibility substantially, thus helping businesses transform customer experience and achieve faster delivery commitments.

According to a recent study by Mckinsey Global Institute, advanced AI technologies have the potential to unlock a global economic impact of $10-15T across all industry segments.

Mckinsey Global Institute, advanced AI technologies

4. Scheduling Maintenance
One of the most exciting applications of this technology is proactive machine maintenance scheduling. Advanced ML algorithms study the signs of machine failure and predict in advance the breakdown or malfunction in the machines soon. By making this prediction, the ML algorithm lets the planners schedule downtime in advance before a breakdown occurs. Logistics and various other elements of the supply chain can also benefit from this technique.

5. Fraud Prevention
Machine learning can reduce the potential for fraud in the supply chain in addition to reducing risk and improving product and process quality. Machine Learning algorithm provides insights that instantaneously reduce the risk of fraud.

If you have had questions about machine learning within supply chain management (or) need to create a process for supply chain using ML technology, please reach us at info@optisolbusiness.com

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