Machine learning is a type of artificial intelligence that allows an algorithm, system or piece of software to learn and adjust without being explicitly programmed to do so. This allows technology to teach itself over time, so that it can improve operations.
Machine learning typically uses observations or data to train a computer model. Patterns in the data, combined with predicted and actual outcomes are analyzed through machine learning and used to improve how the technology functions. This cycle repeats, further refining the technology as it’s exposed to more information.
Machine learning has several applications in the supply chain, including data analysis, supply chain optimization, cost reduction, planning and forecasting.
Modern, international supply chains generate vast amounts of complex data. Machine learning can analyze this information and use the findings to enhance supply chain management (SCM).
Optimize the Speed of the Supply Chain
Machine learning can analyze timings and handovers as products move through the supply chain. It can compare this data to benchmarks and historic performance to identify potential holdups and bottlenecks and make suggestions to speed up the supply chain.
Forecast Likely Demand From Customers
Data can be sourced from many areas like the marketplace environment, seasonal trends, promotions, sales and historic analysis. Machine learning will combine this data to predict demand for specific goods and help to manage the sourcing and manufacture of those products.
Plan the Movement of Goods Based on Demand
Efficient supply chains rely on products being in the right place at the right time. Machine learning can assess customer requirements and optimize the upstream supply chain. It matches the timely supply of goods with marketplace demands.
Manage Suppliers and Documentation
Dealing with suppliers is one of the most challenging parts of SCM. Machine learning can analyze the types of contracts, documentation and other areas that lead to the best outcomes from suppliers and use those as a basis for future agreements and administration.
Ensure Quality from Suppliers, Products and Assets
Quality is vital to good SCM as waste and faulty products create unnecessary rework and increase costs. Machine learning can monitor how quality varies over time and suggest improvements. This doesn’t just apply to materials and products. It can track other areas such as shipping, supplier and third-party quality.
Machine learning delivers several benefits for SCM:
Machine learning depends on reliable, high-quality and timely information. A lack of access to good data can cause significant issues for machine learning in the supply chain. A robust approach to collecting and analyzing data is a priority for supply chain managers:
There are plenty of good use cases for optimizing a supply chain through machine learning:
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