Revolutionizing Supply Chain with Machine Learning

Monday, October 1st, 2018 : 0 Comments











Louis Columbus of published an informative article titled, “10 Ways Machine Learning is Revolutionizing Supply Chain Management”.

Machine Learning has been a transformative tool adopted across multiple industries to quickly detect patterns in data and highlight what the most influential factors are in the data. Columbus states:

“Discovering new patterns in supply chain data has the potential to revolutionize any business. Machine learning algorithms are finding these new patterns in supply chain data daily, without needing manual intervention or the definition of taxonomy to guide the analysis. The algorithms iteratively query data with many using constraint-based modeling to find the core set of factors with the greatest predictive accuracy. Key factors influencing inventory levels, supplier quality, demand forecasting, procure-to-pay, order-to-cash, production planning, transportation management and more are becoming known for the first time. New knowledge and insights from machine learning are revolutionizing supply chain management as a result.”

In particular, Invistics has adopted the following 3 attributes of Machine Learning in order to better manage our customers Supply Chains:

#1 Machine learning algorithms and the apps running them are capable of analyzing large, diverse data sets fast, improving demand forecasting accuracy. One of the most challenging aspects of managing a supply chain is predicting the future demands for production. Existing techniques range from baseline statistical analysis techniques including moving averages to advanced simulation modeling. Machine learning is proving to be very effective at taking into account factors existing methods have no way of tracking or quantifying over time. The example below shows how a wide spectrum is being used to accomplish demand forecasting, and Lennox is using machine learning today.

#3 Machine Learning and its core constructs are ideally suited for providing insights into improving supply chain management performance not available from previous technologies. Combining the strengths of unsupervised learning, supervised learning and reinforcement learning, machine learning is proving to be a very effective technology that continually seeks to find key factors most affecting supply chain performance. Each of the endpoints defined in the taxonomy below is derived entirely by algorithm-based logic, which ensures algorithms scale across a global enterprise.

#10 Combining machine learning with advanced analytics, IoT sensors, and real-time monitoring is providing end-to-end visibility across many supply chains for the first time. What’s needed in many supply chains today is an entirely new operating platform or architecture predicated on real-time data, enriched with patterns and insights not visible with previous analytics tools in the past. Machine learning is an essential element in future supply chain platforms that will revolutionize every aspect of supply chain management.

The beauty of Machine Learning is in its self-learning iterations, where as the data grows and changes, so do the algorithms and pattern-detection of the artificial intelligence. Analyzing all the data points in a complex supply chain system can be a daunting task, but with the capabilities of Machine Learning, the burden of making sense of the Key Performance Metrics falls on the Machine rather than a manual, human process.

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