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QAD 2019 Predictions: Machine Learning Will Continue to Improve Supply Chain Performance in 2019

Industry executives and experts share their predictions for 2019.  Read them in this 11th annual VMblog.com series exclusive.

Contributed by Brent Dawkins, Director, Product Marketing, QAD

Machine Learning Will Continue to Improve Supply Chain Performance in 2019

Global supply chains provide manufacturers with an enormous amount of data. This is especially true as linear supply chains have evolved into interconnected networks of trading partners that include suppliers, customers, carriers, customs agents, supplier's suppliers, customer's customers and others. Depending on the manufacturer, supply chain data includes information covering demand, inventory, logistics, production, warehouse, sales and financial pieces of data. These are all useful pieces of information - and, in many cases, required - when trying to make better supply chain decisions. However, the amount of data generated can overwhelm planners, buyers and other supply chain professionals. This is where machine learning plays a key role moving forward.

The Role of Machine Learning

Machine learning can be defined as the leading edge of artificial intelligence (AI). It's a subset of artificial intelligence where machines can learn by using algorithms to interpret data from the world around us to predict outcomes and learn from successes and failures. For manufacturers, the power of machine learning is exciting with the understanding that business processes, production operations, supply chain activities and strategic decisions can be made better and more accurate. These decisions can include predictions about what a customer is likely to buy next, the best response to an unexpected supply chain disruption or when an expensive shop floor asset is likely to break down.

Machine learning will continue to improve supply chain operations in 2019. Consider the following:

Increase Demand Planning Accuracy

Numerous factors affect future product demand including seasonal trends, customer buying patterns, unexpected weather events and competitive product innovation. Historically, many manufacturing firms have created forecasting processes using information that includes outdated demand patterns. With artificial intelligence, the myriad of factors impacting demand planning can be fully considered and hidden demand patterns within historical demand information can be more accurately identified. With AI and machine learning technology, more accurate demand planning and monitoring allows for appropriate and timely adjustments to better meet customer demands and lower inventory levels.

Boost Procurement Performance

AI powered chatbots deliver conversational interfaces for customers, employees and other roles. Chatbots can rely on AI to conduct conversations designed to simulate human conversations. Most chatbot examples focus on their use in the context of customer service. However, that concept is changing as usage expands into other business areas and a recent example highlights the area of procurement. In one case, a beverage manufacturer and distributor required employees to call a help desk and wait while operators tracked down procurement information. This information included updates on delivery times, shipment status, quantity changes, order confirmations and other information that should have been easily accessible. With the deployment of chatbots supported by AI, this company was better able to access real-time procurement information across their enterprise systems and improve supply chain efficiency.

Develop Predictive Analytics

Nostradamus allegedly said, "Prediction is difficult, especially about the future." And, Yogi Berra is often attributed with saying, "I never make predictions, especially about the future." While predicting the future is inherently difficult, manufacturers continually make predictions about customer demands, supply availability, business processes and production operations. But, what if your predictive decisions could be made better with artificial intelligence and machine learning? This is occurring today as data scientists and other users now have the ability to build predictive models using statistical and machine learning algorithms. How would your organization benefit with improved predictions on the following aspects of supply chain operations?

  • Demand and supply forecasting accuracy
  • Customer and seasonal demand patterns
  • Product tracking and traceability
  • Global transportation performance
  • Supplier performance
  • Asset performance and downtime

Machine learning, along with other advanced technologies, are reaching a maturation point that can deliver positive business outcomes for your global supply chain. Where does your company stand in considering and adopting advanced technologies like machine learning? Because now is the time to consider the positive impact machine learning can have on your 2019 global supply chain operations.

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About the Author

 

Brent is QAD's Director of Product Marketing with over 20 years of manufacturing and supply chain experience. In his spare time, you can find him hiking the Rocky Mountains of Colorado, coaching youth hockey or enjoying time with family.

Published Monday, December 10, 2018 7:39 AM by David Marshall
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