Experts say that machine Learning is a “revolutionary” solution that could solve four major challenges facing supply chains this year.
Supply chains are currently faced with the same innovative, yet challenging movement: technology. The disruption caused by digitization has increased the value of global markets, but has also advanced the way supply chains operate. This translates into an increase in consumer demand and a greater need for optimized processes.
According to Boss Magazine, today’s supply chains strive to achieve the same goal, “to simplify processes while maximizing effectiveness”. However, while emerging platforms have made notable impacts, there is a technology that is gaining considerable traction.
Machine Learning is a methodology analytics leader, SAS, describes as:
A branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
This advanced system of data analytics has helped transform our everyday lives. For example, media service provider, Netflix, creates positive customer experience by predicting the shows viewers may be interested in based on past searches. This form of “analytical model building” could also present revolutionary ways for supply chains to optimize their processes.
This article identifies how machine learning can address four challenges facing supply chains in 2018.
1. Demand Forecasting
As mentioned above, there is considerable pressure on supply chains to provide efficiency and immediacy. Ecommerce and mobile shopping has provided customers with easy-to-use platforms where they can purchase items with the click of a button. Therefore, the expectation of expedited delivery has also increased.
Forbes contributor, Louis Columbus, states that machine learning algorithms can help address one of the top challenges facing supply chains: “predicting the future demands for production”. Machine learning enables companies to make sense of big data in order to recognize patterns and understand how to predict these future demands.
2. Cost Reduction
The advanced forecasting approach of machine learning can also help reduce costs associated with delivery. Supply Chain Dive confirms that just two years ago, 60% of online transactions were expedited with free delivery. This has raised the bar in customer satisfaction but has also put incredible pressure on retailers and supply chains from a cost perspective.
In conjunction with Artificial Intelligence (AI), machine learning can help improve delivery performance and reduce freight costs by considering and avoiding possible deviations.
3. Customer Service
To reiterate, the emergence of online shopping platforms has increased demand, moving organizations to place acute focus on customer service. Machine learning improves efficiency, therefore, improving the ability for companies to provide visible and reliable service. Research on supply chain pain points found that due to inaccurate forecasting, companies struggle understanding ‘market patterns and market fluctuations’.
Forbes states that on top of decreasing inventory and operation costs, machine learning also improves the response time to customers.
4. Optimize Visibility
In a featured article by Morai Logistics, we discuss the importance of transparent supply chains. Being aware of all aspects of the shipment lifecycle is important from both a supply chain and customer standpoint. Research found that,
73% of online shoppers feel more confident making purchases when they have the ability to track their delivery.
In combination with other forms of technology, machine learning can help supply chains achieve end-to-end visibility. It offers advanced insight into real-time data and formulates patterns that can help companies make informed decisions.
Supply chains must leverage technologies that help improve demand forecasting, lower costs, improve their customer service and deliver transparency. Machine learning is a revolutionary tool that supply chains should utilize to overcome unpredictable challenges.
