Supply chain visibility is at a low ebb, prompting leaders to explore machine learning as a way to regain critical insight into future threats.

Supply chain managers in 2024 are faced with an increasingly thorny environment. From shipping disruptions in the Red Sea and Panama (and now in Baltimore), to a rise in extreme weather events, disruption seems less like the exception than the rule. 

This ongoing disruption has highlighted the need for businesses to develop coping strategies. Increasingly, supply chain managers are looking to adopt technologies that let them predict and outmanoeuvre these disruptions. Agility and resilience are cardinal virtues for supply chains in 2024, almost as much as cost containment. 

However, despite the goal being clear, many companies struggle to increase the resilience and agility of their supply chains. According to a recent article in the Harvard Business Review, a lack of accurate forecasting is to blame. As authors Narendra Agrawal et al posit, “how can inventory and production decisions be made effectively when demand forecasts are widely off?” 

Machine learning and demand forecasting

Machine learning and artificial intelligence (AI) have tremendous potential to increase supply chain visibility. 

The growth of IoT devices and oversight platforms is also generating a wealth of unstructured data across the supply chain. This makes machine learning an especially useful tool for tracking and predicting trends or disruptive events. Essentially, the technology is very good at finding complex patterns and relationships within historical data. As a result, machine learning can significantly enhance accuracy when predicting demand.

To use a simple example, let’s imagine a snack company. Using machine learning algorithms, this company could analyse historical and broader contextual data to pick up a pattern where sales of certain snacks tend to spike during specific seasons. During allergy seasons, the demand for grain-free snack foods might increase. Likewise, promotional events, like Veganuary, could cut demand for some products and drastically increase demand for others. Likewise, sourcing disruptions like a crop failure due to extreme weather conditions can be taken into account. 

From a high level, these aren’t decisions that are beyond the scope of an experienced human supply chain professional to notice. However, it’s the ability for a machine learning algorithm to not only pull these insights from vast oceans of seemingly disconnected data, but to translate them into strategic recommendations for action (based on previous successes and failures) that makes the technology truly transformative. It’s doing what (not all) humans can do at speed and scale and, theoretically, with less propensity for error. 

By continuously learning from these data points and recognizing the complex relationships between them, machine learning algorithms can generate highly accurate demand forecasts. As a result, companies can ensure they are stocking the right levels of inventory and ordering the right products at the right times. 

  • AI in Supply Chain
  • Digital Supply Chain

Related Stories

We believe in a personal approach

By working closely with our customers at every step of the way we ensure that we capture the dedication, enthusiasm and passion which has driven change within their organisations and inspire others with motivational real-life stories.