Big data and predictive analytics optimising the supply chain

Everyone in the industry talks about big data, but what does it really mean? Big data is defined as extremely large data sets, both structured or unstructured, that’s analysed to reveal patterns, trends and associations, especially relating to human behaviour and interactions.
In the context of supply chain, big data may provide valuable insights that can be useful to proactively anticipate or quickly respond to events or disruptions. There are many use cases where big data can deliver benefits, but most importantly, big data can help organisations become better trading partners to their customers and suppliers.

Despite the hype, the general industry capabilities to use insights from big data to optimise the supply chain has proven to be far more elusive than collecting the data itself. Some organisations remain unsure about how to implement these large data sets, while others are utilising big data in a fragmented way.
Predicting future supply chain disruptions
Most supply chain systems, such as transportation management systems (TMS), rely heavily on the concept of fixed lead time, but there’s always uncertainty, especially when it comes to ocean shipping.
Relying on lead time and other existing solutions, such as latent EDI-based status updates and alert users to disruptions after the fact, drastically limit an organisation’s ability to remediate issues quickly before problems worsen. This results in lower customer service levels, increased expedited freight, lower margins and the need for more buffer stock.

While supply chain disruptions will always occur, the emergence of newer technologies provides the ability to predict potential future disruptions and act on them accordingly and proactively. Unpredictable consumer behaviour, traffic patterns, port behaviour,, severe weather, natural disasters and labour unrest are all examples of external events that can cause supply chain disruptions that lead to increased costs and customer service challenges.
Newer technologies can give organisations insight into predictive analytics and big data for more certainty of shipment ETAs, down to just a few hours. This creates a more resilient supply chain, enabling organisations to make more active, efficient decisions that reduce network latency, shorten cycle times and protect profit margins.
Driving value from big data
Third party logistics (3PL) providers are becoming more effective with how they leverage big data within the supply chain and are beginning to create more value by dedicating resources and building partnerships with technology providers to apply big data into their service offerings.
Big data isn’t just about collecting information, but it’s the ability to do something with it. Today, organisations expect better visibility into the data and predictive analytics so they can make smarter, quicker and more efficient decisions.