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.

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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.

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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. In addition to using data for supply chain operations, you can turn data into value by providing market insights to customers, suppliers and other trading partners.

Being in logistics, it’s crucial to help upstream and downstream partners grow, which could be as simple as providing consumer sentiment, down to the assortment insights in the case of the retail industry, as well as seasonality patterns and consumption forecasts.

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In the case of industrial procurement, suppliers need to plan months out, however, many retailers and distributors aren’t advanced enough to support this. Big data and predictive analytics can help suppliers plan their businesses beyond the next order horizon, and extend it all the way out to 12 – 18 months.

They can do this without taking too many risks by providing insight into the downstream customer demand and buying behaviour. Providing better visibility helps customers run their businesses better and suppliers to grow their businesses.

The importance of applying proper data science

Most organisations recognise the value and importance of big data, but become overwhelmed and struggle with implementation due to the large amounts of structured and unstructured data. However, it’s what’s done with the data that actually matters and drives value. Big data can be analysed for insights that lead to more efficient and effective business decisions.

Related: Moving from passive to active analytics for data innovation

Many organisations don’t want to get behind and have invested a lot in data centres without having the vision of how to turn their data into real value. It’s critical to make the proper investment and to really understand the data in order to see potential new opportunities, how to achieve them and if it’s sustainable and efficient, before investing large amounts into data science resources.

Leveraging big data to gain a competitive advantage

Before investing resources into a data science department, or until you know what you’re looking to pursue from a data science perspective, it’s recommended that you first start by identifying the biggest opportunities within your data or the data you’re lacking.

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Other recommendations before investing in resources are checking with your peers, local universities and even technology vendors that have invested and “been there, done that,” so you can draw your own points of view. Look for technology vendors that offer “data science as a service” to explore the possibilities. Identifying these opportunities leads to better return on investment from big data and gives organisations a competitive edge they’ve never had before.

Big data is here to stay and can provide significant benefits to the supply chain, so organisations should embrace it. This is especially true to survive and grow. Organisations that properly implement big data will continue in newfound efficiency, and will remain competitive, more streamlined, responsive and proactive to supply chain disruptions.

 

Sourced by Danny Halim, vice president, Global Wholesale-Distribution and 3PL Industry Strategy at JDA Software

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Nick Ismail

Nick Ismail is the editor for Information Age. He has a particular interest in smart technologies, AI and cyber security.