Big Data, Analytics, and the Changing Face of Supply Chain Management

Discover how big data and analytics are transforming the face of supply chain management. Learn how to leverage these key tools and stay ahead of the curve!

5 Min Read
Shutterstock Licensed Photo - 763228831 | By metamorworks

Over the past three to five years, growth in big data and analytics technology has led to improvements in dozens of other industries. In terms of supply chain management, there’s a lot of noise regarding the promise new technologies have for changing how organizations do business forever.

The Role of Big Data and Analytics

When you study supply chain management, it becomes very clear that this is a field where big data and analytics can have a really positive and evident impact. Anytime you can tap into raw data and shape that information into powerful insights that can be used to enhance, improve, or speed up processes and tasks, you have a recipe for success.

But up until just a few years ago, there was very little being done in this niche of the big data world. Businesses didn’t seem super interested in adopting big data tools, which meant those in the analytics arena weren’t overly enthusiastic about creating applications for these archaic organizations.

Thankfully, this has all started to change in the last 24-36 months. Heavy research on the applications of big data was spurred by a 2013 Journal of Business Logistics white paper, in which industry experts called for maximizing the potential of big data within supply chain management. Today, big data and accompanying analytics technologies are influencing the following issues:

1. Supply Chain Visibility

Visibility is a huge buzzword in a handful of industries these days. Many businesses are under increased pressure from regulating agencies and consumers to be more transparent with their supply chains. As a result, new technologies are arising to give these companies better access to quantifiable data that can be used to enhance transparency at each link of the chain.

Take the food industry as an example. Farm-to-fork traceability is something the marketplace is really adamant about. Thankfully, food traceability software has emerged, which leverages data and provides relevant and real-time insights that can be used to enhance visibility. This is what those in the industry call a “game changer” and is just one example of how big data can produce big change.

2. Inventory Management

In 2013, just 11 percent of companies had the capabilities needed to evaluate “what-if analysis.” Furthermore, only 24 percent of companies were able to successfully model the impact of changing conditions on their profitability.

While the exact numbers today aren’t known, it’s clear that these percentages have increased. Supply chain and logistics platforms have been hugely enhanced by big data and, as a result, inventory management has become that much more accurate.

3. Forecasting

If we only knew what to expect, we could be better prepared. How many times have you mulled over that thought in the past? Better, more accurate forecasting is something that has the potential to improve profitability, decrease waste, and lead to increased satisfaction among customers and clients. The problem is that accurate forecasting often feels like a misnomer. With so many changing variables, is it even possible to consistently produce accurate forecasts?

While 100 percent accurate forecasting is still not feasible, access to big data is making it more of a reality than ever before. Advanced organizations are using this data to better predict customer needs and preferences, while simultaneously accounting for external factors in the marketplace. The result is more satisfied customers and fewer lost sales.

Don’t be on the Outside Looking In

Businesses that deal with supply chain management don’t want to sit back any longer and ignore the potential of big data. It has the ability to totally overhaul and revolutionize major aspects of the field – and is already doing so in many organizations. It’s 2017, which means it’s finally time to take big data and analytics seriously.

Share This Article
Exit mobile version