Data is one of our most critical, but underused resources. If used correctly, it helps us make better product and pricing decisions, spurs business development and leads to greater revenue. Big data is especially important for logistics organisations, who must collect the right metrics to feed intelligence for critical activities like circumstance monitoring. With technology like data logging, the right data points are collected throughout the entire supply chain to set logistics companies up for scalable success.
The effects of analytics and insights on supply chain management
Supply chain managers may feel like they already have more data than they can manage. Rather than shying away from collecting and using big data, they should focus on using data collection and analysis to enable knowledge sharing across complex supplier networks. In doing so, they will improve forecasting, mitigate risk and streamline issue management throughout their entire organisation. Data doesn’t replace human decisions. It enables them.
How to integrate Big Data into your business
There are typically two obstacles for supply chain managers to overcome before they can begin to successfully use big data: knowing what data points to collect and how to automate the collection process.
The good news is that many companies have already set the groundwork to accelerate an automated analytics strategy. Transportation companies, for example, use analytics in combination with technologies like GPS to predict fuel consumption and to streamline pickup and drop-off processes by managing warehousing space in advance.
The key to scaling an analytics strategy is the implementation of a big data management platform that not only automates data collection but also uses artificial intelligence and machine learning to continually refine how data is used.
We can collect all of the data in the world, but if we don’t know how to use it, it’s worthless. Supply chain management will only continue to move at a faster pace and become more complex. It’s time for logistics organisations to start viewing big data as a resource instead of a distraction.