Unlocking Big Data and Data Analytics in the Supply Chain
We are entering into the era of big data. With the advent of accessible cloud storage and digitized supply chain applications, businesses can actually suffer from having too much data. What comes next is paralysis -- businesses are not able to turn these mountains of data into actionable insights.
A 2018 study by American Shipper found that by 2020, there will be 44 trillion gigabytes of data in the digital universe... Businesses will earn success through the efficient extraction of relevant insights from immense and endlessly complex datasets. However, it doesn’t stop there -- they will need to use these insights to effectively reformulate their internal processes and customer relationships.
The problem? Businesses can’t keep up
The sheer amount of data produced by today’s supply chain networks and newly implemented digitization efforts presents businesses with a new challenge. Rapid advancements in supply chain technology has enabled businesses to streamline their networks and collect data on their processes.
However, without a sizeable data analysis strategy and active enterprise data management, there is a risk of drowning in incoherent data - or worse, obtaining faulty insights. Further complicating things, the profession of big data analysis in business intelligence is relatively new, which makes finding qualified employees tricky.
While data analysis has the incredible potential to shape and consolidate the supply chain network, too much data can actually slow businesses down, if not dealt with correctly. An overload of data can actually hide potential insights, or lead to incorrect conclusions through drawing random correlations that have no real world relation.
Why is measuring supply chain metrics valuable?
Data, when collected meaningfully, can help businesses improve their supply chain processes with analytics-driven insights. Every given moment, there are countless shipments moving around the world, at every point in the network.
With these countless shipments comes insurmountable data. Of course, in any supply chain the goal is to get the shipment delivered as quickly and efficiently as possible.
In order for businesses to do this, they need to have efficient, meaningful ways of analyzing the mountains of data they produce every day, every hour, every minute.
The benefits of employing big data analytics to further develop the supply chain network include:
- Improved demand planning
- Quicker, more efficient response times
- Network optimization
- More accurate inventory management
Setting up infrastructure to capture, clean, and analyze data is crucial
Once a data strategy is established, it must be employed in a way that simplifies data management at all costs. In the case of data, standardization is the key to simplification.
The intention is to follow non-causal, non-random correlations that will inevitably materialize. This is the key methodology behind big data analysis -- where the end-goal is to find insights that can be implemented into the supply chain strategy, and business model.
The implementation of big data analysis is becoming crucial to optimizing a supply chain network. In a study by Accenture, it is revealed that, while 97 percent of supply chain professionals report having an understanding of the benefit of big data analytics, only 17 percent report having implemented these analytics within their supply chain processes.
The businesses that are able to shift their priorities and implement a robust data strategy will get ahead in today’s market.
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