Companies produce an ever-growing amount of data, that often goes to waste. But how can the information hidden in this data be extracted and used to a business’ advantage? And what potential applications do data analytics have for the supply chain?
Topics in this article
What is data analytics?
Data analytics is the science of analyzing large amounts of data in order to draw conclusions. These conclusions that would otherwise simply be lost in a sea of information. Essentially, data analytics brings order to the vast amounts of data points businesses can easily collect nowadays.
Visualization is thereby a key element in data analytics. Visualizations are created through automation tools that spit out condensed information in the form of visualizations. With their help, businesses can make decisions based on a summary of relevant and trust-worthy information.
Even today, the most common (and easiest) method to create visualizations is to turn an excel spreadsheet into a pie chart, graph, or table. However, recent technologies allow for the creation of more complex visualizations, such as heat maps, infographics, or bubble clouds. Depending on the type of data, a simple graph may be sufficient, whereas other data requires more complex graphics.
Big data and the Internet of Things (IoT)
As the amount of data processed increases, the concept of Big Data has surfaced and essentially covers three aspects. These are:
- Greater variety
- Higher velocity
- Bigger volume
Everything and everyone is producing data points constantly. And, as an increasing number of devices is connected to the internet, a growing number of data is collected, stored and analyzed.
This has led to the creation of the Internet of Things (IoT). This refers to the growing network of physical objects that have internet connectivity and the communication happening among these objects. Big Data and The Internet of Things (IoT) are thereby very connected. Even more so, Big Data powers the IoT.
Why is data analytics important?
With increasingly more complex business operations, companies contribute their own share to the IoT and the increase in Big Data – demand patterns, sales, supply patterns, and much more. In order to digest and draw conclusions from this data, businesses need data analytics. If a demand spike occurs, but the company is unable to understand what has caused the increase, what is that piece of information even worth? Drawing conclusions from such a spike, figuring out what caused it, and implementing concrete actions that follow from that understanding, can have a big impact on success.
Data analytics and the supply chain
Supply chains are typically a part of a business that generate massive amounts of data. Supply chain analytics helps to make sense of all this data by uncovering patterns and generating insights, that would otherwise go left unnoticed.
With increasingly global supply chains, being able to manage that all data becomes extremely important and demands careful data analysis for a variety of reasons. For example, using supply chain analytics can help a business better prepare for the future by taking a look at past patterns.
If ice cream sales increase when it is sunny, production can already be ramped up if good weather is predicted. Making these preparations for the future can allow a company to achieve a more responsive and more effective supply chain, that is tailored exactly to the company market’s needs.
Want to know more? Experience analyzing data in our business simulation games!
Every operation within a company produces data – and this data can be used to the company’s advantage. Using data analytics, companies can draw more accurate conclusions, allowing them to make apt decisions and prepare for the future. This allows them to achieve a leaner and more efficient supply chain.
Do you want to learn how to take advantage of data analytics for your business? Data analytics can be incorporated in the simulation-based learning programs Inchainge offers. Our business simulation games depict virtual companies, that harbor, like in real-life, a ton of data. Data that can be evaluated in preformatted reports, in self-created reports or in some cases, be exported in formats to be further dig through by Excel sheets. A great example of experiential learning about this important topic.
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