Leveraging The Use Of Data For Supply Chain Improvement

supply chain management

Data is the new currency for a successful Business operation. In recent times, Data has become the most valuable resource over Oil. It might seem shocking but this is the current fact. The process of extracting data has become even a much more complicated process than ever. Large multi-million dollar companies are running their business overselling these data to other sources.

Supply chain management is one such sector that has been gaining momentum over the years. It has become even more complex with the increasing demand in various industries which has urged other logistics companies to invest millions to handle all the operations. To tackle this demand and create better performance, data analytics plays a huge role.

In supply chain management, data has seemed to offer multiple ways that can leverage the scope of improvement in the years to come. Well, data analysis starts from even a very small source and can provide great results resulting in better business operations. 

For example, any supply chain companies such as Nestle, Unilever, Nike, etc can gather data from social media where they can understand the behavior of individuals and what is trending. Accordingly, these companies can gather tons of data from all its logistics suppliers and make a collection for further implementation accordingly.

There are companies that are using a much more efficient way to collect these data i.e. through the use of IoT and have already taken edge above all the others.

Use Of Data for Supply Chain Improvement

Data extraction surely has become a difficult task altogether. To extract data, the organization needs specific tools and a proper understanding of complex algorithms to fetch the right data for better business activities and operations. 

It is not always right to say if the data received is suitable or 100% accurate which otherwise creates an issue of mistrust among businesses worldwide. In order to solve this issue, data that has been extracted needs to be properly refined and checked with the use of analytical tools to provide the desired results.

Here are some of the examples of how the use of data can improve Supply Chain.

  1. AI tools are being used along with the IoT sensors, and other insights to create a much better experience for customers within the Supply Chain industry.
  2. GPS maps and data associated with it can be used to guide the delivery vans to take better roads and avoid the ones that are crowded or broken. This data will help to cut costs on unwanted costly affairs.
  3. An organization can use data analytics to understand the customers and get a 360-degree view of the market. It will help to understand their needs, preferences, and this creates a better opportunity ahead.
  4. With the use of data and predictive analysis, organizations can easily assess the probability of an occurrence of a problem. It can help in analyzing the risks involved in supply chain management by the data received and can help to minimize these risks before the problems occur.
  5. Another great way these data can improve the supply chain is by providing real-time updates. As quick updates would be, better actions can be taken to improve customer satisfaction and management activities.

Verdict

Well, it is clear the ways in which the supply chain can be improved with the help of future technology and data analytics. But there are always certain risks involved which need to be taken care of before implementing these techniques. Not all the data received are accurate and thus may lead to false results and many more issues. 

It is always recommended to develop trust in organizations that can give you accurate data so that they can be put to better use and improve the supply chain. For any further research, the accuracy of the data generated from processes such as manufacturing and logistics, improvement in the sensor accuracy on to the machines along with enhancements in the data integration technology in various business processes is necessary overall.

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