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Key Topics
Requirement
Supply Chain Integration with Bigdata and the Internet of things. What can be expected in the future with big data and the Internet of things Implementation in the Supply Chain?
Solution
Supply Chain Integration with Big Data
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The supply chain is a sequence of the processes that are undertaken for producing and distributing a commodity (Christopher, 2016). Big Data is the data that is beyond the processing capacity of the traditional database systems (Mayer-Schönberger, 2013).
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Big data has an impact on the drivers of the supply chain, and the big data adds value to the overall operations of the supply chain by improving the efficiency of its operations depending on the result of the analysis. Thus, the integration of the supply chain needs to be done with Big Data.
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The qualitative and quantitative methods from the various disciplines are used by the supply chain management data science for predicting the outcomes and solve the problems related to SCM. This helps in predicting the past and future integration levels of the business processes of the companies and the costs and service levels associated with it.
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The phases of the flow of information can be analyzed where it can be seen that which data is coming from which of the SCM system like the CPFR, EDI, etc.
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Supply chain integration with Internet of things
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The businesses focus on getting direct feedback from the customers by using technologies and social platforms. They do this by obtaining the ratings, reviews from them and from the blog comments. The data which is obtained from the mobile, social platforms and e-commerce is integrated with the data which is obtained from the enterprise systems.
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The phase of manufacturing has also changed from the event-based planning to the real-time sensing because of the introduction of the internet of things and the communication that happens via machine-to-machine (Pang, 2015). The sensor technology has evolved which has provided real-time equipment and the conditions of the product data that have resulted in the automated maintenance and the process adjustments.
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The volume of the data has grown tremendously, both in volume and in velocity. Thus, it can bring tremendous value if it is exploited in the right way using the internet of things.
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The competitive level of companies cannot be achieved if the technologies using the internet of things is not there. Therefore, all the model of the company has to be synchronized with the internet, and this can be done by creating a separate information flow that will help the companies in capturing, processing, analyzing, storing and exchanging the data for the each and every operation via the internet that they perform (Schoenherr, 2015).
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For example, in Germany, the internet is available at very less cost and the people can access it from any part in bulk. The availability of the internet is easy and it is inexpensive too. So, all the business in Germany is driven by the internet and there are very few physical setups at that place.
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Place Your OrderExpectations in the Future
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In the future, it is expected that the productivity of the companies will be driven along the entire length of its supply chain and the big data and the internet of things will be integrated in such a way that it will spread to the global companies and help them to do the business.
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For example, companies will create a strong enterprise-wide analytics strategy. They will be able to embed the analytics of the big data in their operations of the supply chain (which will happen with the help of internet of things). Also, these companies will have the right people in the future that could produce actionable insights from the big data.
References:
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Christopher, M., 2016. Logistics & supply chain management. Pearson Higher Ed.
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Mayer-Schönberger, V. and Cukier, K., 2013. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
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Pang, Z., Chen, Q., Han, W., and Zheng, L., 2015. The value-centric design of the internet-of-things solution for food supply chain: value creation, sensor portfolio, and information fusion. Information Systems Frontiers, 17(2), pp.289-319.
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Schoenherr, T., and Speier?Pero, C., 2015. Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), pp.120-132.
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