Dear Researchers,
I have recently been thinking to research on the above given topic, however my difficulty is I don't want to make it technical. Involving statistical and algorithm based interpretation is the last thing I want to incorporate in my research as I wanted to study 'how efficient use of Big Data can optimise the supply chain operations'.
Requesting the experts here to suggest how I can make my research specific without making it technical? is it really possible to exclude the algorithms from my research based on the topic?
If No then please suggest any other similar topic?
Thank you,
Best Regards,
Sapna Nayyar
Dear Sapna Nayyar,
I suggest you to see links and attached files on topic.
https://bconsult.io/2018/04/09/the-impact-of-big-data-on-supply-chain/
https://www.forbes.com/sites/louiscolumbus/2015/07/13/ten-ways-big-data-is-revolutionizing-supply-chain-management/#589c5b7f69f5
https://www.sciencedirect.com/science/article/pii/S004016251731329X
https://www.cleverism.com/how-to-optimize-supply-chain-management-big-data/
https://assets.kpmg/content/dam/kpmg/au/pdf/2017/big-data-analytics-supply-chain-performance.pdf
Article Big data and its impact on digitized supply chain management
Conference Paper Big Data Analytics in Supply Chain: A Literature Review
Best regards
Ma'am
In simple terms
Artificial intelligence (AI) - Level One
Machine Learning (ML) - Level two (which is a subset of AI)
Deep Learning - Level three (Which is a subset of ML)
AI focuses on thinking like the human brain. So, there are three outcomes
- Descriptive
- Predictive
- Prescriptive
In order to achieve these outcomes, data is instrumental. So bigger the data better will the results.
Therefore Big Data shouldn't be your focus, your focus should the SCM problem with the expected outcome out the three scenarios. Or building a model based on the three-level where BIG DATA can play a vital role.
Please check the following:
Article Impacts of Internet of Things on Supply Chains: A Framework ...
https://ideas.repec.org/a/eee/tefoso/v130y2018icp135-149.html
Conference Paper Big Data Analytics in Supply Chain: A Literature Review
Data or Big Data is a raw input to any hypothesis you can come up with. Churning insights requires a thorough understanding on the problem statement, here SCM - this I would say is a vast topic and can include any industry vertical. Generally, Big Data and insights will be helpful but innovative solution requires one to not to reinvent the wheel. So, I would suggest to explore a bit more on unsolved SCM real world problems and try to form a suitable hypothesis and use Big Data solution to address it. Problem solving may require you to dissect and arrive at operation KPI's and metrics that can add business value in the SCM value chain.
Hello Sapna, Concerning the effects of Big Data on SCM, I think it would be very interesting to measure the added value of Big Data in improving the performance of city logistics (http://arno.uvt.nl/show.cgi?fid=144834 - by Deckkers) or still in e-commerce sector (https://innovecs.com/blog/impact-of-big-data-on-logistics-and-supply-chain/).
Have a very good reading
Best regards
Distinguished Sapna Nayyar, I understand that you want to do research on a subject that seems to have a quantitative approach (Big Data), but wants to do it from a qualitative level. Having understood correctly, we did a research with Industry 4.0, in general, which we related to the Logistics Model Based on Positions (MoLoBaC). We did the analysis from the qualitative and quantitative point of view. But the quantitative approach was using very simple and easy to handle multicriteria models.
An introduction to this work is in Research Gate (https://www.researchgate.net/publication/333520813_The_Logistic_Model_Based_on_Positions_LoMoBaP_MoLoBaC_and_Industry_40_Source_Title_Technological_Developments_in_Industry_40_for_Business_Applications_Chapter_Preview_Top?_sg=jwseqqb1-Brr5MQzBN9fRFHWa6pwwUI91NKLkOblhloOJjYvFOTGeYB_1bggZJeBFtKVdbu339fy5z33iGCkaKNfP6rg625GUgKGa3bW.UUuTeUJe8GLNj7EeY_ZDFAE4WikuTgSg2yhmg2Mj4FAZfMMQlKSIpsGtEgqU5oJP7fVBkMhKD8QqZH0zEmWqog). Unfortunately, due to confidentiality conditions the work is not complete. But of interest, sure, you can locate it.
I hope this suggestion is of some help and many successes in your research.
Best regards,
José Hernández.
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