Dow Chemical Analysis

Problem Statement
Can Lloyd Colegrove expand the pilot project from the R&D lab using data analytics and then implement it into a larger enterprise manufacturing intelligence (EMI) system at Dow. How can he use big data analytics, in real time, and at the point of origin, to improve manufacturing efficiency at Dow?

** I have began the background information, just missing analysis.

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In 1897 H.H. Dow founded the Dow Chemical Company in Midland, Michigan. Dow was an industry pioneer in electrochemicals and in 1891 he introduced Dow’s first product, when he used electric currents to separate bromides from brine. (Dow, 2020). Today over 123 years later Dow is one of the leading manufacturers in the world for many different types of products from plastics, agriculture, energy, electronic materials, and feedstock. Some of Dow’s most well-known products are Styrofoam, Saran Wrap, Ziploc bags.

In 2012 Lloyd Colegrove was the Director of Data Services at Dow Chemical Company. Lloyd had a vision and a plan (hand drawn on a napkin) that could revolutionize how data is used in manufacturing processes. He recognized an issue with how data was being used (and not used) efficiently in process run design at Dow. Lloyd saw that his engineers were working for the data and not having the data work for them. The data systems that Dow and many other chemical manufacturing companies were using was more than 40 years old, but Lloyd knew how that we view our data was much different due to improved business and manufacturing processes that occurred over the years. The data and the analysis of data was lagging way behind in time.

How do we use the manufacturing data that consisted of process controls, product data, and safety monitoring and more, that eventually will lead to finished products, strategically and to continue to improve processes? He wanted to bring all the databases together to what we now call as Enterprise Manufacturing System (EMI). This will have several benefits such as saving time, money, labor, materials, and increased efficiency. Big data analytics was a new term back in 2012 when Lloyd was testing this new data merging program at Dow. Dow was very good at collecting and storing data, massive amounts of data, but they were using it with a reactive approach after time and money have already been lost. His vision was to make the EMI system use the data in a proactive fashion by measuring the data in real time so that the scientists and engineers can take corrective action immediately and make more informed decisions. Having the data work for the engineers through the EMI can alert them when the system anticipates a problem before it even happens, and they can make corrections to prevent these issues from occurring.

Lloyd also knew that in having the data work for his team. He would use big data analytics in a predictive modeling, such as how customized products could most efficiently work within already established manufacturing processes. If a customer had certain requirements and specifications, he could use the EMI to see what the best materials and processes would be best for that specific product.

EMI was also critical to solve the issue of data management and storage. Since Dow had such a massive library of data from many different products and processes over the years, EMI and data analytics will be able to sort and categorize the data, join the data together, then retrieve the relevant data, and finally perform the analysis on the data automatically before it pops up on a dashboard on the computer screen of an engineer. Again, saving time and having the data work for you. All this background information leads us to understating the root of the problem that Lloyd is trying to solve.

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