Some Comments about “Big Data” Use in the Chemcial Industry

My recent searches on the internet for the use of “big data” analysis indicates to me that such analysis is growing in the chemical industry.

A leading chemical company that seems to be very active in pursuing the use of big data analysis in support of its operations is the Dow Chemcial Company.  I was able to identify from information on the internet the following 15 areas in which Dow is, or has been, pursuing use of big data analysis:

1.   Predict what products to invest in or divest.

2.   Predict the quality of a product before it is manufactured.

3.   Assess consumer sentiment.

4.   Predict how much product to make.

5.   Monitor plant equipment processes for problems.

6.   Monitor equipment processes for problems at multiple plants from a single location.

7.   Reduce error rates in sales forecasts from 40% to 10%.

8.   Assess the trustworthiness of external sources of data and information.

9.   Reduce errors in forecasting models

10.  Develop freight and logistics costs models.

11. Analyze raw material spending.

12.  Price finished products.

13.  Assist the agriculture industry through big data analysis.

14.  Make better personnel-related decisions.

15.  Monitor raw material characteristics. 

Successes by Dow in many of, if not all, of these 15 examples of using big data analysis should bring great value to a company, value which otherwise would be difficult to obtain.  Finding this information about Dow’s interest in big data analysis, I believe, likely indicates the potential of using big data analysis in the chemical industry.   Another indicator of the perceived potential of the use of big data analysis in the chemical industry might be recent increases of scholarly articles on big data and the chemical industry.

Using the search engine Google Scholar, I discovered that for 2014, Google Scholar found 106 scholarly articles that have in them the terms “big data” and “chemical industry”.  This is about a 2000% increase for such articles found for 2010.   The follow table shows the progression of the number of articles (and consequently the progressive interest in the subject) that has both “big data” and “chemical industry” terms in the articles: 

articles with "big data" and "chemical industry" using Google Scholar as the search engine
year	number of articles
2014	106
2013	63
2012	30
2011	8
2010	5

Based on the above indicators, it is likely that we are entering into a very exciting/productive period of using big data analysis leading to more efficient and effective decisions in the chemical industry.