As financial pressures in the chemical industry rise, companies are exploring analytics to give them a competitive edge. With slim margins and volatile markets, the chemical industry must utilize the power of new in-memory computing technologies to increase profits and mitigate financial risks. 

One of the techniques used to accomplish this is called predictive analytics. By combining mountains of historic and real-time data, chemical companies are able to spot meaningful divergences to anticipate changes and adjust strategies. A specialty chemicals manufacturer, for example, will know by the middle of the month if monthly productions targets will be met. The company can then adjust operations and pricing to ensure profit margins are unaffected.

Companies can also use predictive analytics to anticipate and prevent equipment failure. Operating in a capital intensive industry, chemical companies face big financial risks if maintenance issues arise. By compiling and analyzing equipment data 24/7, companies are able to predict when maintenance repairs are needed. Historically, many chemical companies set maintenance repairs on a regular schedule (every week, two weeks, month, year, etc.). However, often repairs are not needed and maintenance is done “just in case”. With the power of in memory computing, companies will no longer need to do these “just in
case” repairs and will be able to use real time data to pinpoint the exact time maintenance is required.

Unfortunately as our companies grow larger and smarter, simple maintenance checks become more complicated. Welcome to the world of multivariable predictive analytics. We all know that maintenance issues are the result of many variables acting together. The right outside temperature combined with the
correct pressure, for example, might be the actual reason repairs are needed. But how can we determine which variables act together? And at what levels do they affect the performance of our machinery? Add to the mix that not all variables are known and you can see why predictive analytics becomes a lot more complicated then predicting a time of maintenance. Various Predictive Analytics solutions have been introduced to the market to combat these challenges. Be on the lookout for a blog dedicated entirely to multivariable predictive analytics later this year.    

Finally, predictive analytics is used in the chemical industry to improve the efficiency of the extended supply chain. With information to historic and real time prices, procurement teams are able to predict trends in raw materials prices to increase margins. Teams will know the optimal time to buy each material at  the lowest cost.

The four areas discussed in this blog are just the beginning of an extensive list where analytics can help chemical companies streamline processes to increase  margins. Studies have shown that the chemical industry is among one of the industries expected to have the highest returns from big data investments.

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