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Big Data: Getting Started on the Journey

This article discusses some experiences and challenges in establishing an enterprise manufacturing intelligence (EMI) platform at a major chemical manufacturing company, and recommends steps you can take to convince your management to harness big data.

It’s often said that a journey of a thousand miles starts with one step. This holds true for any effort to harness the power of the data in plant historians, laboratory information management systems (LIMS), and online analyzers.

The previous two articles in this special section discuss what big data is and why engineers should care about data. This article instructs you on how to tackle the vexing problem of convincing your organization to start the journey into big data. It discusses how we helped the Dow Chemical Co. move from merely collecting data to actively harnessing the data. We offer our experiences, reveal some of the challenges we faced and successfully met, and explain how we overcame these obstacles.

The what and the why

In many sectors, big data means using large sets of unstructured data to predict buying patterns or market trends. These data can also be used to define triggers — also called the what — that alert companies to engage in an activity.

What refers to an event that signals an action is required. For example, data analysis indicates Internet shoppers are interested in a particular product. This trend might prompt you to meet with suppliers, restock, or increase production.

The what allows companies to react appropriately in their supply chain and market context. Because of the fickleness of trends in business, the what that matters today might not matter tomorrow. In this example, you are not necessarily interested in why a particular product’s sales pattern is behaving this way, you just want to respond to the market trend. In this big data journey, the why is often irrelevant or ignored.

The chemical industry cannot afford to ignore the why that accompanies the what. The what might be the plant trending outside of normal operating conditions; the why is the reason for that trend. The what might be that product quality does not meet customer requirements; the why is the reason for that as determined from the data.

In the chemical industry, engineers typically should not respond until they know the why. Chemistry and chemical engineering principles do not trend up or down based on consumer whim or stock market variation. Ignoring the why can lead to operational (i.e., reliability and productivity) and safety peril. So, given our focus on both the what and the why, how do the ideas of big data translate to the chemical manufacturing industry?

At a chemical plant that has taken advantage of big data, the right people will instantly receive alerts to let them know when continuous processes are trending outside of normal or batch processes are not progressing properly. These alerts will be accompanied by tools for making improvements — that is, a method for efficient diagnosis of the problem, a collaborative environment for discussion about what to do, and a way to store the experience to learn from it.

Typically, both the alerting system and the tools for making improvements are built into a single platform, referred to as enterprise manufacturing intelligence (EMI). EMI can mean different things to different people; in this instance, we mean a platform that encompasses the automated sampling of data from data sources, as well as collation, affinitization, and analysis of those data — all in real time. We are not adding new measures or systems to generate more data. We are using the data we already collect and store to achieve ever-increasing value.

In the program that we established at Dow, we aimed to achieve “total data domination.” This entailed mastering all digital and alphanumeric data that related to a given operation, and correlating and displaying the data to be meaningful for every user.

Where to find big data opportunities

To determine where better data tools are needed, listen to the way that data are discussed on a daily basis. For example, at the beginning of our process, we often heard, “I don’t trust the lab data.” In attempting to ensure accuracy in the analytical measurement — and raise the trust within our operations — we discovered two things:

  • The analytical measurement was typically performing its duties as prescribed and expected.
  • The recipients of the data (e.g., process operations, plant engineering, and improvement and quality release functions, among others) were unable to use the data properly.

The recipients did not understand the effect of natural variation inherent in the manufacturing process, the analytical method, the sampling technique, etc. This lack of understanding was costing a lot of money, and frustrating plant custodians and customers alike. Data can bring great value if understood within the proper context, but the context was almost always missing.

Look for operations in continual crisis. Many big data opportunities are identified during post-mortem root-cause investigations. Other opportunities stem from known crisis situations, where the plant works feverishly to solve a recognized problem but the issue continues to escalate. Failing to identify a root cause may result in reduced production rates or even unexpected downtime.

In both of these cases, it’s easy to demonstrate to a desperate audience that the issue could have been avoided if the data had been evaluated in a timely manner. After a process disruption, it’s clear that use of data analytics can help the plant avoid a shutdown, reduce production delays, and/or deliver better value and service to its customers.

After the storm has passed. Once the root causes of a crisis are understood, plant personnel may think they have the situation under control. But, as time passes and engineering personnel change,...

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