Metrics Versus Analytics … from Scorekeeping to Scoring

Companies in the process industry have started to recognize that the value of data created within their plants is underutilized, and often does not extend past manufacturing operations.  Some are taking steps to provide greater visibility and integrate that data into wide-ranging business and operational analytics.  These industrial organizations have the expectation that combining manufacturing, supply chain, and financial data, and even data external to the company, will drive higher profitability by uncovering “hidden” opportunities and previously unrecognized synergies for improvement.

We looked to our original Analytics That Matter research and sliced the aggregate data into the process industry collectively and the chemical industry as a sub-segment.  Then, we examined how companies use metrics versus advanced analytics.  Our researchers discovered that simple look-back metrics are no longer sufficient to manage the business. Companies are using advanced analytics to diagnose current and past performance and predict future performance. When an organization embarks on such a path, there may be confusion about the difference between metrics and analytics. Let’s revisit the two terms, define them, and explain how they are related.

No more confusion between metrics and analytics

In simple terms, METRICS are a method of measuring something or the results obtained from doing so. Metrics represent the values of what you’re measuring. When applied to business, the most important metrics —the indicators of business performance—are called key performance indicators (KPIs); it’s a widely understood term that’s been used for decades. Companies can apply KPIs to all areas and levels of the business.

For example, at the highest level, they may include:

•       Revenue
•       Profit margin
•       Return of investment (ROI)
•       Free cash flow
•       Working capital
•       Environment, health, and safety (EHS)

At the plant level we usually see:

•       Throughput
•       Yield
•       Conversion
•       Utilization
•       Energy intensity
•       On-time delivery
•       Quality
•       EHS

And at the asset performance level, companies may have:

•       Planned downtime
•       Unplanned downtime
•       Overall equipment effectiveness (OEE)

ANALYTICS can be defined as the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns toward effective decision-making. When we consider the LNS Research framework for analytics, we see that metrics are primarily descriptive in nature. That is, they describe what happened in the past or what’s happening now. Inspecting metrics may shed light on the diagnostic side but lacks an explicit explanation of the “why,” or reasons behind them.

Analytics reveal the “why”

When we use analytics to find the reasons behind the metrics and predict future performance, with options to take a specific action, then we are talking advanced analytics. From an operational perspective, the ability to analyze performance within a single plant, and relate it to the performance of other plants across the supply chain is invaluable to uncovering hidden opportunities and potential synergies for improvement in the enterprise.

This capability, which can be called operational performance analytics, is particularly valuable for those in the chemical industry. Plants do not exist and perform in isolation but are part of an overall value chain within a company and with suppliers and customers. Even in a large-scale continuous petrochemical operation such as an ethylene plant, there are byproducts that the company must leverage, i.e., consumed, converted, or sold. The same is true for companies that produce bulk and basic chemicals.

However, perhaps the most complex challenge is in specialty chemicals in which there are multi-product plants, some producing only intermediates for consumption by other plants, be they company owned or customers. As value chain complexity increases, the opportunities for uncovering added value does as well. Value chain is a tremendously ripe area for operational analytics to contribute to “scoring” rather than just the “scorekeeping” that comes with metrics alone.

Putting predictive and prescriptive analytics to work

Our research reveals three distinct trends.  First, predictive analytics has taken off and is as widespread as diagnostic analytics, but occurring primarily at the plant level. Second, there is a significant drop off between predictive and prescriptive analytics, with predictive being most prevalent in maintenance. Options and actionable steps are missing. Finally, the first two trends indicate that there is a significant opportunity for companies to address operational analytics at the corporate level across the value chain, as well as add prescriptive analytics at the plant level.

Lean into KPIs to generate actionable insights

Industrial companies are eager to put advanced analytics and data from manufacturing, supply chain, financial systems, and beyond to work to drive ever greater business performance. Businesses throughout the process industry, particularly those in chemical, have a lot to gain by finding hidden performance opportunities.  Regardless of where the organization is in its quest to apply analytics, it can benefit from these additional steps:

• Every industrial organization should make sure it has good access to plant data and KPIs. If not, address architecture and data issues.

• Develop initial use cases from simple KPI comparisons, such as quality, energy efficiency, and reliability (OEE) of critical equipment across plants. With these in hand, more comparisons and benchmarks will become evident.

• Use analytics to generate actionable steps for improvement.

Companies that make the connection between plants across the value chain using enterprise operational analytics yield a deeper understanding of performance and actions to improve it. It’s good to know and understand; it’s better to do something about it.

Learn how industrial companies drive business results in the webcast with LNS Research, “Driving Profitability with Plant and Process Data.”  To learn how GE can support your Industrial Transformation click here.