Planning the Move From Metrics to Analytics

Aug. 23, 2018
More manufacturers are pursuing digital transformation via IIoT technology, with the move from metrics to analytics leading the way. Companies should carefully consider their operational architecture as a key part of the plan.

For several years, LNS Research has partnered with MESA International to study and publish a research report titled “Metrics That Matter,” with data on which metrics are tracked by leading manufacturing enterprises and which metrics have shown significant year-over-year improvements by survey respondents. Industrial organizations have always found the benchmarks useful when measuring their own performance. The 2018 version of the study, “Analytics That Matter,” moves from a discussion about metrics to planning for advanced analytics, which mirrors the shift we see among our industrial clients.

The manufacturing industry has seen a wide range of new analytics applications launched over the past three to five years. This activity includes highly focused applications in asset performance management (APM) and other maintenance-related processes, typically focused on high-cost resources. Historically, companies started with a dashboard of simple metrics to show up-to-the-second status of machines and operations, and they focused on improving response time and time to resolution when issues occurred. However, very little live data from systems actually move into data stores outside the plant. That’s about to change, however, and it promises something new: prescriptive control of operations from live analytics. This is the opportunity that prompted our shift in focus from metrics to analytics.

A high percentage of companies we talk to are putting teams in place to examine this shift, what it means to their business, and what they should do about it. Although every company starts from a different level of capabilities maturity, a critical element of digital transformation is an operational architecture that unites and aligns the key components of a digital enterprise. For that very reason, we published a guide to operational architecture for manufacturers considering the journey.

Of course, the final operational architecture will be unique for every manufacturer. However, the core components described in the report (and below) are a great starting point to fuel internal discussions.

Industrial operations

Industrial operations is manufacturing—the machines, processes and personnel that make up the business. In other words, if you’re a manufacturer, industrial operations is your core function. And operations improvement is the primary objective of digital transformation. The focus should span improving asset performance, increasing throughput, increasing first pass yield, providing more accurate information to the business, and delivering high-quality data to digital systems (e.g., analytics).

Compute and storage

Industrial operations and business processes throughout a manufacturing organization produce enormous amounts of data. Historically, most of that data is used only for control purposes. However, the digital world requires much more. Connectivity will be enabled through smart devices with IP (Ethernet) capability, edge devices will collect and analyze enormous amounts of data, and data from business operations and manufacturing operations will be combined. Additionally, the whole world outside the plant will become a source of data and a place to which data needs to be sent. Connections to this data and a place to store and analyze it are core requirements of an operational architecture.

Big Data model

Once data is being collected and stored, organizations must build Big Data capability to do something with the data. The Big Data model must be able to handle all types of data used in a digital enterprise. We categorize data in three ways:

  • Structured, which comes from traditional databases and equipment.
  • Time series, which is very specific to manufacturing and includes continuously changing and event-based data in specialty databases (often data historians).
  • Unstructured, such as video, images (e.g., machine vision systems), weather and all sorts of information that has not typically been used in manufacturing but will enhance analytics and application development as digital transformation continues.

Industrial analytics and apps

Finally, at the summit of the operational architecture is where new things happen. To get started, companies must design a hardware and software architecture to support the promise of analytics. Many types of analytics run on multiple levels of the operational architecture, and different processes might be analyzed closer to the plant floor or closer to the executive suite. The technology stack selected by your company must support levels of analytic sophistication from diagnostic (merely looking at what is happening) to prescriptive (controlling the process and business) and, most importantly, supporting the people who run the business from shop to top (or operator to CEO).

If your company is considering its own digital transformation, the “Analytics That Matter” research report from LNS Research and MESA International describes operational framework in context of analytics and explains considerations industrial companies should address.

>>Patrick Fetterman is vice president customer operations of LNS Research, managing company operations across new customer acquisition and customer success. He is also a research analyst providing collaborative coverage across the industrial value chain, including industrial analytics, manufacturing operations technologies, digital transformation and the Industrial Internet of Things (IIoT).

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