Use machine learning to advance operational excellence

Bring existing Lean, Six Sigma, TPS frameworks into real-time domain.

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By Jim Barkley, UI Labs, and Rajiv Anand, Quartic.ai

Applying any new technology to business has its challenges. Early adopters must wrestle with nascent and untested methodologies, overcome a lack of understanding and resources, and suffer risks to existing operations. And for pic resizemanufacturing organizations considering the impact of transformative digital technologies such as those represented by machine learning, big data analytics, and the Industrial Internet of Things (IIoT), these challenges are only amplified by the accelerating pace of change and broad impact of these technologies across the industrial enterprise.

The rewards of successful deployment (for the leaders) or the consequences of failure (for the laggards) will indeed prove transformational. Yet the hesitation to implement a technology that impacts the entire business—without a proven methodology or framework—is obvious and understandable. Meanwhile, waiting for a new framework or strategy platform to evolve may not be a viable business choice either.

This conundrum can be addressed in part by applying new technology within existing business process frameworks. For example, by applying machine learning technology to established operational excellence (OE) programs such as Lean, Six Sigma and the Toyota Production System. In fact, today’s machine learning technology holds the potential to reinvigorate such manufacturing and business programs that have long been limited to historical analysis. Machine learning can help to bring OE concepts forward into the realm of real-time, in-process manufacturing quality control and business optimization. It can also help to develop new and rewarding flexible manufacturing opportunities.

Machine learning basics

james barkley photocropped

Jim Barkley, UI Labs

In short, machine learning is closely related to data analytics and describes the use of algorithms to learn directly from data and to automatically adjust machine behavior based on such new information. This is in contrast to traditional, less flexible approaches that rely on explicit programming and an a priori understanding of machine dynamics. Another way to think about machine learning is that the machine “creates” its own program by looking at patterns and relationships in data. The complementary term “deep learning” also is used to describe multi-layered algorithms for detecting and responding to especially complex patterns.

The impact of machine learning on OE in the manufacturing industry will be significant, especially given the integrated view of industrial supply chains that today transcend organizational boundaries. These effects, which may well exceed the initial impact realized when OE concepts and methodologies were first introduced, will accelerate with the continued development of communications and collaboration platforms such as the Digital Manufacturing Commons (DMC) currently under development by the Digital Manufacturing & Design Innovation Institute (DMDII).

Solution characteristics
Just as implementation details can determine whether a theoretically sound OE program is successful in actual practice, there are important characteristics to

rajiv anand quartic

Rajiv Anand of Quartic.ai

consider when selecting a development platform for machine learning applications.

Training on partial or sample data may yield an unreliable model that faces adoption challenges. A platform that can efficiently and cost effectively develop and train models on large amounts of complete data sets with complex initial dimensions is required for such applications. (“Dimension” in machine learning refers to the unique variables or attributes in a data set that together describe a property.)

Minimize feature engineering requirements to enable the average process or manufacturing engineer to deploy and maintain algorithms and models. A platform that provides easy, minimal (and ideally, no) feature engineering is desirable. (A “feature” in machine learning is an individual measurable parameter or attribute—in our existing process control and manufacturing automation vernacular, we refer to a feature as a “process variable.” Feature engineering is the process of choosing informative, discriminating, and independent features from a data set.)

Allow for machine applications that can live and operate at the edge. For widespread adoption and speedy deployment, models should be as lean and platform-agnostic as possible with the scalability to co-exist in cloud-based, enterprise environments and at the edge (the manufacturing floor) to stretch the manufacturing “loop” to encompass the entire supply chain as needed.

Plan for aggregation and fusion of different data sources across organizations to provide for benchmarking and macro trend analysis. Long-term analysis of key performance indicators across a distributed manufacturing value chain is a natural extension of current OE programs. A machine learning development platform must provide a common, distributed architecture for mixing manufacturing data and machine learning recipes or “apps” in an easy-to-use, intelligent fashion.

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  • Most importantly, diagnostics of "things" like pumps etc. must be easy to use because maintenance and reliability engineers and technicians are not data analysts. They need simple actionable information like "clean", "repair", "replace". The user of the analytics app shouldn't have to know what the underlying analytics is; model-based, statistical, or machine learning. They should just get the actionable information. Learn how plants are doing it: https://www.linkedin.com/pulse/real-end-users-analytics-data-scientists-jonas-berge

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