As the manufacturing industry matures technologically, predictive maintenance is more valuable than ever as the ability to harness extremely large volumes of data becomes widely accessible. The benefits, including reduced machine downtime and lower annual maintenance costs, are abundantly clear. However, the road to efficiently realizing these perks is not without its challenges, evidenced by the fact that many organizations have yet to adopt a structured approach to predictive maintenance at all.
While people are the salient factor in any transformation effort, they must work collaboratively to leverage machine learning (ML).
Chief among the barriers to successfully integrating predictive maintenance into daily operations are:
• Separating the signal from the noise: data is available in troves, but without serious data science horsepower, finding insightful data in the proverbial haystack is challenging.
• Antiquated hardware: even the most capable analytics infrastructure and team will fall short without a way to source and ingest rich data. Production floors must be equipped with IIoT sensors to measure key variables such as vibration, gas, humidity, temperature, security, and pressure.
• Building organizational habits: building several models to predict developing machine defects is one thing, but integrating these insights into daily workflows requires diligent ML Ops and forward-thinking leadership to catalyze the adoption of new tools and methods.
• Achieving enterprise scale: scaling predictive maintenance methods, procedures, resources, and data & analytics infrastructure across facilities and regions requires strong commitment and buy-in from the C-suite. Innovative teams must actively showcase positive results and work to promote new technology and approaches across the company.
The Key Ingredient: Machine Learning
While people are the salient factor in any transformation effort, they must work collaboratively to leverage the key ingredient in effective predictive maintenance: machine learning (ML). A subset of AI, machine learning allows for the early detection and prevention of machine defects and failures, and is fueled by high volumes and velocities of source data. Mckinsey & Co reports that ML-based predictive maintenance can boost availability by up to 20%, all while reducing inspection costs by 25% and annual maintenance fees by up to 10%. At enterprise scale, this impact is both immediate and drastic. Put another way, the failure to successfully implement predictive maintenance is a clear competitive disadvantage in the digital age.
Flavors of ML
The cloud computing and bigdata revolutions of the 2010s catapulted machine learning from the confines of academia to boardrooms across the globe. By pairing tried-and-true statistical methods such as correlation, regression, and bayesian analysis with cloud-based data processing technology, businesses can derive valuable insights from the data their machines produce.
ML is largely divided into two distinct but related methodologies, known as supervised and unsupervised learning. The supervised learning process is steered by a specific desired output, such as the classification of images by the animals they depict. A supervised learning model will examine photos and conclude that, for example, this picture contains a bear. In contrast, unsupervised learning doesn’t require any desired output, and instead sifts through data in search of new, undiscovered patterns and trends.
In our context of predictive maintenance, supervised learning is useful in determining the overall health of a given machine at a given time, while unsupervised learning may uncover the formation of a defect that humans could not detect by themselves. When used in tandem, this powerful duo allows for proactive repair & maintenance and more accurate foresight for material and resource planning.
Assess the fundamental dynamics of your production processes and constantly review the relevance of existing ML models with your stakeholders. While talking to your stakeholders, ask these questions:
- Why do you want to have these insights? What outcomes will they enable?
- Who owns the insights coming out of our models? Who is accountable when it comes to transforming insights into decisions and actions?
- What are the relevant data attributes required for the model to derive accurate and timely insights?
Fueling Business Outcomes
Translating effective machine learning outputs into more proactive, intelligent decisions requires an understanding of what insights are needed (such as recommended maintenance time for machine ABC or a real-time status indicator for machine XYZ), why they are useful (such as allowing repair and maintenance to occur before machines fail), who they must be delivered to (plant managers, repair & maintenance crews, floor engineers, upper management, etc.), and how they must be delivered (in what format and at what cadence, depending on the decision-making process of each stakeholder).
With a comprehensive approach to predictive maintenance, complete with a modern approach to leveraging supervised and unsupervised learning, modern manufacturing organizations are one step close to protecting, or even improving, their margins in a period of global economic turmoil.
Interested in improving predictive maintenance within your team or organization? Getting started typically involves one or multiple pilot projects focused on a specific machine and facility. This allows teams to limit their financial risk and generate tangible outcomes quickly, which can be evangelized across the organization once they yield a measurable ROI.
About Us
Astral Insights is a boutique decision intelligence consultancy focused on helping transform our clients into insight-driven organizations that outperform their industry peers. As an end-to-end transformation partner, we offer strategic consulting, technology development and implementation, and managed services to support our mid-market and enterprise clients. Our innovative methodology allows our clients to transform data into profitable outcomes with personalized insights for decision makers across the business, from sales to supply chain & operations.
