Drive Contextual Analytics by Transforming Engines in Industry 4.0

Manufacturing intelligence is one of the most significant focus areas in the industrial sector today. Manufacturing organizations are implementing more contextual analytics capabilities than ever before with the hopes of leveraging insights from their machines, people, and processes to drive positive outcomes that ultimately drive revenues. Unfortunately, simply connecting your machines, extracting that data, and then hoping for the best will set you up to fail.

A recent survey from LNS Research revealed that there has been a 52% increase in the share of industrial companies with a formal analytics program, a 102% increase in diagnostic capabilities, and a 66% increase in predictive capabilities. However, there is only a 39% increase in prescriptive analytics. This isn’t surprising given the data challenges that are roadblocks to achieving next-level insights.

Issues like data inconsistencies, disparate silos, lack of standardization, and poor contextualization will fuel the growth of a digital "beast" in the factory. That beast increases risks, costs, inefficiencies and eventually scares off customers. To transform this beast into a beauty, manufacturers require an efficient platform with a dynamic analytics engine that gives them the ability to quickly access, analyze, and act on intelligent, contextualized information.

In this blog post, we'll identify critical success factors to make your analytics approach a thing of beauty and discuss actionable steps to get your business on the path to analytical success.

Critical Success Factors to Unleash the Beauty in Your Analytics

Individual metrics by themselves aren't sufficient anymore. Robust, contextualized analytics that allows teams to make data-driven decisions are essential for the factory of the future. There are a few key attributes that enable contextual analytics. They include:

• High Data Quality: To drive contextual analytics in Industry 4.0, the quality of your data must be reliable. To ensure high-quality data, you need to create a sense of data ownership as close to the source as possible. In addition, make sure data owners are using their data regularly, and this can be done by making analytics easily accessible and configurable to everyone that needs it.

• Native Edge Approach: A native edge approach that easily standardizes and contextualizes data across products, processes, and machines is the best path forward for manufacturers who want to embrace Industry 4.0 and transform their business into a future-ready enterprise. Manufacturers should be able to easily change factory floor machinery without needing any customization to consume, contextualize, and get immediate insights from those new machines.

• Domain Expertise: One of the most critical pieces to real contextualization is the level of manufacturing domain expertise built into the analytics platform's ontological schema. The schema is heavily dependent upon knowledge of the market segment, the context of that data, and the use models. Understanding where, what, and when to pull key data elements together requires a deep understanding of the application, the market, and how that data can and should be used. Today, many solutions on the market simply do not even have an ontological schema built into their data models.

• Scalable Data Model: The lack of consistently defined and harmonized data models is a huge obstacle to being able to contextualize large volumes of data beyond basic dashboard reports. What's needed is an intuitive data model that can be mined, modeled, simulated, and scaled.

In a fast-paced manufacturing environment, effective business analytics depends on three main things: powerfully enabled by the attributes discussed above. First, operators and other decision-makers need access to an array of data that can help enrich their understanding of the business. Second, they need this information delivered on time, while it can still impact the decision at hand. And third, they need to convert insight into action and execute against opportunities to do things more efficiently. Without context, data is meaningless. And with a beast of meaningless data, beauty can never be found.

A Starting Point to Drive Your Business Forward

Looking ahead to the future, what should manufacturers make sure they have on their analytics to-do lists? The following actionable steps will help position your organization to drive long-term success through contextual analytics:

• Define and implement a data governance strategy. Ensure those who need access to data have it, and those who own data are given the tools to manage it. Creating data governance transforms your organization by moving beyond the 'tools'.

• Leverage a contextualized data model. This initiative can be based on an IIoT-based solution that supports contextualized analytics and ensures data standardization and quality. Decide what will be addressed, both from a data standpoint and architecturally.

• Implement some start-up analytics. Involve leaders at all levels of the organization and work with them to build trust in and demonstrate the value of analytics. Increase both prescriptive and prognostic analytics to promote a data-centric and learning-based organization.

• Address real scenarios to improve analytics maturity. Address analytics that will help leaders improve performance and better support their teams. Break down data silos by using an industrial data model to gather important insights for decision-makers. Implement analytics that will help develop your governance strategy and data model while building trust in analytics.

At the end of the day, analytics should stretch beyond the single plant to envelop the entire enterprise. The opportunity to share data and analytics applications across the value chain is virtually unlimited. New technologies like Machine Learning (ML) and Artificial Intelligence (AI), coupled with a trusting workforce and optimized processes, will ultimately enable you to turn analytical beasts into beauties.

Conclusion: Putting Analytics Front and Center

Analytics are at the heart of any Industrial Transformation/Industry 4.0 program. Without data duly gathered, cleansed, calculated, and contextualized, it's difficult even to begin the journey towards becoming a more future-focused factory. Companies that want to be analytics leaders in Industry 4.0 have to put analytics at the front and center of their transformation programs, focusing on solutions that drive innovation and efficiency.

An IIoT-enabled, Industry 4.0-ready Manufacturing Operations platform—like Aegis' FactoryLogix—offers manufacturers the ability to gain transformative, real-time operational visibility and control. Aegis’ manufacturing intelligence platform delivers a unique ontological, contextualized data model that was built with industry-specific domain knowledge and expertise. With the capacity to operate seamlessly and connect all areas of the factory, the contextual insights and analytics gleaned from FactoryLogix enable manufacturers to make smarter, insight-based decisions faster than ever before.

Watch the From Beast to Beauty webinar to learn more about how you can transform your data beast into a contextual analytics beauty. If your organization is struggling with how to unlock contextual analytics in industry 4.0, contact Aegis to learn how FactoryLogix can help.