The Team-First Approach to Industry 4.0

As companies look to make Industry 4.0 applications like AI/ML, digital twins, and operational orchestration a central part of their operations, they are again confronting challenges with silos. Especially when different teams or departments have different degrees of data access and readiness, and sometimes just struggle to get user licenses to tap into their data, priorities around digital transformation can reflect the demands of individual business units.

Through cloud computing, teams across data science, maintenance, and business analytics can now drive enterprise improvement without the traditional misalignment.

The specific challenges and needs of individual teams have revealed the need for purpose-built digital solutions unlocking applications like AI, digital twins, and automated reporting in order to drive larger enterprise goals.

However, unclear or conflicting priorities in an organization can make the adoption of advanced analytics difficult. False starts with digital transformation are costly and difficult to rein in on the original budget and timetable, in addition to their impact on worker fatigue when it comes to pursuing new initiatives.

As companies realized the need for solutions like remote monitoring and on-demand expertise during the pandemic, many also increased their spending on advanced analytics.

Research from Harvard Business Review shows that $1.3 trillion was spent on digital transformation initiatives in 2018 alone, with an estimated $900 billion in waste from companies reporting that they did not meet their goals. That spending is set to rise to $2 trillion annually by 2022.

When projects meet their mark, companies have sustained forward-thinking initiatives as a foundation for future excellence. In an analysis across asset-intensive industries, McKinsey found that industrial facilities and companies with low digital maturity improved their EBITDA by 3-5 percentage points on average through digital transformation initiatives.

Even organizations experienced with digitization initiatives stood to gain from adopting maintenance strategies that improved their preventative maintenance (PM) program — about 1-3 percent according to the same study.

In these initiatives, a data management strategy like a data lake was key. In the case of operational technology (OT) data, secure storage and user-friendly retrieval are catching industrial control systems up with the maturity of IT infrastructure.

Uptake Fusion, for example, makes OT data legible throughout the organization to different stakeholders with the data models that matter to them. It has enabled teams to steer clear of data compression and limited user licenses, allowing internal and third-party data consumers to take advantage of data with high granularity and in an open format. In turn, companies have accelerated the deployment of operational applications from years and months to weeks.

As a result, teams of data consumers gain visibility into conditions at the level of the facility. A data lake strategy empowers teams to tackle their specific strategic initiatives that ladder up to enterprise business objectives, sustainability goals, and regulatory requirements — all from a single repository of contextualized data. Not just limited to the traditional power users of industrial control systems, the cloud enables teams throughout the organization to capitalize on the value of OT data.

For example:

Data science teams have cost-effectively integrated key data sources to develop digital twins and advanced analytics applications.

IT and operations teams have uncovered insights that improved asset performance by storing OT data in a cloud environment for further analysis.

Engineering, maintenance, and reliability teams have easily accessed cost avoidance insights from their OT data in the cloud to better oversee the lifecycle of asset utilization through data-backed planning, optimization, execution, and tracking of preventative maintenance activities.

Executives and finance, accounting, and business analytics teams have used Microsoft Power BI, Power Apps, and Azure Time Series to track, measure, and visualize KPIs, moving from pen and paper to automated reporting.

Through the power of data access backed by the cloud, teams enjoy a new agility to develop Industry 4.0 applications without diminishing the value of use cases to the rest of the enterprise. Business units can now cost-effectively unlock incremental and compounding improvements with a clear line of sight towards better productivity, maintenance, sustainability, and safety.

Empower your People. Put Your Data to Work.