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Industry 4.0

Why Industry 4.0 Strategies Can Optimize the Systems and Data You Already Have


Conrad Leiva

Chariman, MESA International Smart Manufacturing Working Group

We tend to use the term “digital transformation” when we talk about Industry 4.0, but the term “digital evolution” might be more appropriate to describe the journey.

We do not want to imply that Industry 4.0 is a rip-and-replace all systems proposal. Many companies have already begun the transformation and have systems in place that are important foundational lynchpins. For these companies, an integrate-and-extend strategy to leverage existing systems is more appropriate.

Many manufacturers are already taking action to leverage their existing systems. A recent survey from Gartner and MESA International revealed that 98 percent of manufacturers believe there is more value to capture from their current manufacturing execution system (MES) and have identified next steps to extend and further integrate systems in their digital journey.

These manufacturers have identified the following areas as small, evolutionary steps toward the Industry 4.0 goals:

Mine and join data across silos

Manufacturers have more data on-hand than they realize. Existing data might be trapped in silos or may not be organized to enable joining data across systems. For example, MES data is usually leveraged for operational metrics, but could also be made available to join with data in other enterprise systems like enterprise resource planning and product lifecycle management for higher levels of business analysis and optimization.

Industrial Internet of Things platforms are capturing data straight out of sensors and smart machines for predictive analysis tied to equipment maintenance. This data could also be joined to MES data for more automated and accurate real-time data collection.

Integrate via APIs

Application Programming Interfaces (APIs) can not only facilitate integration into a system of systems, but also expose data for mining across the enterprise.

API strategies that extend into the supply chain uncover a substantial amount of valuable data. This is because most supply chain visibility applications concentrate on connecting plan, source, make, and deliver domains, and rely on ex-post-facto production data. In turn, most manufacturing enterprise applications provide real-time data and analytical tools focused solely on internal plant optimization and asset performance. Integration of these systems would provide better orchestration and optimization opportunities for the plan-to-produce and order-to-cash processes.

Analyze with AI

Artificial intelligence (AI) and robotic processes are helping manufacturers sense, extract, synthesize, and analyze data across traditional silos. AI can use data from more than just internal systems, bringing in data from partners, suppliers, and customers. AI can make it practical to organize these varied data sources into meaningful insights to fulfill the higher analytical and optimization goals of the Industry 4.0 integrated enterprise.

Conrad Leiva, Chariman, MESA International Smart Manufacturing Working Group, [email protected]

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