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Challenges, Opportunities and Strategies for Integrating IIoT
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Across all industries, there are assets everywhere, all generating critical time series data. As the volume and sources of asset data constantly expand, there are multiple challenges to creating insights that impact operational performance. Operators, engineers and managers face a data deluge that comes from diverse systems and has a heterogeneous array of units, protocols and formats. For analysis and reporting, users often wrestle with data stored in multiple systems with very little associated context. The difficulty surrounding accessing, finding and integrating vital operational data in a timely manner often results in underutilization for initiatives such as asset health, product quality and genealogy or process efficiency. What's needed is an environment that breaks down these barriers. An environment that connects all operational information sources in a coherent and scalable way and empowers people to leverage data to generate the insight that leads to actionable information, best practices, analysis and continuous improvement.
As technology lowers barriers to large scale data capture, industries are searching for ways to capitalize on information; however, very few have standardized technologies that allow data to create impact at an enterprise scale. More often, operational ecosystems encompass single purpose technologies, such as traditional historians, that limit exposing information to multiple people or purposes. While invaluable for local visibility, implementing numerous single purpose technologies across the enterprise leads to multiple versions of the truth, information islands and layers of legacy systems. In this environment, people often struggle to access, analyze or share data especially outside organizational, geographical or security boundaries. Data remains underutilized or “dark” for key business drivers such as asset health, process efficiency and quality.
Today, every mill faces the challenge of optimizing production management decisions. These decisions, such as rationalizing grade structures, enable the supply chain to respond to the realities of shorter product life cycles. In this environment, treating data as an enterprise asset enables the stakeholders to operate and manage independently based on near real-time, data-driven insights. Given the potential return, every mill can justify retooling their decision-support systems so they can track profitability by asset, by customer and by run throughout the life cycle of each grade. Accelerating time-to-market through data-driven decision-making may be one of the more compelling justifications for treating data as an enterprise asset. Recent advances in how process data are collected, stored and visualized across the enterprise are revolutionizing the way mills relate operational performance to cost structure.
Learn how ArcelorMittal, the world's number one steelmaker, optimizes supply chain logistics with predictive analytics.
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