In AI, How-To Articles

Chemicals manufacturing encompasses a broader array of sub-verticals — ranging from petrochemicals and pharmaceuticals to mining and oil & gas processing — than virtually any other industry.

Yet, all large-scale chemicals manufacturers share a common challenge: minimizing unplanned downtime, particularly for continuous manufacturing.

Continuous production depends on flawless execution of many interdependent processes. Bottlenecks at one or more points in the process can slow or completely halt production — negatively impacting everything from quality and profitability to worker safety.

This unplanned downtime plagues companies throughout the manufacturing industry. In fact, one study estimated that the industry squanders more than $50 billion annually due to unplanned downtime. For process industries such as chemicals, the average annual cost of unplanned downtime is estimated at $20 billion. In the petrochemicals industry, unplanned downtime fuels production losses of 2 to 5 percent.

How can chemicals manufacturers improve process reliability — and limit unplanned downtime?

The answer lies in data — or, more specifically, in using data from the manufacturing process to build a real-time model of the complex relationships among the equipment, systems, and output involved in the production process. That model can then be used to optimize the entire process, not just its individual elements.

Unfortunately, many chemicals manufacturers suffer from DRIP — they’re Data-Rich, but Information-Poor. The reason? They’re hindered by “data silos” — multiple, discrete process areas that don’t have the ability to talk with each other. These silos often result from earlier, one-off “point” projects built on limited data sets that don’t scale to deliver transformational value.

Simply put, chemicals manufacturers must change their approach to using data.

What’s needed is a “data first” platform that combines AI, machine learning, and massive cloud storage and compute to ingest, aggregate, and transform myriad data types into a universal format. Companies can then model complex dependencies and interrelationships, uncover hidden patterns, and produce actionable insights.

This system allows manufacturers to immediately detect if and when any condition deviates from the normal range during a production run. Because the Sight Machine platform also knows which processes will be affected downstream, and how soon, its AI engine can generate predictive, actionable alerts, enabling manufacturers to take corrective steps before a sudden outage or failure occurs.

In addition to improving reliability and quality by limiting unplanned downtime, these capabilities can:

  • Improve worker safety: Safety can suffer when employees are forced to investigate system breakdowns due to the often non-routine conditions required to make repairs. Companies should strive to use data-derived insights to prevent the need for maintenance and repair.
  • Increase energy efficiency: With fewer slowdowns or stoppages, systems run more efficiently, reducing costs and a company’s carbon footprint.
  • Lower raw materials costs: Less unplanned downtime also helps reduce raw materials waste—directly benefiting the manufacturer’s bottom line.

To start realizing these benefits for your organization, here are three key questions to consider:

  1. What is the condition of your data?

Manufacturers often embark on projects in the mistaken belief that their data is accessible, usable, and relevant, only to discover that most of it isn’t ready to support a digital initiative. Sight Machine has developed a process for assessing production data against the key attributes required for successful digital manufacturing initiatives.

  1. Which use cases will deliver the greatest value?

In the beginning, choose use cases (e.g., predictive maintenance) that will generate real value in a short timeframe. To know which projects will quickly deliver the most bang for the buck, you have to quantify the impact of manufacturing production improvements on corporate profitability. Sight Machine has developed a productivity metric, the Manufacturing Performance Index (MPI), that lets you translate performance improvements into output gains and dollars generated.

  1. Is your organization ready to act?

Do you have the commitment (a “project champion”), organizational buy-in, budget, and skillsets for your digital initiative to succeed?

Using these steps as a starting point, you’ll be on your way to converting downtime to uptime.

Please contact us for a live demonstration of how Sight Machine’s AI-driven platform and CI services can achieve these goals.

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