AI and multi-variate analysis are challenging how manufacturers approach digital transformations and harness the benefits that these transformations can bring. These technologies and techniques are the building blocks for predicting what is going to happen in the future with enough time to adjust.
As a result, every manufacturer, ranging from chemical makers, oil and gas producers, food and beverage processors, consumer product companies, power generators, and metal and mining companies, is facing a dilemma: How can they transition to running operations with AI and multi-variate analysis while also sustaining ongoing success in key performance indicators?
Many manufacturers aren’t sure what to do with fully booked staff who need to acquire knowledge to work with AI and multi-variate analyses. They might have a science project in one plant that has demonstrated success, but they aren’t sure how to roll out a reliable solution across a fleet of plants. They could be having trouble hiring a team that has both the engineering know-how and the experience in handling many types of data, including time series, vibration, image, video, business information, etc. Many are still working hard on creating the “talent density” to execute new tech.
But these manufacturers also know that the opportunities are great if they can undergo digital transformations successfully. Doing nothing is not an option.
Manufacturers have been collecting data for years, but today too many firms use it only retrospectively. To monitor KPIs, they tend to pull together data from a given period and apply statistical models that measure performance in a siloed manner. AI, in contrast, is self-learning based on the data it is fed. It transcends the limitations of manmade statistical models, deriving new insights from context, patterns, industry standards, and, importantly, input from multiple manufacturing processes, to note associated risks and suggest potential actions.
For companies that struggle to document machine and process faults year after year, the opportunity to improve is vast. For those who suspect sensor information can be unreliable, the possibilities to improve are great. For manufacturers who have always dreamed of combining the swim lanes of machine and process condition data into one meaningful advisory, the benefits are transformational.
What to look for in a KPI system driven by AI
The best AI-driven KPI systems contain both a middle manager’s tool to manage plant operations with actionable insight in advance (e.g., prediction and forecasting) as well as a CEO or business unit’s tool to manage profitability and the manufacturer’s carbon footprint. The systems transform OEE dashboards from backward-looking records or past performance into business planning tools enabling faster responses to constantly changing internal and external events while keeping tabs on metrics like plant capacity and commodity prices.
Traditional OEE systems only relate a plant’s performance at a given point in time. They can tell factory managers what went wrong. They don’t provide much help in improving systems to avoid stoppages, breakdowns, and other setbacks, however. AI, in contrast, uses predictive modeling to analyze throughput in real time and suggest changes like, say, adding an extra shift one night a week to make up for slowdowns but still make production deadlines on time.
AI is a game-changer for anomaly detection, auto-identification of operating regime changes in production systems, and forecasting using deep learning algorithms on the next possible outcomes and the level of risk in achieving a plant or company’s goals. Companies that use solutions deploying it can transform their businesses, especially since generative AI can easily translate industrial data into digestible text, graphs, charts, and images for people on the shop floor.
Production managers who traditionally plan out product runs, from obtaining the necessary materials and other resources to meeting customer deadlines and other challenges, operate on razor-thin margins in part because their data has been backward-looking. AI lets them make decisions in real time as they measure their KPIs to help drive significant growth.
The companies that deliver these superior platforms for manufacturers start by first asking customers about their businesses according to a proven methodology:
- Look for areas of potential improvement.
- Assess data readiness for creating new digital processes necessary to realize the improvement.
- Draft an implementation roadmap to achieve the improvement at scale and on time.
- Ensure consistent, continuous operations using best practices that yield improvement.
These methodologies have led to real-world success in many industrial sectors:
- Mining: Profiling ore through crushing and milling operations and providing ore-blending advisories to achieve target ore grades with optimal recovery.
- Power generation: Monitoring turbine-generator cycles to maintain optimal plant heat rate and fuel efficiency.
- Petroleum refining: Monitoring naphtha catalytic cracking to manage product yields of propylene, ethylene, etc.
- Food and beverage: Balancing water needs to reduce yield rate reduction and unplanned downtime, predicting centrifuge performance; monitoring boiler performance for heat, fuel efficiency, and condenser performance.
Every manufacturer can gain from a smart, informed assessment of how AI, multi-variate analyses, and other innovations can help them meet and surpass their KPIs by seizing opportunities, building solutions, and deploying the same into production.