Internet of Things in Action: Smarter Manufacturing, Predictive Maintenance and Quality Control
Customer Company Size
Large Corporate
Country
- Worldwide
Product
- Zementis Predictive Analytics
Tech Stack
- Data Mining Tools
- Predictive Analytics
- Internet of Things Systems
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
- Innovation Output
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Big Data Analytics
Applicable Industries
- Aerospace
- Automotive
- Electronics
Applicable Functions
- Discrete Manufacturing
- Quality Assurance
Use Cases
- Predictive Maintenance
- Manufacturing System Automation
Services
- Data Science Services
About The Customer
The company is a global industrial powerhouse. Born of humble origins with a focus on a single market, this leading manufacturer in advanced coatings, aerospace, automotive, electronics, and energy systems operates a vast production network across international boundaries. Its annual revenue exceeds $60 billion, with an operating income of over $7 billion. As the company transitioned from a single market focus to becoming a digitalized global enterprise, rapidly growing data complexity became a major threat to the business. The company needed to pay close attention to an increasingly demanding customer base—able to source products and services from more suppliers around the globe than ever before. But this couldn’t come at the expense of industry-leading quality controls.
The Challenge
As the company transitioned from a single market focus to becoming a digitalized global enterprise, rapidly growing data complexity became a major threat to the business. The company needed to manage complex product life cycles, control financial and human risks, and work with dozens of independent systems. Earlier attempts to solve these problems with a small data science team focusing on production had promising results. But the company quickly ran aground of business and technology liabilities, such as: human errors in manually coded models, scalability bottlenecks, burgeoning data volumes, and an inability to achieve real-time data processing goals.
The Solution
The company turned to Zementis Predictive Analytics, designed from the start to handle streaming data flows from connected, Internet of Things systems and their innumerable sensors, actuators, and other components. Its core capabilities of automated decision making and platform-agnostic interoperability enabled growth while capitalizing on predictive maintenance to cut costs and increase manufacturing precision and quality. The platform-agnostic architecture, built into Zementis Predictive Analytics by design, was the key differentiator from the competition. With foundational predictive analytics utilized across the company’s large, multi-industry product portfolio, the next step was to go real time—and then further.
Operational Impact
Quantitative Benefit
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