Software AG Case Studies Internet of Things in Action: Smarter Manufacturing, Predictive Maintenance and Quality Control

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	Internet of Things in Action: Smarter Manufacturing, Predictive Maintenance and Quality Control Software AG

Internet of Things in Action: Smarter Manufacturing, Predictive Maintenance and Quality Control

Software AG

Technology Category	Analytics & Modeling - Big Data Analytics Analytics & Modeling - Predictive Analytics
Applicable Industries	Aerospace Automotive Electronics
Applicable Functions	Discrete Manufacturing Quality Assurance
Use Cases	Manufacturing System Automation Predictive Maintenance
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Analytics & Modeling - Predictive Analytics

Applicable Industries

Aerospace

Automotive

Electronics

Applicable Functions

Discrete Manufacturing

Quality Assurance

Use Cases

Manufacturing System Automation

Predictive Maintenance

Services

Data Science Services

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.

About 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.

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.

Impact #1	Efficiency went up, costs went down, and everyone involved was a winner.
Impact #2	Data streaming from thousands of components and sub-systems were easy to ingest, analyze, and make actionable.
Impact #3	Benefits extended to all steps of manufacturing: measuring product performance and making engineering adjustments during development, monitoring quality control and making process tweaks during production, configuring components during delivery, and providing predictive services for optimal operation post-sale.

Impact #1

Efficiency went up, costs went down, and everyone involved was a winner.

Impact #2

Data streaming from thousands of components and sub-systems were easy to ingest, analyze, and make actionable.

Impact #3

Benefits extended to all steps of manufacturing: measuring product performance and making engineering adjustments during development, monitoring quality control and making process tweaks during production, configuring components during delivery, and providing predictive services for optimal operation post-sale.

Benefit #1	Lowered costs
Benefit #2	Extended scalability company-wide
Benefit #3	Improved quality controls

Benefit #1

Lowered costs

Benefit #2

Extended scalability company-wide

Benefit #3

Improved quality controls

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Overview

Internet of Things in Action: Smarter Manufacturing, Predictive Maintenance and Quality Control

Operational Impact

Quantitative Benefit