Download PDF
OYAK Cement Boosts Alternative Fuel Usage from 4% to 30% — for Savings of Around $39M
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Machine Learning
Applicable Functions
- Discrete Manufacturing
- Quality Assurance
Use Cases
- Predictive Maintenance
- Process Control & Optimization
Services
- Data Science Services
The Challenge
OYAK Cement, a leading Turkish cement maker, was facing a significant challenge. The company operates 18 plants in six countries with a production capacity of 33 million tons of cement each year. It was estimated that up to eight percent of CO2 emissions stem from manufacturing cement, the raw material needed for concrete. This was a major concern for OYAK Cement as it was contributing to the environmental problem and also risking costly penalties from exceeding government emissions limits. The company recognized that increasing operational efficiency by five percent would result in four to five percent cost-savings, along with reducing CO2 output by two percent — preventing the release of nearly 200,000 tons of CO2 emissions and eliminating $10M+ worth of CO2-related social impact costs per year.
About The Customer
OYAK Cement is a leading Turkish cement maker. The company operates 18 plants in six countries with a production capacity of 33 million tons of cement each year. It is a major player in the cement industry, contributing significantly to the world's infrastructure with its products. However, the company was facing challenges related to CO2 emissions and the associated environmental impact. It was also at risk of incurring costly penalties for exceeding government emissions limits. To address these issues, the company initiated the Cement 4.0 project, aimed at optimizing and automating its processes.
The Solution
OYAK kicked off an initiative called Cement 4.0 with the goal of optimizing and automating its processes. Among its goals, OYAK sought to understand vast amounts of data across its 18 plants, which run a mix of different DCS (distributed control systems) and SCADA (supervisory control and data acquisition) systems acquiring streaming data from multiple sensors. It simply wasn’t possible to analyze the data manually. Berkan Fidan, Performance and Process Director, suggested AI as a means to make sense of OYAK’s data. In the trial project, OYAK found that it could predict and prevent mechanical failures in one-quarter the time it took previously. Results from the DataRobot trial convinced management to roll out the solution to all plants and build a team of data scientists. Additionally, OYAK empowered engineers and maintenance team members throughout the organization to use DataRobot as citizen data scientists.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
System 800xA at Indian Cement Plants
Chettinad Cement recognized that further efficiencies could be achieved in its cement manufacturing process. It looked to investing in comprehensive operational and control technologies to manage and derive productivity and energy efficiency gains from the assets on Line 2, their second plant in India.
Case Study
Airbus Soars with Wearable Technology
Building an Airbus aircraft involves complex manufacturing processes consisting of thousands of moving parts. Speed and accuracy are critical to business and competitive advantage. Improvements in both would have high impact on Airbus’ bottom line. Airbus wanted to help operators reduce the complexity of assembling cabin seats and decrease the time required to complete this task.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
