Optimise resources and eliminate asset failure to reduce costs and improve operational efficiency
Predictive Maintenance and Quality solutions access multiple data sources in real-time to predict asset failure so that your organisation can avoid costly downtime, reduce maintenance costs and improve operational efficiency.
Driven by predictive analytics, these solutions detect even minor anomalies and failure patterns. This in turn, helps determine the assets and operational processes that are at the greatest risk of failure or that can result in an increased number of faulty products and warranty claims.
This early identification of issues helps you deploy limited maintenance resources more cost-effectively, maximise equipment uptime and enhance quality and supply chain processes. The end result is an improved financial position, enhanced customer satisfaction and a stronger brand value.
Version 1’s SPSS experts can consult and deliver a wide variety of Predictive Maintenance solutions. Read more within the sections below.
Enabling your organisation to predict when and where asset failures are likely to occur Leverage from the Internet of Things (IoT) sensors readings on instrumented assets.
Combine external data with internal data, such as environmental and weather data with data from Asset Management and Supervisory Control Systems
Avoid asset downtime and reduce maintenance costs, and perform root-cause analyses of asset and process failures. Mine maintenance logs to determine the most effective repair procedures and failure patterns.
Visualize maintenance and operational insights and ensure Operational Efficiency through alarms prioritization and recommendations
Detect emerging quality problems and reliability issues early enough to avoid costly callbacks and lost production alongside reducing scrap work, Inspections effort and Warranty claims.
Make adjustments to predictive maintenance schedules and corrective actions, and maximise shipping rates cost-effectively.
Improve Brand Value and After Sales Support recommendations.
Innovative companies consistently look for ways to achieve their goals more efficiently, quickly respond to market changes and take advantage of emerging opportunities. Predictive Maintenance and Quality solutions can help your organisation continuously improve Operational Efficiency by optimizing existing processes and turning operational data into actionable insights.
Opportunities to improve operational efficiency, reduce operational costs and maximise productivity, exist in various operational areas, including:
- Workforce and Resources Scheduling
- Equipment and Spare-Parts Inventory
- Capital and Strategic Planning
- Supply Chain Management
- Building Management
- Energy Efficiency
- Facilities Management
“Choosing SPSS Statistics Base and SPSS Modeler has been very advantageous. Proof lies in the results achieved and the fact that we rely so much on the tools. We will continue to seek other SPSS Inc. tools that will help us gain competitive advantage.”
“With predictive analytics, we were basically able to close a hole in our pocket where money was leaking out steadily. We were also ready to show that this was just the beginning.”
“IBM SPSS predictive analytics software has given us the means to learn systematically from our experience. By learning from the history, we can know today who will buy a given product in the future.”
“Analytics gives us a method of assessing impact of factors that we may not be able to measure directly – so that we can do more with the data we already have, instead of making big investments in high-tech equipment.”
“Course was excellently presented with a clear overview of research and statistics principles and the IBM SPSS Statistics software package. I found it challenging but very informative and a useful introduction to IBM SPSS Statistics.”
Predictive Maintenance Case Studies
Case Study – Daimler Group
Daimler AG was looking for a way to maximize the number of flawlessly produced cylinder-heads by making targeted process adjustments. They also wanted to increase productivity and shorten the ramp-up phase of its manufacturing process. Using IBM SPSS software, Daimler is now able to analyse the plethora of data obtained in the production process and gain deep insights into the key factors that influence product quality.
Case Study – Santos
When a critical asset fails in one of Santos’ operations, the result can be lost revenues and more time on the road for engineers. Santos needed to find a way to identify faults before failures occurred. To maximize production uptime and promote safe, efficient maintenance, Santos predictively models data from assets connected to the Internet of Things, providing early warnings of equipment failure.
Case Study – Israel Electric
Israel Electric Corporation (IEC) generates 95 percent of Israel’s electricity. To meet peak demand, its turbines need to run at full capacity – so it is vital to keep them online and running efficiently. IEC uses predictive maintenance and other technologies to model the behavior of its turbines and monitor their performance in real time. When anomalies are detected, it can quickly trigger maintenance resources to fix problems before outages occur or efficiencies are reduced
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