Big Data Analytics Challenges

Who we are

Established at Malaysia and United Kingdom with affiliates located at South East Asia region, Czech Republic, Oman, South Africa, United State, Netherland, Sweden and China. We offer robust and scalable Asset Reliability maintenance to helps our clients achieving maintenance optimization and operational excellence.

Big data technologies provide the means to collect, store and process large amounts of data that indicate the condition of the equipment’s. Without an adequate data foundation, asset and maintenance systems will fail to support the business. Our solutions help to improve, enhances, optimize and manage insight of your assets and protect what matter to your business.

 

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Evolution of Maintenance and Reliability

 

Take advantage of having real-time sensors installed on equipment and its will provide real-time data that you can put into predictive models to help you determine when something is about to fail or what the remaining useful life is for that equipment. Combine Real-Time & Contextual Data, The traditional challenges with predictive maintenance were that the data was limited to sensor data and it was mostly used for operational control, where we wanted to control the machines, make them faster, slower or switch them on and off. From an automation perspective just around the control side of things.

At Worke Group, we combine your maintenance program data vs equipment real-time health monitoring. We help you to measure maintenance performance standard vs real-time machine health, work order deferment implication, maintenance unplan intervention, improvement maintenance plan, managing bathtub curve, MRO, spares and optimize your assets.

 

Predictive maintenance was often based on predictive analytics that required expert data scientists and complex machine learning models. We have pool of subject matter experts from machine or equipment technical specialists, data scientists, IT crooks, business improvement team and reliability maintenance experts. Our personnel has vast industries experience with background came from manufacturing, petrochemical, energy, utilities and more. 

Not all equipment behaves in a way that you can predict it’s likelihood to fail. Because of that, you also need to do a further analysis by taking a reliability centered maintenance approach. Define what the failure modes are. 

You now have the ability to continuously parse data through machine learning models and continuously get predictions back. Our solutions it’s not just reserved for large organizations but available for small and medium-sized businesses.