Predikto prevents failure of critical assets

With the rapid acceleration of information technology advancement and proliferation into business, success depends on how businesses leverage their access to information and data while processing and analyzing them for clearer insights.

A recent study revealed “failure of critical assets” to be the biggest problem faced in operations. This is attributed not only by the lack of insight but possibly the absence of “predictability” in the health and performance of assets. This reduces asset productivity and reduces efficiency which is fundamental to a successful business. Here are 3 value propositions of predictive analytics to make better decisions:

    1. Monitor, maintain and maximize assets to gain better utilization and performance in real-time: With real-time data, it has become even more challenging to provide immediate actionable solutions to boost productivity and efficiency across machinery and platforms and market changes. Without the proper manpower or skills, it could prove to be an uphill task for most IT departments. Hence, appropriate IT investments to engage in professional teams could potential help businesses revamp their decision-making processes and effectiveness so the right management decisions can be made instantly.

 

  • Predict asset failures to improve quality and supply chain processes: The key difference between preventive maintenance from predictive maintenance is that it accounts for frequency of usage, wear and tear and environmental factors of the asset to accurately discern its failure. This prediction is vital in resource planning and placing orders for spare parts and financial planning for budgeting as well.

 

 

  • Eliminate risk and uncertainty in the decision making process: Capital equipment utilized in production facilities is subjected to high cost and unplanned downtime. By adopting predictive analytics, we can optimize decisions within resource constraints by visualizing issues to allow for immediate recommended actions, eventually maximizing efficiency and production for the company

Example – By adding predictive maintenance capabilities to its production lines, a steel manufacturing plant can reduce production delays predicting if a batch will have delays prior to starting the rolling process, improving reliability, and thereby significantly reducing maintenance costs.