DATA-BASED OPTIMISATION OF MAINTENANCE AND OPERATIONS PROCESSES
Manufacturing / Industry
Description of the DVC THEME
Industry 4.0 and technological advances have revolutionized the way industrial processes and machinery are analyzed and optimized, by prefiguring a new age of innovation. Wireless interconnectivity of machinery and devices has allowed for more efficient collection and monitoring of data. Modern industrial processes can be analyzed and adapted more than ever. Companies have greater insight into the operation of plants and machinery, allowing them to make better-informed decisions and reach tough industrial KPIs.
By combining advanced sensor capability, IoT technology, and data analytics algorithms, maintenance in the era of Industry 4.0 has experienced a rapid shift from reactive to proactive: instead of performing maintenance only when failure has already occurred, the state-of-the-art strategy is to actively anticipate system degradation and schedule maintenance just-in-time.
Large and midsize process organizations with sophisticated equipment in asset-intensive industries tend to consider and use all types of proactive maintenance strategies (like preventive, predictive, or prescriptive maintenance). Smaller organizations often focus on trying to improve their preventive maintenance performance. Being more preventive, predictive, or prescriptive depends on being able to have an analytic strategy and data.
All areas of the industry will benefit from the increased use of data in terms of increased efficiency and productivity, optimization, and automation.
Sub-challenges composing this experiment:
This DVC THEME is composed of the challenge:
Expected global results:
- Optimize plant and machinery usage, by minimizing machine downtime and eliminating complete failure or serious damage.
- Improve process efficiency, by using data analytics to keep targeted data manageable and streamlined.
- Reduce energy usage, by reaching considerable cost savings over the long term.