Maintaining legacy physical assets can be a significant challenge for many organizations. It can be both time-consuming and costly to keep older assets in good working order, especially if they were not designed with modern maintenance techniques in mind. However, there is a solution to this problem that can help reduce maintenance time and cost: the transformation and use of asset unstructured data.
Asset unstructured data refers to the vast amounts of data generated by physical assets that are typically not well-organized or easily accessible. This can include data such as maintenance logs, emails, and other types of operational data. By analyzing this data and extracting meaningful insights, organizations can identify patterns and trends that can help them optimize their maintenance processes, reduce downtime, and extend the lifespan of their assets.
One industry that has seen significant benefits from this approach is the oil and gas industry. By analyzing data from sensors and other sources, companies can identify potential equipment failures before they occur, allowing them to schedule maintenance proactively and avoid costly downtime. For example, Shell Oil Company was able to save $11 million by using predictive analytics to optimize maintenance schedules for their offshore oil rigs.
Another industry that has seen significant benefits from this approach is manufacturing. By analyzing data from sensors and other sources, manufacturers can identify opportunities to improve efficiency, reduce waste, and optimize production schedules. For example, one automotive manufacturer was able to save $3 million per year by optimizing the maintenance schedules for their production equipment.
To transform and make use of this unstructured data, many organizations are adopting enterprise SaaS solutions that provide advanced analytics capabilities. These solutions can help organizations automate the process of analyzing data and extracting insights, making it easier and more cost-effective to identify opportunities for improvement.
The benefits of adopting enterprise SaaS solutions to transform and use asset unstructured data can be significant. In addition to reducing maintenance time and cost, these solutions can help organizations improve efficiency, extend the lifespan of their assets, and avoid costly downtime. They can also help organizations reduce the risk of safety incidents and improve regulatory compliance, which can be a significant cost saver in highly regulated industries such as oil and gas.
Financial examples of cost savings resulting from the adoption of enterprise SaaS solutions are plentiful. For example, a report by McKinsey & Company found that predictive maintenance can reduce maintenance costs by 10-40%, reduce downtime by 20-50%, and increase equipment uptime by 10-20%. Similarly, a report by Deloitte found that predictive maintenance can increase equipment lifespan by 20-40%, reduce maintenance costs by 5-10%, and reduce downtime by 20-50%.
In conclusion, the transformation and use of asset unstructured data can help organizations reduce maintenance time and cost for legacy physical assets. By adopting enterprise SaaS solutions that provide advanced analytics capabilities, organizations can extract meaningful insights from this data, identify opportunities for improvement, and reduce the risk of costly downtime. The financial benefits of adopting these solutions can be significant, making it a smart investment for any organization looking to optimize their maintenance processes and extend the lifespan of their assets.