Tesser Insights Case Study: Predictive Maintenance Optimization

Client: A manufacturing company that produces industrial equipment for aviation industry

Challenge: The client was facing the challenge of predicting maintenance needs for their equipment, resulting in frequent breakdowns and production delays. They had a large amount of data from sensors on the equipment but were struggling to extract insights from it and use it to make informed maintenance decisions.

Solution:

Gathering and Analyzing Data: Tesser Insights worked with the client to gather data from various sensors on their equipment, including temperature, pressure, and vibration sensors. They performed data analysis to identify patterns and correlations in the data, using techniques such as clustering, dimensionality reduction, and anomaly detection.

Developing Predictive Models: Based on the insights gained from the data analysis, Tesser Insights developed predictive models to forecast maintenance needs for the equipment. The models included time-series analysis using ARIMA models, and machine learning models such as random forest, decision trees, and neural networks.

Validating and Refining Models: Tesser Insights worked with the client to validate the accuracy and effectiveness of the predictive models. They tested the models against new data and compared them to other methods of analysis. Based on feedback and new data, Tesser Insights refined the models to improve their accuracy and effectiveness.

Providing Actionable Insights: After the predictive models were validated and refined, Tesser Insights provided the client with actionable insights that could be used to inform their maintenance decisions. They recommended specific maintenance schedules, identified equipment that required immediate attention, and provided early warning for equipment failure.

Continuously Monitoring and Updating Models: Tesser Insights continued to monitor the performance of the predictive models and updated them as needed based on new data and changing business needs. This ensured that the insights provided remained accurate and relevant over time.

Results:

Using the insights provided by Tesser Insights, the client was able to optimize their maintenance schedule, reduce equipment breakdowns, and increase overall production efficiency. They were also able to identify potential issues before they become major problems, allowing them to address them proactively.

Conclusion:

Through the use of advanced analytics and predictive modeling, Tesser Insights helped client gain insights from their data and make more informed maintenance decisions. By following the steps outlined in this example, Tesser Insights was able to help their client reduce downtime, increase production efficiency