[Case Study] Avoiding tens of millions of yen in losses from unexpected shutdowns | Chemical Plant
"Although there are sensors, they cannot be used" and "Neither PoC nor approval can be obtained." To those in charge of plant maintenance: We are revealing all the procedures that have avoided losses of tens of millions of yen annually due to unexpected shutdowns.
"Although we have sensor data, it cannot be used on-site," and "We tried a PoC but couldn't achieve accuracy, and management approval has not been granted" — equipment maintenance often comes to a standstill at this "one step away." This case study reveals the steps taken to overcome three challenges hindering equipment maintenance in aging plants: 1. The risk of unexpected shutdowns due to aging, 2. The retirement of skilled maintenance personnel and the loss of tacit knowledge, and 3. The barrier of lacking a data infrastructure that halts PoC efforts. We will outline a four-step process centered around the Databricks Data Lakehouse (digitizing records → data integration → assetizing "intuition" → automating maintenance planning). [Recommended for those who:] - Want to implement equipment maintenance but are struggling with data infrastructure development - Have not achieved accuracy in their PoC and have not received management approval - Face challenges in inheriting tacit knowledge due to the retirement of skilled maintenance personnel - Have sensor data but are not fully utilizing it.
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basic information
What can be understood from this document: - Four steps to break away from unexpected shutdowns in equipment maintenance (digitization of inspection records → data integration → AI implementation → automation of maintenance planning) - Approach to make "disparate data by equipment and department" AI-ready using Databricks - Approach to incorporate the "intuition" of experienced maintenance personnel into AI models, creating a digital asset that can be used even after their retirement - Quantitative effects: Avoidance of losses estimated at 30 to 70 million yen per year / Approximately 90% reduction in maintenance planning man-hours / Detection of anomalies up to one week in advance
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Avoid losses of 30 to 70 million yen per year due to unexpected shutdowns (estimated), reduce maintenance planning man-hours by approximately 90%, and detect abnormal signs up to one week in advance. This is a record of our support in establishing "AI that is actually used on-site and continues to function" without stopping at the PoC stage. *For details, please download the materials and check.
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NTP Corporation is a company that designs the future of manufacturing from the front lines of the industry. We do not engage in consulting that is solely based on documents. We redefine the roles of consultants and engineers, deeply immersing ourselves in the front lines of our clients' businesses and delivering tangible results in the manufacturing field through fast implementation that directly connects strategy and technology. Please feel free to contact us first. Professionals who understand the field will propose suitable solutions.
