Enhancing Productivity at PT Bakrie Pipe Industries
In the competitive world of manufacturing, production machine reliability is a determining factor for company success and profitability. Unplanned machine downtime can result in significant financial losses, disruption of supply chains, and decreased customer satisfaction.
At PT Bakrie Pipe Industries, a leading steel pipe producer, the High Frequency Welding (HFW) Machine at Plant KT 24 has drawn attention due to experiencing the longest breakdown time compared to other machines—reaching 80.66 hours over a one-year period 1 2 .
Unplanned downtime doesn't just stop production—it creates a ripple effect that impacts the entire supply chain and customer relationships.
High Frequency Welding (HFW) is an electrical resistance welding process that uses high-frequency alternating current to generate the heat needed to join metal materials 5 .
To analyze the reliability of the HFW machine, researchers at PT Bakrie Pipe Industries used two complementary methodological approaches: Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) 1 .
A deductive technique that starts from an undesired system failure and determines all possible ways that could cause that failure.
A systematic approach to identify all possible failures in a design, process, or product.
The research was conducted by collecting historical breakdown data of the HFW machine over a one-year period. This data was then processed using reliability analysis methods to obtain the machine's reliability value, which was only 0.54 on a scale of 0-1 (with 1 being the highest reliability value) 1 .
Based on FTA and FMEA analysis, the main failure modes were identified as:
| # | Failure Mode | RPN Value | Criticality |
|---|---|---|---|
| 1 | HFW trip | 144 | |
| 2 | HFW alarm PMGI module | 144 | |
| 3 | Alarm PMGI machine | 96 | |
| 4 | Recorder problem | 90 |
In conducting the HFW machine reliability analysis, several key tools and methodologies were employed:
| Tool/Methodology | Function |
|---|---|
| Reliability Analysis | Measures machine reliability value based on time between failures and repair time data |
| Fault Tree Analysis (FTA) | Analyzes combinations of events that can cause system failure |
| Failure Mode and Effect Analysis (FMEA) | Identifies and prioritizes failure modes based on their risk level |
| Risk Priority Number (RPN) | Quantitative tool to prioritize failure modes based on severity, occurrence, and detection |
Based on the analysis conducted, the research team recommended improvements to the Standard Operating Procedure (SOP) as part of periodic maintenance activities 1 . SOP improvements focused on failure modes with the highest RPN values, namely HFW trip and HFW alarm PMGI module.
The Six Sigma approach with the DMAIC method (Define, Measure, Analyze, Improve, Control) was also implemented to reduce the rejection rate of produced steel pipes .
| Parameter | Before Improvement | After Improvement | Improvement |
|---|---|---|---|
| Breakdown Time (hours/year) | 80.66 | Data not available | Significant |
| Reliability Value | 0.54 | Data not available | Significant |
| Pipe Rejection Rate | High | Decreased | Significant |
The reliability analysis conducted at Plant KT 24 of PT Bakrie Pipe Industries not only brought positive impacts to the company but also became a valuable case study for the manufacturing industry as a whole.
The scientific approach to machine maintenance has proven to increase productivity and reduce operational costs. In a broader context, the findings from this research align with similar studies in other industries.
A study on spot welding machines in the automotive industry found that hydraulic subsystems significantly affect the overall reliability level of the machine 6 . That study also highlighted the importance of determining appropriate preventive maintenance intervals based on the reliability characteristics of each component.
Future integration with IoT and predictive analytics can further enhance machine reliability through real-time monitoring.
The analysis of High Frequency Welding machine reliability at Plant KT 24 of PT Bakrie Pipe Industries demonstrates the critical importance of systematic reliability assessment in industrial settings.
Investment in machine reliability analysis is not merely an additional cost but rather a long-term strategy that can provide competitive advantage in an increasingly tight global market.
Looking ahead, integration of Industry 4.0 technologies such as Internet of Things (IoT) and predictive analytics can further improve machine reliability by enabling real-time machine condition monitoring and failure prediction before it occurs.
This approach will not only reduce downtime but also optimize maintenance costs and improve overall productivity.