This project aimed to optimize machine settings for a packaging department in the consumer goods industry. By applying Lean Six Sigma methodologies, specifically the DMAIC (Define, Measure, Analyze, Improve, Control) approach, the team targeted a pressing issue that disrupted production and increased costs: excessive adjustments to the machine’s servo-driven settings. This project illustrates how Lean Six Sigma tools can provide solutions to complex manufacturing problems while achieving significant cost savings and efficiency gains.
The Challenge: Reducing Machine Adjustments
The project team focused on a packaging machine that reverted to default settings after every batch load. Each batch required an average of six additional adjustments, adding time and resources to ensure the equipment operated correctly. This problem not only hampered productivity but also frustrated team members who constantly dealt with interruptions. Following discussions with a project sponsor and department managers, the goal was set to reduce these manual adjustments by 75%, which would allow the machine to run with minimal intervention.
Setting Up the Project Team
A critical success factor in this Lean Six Sigma project was forming a well-balanced team with individuals who understood both the machine operations and the broader goals. The team included experienced operators from each shift and a formulation manager. Including operators in the process proved to be strategic, as their insights into daily operations provided essential data that helped guide the project’s progress.
Assembling the team required careful consideration of team roles, leadership dynamics, and individual motivation levels. Early on, it became apparent that including operators from different shifts would foster cross-team understanding and bring a range of perspectives on the machine’s behavior. Their input was valuable, as they had a direct stake in the project’s success and were instrumental in identifying operational inefficiencies. Lean Six Sigma projects emphasize teamwork and engagement, and this project reinforced that including front-line workers adds depth to the problem-solving process.
Tools and Techniques Used
The DMAIC structure guided the project, helping to break down complex issues into manageable phases. To identify root causes of the excessive machine adjustments, the team applied several Lean Six Sigma tools:
- Fishbone Diagram (Ishikawa): This tool helped the team brainstorm and categorize possible causes of the issue, grouping them into themes like equipment, methods, materials, and human factors. Team members with prior experience with the fishbone diagram found it useful for organizing ideas.
- Failure Mode and Effects Analysis (FMEA): This tool was crucial in pinpointing potential failures within the machine’s processes and prioritizing issues that would most impact the project’s goals. It also highlighted areas where adjustments to materials or processes might reduce the number of interventions required.
- Measurement System Analysis: Ensuring the accuracy of the data was essential for tracking project success. This tool validated that data collected on machine settings was reliable and consistent, laying a solid foundation for later phases of the project.
- Control Plan: To ensure that improvements would be sustainable, the team developed a control plan to track adjustments and measure any deviations over time. The control plan served as a proactive approach to managing the adjustments needed for different batches and acted as a final check before declaring the project complete.
Challenges and Practical Solutions
One of the practical challenges faced during the project was coordinating meeting times with production schedules. Allocating time for operators to participate in the project meetings was essential, but it could not interfere with ongoing production. To overcome this, the team carefully scheduled sessions during machine downtime and worked closely with shift coordinators. These adjustments ensured minimal disruption to production while maintaining the momentum of the project.
Another challenge was gaining alignment with external stakeholders, such as quality control personnel and an in-house pharmacist. When attempting to modify a cleaning checklist as part of the broader machine maintenance plan, the project team faced initial resistance from the pharmacist, who felt the adjustments didn’t pertain to cleaning procedures. Through open dialogue, a compromise was reached by adding a reference in the checklist to a supplementary work instruction. This experience highlighted the importance of clear communication and the need for compromise when implementing procedural changes that affect various departments.
Results and Benefits of the Project
The project culminated in an end-of-project assessment meeting, where the team measured and validated the final results. By standardizing machine settings and reducing manual adjustments, the team achieved a dramatic reduction in interventions per batch, from an average of 6 to approximately 0.78. This improvement surpassed the original goal, delivering an estimated $15,000 in annual savings and freeing up operator time to focus on other tasks.
This result had significant implications for the packaging department:
- Increased Productivity: Operators spent less time troubleshooting the machine, allowing them to focus on other critical tasks within the department. This shift contributed to an overall increase in output without adding more resources.
- Improved Team Morale: The operators appreciated the project’s impact, as it addressed one of their routine pain points. Their involvement in the project fostered a sense of ownership and accomplishment, which positively affected motivation and team cohesion.
- Sustainable Improvement: The control plan created during the project ensured that these adjustments would remain consistent over time. By regularly checking machine settings and adjusting preventive measures, the department could maintain the improvements without needing frequent recalibrations.
Key Takeaways and Lessons Learned
Reflecting on the project, team members highlighted several critical insights:
- Data Validates Insights:The power of data in Lean Six Sigma cannot be overstated. Initial data collection and validation through Measurement System Analysis helped the team make informed decisions, reducing the risk of addressing symptoms instead of root causes.
- Communication is Essential:Throughout the project, clear communication and well-planned meetings were vital. Operators were informed about project activities, which helped manage expectations and build a culture of collaboration. Better planning for meeting times and transparent communication about project goals were especially valuable.
- Cross-Functional Support Strengthens Outcomes:Working with stakeholders from other departments provided additional expertise and broadened the scope of possible solutions. Engaging with quality control and pharmacy teams, even when initial resistance occurred, enriched the final outcome and ensured that adjustments complied with department-wide standards.
- Ownership Drives Success: Involving team members directly impacted by the machine’s performance was instrumental to the project’s success. Their input provided practical perspectives on machine settings and revealed opportunities for improvement that might have gone unnoticed by external observers.
Final Thoughts
This Green Belt project demonstrated the transformative potential of Lean Six Sigma within manufacturing operations. By following the DMAIC approach, using reliable data, and fostering open communication among departments, the project team was able to achieve a substantial improvement in machine efficiency and reduce costly adjustments. The success of this project serves as a testament to the value of Lean Six Sigma methodologies in tackling industry-specific challenges, promoting cross-functional collaboration, and delivering quantifiable results.
For those considering their own Lean Six Sigma project, this experience underlines the importance of structured problem-solving tools, data-driven decisions, and the commitment to continuous improvement that Lean Six Sigma embodies.
