{"id":13620,"date":"2025-06-18T09:18:47","date_gmt":"2025-06-18T08:18:47","guid":{"rendered":"https:\/\/www.theleansixsigmacompany.com\/tlssc-preview3\/?p=13620"},"modified":"2026-02-10T09:09:31","modified_gmt":"2026-02-10T08:09:31","slug":"cutting-waste-in-corrugated-packaging-a-lean-six-sigma-approach","status":"publish","type":"post","link":"https:\/\/www.theleansixsigmacompany.com\/uk\/library\/cutting-waste-in-corrugated-packaging-a-lean-six-sigma-approach\/","title":{"rendered":"Cutting waste in corrugated packaging: a lean six sigma approach"},"content":{"rendered":"
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Industry:<\/strong> Industry<\/span><\/div>\n<\/div>\n<\/div>\n
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Department:\u00a0<\/strong>Production,\u00a0Quality,\u00a0Maintenance,\u00a0Planning<\/span>\u00a0<\/span><\/div>\n<\/div>\n<\/div>\n
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Project\u00a0Type:<\/strong>\u00a0Green\u00a0Belt<\/span>\u00a0<\/span><\/div>\n<\/div>\n<\/div>\n
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Project\u00a0Duration:<\/strong>\u00a0Not\u00a0specified<\/span>\u00a0<\/span><\/div>\n<\/div>\n<\/div>\n
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Financial\u00a0Benefits:<\/strong>\u00a0Approx.\u00a0$50,000\u00a0savings<\/span><\/div>\n
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Waste reduction is one of the simplest ways for manufacturers to save money, improve efficiency, and boost sustainability. But where do you start?\u00a0<\/span><\/p>\n

This Green Belt<\/a>\u00a0project focused on reducing dry end waste in the corrugation process\u2014the final stage where large sheets of corrugated board are cut, stacked, and prepared for conversion into boxes. The goal was to bring waste levels down from <\/span>1.9% to 1.4%<\/b>, resulting in estimated savings of <\/span>$50,000<\/b>.\u00a0<\/span><\/p>\n

The challenge wasn\u2019t just in identifying where the waste was coming from, but in making sure any improvements were practical, measurable, and sustainable.\u00a0<\/span><\/p>\n

Finding the problem: why was waste so high?\u00a0<\/strong><\/h2>\n

Corrugated board production is complex, with multiple variables affecting waste. The dry end of the process\u2014the final stage before the sheets are sent for printing and cutting\u2014was consistently producing more scrap than expected.\u00a0<\/span><\/p>\n

Before jumping to solutions, the project team took a <\/span>data-driven approach<\/b> to confirm the issue. They analyzed the last <\/span>12 months of production data<\/b>, comparing actual waste levels to standard benchmarks.\u00a0<\/span><\/p>\n

To ensure accurate decision-making, they first verified that the waste measurement process itself was reliable. A <\/span>Gage R&R study<\/b> (a method to test the reliability of measurements) confirmed that the waste tracking system was consistent, meaning the problem was real\u2014not just an issue with data collection.\u00a0<\/span><\/p>\n

Investigating root causes: what was driving the waste?\u00a0<\/b><\/h2>\n

Once the data was validated, the next step was to break down why waste levels were too high. The team used several Lean Six Sigma tools to get a clear picture of the problem.\u00a0<\/span><\/p>\n

A <\/span>Fishbone Diagram (Ishikawa)<\/b> was used to categorize potential causes into six areas: Man, Machine, Method, Material, Measurement, and Environment. This helped structure the discussion and ensure that no potential issue was overlooked.\u00a0<\/span><\/p>\n

A <\/span>Process Capability Analysis<\/b> was performed to check whether the process could consistently meet the waste reduction target. Additionally, a <\/span>Failure Modes and Effects Analysis (FMEA<\/a>)<\/b> helped rank different causes based on their impact and likelihood of occurrence.\u00a0<\/span><\/p>\n

Through these analyses, several key issues became clear. Machine settings were inconsistent, leading to unnecessary cutting errors. Operators followed different procedures, meaning waste levels varied depending on who was running the machine. Preventive maintenance was not scheduled properly, causing occasional performance issues. Cleaning and adjustments were also not standardized, leading to inefficiencies in setup and operation.\u00a0<\/span><\/p>\n

Implementing solutions: fixing the waste problem\u00a0<\/b><\/h2>\n

With the root causes identified, the team moved to the Improve phase of the DMAIC<\/a>\u00a0(Define, Measure, Analyze, Improve, Control) methodology.\u00a0<\/span><\/p>\n

The first step was standardizing machine settings. Operators were given precise cutting and stacking settings to follow, and a checklist was introduced to ensure that machines were calibrated before each shift.\u00a0<\/span><\/p>\n

To ensure consistent operation, the team developed and implemented Standard Operating Procedures (SOPs). Clear, step-by-step instructions were introduced so that all operators followed the same process. Training sessions were conducted to reinforce these new standards.\u00a0<\/span><\/p>\n

A preventive maintenance schedule was put in place, with weekly maintenance set for Wednesdays during the first shift. This ensured that machines remained in optimal condition, reducing unexpected failures that contributed to waste. The importance of routine maintenance was explained to operators, showing how sticking to the schedule would improve long-term performance and reduce rework.\u00a0<\/span><\/p>\n

Finally, the team introduced a real-time monitoring system to track waste levels. A dashboard was created to display ongoing waste performance, and monthly review meetings were scheduled to ensure accountability and continuous improvement.\u00a0<\/span><\/p>\n

Overcoming challenges: resistance and time constraints\u00a0<\/b><\/h2>\n

Every Lean Six Sigma<\/a> project faces some resistance. In this case, convincing the maintenance team to stick to a fixed preventive maintenance schedule was a challenge. They were initially concerned that scheduled downtime could interfere with production deadlines.\u00a0<\/span><\/p>\n

To address this, the project leader emphasized the long-term benefits of preventive maintenance.\u00a0<\/span><\/p>\n

Data was presented to show how consistent maintenance would lead to fewer breakdowns, less downtime, and more predictable machine performance. Once early results confirmed that maintenance helped reduce waste, the team was fully on board.\u00a0<\/span><\/p>\n

Another challenge was time availability. Many team members were involved in other projects and had limited availability for meetings and improvement activities. To resolve this, meetings were scheduled at optimal times to fit within existing workloads. Some tasks were reassigned to ensure that the project could progress without delays.\u00a0<\/span><\/p>\n

Results: did the project succeed?\u00a0<\/b><\/h2>\n

The project exceeded its original goal. Waste at the dry end of the process dropped from 1.9 percent to 1.33 percent, surpassing the 1.4 percent target. The estimated cost savings were approximately $50,000.\u00a0<\/span><\/p>\n

Beyond financial benefits, the project also improved team engagement. At the start, motivation among team members was low, scoring an average of 2.5 on a five-point scale. By the end of the project, this score had risen to 4.7. As results became visible, confidence in the project grew, and team members became more involved in sustaining the improvements.\u00a0<\/span><\/p>\n

To ensure the gains were maintained, several control mechanisms were put in place. Waste levels were continuously monitored, allowing quick responses to any deviations. Preventive maintenance routines remained in place, reducing unexpected machine failures. Monthly review meetings ensured that all stakeholders remained accountable for keeping waste levels low.\u00a0<\/span><\/p>\n

Lessons learned: key takeaways from the project\u00a0<\/b><\/h2>\n

One of the most important lessons was the value of setting clear targets and following the DMAIC methodology. Having a structured roadmap ensured that every step of the project was backed by data and that improvements were sustainable.<\/span><\/p>\n

Another key takeaway was the importance of standardization. When processes and machine settings were aligned, waste levels became much more predictable. Standard Operating Procedures (SOPs) played a critical role in ensuring consistency across different shifts.\u00a0<\/span><\/p>\n

Finally, routine monitoring and quick action were essential in preventing backsliding. Lean Six Sigma is not just about making improvements once; it is about maintaining those improvements over time.\u00a0<\/span><\/p>\n

Real-time tracking and structured review meetings ensured that waste reduction efforts remained on track.\u00a0<\/span><\/p>\n

Why this project matters\u00a0<\/b><\/h2>\n

For companies in manufacturing and packaging, waste is a major cost factor. This project demonstrated that even small improvements\u2014such as optimizing machine settings and standardizing procedures\u2014can lead to significant financial benefits.\u00a0<\/span><\/p>\n

For those considering a Lean Six Sigma project<\/a> in their own organization, the key takeaway is clear.\u00a0<\/span><\/p>\n

Using a data-driven approach, involving the right people, and focusing on practical, measurable improvements leads to sustainable process improvements. By applying structured problem-solving techniques, this company not only reduced costs but also built a stronger, more efficient process that will continue delivering value in the long run.\u00a0<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

In the high-volume world of corrugated packaging, even small inefficiencies can add up quickly. This Green Belt Lean Six Sigma project targeted waste at the dry end of the corrugation process, where sheets are cut and stacked, cutting scrap from 1.9% to just 1.33%. The result? An impressive $50,000 in estimated annual savings.<\/p>\n

By combining data analysis with frontline insights, the team standardized machine settings, implemented preventive maintenance, and aligned operator practices across shifts. Tools like Fishbone Diagrams, FMEA, and Process Capability Analysis helped expose the root causes, while real-time dashboards ensured ongoing accountability.<\/p>\n

More than a cost-saving initiative, this project boosted team motivation and built a culture of continuous improvement. It\u2019s a strong reminder that structured, data-driven problem-solving isn\u2019t just about fixing issues, it\u2019s about creating lasting value.<\/p>\n","protected":false},"author":107,"featured_media":13622,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[160,159,150,131,127,1],"tags":[],"class_list":["post-13620","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fishbone-diagram","category-fmea","category-dmaic-project","category-green-belt","category-industry","category-lean-six-sigma-topics"],"yoast_head":"\nCutting waste in corrugated packaging: a lean six sigma approach - theleansixsigmacompany.com\/uk<\/title>\n<meta name=\"description\" content=\"Reduce waste and save big! 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