Success Stories Here are four examples of recent Six Sigma Projects
DCR/CAL Gall Elimination
(Six Sigma Project—Yield Improvement)
Objective: Reduce Gall by 50%
Actual Outcome: Eliminated Gall
Company: USS-POSCO Industries
Process: Flat rolled Steel finishing DCR/CAL
Description: Gall is small transverse scratch generated during uncoiling and/or recoiling flat rolled steel. Our integrated yield analysis identified this defect as one of the largest improvement opportunities in the plant. We defined the problem space, scoped the project, developed the schedule, and mapped the process. We analyzed existing data to identify possible failure modes, and then we prioritized them through a Failure Modes and Effects Analysis (FMEA). We then created a Repeatable and Reproducible measurement system (through Gauge RR) for Gall and used the measurement system in three successive industrial Designed Experiments (DOEs). These experiments quantified the effect of 6 different factors and identified the 2 primary contributors. A root cause analysis of those two factors revealed two inconsistent maintenance practices which we eliminated through a relatively simple control plan by redesigning the equipment to eliminate the need for that maintenance task, and by modifying maintenance procedures. This eliminated Gall and is saving over $1M/year.
ETL Trim Reduction
(Six Sigma Project—Yield Improvement)
Objective: Reduce the standard trim
Actual Outcome: Reduced trim by 1/8”
Company: USS-POSCO Industries
Process: Flat rolled Steel finishing Electro Tinning Lines (ETL)
Description: Our integrated yield analysis identified this defect as one of the plant’s largest improvement opportunities. We did extensive analysis on the trimming capability under many different conditions, including: steady state at different speeds, ramp up, ramp down, and around the welds. We also evaluated the capability of hot band suppliers for over width and cracked edges. A quantitative comparison revealed that off-centered trimming was a critical concern. The team then added an additional strip location sensor and tied this signal into the existing control system. This centered our trim distribution and increased our trimming capabilities from 5sigma to 13sigma in the body of the coil and 2sigma to 3.7Sigma around the weld. We reduced the trim and began saving $600k/year.
Absenteeism Reduction
(Six Sigma Project—Operating Cost Reduction)
(IT Project implementation)
Objective: Reduce Plant-wide Absenteeism by 50%
Actual Outcome: 60% reduction
Company: USS-POSCO Industries
Process: Flat rolled Steel finishing Facility
Absenteeism increases the costs of operation through overtime wages, lost productivity, diminished morale, and increased turnover. We researched absenteeism data across the industry and collected surveys from a random sample of employees from the facility. We analyzed why people missed work and grouped them into addressable categories. Taking into account absenteeism data, the corporate work environment, and employee relations, we developed a 5 part solution:
1) Management would provide flexibility in exchange for an early notice of an absence
2) Management would need to address each and every absence
3) Increase absenteeism visibility and accountability company-wide
4) Recognize good attendance performance
5) Implementation of 12 hour shifts in facilities where possible
The team strongly believed that for the Absenteeism Management process to be effective, it would need to be managed by a data management system. For that reason the implementation changed into a software development project. This process began with the development of the use cases, beta testing, and company-wide training and rollout. This quickly reduced the absenteeism rate by 60%.
Cold Mill chatter Reduction on
(Six Sigma Project)
Objective: Reduce mill chatter frequency by 50%
Actual Outcome: 50% reduction
Company: USS-POSCO Industries
Process: Cold Rolling Mill Chatter is a resonance condition on a cold mill which forces slower operational speeds and causes strip breaks. Strip breaks pose safety hazards during clean up, cause loss of material, force roll changes, and can damage equipment. Through brainstorming, data analysis, process mapping, and a Failure Modes and Effects Analysis, we identified 6 operational factors which we believed were the most likely contributors to chatter. We analyzed the measurement systems for all the factors through Gauge RR and then ran a series of 3 designed experiments to quantify the effect of each of the factors on chatter. We utilized ANOVA to create and validate a statistical model that correctly predicted the probability of chatter 80% of the time. We modified the roll roughness in the final stand, added solution heating capacity, and displayed and alarmed a real-time chatter predictor on the operator’s screen. These things gave the operator the information that is required to make operational decisions to prevent chatter and strip breaks.