Six Sigma is a statistical- and data-driven process that works by reviewing limit mistakes or defects. It emphasizes cycle-time improvements while reducing manufacturing defects to no more than 3.4 occurrences per million units or events. This means that an error generally occurs with a six-standard deviation event from the mean because only 3.4 out of a million events along a bell curve would fall outside of six standard deviations.
Six Sigma is a management ideology that focuses on statistical improvements to a business process and advocates for qualitative measurements of success over qualitative markers. As such, Six Sigma practitioners are business people who use statistics, financial analysis, and project management to achieve improved business functionality.3
Six Sigma is a statistical benchmark that shows how (well) a business process works.2 As mentioned above, an error happens when an event occurs with six standard deviations from the mean with no more than 3.4 occurrences per million events. This means that a process is considered to be efficient if it produces less than 3.4 defects per one million chances. A defect is anything produced outside of consumer satisfaction.
Six Sigma Process Steps
The Six Sigma Methodology comprises five data-driven stages — Define, Measure, Analyze, Improve and Control (DMAIC). When fully implemented, DMAIC standardizes an organization’s problem-solving approach and shapes how it ideates new process solutions.
The “Define” stage seeks to identify all the pertinent information necessary to break down a project, problem or process into tangible, actionable terms. It emphasizes the concrete, grounding process improvements in actual, quantifiable and qualifiable information rather than abstract goals.
Examples of terms in the Define stage include:
Project scope charter, including budget, focus and driving motivation
Voice of customer (VoC) research
Value stream maps
In the “Measure” phase, organizations assess where current process capabilities are. While they understand they need to make improvements and have listed those improvements concretely in the Define phase, they cannot go about tweaking and tailoring changes until they have a data-backed baseline.
In other words, the Measure phase initiates two activities:
Measure the current process or activity
Use those current data sets to establish a process capability baseline, which process improvement data will be compared to
The “Analyze” phase examines the data amassed during the Measure stage to isolate the exact root causes of process inefficiencies, defects and discrepancies. In short, it extracts meaning from your data. Insights gleaned from Analyzation begin scaffolding the tangible process improvements for your team or organization to implement.
Organizations can move beyond the Analyze phase once they’ve conducted the following:
Pareto charts and similar Six-Sigma approved data maps tracking the frequency of an issue
Potential capability (Cp) and actual capability (Cpk) calculations
A formal root cause analysis
The “Improve” initiates formal action plans meant to solve the target root problems gleaned from your Analyzations. Organizations directly address what they’ve identified as problem root causes, typically deploying a Design of Experiment plan to isolate different variables and co-factors until the true obstacle is found.
In the final phase, “Control,” Six Sigma teams create a control plan and deploy your new standardized process. The control plan outlines improved daily workflows, which result in critical business process variables abiding by accepted quality control variances.
Each of these five phases creates a repeatable template to improve your business’ process capabilities. When the five stages are fully implemented, organizations can measure both the effectiveness and efficiency of critical manufacturing business processes. Measurements are tracked in a control chart, lending you quantifiable, comparable process-control data that leverage a competitive advantage.
Benefits of Six Sigma
The Six Sigma methodology in the manufacturing industry carries numerous benefits, each quantifiable and backed by charted growth.
1. Bolstered Productivity
Six Sigma presents a quantifiable, actionable template to do so. Through its data measurements and analysis, organizations target bottlenecks, redundancies and error-prone processes, ultimately unlocking that Goldilocks balance between producing more in less time.
2. Increased Throughout
Six Sigma’s philosophy of root cause identification and elimination is the ideal complement to a manufacturer looking to improve throughput rates. Using precise data, you target exact production areas, from processing times or inspections to unit movement, queuing and warehousing.
3. Improved Quality
Six Sigma doesn’t replace the operations and workflows you perform. It aims to improve what you’re already doing, tweaking tactical adjustments here and there to reduce mistakes and eliminate redundancies.
4. Reduced Incidents of Damage Control
Certified Six Sigma manufacturers have better visibility over their complete production cycle. With that knowledge comes decreased “fire drills,” — instances of major product or process defects. Six Sigma peels back the curtain on some of today’s most notorious causes of product damage in the manufacturing industry, from improperly calibrated equipment that mishandles components to complete changeover errors resulting in expensive — and cumbersome — production halts.
5. Leaner Operating Costs
An end-to-end efficient manufacturing operation is a less costly operation. End of story. This leaves more money in the pockets of your manufacturing company, reducing overhead and direct expenses alike and burgeoning your overall bottom line.
Challenges of Six Sigma
1. Differing Definitions and Approaches
The guiding philosophy behind Six Sigma is the five-stage DMAIC process, which assumes every detail and activity in the manufacturing environment involves quantifiable inputs and outputs. While this isn’t misguided, it presents problems with less immediately or obviously quantifiable operations. Without proper statistical training, organizations are burdened with the task of quantifying data points seemingly in the dark.
2. Highly Statistical
Six Sigma relies on a diverse set of statistical tools to identify and validate a process’ root problem. Using the DMAIC model alone, teams may conduct any of the following quantitative calculation techniques:
Control chart creation
Statistical process control (SPC)
Potential capability (Cp) and actual capability (Cpk) calculations
Failure mode and effect analysis
Value stream mapping
At the very least, organizations committing to Six Sigma improvements will need a statistics or data analytics expert to guide, if not spearhead, these imperative toolsets.
3. Wider Organizational Disconnect
Leaders in the wider organization may not have the technical fluency needed for Six Sigma buy-in and continued support. This leadership disconnect is also likely to spur misappropriated resources, with Six Sigma efforts not receiving proper time commitments, budgets, dedicated personnel and other resources needed to guide it to full process-control validation.
Resource: mantec.org, investopedia
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