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FLEXO Magazine : January 2013
Technologies & Techniques The Importance of Statistical Analysis selecting and understanding the Right Data By Alberto Tormin Quality efforts have historically evolved from inspection and detection to prevention. Eradicating inconsisten- cy, doing things right the first time and reducing waste became some of the goals of QC, QA, TQM (Quality Control, Quality Assurance, Total Quality Management), Lean and Six Sigma teams. Very often these initiatives were driven by data collection and analysis. The digital age increased the development of electronic data-gathering systems, making available many observations and a great number of variables to be dissected. The activi- ties around selecting the right data, and gathering, maintain- ing and understanding these observations have created a new challenge for many companies because the selection of significant metrics can influence the path we take to create and achieve goals. These analyses usually include the application of statistical process control and process improvement techniques. To do this in an effective way, we need certain knowledge of statisti- cal tools to analyze data problems. We also need to provide information with statistical significance that will help achieve the company ’s goals. In the process we should ask: Why is the data important? Who produced the data? How was the data produced? If this is not done correctly, we find that despite the team’s best efforts, the solutions are only temporary, and often the same problem can reappear after some time. We need to de- termine the correct cause, and at the same time understand the interaction between processes. Once trends are verified, we need to identify the main theories that may explain them. In order to analyze the cause and implement corrective action, we must first understand the variation. The effort to review our steps and determine where we went wrong can be achieved with the use of many tools at our disposal. Here are some examples of basic analysis and tools: • Root Cause Analysis • Ishikawa Diagrams (Cause and Effect) • Drill Down (5 Whys) • Histograms (Trend Data) • Pareto Charts or Scattergrams (Frequency) • Control Charts (Outliers) • FMEA (Failure Mode and Effects Analysis) • PDCA (Plan, Do, Check, Act) • SIPOC (Supplier, Inputs, Process, Outputs, Customer) • 8 Wastes of Lean (Overproduction, Motion, Inventory, Transportation, Waiting, Underutilized People, Defects and Over-Processing) Additionally, it is important to remember that leadership is a key component when putting your quality Initiatives into action. Often we fail because the leadership is strong but the problem solving method is ineffective. A company with a problem- PROCESS IMPROVEMENT STRATEGY • Leadership is a key component when putting your quality initiatives into action • Remaining competitive in the era of globalization requires ensuring the efficiency of operations • Organizational excellence involves quality education and statistical training • Statistical analyses drives process improvement, as well as production and individual performance • Eradicating inconsistency, doing things right the first time and reducing waste is the ultimate goal 46 FLeXO jAnuARy 2013 www.flexography.org