Author: Alphadi Team

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MSA & SPC

MSA & SPC

Quick Info

In this module, you will receive an introduction to the use of measurement systems. You will learn the scattering components of MSA and develop a deep understanding of measurement system analysis. The module guides you step-by-step through the procedures of measurement system analysis (MSA), starting with the basics and moving on to a detailed look at important key figures such as Cg and Cgk for continuous and attributive measurement data.
You will also learn how to use control charts to effectively monitor process behavior. This preventative strategy allows you to proactively intervene in processes to identify and correct potential errors at an early stage or prevent faults from occurring before they manifest themselves.

  • Duration:
Dauer auf Anfrage
  • Next Starts
  • Live webinar dates in:
- 1
Termine auf Anfrage
  • Alternatives:
  • One-to-one coaching
  • In-house
  • Small groups
  • In the languages:
- 3
- 5
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Contents

● Basics of MSA

● Variation component

● Resolution

● MSA 1

● MSA 2 (continuous, attributive)

● Key figures (Cg, Cgk)

● Measurement deviation (BIAS)

● Scattering components

● Reproducibility

● Repeatability

● Control chart technology

● I/MR chart

● P-chart

● Linearity, stability

● Sample size

● Warning limit

● Intervention limit

● Specification limit

● Trends in control charts

● Process variations

Key information

MSA (Measurement System Analysis) and SPC (Statistical Process Control) are statistical methods used in quality control to check measurement accuracy and monitor processes. MSA focuses on analyzing the measurement system to identify sources of error such as bias and scatter. SPC, on the other hand, uses control chart techniques to monitor and control process stability and variation.

History

The development of SPC dates back to the 1920s, when Walter A. Shewhart of Bell Laboratories developed the first control charts. MSA was later introduced as a complement to SPC to ensure the reliability of the measurement data used in control charts. Both methods were further developed during the Second World War and in the post-war period.

Usage

MSA and SPC are used in a variety of industries, including manufacturing, automotive, pharmaceutical and other industries where high quality standards are required. These methods help companies to ensure the quality of their products by evaluating the accuracy of measurement systems and continuously monitoring processes.

Benefits

  • ● Improve product quality
  • ● Reduction of variability
  • ● Monitoring of processes
  • ● Ensure the capability of measurement processes
  • ● Recognize trends

Risks

  • ● Ongoing monitoring required
  • ● Qualified personnel required for data analysis
  • ● Less effective in dynamic environments
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Process Capability Analysis

Process Capability Analysis

Quick Info

The process capability analysis module builds on the basics of statistics. Here you will gain a deeper insight into data types and distribution forms.
In this module, you will learn, among other things, how to present and interpret the performance of processes in various key figures. You will gain a basic understanding of whether the statements in your data are statistically relevant or not. You will learn to interpret diagrams correctly and to think and act in terms of probabilities.

  • Duration:
Dauer auf Anfrage
  • Next Starts
  • Live webinar dates in:
- 7
Termine auf Anfrage
  • Alternatives:
  • One-to-one coaching
  • In-house
  • Small groups
  • In the languages:
- 9
- 11
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Contents

● Data acquisition

● Types of diagrams

● Normal distribution

● Binomial distribution

● Poisson distribution

● Weibull distribution

● Sigma level

● z-value table

● Process performance

● Specification limits

● Accuracy

● Precision

● Cp & Cpk

● z-value vs. Cpk

● Process capability DPMO

● DPU

● Ppm

● Long-term & short-term capability

● Sigma Shift

● Test for normal distribution

Key information

Process capability analysis is a statistical method used to evaluate the ability of a process to produce products or results within defined specification limits. This tool is used to measure and improve the consistency and reliability of production processes by evaluating data on process performance and comparing it with the specified tolerances.

History

Process capability analysis has evolved as an essential part of QA in manufacturing and other operational areas since the mid-20th century. With the increasing importance of Six Sigma and other QA programs in the 1980s, these techniques gained importance to ensure lower defect rates.

Usage

Process capability analysis is widely used in the manufacturing industry as well as in other sectors that control complex processes, such as pharmaceuticals, automotive and electronics manufacturing. It helps organizations understand, monitor and optimize processes to ensure consistent quality.

Benefits

  • ● Ensure the reliability of processes
  • ● Reduction of waste
  • ● Erhöhen der Kundenzufriedenheit
  • ● Identify the causes of fluctuations
  • ● Reduction of error costs

Risks

  • ● Precise data acquisition required
  • ● Possession of expertise in statistics
  • ● Misinterpretation of the results
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Statistics Basics

Statistics Basics

Quick Info

Understanding statistical correlations is essential for all employees in a company. From management, to work systems, to continuous improvement – everywhere decisions are made by numbers.
This module enables you to collect and analyze the right amount of data from your processes, depending on the type. You will learn how data can be categorized and presented graphically in an appropriate chart form to provide a better basis for later decisions.

  • Duration:
Dauer auf Anfrage
  • Next Starts
  • Live webinar dates in:
- 13
Termine auf Anfrage
  • Alternatives:
  • One-to-one coaching
  • In-house
  • Small groups
  • In the languages:
- 15
- 17
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Contents

● Introduction to statistics

● Statistical population

● Sample

● Evaluating data

● Correlation

● Different types of data

● Definition: Measured variable

● Types of data acquisition

● Frequency diagram

● Time series chart

● Histogram

● Scatter diagram

● Boxplot diagram

● Mean, median, mode

● Range, variance

● Standard deviation

● Define anomalies

● z-value

● Types of distribution

● Normal distribution

Key information

Fundamentals of statistics refer to the methods and techniques used to collect, analyze, interpret and present data. This area of knowledge is critical to making data-based decisions in science, industry and many other fields. Statistical principles include understanding different types of data, collecting and analyzing samples, and using descriptive and inferential statistical methods.

History

The origins of modern statistics can be traced back to the 17th century, when the first systematic methods for collecting and analyzing data were developed. In the 19th century, statistical methods were further formalized and expanded, particularly through the work of Karl Pearson and Ronald Fisher, in order to analyse phenomena in biology and the social sciences.

Usage

The statistical principles are used in almost every field that requires data-based decisions. These include science, medicine, business, engineering, government work and social media. Statistical methods help in the evaluation of experiments, monitoring production quality, predicting trends and other applications.

Benefits

  • ● Analyze complex data effectively
  • ● Making better decisions
  • ● Quantify uncertainties
  • ● Scientific approach
  • ● Understanding of connections

Risks

  • ● Misleading if used incorrectly
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Lean Basic Tools

Lean Basic Tools

Quick Info

With lean management, you optimize processes by finding errors whose core causes are of an organizational nature. These lead to waste and a reduced process speed. The Lean Basic Tools help you to identify these causes and become more efficient as a result. The application of lean tools ensures that you increase added value and achieve a perfect flow in the organization.
The benefits are high-quality products, a fast market entry, flexibility, customer satisfaction, sustainability and higher sales.

  • Duration:
Dauer auf Anfrage
  • Next Starts
  • Live webinar dates in:
- 19
Termine auf Anfrage
  • Alternatives:
  • One-to-one coaching
  • In-house
  • Small groups
  • In the languages:
- 21
- 23
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Contents

● Lean Management

● Shopfloor

● Lean key indicators

● Lean tools

● Process Cycle Efficiency

● Poka Yoke

● Bottleneck & Constraint

● Overall Equipment Effectiveness

● Total Production Maintenance

● Changeover time optimization

● Spaghetti diagram

● Workplace organization

● Kaizen events

● Kaikaku

● Gemba Walks

● Visualization techniques

● Andon Signal

● Jidoka

● PDCA Cycle

● Gemba

Key information

Lean Basic Tools are a collection of principles and techniques aimed to eliminate waste and maximize efficiency in production and business processes. They are a central component of lean management. These tools include techniques such as Poka Yoke for preventing defects, Jidoka for automatic defect detection and the 5S method for workplace organization, in addition to methods such as Kaizen for continuous improvement and Gemba Walks for direct observation.

History

The origins of Lean Basic Tools can be traced back to the Toyota Production System (TPS) in Japan, which was developed after the Second World War. This philosophy and its tools have spread worldwide and have been adapted in various industries.

Usage

Lean tools are used to optimize processes and improve productivity and quality. They are used in manufacturing, in the service sector, in hospitals and in many other environments where process efficiency is the focus.

Benefits

  • ● Reduction of waste
  • ● Low costs
  • ● Better efficiency
  • ● Short lead times
  • ● Improving the CIP culture

Risks

  • ● Resistance through cultural problems
  • ● Management support required
  • ● No understanding of lean principles
  • ● Traditional work has to be abandoned
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CIP Tools

CIP Tools

Quick Info

CIP stands for the Continuous Improvement Process, which is often associated with Kaizen. KVP focuses exclusively on improving workplace processes. In contrast, Kaizen is an integral part of the Japanese mindset, embodying the principle of continuous optimization beyond just the work environment.

KVP tools are used in quality management, project management, lean management, and Six Sigma. Through KVP, employees expand their methodological knowledge and adopt a quality-oriented mindset, which is crucial for the success of a sustainable KVP initiative.

  • Duration:
Dauer auf Anfrage
  • Next Starts
  • Live webinar dates in:
- 25
Termine auf Anfrage
  • Alternatives:
  • One-to-one coaching
  • In-house
  • Small groups
  • In the languages:
- 27
- 29
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Contents

● Introduction to CIP Tools

● CIP Philosophy

● PDCA Cycle

● Quality Tools

● Management Tools

● Error collection plan

● Quality control chart

● Histogram

● The Pareto principle

● Correlation diagram

● Process flow diagrams

● Turtle diagram

● Ishikawa method

● Affinity diagram

● Relationship diagram

● Tree diagram

● Matrix diagram

● Portfolio diagram

● Network diagram technique

● Utility value analysis

Key information

CIP tools are essential instruments in the Continuous Improvement Process (CIP). The goal is to continuously optimize work processes and quality. These tools encompass a variety of methods and techniques, including the PDCA cycle for systematically planning, executing, reviewing, and adjusting processes. Other important tools include quality and management tools, error prevention and analysis methods such as check sheets, Ishikawa diagrams, and the Pareto principle. By effectively utilizing these tools, companies can enhance efficiency, reduce errors, and continuously improve product and service quality.

History

CIP tools have their roots in quality control, which developed over the course of the 20th century, particularly after the Second World War. The CIP approach was influenced by the principles of Total Quality Management (TQM), which were promoted in Japan by pioneers such as W. Edwards Deming and Kaoru Ishikawa. In the 1980s, Western companies also began to adapt these principles, which led to the development and dissemination of specific tools such as PDCA cycles and Ishikawa diagrams.

Usage

CIP tools differ from DMAIC and Six Sigma mainly in terms of their flexibility and broad range of applications. DMAIC is used to systematically reduce errors and increase efficiency in measurable processes. CIP tools are intended for incremental optimization in operational environments. This makes CIP particularly versatile and adaptable to different operational cultures and processes, from small changes in daily operations to larger, cross-functional initiatives.

Benefits

  • ● Universally useful in all processes
  • ● Useful for continuous improvement
  • ● Also suitable for small improvements
  • ● Usable in all industries
  • ● Easy to understand

Risks

  • ● Employee commitment required
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Lean Six Sigma DMAIC

Lean Six Sigma DMAIC

Quick Info

The DMAIC roadmap is a structured approach for executing Lean Six Sigma projects. It consists of five phases, each with its corresponding tools, which are applied to help carry out projects successfully.

The DMAIC roadmap is ideally suited for improving existing products or processes. It provides a clear thread from the problem definition to the solution in your project.

  • Duration:
Dauer auf Anfrage
  • Next Starts
  • Live webinar dates in:
- 31
Termine auf Anfrage
  • Alternatives:
  • One-to-one coaching
  • In-house
  • Small groups
  • In the languages:
- 33
- 35
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Contents

● DMAIC Roadmap

● Goals of Lean Six Sigma

● Cost of Poor Quality

● Quick Wins

● Hidden Factory

● Cause & Effects Matrix

● TIMWOOD

● Project Requirements

● Gate Review Process

● Project Breakdown

● SIPOC Diagram

● Voice of Process

● Six Sigma Process Model

● Data Collection Plan

● Ishikawa

● Benefit & Effort Matrix

● Implementation Plan

● Risk Analysis

● Control Plan

● Project Handover

Key Information

DMAIC is the core method (approach) of Six Sigma and stands for the five phases: Define, Measure, Analyze, Improve, and Control. This systematic approach was developed to improve processes within companies by minimizing defects and increasing efficiency.

History

DMAIC has its roots in quality assurance, which was developed by Motorola in the 1980s. Motorola introduced Six Sigma to improve the quality of its products. The approach was later adopted and further developed by other leading companies such as General Electric.

Usage

DMAIC, or Six Sigma, is used in a wide range of industries, including manufacturing, healthcare, finance, IT, and more. It is particularly useful in environments that are highly process-oriented, where reducing defects and increasing efficiency are critical.

DMAIC is the core methodology in Six Sigma. However, the application of DMAIC is often perceived as too complex. Additionally, the introduction of changes can meet resistance, as it involves moving away from established practices. Effective use of DMAIC requires specialized knowledge in statistics and project management. Often, individuals are trained but not professionally supported, which can lead to project failures.

Benefits

  • ● Structured Problem Solving
  • ● Clear Phase Model for Every Project
  • ● Universal Applicability
  • ● Integration of Tools with the Phase Model
  • ● Integrated Six Sigma Process Model

Risks

  • ● Professional Support is Often Lacking
  • ● Complexity is Often Underestimated
  • ● Established Practices Must Be Abandoned
  • ● Specialized Knowledge Required
  • ● Management Support is Often Lacking

With more than 4,000 projects and case studies, Alphadi is a leader in the field of Lean Six Sigma and Sales Process Engineering. We advise you holistically so that your goals are achieved sustainably and in the long term.

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