IFQ India’s Minitab Data Analysis is a procedure of gathering, changing, cleaning, and displaying information with the goal of finding the required data. The results so got are communicated, recommending conclusions and supporting basic leadership. Data visualization is an occasion used to depict the data for the simplicity of finding valuable examples in the information. The terms Data Modelling and Data Analysis mean the equivalent.
Minitab for data analysis helps you to research the records. This is designed essentially for the Six Sigma experts. It gives a simple, powerful manner to input the statistical data, control that information, and pick out tendencies and styles, after which extrapolate solutions to the modern issues. This is the maximum widely used software program for the enterprise of all sizes-small, medium, and massive. Minitab affords a short, effective solution for the level of evaluation required in the maximum of the Six Sigma initiatives.
Who Should Attend?
For all Middle/Senior management employees from Mfg/Operation, Sales & Service, HRD, Finance department, & other department employees of an Organization.
In house training program can be organized for a group of 15 to 20 nos of participants your work place(per batch).
Course Duration : 2 days(9.30 AM to 5 .00 PM)
- Introduction to Minitab software
- Data types entry
- Basics graphical techniques, Histogram
- Box plots, Dot plots
- Parato analysis,
- Fish Bone diagram
- MSA, Cp, Hypothesis
- Anova with Minitab
- Correlation & regression
- Multiple Regression
- Multi variation analysis
- DOE & Control charts with Minitab
- Practical exercise on Minitab
- Case study for practice
Take Away of the Program
- For Quality& operational department professionals
- Hands on training on software to handle statistical tools for analysis of shop floor data on defects, trials, rejections etc.
- Improving ability of an individual for using own data while practicing Minitab
- 2 days training program gives special exposure to DOE tools for use of Design professionals.
IFQ India’s Minitab For Data Analysis Details
Minitab is a statistics tool created by some researchers to assist six sigma experts in analyzing and interpreting data to aid in the business process. The data input has been simplified to make it easier to use for statistical analysis and to manipulate the dataset. If trends, patterns, or charts are provided, they are evaluated and interpreted in order to reach a final decision. The responses are offered, and they are amplified with the products or services provided to assist in the business. The Minitab tool makes problem-solving simple and quick.
Why do we need Minitab if we have MS Excel?
- MS Excel is excellent for analyzing and summarizing data. Sorting, highlighting, and other functions are available here.
It is, nevertheless, the sole option when it comes to statistics and specialized mathematical functions. Even visualization that is specific to the statistical situation is superior. All you have to do is give clean raw data, and you’ll have gorgeous charts in no time.
- When you have a lot of spreadsheets and charts, MS Excel might become cluttered. It also features a “Project Manager” option that allows you to switch between spreadsheets, graphics, and statistics output.
- MS Excel is a versatile tool that can be used with a wide range of applications. However, statistics, hypothesis testing, statistic visualization, and other techniques are used to improve the quality of processes, products, and services.
Minitab when opened, typically has three types of windows
- Session window: This is the uppermost window, where the results of your specified statistical analysis will be displayed.
- Worksheet window: Data is copied and pasted into a worksheet in the Worksheet window, which is located at the bottom of the screen.
- Graphics window: When this sort of window opens, it is not visible. This only occurs when it is asked to plot something, such as a scatterplot or histograms. So here we have the graphics window.
Key Features of Minitab
The following are the eight key characteristics:
- Basic Statistics: This section includes statistical tests, descriptive statistics, correlations, and covariances.
- Graphics: Users can create scatter plots, histograms, boxplots, matrix plots, marginal plots, bubble charts, and other statistical graphs with this feature.
- Regression: Users can utilize this function to determine the link between variables (which is a key feature of any statistical tool). Linear, non-linear, ordinal, nominal, and other types of regression are all available.
- Analysis of Variance: The difference between the group means is investigated using analysis of variance, or ANOVA.
- Statistical Process Control: This feature aids in the creation of cause-and-effect diagrams, variable-control charts, multivariate-control charts, time-weighted charts, and other graphs.
- Measurement System Analysis: MSA is a mathematical technique for calculating the amount of variance in a measurement process. A process’s variability has a direct impact on the process’s overall variation.
- Design of Experimentations: This feature aids in the deciphering of cause-and-effect relationships. This aids in the creation and testing of various designs by recording all essential outputs. This assists you in finishing and optimizing a technique.
- Reliability/Survival: It enables you to select the best distribution for modeling data. It helps you in identifying which is the best function that best describes your data.
What is APQP?
APQP stands for Advanced Product Quality Planning.
In the manufacturing industry, as a product’s complexity rises—a car, for example, is made up of 30,000 parts, including the tiniest screws, nuts, and bolts—so does the risk of error in the product lifecycle as work moves downstream through the process chain in design, manufacturing, quality, supply chain, and other internal and external teams.
This is particularly true when it comes to new products and procedures.
Through a mutual understanding of the requirements and a thorough risk assessment, APQP is a set of standard procedures that documents the ability to produce a capable part with a dependable and repeatable process.
APQP is one of five IATF 16949-compliant Core Tools for successful quality management, which also includes PPAP, FMEA, MSA, and SPC.
Why is APQP important?
By having a defined agreement and protocol for product definitions and needs, APQP benefits both the OEM (customer) and the supply chain (contractor). It gives a platform for making quick and informed judgments and communicating effectively.
This eliminates any ambiguity or misinterpretation that could cause manufacturing to be delayed, a sub-standard part to be produced, and/or an increase in expenses.
This gives the following benefits to OEMs dealing with various suppliers and their sub-tier suppliers:
- Across-the-board product conformity.
- The time it takes for a product to reach the market is reduced.
- Communication with the supplier that is open and honest (s).
- Determine a supplier’s ability to meet specifications.
This covers what’s required in the supply chain to get part approval and compliance:
- Communication and comprehension of the process and product requirements in a structured manner.
- Errors are detected early on.
- Customer communication that is open and honest.
- More business comes from a satisfied customer.
Because the cost of rectification is substantially lower earlier in the product lifecycle, APQP helps reduce risk by discovering problems earlier in the design phase.
The cost of correction is substantially higher than the expense of later stages such as operations and support.
When is APQP necessary?
When the OEM and suppliers collaborate to design a new product and process, or when modifying product or process changes after release, APQP is frequently employed.
How does the APQP process work?
The APQP process includes a cross-functional collaborative team with members from engineering, manufacturing, quality, procurement, and other functions, as well as a project team leader who oversees the planning process.
The APQP process consists of 5 phases:
Phase 1: Planning
Phase 2: Product Development and Design
Phase 3: Design and Development of Processes
Phase 4: Validation of the Product and Process
Phase 5: Feedback & Continuous Improvement
Each step is built on the basis of the previous phase and then continues to build on the next, much like a pyramid.
And, each phase has inputs (data) and outputs (results) (deliverables). The output of one phase is used as the input of the next.
Who maintains the APQP standard?
The APQP standard is maintained by the Automotive Industry Action Group (AIAG).
The organization has nearly 4,000 members, including major automakers such as Ford, GM, Honda, Nissan, PACCAR, Stellantis, Tesla, Toyota, Volkswagen, and many of its component suppliers.
Other Original Equipment Manufacturers (OEMs) beyond the automotive sector include Boeing, Caterpillar, BAE Systems, Deere, and more.
What is Measurement System Analysis (MSA)?
MSA stands for Measurement System Analysis, and it’s a rigorous statistical study that examines whether your measurement systems, whether they’re measuring gadgets or people, are capable of giving trustworthy data so you may make the best data-driven decisions possible. The method used for discrete data is termed an Attribute Agreement Analysis, and the statistical study used for continuous data is called a Gage R&R research.
You will evaluate your observed measurement from a variety of angles during your MSA research. They are as follows:
- Repeatability: If the same individual measures the same object numerous times with the same device and gets the same findings, we call the measurement system Repeatable.
- Reproducibility: If numerous persons measure the same thing with the same device and get the same findings, the measuring system is said to be repeatable.
- Stability: If the fluctuation in a measurement system remains constant throughout time, it is said to be stable.
- Bias: The measurement system’s bias is a one-way tendency. Your scale, in our case, always appears to weigh more than it should. The scale’s setting can be changed to adjust the bias.
- Linearity: Is the measurement system capable of remaining stable over the whole range of intended measurements? You want your scale to accurately and precisely measure you and your husband, but you also want it to measure your pet mouse and elephant. That’s the range of possible weights you’re looking for. However, at extremes, the scale is unlikely to be useful.
- Discrimination or resolution: Is the measurement system capable of generating values that are adequate to be meaningful? To measure out food for your diet, you utilise a tiny kitchen scale. The scale can only measure in pounds, but you need to measure out ounces. Resolution always comes at a cost, so think carefully about how much you truly need to get useful data.
3 advantages of doing an MSA
You will gain a better understanding of the nature of your measurement system and, as a result, your data by conducting an MSA study.
- You’ll be able to make more informed judgments based on data.
- You’ll be able to detect minor variations in the procedure.
- It enables a scientifically and statistically sound evaluation of performance.
Why is MSA important to understand?
The concept of MSA is simple to grasp. Gage R&R and Attribute Agreement Analysis, the actual statistical investigations, are more difficult to comprehend and use. You need to know about MSA because:
- It’s critical to make decisions based on credible and trustworthy data. MSA gives you the tools to evaluate your measurement system and, if necessary, improve it.
- You must be aware of the realities of your procedures. If you can’t trust your measurement data, your operations may be producing defective items that make it all the way to the client. If they are measuring your product during incoming inspection, for example, both you and the customer must have comparable capable measurement devices.
- Understanding data as a foundation for decision-making is becoming increasingly important in the business sector. Your competitors may already be utilising MSA to better analyse their data and using that knowledge to improve their processes and respond to customers. Walmart, for example, has an enormous quantity of data on its customers, and they are a dominant force in the retail sector because they trust that data.