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AIAG VDA FMEA Key Changes | Overview | 7 Step FMEA |PFMEA |1st Edition 2019 | Training

26/03/2024 by TECHIEQUALITY

AIAG VDA FMEA Key Changes | Overview | 7 Step FMEA |PFMEA |1^st Edition 2019 | Training

The AIAG VDA FMEA 1^st edition handbook has been released first time jointly by AIAG (Automotive Industry Action Group) & VDA (Verband der Automobilindustrie) in 2019. Basically, FMEA is a core tool that is used to identify the potential failure modes of process and product & their cause and effects. Mainly FMEA is three types i.e. [1] PFMEA (Process failure mode and effect analysis), [2] DFMEA (Design failure mode and effect analysis), [3] MSR-FMEA. In this post, we will cover the PFMEA with a manufacturing example.

DOWNLOAD-PDF files of AIAG-VDA FMEA key changes.

If your organization is manufacturing Automotive /Automobile parts/items/products or IATF 16949 certified companies then, you are compulsory to identify the process/ product /design related failures /risks in the FMEA worksheet. It means you have to prepare and implement the FMEA in your organization as a live document. If a customer mentions in their CSR (Customer Specific Requirement) for FMEA requirement then, it’s mandatory for their supplier to fulfil the FMEA requirement, but it does not matter whether your organization is IATF-16949 certified or not. So it’s more important to understand the complete requirements of FMEA before its application in industries. In standard IATF-16949, the application of FMEA is clearly mentioned in several clauses. Now we are going to explain each and every clause of IATF-16949 those are relevant to the FMEA application.

What are the requirements of IATF 16949 for FMEA application?

There are almost 12 sub-clauses in the IATF-16949 standard, where the application and implementation of FMEA are widely explained in the IATF standard. These clauses’s requirements as explained below;

IATF-16949 Clauses /Sub-clauses No: 4.4.1.2: Special approval of PFMEA and DFMEA is required.

8.3.2.1 (IATF Sub-clause): During product development, the organization shall develop the DFMEA and require reviewing the product design risk to reduce the potential risks.

8.3.3.3 (IATF Sub-clause): Special characteristics like fit & function, Critical characteristics, significant characteristics, safety characteristics, etc. are need to be included in the FMEA worksheet.

8.3.5.1 (IATF Sub-clause): This sub-clause talks about the design risk analysis. DFMEA is the mandatory requirement for design and development output.

8.3.5.2 (IATF Sub-clause): Manufacturing process-related risks need to be addressed in PFMEA.

8.5.6.1.1 (IATF Sub-clause): Process-related risks need to be addressed in PFMEA due to temporary changes in process controls. If your organization has temporarily changed the existing process controls and using the alternative new control method then, the organization shall include the risks of new process control methods in the PFMEA worksheet.

8.7.1.4 (IATF Sub-clause): The organization shall identify the risks related to the product rework process in FMEA.

8.7.1.5 (IATF Sub-clause): The organization shall identify the risks related to the product repair process in FMEA.

9.1.1.1 (IATF Sub-clause): This clause is about the Implementation of PFMEA.

9.3.2.1 (IATF Sub-clause): Field failure /warranty failure-related risk needs to be addressed in FMEA.

10.2.3 (IATF Sub-clause): As and when problems arise in the process, accordingly review and updation of PFMEA are required.

10.2.4 (IATF Sub-clause): Error proofing details methodology needs to be addressed in PFMEA.

Key Changes of AIAG-VDA PFMEA, 1^st edition 2019:

*4^th edition FMEA (Old version manual)*	*AIAG-VDA FMEA (New version handbook)*
RPN (Risk priority number) = S x O x D	Action Priority (AP) Table.
Old Severity, Occurrence & Detection Ratings	Revised Severity, Occurrence & Detection Ratings
Old worksheet (Format/ Template)	Revised worksheet (Format/ Template)
PFD was not an integral part	The Process flow diagram is an integral part of the new manual
Recommended action	Prevention & detection action
Classification	Special Characteristics
	4M-Men, Machine, Material, and Method approach

The complete PFMEA worksheet has made considering 7 Steps FMEA approach, i.e. [1] Planning & Preparation, [2] Structure Analysis, [3] Function Analysis, [4] Failure Analysis, [5] Risk Analysis, [6] Optimization and [7] Risk Communication.

7 Steps PFMEA Approach:

Planning & Preparation:

This is the first step of the FMEA 7-step approach, A Cross-functional team needs to be formed and their roles and responsibilities need to be assigned. In the worksheet, you have to mention those things like Company name, location, PFMEA ID, Confidential Level, etc.

Structure Analysis:

In SA, You have to identify the Process steps i.e. PFD, here you have to identify mainly three things, Process Item, step, and work element (4M).

Function Analysis:

You have to describe the function process item in the next plant process, end-user, and customer, and Simultaneously Process and product characteristics need to be identified.

Failure Analysis:

Here you have to identify the failure effect, mode, and cause.

Risk Analysis:

Current prevention & detection control, AP, SC, etc. need to be addressed.

Optimization:

According to the Level of AP (Action Priority), if required to take the action then, you have to complete the optimization step and again you have to level the PFMEA-AP.

In the previous version, there is a calculation of RPN, but in the new FMEA there is no calculation, just you have to identify the level from the Action Priority Table. Priority High AP level means you must be required to identify appropriate action, Medium priority AP level means you should identify appropriate actions and finally Low priority level (L) means you could identify actions to improve prevention or detection controls.

Result Documentation / Risk Communication:

Now, you have to keep the documents like result communication, CFT meeting report, etc.

7 Steps to Implement the New AIAG-VDA PFMEA in Industry?

In each and every post our authors are trying to share their experience with industrial examples so that the common man can easily understand, learn, and implement it in their workplace. Also, we are always trying to enhance the skill base knowledge of our readers. Anyway, go through the below steps to implement the new PFMEA manual.

Step-1:

To form a CFT team, for example, members should be from different functions like Production, Maintenance, Process quality, product quality, R&D function, etc.

Step-2:

Use the process flow diagram to identify each step of all processes.

Step-3:

Visit the workplace and understand the operational function of each and every step of the process and 4M factors.

Step-4:

Brainstorm the potential failure mode and its effects. accumulate the previous failures from any resources like from shop floor supervisors, operators, from old data, failure due to 4M factors (Man, Machine, Material, Method, etc), and list up the potential effect of failures as well.

Step-5:

To rank the Severity, occurrence, and detection and accordingly choose the level of Action Priority from the AP table.

Step-6:

To address the Current control mechanism in the worksheet.

Step-7:

Take action as per AP level and after implementation of the Action Plan, you have to re-level the PFMEA action priority.

FAQ:

Q1: Shall I need to convert our Approved old PFMEA worksheet to the new one?

A1: Actually it depends upon your customer’s needs and what they want, they need to keep the old approved copy or need the re-approved copy as per the new manual. If your customer asks for a new worksheet as per the 1^st edition of AIAG-VDA PFMEA then only you need to change the old one.

Example:

Let’s take an example related to field failure, there is a leakage issue found in the sump chamber, and the same issue was communicated by customers to their supplier to submit the CAPA and updated FMEA. when the supplier investigated the defective product, they found leakage caused due to sand inclusion in the sump base wall. Basically, here, we will focus on how to identify the action priority level of a failure mode. Let you have selected the Severity of FE (Failure effects), Occurrence of FC (Failure cause), and Detection of FC/FM as 9, 2, 1

Effect	S	Prediction of failure cause occurring	O	Ability to detect	D	Action Priority (AP)
Product or Plant effect very high	9-10	Low	2-3	Low-Very low	7-10	H
Product or Plant effect very high	9-10	Low	2-3	Moderate	5-6	M
Product or Plant effect very high	9-10	Low	2-3	High	2-4	L
Product or Plant effect very high	9-10	Low	2-3	Very High	1	L

Action Priority Table (AP)

In the above example, the rating of Severity (S) is 9, Occurrence (O) is 2, and Detection (D) is 1. So now, we will match the value in the AP table to find out the AP level. Go through the below picture to know the AP level.

NEW AIAG VDA FMEA AP TABLE EXAMPLE

action priority table — NEW AIAG VDA FMEA AP TABLE

Free Templates / Formats of QM: we have published some free templates or formats related to Quality Management with manufacturing / industrial practical examples for better understanding and learning. if you have not yet read these free template articles/posts then, you could visit our “Template/Format” section. Thanks for reading…keep visiting techiequality.com

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Simple Moving Average Formula | Calculation | Excel Template | Example

26/03/2024 by TECHIEQUALITY

Simple Moving Average Formula | Calculation | Excel Template | Example:

A simple moving average (SMA) is a method to get an overall idea about the forecast value of future prediction. If you are planning for Sales, Manpower planning, Production planning, marketing, etc. then SMA will be helping you to forecast future planning. So easily you can do future plans in your business area with the help of expected value based on past demands. In this article, we will be covering the simple moving average formula, calculation, and type of errors with examples. Also, we shall describe how to calculate MA (moving average) with the help of an excel sheet (Data analysis method and function Method).

DOWNLOAD-Sample Example of Moving Average Excel Template.

Simple Moving Average Formula (SMA):

If you would like to calculate the forecast for the coming period based on the Simple Moving Average Method, then formula {F (t, n)} will be the sum of Actual Occurrences or Demands in the past period up to “n” periods divided by the number of periods to be averaged.

Where, F = Forecast for the upcoming period.

n = Number of periods to be averaged.

At-1, At-2, At-3 = Actual demands or occurrence in the past period up to “n” periods.

How to calculate simple moving average forecast value?

Let’s use a simple example, suppose a company would like to use a 3-month, 5-month, and 7-month simple moving average for forecasting sales of the company. The actual sales of the last 11 months are given below;

Calculate the Forecast value of December by using a 3-month, 5-month, and 7-month simple moving average method. i.e.

F=?, (Use 3-month SMA method)
F =?, (Use 5-month SMA method)
F =?, (Use 7-month SMA method)

Month	Sales in Million Dollar ($M)
January	150
February	162
March	155
April	165
May	170
June	172
July	164
August	173
September	168
October	174
November	169
December	???

Answer:

Where, F = Forecast for the upcoming period.

n = Number of periods to be averaged.

At-1, At-2, At-3 =Actual demands or occurrence in the past period up to “n” periods.

Forecast for December using the 3-month SMA method:

= (169 + 174 + 168)/3

= 170.33

Note: we have calculated the average of the past three month’s sales i.e. sales of November, October, and September.

The past 5-month sales values from July to November are 164, 173, 168, 174 & 169 (See the above table).

Forecast for December using a 5-month simple moving average method:

= (164 + 173 + 168 + 174 + 169)/5

= 169.6

Forecast for December using the 7-month SMA method (May to November):

= (170 + 172 + 164 + 173 + 168 + 174 + 169)/7

= 170

Forecast Table:

Month	Sales in Million Dollar ($M)	3-month SMA	5-month SMA	7-month SMA
January	150
February	162
March	155
April	165
May	170
June	172
July	164
August	173
September	168
October	174
November	169
December	???	170.33	169.6	170

How to calculate different types of SMA errors?

Here, we are going to calculate the main three types of Error i.e. [1] Mean Absolute Deviation (MAD). [2] (MSE) Mean Squared Error. [3] Mean Absolute Percent Error (MAPE). Suppose a company wants to use the 3-month simple moving average method to calculate the forecast value of sales and errors of each month w.r.t actual value. Month-wise sales are given below;

Month	Sales quantity (Nos.)
April	3000
May	3500
June	3300
July	3400
August	3450
September	3501
October	??

By using a 3-month SMA method, we have calculated the forecast sales quantity from July to October as mentioned in the below table.

Month	Sales quantity (Nos.)	3-month SMA Forecast
April	3000
May	3500
June	3300
July	3400	3266.67
August	3450	3400
September	3501	3383.33
October	??	3450.33

Now, we have to calculate the Error from July to September simply by subtracting the 3-month SMA forecast value from the Actual sales quantity.

Error of July = 3400 – 3266.67

= 133.33

August error value = 50

September error value = 117.67

There is no negative error value, so no need to calculate the absolute value, else calculate the absolute value.

Mean Absolute Deviation (MAD) = (133.33 + 50 + 117.67) / 3

= 100.33

Mean Squared Error (MSE) = (133.33²+ 50² + 117.67²) / 3

= (17776.89 + 2500 + 13846.22) /3

=11374.37

Absolute percent error of July = (133.33/3400) X 100

= 3.92

Absolute percent error of August = (50 /3450) X 100

= 1.45

Absolute percent error of September = (117.67 /3501) X 100

= 3.36

Mean Absolute Percent Error (MAPE) = (3.92 + 1.45 + 3.36) / 3

= 2.91%

How to calculate an SMA forecast in an Excel sheet using a data analysis option?

We have taken the same data table that was already considered for example and also we will cross-check the forecast value of both the methods (manual and Excel data analysis) by using a 3-month simple moving average.

Step-1: Open the Excel sheet and then follow the below options as;

Click on the “Data” option in the Excel sheet.
Enter on the “Data Analysis” option.
Select the “Moving Average “ option.

Step-2: After click on the “Data Analysis” option, a pop-up will appear on the screen. Now, you have to select the input range and enter the interval value. The details process is clearly mentioned in the below figure.

Now, as you can see the 3-month SMA forecast value of both the method is same and that is 170.33

How to calculate Moving Average forecast in an excel sheet using function option?

Step-1: First of all go through the data table given in below image. and based no the below data we are going to calculate the forecast value using 5-month simple moving average in excel sheet by applying function option, for doing so, you have to select the cell first, then apply the “Average’ function on that cell.

Step-2: Drag the “already applied average function cell” in the bottom right corner down to move the function formula to all cell.

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Gage R and R |Attribute type MSA |How to do MSA Study | Acceptance Criteria

26/03/2024 by TECHIEQUALITY

Gage R and R |Attribute type MSA |How to do MSA Study | Acceptance Criteria:

Hi readers! Today we will be discussing here on Measurement System Analysis. Basically on variable (Gage R and R) and attribute type MSA, how to do both MSA studies, and finally on the interpretation of result with manufacturing example.

Measurement System Analysis (Variable type MSA):

MSA is nothing but it’s a Measurement System Analysis. And also it is one of the AIAG core tools. As you know how important the process data that you are measuring, monitoring, and collecting for analysis. If unfortunately, those data may be wrong due to instrument error or human (appraisal) error then your whole analysis and decision will be incorrect. Some people think that we are doing calibration of our instrument, so why will we do the Variable MSA of the instrument?

Hence I’m trying to illustrate why it is required, suppose you are measuring the length of a product and during an inspection of the product inadvertently the vernier caliper has fallen on the ground and you used it in next time because you know your vernier caliper is calibrated and within the period. However, the condition of the instrument and instrument error may have varied after this incident happened. So we should analyze the gage repeatability and reproducibility of the instrument and need to make the decision accordingly. Similarly, in dusty and high moisture environments your instrument’s reading may be incorrect (more instrument error) due to bad conditions. So it is very important to do the variable type MSA on a periodic basis considering various factors, customer requirements, and standard requirements, etc.

Attribute type MSA:

Similarly, we have to give attention to those who are doing inspection because the report of the inspection may be incorrect due to human error. For example, an inspector was doing a visual inspection of the colour box. And he accepted the lot of boxes and allowed to next process for operation. But the line manager has rejected the box due to a mismatch in the colour w.r.t approved master sample. When a quality manager tried to find out the cause of the incident later, he came to know that the person involved in the visual inspection was colour-blind. So it’s clearly indicating that the performance of the inspector was very low and miss-alarm & false-alarm was very high. Hence to prevent such types of mistakes we should periodically do the Attribute type of analysis to know the performance level of appraisal. A detailed interpretation is given below.

Below are some factors that may cause human error but these are not limited;

Vision problem
By following the wrong methods.
Insufficient knowledge.

How to do the Variable type MSA (Gauge repeatability & reproducibility)?

Step -1: First of all prepare the master calendar of MSA considering all instruments.

Step-2: List out the names of appraisers.

Step 3: E.g.- Every individual appraiser should measure the 10 samples with 3 trials, so in total there will be 30 measurements of individuals and 90 measurements of three appraisers.

Step-4: Calculate the % Gage R&R and “ndc” (No of distinct categories).

How to do the attribute type MSA?

Step-1: E.g.-Need to have 50 references and at least 10 bad parts. (At least 20 to 30 % of total reference-general practics).

Step-2: e.g.-Do a study and record the data of 50 parts with 3 trials in individuals of three appraisers.

Step-3: Calculate the % of Effectiveness, Miss-alarm, and false alarm of all three appraisers.

Interpretation of result:

Both the type of MSA result needs to be evaluated to determine, whether the measurement device is accepted or not. The acceptability criteria may depend on several factors but the general guidelines of acceptance criteria of both MSA (Variable and Attribute type) are given below.

General Acceptance Criteria of Gage R and R (Variable type MSA):

Generally, the measurement device to be acceptable when GR&R will be < 10% or NDC (Number of distinct categories) ≥ 5.
May be acceptable for some applications when Gage R&R will be 10% to 30%, during the decision making you should consider factors like the importance of application measurement, cost of rework, repair, and cost of the measurement device. But you should take approval from your customer as well.
The measurement device to be unacceptable when the GRR will be >30%.

General Acceptance Criteria of Attribute type MSA:

The decision to be an acceptable for the appraiser when Effectiveness ≥90% & Miss alarm ≤2% & False alarm ≤5%.
Marginally acceptable for the appraiser when Effectiveness ≥80% & Miss alarm ≤5% & False alarm ≤10%.
Unacceptable for the appraiser when Effectiveness <80% & Miss alarm >5% & False alarm >10%.
If you are considering the Kappa value to make a decision then, value >0.75 indicate good to excellent agreement.

Note: the above criteria are the general guideline for acceptability but you may consider the other factors during decision-making, for example, measurement system environment, Purpose, application, Cost of the measurement device, Cost of rework, cost of repair, total variation, etc.

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Correlation analysis in excel |3 best method |step by step guide with example

22/03/2024 by TECHIEQUALITY

Correlation analysis in excel | 3 best method |step by step guide with example

Hi reader! Today we will discuss on Correlation analysis in excel, this tool is generally used to know the correlation between two variables. There is so many software available in the market that you can execute the correlation test. But in this tutorial, we will explain to you how to do a correlation test in excel with an industrial example. There are three common methods that you can execute for the test i.e. [1] shortcut function method [2] direct function method [3] Through data analysis method. For doing the data analysis method you have to install the analysis tool pack if you have not yet installed then follow the steps to install it. The Link is given below.

Step by step guide for installation of Data Analysis tools in excel.

Processes of Correlation analysis in excel:

There are three common methods that we are going to explain it step by step. Here we have analyzed the correlation between variables “water tank (volume) vs Tank capacity” to know the interpretation of correlation and value of the coefficient of correlation. A Data table is given below;

Water Tank (Volume in m³)	Tank Capacity in liters’
2	2000
2.5	2500
3.5	3500
4	4000
4.3	4300
5	5000
5.5	5500

Method -1;

Step-1:

Open the Excel sheet, then create a table of two variables, and next, click on the function button. Follow the below figure.

Step-2:

After clicking on the function button, the below interface will appear.

Step-3:

Type “correlation” on the search bar and search the function, then select the function “CORREL”.

step by step guide of correction analysis

Step-4:

Select the data for array1 and array2; here we have selected the column of water tank volume as array1 and tank capacity as array2.

Step-5:

The Correlation coefficient will be calculated automatically. You can see in the below figure the value of the coefficient of correlation is 1. For better understanding, we have plotted the scatter diagram. And the graph and value of the coefficient of correlation indicate that there is a perfect positive correlation between the two variables.

Method-2;

Step-1:

Select the correlation function from the statistical option, and go through the below figure.

Step-2:

Select the data for array1 and array2 from the data table.

Step-3:

The Value of the coefficient of correlation will be calculated automatically.

Method-3;

Step-1:

Ensure that the data analysis tool has been installed already in Excel, else click here to learn the step-by-step process. Now go to the data option and select the data analysis option.

Step-2:

Select the input data range

Step-3:

The Correlation coefficient will be calculated automatically.

Interpretation of Correlation coefficient (r):

Correlation Coefficient (r )	Interpretation
r=0.5	Low positive correlation
r=0.9	High positive correlation
r=1	Perfect positive correlation
r=0	No correlation
r= -0.5	Low negative correlation
r= -0.9	High negative correlation
r= -1	Perfect negative correlation

Correlation Analysis in Minitab |Step by step guide with example

22/03/2024 by TECHIEQUALITY

Correlation Analysis in Minitab |Step by step guide with example

Hi reader! Today we will discuss on Correlation Analysis in Minitab, this tool is generally used to know the correlation between two variables. Alternatively, you can do the test of two variables in Excel also. If you are interested in executing the correlation analysis in Excel then read our below article (link is given below). But here in this tutorial, we will explain to you how to do a correlation test in Minitab with an example.

How to do correlation analysis in excel? Step by step guide.

Correlation Analysis in Minitab (Step-by-Step guides):

Here we are going to analyze the correlation between variables “water tank (volume) vs Tank capacity” to know the interpretation of correlation and value of the coefficient of correlation. The Data table is given below;

Water Tank (Volume in m³)	Tank Capacity in litres
6	6000
6.2	6200
6.5	6500
6.8	6800
7.2	7200
7.6	7600
8	8000

Step-1:

Open the Minitab software. You will see an interface like the below figure.

Step-2:

Type the data of two variables in Minitab’s worksheet.

Step-3:

Just follow the below path to select the Correlation option. Path: Stat » Basic Statistics » Correlation

Step-4:

Select the variables, here we have selected Water tank volume and tank capacity then enter the “OK” button to execute the test.

Step-5:

After the execution of correlation analysis, you will get the Pearson correlation value; here we got the value i.e. 1 of two variables “water tank volume vs tank capacity”.

Interpretation of Correlation coefficient (r):

Correlation Coefficient (r )	Interpretation
r=0.5	Low positive correlation
r=0.9	High positive correlation
r=1	Perfect positive correlation
r=0	No correlation
r= -0.5	Low negative correlation
r= -0.9	High negative correlation
r= -1	Perfect negative correlation

In the above example, we got the Pearson correlation value is 1, which means it indicates that there is a perfect positive correlation between two variables.

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Correlation Analysis Example and Interpretation of Result

22/03/2024 by TECHIEQUALITY

Correlation Analysis Example and Interpretation of Result

Hi readers! Today we will discuss on Correlation Analysis Example and Interpretation of the Result, let me tell you one thing correlation analysis is generally used to know the correlation between two variables. We have published two articles on how to do correlation analysis in Excel and Minitab (both links are given below). But here in this tutorial, we will explain to you the Interpretation of correlation results with industrial examples.

How to do correlation analysis in excel? Step by step guide.

Correlation analysis in Minitab? Step by step guide.

Correlation Analysis Example and Interpretation of Result:

We have explained here both positive and negative correlation analyses with industrial examples. Details are given below.

Example-1:

The Forging force has been applied in the billet at four different stages, as you can see in the above figure. At every stage, there is a reduction of height per stroke of the billet. The original height of the billet is 140.0mm. The details data for every stage is mentioned in the below table.

Forging Force in “N”	Reduction of height/Stroke of Billet in “mm”
500	6.5
750	19.5
1000	30
1250	36

We are going to analyze the correlation test of the above two variables i.e. “forging force vs reduction of the height of billet” by two different methods [1] Analysis by Excel [2] analysis by Minitab.

Note that in both the methods, the correlation coefficient value is 0.98; it means the value lies between 0.91 to 1.0, which indicates us there is a perfect positive correlation between the two variables.

Example-2:

This example is part of Example-1, here we are going to analyze the correlation between the forging force applied and the height of the billet after each stroke in two different methods (both in Excel and Minitab). A Data table is given below;

Forging Force in “N”	Height of Billet after stroke
500	133.5
750	120.5
1000	110
1250	104

Note that in both methods, the correlation coefficient value is -0.98; it means the value lies from -0.91 to -1.0, which indicates us there is a perfect negative correlation between the two variables.

Example-3:

A company ABC Ltd has advertised its product for seven months at different frequencies and respectively collected the sales volume to know the effectiveness of advertisement against sales quantity. Details data is given below;

Advertisement frequency/month in days of last 7 months	Sales Quantity
10	10000
8	30000
15	72000
20	55000
6	15000
9	80000
11	15000

Note that in both the methods, the correlation coefficient value is 0.44; it means the value lies in 0.00 to 0.5 (refer the Table-A), which indicates us there is a low positive correlation between the two variables.

Interpretation of Correlation coefficient (r):

Correlation Coefficient (r )	Interpretation
r=0.5	Low positive correlation
r=0.9	High positive correlation
r=1	Perfect positive correlation
r=0	No correlation
r= -0.5	Low negative correlation
r= -0.9	High negative correlation
r= -1	Perfect negative correlation

Table-A

How to Calculate Correlation Coefficient (r) | Correlation Coefficient Formula

22/03/2024 by TECHIEQUALITY

How to Calculate Correlation Coefficient (r) |Correlation Coefficient Formula

Hi readers! Today we will discuss How to Calculate Correlation Coefficient (r)? Basically coefficient of correlation gives an idea about the nature of the correlation between two variables, i.e. No correlation, positive correlation, and negative correlation. A detailed interpretation of the coefficient of correlation is given in Table-A. We have published three similar articles on correlation analysis and links of the individuals mentioned below. But here we will merely explain the calculation part of the correlation coefficient with an example.

How to do correlation analysis in excel?

Step by step guide of Correlation analysis in Minitab.

Correlation Analysis Examples and its Interpretation.

How to Calculate Correlation Coefficient (r) |Correlation Coefficient Formula:

Let’s consider a manufacturing-related example to calculate the correlation coefficient (r). The process engineer has applied the Forging force in the billet at four different stages, as you can see in the above figure. At every stage, there is a reduction of height per stroke of the billet. The original height of the billet is 140.0mm. The details data for every stage is mentioned in the below table.

Forging Force in “N” (X)	Reduction of height/Stroke of Billet in “mm” (Y)
500	6.2
750	19
1000	29.5
1250	36
1500	39.5

From the above data table, we are going to calculate the correlation coefficient (r). And we will verify the manual calculation of the “r” value against the value calculated by Minitab and Excel.

Correlation Coefficient Formula:

Calculation of Correlation Coefficient “r”:

Data table:

Forging Force in “N” (X)	Reduction of height/Stroke of Billet in “mm” (Y)
500	6.2
750	19
1000	29.5
1250	36
1500	39.5

The standard deviation of “X”:

Forging Force in “N” (X)	X²
500	250000
750	562500
1000	1000000
1250	1562500
1500	2250000

Σ(X²) =	5625000

(ΣX) =	5000
(ΣX)² =	25000000
(ΣX)²/n =	5000000
n-1 =	4
Σ(X²)-(ΣX)²/n =	625000
[{Σ(X²)-(ΣX)²/n}/(n-1)] =	156250
Square root of [{Σ(X²)-(ΣX)²/n}/(n-1)] =	395.2847075

The standard deviation of “Y”:

Reduction of height/Stroke of Billet in “mm” (Y)	Y²
6.2	38.44
19	361
29.5	870.25
36	1296
39.5	1560.25

Σ(X²) =	4125.94

(ΣX) =	130.2
(ΣX)² =	16952.04
(ΣX)²/n =	3390.408
n-1=	4
Σ(X²)-(ΣX)²/n =	735.532
[{Σ(X²)-(ΣX)²/n}/(n-1)] =	183.883
Square root of [{Σ(X²)-(ΣX)²/n}/(n-1)] =	13.5603466

Correlation Coefficient “r”:

Forging Force in “N” (X)	Reduction of height/Stroke of Billet in “mm” (Y)	X-X̅	Y-Y̅	(X-X̅) * (Y-Y̅)
500	6.2	-500	-19.84	9920
750	19	-250	-7.04	1760
1000	29.5	0	3.46	0
1250	36	250	9.96	2490
1500	39.5	500	13.46	6730

X̅ = 1000	Y̅ = 26.04
Σ(X-X̅) (Y-Y̅) =	20900
(Sx) * (Sy) =	5360.197641
(n-1) ((Sx) (Sy)) =	21440.79056
r=	0.9748

Similarly, we have done the correlation analysis in excel and Minitab and found the value of the Correlation coefficient is 0.9748.

Interpretation of Correlation coefficient (r), Table-A:

Correlation Coefficient (r )	Interpretation
r=0.5	Low positive correlation
r=0.9	High positive correlation
r=1	Perfect positive correlation
r=0	No correlation
r= -0.5	Low negative correlation
r= -0.9	High negative correlation
r= -1	Perfect negative correlation

Summary of the Above Example:

From the above example, we found the value of “r” (Correlation coefficient) 0.975, which means there is a perfect positive correlation between the two variables.

When and How to Apply Correlation Analysis Tool in Manufacturing Industries?

We are always trying to share our own manufacturing experience so that our readers can easily understand, and learn the concept and can apply it in the manufacturing process to solve the problem. we think the calculation part is clear to all and the next, part we’re going to explain is the application of the Correlation tool. Always keep in your mind that correlation analysis can be applicable only between two variables. it’s a very useful tool generally used in industry to resolve the quality-related problems.

let’s say for example, you have 10 no cast iron blocks in the same shape and size, each and every block has a different percentage of “Mn”, but other compositions are the same in all the blocks. All blocks were tested to know the hardness. After getting the result we analyze both the variables by applying the Correlation tool to know whether block hardness is varied w.r.t Mn % or not. it means whether is there any relation between variables or not. Similarly, you can apply this tool between two quality-related factors like cause and effect to know whether significant relation or in-signification relation between cause and effect. so that you take the action plan to fix-up the problem.

Also Read…

How to Plot Scatter Diagram in Excel? |Guides with example | Interpretation

Scatter Diagram Template |Industrial Example |Download Excel Format

How to do data analysis by excel sheet? Step by step guides

15/03/2024 by TECHIEQUALITY

How to do data analysis by excel sheet? i| Step by step guides

Hi Readers! Today we will discuss an important topic i.e. How to do data analysis by excel sheet. Data analysis is very important and it helps to make the correct decision on fact basis. While data analysis we generally use many tools/ techniques like 7QC Tools (Pareto chart, fishbone diagram, histogram, scatter diagram, control chart, checklist, diagram & PFC) and Statistical tools, etc. Basically, we use several platforms to execute the test or plot the graph to know the interpretation of results. But with the help of a simple Excel sheet you can analyze your data by doing advanced tests like Regression analysis, Correlation, ANOVA, Z-test, etc. but you can’t get the option of those tests directly in your Excel sheet, for getting the option you have to install the data analysis tools in your excel sheet, so we will aid you through this article for easy installation of analysis tools.

DOWNLOAD:-Several Data Analysis Tools

Below are the step-by-step installation processes of Data Analysis Tools:

Step-1:

We cannot find the Data Analysis option in excel sheet because this tool is not pre-installed. You have to install it manually, so open your excel sheet and then click on the Office Button, and then follow the step-2 mentioned in the below figure.

Step-2:

Now select the Add-Ins” option as shown in the below figure, next select Analysis ToolPak, and then follow the step-5 of below figure to install the Analysis Tool Pack.

Step-3:

You have successfully installed the Data Analysis Tools Pack, and now you are ready to use the Data Analysis options. Just go to the Data section in excel sheet for usages of Analysis Tools.

For doing individual tests like correlation, regression analysis, ANOVA, Z-test, etc, open the data analysis option and select the data as per individual test instructions.

FAQ:

How to organize data in excel for analysis?

Ans.: it depends on which data analysis tools you are going to use, for example, if are going to do the ANOVA-single factor test then the selection criteria and data arrangement will be different than other tests like paired comparison, 2P test, etc.

What are the common data analysis tools used in manufacturing industries?

There are many common tools used in the manufacturing industry but some are 7QC tools, why-why analysis, 5 Core tools, hypothesis testing tools, etc. Directly you can also use the data analysis tool in excel which is given in the “Data” section in excel as Data Analysis. just you have to install the same by following the above process. so follow the above steps and use it for data analysis. you can also use the excel function for data analysis, it purely depends on the data type, nature of the test, etc.

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Scatter Diagram Template | Industrial Example | Download Excel Format

07/04/202615/03/2024 by TECHIEQUALITY

Industrial Example of Scatter Diagram | Interpretation of result | Scatter Diagram Template:

Hi readers! Today we will discuss on Scatter diagram Example with the interpretation of its results. The scatter diagram is one of the popular tools of 7QC tools. It’s a type of diagram to displays the value for typically two continuous variables for a set of data. One variable can be positioned on the X-axis and another variable can be positioned on the Y-axis. This diagram will help you to find out the significant causes among the total collection of potential causes. When you have variable types of data collection of potential causes and you do not know the positive or negative relationship that time you can plot the scatter diagram to know the relationship among them. You can download our simple Excel Scatter Diagram Template from the below link.

DOWNLOAD– Sample Scatter Diagram Excel Template/ Format.

Example:

An organization has tried to know the significant causes for the high compressive strength of “X” quantity sand, so initially quality engineer drew the cause& effect diagram with the help of CFT team members and then he started the validation of each potential cause. The same C&F diagram is mentioned below.

Here, we have not mentioned the other potential causes like mixing time, water%, etc. because these are already validated but now we have to know the relationship among the two variables as additive quantity v/s compressive strength through a scatter diagram. To do so data has to be collected and then a scatter diagram needs to be drawn.

Data table:

Additives in Kg.	Compressive Strength (gm/cm²)
2.5	1245
3.5	1290
5	1330
6.5	1395
7.5	1435

Scatter Diagram:

Interpretation of result:

The above scatter diagram indicates us there is a perfect positive correlation between two variables i.e. Additives in Kg. vs. Compressive Strength (gm/cm²). So we can conclude that more the additive addition can result in high compressive strength.

Interpolation: you can guess the value from the set of data points. From the above graph, I would like to know the compressive strength if I will add 5.5 Kg additives in “X” Kg of Sand.

FAQ:

Q1: What are the common possibilities of correlation between two variables of the scatter diagram?

Ans.: There are so many possibilities but three common correlations are positive, negative, and no correlation. Positive and Negative correlations are further categorized into three types as.

Positive Correlation:

Low positive correlation
High positive correlation
Perfect positive correlation

Negative Correlation:

Low negative correlation
High negative correlation
Perfect negative correlation

Q2: What types of data are used to plot the scatter diagram?

Ans.: Continuous variable type data.

Useful Articles:

Types of Fishbone Diagram |Dispersion Analysis |Enumeration |Process Classification

7 QC Tools Template.

Repeatability vs Reproducibility | Discussion of Key difference.

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How to Plot Scatter Diagram in Excel |Guides with example | Interpretation:

Hi reader! Today we will discuss on How to Plot Scatter Diagram in Excel? The scatter diagram is a type of tool that is generally used to know the correlation between two variables. We have published a separate post on the concept of scatter diagrams with industrial examples and interpretation of results, if you are interested in knowing the concept before reading this article then you may read more, the link is provided below.

Scatter Diagram Template |Industrial Example |Download Excel Format.

How to Plot Scatter Diagram in Excel?

We would like to explain it with examples.

Example:

A class teacher tried to survey their top five students’ marks obtained in % v/s study hours. Details of the data are given below.

Variable-1	Variable-2
Study hours	Mark obtained in %
5	40
6	50
7	65
8	82
10	89

Now class teacher has drawn the scatter diagram to know the correlation between variables. Step by step guide is mentioned below;

Step-1:

Open the excel sheet and make a table.

Step-2:

Select the table to plot the diagram.

Step-3:

Choose the pattern of the Scatter diagram as you wish, To do so first go to the option “Insert” and then select the scatter diagram option with your preferred pattern. Details are mentioned in the below figure.

Step-4:

Now, your Scatter Diagram is ready.

Interpretation of the above scatter diagram:

All sample points are nearer to the trend line and in a positive direction. So the scatter diagram indicates us there is a perfect positive correlation between two variables. Read more…to know more about common possibilities of correlation.

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DOWNLOAD-PDF files of AIAG-VDA FMEA key changes.

What are the requirements of IATF 16949 for FMEA application?

8.3.5.1 (IATF Sub-clause): This sub-clause talks about the design risk analysis. DFMEA is the mandatory requirement for design and development output.

8.7.1.4 (IATF Sub-clause): The organization shall identify the risks related to the product rework process in FMEA.

10.2.3 (IATF Sub-clause): As and when problems arise in the process, accordingly review and updation of PFMEA are required.

Key Changes of AIAG-VDA PFMEA, 1st edition 2019:

7 Steps PFMEA Approach:

Planning & Preparation:

Structure Analysis:

Function Analysis:

Failure Analysis:

Risk Analysis:

Optimization:

Result Documentation / Risk Communication:

7 Steps to Implement the New AIAG-VDA PFMEA in Industry?

Step-1:

Step-2:

Step-3:

Step-4:

Step-5:

Step-6:

Step-7:

FAQ:

Example:

NEW AIAG VDA FMEA AP TABLE EXAMPLE

Simple Moving Average Formula | Calculation | Excel Template | Example:

Simple Moving Average Formula (SMA):

How to calculate simple moving average forecast value?

Answer:

Forecast for December using the 3-month SMA method:

The past 5-month sales values from July to November are 164, 173, 168, 174 & 169 (See the above table).

Forecast for December using the 7-month SMA method (May to November):

How to calculate different types of SMA errors?

There is no negative error value, so no need to calculate the absolute value, else calculate the absolute value.

How to calculate an SMA forecast in an Excel sheet using a data analysis option?

How to calculate Moving Average forecast in an excel sheet using function option?

Gage R and R |Attribute type MSA |How to do MSA Study | Acceptance Criteria:

Measurement System Analysis (Variable type MSA):

Attribute type MSA:

Below are some factors that may cause human error but these are not limited;

How to do the Variable type MSA (Gauge repeatability & reproducibility)?

How to do the attribute type MSA?

Interpretation of result:

General Acceptance Criteria of Gage R and R (Variable type MSA):

General Acceptance Criteria of Attribute type MSA:

Correlation analysis in excel | 3 best method |step by step guide with example

Processes of Correlation analysis in excel:

Method -1;

Step-2:

Step-3:

Step-4:

Step-5:

Method-2;

Step-2:

Step-3:

Method-3;

Step-2:

Step-3:

Interpretation of Correlation coefficient (r):

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Correlation Analysis in Minitab |Step by step guide with example

Correlation Analysis in Minitab (Step-by-Step guides):

Step-1:

Step-2:

Step-3:

Step-4:

Step-5:

Interpretation of Correlation coefficient (r):

Correlation Analysis Example and Interpretation of Result

Correlation Analysis Example and Interpretation of Result:

Example-1:

Example-2:

Example-3:

Interpretation of Correlation coefficient (r):

How to Calculate Correlation Coefficient (r) |Correlation Coefficient Formula

How to Calculate Correlation Coefficient (r) |Correlation Coefficient Formula:

Correlation Coefficient Formula:

Calculation of Correlation Coefficient “r”:

The standard deviation of “X”:

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Key Changes of AIAG-VDA PFMEA, 1^st edition 2019: