IE 673 Total Quality Management

 Process Control & Control Charts

The assignment was completed by Abhishek Patil with the use of resources from 3D e-book: chapter 

10, 11, 12 and the control chart templates created by Paul G. Ranky.

Introduction and Objectives of Project

A&P eco-MHE is a private company which manufactures automated Omni – directional forklifts. We 

provide a wide range of customization and material handling solutions to customers globally. The 

company uses mecanum wheels to give Omni directional mobility to a lift truck. This allows normal 

forward and backward motions like the conventional wheel, but also provides sideways motion and 

rotation about it center of gravity. The parts used in the assembly of each forklift are 

interchangeable. Our cutting edge technology ensures high efficiency and quality by obtaining high 

mobile accuracy and sustainable engineering. We also provide onsite training sessions for 

operating the forklifts along with maintenance service.

The main objective of this project is to analyze data, process variables and attributes to determine if 

the process is in control and if not take corrective steps in order to obtain control over process and 

quality as an output. The control charts will be used to analyze the data collected for variables and 

attributes in order to maintain the customer specified quality through process control. The main 

objective of A&P Eco – Material handling Equipments will be to reduce the variations in order to 

improve the process and lower the waste and cost. The aim will be to maintain the process 

parameters along with the quality within a certain range of acceptable standards in order to obtain 

operational efficiency and the customer defined quality. Our main aim will be to establish process 

sustainability through process control with the help of control charts.

The control charts represents deviations from the mean and spread of measured values graphically. 

Control charts can be used for hypothesis testing for quality checks and make accept or reject 

decisions. The important feature of the control charts is that effects of several factors on the 

process can be obtained in a graphical representation which is easy to interpret visually (by the use 

of MINITAB).  

    

Description of Methodologies Applied

A&P Eco-MHE concentrates on the aim of quality in order to obtain customer satisfaction along with 

the functional requirements defined for the product. The quality of the product is mainly driven by 

manufacturing process, thus process control becomes the main aim of the company. The out of 

control processes are effectively managed by the use of control charts Beta Version provided by 

Prof. Paul Ranky. This procedure helps reduce the variations. Control charts are plotted for 

variables which are measured in quantitative units and the attributes which are measured as count. 

The control charts are used in A&P Eco-MHE on a large scale to observe the efficiency of machines 

to reduce the defects in batch system. By continuous monitoring the process, we can ensure 

reduced variability in the products by confirmation to the specifications. A data sample is created to 

determine defects and the effects of critical factors on the process and thus the source of variation 

can also be determined.

Thus the statistical control charts are the continuous monitoring tool, which monitors the entire 

process and maintains the process in control by eliminating the variations. The plotted values in the 

control charts help to evaluate the mean value for the sample as well as the standard deviation 

(variation). The mean value is used to plot the X bar charts and the standard deviation is used to 

plot the R chart.

Variable control Charts

A&P Eco-MHE uses variable control charts to focus on production quality.

X bar chart: It is a control chart for variation of the mean value of characteristic. The X bar 

                     chart is developed form the range of each subgroup data.        

R chart: R chart is developed from the ranges of each subgroup data, which is calculated  

               by subtracting the maximum and minimum value in each subgroup.

X bar and R charts are used for those processes that have high sample size (more than 30).

Attribute Control Charts

These charts control quality utilizing either mean or range of unit sets. They have a purpose to 

control variability and mean. P-chart, C-chart and U-chart are the attribute control charts.

P chart: This control chart shows the fraction of non-conforming or defective product produced by 

               manufacturing process.

C chart: This chart shows the number of defects or non-conforming products produced by the 

               manufacturing process.

U chart: This chart shows the non-conformity per unit produced by the manufacturing process.

              Main Body of the Project

A&P Eco-MHE will focus on the manufacturing of the mecanum wheels for the forklifts. The 

mecanum wheel is a critical part of the forklift as all the movements of the forklift and the 

smoothness of the movement on the shop floor depends upon the precision of mecanum wheel.

Following diagram gives an overview of the mecanum wheel assembly.

Variable Control Charts (X chart & R chart)

There are several ways to identify if the process is Out-Of-Control:

-    One or more points are outside of the control limits.

-    Two or three consecutive points are outside the two-sigma warning limits.

-    Four or five consecutive points are beyond the one-sigma limits.

-    A run of 8 consecutive points are on one side of the center line.

The design specifies that the outer diameter of the central hub should be 435mm (core diameter). 

We will analyze if the samples we selected are between control limits with the use of X chart and R 

chart.

Control Chart for Variable

Out of Control

 

Analysis of Control Chart for Variable

Here the area of focus is a variable control which is a single quality characteristic dimension. 

As we are dealing with a variable control we monitor both the mean value of the quality 

characteristic and the variability. For testing the outer diameter of the center hub we pick 5 

different sample sizes a day and then run it for 25 days. When setting up X-bar and R control 

charts, it is best to begin with the R chart. Because the control limits on the X-bar chart depend 

on the process variability, unless process variability is in control.

According to the original specifications provided the outer diameter should be 436mm plus or minus

3mm. The analysis for 25 samples for 5days done is showed in the spreadsheet attached below. 

Analysis of R-chart shows the values are within control limits and hence process is in control. But 

analysis of X-chart shows that one point (i.e. point 14) is above the upper control limit and hence the 

process is out of control. Therefore the process has to be checked and made sure that it is in control. 

The chart shows the variable data collected from our manufacturing process of central hub. 

Reasons:

There are 3 possible reasons for such an error to occur and a combination of these reasons:

1.    Tool: The tool has worn out or the tool wasn’t mounted properly

2.    Machining error: The personnel gave wrong cutting parameters while machining. 

 

Solution: 

The measuring instruments were checked for errors and were found to be accurate.

There was no chance that the parameters fed into the machine were wrong as the cutting is done on 

a CNC machines and in batches with an automatic feed system. So if an error would have occurred 

due to wrong machining parameters, errors would have been caused in all the jobs. So the 

machining error rules out. 

The tools were tested under several work loads and it was found that the cutting edge of the tool 

underwent deformation after completion of specific number of jobs. QCT supplies single point cutting 

tools to A&P Eco-MHE. QCT found out that the tools used had inserts did not have sufficient wear 

resistance. QCT provided tungsten carbide inserts which were more stable under the heat produced 

during machining.  

Thus Quality carbide tools developed new set of tools which were featured by:

1.    Higher cutting speeds

2.    Larger depth of cuts

3.    High resistance to abrasion

4.    Hardness of HRC 93

Control Chart for Variable

In Control

 Analysis of Control Chart for Variable (In Control)

As it can be seen that both the charts satisfies the tolerance criteria and no point  on the chart are 

out of limits and thus we can conclude that the process is in control. Analyzing the control charts for 

variable is a significant step towards quality assurance as our main motive is customer satisfaction. 

By using this, the company can invariably catch any defects in the lots and can take the corrective 

steps as needed.

As can be seen on the R chart and the X bar Chart, there are no points past the control limits. There 

are no seven consecutive points above or below the central lines (R-bar and the X-bar). And there 

are no seven consecutive points in an increasing or decreasing trend. This tells us that the R and the 

X bar processes are in control.

Thus use of tungsten carbide inserts have increased the wear resistance of the single point cutting 

tool. Hence the process was brought under control. Note that Use of tungsten carbide inserts came 

with a added benefit. As these tungsten carbide allowed larger depth of cuts the machining time 

reduced.

Attribute Control Charts (C chart & U chart)

Some of the quality characteristics cannot be conveniently represented numerically, therefore, we 

will use "defective" or "non-defective" to identify our quality characteristics. The fraction 

nonconforming control charts has three parameters that must be specified: the sample size, the 

frequency of sampling, and the width of the control limits.

C chart

Analysis of Control Chart for Attribute (C chart)

C-chart is a type of control chart used to monitor “count”-type data, typically total number of

non conformities per unit. Poisson distribution is the basis for the c-chart. It not only tracks the 

number of non conformities, but also the type or location and therefore could provide vital information 

on how to pin point the assignable cause.

The company makes use of the C-chart to detect and keep a close eye on the surface defects of the 

rollers. As the rollers are made of soft rubber the surface of the rollers are to be inspected for surface 

irregularities restricted to visibility to eye. The surface of the rollers play an crucial role in the smooth 

mobility of the whole forklift and hence in the customer satisfaction.  

From this we calculated the average number of non-conforming units, Upper Control Limit (UCL) and 

Lower Control Limit (LCL) of the 30 samples. After plotting the average data of the 30 samples, when 

the analysis of the process was done, it was found that the points are within the control limits and 

there were no points outlying in non-conforming parts and also there were no 7 consecutive points 

over or below the bar values so over all the process was classified as an in control process.  

Thus to ensure that customer does not receive any item that is defective we ensure that all the 

rollers have surface irregularities within tolerance total customer satisfaction.

U Chart

Analysis of Control Chart for Attribute (U chart)

The u- chart is often used in quality control if the sample size is not a constant, but a variable. It plots 

the number of nonconformity per unit tested.

The collaborating companies supply us with certain products such as cutting tools in mass. The 

quality of these cutting tools are critical as quality of all the cutting processes depend upon the 

quality of the tools. 

It can be seen that the upper control limit and the lower control limit is varying and is not at a 

constant value. The values are almost evenly distributed above and below the central line. The 

spreadsheet attached below shows that all the points are within the control limits and hence the 

process is in control.  In the above process, there were no points outlying in non-conforming parts 

and also there were no 7 consecutive points over or below the bar values so over all the process 

was classified as an in control process.     

     

Collaboration

With the process control analysis we A&P Eco-MHE can understand the process defects in details 

and how engineering functions can be applied in order to satisfy the customer requirements with the 

help of engineering solutions. A& P Eco-MHE had incorporated all the decisions and worked very 

closely with the other four companies on Process control chart for continuous process improvement 

and process analysis purpose. Our collaboration with the other four companies involves getting 

solutions with latest process technology for our manufacturing process. They also provide us with 

more quality consultants and advocate the latest TQM techniques. It includes modules for all the 

phases of manufacturing process thus helping designers and manufactures with the exact data and 

to implement the corrective steps, keeping the process in control. The Numina group will help us 

maintain the product database. The products will be easily tracked back from suppliers customers 

can get quick response and it will also help the company to make alterations to fix the process 

defects.   

The process control analysis will also help implement and trigger changes in order to obtain 

continuous improvements in order to meet the company goals to attain customer defined quality and 

functional requirements with an emphasis on sustainable green focus. 

Summary 

Utilization of process control chart by well trained professionals can prevent organization from 

disaster and give a better future improvement plan. Statistical process control charts help to 

establish mistake proof processes, develop upstream process controls, contribute to process and 

product redesign, and as a result, help to monitor, and keep the processes under continuous 

control. A process out-of-control must always be investigated properly. The quality team must 

identify the sources of variation, and corrective action must be taken to bring the process back 

within the control limits.

Our initial control chart shows that system progress is out of control and quality of central hub was 

improved by improvements in the process and the customers will be satisfied through smooth 

movement of the forklift with added accuracy.

  

References:

1.    Paul G. Ranky, IE673 Total Quality Management eLearning Pack ID: IE673-Spring 2012-95-66

2.    http://www.cimwareukandusa.com/All-Green/Ranky-GreenEngineering.html

3.    http://www.cimwareukandusa.com/CIMpgr1.html. 

4.    http://www.qctc.com/

5.    http://www.boschrexroth.com

6.    http://www.numinagroup.com/

  7.   http://www.sunnen.com/