Afolawemi Afolabi

Assignment A3: Process Improvement and Process Control Analysis, and a Quantitative, Computational Solution: Control Charts for Variables and Attributes.

The main business of Fola Quaity Plastic Toys is to produce the Plastic toys that satisfy all the functional requirements which enables 100% customer Satisfaction. In order to achieve this Goal we employed the CORA (Customer Oriented Analysis) method which enables us to grade the main factors and execute them in characteristic graphs so that we can improve the processes with respect to all factors and customer requirements and enable 100% customer satisfaction. This gives the collaborative companies better idea and knowledge of the existing plastic toys, one example is Legos. Considering the competitive market in today’s world my company will concentrate on the dimensions of quality such as the features, durability and performance which will be excellent and a raw model for other existing companies present in market.

According to encyclopedia .com, most of the chain of big and giant supermarkets like Wal-Mart, Shoprite and Kmart sell different kind of products with different brand names. Apart from these products, they also sell their own manufactured products. So in order to keep and maintain their own products as well as the different products, they have to keep all of them in a warehouse. This is a vital issue because the most important and complicated part is to maintain and manage the warehouse. To know where the particular product is and how many of them are there, it is pretty much problematical. We provide a well-managed warehouse with efficient inventory management which provides cost savings, planning tools and satisfied customers. This level of quality is achieved through a modern Total Quality Management approach (TQM) of preventing problems before they develop.

Fola Quality Plastic Toys focuses on exceeding customer's needs through providing outstanding customer service and reliability to ensure that the warehouse has the items that the customer has requested.

Control charts are effective continuous quality improvement tools. The data which is obtained from the process through Control Charts will be compared to the product specifications to measure the variations in the process that is taking place. There is a pre-specified limit for the component within or outside the upper or lower limit being specified. The chart in it indicates zones where the data will show a slight area to improve the process. The Control chart can therefore be interpreted as an on-line tool used for 1.Assuring that the process does not go out of control.2.Securing the elimination of the variability in the process.

In addition to these, a control chart is a specific kind of run chart that allows significant change to be differentiated from the natural variability of the process. This is key to effective process control and improvement. The control chart is one of the seven basic tools of quality control along with the histogram, Pareto chart, check sheet, cause-and-effect diagram, flowchart, and scatter diagram.

There are two types of measurement which you can measure and plot on a Control Chart.

· Variables answer the question ‘how much?’ and are measured in quantitative units, for example weight, voltage or time.

· Attributes answer the question ‘how many?’ and are measured as a count, for example the number of defects in a batch of products.

When you are measuring variables, there are three types of Control Chart that you can use (X/MR, X-bar/R and X-bar/S). This decision is based on the number of measurements that you make and consequently how many measurements you can combine into a single point (subgroup). Variables charts are useful for such as measuring machine tool wear and predicting when the tool needs changing before it creates defective products. Variables charts are more sensitive to change than Attributes charts, but can be more difficult both in the identification of what to measure and also in the actual measurement.

A different attribute Control Chart is needed depending both on whether you are counting the number of defects per item or whether you are just counting total defects. Thus, for example, a production line might output 100 televisions, with 100 defects. This could mean that they are all defective or only one television is defective (or anything in between). The right attribute chart is also selected based on the whether there is constant number of measurements in each point (subgroup) on the chart. Thus there are four types of attribute chart to choose from (u, c, p and np).

Attribute charts are useful for both machine- and people-based processes. Data for them is often readily available and they are easily understood. It can thus be easier to start with these, then move on to Variables charts for more detailed analysis.

Statistical methods were used to set the upper class limit (UCL) and the lower class limit (LCL), Thereafter, we ran the process that is short of control and stability to estimate the expected inconsistency. We used control chart for variables and the control chart for attributes, also the P-chart with a constant sample size and the U-chart with a variable sample size. The formula is given as

Variable Control Charts: It focuses on production in which the quality characteristic can be measured. The goal is to control the mean and variability of the process.

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

Attribute Control Charts: It focuses on the quality characteristics. The measurement of this chart is numerical. The purpose is to control variability and mean.

Control charts are for monitoring and controlling the processes. The X-bar and R values for plotting the graphs tell us whether there are any variations in our process or not. If there are points that are out of the control limits or seven consecutive points above or below the central line, those points have to be rejected to plot new charts. This method need to be repeated until charts without any patterns of out-of-control process is obtained. If there are no points are out of the control limits or seven consecutive points above or below the central line this means that the process is statistically under control.

For the purpose of this exercise we chose three separate methods to measure the same process. The control charts for variables and for the control charts for attributes, the P-chart with a constant sample size and U-chart with a variable sample size for the Network Delay for the Game Engine

ControlChartforVariables

UChart

PControlChart

Three samples were taken a day over 20 days The samples were monitored and measured and the data was entered into an excel spreadsheet. In analyzing the plots and charts it was realized that the samples 15, 16 and 17 were beyond the control limits. An investigation took place and it was realized that for those Three days there was a Manufacturing Fault that caused more delay. In order to maintain control over the process in the future it was decided to check the working of Machines more strenuously before adding them to the line. Once the cause was determined these points were rejected and the calculations were redone. The Xbar Chart revealed that there were no points out of control, nor 7 consecutives points above or below the central line. Therefore Fola Quality Plastic Toys determined that this process was under control. In looking at the P where 12 samples within 150 users were looked at and U charts where 15 sample within n units we see that both charts show the process as being in control as well. If these charts showed that the process was not in control the same steps as that used for the control chart for variables would be taken. The cause would be investigated and any necessary actions would take place.

After focusing on the X-Bar chart, we found that there are no points out of the control limits (UCL and LCL) and also no consecutive seven points were found out of the central line. same was found for the R-Chart. No point were out of the control limits and no consecutive seven points were out of the central line.

Process analysis was performed using control charts to determine if any action needed to be taken to bring the process into control. The point of control is to use a chart/method that will accurately measure the inventory management of the warehouse. This method will help brings us into control and as a result provide unsurpassed customer satisfaction.In the situation of Fola Quality Plastic Toys the control method employed (control charts) observed a variation that put the process out of control. Fola Quality Plastic Toys was able to graphically see this and perform immediate preventive action. This method will help to produce Plastic Toys to the ultimate goal of superior quality.

For the continuous improvement, continuous monitoring is required, therefore calculations should be done for all of the requirements to find the changes in the process and prevent defects from taking place by identifying and eliminating the variations that are causing.

Paul G. Ranky, IE673 Total Quality Management, Fall 2008, E-Learning Pack ID # IE673-Fall 2008-70-18

	Central line		Control Limit
	Definition	Formula	Upper limit	Lower limit
X_bar- chart	Average samples of quality/unit tested per sample	X_bar=∑Xj/m	UCLx=Xbar+A2R_bar	LCLx=Xbar-A2R_bar
R- chart	Average sample on range/sample units	R_bar =∑R/m	UCLr = D4 R_bar	LCLr=D3 R_bar