Statistical Process Control. The business, commercial and public-sector world has changed dramatically since John Oakland wrote the first edition of Statistical Process Control — a practical guide in the mid-eighties. Statistical Process Control for the Food Industry. A comprehensive treatment for implementing Statistical Process Control SPC in the food industry This book provides managers, engineers, and practitioners with an overview of necessary and relevant tools of Statistical Process Control, a roadmap for their implementation, the importance of engagement and teamwork, SPC leadership, success factors of the readiness.
Mastering Statistical Process Control. Mastering Statistical Process Control shows how to understand business or process performance more clearly and more effectively. This practical book is based on a rich and varied selection of case studies from across industry and commerce, including material from the manufacturing, extractive and service sectors. It will enable readers to. One good lowchart can replace pages of words.
More info here. Because of the way they visually layout the sequence of process steps, these can be useful in training employees to perform the process according to standardized procedures. Once you break down the process steps and diagrams, problem areas become more visible. It is easy to spot opportunities for simplifying and reining your process by analyzing decision points, redundant steps and rework loops.
When you are developing a Flowchart, consider how it will be used and the amount and kind of information needed by the people who will use it.
Generally, a macro-level Flowchart has fewer than six steps. Mini Level. Typically, it focuses on only a part of the macro- level Flowchart. Using the airplane analogy, you see the level of detail as if looking at the ground from 10, feet.
Micro Level. People trying to improve the way a job is done need a detailed depiction of process steps. It is commonly used to chart how a particular task is performed. Besides the three levels of detail used to categorize Flowcharts, there are three main types of Flowcharts — Linear, Deployment, and Opportunity.
A Linear Flowchart Figure 2. Deployment Flowchart. A Deployment Flowchart shows the actual process low and identiies the people or groups involved at each step Figure 2. Horizontal lines deine customer-supplier relationships. Opportunity Flowchart. An Opportunity Flowchart — a variation of the basic linear type — diferentiates process activities that add value from those that add cost only Figure 2. In other words, the output cannot be produced without them. For example, end-of-process inspection might be instituted because of defects, errors, or omissions that occurred in the past.
Other CAO steps may depend on actions in supplier processes — waiting for approvals or the availability of equipment, for example. If your team is not sure about a step, mark it to be investigated later. Sometimes, it is easier to start with the last step and work back to the irst step. To construct an Opportunity Flowchart, you need to distinguish value-added from cost-added-only steps. You may want to review how to diferentiate these steps under the description of Opportunity Flowcharts that precedes this discussion.
A Flowchart will help you understand your process and discover ways to improve it only if and only if you use it to analyze what is happening.
By streamlining a business process only means inding ways to be more productive while maintaining cost efectiveness. Another illustration is when you have three 3 processes, Process 1 can produce units, Process 2 can accommodate 90 units and the last process, Process 3, can work on units.
It can be observed that there is a lack of capacity in Process 2 that can consequently bring delays in the three process activity. Weak links. Steps which are not well-deined and oriented may be interpreted, perceived and performed in a diferent way by each person involved, leading to process variation.
Cost-added-only steps. Such steps add no value to the output of the process and should be subjected for elimination. If the answers in any decision point go one way rather than the other, you may opt to remove the decision point. Processes with numerous checks generate rework and waste. Examine the activities preceding the rework loop and identify those that need to be improved. Minimize the loop of rework and if possible, eliminate the loop.
If the activities do not build value, eliminate those. Paired measurements are taken on each item and plotted on a standard X-Y graph. Statistical tests quantify the degree of correlation between the variables. Say, the irst variable is said to be the measurement of a part continuous in relation to the second variable, number of rejected units with those measurements discrete.
Ater plotting all paired data, you can draw a straight line in the cluster of points in order to see the possible relationship. For analysis reference, please see succeeding paragraphs. Again, it should be kept in mind that A strong correlation does not necessarily mean that one variable caused the other.
In particular, there could be a third variable that is the cause for changes in both of the plotted variables, and it is the causal relationships involving this third variable that result in a clustered pattern in the scatter diagram. Basically, there are three 3 types of correlation. If the x increases, y increases too and if x decreases, y decreases too.
Say, when x increase, the value of y decreases and when the value of x decreases, the value of y increases. To assure correct interpretation we can use the Linear Correlation Coeicient r.
It is a measure of how strong is the linear relationship between two variables. You can use the mathematical equation below to calculate r. Consequently, when there is an evident correlation coeicient calculated, we can use the x variable to predict the possible value of the y variable. He noted that the sons of very tall fathers tended to be tall, but not quite as tall as their fathers. Also, sons of very short fathers tended to be short, but not quite as short as their fathers.
He called this tendency, regression. Variation makes defects and poor quality possible — not something we want. Statistical control charts monitor and display the variation in process output and can be an important tool for product and process improvement.
Deine the key characteristic or quality characteristic to be measured. Deine where in the process the key characteristic will be measured. It should be at the earliest possible point in the manufacturing process where the characteristic can be measured. Select which control charts to use. Determine subgroup size and frequency of measurement. Take measurements. Plot measurements or summary statistics on the chart.
Connect the plot points. Ater at least 20 plot points, calculate the centerline and control limits the actual number of plot points depends upon the circumstances. Identify any out-of-control points. Analyze for special causes of variation and remove them. Remove subgroup data corresponding to any out-of-control points from the calculation of the control limits.
Add a corresponding number of plot points and recalculate the control limits using data from all in-control plot points. Extend the control limits into the future. Do not recalculate the control limits until signiicant and identiiable process changes occur. Do not change the control limits continually as new data is added. Need help with your dissertation?
Find out what you can do to improve the quality of your dissertation! Get Help Now Go to www. In selecting a control chart to be used, the irst to do is to identify whether the data to be collected and studied is variable continuous data , there are measurable data like length, weight, diameter, thickness or attribute discrete data , these are countable data such as number of defects or defective in a lot or average number of defects per unit.
When using variable data, both average and variability of the process must be monitored. With precision and accuracy of the choice made, a better understanding of the process and the sources of its variations can be strongly established.
With the use of a control charts with all data plotted on it, we can analyze the behavior of the process and determine the current condition of it thru the use of the Sensitizing Rules for Control Charts.
Whenever one of these rules had been evident in the control chart being created, we can say that the process has a higher chance to produce out of control units. One or more points outside of the control limits. Two of three consecutive points outside the two sigma warning limits still inside the control limits. Four of ive consecutive points beyond the one sigma limits. A run of eight consecutive points on one side of the center line. Six points in a row steadily increasing or decreasing.
Fiteen points in a row in Zone C both above and below the center line. Fourteen points in a row alternating up and down. Eight points in a row on both sides of the center line with none in Zone C.
An unusual or non-random pattern in the data. One or more points near a warning or control limit. In order to construct a X Bar and R Chart, we must plot averages and ranges on separate charts and adding the centerline and control limits to each part. These can be reduced dramatically thanks to our systems for on-line condition monitoring and automatic lubrication.
We help make it more economical to create cleaner, cheaper energy out of thin air. By sharing our experience, expertise, and creativity, industries can boost performance beyond expectations. Therefore we need the best employees who can meet this challenge!
Visit us at www. Remember that, there is no mathematical or statistical relationship between speciications and control limits. It also depicts the ratio of how much of the tolerance limits is used up. In Figure a, the process capability is greater than unity or 1. Any slight change or shit in the process can be absorbed by the remaining unused tolerance band.
With this situation, the number of non-conforming units will be relatively few. In Figure b, the process capability is equal to unity. For a normal distribution, it will imply about 0.
In Figure c, the process capability is greater than the unity. With this a large number of non-conforming units will be produced. For practicing professionals in the ield, this is done weekly, monthly, quarterly or in a lot by lot model such as every 25, 50 or samples.
At times, the x bar will be replaced by a target value to attain controllability. If the R chart exhibit control, this can be helpful in shiting the process average to the desired value, that is, by a fairly simple adjustment of a manipulatable variable in the process.
If the R chart has out of control points, we oten eliminate the out of control points and recompute a revised value of R bar. With this action, the limits will be tightened on both charts due to the revision made.
Compared to the R chart, this chart ofers greater precision. Subgroup sizes are greater than 1 and certainly used when subgroup sizes are 10 or larger. It is best used when opportunities to obtain data are limited, such a low production volume or testing. Sampling sizes that are greater than one simply do not apply, such as when sampling from homogenous batches, or when samples have very small short term variation or for business processes.
In this case it can be used to mean nonconformance to speciication, but it can also be used to mean that customer expectations were not met. Subgroup samples are taken from groups of items or lots. Consult a statistical text for estimating the needed subgroup size to ensure a suiciently high probability that the subgroup will contain at least one defective. It is also possible to base a control chart on the number nonconforming rather than the fraction nonconforming. A unit can be a single part, an assembly, an area of material, or any rational grouping of units in which the likelihood of defects is constant from unit to unit.
A nonconforming item is a unit of product that does not satisfy one or more of the speciications for that product. Each speciic point at which a speciication is not satisied results in a defect or nonconformity. Consequently, a nonconforming item will contain at least one nonconformity. However, depending on their nature and severity, it is quite possible for a unit to contain several nonconformities and not be classiied as nonconforming. As a rule, the average number of defects per unit or grouping should be at least ive.
Before we discuss what problem solving process is, let us irst discuss the three words encompassing the phrase. A problem may be a practical poor quality or statistical spread or centering. A solution is a deined action, set of activities or a program, that aims to address the gap. A process is an activity of converting inputs to outputs. It is also known to be a systematic approach to execute initiatives.
It is usually represented by a general set of steps applied to speciic setting. Furthermore, these are approaches for identifying a problem or an improvement initiative, discovering the root causes of a problem, identifying and evaluating solutions, implementing the solution, measuring the improvement, and ensuring holistic integration of the solution so the problem will not recur.
Once ire recurs, they will just intend to do the same ireighting as mentioned. Being reactive, this tends to be more costly as ire is contained not prevented. Every ire occurrence can cost most likely the same as the previous cost of containing it or even more. Once the ire is contained, the organization put initiative to create a structure to deploy problem analysis and there by generation solutions and choosing the best solution in order to avoid the recurring ire, counter checking efectives of the chosen solution by measuring results and standardizing the successful solution.
With this approach, the probability of ire recurrence is set to be minimized and it generates long term results that are efective and cost eicient. Figure 3. First it requires the identiication of the need for a problem solving process, that is, through actual results and comparing to the objectives.
With causes identiied, we can now devise particular solutions so that the problem identiied or improvement initiated can be addressed. We will develop at least three 3 signiicant alternatives for our solutions and will implement what is doable based on the consensus of the team.
In this case that it is not bearing what is required; a need to implement another feasible solution will be done. Improvement is a continuous process that is why we must review regularly the results of our operations thru the use of Key Performance Indexes KPI.
It is oten conceptually drawn as a wheel showing the feedback nature of the process as shown in Figure 3. Table 3.
0コメント