SPC Teory

Introduction and Background
The concepts of Statistical Process Control (SPC) were initially developed by Dr. Walter Shewhart of Bell Laboratories in the 1920's, and were expanded upon by Dr. W. Edwards Deming, who introduced SPC to Japanese industry after WWII. After early successful adoption by Japanese firms, Statistical Process Control has now been incorporated by organizations around the world as a primary tool to improve product quality by reducing process variation.
Dr. Shewhart identified two sources of process variation: Chance variation that is inherent in process, and stable over time, and Assignable, or Uncontrolled variation, which is unstable over time - the result of specific events outside the system. Dr. Deming relabeled chance variation as Common Cause variation, and assignable variation as Special Cause variation. Based on experience with many types of process data, and supported by the laws of statistics and probability, Dr. Shewhart devised control charts used to plot data over time and identify both Common Cause variation and Special Cause variation.
This tutorial provides a brief conceptual background to the practice of SPC, as well as the necessary formulas and techniques to apply it.
Process Variability
If you have reviewed the discussion of frequency distributions in the Histogram module, you will recall that many histograms will approximate a Normal Distribution, as shown below (please note that control charts do not require normally distributed data in order to work - they will work with any process distribution - we use a normal distribution in this example for ease of representation):
In order to work with any distribution, it is important to have a measure of the data dispersion, or spread. This can be expressed by the range (highest less lowest), but is better captured by the standard deviation (sigma). The standard deviation can be easily calculated from a group of numbers using many calculators, or a spreadsheet or statistics program.
Example
Why Is Dispersion So Important?
Often we focus on average values, but understanding dispersion is critical to the management of industrial processes. Consider two examples: If you put one foot in a bucket of ice water (33 degrees F) and one foot in a bucket of scalding water (127 degrees F), on average you'll feel fine (80 degrees F), but you won't actually be very comfortable! If you are asked to walk through a river and are told that the average water depth is 3 feet you might want more information. If you are then told that the range is from zero to 15 feet, you might want to re-evaluate the trip.
MoreSteam Hint: Analysis of averages should always be accompanied by analysis of the variability!
Control Limits
Statistical tables have been developed for various types of distributions that quantify the area under the curve for a given number of standard deviations from the mean (the normal distribution is shown in this example). These can be used as probability tables to calculate the odds that a given value (measurement) is part of the same group of data used to construct the histogram. Shewhart found that control limits placed at three standard deviations from the mean in either direction provide an economical tradeoff between the risk of reacting to a false signal and the risk of not reacting to a true signal - regardless the shape of the underlying process distribution. If the process has a normal distribution, 99.7% of the population is captured by the curve at three standard deviations from the mean. Stated another way, there is only a 1-99.7%, or 0.3% chance of finding a value beyond 3 standard deviations. Therefore, a measurement value beyond 3 standard deviations indicates that the process has either shifted or become unstable (more variability). The illustration below shows a normal curve for a distribution with a mean of 69, a mean less 3 standard deviations value of 63.4, and a mean plus 3 standard deviations value of 74.6. Values, or measurements, less than 63.4 or greater than 74.6 are extremely unlikely. These laws of probability are the foundation of the control chart.
Now, consider that the distribution is turned sideways, and the lines denoting the mean and ± 3 standard deviations are extended. This construction forms the basis of the Control chart. Time series data plotted on this chart can be compared to the lines, which now become control limits for the process. Comparing the plot points to the control limits allows a simple probability assessment. We know from our previous discussion that a point plotted above the upper control limit has a very low probability of coming from the same population that was used to construct the chart - this indicates that there is a Special Cause - a source of variation beyond the normal chance variation of the process.
Implementing Statistical Process Control
Deploying Statistical Process Control is a process in itself, requiring organizational commitment across functional boundaries. The flow-chart below outlines the major components of an effective SPC effort. The process steps are numbered for reference.
1. Determine Measurement Method
Statistical Process Control is based on the analysis of data, so the first step is to decide what data to collect. There are two categories of control chart distinguished by the type of data used: Variable or Attribute. Variable data comes from measurements on a continuous scale, such as: temperature, time, distance, weight. Attribute data is based on upon discrete distinctions such as good/bad, percentage defective, or number defective per hundred.
MoreSteam Hint: Use variable data whenever possible because it imparts a higher quality of information - it does not rely on sometimes arbitrary distinctions between good and bad.
2. & 3. Qualify the Measurement System
A critical but often overlooked step in the process is to qualify the measurement system. No measurement system is without measurement error. If that error exceeds an acceptable level, the data cannot be acted upon reliably. For example: a Midwest building products manufacturer found that many important measurements of its most critical processes had error in excess of 200% of the process tolerance. Using this erroneous data, the process was often adjusted in the wrong direction - adding to instability rather than reducing variability. See the Measurement Systems Analysis section of the Toolbox for additional help with this subject.
4. & 5. Initiate Data Collection and SPC Charting
Develop a sampling plan to collect data (subgroups) in a random fashion at a determined frequency. Be sure to train the data collectors in proper measurement and charting techniques. Establish subgroups following a rational subgrouping strategy so that process variation is captured BETWEEN subgroups rather than WITHIN subgroups. If process variation (e.g. from two different shifts) is captured within one subgroup, the resulting control limits will be wider, and the chart will be insensitive to process shifts. The type of chart used will be dependent upon the type of data collected as well as the subgroup size, as shown by the table below. A bar, or line, above a letter denotes the average value for that subgroup. Likewise, a double bar denotes an average of averages.
Consider the example of two subgroups, each with 5 observations. The first subgroup's values are: 3,4,5,4,4 - yielding a subgroup average of 4 (X̅1). The second subgroup has the following values: 5,4,5,6,5 - yielding an average of 5 (X̅2). The average of the two subgroup averages is (4 + 5)/2 = 4.5, which is called X double-bar (), because it is the average of the averages.
You can see examples of charts in Section 9 on Control Limits.
6. & 7. Develop and Document Reaction Plan
Each process charted should have a defined reaction plan to guide the actions to those using the chart in the event of an out-of-control or out-of-specification condition. Read Section 9 below to understand how to detect out-of-control conditions. One simple way to express the reaction plan is to create a flow chart with a reference number, and reference the flow chart on the SPC chart. Many reaction plans will be similar, or even identical for various processes. Following is an example of a reaction plan flow chart:
MoreSteam Note: Specifications should NEVER be expressed as lines on control charts because the plot point is an average, not an individual. The only exception is the moving range chart, which is based on a subgroup size of one. Consider the case of a subgroup of three data points: 13, 15, 17. Suppose the upper specification limit is 16. The average of the subgroup is only 15, so the plot point looks like it is within the specification, even though one of the measurements was out of spec.! However, specifications should be printed on the side, top, or bottom of the chart for comparing individual readings.
8. Add Chart to Control Plan
A control plan should be maintained that contains all pertinent information on each chart that is maintained, including:
Chart Type Chart Champion - Person(s) responsible to collect and chart the data Chart Location Measurement Method Measurement System Analysis (Acceptable Error?) Reaction Plan Gauge Number - Tied in with calibration program Sampling Plan Process Stability Status Cp & Cpk
The control plan can be modified to fit local needs. A template can be accessed through the Control Plan section of the Toolbox.
9. Calculate Control Limits After 20-25 Subgroups.
Terms used in the various control chart formulas are summarized by the table below:
Formulas are shown below for Attribute and Variable data.
(Here n = subgroup or sample size and k = number of subgroups or samples)
Values for formula constants are provided by the following charts:
X-bar and R Chart Subgroup Size (n) A2 D3 D4 d2 2 1.880 0 3.267 1.128 3 1.023 0 2.574 1.693 4 0.729 0 2.282 2.059 5 0.577 0 2.114 2.326 6 0.483 0 2.004 2.534 7 0.419 0.076 1.924 2.704 8 0.373 0.136 1.864 2.847 9 0.337 0.184 1.816 2.970 10 0.308 0.223 1.777 3.078
X-bar and S Chart Subgroup Size (n) A2 B3 B4 11 0.927 0.322 1.678 12 0.886 0.354 1.646 13 0.850 0.382 1.619 14 0.817 0.407 1.593 15 0.789 0.428 1.572
Chart examples:
X and R Chart
p-Chart
The area circled denotes an out-of-control condition, which is discussed below.
For more specific help in constructing SPC charts, see the MoreSteam Online SPC Course offering.
10. Assess Control.
After establishing control limits, the next step is to assess whether or not the process is in control (statistically stable over time). This determination is made by observing the plot point patterns and applying six simple rules to identify an out-of-control condition.
Out of Control Conditions:
A. If one or more points falls outside of the upper control limit (UCL), or lower control limit (LCL). The UCL and LCL are three standard deviations on either side of the mean - see section A of the illustration below. B. If two out of three successive points fall in the area that is beyond two standard deviations from the mean, either above or below - see section B of the illustration below. C. If four out of five successive points fall in the area that is beyond one standard deviation from the mean, either above or below - see section C of the illustration below. D. If there is a run of six or more points that are all either successively higher or successively lower - see section D of the illustration below. E. If eight or more points fall on either side of the mean (some organization use 7 points, some 9) - see section E of the illustration below. F. If 15 points in a row fall within the area on either side of the mean that is one standard deviation from the mean - see section F of the illustration below.
When an out-of-control condition occurs, the points should be circled on the chart, and the reaction plan should be followed.
When corrective action is successful, make a note on the chart to explain what happened.
MoreSteam Hint: Control charts offer a powerful medium for communication. Process shifts, out-of-control conditions, and corrective actions should be noted on the chart to help connect cause and effect in the minds of all who use the chart. The best charts are often the most cluttered with notes!
11. & 12. Analyze Data to Identify Root Cause and Correct
If an out-of-control condition is noted, the next step is to collect and analyze data to identify the root cause. Several tools are available through the MoreSteam.com Toolbox function to assist this effort - see the Toolbox Home Page. You can use MoreSteam.com's Traction® to manage projects using the Six Sigma DMAIC and DFSS processes.
Remember to review old control charts for the process if they exist - there may be notes from earlier incidents that will illuminate the current condition.
13. Design and Implement Actions to Improve Process Capability
After identifying the root cause, you will want to design and implement actions to eliminate special causes and improve the stability of the process. You can use the Corrective Action Matrix to help organize and track the actions by identifying responsibilities and target dates.
14. & 15. Calculate Cp and Cpk and Compare to Benchmark
The ability of a process to meet specifications (customer expectations) is defined as Process Capability, which is measured by indexes that compare the spread (variability) and centering of the process to the upper and lower specifications. The difference between the upper and lower specification is know as the tolerance.
After establishing stability - a process in control - the process can be compared to the tolerance to see how much of the process falls inside or outside of the specifications. Note: this analysis requires that the process be normally distributed. Distributions with other shapes are beyond the scope of this material.
MoreSteam Reminder: Specifications are not related to control limits - they are completely separate. Specifications reflect "what the customer wants", while control limits tell us "what the process can deliver".
The first step is to compare the natural six-sigma spread of the process to the tolerance. This index is known as Cp.
Here is the information you will need to calculate the Cp and Cpk:
Process average, or Upper Specification Limit (USL) and Lower Specification Limit (LSL). The Process Standard Deviation (). This can be calculated directly from the individual data, or can be estimated by:
Cp is calculated as follows:
Following is an illustration of the Cp concept:
Cp is often referred to as "Process Potential" because it describes how capable the process could be if it were centered precisely between the specifications. A process can have a Cp in excess of one but still fail to consistently meet customer expectations, as shown by the illustration below:
The measurement that assesses process centering in addition to spread, or variability, is Cpk. Think of Cpk as a Cp calculation that is handicapped by considering only the half of the distribution that is closest to the specification. Cpk is calculated as follows:
The illustrations below provide graphic examples of Cp and Cpk calculations using hypothetical data:
The Lower Specification Limit is 48 The Nominal, or Target Specification is 55 The Upper Specification Limit is 60 Therefore, the Tolerance is 60 - 48, or 12 As seen in the illustration, the 6-Sigma process spread is 9. Therefore, the Cp is 12/9 or 1.33.
The next step is to calculate the Cpk index:
Cpk is the minimum of: 57-48/4.5 = 2, and 60-57/4.5 = 0.67
So Cpk is 0.67, indicating that a small percentage of the process output is defective (about 2.3%).
Without reducing variability, the Cpk could be improved to a maximum1.33, the Cp value, by centering the process. Further improvements beyond that level will require actions to reduce process variability.
16. Monitor and Focus Efforts on Next Highest Priority
The last step in the process is to continue to monitor the process and move on to the next highest priority.
Source --> http://www.moresteam.com/toolbox/t402.cfm  MoreSteam Hint: Statistical Process Control requires support from the top, like any program. The process will be most effective if senior managers make it part of their daily routine to review charts and make comments. Some practitioners initial charts when they review them to provide visual support. Charts that are posted on the floor make the best working tools - they are visible to operators, and are accessible to problem-solving teams.

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Example SPC Implementation

SPC Concept
> All processes vary. 
> The aim of SPC is to minimise variation.
> Variation is expressed in "sample standard deviations" (SD)
> SD is a mathematical term, based on probability theory.
Consider the process of driving a car along a dry, straight, level road.
Look at your hands on the steering wheel - they move as you make slight adjustments.
Q:    Why are the adjustments necessary ?
A:    Because the road is not totally flat, there's some play in car's suspension, etc.














If you draw a graph of the movements you will have a "normal distribution curve"
In a normal distribution, most of the time your hands are in the middle
(In the secret language of SPC the "middle" is called the "average" or "mean" or "x-bar")
But sometimes your hands are a little to the left, at other times they're a little to the right
And on rare occasions they're on the extreme left or extreme right.

SPC Application
Using the "sample standard deviation" (SD), you can then calculate and chart the usual extremes of a process under a given set of conditions.  This variation is "normal" and we cannot reduce the variation without making fundamental changes to the process (eg going on an advanced drivers course, buying sports suspension, etc.)
Because of the properties of the normal distribution it is usual in SPC or quality control applications to multiply the standard deviation by three and then add that to and take it away from the average which gives a confidence level which encompasses 99.73% of the observations or "population".
For example:       
average = 20
1 SD        = 1.3
3 SD        = 3.9
average + 3 SD    = 23.9
average - 3 SD      = 16.1
therefore 99.73% of all that we see will be between 16.1 and 23.9

Back in the car... 
A child runs out in front of you and you swerve to avoid him.
The chart now has a "blip" which is outside the normal curve.  This indicates that something unusual has happened.














 
In SPC applications it is often useful to recognise what is "normal" and what is "non-normal". This case is clearly "non-normal".
In some situations also helps to know "when" things happen. Therefore, an SPC control charts also has a time axis.

SPC Control charts
We can then apply these concepts in an SPC chart for monitoring customer complaints.















Having collected the basic information we can then add the average and +/- 3 SD markers.














What we are saying is this:
1.    Given the normal variation of the process we will always have a level of complaints.  There is nothing we can do without making fundamental changes to the process.
2.    As long as the individual plots are between the red lines then the process is exhibiting normal variation.
3.    BUT clearly something unusual (non-normal) happened in March - we had far more complaints than usual.  We need to investigate and find out why.
4.    THIS IS THE CLEVER BIT - February's low result is equally unusual. We need to find why it happened and build that in to the process. Do it often enough and the average line will go down, and eventually become zero.
To help the investigation, use appropriate problem solving tools

The problem with traditional SPC
Most SPC control chart techniques are based around the +/- 3 SD method, which covers 99.73% of a population.  99.73% sounds good until you realise that this means that 0.27% are not accounted for, there is 1/4 percent chance of something slipping through the net. 
If you are trying to control critical processes or very high volume (eg aerospace, building nuclear reactors, making silicone chips, etc.) then you will get it wrong once in every 370 attempts. 
Such critical industries tend to use Six Sigma (+/- 6 SD) methods as it gives a 99.9997% (3 in a million failures) confidence level.
Three in a million sounds much better than one in three hundred and seventy.

Need more ?
Click here for on-line SPC training. And check their toolbox for other quality improvement tutorials
Or buy R H Caplen's excellent book  A Practical Approach to Quality Control. 

Source -->http://www.iso9001help.co.uk/Introduction_to_SPC.htm

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TEORI DASAR STATISTICAL PROCESS CONTROL

Bagi kalangan praktisi di dunia industri tentunya sudah tidak asing lagi dengan terminologi-terminologi Quality yang sekarang sedang banyak sekali dipelajari dan dikembangkan oleh berbagai pihak, baik dari kalangan akademis sebagai dasar referensi teori maupun dari praktisi didunia industri sebagai subjek sekaligus objek atas “Quality knowledge” yang sekarang sedang berkembang.

Salah satu metode Quality yang erat kaitannya dengan hal tersebut adalah Statistical Process Control (SPC). Secara Etimologi, Statistical Process Control
1. Process : adalah suatu kegiatan yang melibatkan penggunaan mesin (alat), penerapan suatu metode, penggunaan suatu material dan atau pendayagunaan orang untuk mencapai suatu tujuan.
2. Control : adalah suatu rangkaian kegiatan umpan balik (reciprocal) untuk mengukur suatu hasil yang harus dicapai apabila dibandingkan dengan standard serta melakukan tindakan jika terjadi penyimpangan (abnormality)

Sedang secara epistimologi, Statistical Process Control (SPC): adalah penerapan teknik statistik untuk mengukur dan menganalisa variasi yang terjadi selama proses (produksi-red) berlangsung.

Jenis-jenis Variasi
Satu hal yang harus menjadi filosofi dasar dan harus dipahami oleh kita bahwa setiap produk ataupun jasa yang dihasilkan dari suatu proses (produksi-red) itu tidak akan 100% sama, hal ini terjadi karena adanya variasi selama proses (produksi-red) berlangsung. Variasi dapat didefinisikan sebagai ketidakseragaman produk atau jasa yang dihasilkan. Dapat pula didefinisikan sebagai produk atau jasa yang dihasilkan tidak memenuhi spesifikasi standard yang telah ditetapkan. 


Variasi dikelompokan menjadi 2 jenis :
1. Variasi yang tidak bisa dihindari (uncontrollable variation/chance/common/random variation) contoh: kelembaban udara, suhu ruangan yang berubah-ubah, getaran mesin penggilingan padi, perubahan voltage PLN, dll
2. Variasi yang bisa dihindari (controllable variation/assignable variation)
Contoh: kurang homogennya bahan baku, kurang cermatnya operator, dll.
 
Manfaat Umum Penerapan SPC
Secara Umum dengan menerapkan SPC akan diperoleh beberapa manfaat, antara lain :
1. Meningkatkan daya saing produksi dengan menekan terjadinya variasi.
Mengurangi biaya-biaya yang seharusnya tidak perlu dikeluarkan, misalnya : rework cost, sorting cost, Punishment cost akibat customer complaint, dll.
2. Meningkatakan mutu bahan dan material yang dibeli melalui penerapan Incoming Inspection.
3. Meningkatkan produktivitas dengan menekan persentase cacat, kesalahan ataupun rework.

Lima langkah praktis dalam menerapkan SPC
a. Mendefinisikan, menggambarkan dan memahami tentang proses (produksi-red) yang akan dilakukan perbaikan.
b. Mengidentifikasi parameter proses yang kritis (critical process parameter)
c. Memindahkan data-data yang sudah diperoleh kedalam format grafik statistik (menerapkan teknik kendali statistik) d. Memonitor proses pengendalian
e. Mereview dan tindak lanjut

terdiri dari :Pada dasarnya “inti permasalahan” ini terletak pada terjadinya variasi pada proses (produksi-red) yang disebabkan oleh berbagai faktor secara kompleks. Faktor-faktor tersebut dapat diklasifikasikan melalui pendekatan 4M +1E (Man, Material, Measurement, Methode and Environment) dan suatu analisa yang tidak dapat dilepaskan dengan adanya variasi ini adalah Process Capability Analyze.
 
Process Capability Analyze
Process Capability Analyze dapat didefinisikan sebagai suatu analisa untuk mengetahui apakah proses kerja yang sedang berjalan memenuhi spesifikasi yang telah ditetapkan. Proses disebut capable jika mampu menghasilkan hampir 100 % output sesuai dengan spesifikasi yang telah ditetapkan. Capability adalah kemampuan suatu proses untuk menghasilkan output sesuai dengan spesifikasi yang telah ditetapkan. Process Capability ialah suatu kemampuan proses yang merefleksikan derajat keseragaman dalam memproduksi suatu produk.Capability index adalah suatu index yang mengggambarkan seberapa jauh proses tersebut dapat memenuhi spesifikasi yang diharapkan. Dengan mengetahui Capability index, hal ini akan membantu kita dalam memfokuskan pada target value, target value yaitu value yang paling diinginkan pelanggan. Meskipun output 100% berada di dalam spesifikasi limit, bisa jadi pelanggan tidak puas dan memungkinkan hilangnya bisnis.


Index untuk mengukur Process Capability Analyze : 
1. Cp : Index yang menunjukkan kemampuan suatu sistem dalam memenuhi spesifikasi limit (limit atas-USL dan limit bawah-LSL).
2. Perhitungan Cp menggunakan estimasi sigma dan dapat digunakan untuk menunjukkan potensi suatu sistem dalam memenuhi spesifikasi.
3. Dalam Cp, tidak memperhitungkan rata-rata proses, hanya terfokus pada spread (persebaran data). Jika sistem tidak centered di dalam batas spesifikasi, maka nilai Cp kurang memberikan gambaran yang sebenarnya.
4. Cpk : Index yang menunjukkan seberapa baik suatu sistem dapat memenuhi spesifikasi limit.
5. Perhitungan Cpk menggunakan estimasi sigma dan dapat digunakan untuk menunjukkan potensi suatu sistem dalam memenuhi spesifikasi.
6. Dalam Cpk, rata-rata proses diperhitungkan sehingga proses tidak perlu centered terhadap target.

Mengukur Process Capability Analyze :
Hal-hal yang perlu diketahui :
a. Control Limit merupakan garis batas yang menggambarkan kemampuan proses berdasarkan pengalaman dan kemampuan teknik. Control Limit ada 2 jenis, yakni : Upper Control Limit (UCL) dan Lower Control Limit (LCL).
XBAR Control Limit :
- UCL = X+ (A2)*(R)
- LCL = X - (A2)*(R)
R Control Limit :
- UCL = (D4)*(R)
- LCL = (D3)*(R)

 
b. Spesifikasi Limit merupakan batas-batas yang ditentukan oleh konsumen (internal maupun eksternal) ataupun target yang harus dicapai. Specifikasi Limit ada 2 jenis, yaitu : Upper Specification Limit (USL) dan Lower Specification Limit (LSL).
c. Mean (Rata-rata) adalah nilai yang mewakili data secara keseluruhan.
d. Median adalah nilai tengah dari data yang telah diurutkan.
e. Modus adalah nila data yang mempunyai frekuensi tertinggi.

 
f. Standard Deviation (Sigma) bisa dianggap sebagai akar dari variance sedangkan variance ialah rata-rata kuadrat dari tiap-tiap titik ke rata-rata.

 
g. Bias ialah Perbedaan antara data yang dikumpulkan dalam sampel dengan kondisi yang sebenarnya dalam populasi.
h. Populasi ialah keseluruhan object yang ingin kita ukur dan analisa.Sample ialah sebagian (kecil) dari populasi dimana kita benar-benar melakukan pengukuran dan dengan ini kita dapat menarik kesimpulan.
 
Pengumpulan Data
Dalam melakukan suatu observasi dibutuhkan data-data yang accountable. Data yang baik apabila diolah maka akan menghasilkan informasi yang berguna atau bermanfaat. Jadi yang dimaksud dengan data adalah sekumpulan fakta, angka atau segala sesuatu yang dapat dipercaya kebenarannya sehingga dapat digunakan sebagai referensi dalam mengambil keputusan. Data terbagi dalam data variable dan data attribute.

1. Data variable : disebut juga data continues atau measurement. Data ini berasal dari hasil pengukuran dan nilainya berada dalam suatu interval atau jangkaun tertentu, contoh : Hasil pengukuran berat badan dari 46 Inspector di PQA, hasill pengukuran panjang Frame Main DV28EC selama 1 bulan, dll.
2. Data attribute : disebut juga data diskrit atau data non continues. Umumnya data ini merupakan hasil perhitungan dan berupa bilangan bulat, contoh : Jenis suku bangsa Inspector PQA, jenis kelamin (pria/ wanita), jumlah karyawan yang tidak masuk per hari, dll.
Dalam pengumpulan data-data dilapangan, ada beberapa faktor yang mempengarui hasil pengukuran, diantaranya : kesalahan alat ukur (repeatability), kesalahan operator (reproducibility), kesalahan alat hitung, kesalahan metode pengukuran, dll.
 
Control Chart
Pada dasarnya kurang lebih ada 7 buah QC Tools yang dapat dalam pengendalian mutu (Quality Control), yakni :
1. Flow Chart
2. Check Sheet
3. Histogram
4. Scatter Diagram
5. Pareto Diagram
6. Cause-and-Effect Diagram
7. Control Chart
 
 
Dan dalam hal ini pembahasan akan dikonsentrasikan pada Control Chart (Peta kendali). Control Chart ialah suatu Quality Tool yang dapat digunakan untuk mendeteksi apakah sebuah proses tersebut dalam kondisi terkontrol secara statistik (statistically stable) ataukah tidak. Proses yang tidak dalam kondisi terkontrol secara statistik akan menunjukan suatu variasi yang berlebih sebanding dengan perubahan waktu.





 
Control Chart membedakan antara Common Cause dan Special Cause. Common Cause ialah Penyebab yang agak susah untuk bisa dihilangkan (Natural variation) sedang Special Cause ialah Penyebab yang masih mungkin bisa dihilangkan, misalnya : Kesalahan Operator, materialnya retak dan kotor, Operator masih baru, tidak ada Standard Operasional Procedure untuk menjalankan suatu mesin produksi, dll.
 
Manfaat Control Chart 
1. Mengetahui perubahan-perubahan yang terjadi selama satu periode produksi.
2. Memberikan informasi proses secara kronologis, yakni menunjukkan bagaimana pengaruh berbagai faktor, misalnya : material, manusia, metode, dll. terhadap proses produksi.
3. Mengidentifikasi gejala penyimpangan suatu proses yakni dengan memperhatikan pola atas pergerakan titik-titik sehingga dapat dihindari Over Control yaitu pengontrolan terlalu ketat sehingga dapat menurunkan efisiensi maupun Under Control yaitu pengontrolan terlalu longgar sehingga dapat menurunkan mutu.

8 Kategori adanya pola yang Out of Control pada special Cause yang menunjukan bahwa proses belum stabil secara statistik (Uncontrolled)
a. Dua titik berada lebih dari 3 sigma dari garis tengah.b. Sembilan titik berada pada lajur baris yang sama dari center line.c. Enam titik pada gambar kecenderungannya semuanya naik atau turun.
d. Keempat belas titik yang terdapat pada gambar naik dan turun.
e. Titik-titik yang dilingkari berada lebih dari 2 sigma pada CL. f. Titik-titik yang dilingkari melebihi 1 sigma dari CL.
g. Kelima belas titik berada pada batas 1 sigma dari CL.
h. Kedelapan titik yang dilingkari melebihi 1 sigma dari CL.. Jenis Control Chart
Sebagaimana telah disinggung pada pembahasan diatas bahwa pada dasarnya data diklasifikasikan menjadi 2, yakni : Data attribute dan data variable, sehingga dengan demikian jenis-jenis Control Chart terbagi atas :
1. Variable Control Chart, yaitu suatu jenis Control Chart dimana data yang dikumpulkan dan akan dianalisa merupakan data-data variable, misalnya : X-R Bar Chart dan X-S Bar Chart.
2. Attribute Control Chart, yaitu suatu jenis Control Chart dimana data yang dikumpulkan dan akan dianalisa merupakan data-data attribute, misalnya : p-chart
 
Contoh Langkah kerja pada penggunaan X-R Bar Control Chart
X-R Bar Control Chart merupakan salah satu variable Control Chart dimana data yang dikumpulkan dalam setiap pengamatan berbentuk sub-group yang besarnya sekitar 2 hingga 9 sampel.

Koreksi dalam menentukan frekwensi dan besarnya sample, jika ditemui kondisi sebagai berikut :
a. Sample size terlalu kecil
Jika Control Chart tidak bisa cepat mendeteksi perubahan ekonomis penting.
b. Sample Size terlalu besar
Jika Control Chart “Out of Control” untuk perubahan yang tidak bernilai ekonomis.
c. Frekwensi Penyampelan terlalu sering
Jika sampling and plotting cost melebihi keuntungan ekonomis yang diperoleh dari proses control tersebut.
d. Frekwensi penyampelan terlalu jarang
Jika Economic loss lebih tinggi biayanya dibandingkan dengan waktu tambahan.

Langkah-langkah Kerja :
1. Tentukan tujuan dari penelitian.
2. Buatlah Blue Print (Rancangan-rancangan) sistematis dari penelitian yang akan dilakukan.
3. Buatlah lembar data (Check Sheet) dengan menentukan : jenis data yang dibutuhkan, Critical parameter yang akan dikontrol, besar sample (sub group), frekuensi pengambilan sampel, dll.
4. Lakukan pengumpulan data.
5. Buatlah Control Chart. Hal ini dapat dilakukan dengan bantuan software (MINITAB) maupun secara konvensional dengan rumus sebagaimana telah dibahas dimuka.
6. Perlu diperhatikan bahwa Control Chart yang dibuat pertama kali merupakan Control Chart “percobaan”.
7. Periksa apakah ada titik-titik yang Out of Control ataukah tidak dengan menggunaikan kaidah pengujian 8 titik Out of Control sebagimana telah dikemukakan diatas. Jika terdapat titik yang Out of Control maka Control Chart harus diperbaiki (revisi)
8. Merperbaiki (revisi) Control Chart dengan membuang data-data yang Out of Control (tidak stabil), kemudian hitung kembali dan tampilkan dalam Control Chart. Perlu diingat bahwa pembuangan data-data yang Out of Control harus disertai dengan penjelasan logis 5W+1H dan dilengkapi dengan Corrective Action. Setelah dianggap jelas (close) maka data-data yang Out of Control dapat dibuang.
9. Ulangi proses 5 ~ 8 dan hingga seluruh titik berada dalam Chart serta dalam keadaan In Control. Perlu diperhatikan juga bahwa dalam pembentukkan Control Chart ini dalam satu periode proses pengambilan data harus diperhatikan dalam keadaan normal serta tidak mengalami perubahan proses kerja yang signifikan (perubahan material, mesin, sistem kerja, dll.
10. Jika telah tercapai, maka garis kendali yang diperoleh dapat digunakan untuk mengontrol proses pada periode berikutnya.
11. Menerapkan pengontrolan proses. Sebagai informasi tambahan saja bahwa seiring dengan mobilitas produksi yang tinggi maka alangkah baiknya jika dalam aktual penerapan pengontrolan proses itu menggunakan bantuan software (misal : MINITAB). Hal ini bertujuan jika terjadi Out of Control (abnormality) akan segera ketahuan dan terdeteksi. 

dDitulis oleh Widianto

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