İlaç sektöründe ürün değerlendirmelerinde kontrol grafiklerinin ve proses yeterlilik analizlerinin kullanılması
Use of control charts and process capability indices in the pharmaceutical industry for product reviews
Yazar: BÜŞRA TEMELATMA
Danışman: PROF. DR. HATİCE CAMGÖZ AKDAĞ
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / İşletme Ana Bilim Dalı / İşletme Bilim Dalı
Derece: Yüksek Lisans, 2021
Konu: İstatistik = Statistics ; İşletme = Business Administration
Dizin: Kontrol grafikleri = Control charts ; Süreç kontrol = Process control ; Süreç yeterliliği = Process capability ; Üretim kontrolü = Production control ; Ürün değerlendirme = Product evaluation ; İlaç sektörü = Pharmaceutical sector ; İlaç üretimi = Drug production ; İlaçlar = Drugs

Özet:
Üretim sektöründe giderek artan rekabetçi ortam ve teknolojik gelişmeler, kalite kavramının önemini gün geçtikçe arttırmaktadır. Rekabetçi ortamın artması, firmaları mükemmele en yakın ürünleri üretmeye zorlamaktadır. Bu nedenle, firmalar istenilen kalitede ürün üretebilmek adına, üretim süreçlerindeki değişkenleri daha iyi tanımaya ve olası sorunları henüz ortaya çıkmadan önce tespit edebilmeye yönelik çalışmalar yapmaktadır. Çetele tabloları, pareto analizi, balık kılçığı diyagramları, histogramlar, serpilme – dağılma diyagramları ve kontrol grafikleri gibi istatistiksel süreç kontrol yöntemleri de bu adımda firmaların sıkça kullandığı yöntemlerdendir.
İstatistiksel süreç kontrol yöntemleri, üretim süreçlerinin sürekli olarak denetlenmesi ile süreçlerdeki değişkenlerin kontrol altına alınabilmesi amacıyla kullanılmakta ve olası sorunların önceden tespit edilmesini hedeflemektedir. İstatistiksel metotlar, kalite uygunluk şartlarının sağlanıp sağlanmadığını, müşteri isteklerine uygun üretimin yapılıp yapılmadığını, önceden belirlenen spesifikasyonlara ve standartlara uygun çalışılıp çalışılmadığını kontrol edebilmek, hatalı üretimi en aza indirmek amacıyla üretimin tüm aşamalarında kullanılmaktadır. İstatistiksel süreç kontrol yöntemleri ile üretilen ürünlerden örnekler alınmakta ve örneklerin sonuçlarına göre üretilen ürünlerin tamamı için yorumlar yapılmaktadır. Kontrol grafikleri de önemli bir istatistiksel süreç kontrol yöntemi olmakla beraber, bir çok sektörde üretilen ürünlerin kalite kontrolünde sıkça kullanılmaktadır. Üretilen ürünlerin yalnızca belirlenen spesifikasyon limitlerinin arasında olması mükemmele yakın ürün üretiminde tek başına yeterli olmadığından, kontrol grafiklerinden faydalanılarak kontrol limitleri belirlenmekte ve üretilen ürünlerin kritik kalite özelliklerinin bu kontrol limitleri aralığında olup olmadığı incelenmektedir. Üretilen ürünlerin kritik kalite özellikleri için oluşturulan kontrol grafiklerinin yanısıra, üretim sürecinin performansının ve yeterliliğinin de somut verilerle ortaya konması amacıyla süreç yeterlilik veya süreç performans indislerinin de hesaplanması ve yorumlanması son derece önemlidir.
İlaç üretim sektörü, canlı sağlığını doğrudan etkileyen ürünlerin üretilmesi sebebiyle sıkı denetimlere tabi tutulan ve kalite kavramının oldukça önemli olduğu bir sektördür. Bu nedenle, üretilen ürünlerin kalitesinin geliştirilebilmesi için istatistiksel süreç kontrol yöntemlerinin kullanılması ve kalite sorunlarının en az seviyeye indirilmesi önemli bir gerekliliktir.
Bu çalışmada öncelikle istatistiksel süreç kontrol yöntemlerinden biri olan kontrol grafikleri ile ilgili kapsamlı bir literatür araştırması yapılmıştır. Ardından, ilaç üretim sektöründe faaliyet gösteren bir firmada üretilen ürünlerin üretim süreçleri kontrol grafikleri ile incelenmiş, süreç yeterlilik ve süreç performans analizleri gerçekleştirilmiştir. Çalışmanın literatüre katkısı, kontrol grafikleri, süreç yeterlilik ve süreç performans indislerinin bir arada kullanılmasıdır. Ayrıca literatüre katkı olarak, kritik kalite özelliklerinin yalnızca belirlenen spesifikasyon limitleri arasında olmasının yeterli olmadığı, kontrol grafiklerinin de mutlaka incelenmesi gerektiği ve istatistiksel anlamda kontrol altında olmayan süreçlerde süreç yeterlilik indisinin yanıltıcı olduğu, bu tür süreçlerde süreç performans indisinin kullanılmasının gerekli olduğu saptanmıştır.

Abstract:
With the passing of time and increasing of competition the term of quality started to be seen as a strategy tool and started to gain importance in terms of management especially in the production sector. Considering all areas of use, the importance of the quality, the most basic word meaning of which is the conformity with the desired functions and the standards expected from the product, is increasing day by day, especially in the production sector, due to the increase in national and international competition brought about by rapid changes.
As a term, quality has been defined in so many ways. While Garvin interpreted the term of quality as the degree of the performance, features, usability and aesthetics of the product which satisfies the customer, Feigenbaum associated it with the usability and selling price of the product. Deming; stated that variability should be reduced and errors should be reset to prevent poor quality; emphasized that quality is a multi-dimensional concept.
Quality is an important term for both the manufacturer and the customer. Quality, which can be defined as a product or service that is error-free in terms of the customer, meets the expectations and has the promised features, can be expressed for the manufacturer as the production of the correct and error-free product by minimizing the losses such as waste product, disposal and time while providing customer satisfaction.
The most important way to prevent quality problems is to reduce the variability in processes, and these variables are monitored with statistical process control methods and interventions are made when necessary.
Quality manifests itself as an important term in all sectors such as production, education, service and automotive, and directly affects customer satisfaction which is one of the basic criteria that determines the success of a business in every sector. In order for businesses to survive in a competitive environment, companies must keep customer satisfaction at a high level and at the same time keep their expenses at a minimum. In order to achieve this, a quality-oriented management must be displayed and the quality philosophy must be adopted in every step of the business.
The increasingly competitive environment and technological developments in the production sector increase the importance of the quality term day by day. The increasing competitive environment forces companies to produce products that are closest to perfection. For this reason, companies are working to identify the variables in their production processes to understand the process better and to identify potential problems before they arise in order to produce products of the desired quality.
The biggest obstacle to reaching the desired quality level is the variables in the processes and as Deming stated, variability should be reduced in order to prevent poor quality. Statistical Process Control is used to control the variability in processes by constantly monitoring the processes and aims to prevent errors by determining them in advance. Statistical methods are used in all stages of production in order to check whether the quality conformity conditions are met, whether the production is made in accordance with the customer requests, whether it is working in accordance with the predetermined specifications and standards, and to minimize the defective production.
In order to control the variables that exist in the processes and to make improvements, samples are taken from the products produced by statistical process control methods and analyzed and comments are made for all products produced according to the results. The main purpose of statistical process control is to take the process under control with the measures taken and to interfere to find and eliminate the special reasons causing poor quality.
Frequently used statistical process control tools are; Histograms, Check Sheets, Pareto Charts, Cause and Effect Diagrams, Scatter Diagrams and Control Charts. The biggest advantage gained by using statistical process control techniques is to provide error-free product delivery to customers.
When the historical development of the studies to improve the quality control and the detected nonconformities are examined, it is observed that the use of statistics in quality management was with the control charts developed by Shewhart in 1924. While controlling whether the process is under control in statistical process control studies, process capability indices and control charts are also used.
Control charts are used to evaluate the process data and the results of systematic or coincidental causes affecting critical quality parameters determined in products by statistical methods. Process characteristics are numerically determined, interpreted and possible problems are identified with control charts.
Control charts which also known as Shewhart control charts are used to analyze the results of general and special causes that have an impact on the critical quality characteristics of a process and to perform statistical evaluations. The type of control charts to be created is selected and interpreted according to the number of subgroups taken from the process. Control charts can be created for quantitative data as well as for qualitative data.
Quantitative control charts are created for the properties of the product that can be expressed with numerical values such as thickness and length. There are quantitative control charts that examine mean value and width and examine mean value and standard deviation, as well as quantitative control charts in which data obtained from single sample measurements are evaluated.
Qualitative control charts are created for the features of the product that are expressed as "appropriate" or "not suitable", "accepted" or "rejected", and that cannot be measured with numerical values. The type of control chart to be created based on searching for specific defects in samples within the selected samples or searching for any defects within the selected sample.
While searching for a specific defect in the samples within the selected sample, the c or u graph is used, and the np or p graph is used when searching the sample with any defects in the selected sample.
With the use of control charts, possible errors can be predicted and necessary measures can be taken. In addition, the capability of the process can be evaluated.
Shewhart control charts are used to determine the standards that products should have. Another purpose of Shewhart control charts, which serve as a tool to reach the targeted standards, is to check whether the targeted goal is achieved by making certain calculations.
Control charts are also used in evaluating the capability and performance in the processes. It is evaluated whether the process is under control and whether it requires intervention by interpreting the standard deviation, average value and the location between the specified specification limits and control limits of the values of the process.
Classical Shewhart control charts are widely used in detecting outcomes that are out of control. Alternative control charts such as EWMA, CUSUM, MA have been developed over time to detect small deviations in the process. Developed alternative control charts can be used in low volume production, as well as in examining the quality characteristics of products produced simultaneously or at different times.
The methods used in statistical process control applications are made according to the normal distribution and the normal distribution approach is taken as basis when evaluating the data obtained from the process with control charts. Therefore, the normality of the distribution should be examined while creating control charts. However, there are studies in the literature indicating that normality test is not required since all data obtained from the process are used in the Individuals - Moving Range control charts where samples are not used, and that this control chart can also be used for data that do not show normal distribution.
Although control charts are an important statistical process control method, they are frequently used in the quality control of products produced in many sectors. Since the products produced are only within the specified specification limits, it is not enough alone in the production of near-perfect products, control limits are determined using control charts and it is examined whether the critical quality characteristics of the produced products are within these control limits. In addition to the control charts created for the critical quality characteristics of the products produced, it is extremely important to calculate and interpret process capability or process performance indices in order to reveal the performance and capability of the production process with concrete data.
Process capability analyzes are used to numerically express the conformity of the processes performed to predetermined specifications or, in other words, their conformity to customer requests. Process capability analysis is used to evaluate the capability of the process by examining the mean and standard deviation of the process, and it is desirable that the process average is at the targeted value and the spread is as narrow as possible. Cp, Cpk, Pp and Ppk parameters are commonly used to determine the conformity of the process to the desired specifications.
In new processes that are not under statistical control, the performance of the process is interpreted with Pp and Ppk values. In the current processes that are under statistical control, interpretations are made with Cp and Cpk values.
In a statistically controlled process, variability in output is only due to general reasons. In case of variation in process outputs due to special reasons, the process is not under statistical control. Whether the processes are under statistical control is determined using control charts. While the Cpk value indicates the potential capability the process will have when all special causes that may cause variability are eliminated; Ppk value shows how the process has performed in the past without considering the stability. While comments about the future of the process are made with Cpk value, comments about the past performance of the process are made with Ppk value.
In a normally distributed and controlled process, the difference between the sample standard deviation and the estimated standard deviation will be small, so the Ppk value is expected to be equal to the Cpk.
The pharmaceutical production sector is a sector that is subject to strict controls due to the production of products that directly affect the creatures health and where the concept of quality is very important. For this reason, it is an important necessity to use statistical process control methods and to minimize quality problems in order to improve the quality of the products produced.
In this study, first of all, a comprehensive literature search on control charts, one of the statistical process control methods, was conducted. Then, the production processes of the products produced in a company operating in the pharmaceutical manufacturing sector were examined with control charts, process capability and process performance analyzes were performed. The contribution of the study to the literature is the combined use of control charts, process capability and process performance indices. In addition, as a contribution to the literature, it has been determined that it is not enough to have critical quality features only within the specified specification limits, control charts must also be examined, and process capability index is misleading in processes that are not under statistical control, and it is necessary to use process performance index in such processes.