Special cause variation in Stastical Process Control

Let's consider a manufacturing scenario where Statistical Process Control (SPC) is used to monitor and improve the quality of a production process for a specific product, such as automobile brake pads. 

In this example, the critical quality characteristic of interest is the thickness of the brake pads, which must fall within a specified range to meet safety and performance requirements.

Here's how SPC can be applied to monitor and control the thickness of brake pads:

1. Data Collection: The manufacturing process involves producing brake pads of a certain thickness. Measurements of the thickness are taken periodically from samples of brake pads produced during the production run.

2. Control Chart Creation: A control chart is created to monitor the thickness of the brake pads over time. The control chart typically consists of a central line representing the process mean and upper and lower control limits based on process variability.

3. Data Analysis: As measurements are collected and plotted on the control chart, the process variability and trends are analyzed. Any points that fall outside the control limits or exhibit non-random patterns (such as trends, shifts, or cycles) are investigated as potential special causes of variation.

4. Process Adjustment: If special causes of variation are detected, corrective actions are taken to address the root cause of the issue. This could involve adjusting machine settings, changing materials or processes, or providing additional training to operators.

5. Continuous Monitoring and Improvement: The process is continuously monitored using SPC techniques to ensure that the thickness of the brake pads remains within the desired range. Regular data collection and analysis help to identify opportunities for process improvement and prevent quality issues from recurring.

By using Statistical Process Control in this example, the manufacturer can ensure that the thickness of brake pads meets quality standards consistently, leading to improved product performance, customer satisfaction, and cost savings through reduced rework and scrap.

give me real world example for special cause variation in Stastical process control

Here are some real-world examples of special cause variation in Statistical Process Control (SPC):

Few other examples 

- A machine breaks down unexpectedly, causing a spike in cycle time or defect rate[1][2][3]

- A shipment of raw materials is contaminated, leading to an increase in product failures[1][2][4] 

- A power outage shuts down production for several hours, disrupting the normal process[2][3]

- A new operator makes a mistake in a critical process step, resulting in off-spec product[1][3]

- An earthquake or severe weather event damages equipment and facilities[2][4]

Special causes are unexpected, non-routine events that are not part of the normal process variation. They are assignable to a specific cause and can be corrected by adjusting the process. When special causes occur, they will show up as points outside the control limits or non-random patterns on a control chart[1][4].

The key is to quickly identify the special cause, determine the root source, and implement corrective action to bring the process back into a state of statistical control[1][3]. Preventing special causes requires proactive measures like preventive maintenance, operator training, and supplier quality audits[3].

Citations:

[1] The Power of Special Cause Variation: Learning from Process Changes https://www.isixsigma.com/dictionary/special-cause-variation/

[2] Common Cause Variation Vs. Special Cause Variation - Simplilearn.com https://www.simplilearn.com/common-vs-special-cause-of-variance-article

[3] Common and Special Causes in Statistical Process Control - LinkedIn https://www.linkedin.com/advice/0/what-some-common-tools-techniques-identifying

[4] How to Identify Causes of Variation in Statistical Process Control https://safetychain.com/blog/identifying-variation-statistical-process-control

[5] Common cause and special cause (statistics) - Wikipedia https://en.wikipedia.org/wiki/Common_cause_and_special_cause_%28statistics%29

A Guide to Statistical Process Control (SPC)

In today's competitive business landscape, maintaining consistent quality is paramount for success. Statistical Process Control (SPC) is a powerful tool that enables organizations to monitor and improve processes, ensuring that products and services meet the highest standards. By harnessing the principles of SPC, businesses can reduce variability, enhance efficiency, and ultimately drive customer satisfaction.

What is Statistical Process Control (SPC)?

Statistical Process Control is a methodical approach to quality management that uses statistical tools to monitor and control processes. By collecting and analyzing data over time, SPC helps organizations understand process performance, detect variations, and make informed decisions to maintain quality standards.

Key Concepts of SPC:

1. Control Charts: Control charts are a fundamental tool in SPC that visually display process data over time. By plotting data points against control limits, organizations can identify trends, shifts, or patterns that indicate whether a process is in control or out of control.

2. Variation: SPC distinguishes between common cause variation (inherent to the process) and special cause variation (resulting from external factors). Understanding these variations is crucial for taking appropriate action to improve processes.

3. Process Capability: Process capability analysis assesses the ability of a process to meet specifications. By comparing process variation to specification limits, organizations can determine whether a process is capable of producing products or services within desired quality standards.

Benefits of Implementing SPC:

1. Improved Quality: SPC helps organizations identify and address issues proactively, leading to higher quality products and services.

2. Cost Reduction: By reducing waste, rework, and defects, SPC helps organizations optimize processes and lower production costs.

3. Enhanced Decision-Making: Data-driven insights provided by SPC enable informed decision-making and continuous improvement initiatives.

4. Customer Satisfaction: Consistent quality assurance through SPC results in products and services that meet or exceed customer expectations, fostering loyalty and retention.

In conclusion, Statistical Process Control is a valuable methodology for organizations looking to achieve operational excellence and deliver superior quality. By embracing SPC principles and tools, businesses can drive efficiency, reduce defects, and gain a competitive edge in today's market. Start mastering SPC today to unlock the full potential of your processes and elevate your quality standards.

Sun Pharma's Dadra unit gets USFDA warning letter

Sun Pharma's Dadra unit had received an Official Action Indicated (OAI) status from the USFDA on April 11 this year. This was after the regulator had inspected the facility between December 4 to December 15, 2023.

Sun Pharma's Dadra unit is involved in the production of oral solid dosage forms and in the manufacturing of the generic Revlimid, which has been a key driver of sales for many pharma companies including Dr. Reddy's, in recent times.

Warning Letter to Indian Sterile Manufacturer due to egregious GMP Deficiencies 17.06.2024

The nine-day FDA inspection of an Indian pharmaceutical manufacturer had already taken place in October 2023. Due to the numerous and, as the FDA itself writes, egregious GMP violations and the inadequate response to the list of deficiencies by the manufacturer, a Warning Letter has now been issued. 

Insanitary conditions

The FDA classifies the medicinal products manufactured by the Indian pharmaceutical manufacturer as contaminated because they were manufactured, packaged or stored under insanitary conditions. The FDA inspectors found the manufacturing facilities to be in a state of disrepair, inadequately cleaned and maintained. Among the findings mentioned were residues next to the HEPA filters in the ISO 5 area, several barefoot employees in an ISO 8 area and employees bringing materials into the ISO 7 area without the required protective clothing and gloves. Sterile filling takes place in a RABS, which was visibly dirty.

And even if these deficiencies would be enough, the FDA lists further critical GMP deficiencies in its Warning Letter.

Inadequate, aseptic working methods

The FDA describes deficiencies that were observed during the manufacture of sterile medicinal products: Employees used a cloth to wipe down parts of the filling line and conveyor belt in the ISO 5 areas. In addition, employees blocked the protective air flow by leaning over open product. Employees did not wear goggles and had exposed skin during line set-up and aseptic operations. In addition, the FDA criticised the lack of monitoring of aseptic behaviour and the effectiveness of employee training.

Deficiencies in the media fill

During the inspection, it was noted that the aseptic operations simulated during the media fill were not sufficiently representative of commercial aseptic manufacturing operations. As routine production staff do not record manual interventions in the batch documentation, the media fill programme lacks representative data to determine the number and duration of interventions to be simulated during the media fill. In addition, during the inspection, numerous procedures were performed during routine manufacturing that were not simulated in the media fill.

Inadequate smoke studies

The FDA also criticised the smoke studies conducted to show the flow in the aseptic processing areas. Several instances of turbulent airflow were noted in critical areas of the filling line, even directly above the filling line. In addition, the smoke study videos did not include the manual filling operations that simulate actual production. Also, not all parts of the line were assembled as used in routine production.

Deficiencies in environmental monitoring and data falsification

The FDA criticised the fact that the required number of samples for environmental and personnel monitoring were not collected in accordance with the manufacturer's procedures. Instead, interviews with employees and the management of the microbiology laboratory revealed that it is common practice to fabricate results for samples that were never taken. In addition, results were altered that would otherwise not fulfil the defined specifications. During the inspection, numerous limit violations were also found in environmental and personnel monitoring samples.

Ineffective quality system

According to the FDA, the manufacturer does not have an effective quality system in accordance with GMP. In addition to the lack of effective management oversight of its manufacturing and laboratory operations, the FDA found that the quality department was unable to exercise appropriate authority or had inadequately implemented its responsibilities. Company management should promptly and comprehensively evaluate the company's global manufacturing operations to ensure that systems, processes and products are in compliance with FDA requirements.

Delay in the recall of medicinal products

Due to the serious GMP violations and the associated patient risk, the FDA had already advised the manufacturer in a conference call on 25 October 2023 to recall medicinal products from the inspected facility. On 27 October, the FDA published its own announcement. Despite numerous attempts by the FDA to obtain a decision on a recall, the Indian manufacturer did not initiate the necessary recall until 15 November 2023, i.e. approximately three weeks after the initial discussion.

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CHMP confirms Suspension of Marketing Authorizations with Studies of Synapse Labs

The re-examination by the Committee for Medicinal Products for Human Use (CHMP) confirms the suspension of marketing authorizations with bioequivalence studies of Synapse Labs Pvt. Ltd.

In July 2023, critical inspection findings at Synapse Labs Pvt. Ltd, a contract research organization (CRO) in India, led the EMA to initiate an Article 31 referral procedure to assess the impact on the benefits and risks of medicinal products authorized on the basis of studies conducted at the CRO. After reviewing all information for the over 400 medicinal products tested by Synapse Labs Pvt. on behalf of EU companies, the CHMP has recommended the suspension for numerous generic marketing authorizations. For some marketing authorizations, sufficient data are available to demonstrate bioequivalence. For all other medicinal products, no or insufficient data were available to demonstrate bioequivalence. National authorities can defer the suspension of medicinal products of critical importance for a maximum of two years and companies must submit the required bioequivalence data for these medicinal products within one year. Applicants and marketing authorization holders have requested the CHMP to re-examine its opinion. In March 2024, the CHMP confirmed and adopted its final opinion.

This confirmation concludes the re-examination for some of the medicinal products concerned. The opinion will now be forwarded to the European Commission, which will take a final decision that is legally binding in all EU Member States.

Checklist for Implementation of GDP Principles - Part 8: Self-Inspections

According to Chapter 8 (Self-Inspections) of the EU GDP Guidelines (Guidelines of 5 November 2013 on Good Distribution Practice of medicinal products for human use - 2013/C 343/01) "self-inspections should be conducted in order to monitor implementation and compliance with GDP principles and to propose necessary corrective measures."

The RP is responsible to ensure this program is maintained and thus is responsible for keeping up to date with changes in legislation and regulations impacting the business with regards to GDP. Other departments may support the RP by performing local audits and/or participating in the organisation’s self-inspection program.

Checklist: Implementation of GDP Principles at Wholesale Distributors

The following checklist provides a generic overview and could be used as a minimum requirement related to Chapter 8 of the 2013 guidelines:

• A self-inspection programme is implemented to cover all aspects of GDP and compliance within a defined time frame
• Self-inspections are conducted in an independent and detailed manner (by designated competent person(s) from the company and independent external experts)
• Subcontracted activities are a part of the self inspection programme
• Reports contain all observations
• A copy of the report is submitted to the organisation’s management and other relevant personnel
• Causes of irregularities and/or deficiencies are determined and the CAPA is documented and followed-up

EU adopts Supply Chain Law - Implications for pharmaceutical Companies

On 24th of May 2024, the EU member states adopted the European Supply Chain Law which requires companies of a certain size to monitor and prevent negative impacts on human rights and the environment from their supply chain activities.

The directive applies to companies with more than 1,000 employees and a turnover of more than 450 million euros.

Companies addressed by the directive are obliged to:

Establish a risk-based system to remediate and prevent human rights and environmental impacts throughout the life cycle of production, distribution, transport and storage of a product or the provision of a service.

Ensure that their entire supply chain (e.g. subsidiaries or business partners) meets these obligations.

Take appropriate measures to prevent and remedy abuses caused by their own activities or by other actors in the value chain. Companies can be prosecuted under civil law and must pay full compensation for the damage caused.

Implement a climate change transition plan in accordance with the Paris Agreement.

The directive will be introduced gradually (depending on the size of the company):

3 years after entry into force, the directive will apply to companies with more than 5,000 employees and a turnover of 1.5 billion euros

4 years after entry into force, the directive will apply to companies with more than 3,000 employees and a turnover of 900 million euros

5 years after entry into force, the directive will apply to companies with more than 1,000 employees and a turnover of 450 million euros

Once signed by the President of the European Parliament and the President of the European Council, the Directive will enter into force 20 days after its publication in the Official Journal of the European Union. Member states have two years to implement the regulation at national leve

What is the history of current good manufacturing practices

The history of Current Good Manufacturing Practices (cGMP) dates back to the early 20th century in the United States. The first major event was the passage of the 1906 Food and Drugs Act, which required dangerous ingredients to be declared on product labels and prohibited adulterated and misbranded drugs[4]. This act led to the creation of the Bureau of Chemistry, which later became the Food and Drug Administration (FDA)[4].

In the 1920s, the FDA began to regulate the pharmaceutical industry more closely. The 1937 Elixir Sulfanilamide tragedy, which killed over 100 people, further emphasized the need for stricter regulations. This led to the development of more detailed guidelines for pharmaceutical manufacturing, including the creation of the Food, Drug and Insecticide Administration (FDIA) in 1937[4].

The modern cGMP regulations were formalized in 1969 with the publication of Part 128 of the Code of Federal Regulations (CFR). These regulations were later recodified as Part 110 in 1977. The FDA continued to refine these regulations over the years, including revisions in 1986 and ongoing efforts to modernize them in the 21st century[3][4].

The main objectives of cGMP are to ensure the quality and safety of products by controlling manufacturing processes, maintaining clean and hygienic facilities, and implementing quality control measures. The regulations have evolved to address specific industry needs and technological advancements, with a focus on preventing harm to consumers and patients[1][2].

Automated Visual Inspection: False rejections

During the validation and operation of fully automated inspection machines (AVI), there are always problems with high false reject rates, i.e. the incorrect rejection of good objects. Are there GMP requirements in this regard?

As the automatic inspection systems are also able to recognise much smaller particles than humans, more or less large quantities of objects that were rated as good by an employee in the manual inspection [humans are still considered to be the gold standard in the 100% visual inspection] appear again and again in the eject of the AVI.

 In addition, a fully automated system could also have problems with air bubbles and reject objects with air bubbles as faulty. Ultimately, the trick is to configure the system in such a way that no objects with very small particles (invisible to humans) are rejected. There are companies in which objects rejected by the automatic system are checked again by manual inspection staff. However, this method harbours the risk that objects that actually have defects are suddenly judged to be free of defects by the employee. The FDA could also regard this procedure as unauthorised 'testing-into-compliance'.

Two conclusions can be drawn from the perspective of GMP. The rejection of objects without defects does not harbour any risk for patients and therefore appears to be practicable. On the other hand, qualification as such could also be criticised, as a system that makes systematic errors is not sufficiently qualified. In any case, acceptance criteria for false rejects should be defined. If these are exceeded, certain measures must be taken, such as an investigation into the cause and, if necessary and justifiable, an additional 100% inspection.

To summarise, it can be said that there is no requirement to set a limit value for false rejects. On the other hand, it is advisable to set a limit value for these false rejects as well, usually during qualification. This is because a missing limit value and, above all, a very high number of good objects in the false rejects can jeopardise the entire qualification during a GMP inspection.

Update of Appendix 1 for Nitrosamines

The nitrosamine Q&A document "Questions and answers for marketing authorization holders/applicants on the CHMP Opinion for the Article 5(3) of Regulation (EC) No 726/2004 referral on nitrosamine impurities in human medicinal products" of the EMA/CMDh contains three appendices (Appendix 1-3) in its current version of January 2024. These documents are published on the EMA website and can be viewed under "Questions and answers".

Appendix 1 "Acceptable intakes established for N-nitrosamines" was prepared by the "Non-clincal Working Party (NcWP)" and the information provided there for the acceptable intakes (AIs) is based on the "Carcinogenic Potency Categorization Approach (CPCA)". In May 2024, new substances were added to Appendix 1, which consists of a tabular list of substances, and some existing entries in the list were updated. Sixteen substances were added and are marked in red and clearly recognizable in the list of acceptable intakes (AIs). These include:

2-(4-nitrosopiperazin-1-yl)ethanol

3-((ethyl(nitroso)amino)methyl) benzenesulfonate

4-nitroso-methyl piperazine-1-carboxylate

N-(2-hydroxy-2-phenylethyl)-N-(4-nitrophenethyl)nitrous amide

N-(4-aminophenethyl)-N-(2-hydroxy-2-phenylethyl)nitrous amide

N-nitroso-articaine

N-nitroso-desmethyl-edoxaban

N-nitroso-fenfluramine

N-nitroso-furosemide

N-nitroso-N-desmethyl-dextromethorphan

N-nitroso-posaconazole Impurity 1

Rivaroxaban Nitroso Impurity 1

Rivaroxaban Nitroso Impurity 2

Rivaroxaban Nitroso Impurity 5

N-nitroso-trientine Impurity 1

N-nitroso-vibegron

The entries for the substances "N-nitroso-folic acid" and "N-nitroso-lisinopril" have been updated.

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