Quality Assurance Operations – Make Workflow Seamless

Who is this article for?

• Software companies looking to improve their development and testing processes
• Manufacturing and service companies aiming to enhance quality assurance
• Quality assurance managers
• Software development managers
• Product managers
• Project managers

Quality assurance operations are critical for any company that wants to deliver high-quality products and services consistently to their customers. Integrating QA testing throughout the development process, rather than just at the end, allows issues to be identified and fixed much faster.

What are the key elements of effective quality assurance operations?

1. Establishing clear quality standards: Define the specific requirements and quality criteria that the product or service must meet. These serve as the benchmark for testing and evaluation.
2. Integrating QA into the process: Rather than testing only at the end, QA should be incorporated throughout development. For software, this means developers frequently integrate code changes and automated tests are run regularly to catch issues early (Leopold et al., 2012).
3. Focusing on the right areas: Prioritize testing on the aspects that have the greatest impact on quality from the customer perspective. For services, dimensions like reliability, responsiveness and assurance are often most important (Rezaei et al., 2018).

What are the challenges with implementing quality assurance?

While the benefits of QA are clear, companies often struggle with implementation challenges such as:

• Resistance to change from development teams who are used to testing only at the end
• Difficulty standardizing quality across different teams and departments (Mak, 2011)
• High cost and effort required to establish formal QA processes and train people
• Ensuring QA keeps pace with rapid development cycles and tight deadlines

How can workflow management tools help standardize QA?

Consistency is key for effective quality assurance. Workflow management software like Tallyfy can automate processes with if-this-then-that rules to ensure QA tasks are always completed by the right people at the right stage.

With Tallyfy, you can:

• Document processes once and the system guides people through the steps
• Structure intake forms to standardize how information is captured
• Track task status in real-time for full visibility into the QA process
• Quickly create test cases and reports from customizable templates

Risks and warnings about complex quality assurance software

• Difficult to use systems can slow down QA rather than improve it
• Overly rigid processes can stifle innovation and frustrate employees
• Focusing only on meeting specifications can miss bigger picture quality issues
• Expensive enterprise QA tools are often overkill for small and mid-sized companies

Quality assurance doesn’t have to be complicated. With the right approach and tools, any company can implement QA operations to improve quality, efficiency and customer satisfaction. Tallyfy makes it easy to digitize and track QA workflows so you can spend less time managing the process and more time delighting your customers.

How is AI Changing Quality Assurance in Business Operations?

Quality assurance (QA) is a critical function for ensuring the reliability and performance of products, services, and business processes. Traditionally, QA has relied heavily on manual inspection and testing by human workers. However, the rapid rise of artificial intelligence (AI) and related technologies is now transforming how QA is performed.

One key impact of AI is the automation of many QA tasks. Machine learning algorithms can be trained on historical quality data to automatically detect defects or anomalies, often with greater speed and accuracy than human inspectors. Computer vision systems using deep learning can visually inspect products on assembly lines to spot flaws. Intelligent software bots can automatically test software and web applications to ensure proper functionality. By automating routine QA activities, AI enables human workers to focus on more complex and judgement-intensive aspects of quality management.

AI is also enabling more proactive and predictive approaches to quality management. By analyzing vast amounts of sensor data from connected equipment and products, machine learning models can predict potential quality issues or failures before they occur. This allows companies to take preventive action to avoid costly defects and downtime. Predictive quality is becoming increasingly important as products become more complex and digitized (Rezaei et al., 2018).

Another major impact of AI is the ability to gain deeper insights into the drivers of quality problems and prioritize improvement efforts. Advanced analytics tools can mine structured and unstructured quality data to uncover hidden trends, patterns and root causes that may not be apparent to human analysts. Natural language processing (NLP) techniques can analyze large volumes of customer feedback and service logs to identify common quality complaints and pain points (Cheng et al., 2021). Armed with these insights, QA teams can focus their efforts on the quality issues that matter most to customers and the business.

What Challenges Does AI Pose for Quality Assurance?

While AI offers many benefits for QA, it also introduces new challenges. One issue is the potential for bias in AI-based quality systems. If the historical data used to train AI models contains human biases or skewed sampling, the resulting algorithms may make biased decisions that unfairly disadvantage certain products or customer segments (Stoel et al., 2012). Careful auditing and testing for bias is essential.

Another challenge is the “black box” nature of some AI systems, where it can be difficult to understand or explain how the algorithm arrived at a particular quality assessment. This opacity can make it harder to audit AI quality systems for accuracy and reliability (Mak, 2011). Techniques like Local Interpretable Model-Agnostic Explanations (LIME) can help by providing explanations of individual AI model predictions.

Finally, the use of AI requires new skills and ways of working for QA professionals. QA teams may need to collaborate much more closely with data scientists and machine learning engineers to develop and monitor AI quality systems. A mindset of continuous experimentation and learning is needed as AI models are refined over time based on incoming quality data (Li et al., 2011). Change management is also critical to get frontline workers to embrace AI tools and trust their outputs.

How Will Emerging Technologies Continue to Transform Quality Assurance?

Looking ahead, several emerging technologies are poised to further transform quality assurance in the coming years:

• Digital twins – virtual models that exactly replicate physical assets, products and processes – will allow proactive simulation and optimization of quality
• Blockchain will enable tamper-proof, end-to-end traceability of quality data across complex supply chains, increasing transparency and trust
• Augmented reality will give QA workers real-time visual guidance and information to make better quality decisions
• 5G networks will allow real-time gathering and analysis of quality data from massive networks of sensors in products and equipment

Businesses that embrace these technologies will be able to take quality assurance to new levels of proactivity, personalization and business value. However, they will also need to navigate complex issues around data privacy, algorithm governance, and workforce transformation. The future of quality assurance is tightly linked to AI – and organizations’ ability to harness its power while mitigating its challenges will be a key source of competitive differentiation.

Tallyfy Tango – A cheerful and alternative take

Scene: Inside the Quality Assurance department at Acme Corporation. Quincy, the QA manager, is reviewing some product defects with his top analyst, Anita.

Quincy: Anita, we have a problem. Customer complaints are through the roof about the Acme Rocket Skates. Looks like another quality issue slipped through.

Anita: Again? (sighs) Let me guess… The rocket boosters are engaging randomly and sending people flying?

Quincy: Yep. Marketing pitched it as an “exhilarating experience” but I don’t think this is what they had in mind. We need to tighten up our quality assurance operations, pronto!

Anita: No kidding. Hey, maybe we should take a page from Wile E. Coyote’s playbook…

Quincy: How so? Strap ourselves to a rocket and light the fuse?

Anita:(laughs) No, I meant be more methodical in our testing. Really put the product through its paces. Wile E. is the ultimate QA guy – he pushes everything to the limit to find the breaking point.

Quincy: True, although his breaking point usually involves a 1000-foot cliff. I’d prefer to keep our testing in the lab. Let’s review the QA checklist and make sure we didn’t miss any key steps.

Anita: Good call. And from now on, no product goes out the door until it gets the full Quincy Quality Seal of Approval!

Quincy: I like the sound of that. With tighter quality assurance operations, we’ll have customers raving about Acme products… and not because they’re ducking for cover!

Anita and Quincy high-five as the scene ends.

Related Questions

What is quality assurance operations?

Quality assurance operations is the process of ensuring that products or services meet the required quality standards before they reach the customer. It involves planning, monitoring, and controlling various activities throughout the production process to prevent defects and maintain consistency. Quality assurance operations aim to build trust with customers by delivering high-quality outputs that meet their expectations every time.

What are the 5 functions of quality assurance?

The five main functions of quality assurance are:

1. Planning: Establishing quality standards and defining the processes needed to achieve them.
2. Monitoring: Continuously observing and measuring the production process to identify potential issues.
3. Control: Taking corrective actions when deviations from the quality standards are detected.
4. Improvement: Constantly seeking ways to enhance the quality of products or services and optimize processes.
5. Documentation: Maintaining accurate records of quality assurance activities for traceability and compliance.

What is quality assurance in operational phase?

In the operational phase, quality assurance focuses on maintaining the quality standards set during the planning stage. This involves regularly monitoring the production process, conducting inspections and tests, and addressing any issues that arise. Quality assurance teams work closely with production staff to ensure that everyone understands and follows the established quality procedures. They also analyze quality data to identify trends and opportunities for improvement.

What are the four types of quality assurance?

The four main types of quality assurance are:

1. Process QA: Ensuring that the production process is designed and executed in a way that consistently delivers high-quality outputs.
2. Product QA: Verifying that the final product meets the specified requirements and is free from defects before it reaches the customer.
3. Software QA: Testing software applications to identify and fix bugs, ensure proper functionality, and optimize performance.
4. Service QA: Monitoring and evaluating the quality of services provided to customers, such as customer support or after-sales service.

References and Editorial Perspectives

Edvardsson, B. (1998). Service Quality Improvement. Managing Service Quality, 8, 142 – 149. https://doi.org/10.1108/09604529810206972

Summary of this study

This study focuses on quality improvement in service operations, including private and public services as well as services within manufacturing companies. The author notes that despite the significant role services play in GDP and employment in OECD countries, there is still limited knowledge about quality management in service operations compared to manufacturing. The study aims to address this gap and provide insights into service quality improvement.

Editor perspectives

At Tallyfy, we find this study highly relevant as it sheds light on the importance of quality assurance in service operations, an area that is often overlooked compared to manufacturing. As a workflow management platform, we understand the critical role of streamlined processes in delivering high-quality services consistently. This research reinforces our mission to help organizations digitize and optimize their workflows for improved quality and efficiency.

Li, L., Su, Q., & Chen, X. (2011). Ensuring Supply Chain Quality Performance Through Applying the SCOR Model. International Journal of Production Research, 49, 33 – 57. https://doi.org/10.1080/00207543.2010.508934

Summary of this study

This study examines how the Supply Chain Operations Reference (SCOR) model can be used to ensure quality performance in supply chains. By surveying 232 ISO 9000 certified companies, the authors extend the five decision areas of the SCOR model (Plan, Source, Make, Deliver, and Return) by integrating quality assurance measures. The results show that each decision area positively impacts both customer-facing supply chain quality performance and internal business performance.

Editor perspectives

This research is particularly interesting to us at Tallyfy because it demonstrates the value of integrating quality assurance measures into supply chain processes. By leveraging a structured framework like the SCOR model, organizations can ensure consistent quality performance across their operations. As a workflow management solution, we strive to help our clients embed quality checkpoints and best practices into their processes to drive continuous improvement.

Okerekehe, O. (2014). The Impact of Inspection on the Quality Assurance and Reliability of Projects, Manufacturing, Operations and Maintenance. British Journal of Applied Science and Technology, 4, 3884 – 3901. https://doi.org/10.9734/bjast/2014/12081

Summary of this study

This study reviews the impact of inspection on quality assurance and reliability across projects, manufacturing, operations, and maintenance. The author identifies the types of inspections and methods for establishing inspection frequency. The research reveals that too little inspection leads to more rejects, while too much inspection results in lost revenue due to excess inspection costs. An optimal balance is found between the cost of out-of-limit work and inspection costs, leading to an optimal value for the Change Multiple in inspection frequency.

Editor perspectives

At Tallyfy, we appreciate the insights this study provides on optimizing inspection frequency for quality assurance. Finding the right balance between inspection costs and the cost of quality issues is crucial for efficient operations. Our workflow management platform allows organizations to build inspection tasks into their processes at the optimal frequency, ensuring quality without overburdening the system. This research reinforces the importance of data-driven decision making in quality assurance.

Rezaei, J., Kothadiya, O., Tavasszy, L., & Kroesen, M. (2018). Quality Assessment of Airline Baggage Handling Systems Using SERVQUAL and BWM. Tourism Management, 66, 85 – 93. https://doi.org/10.1016/j.tourman.2017.11.009

Summary of this study

This study assesses the perceived service quality of airline baggage handling systems using the SERVQUAL model and Best Worst Method (BWM). The authors identify criteria for each SERVQUAL dimension through a literature review and use BWM to calculate the weights of the criteria based on passenger data. The results show that ‘reliability’ is the most important dimension, followed by ‘responsiveness’, ‘assurance’, ‘tangibles’, and ’empathy’. A cluster analysis reveals that passengers may have different service quality priorities.

Editor perspectives

As a workflow management platform, Tallyfy is always interested in how service quality can be measured and improved in various industries. This study’s application of the SERVQUAL model and BWM to airline baggage handling provides a structured approach to assessing service quality in a complex operation. The findings highlight the importance of reliability and responsiveness, which are key factors we help our clients optimize through streamlined workflows and real-time monitoring. The study also reminds us that different customer segments may have varying quality priorities, emphasizing the need for adaptable processes.

Mak, B. (2011). ISO Certification in the Tour Operator Sector. International Journal of Contemporary Hospitality Management, 23, 115 – 130. https://doi.org/10.1108/09596111111101706

Summary of this study

This study investigates why tour operators implement quality assurance and the challenges they face in doing so. Through interviews with senior managers of three ISO 9000 certified tour operators, the authors find that the purposes for certification include image building, standardizing operations across departments, understanding operations, reviewing procedures and systems, and legitimizing senior management’s change initiatives. However, the study also identifies negative aspects such as high implementation costs, lukewarm staff reception, reduced effectiveness over time, and the fact that consistent procedures do not necessarily equate to good or improving quality.

Editor perspectives

At Tallyfy, we recognize the challenges of implementing quality assurance in service-based businesses like tour operators, where standardizing service quality can be difficult. This study provides valuable insights into the motivations and pitfalls of ISO 9000 certification in this sector. While certification can help reduce human error and improve consistency, it’s not a silver bullet for quality improvement. As a workflow management solution, we believe in empowering organizations to continuously optimize their processes based on real-world performance data, rather than relying solely on static certifications. This study underscores the importance of engaging staff, monitoring effectiveness, and focusing on actual quality outcomes in any quality assurance initiative.

Leopold, H., Smirnov, S., & Mendling, J. (2012). On the Refactoring of Activity Labels in Business Process Models. Information Systems, 37, 443 – 459. https://doi.org/10.1016/j.is.2012.01.004

Summary of this study

This study addresses the problem of activity label quality in business process models. The authors design a technique for recognizing labeling styles and automatically refactoring labels with quality issues. They develop a parsing algorithm that integrates natural language tools like WordNet and the Stanford Parser to deal with the shortness of activity labels. Using three business process model collections with different labeling style distributions, the authors demonstrate the applicability of their technique, which provides more stable results compared to standard natural language tools. The technique shifts the boundary of automatically checkable process model quality issues from syntactic to semantic aspects.

Editor perspectives

As a workflow management platform, Tallyfy is deeply invested in helping organizations create clear, consistent, and high-quality process models. This study tackles a critical issue in process modeling: the quality of activity labels. By developing a technique to automatically detect and refactor labeling issues, the authors push the boundaries of what can be checked automatically in process models. At Tallyfy, we strive to provide our users with intuitive tools for creating well-structured, semantically clear process models. This research inspires us to continue exploring innovative ways to enhance the quality and clarity of process documentation, ultimately leading to better process understanding and execution.

Stoel, D., Havelka, D., & Merhout, J., W. (2012). An Analysis of Attributes That Impact Information Technology Audit Quality: A Study of IT and Financial Audit Practitioners. International Journal of Accounting Information Systems, 13, 60 – 79. https://doi.org/10.1016/j.accinf.2011.11.001

Summary of this study

This study identifies and evaluates potential constructs that impact IT audit quality, building on prior work that proposed general frameworks. The authors develop a survey tool and ask IT and financial accounting practitioners to assess the impact of these items on IT audit quality. Using factor analysis, they refine the set of IT audit quality factors and provide insight into the prioritized impact of each factor. The study finds that additional factors are significant for IT audit quality compared to prior research, and the relative importance of the factors differs between IT and financial auditors.

Editor perspectives

At Tallyfy, we recognize the growing importance of IT audits as organizations increasingly rely on technology for their operations. This study’s approach to identifying and prioritizing factors that impact IT audit quality is highly relevant for ensuring the effectiveness of these audits. By understanding the key attributes that contribute to audit quality, organizations can better structure their IT audit processes and focus on the most critical areas. As a workflow management platform, Tallyfy can help streamline IT audit workflows based on these prioritized factors, ensuring that audits are conducted efficiently and effectively. The study also highlights the need to consider the perspectives of both IT and financial auditors in designing audit processes.

Cheng, C., Chang, Y., & Chen, C. (2021). Construction of a Service Quality Scale for the Online Food Delivery Industry. International Journal of Hospitality Management, 95, 102938 – 102938. https://doi.org/10.1016/j.ijhm.2021.102938

Summary of this study

This study constructs a service quality scale for the online food delivery (OFD) industry by integrating key service factors extracted through Internet Big Data Analytics (IBDA). The authors use qualitative and quantitative research procedures, taking OFD customers in Taipei City as the objects. The results show 20 key service factors for the OFD industry, and the developed OFD-SERV scale contains six dimensions: reliability, maintenance of meal quality and hygiene, assurance, security, system operation, and traceability, with a total of 28 items. Structural equation modeling reveals that reliability, assurance, and system operation positively impact customer satisfaction.

Editor perspectives

The online food delivery industry has experienced tremendous growth in recent years, and at Tallyfy, we’re excited to see research focused on improving service quality in this sector. This study’s approach of using IBDA to identify key service factors and construct a tailored service quality scale is innovative and practical. By understanding the dimensions that matter most to customers, OFD businesses can prioritize their efforts and allocate resources effectively. As a workflow management platform, Tallyfy can help OFD companies operationalize these service quality dimensions by building them into their delivery processes and monitoring performance. The study’s findings on the impact of reliability, assurance, and system operation on customer satisfaction provide valuable guidance for optimizing OFD workflows.

Sethi, S., P., Rovenpor, J., L., & Demir, M. (2017). Enhancing the Quality of Reporting in Corporate Social Responsibility Guidance Documents: The Roles of ISO 26000, Global Reporting Initiative and CSR‐Sustainability Monitor. Business and Society Review, 122, 139 – 163. https://doi.org/10.1111/basr.12113

Summary of this study

This article reviews the growth of Corporate Social Responsibility (CSR) reports published by large corporations worldwide. These mostly voluntary reports allow companies to inform society about the impacts of their business operations on environmental, socio-political, and governmental aspects. However, the voluntary nature places the burden on corporations to provide adequate information, cover all relevant issues, and ensure the accuracy of the information. The authors compare three institutional approaches that have played a role in improving the quality and consistency of these reports: ISO 26000, Global Reporting Initiative (GRI), and CSR-Sustainability Monitor. The article assesses their different approaches to guiding CSR reporting and their relative strengths and limitations.

Editor perspectives

At Tallyfy, we believe that CSR reporting is a crucial aspect of modern business operations, as it promotes transparency and accountability. This study’s comparison of the three major institutional approaches to CSR reporting guidance is valuable for organizations looking to improve the quality of their CSR disclosures. By understanding the strengths and limitations of each approach, companies can make informed decisions about which framework to adopt or how to combine elements from different guidelines. As a workflow management platform, Tallyfy can support the implementation of these CSR reporting frameworks by embedding their requirements into the relevant business processes and ensuring consistent data collection and reporting. The study highlights the importance of standardization and comparability in CSR reporting, which can be facilitated through well-designed workflows.

Leopold, H., Eid-Sabbagh, R., Mendling, J., Azevedo, L., G., & Baião, F. (2013). Detection of Naming Convention Violations in Process Models for Different Languages. Decision Support Systems, 56, 310 – 325. https://doi.org/10.1016/j.dss.2013.06.014

Summary of this study

This study introduces an automatic technique for detecting violations of naming conventions in process models. The technique is based on text corpora and independent of linguistic resources like WordNet, making it easily adaptable to languages for which corpora exist. The authors demonstrate the applicability of the technique by analyzing nine process model collections from practice, covering over 27,000 labels and three different languages. The results show that the technique yields stable results and can reliably deal with ambiguous cases, contributing to the field of automated quality assurance of conceptual models.

Editor perspectives

Consistent naming conventions are essential for creating clear, understandable, and maintainable process models. At Tallyfy, we appreciate the value of this study in providing an automated technique for detecting naming convention violations across different languages. As organizations increasingly operate in global environments, the ability