BUSN361 Introduction to International Business

Quality Function Deployment in Manufacturing

Over the past decade, product manufacturing has been experiencing significant growth. As a result, consumers in all parts of the globe have various products or services options from which they can make selection (Cudney and Elrod, 2011). Product or service perception is a key a factor, which a consumer considers before choosing a product or service. Typically, consumers look for products that offers extra value proposition in addition to what one they seek. For this, organizations should strive to establish what drives perception of the consumer in considering product or service quality. To attain competitiveness, firms must clearly define product feature like performance, reliability and value from consumer’s point of view.

Concerning product design and manufacturing, firms should collect and integrate the consumer feedback in these processes. Companies engage in active manufacture of products or services and consumers judge the value of the items (Vinayak and Kodali, 2013). In order to fully satisfy consumers, organizations employ structured process to identify the needs and wants of customers. After identifying and defines consumer needs and wants, organizations plan to manufacture products with specific designs to promote customer experience.

In most instances, organizations utilize quality function deployment tool to define consumer expectations or requirements. After defining requirements of the customer, organizations utilize them to manufacture products that meet the expectations. The process of manufacturing is very critical in companies since it produces products, which plays a central role in the profitability of a company (Vinayak and Kodali, 2013). The quality operation deployment tool translates the voice of the consumer into specific design specifications, which are then transferred to different sections of the manufacturing department. Additionally, QFD tool incorporates quality management in the manufacturing process to ensure quality consistency of all products.

Drawback of QFD in Manufacturing

The customer requirements are the start point of the QFD methodology. Typically, customer requirements address product aspects like “how it looks, durability and value position.” The QFD methodology converts customer requirements into technical specifications. The drawbacks of a QFD methodology necessitates its integration with other project approaches like artificial neural networks, and fuzzy logic.

Fuzzy Logic

QFD chart require various inputs in form of evaluations and judgement from marketing department. Uncertainties arise while quantifying information form the sales department. Fuzzy logic helps in reducing uncertainty in data collection in QFD methodology (Dehe and Bamford, 2017). Fuzzy logic helps in reducing uncertainty in data collection by eliminating vagueness in data. The techniques do so by manipulating fuzzy qualitative data using linguistic parameters. Fuzzy logic brings approximate reasoning in quality function deployment to handle uncertainty (Dehe and Bamford, 2017).

Fuzzy logic and quality function deployment integration happens when linguistics ideas play a critical role in interpreting customer voice. Usually, customer voice comes in qualitative forms and it requires quantitative expression to flow downstream in the manufacturing system. The voice requires quantitative expression because it contains different and ambiguous meanings (Wu and Liao, 2018). For instance, adjectives “durable product” from customer perspective is not specific. Fuzzy logic uses symbols to represent customer requirements and engineering specification. Therefore, Fuzzy logic assists in building the house of quality relationship matrix.

Artificial Neural Networks

ANN is a simple mathematical model of the human brain that resembles computer networks. The approach uses the way human brain functions after learning to represent information in mathematical algorithms, similar to that of computers (Wu and Liao, 2018). ANN argue that human brain has the ability to learn from real examples and then use the observations to manage systems. QFD requires ANN because it has the ability to deal large data inputs and can tolerate faults of imprecise data and ill-defined tasks. Also, ANN learns underlying relationship between engineering parameters and use it in reducing development time. Since QFD is unable to deal with large amounts of data, artificial neural networks can help the methodology to deal large customer data.

Brainstorming the QFD Tool

Just like effective communication, deployment of quality function in an organization is a critical and impactful success aspect (Erdil and Arani, 2019). The tool communicates customer requirements to a firm’s operations, including manufacturing, quality management, marketing, product design and sales function. The effective communication of customer voice ensure that departments of an organization operate in a collaborative manner to produce products that enhances customer perception.

Additionally, quality function tool places focus on needs and wants of the customer. Therefore, organizations should produce what the customer desire and not what they believe the consumer requires (Erdil and Arani, 2019). Here, QFD translate consumer voice into technical design specification to align product quality with customer needs. In the manufacturing process, the work of QFD is to drive the design specification form customer voice from facility level to component level. The tool ensures control of design specifications by assembling all processes of a company to meet customer needs.

QFD saves time and reduces production cost by avoiding unplanned design changes by using consumer voice to manufacture products that are in line with the requirements (Bolar et al., 2017). The maintenance of effective QFD tool is essential to an organization as it saves valuable project resources from going to waste in the manufacture of products that do not add value to customers. Similarly, QFD provides a structure for documenting lessons learned and decisions formulated during product manufacturing process. The records act as a knowledge base for future manufacturing projects.

Consequently, QFD advises companies to participate in active creativity and innovative processes to launch new products to the market. For the new product to satisfy the needs of customers, it should be made from the customer perspective. Since the customer is the “king,” organizations should incorporate quality function in the manufacturing process by listening to the customer voice before designing products.

The implementation process of QFD requires four phases in the product development cycle. QFD tool utilizes a series of matrices in all the four phases to ensure all the systems, subsystems, and components of the manufacturing process are in line with customer voice. The first phase is product definition, which includes gathering customer data on needs and wants and then translating it to design specifications. It is also essential to carry a competitive analysis in this phase to assess how effective substitute products form rivals are in fulfilling customer needs and wants.

After defining the product, the second phase is product development. In the second phase, QFD identifies and assembles critical parts of the product. The main characteristics of the product are clarified in the second phase by cascading the features down the manufacturing system to make customer specifications clear to human capital. Eventually, the product development phase defines functional specifications and requirements to attain the overall manufacturing goal.

The third phase is the development of the process and involves designing assembly and manufacturing processes in alignment with product specification. Here, QFD develops process flow and identifies significant process features. The fourth and the final phase in the QFD implementation is quality control of manufacturing process. Regarding the phase, QFD determines the parameters of the process to develop and implement appropriate quality control methods in manufacturing process. Throughout the phases, it is vital for organizations to note that team discipline and participation is essential for effectiveness in QFD.

House of Quality (HOQ) in Product Manufacturing

The HOQ relates to a matrix that facilitate product planning by showing relationship between customer requirements and the methods or ways that organizations utilize to attain the requirements. Most investment engineers take house of quality analysis as one of essential evaluation, which makes easier to trace equipment throughout the project. The methodology creates a matrix by utilizing a certain criterion for every project cell. The matrix organizes customer requirement date by ranking it using a general house shape (Tang and Dinçer, 2019).

Competitor analysis tool

In the competitor analysis, HOQ facilitates direct comparison on how product designs strive to satisfy customer voice (Tang and Dinçer, 2019). An analysis of the competitor product can help a company by acting as a turning point for making design decisions to place the firm ahead of competition. HOQ uses technological and competitive benchmarking information to create a design that resembles a house. Typically, HOQ is the fundamental tool that facilitates group decision-making in quality function deployment.

Technical tool

Technical tool relates to “HOWs” the production process happens and includes the ideas of the project engineer. Deployment of an HOQ tool in an organization facilitates designs in the implementation of quality function. Many organizations see and regard HOQ as the most crucial tool in the integration and implementation of the quality function (Tang and Dinçer, 2019). Typically, HOQ implementation starts by satisfying customer needs and then moves to other critical technical designs (Tang and Dinçer, 2019). The tool follows a planning matrix to cater for customer needs systematically. In the deployment of quality function, managers regard HOQ results as the most imperative from the technical perspective of consumer needs in order to fully satisfy them.

Customer requirements (WHATs)

In the methodology, customers’ requirements compose the walls of the house. Therefore, the walls in the matrix indicate the value propositions that a customer looks in a product. Typically, customers requirements are general statements describing the characteristics of the product the customer is seeking. In the stage of HOQ, project engineers do not state customer requirements in the context of specifications or engineering requirements.

 Planning tool (WHYs)

As a rule of thumb, engineering specifications appear at the upper story of HOQ. The story defines product characteristics rather than customer requirements. The requirements of engineers are measurable attributes that are part of a hypothetical product. Generally, engineering requirements have some relation and HOQ methodology records the interrelationship on the roof or the matrix (Efe et al., 2020). Concerning the interrelationship, project engineers record them in terms of strength. There can either be a strong or weak positive relationship and strong or weak negative relation or no relationship whatsoever.

As a result, project engineers in manufacturing are able to identify relating and contradictory engineering requirements. For instance, incorporating quality control techniques in the manufacturing system has a positive relationship with the final quality of the product. an example of strong relationship would be measuring the desirable quality of the final product while also defining the overall desirable value of the product.

Relation matrix  

The actual measurement of the association between engineering requirements happens at the basement of a HOQ. Here, project engineers give the requirements weights to ensure systematic tracking of competing options (Idrees et al., 2019). Additionally, the engineers may use the weighted values to benchmark the project with competitors’ one. As a result, the design of the product under development helps in gaining greater market traction.

Technical connection tool

In a HOQ, the center feature is the basic motive force that drives the methodology. Here, engineers have the discretion to work on the rankings in the way that seems most desirable. However, the commonly used ranking in a house of quality is the relative ranking. For instance, 1-3-9 is a popular method in a house of quality (Shahin and Ebrahimi, 2020). The methods argue that the weights of the most critical aspect in a project is about 9 times as valuable as the least crucial aspect. Also, absolute ranking is another common method in the house of quality. The method allows ranking of the requirements relative to one another where there is clear preference for individual requirements relative to others. Another method that projects engineers use in a HOQ is the binary ranking. It allows rankings to be made in simple terms of relative or irrelevant to simplify the final ranking computation.

A quality function deployment methodology that utilizes HOQ tool yield various competitive advantages from customer satisfaction perspective like reducing flowtime, and improving the general performance of the product. Since QFD focus on ensuring customer voice in product manufacturing, establishing a HOQ matrix helps in ranking customer desires. Project managers ranks desires in terms of customer importance and engineering necessity (Shahin and Ebrahimi, 2020).

 Project managers expands HOQ framework to other areas that relate to manufacturing process. They use the same HOQ process to determine parts to configure by importing engineering features from the previous HOQ. Similarly, project mangers can accomplish process planning by importing the part characteristics from prior HOQ. Finally, the previous process operations can help in determining the production requirements. Therefore, missing data for the manufacturing process can be collected form previous house of quality analysis.

Survey

A survey was conducted on manufacturing companies in the United States to obtain response on their view on application of QFD methodology in the manufacturing system. All the participating manufacturing firms had an opportunity to provide their view on implementation of quality functions deployment in the manufacturing process and its impacts as follows:

Kano Model

In the model, revealed customer needs represents normal requires and firms need to provide extra value proposition to enhance client experience. The expected client requirement represents customer needs that firms assume in the manufacturing process. They include basic product requirements that customers expect. Exciting consumer requirements relate to delighters, meaning that they are out of ordinary needs. These requirements help firms to maintain their market position.

Outcomes of QFD in Manufacturing

The period of increasing professionalism in manufacturing is gone and firms are realizing the new era of embracing the integration of various methodologies to form a multi-functional discipline in the system. QFD in manufacturing help in tracking activities and resources to save time and avoid wastage. The methodology coordinates operations in manufacturing system form the customer point of view (Dinçer et al., 2016). It ensures that human capital in the manufacturing system is aware of the customer requirements to align product characteristics with the expectations.

Additionally, quality function deployment assists companies to collaborate marketing and manufacturing department. Marketing department is very critical in implementation of QFD as it provides critical data in form of customer requirement (Mohamadi et al., 2019). The department is able to quickly obtain customer information as it is always in direct touch with clients during sales operations. Consequently, QFD in manufacturing help in ensuring that the final product meet customer requirements by satisfying their needs (Purwanto, 2020).

Aligning products with customer requirements translates to overall financial performance of a company as it increases sales volume. The main goal of a company is to make profits to maximize shareholders’ wealth and product improvement using QFD is critical in ensuring sales performance (Shahin and Ebrahimi, 2020). QFD in manufacturing assist in reducing start-up costs as it helps firms to avoid wastage of resources in product piloting. Quality planning in manufacturing system contributes to the organization by bringing positive reaction to the marketing as it opens new markets for products.

The fundamental outcome of QFD in manufacturing is that it enhances the likelihood of a product to succeed in the market as it is designed to target specific customer requirements. As a result, QFD reduces the overall design cycle time since it eliminates design changes (Song, 2020). Customer requirements take substantial period before changing and aligning production with client needs can be helpful to a firm.  

Recommendations

Organizations should acknowledge the idea that the growth of internet and technology plays a critical role in providing customer with information regarding products. Customers are able obtain information about the best performing products in the market and utilize it in making purchase decisions. “Adding value” in QFD requires integrating the methodology with tools like Fuzzy logic and artificial neural networks to better satisfy customer needs (Orner et al., 2017). Customers look for extra value propositions in a product and requires consideration in manufacture of products. Firms should use internet to engage customers more and produce products as per the client specifications to eliminate slow moving stock. Global firms deal with huge customer information and QFD cannot handle such information. Therefore, global firms should integrate QFD with Fuzzy logic tool to enable quantitative computing.

Conclusions

In conclusion, QFD brings benefits to operations of an organization since in focuses on customer requirements. As the world is ushering new era of using systems in operation instead of enhancing professionalism, QFD is gaining significant application in firms. Particularly, manufacturing operations require alignment with customer requirements to ensure that final products are satisfying consumer needs. The structure of QFD is rigid and requires integration with artificial neural networks to enhance its flexibility. ANN enables use of human judgment to change the methodology as the customer requirements changes. Finally, researchers need to conduct research to integrate QFD with technologies like python and artificial intelligence.

References

Bolar, A.A., Tesfamariam, S. and Sadiq, R., 2017. Framework for prioritizing infrastructure user expectations using Quality Function Deployment (QFD). International Journal of Sustainable Built Environment, 6(1), pp.16-29.

Cudney, E.A. and Elrod, C.C., 2011. Quality function deployment in continuous improvement. Six Sigma projects and personal experiences, pp.45-78.

Dehe, B. and Bamford, D., 2017. Quality Function Deployment and operational design decisions–a healthcare infrastructure development case study. Production Planning & Control, 28(14), pp.1177-1192.

Dinçer, H., Yüksel, S. and Kartal, M.T., 2016. Based Performance of Participation Banks in Turkey with the House of Quality Using an Integrated Hesitant Fuzzy MCDM. BDDK Bankacılık ve Finansal Piyasalar Dergisi, 10(1), pp.9-33.

 Efe, B., Yerlikaya, M.A. and Efe, Ö.F., 2020. Mobile phone selection based on a novel quality function deployment approach. Soft Computing, 24(20), pp.15447-15461.

Erdil, N.O. and Arani, O.M., 2019. Quality function deployment: more than a design tool. International Journal of Quality and Service Sciences.

Idrees, A.M., EL SEDDAWY, A.I. and Zeidan, M.O., 2019. Knowledge Discovery Based Framework for Enhancing the House of Quality. International Journal of Advanced Computer Science and Applications, 10(7), pp.324-331.

Mohamadi Zanjiran, D., Hashemkhani Zolfani, S. and Prentkovskis, O., 2019. LARG supplier selection based on integrating house of quality, Taguchi loss function and MOPA. Economic research-Ekonomska istraživanja, 32(1), pp.1944-1964.

Orner, K.D., Ozcan, O.Y., Saetta, D., Boyer, T.H., Yeh, D.H., Anderson, D. and Cunningham, J.A., 2017. House of quality planning matrix for evaluating wastewater nutrient management technologies at three scales within a sewershed. Environmental Engineering Science, 34(11), pp.773-784.

Purwanto, A., 2020. Design of Food Product Using Quality Function Deployment in Food Industry. Journal of Industrial Engineering & Management Research, 1(1), pp.1-16.

Shahin, A. and Ebrahimi, S., 2020. Revising the interrelationship matrix of house of quality by the Kano model. The TQM Journal.

Song, C., Wang, J.Q. and Li, J.B., 2020. New framework for quality function deployment using linguistic Z-numbers. Mathematics, 8(2), p.224.

Tang, Z. and Dinçer, H., 2019. Selecting the House-of-quality-based energy investment policies for the Sustainable Emerging Economies. Sustainability, 11(13), p.3514.

Vinayak, K. and Kodali, R., 2013. Benchmarking the quality function deployment models. Benchmarking: An International Journal.

Wu, X. and Liao, H., 2018. An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making. Information Fusion, 43, pp.13-26.

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