Sustainable Prototyping: Linking Quality and Environmental Impact via QFD and LCA

Abstract

Sustainable development requires manufacturers to deliver products that are not only of good quality but also environmentally friendly. The materials used play a crucial role in product manufacturing. They not only directly determine quality but also influence the environment throughout the product’s life cycle. Therefore, the aim was to develop an innovative approach based on the modelling of the relationship between materials and product quality within the framework of the sustainable design of alternative product solutions. The model framework includes selected elements of Quality Functional Development (QFD) and the first stage of life cycle assessment (LCA), namely, material acquisition and extraction. Its novelty lies in supporting the modelling process with life cycle data on materials characterised by their environmental burden. This modelling is based on their potential negative environment impact. Using this foundation, it becomes possible to consider alternative design solutions in terms of both quality (i.e., fulfilling customer satisfaction during use) and environmental performance (i.e., reducing the negative impact throughout the life cycle). The proposed modelling process was also tested, demonstrating its effectiveness in the material analysis of products. The solution can be applied to any material, and, with minor modifications, adapted to various product types.

1. Introduction

There are types of materials and raw materials that are essential in products, and their acquisition is difficult [1,2]. They are often used to produce new products, including those that can significantly modify existing products. These modifications usually lead to an improvement in quality. However, in the era of sustainable development, design and production activities are increasingly focused on the use of new, environmentally friendly materials [3,4,5]. Despite this, the search for materials that simultaneously consider quality and environmental aspects remains an open area of research [6,7]. Most techniques have been observed not to support decision making related to modifying product quality while taking into account changes in materials and their environmental impact [8,9,10,11]. Additionally, the lack of specific quality and material indicators makes it difficult to develop efficient product structures and functions in the context of seeking favourable quality and material solutions [12,13,14].

For example, the authors of [6] analysed potential materials within the framework of sustainable product development, taking into account material quality, environmental impact, and costs. They used a three-dimensional analysis technique within a mathematical model. The authors of [15] explored ways to increase the quality of products made with polyethylene terephthalate (PET). As part of their analysis, they also investigated possibilities for achieving minimal environmental pollution while maintaining product quality. In turn, [16] examined secondary semi-finished products from wood fibres, which were processed using a dry-type rotary cutting mill. These materials are used in finished products for various purposes. The quality of the wood fibres was evaluated under different processing conditions, as was the impact of their grinding on the natural environment. Another example is the work of the authors of [17], who developed a process for manufacturing a matrix used in the production of arc additives with cold metal. The aim was to minimise hydrogen porosity and refine the columnar grains, which contribute to environmental burdens, while also improving material quality by enhancing the physical properties of the deposition wall. The quality and environmental issues of materials used in products were also addressed in [18], which focused on the mechanical recycling of polymers. The study analysed the possibility of reducing pollution by ensuring environmentally friendly waste disposal practices, including material saving. The main problem identified was the presence of pollutants in both the collection and mechanical sorting of waste that negatively affected the quality of the recycled product, resulting in low-quality secondary materials. Meanwhile, the authors of [19] studied the processing of hard coal, one of the main stages of mineral resource management. This process supports the adjustment of product quality parameters at various stages. Clean coal technologies were considered, including quality adaptations of coal products with respect to environmental impact factor. The authors of [12] proposed an approach to quality and material analysis of products in which a circularity index for plastic was developed. This approach was based on the quality of recycled polymers taking into account their environmental impact. On the other hand, the authors of [20] analysed the mechanical recycling of concrete made from high-quality aggregate. They also considered the environmental impact, including the potential to reduce the need for transporting building materials in urban areas. Subsequently, the authors of [21] analysed the effect of irradiation time and microwave power level on the drying kinetics in wood processing. Compared with the traditional drying process, microwave drying resulted in better quality of the final product and additional environmental benefits such as energy savings and shorter processing time.

There remains a lack of clear research that considers the relationships between different materials and their direct impact on both the environment and the quality of products and their prototypes. Although previous studies have addressed quality, environmental, and sometimes cost aspects, these have not been considered within a coherent framework based on modelling these factors in relation to developing the most beneficial product solutions. In the case under consideration, “most beneficial” is understood as environmentally friendly throughout the product’s life cycle and satisfactory to customers during use.

Therefore, the aim of this study was to develop a model supporting the creation and analysis of the relationship between materials and quality towards the sustainable design of alternative product solutions. The model was developed in ten main stages, illustrated, and tested.

The novelty of the presented model is demonstrated by means of a compilation of selected works that concern a similar research area, as presented in

Table 1. Analysis of the literature on the subject and novelty of the research.

Study	Methodology	Techniques	Case Study	Comparison and Novelty of This Study
[22]	Method that supports the sustainable development of materials and the products made from them based on an aggregated indicator of quality and environmental load in the life cycle (QLCA)	Weight Sum Model (WSM), Entropy, Ecoinvent with OpenLCA	Passenger car tyre	The QLCA method provides an analysis of final products in terms of quality and environment, where modifications to the states of product quality criteria and the impact of these modifications on the environment are analysed in LCA; this model focusses on a detailed analysis of the materials obtained and processed for the production of products, taking into account their burden on the environment in the context of LCA, including correlations between planned modifications of quality criteria.
[23]	Framework for a model integrating design thinking (DT) with the Quality Life Cycle Assessment (QLCA) model	Design thinking (DT), Ecoinvent with OpenLCA, Quality Life Cycle Assessment	Framework	The model supports the development of products according to traditional design thinking integrated with quality and product life cycle assessment (QLCA); compared with the model offered in this study, design solutions are sought depending on the materials obtained and processed in the final product and its prototypes, where this is supported by the assumptions of the QFD method (Quality Functional Development).
[24]	A method supporting the design process by assessing innovative product solutions that take into account customer requirements and life cycle assessment	LCA, QFD, and TRIZ (Theory of Inventive Problem Solving)	Hair dryer	Product improvement according to integrated methods based on the search for innovative design solutions and taking into account the aspect of quality and environmental impact in the life cycle, where this analysis takes place in QFD; the offered model searches for product design solutions based on QFD assumptions and takes into account the aspect of quality and environment, but in the form of identifying the increase in negative impact on the environment through unfavourable changes in the product quality criteria, which are directly related only to materials from the first phase of the life cycle and in the overall approach of the final product.
[25]	Eco-efficiency quantification method using Quality Function Deployment (QFD) and life cycle impact assessment (LCA)	QFD, LCA	Different case studies about eco-efficiency	Eco-efficiency analyses of products that do not consider correlation: environmental impacts resulting from changes in the environmental burdens of materials that depend on various modifications of the product quality criteria.
[26,27,28]	Analyses of the environmental impact of a product throughout its life cycle focussing on identifying environmentally friendly solutions that simultaneously meet customer requirements for product quality		Action design research (ADR), inflatable dinghy	Determining environmental requirements using LCA and then prioritising them, taking into account customer requirements; the offered model ensures determining the direction of product quality development by taking into account customer satisfaction with the use of the product, and thus, focussing on criteria that have a significant, direct impact on generating environmental burdens, where these burdens include materials obtained and processed for the production of the final product.

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The novelty lies in supporting the modelling process with materials based on data from the product life cycle (material acquisition and extraction phase) characterised by the level of environmental burden. This modelling is carried out based on the quality criteria of the final product, from which the key ones are selected in terms of their negative impact on the natural environment. On this basis, it is possible to consider alternative design solutions in terms of both quality (ensuring customer satisfaction in use) and environmental performance (limiting the negative environmental impact throughout the life cycle). The originality of this model lies in its integration with the Quality Function Deployment (QFD) method [29], which has not been previously adapted in this manner. Additionally, aspects of life cycle assessment (LCA), according to ISO 14040 [30], are incorporated into the model, consistent with the concept of data modelling in the proposed approach.

Section 1 presents the introduction, literature review, and the novelty of the study. Section 2 outlines the model in a synthetic and general form, allowing its application to any product. Section 3 illustrates and tests the model on a tyre used in light passenger cars. Section 4 offers a discussion covering the model’s novelty in relation to the QFD method and LCA method including its advantages, limitations, and directions for future research.

2. Development of Model: Concept and General Presentation with Description

The framework of the model supporting the creation and analysis of the relationship between materials and product quality includes elements of the QFD method (Quality Functional Development, or House of Quality, HoQ) [31] and the first stage of the product life cycle assessment (LCA), i.e., material acquisition and extraction [22,32].

The QFD method is considered one of the most popular techniques for transforming the voice of customers (VoCs) into the technical language of product design. It involves gathering customer requirements, considering product quality criteria, and defining their equivalents from the designer’s perspective. Correlations and interactions between customer and designer criteria are identified, which then guide product development by prioritising the changes that are most significant to customers, as described in [33,34,35].

In turn, the LCA method is the fundamental approach for assessing the environmental impact of a product or system. It is based on the ISO 14040/44 [30] standards and involves defining the goal and scope, conducting an inventory analysis, assessing the impact, and interpreting the results. LCA typically follows a “cradle to grave” approach, covering material acquisition and extraction, production, use, and end-of-life stages. This method allows for estimating key environmental burdens over the product’s life cycle [36,37].

It was assumed that the results from selected stages of the model would be incorporated into the House of Quality template [38]. To achieve this, the HoQ was modified—specifically, customer-specified quality criteria were replaced with material-related criteria from the first phase of the product life cycle. The weights (importance assessments) of the quality criteria were substituted with values representing the amount of material used during the life cycle. The remaining modifications are shown in Figure 1.

The concept of the proposed model is to select important materials (included in the LCA) that are acquired and processed during the product life cycle. The quantities of these materials are analysed in terms of their environmental burden in the LCA. Then, the product’s quality criteria are identified. Correlations between quality criteria are determined, supporting decision making in later stages of the model. Interactions between product materials and quality criteria are also established. A ranking is developed for the quality criteria that have the greatest negative environmental impact in the product life cycle. Based on this, alternative product solutions are developed and analysed in terms of quality. Among them, the most advantageous solutions are selected.

The characteristics of the proposed model, including the process of integrating HoQ, are described across its main stages.

• Selection of the product for testing. The product is optional and is selected based on the needs of the entity using the model. It may be in the development or maturity phase, newly designed, or require improvements. Market research, observations, or business development goals may guide the selection. The chosen product is treated as a reference, representing a generalisation of products with similar functions.
• Defining the functional unit and system boundaries. The functional unit quantifies the product’s function, allowing for data normalisation and comparison. It is defined using international metric units [39]. From example, a unit could be one product item or its weight. System boundaries refer to the analysed inputs and outputs, including environmental loads or analysis scope. A “cradle to grave” approach is assumed, covering material acquisition, production, use, and end of life. Temporal and spatial boundaries may be defined depending on data availability [39,40].
• Defining the criteria for the material of the product. According to the defined system boundaries (in the concept “from cradle to grave”), materials used in the product are identified [41,42]. The type and quantity of materials depend on the research subject. Material selection and characterisation are performed by expert teams (for example, designers or sustainability specialists) [22,43], potentially with support from LCA databases, e.g., OpenLCA. Each material should be described by a value (parameter) relative to the functional unit.
• Definition of quality criteria. The criteria that influence customer satisfaction during use are identified [44]. These criteria form the basis for evaluating product quality and are selected by expert teams, e.g., based on product catalogues. Typically, about 10 criteria are selected (or more for complex products) [45]. Quality criteria must be quantified using international metric units.
• Determining the correlation between quality criteria. Positive (+), negative (−), and neutral (no mark) correlations are identified [46]. A positive correlation means improving one criterion does not compromise another; a negative correlation implies trade-offs. No correlation indicates independence [33,34]. This supports the product development process in later model stages.
• Determination of mutual influences between material and product quality criteria. The influence of quality criteria on the environmental burdens associated with product materials is assessed [47]. For instance, achieving a certain quality may require more of a specific material, e.g., a plastic product element will have an influence on the amount of plastic used (being one of the materials). Using HoQ principles, these interactions are evaluated by experts using a rating scale: weak ($∆$—rating 1), medium (O—rating 3), strong (∎—rating 9), or no influence (no markings—rating 0) [29,33,35].
• Estimation of the material change significance index (I) and ranking development. The significance index (I) combines the normalised environmental burden (b) of materials with the interaction rating (o) between materials and quality criteria [48]. In this case, it is the values of the sum of the products of the material parameters with the assessments of their dependencies on the quality criteria (1):

$${\mathrm{I}}_{\mathrm{j}}=\sum _{\mathrm{i}=1}^{\mathrm{l}}{\mathrm{b}}_{\mathrm{i}}{\mathrm{o}}_{\mathrm{i}\mathrm{j}}$$

(1)

where I—material change significance index; b—quantitative value of environmental burden for product materials in their life cycle; o—assessment of mutual influences between materials and quality criteria; i—product material; j—quality criterion; i, j = 1, …, n.

A ranking is developed based on the I indicator. The higher the I value, the greater the criterion’s impact on material-related environmental burden. The classification supports identifying solutions that increase customer satisfaction and reduce environmental impact. Additionally, quality criteria weights are assigned using a Likert scale from 1 (low importance) to 5 (high importance). Assigning weights to the criteria ensures differentiation of the significance of the impact not only in terms of customer satisfaction with use but also in terms of reducing the negative environmental impact in the life cycle.

8.

Development of proposals to modify the states of the product quality criteria. Various modifications of current quality criteria states are proposed. A maximum of 7 ± 2 [45] modified states per criterion is recommended, including values above/below the current state or value with verbal descriptions.

9.

Estimation of the quality of product prototypes. Product prototypes are initially assessed based on the current and modified states of quality criteria. Scores are assigned using a Likert scale, where 1 is low customer satisfaction with product use and 5 is high customer satisfaction with product use [49]. Scores across all states for a given criterion should sum to 1. As part of a comprehensive analysis of the quality of prototypes, it is proposed to take into account the importance of quality criteria. The quality indicator (Q) is calculated using a Weighted Sum Model (WSM) (2) [50]:

$${\mathrm{Q}}_{\mathrm{j}}=\sum {\mathrm{w}}_{\mathrm{i}}·{\mathrm{q}}_{\mathrm{i}\mathrm{j}}\pm \cdots \pm \sum {\mathrm{w}}_{\mathrm{i}}·{\mathrm{q}}_{\mathrm{i}\mathrm{j}}$$

(2)

where Q—prototype quality; w—criterion weight; q—criterion state quality; i—criterion; j—prototype; i, j = 1, 2, …, n.

Quality is estimated taking into account correlations between quality criteria (zone IV). In the case of positive correlation, the summation of weighted assessments of the states of criteria with the analysed criterion is considered. In turn, for negative correlation, the differences of weighted assessments of the states of criteria with the analysed criterion are considered. Lack of correlation means accepting the current state for uncorrelated criteria and their summation in the form of a weighted assessment of these states. The higher the Q indicator, the more beneficial the prototype is for customers from a user point of view.

10.

Interpretation of results. The results are interpreted through a normalised quality indicator, depending on environmental aspects. A prototype classification is established that is created according to the normalised Q indicator (3) [23]:

$${\mathrm{Q}}_{\mathrm{j}}^{\mathrm{N}}={\displaystyle \frac{{\mathrm{Q}}_{\mathrm{j}}-\min {\mathrm{Q}}_{\mathrm{j}}}{\max {\mathrm{Q}}_{\mathrm{j}}-\min {\mathrm{Q}}_{\mathrm{j}}}}$$

(3)

where Q—prototype quality; j—prototype; i, j = 1, 2, …, n.

The values range from 0 (unsatisfactory quality) to 1 (fully satisfactory). A value of 0.75 is considered the threshold for favourable product quality [51,52]. Prototypes within the 0.75–1.00 range are considered a compromise between environmental and quality performance. Hence, the value of 0.75 was adopted as a threshold, constituting a compromise between environmental requirements and the quality level achievable by most manufacturers in current technological conditions. This allows for distinguishing products that not only work well in use but also minimise the negative impact on the environment.

The final choice of prototype is made by the user of the model, considering available resources, feasibility of design changes, and other factors. The selected prototype should maximise both customer satisfaction and environmental performance across the life cycle.

Figure 2 presents the algorithm for modelling the relationship between materials and quality in the sustainable design of alternative product solutions.

3. Results: Test and Illustration of the Model Based on a Case Study

The model was tested and illustrated using a passenger car tyre as an example. Passenger car tyres are one of the basic components of light vehicles and the only element in contact with the road [53]. This product is key in all road vehicles. The popularity of such products depends on the widespread availability of data, which is essential for model testing. As vehicles continue to evolve, there is also a need to improve their components, including the tyre [54]. This product should be modified according to specifications to ensure proper use. Additionally, the search for environmentally friendly solutions is becoming increasingly important. Based on a literature review, for example [55,56,57], it has been concluded that passenger car tyres negatively impact the natural environment. Environmental impact, data availability, and industrial significance were the key factors that led to the selection of car tyres as the subject of this research.

A functional unit was adopted for data normalization to enable further comparison. The research includes modelling a passenger car tyre (the reference product) and its hypothetical prototypes. Following the approach used in other studies [22,57], the functional unit is assumed to be one tyre used over a period of approximately 10 years. The system boundaries were limited to the material acquisition and extraction phase—the first stage of the “cradle-to-grave” approach [58]. Inputs include energy and raw materials, while outputs consist of emissions and waste, as shown in Figure 3.

Then, material criteria for a passenger car tyre were determined. The necessary materials used in the tyre production process were selected. The values represent average material masses obtained based on the GREET v1.3.0.13991 model database and the OpenLCA Ecoinvent database, and a literature review presenting inventory data for the assessment of the tyre life cycle assessment was used in the life cycle assessment of tyres [22,57]. Materials used in negligible quantities were omitted. The main materials (listed in

Table 2. Material data for a passenger car tyre in the context of the life cycle in the material acquisition and extraction phase.

Materials	Unit	Average Value
synthetic rubber	kg	2.330
natural rubber	kg	1.759
black carbon	kg	1.808
precipitated silica	kg	0.928
mineral and vegetable oils	kg	0.570
steel wires	kg	1.073
textiles	kg	0.445
polymers–polyurethanes	kg	0.232
anti-adhesive substance (silicone)	kg	0.242

) were recorded in zone I, while their quantities for a passenger car tyre were listed in zone II.

Then, the quality criteria for the passenger car tyre, which are related to customer satisfaction during use, were determined. They were recorded in zone III. The criteria were determined in accordance with the passenger car tyre catalogues:

• Wet grip;
• Cornering grip;
• Durability;
• Fuel consumption;
• Noise;
• Aquaplaning resistance in a straight line;
• Aquaplaning resistance on a curve;
• Driving stability;
• Rolling resistance coefficient;
• Tyre load capacity;
• Speed index.

Correlations between the quality criteria were then determined and recorded in zone IV. Positive correlations were observed between wet grip and criteria such as cornering grip, noise, aquaplaning resistance on straight and curved roads, and driving stability. Other positive correlations included the following: cornering grip with noise and aquaplaning resistance; durability with load capacity; fuel consumption with noise and rolling resistance coefficient; and driving stability with both rolling resistance coefficient and speed index. Negative correlations were observed between wet braking grip and both fuel consumption and rolling resistance coefficient. Similar negative correlations existed between cornering grip and both fuel consumption and rolling resistance coefficient.

Subsequently, the mutual influences between tyre materials and qualitative criteria were identified. These were categorised as weak, medium, or strong, indicating the impact of qualitative criteria on the environmental burden caused by selected tyre materials. Strong influences were most commonly associated with qualitative criteria such as wet braking grip, cornering grip, and tyre durability. The results were recorded in zone IV.

Finally, the material change significance index (I) was calculated using Formula (1), which sums the products of material parameters and the evaluations of their dependence on the qualitative criteria. For example, for the first quality criterion (wet braking grip), the calculation is shown in Equation (4):

$${\mathrm{I}}_1=\left(2.33·9\right)+\left(1.76·9\right)+\left(1.81·3\right)+\left(0.93·1\right)+\left(0.57·1\right)+\left(1.07·3\right)+\left(0.44·1\right)+\left(0.23·3\right)+\left(0.24·1\right)=48.32$$

(4)

Similar calculations were performed for the remaining quality criteria. Based on these results, a ranking of qualitative criteria was developed and recorded in zone V. The complete structure is shown in Appendix A (Figure A1).

Tyre durability was identified as the most important quality criterion in terms of environmental impact over the tyre’s life cycle (in the context of environmental criteria). It achieved the highest significance index (76.94) and was therefore assigned a weight of 5 on the Likert scale. The next most important criteria (4) were cornering grip (49.17) and wet braking grip (48.32). Criteria of medium importance (3) included load capacity (33.32), driving stability (28.89), and aquaplaning resistance on straight and curve roads (21.18). Fuel consumption and speed index (17.56) were deemed of little importance (2), while noise (13.33) and the rolling resistance coefficient (12.38) were considered practically insignificant (1).

Next, modifications to the reference tyre were proposed based on product catalogues. These proposed modifications are presented in

Table 3. Modifications to the status of the tyre quality criteria.

Tire Quality Criterion	Proposed Modifications to the Criteria
	A0	M1	M2	M3	M4	M5	M6
Wet grip (class)	A	B	C	D	E	-	-
Corner grip (class)	A	B	C	D	E	-	-
Durability (years)	5	6	7	8	9	10	-
Fuel consumption (class)	A	B	C	D	E	-	-
Noise (dB)	67–69	70–72	73–77	-	-	-	-
Aquaplaning resistance	9	10	11	12	13	14	15
in a straight line (litres/second)	9	10	11	12	13	14	15
Aquaplaning resistance in a	150	200	250	300	-	-	-
curve (litres/second)	<6.5	6.6–7.7	7.9–9.0	9.1–10.5	>10.6	-	-
Driving stability (km/h)	75	91–95	100–106	110–115	-	-	-
Rolling resistance coefficient (RRC)	90–110	120–140	150–180	190	210	200–240	270

A0 is the current product, M1–M6 are prototypes. For wet grip and for corner grip (class): A—best grip on the tyre (braking distance is about 3 metres at a speed of 80 km/h.), B—good grip on the tyre (braking distance is about 5 metres at a speed of 80 km/h.), C—average grip (braking distance is about 7 metres at a speed of 80 km/h.), D—lower grip (braking distance is about 18 metres at a speed of 80 km/h.), E—lowest grip (lower than D). For fuel consumption (class): tyres are classified from “A” (best—greatest fuel savings) to “G” (worst—least fuel savings) in terms of fuel efficiency, where “B” class tires consume about 0.1 litres less fuel per 100 km compared with the “C” class, and the difference between the “A” and “E” classes is 0.6 litres per 100 km.

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The modified quality criteria were evaluated using a Likert scale, where 1 = low customer satisfaction with the use of the tyre, and 5 = maximum customer satisfaction with the use of the tyre. The assigned scores are presented in

Table 4. Evaluation of the tire quality criteria and their importance.

Tire Quality Criterion	Weight	Proposed Modifications to the Criteria
		A0	M1	M2	M3	M4	M5	M6
Wet grip (class)	4	3	4	4	5	5	-	-
Corner grip (class)	4	3	4	4	5	5	-	-
Durability (years)	5	2	3	4	4	5	5	-
Fuel consumption (class)	2	3	4	4	5	5	-	-
Noise (dB)	1	5	4	3	-	-	-	-
Aquaplaning resistance	3	5	5	5	4	4	3	3
in a straight line (litres/second)	3	5	5	4	4	3	3	2
Aquaplaning resistance in a	3	3	4	5	5	-	-	-
curve (litres/second)	1	5	4	3	2	1	-	-
Driving stability (km/h)	3	3	4	4	5	-	-	-
Rolling resistance coefficient (RRC)	2	2	3	3	4	4	5	5

A0—current product, M1–M6—prototypes, 1–5—evaluation on Likert scale.

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Subsequently, based on previous established correlations between tyre quality criteria (zone IV), possible quality levels were estimated. For instance, in the case of “adhesion on a wet surface”, five positive and two negative correlations with other quality criteria were identified. Three criteria were uncorrelated. Combinations were created for each state of the positively and negatively correlated criteria in relation to each state of the analysed criterion. Uncorrelated criteria were presented by their current states only. Using Formula (2) and data from Table 4, the quality of tyre prototypes was estimated. An example of the calculation method for the first three prototypes is shown in Formula (5):

$$\begin{gathered} {\mathrm{Q}}_1=\left(4·3\right)+\left(4·3\right)+\left(1·5\right)+\left(3·5\right)+\left(3·5\right)+\left(3·3\right)-\left(2·3\right)-\left(1·5\right)+\left(5·2\right)+\left(3·3\right)+\left(2·2\right)=80 \\ {\mathrm{Q}}_2=\left(4·3\right)+\left(4·4\right)+\left(1·4\right)+\left(3·5\right)+\left(3·5\right)+\left(3·4\right)-\left(2·4\right)-\left(1·4\right)+\left(5·2\right)+\left(3·3\right)+\left(2·2\right)=85 \\ {\mathrm{Q}}_3=\left(4·4\right)+\left(4·3\right)+\left(1·5\right)+\left(3·5\right)+\left(3·5\right)+\left(3·3\right)-\left(2·3\right)-\left(1·5\right)+\left(5·2\right)+\left(3·3\right)+\left(2·2\right)=84 \end{gathered}$$

(5)

As a result, 35 alternative tyre solutions were obtained for the wet grip criterion. The quality of these alternatives was assessed in relation to the remaining quality criteria using a similar procedure. Due to the numerous correlations and various states of the criteria, a total of 277 quality (Q) indices were ultimately calculated.

Using Formula (3), the Q indices were normalised to a range from 0 to 1, where 1 is the most favourable quality indicator (highest customer satisfaction). The results for the developed tyre prototypes are visualised in Figure 4.

Assuming a quality index above 0.75 indicates high customer satisfaction, 37 of the analysed prototypes were deemed satisfactory. The threshold of 0.75 was adopted as the acceptability criterion for comprehensive quality and environmental performance, encompassing both technical (quality) parameters and environmental impact. This means that a tyre must meet at least 75% of the evaluated criteria at a satisfactory level or higher to be considered acceptable in terms of quality and ecological compatibility. A summary is presented in

Table 5. List of satisfactory prototype quality levels.

No.	Indicator	Verbal Description	No.	Indicator	Verbal Description
Q215	0.76	beneficial	Q219	0.84	distinctive
Q217	0.76	beneficial	Q220	0.84	distinctive
Q222	0.76	beneficial	Q226	0.84	distinctive
Q224	0.76	beneficial	Q227	0.84	distinctive
Q76	0.78	beneficial	Q80	0.86	distinctive
Q77	0.78	beneficial	Q81	0.86	distinctive
Q107	0.78	beneficial	Q84	0.86	distinctive
Q108	0.78	beneficial	Q85	0.86	distinctive
Q114	0.78	beneficial	Q87	0.90	distinctive
Q115	0.78	beneficial	Q91	0.90	distinctive
Q212	0.79	beneficial	Q82	0.91	excellent
Q213	0.79	beneficial	Q86	0.91	excellent
Q79	0.81	distinctive	Q88	0.95	excellent
Q83	0.81	distinctive	Q89	0.95	excellent
Q121	0.81	distinctive	Q92	0.95	excellent
Q122	0.81	distinctive	Q93	0.95	excellent
Q128	0.81	distinctive	Q90	1.00	excellent
Q129	0.81	distinctive	Q94	1.00	excellent
Q78	0.83	distinctive

Q—quality indicator.

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Among these prototypes, the most advantageous designs were Q90 and Q94, which achieved the highest quality indicator. The quality criteria are presented in the morphological analysis (Figure 5).

The morphological analysis of these two selected designs demonstrated that small changes to the current product could significantly improve customer satisfaction while reducing environmental impact. Despite requiring minimal design changes, they proved the most beneficial. This was due, in part, to the assumption that the environmental burdens of materials used in modifying the quality criteria were quantitatively assessed. The ranking was also influenced by the assigned weights of product criteria used in generating the quality indicator. The combination of environmental load and criterion significance revealed that, for instance, increasing tyre durability and load capacity alone can result in satisfactory quality and environmental performance. The longer the service life and the greater the load capacity, the higher the customer satisfaction and the lower the negative environmental impact over the tyre’s life cycle compared with the reference tyre.

However, the final decision on prototype selection lies with the entity using the model and may depend on factors such as available resources. The highest quality indicator may not always determine the best choice—prototypes with slightly lower indices may be preferred due to simple design assumptions, fewer required modifications, or lower cost.

4. Discussion

Research on product improvement using the QFD method, including efforts to limit the environmental impact throughout the life cycle, is still in the development phase [59,60]. Various methods supporting the design process have been proposed. For example, in [24], LCA, QFD, and TRIZ (Theory of Inventive Problem Solving) were combined. The reduction in global warming was considered, including the possibility of generating prototypes beneficial to customers in terms of quality. Eco-design was the subject of research in [25], where QFD was integrated with LCA. The analysis focused on the eco-efficiency of products. A similar combination of methods was proposed in [26], where a procedure was introduced to incorporate environmental criteria, establish their priorities, and then combine them with customer requirements. The list of environmental requirements was determined using LCA. Likewise, in [27], a tool was offered to optimise product design (prototypes) at the early stages of development. This tool uses the QFD matrix and accounts for environmental impacts during the product life cycle. In turn, [28] identified environmental criteria and their importance and then processed them in the QFD method to achieve product eco-design.

It has been observed that current approaches do not focus on balancing product quality and the materials used, which are important from an environmental perspective. Moreover, these materials were not analysed in the context of the product life cycle, including an initial search for significant relationships with quality criteria and then the generation of alternative product solutions. Therefore, the objective was to develop an innovative approach based on modelling the relationships between materials and product quality within the framework of sustainable design for alternative product solutions. The modelling was based on material relationships from the product life cycle (LCA) and key product quality criteria.

Table 6. Comparison of the proposed model with the traditional form of the QFD method.

Aspects	Traditional QFD Method	Proposed Model Considering the Stages of QFD Method
Purpose	Analysis of the relationship between quality criteria (customer) and technical criteria of the product to determine the direction of product development that ensures customer satisfaction (required quality).	Analysis of the relationship between material criteria (material acquisition and extraction phase in the product life cycle) and technical product criteria (related to quality) to determine the direction of product development that ensures customer satisfaction (required quality) while simultaneously limiting the negative environmental impact throughout the life cycle.
Scope of analysis	Quality criteria (expressed by customers and translated into technical language), including a possible analysis of the product’s competitiveness, within the framework of the current product (excluding prototypes).	Quality criteria (expressed by customers and translated into technical language), product material criteria with their quantitative environmental impact over the life cycle, and prototyping in various states of quality criteria.
Input	Voice of the customer (VoC), market requirements, prior product designs.	Voice of the Customer (VoC), market requirements, prior product designs, set of materials, and the quantity sourced and processed during product production.
Output	Ranking of technical criteria specifying the order of criteria that need to be improved to achieve the expected quality.	Ranking of technical product criteria specifying the order of criteria that need to be improved to achieve the expected quality while simultaneously limiting the negative environmental impact.
Techniques	Survey, interview, brainstorming.	Survey, interview, QFD method, brainstorming, morphological analysis, elements of the LCA method.
Main benefits	Support for the design process, facilitating the acquisition and processing of customer requirements, and supporting designers in determining the sequence of improvement actions to achieve customer satisfaction; low-cost method.	Support for the design process, facilitating the determination of relationships between materials generating environmental burdens and quality criteria, and supporting designers in determining the sequence of improvement actions in order to achieve customer satisfaction and minimise environmental impact; low-cost model.
Main limitations	Complex form, need for an interdisciplinary team, difficulty in analysing a large number of criteria, time-consuming.	Complex form, modelling with simplified data, anticipated difficulties in analyses involving a large number of different criteria, time-consuming.

Source: own elaboration.

presents the main contribution of the proposed model to the design and improvement process of products using the QFD method.

The proposed model, based on the QFD method, was found to be methodologically consistent and to support correlation analyses, including mutual relationships between materials and product quality criteria. Including selected QFD stages in the model contributes to its methodological soundness, which further enhances its practical efficiency. Expanding the model with additional stages beyond the traditional QFD method adds to its originality.

Next, the proposed model, which incorporates life cycle assessment elements, was compared with the traditional approach of the LCA method. The aim was to demonstrate the relationship between LCA and the proposed model, highlighting its novelty. This is presented in

Table 7. Comparison of the LCA method with the proposed model based on selected LCA elements.

Aspects	Life Cycle Assessment Method	Proposed Model Considering the Elements of LCA Method
Purpose	Life cycle assessment of a product, traditionally “from cradle to grave”, where the stages of material acquisition and extraction, production, use, and end of life are analysed. The assessment is carried out according to the adopted criterion of environmental burden.	Relying on materials from the first stage of the life cycle assessment, i.e., acquisition and extraction of the materials necessary for the product. Taking into account the quantity of these materials per adopted functional unit. A full product life cycle assessment is not performed.
Scope of analysis	Life cycle assessment steps according to the approach adopted, e.g., “cradle to grave”.	The first stage of the life cycle assessment is limited to determining the list of main materials and their quantities (the so-called quantitative environmental burden criteria).
Input	System boundaries, functional unit, and product function, including inventory data (materials, energy, transportation, production, use, end of life).	System boundary, functional unit, and product function, including inventory data for materials used in the final product.
Output	The result of the product life cycle assessment that defines the environmental burden in relation to the analysed criterion.	List of product quality criteria most closely related to the generation of significant environmental burdens from the perspective of the materials used in the product.
Techniques	ISO 14040/44	LCA method
Main benefits	Identification of key environmental burdens that support the process of reducing negative environmental impacts in LCA.	Identifying key relationships between material and product quality criteria to determine which quality criteria may generate the greatest environmental burden.
Main limitations	Problems with access to current and complete data and difficulties in interpreting complex results.	Focusses only on the first phase of LCA, involving simplified modelling of relationships between material and quality criterion.

Source: own elaboration.

.

The main area of LCA applicability in the proposed model is to ensure the inclusion of materials obtained and processed during production manufacturing. At the same time, the environmental impact of these materials is considered by quantifying their share in the product. The greater the share, the greater the environmental burden. Based on this reasoning, the model seeks to balance product quality with the necessary materials used, including their quantity, to ensure customer satisfaction while reducing environmental impact.

Therefore, the proposed model will support sustainable product development by balancing product quality and environmental impact through materials selection. It provides the following benefits:

• Improving product quality while reducing environmental impact;
• Supporting the identification of relationships between materials with significant environmental impact and the planned product quality criteria;
• Enhancing the prototyping process to simultaneously meet customer expectations and minimise environmental impact.

The model is low-cost and therefore suitable for small- and medium-sized enterprises (SMEs). Its application does not require a large team of experts, but rather, an interdisciplinary selection of specialists.

One limitation of the model is its focus on the materials used in the product. This refers to the first life cycle stage concerning material acquisition and processing. Other stages—such as energy use, usage phase, and end of life—are not included. Another limitation is the difficulty of conducting extensive analyses for a large number of materials and quality criteria, which necessitates creating a comprehensive quality house, making practical use inefficient.

Future research will aim to expand the model to include other life cycle stages. It will be adapted for comprehensive life cycle assessment, including simultaneous quality assessment. Plans also include incorporating stages such as cost analysis or competitive product benchmarking. Future developments may involve integrating cost analysis, multi-objective optimisation, or simulation tools such as OpenLCA.

The proposed model could also be applicable to products such as electronics, composites, or modular systems. However, changes to the underlying assumptions will be necessary, as the current ones apply to final, commonly used products intended for end users. For modular products, the model should include separate analyses for each module. In the case of composites, the model could be used for quality–environment evaluation, but it should be applied by specialists familiar with both the environmental impact and specific material properties, which would be treated as quality attributes of specific material types. A similar approach applies to electronic products. Adapting the model for other types of applications requires separate studies, which will be the subject of future research by the authors.

5. Conclusions

Sustainable product development requires the consideration of various criteria such as quality and environmental impact. New techniques are still being explored to support conceptualisation and prototyping, aiming to increase customer satisfaction while reducing environmental burdens. Therefore, the objective was to develop a model that supports the creation and analysis of the relationship between materials and quality towards the sustainable design of alternative product solutions.

The novelty of the proposed model lies in its use of product life cycle data—starting from the material acquisition and extraction phase—to support the modelling process. This data includes information on the degree of environmental impact. Modelling considers selected quality criteria of the final product that are significant in terms of environmental impact. This enables the analysis of alternative design variants in terms of both utility quality (i.e., meeting customer expectations) and environmental impact throughout the product life cycle. The model’s innovativeness stems from its integration with the QFD method while incorporating life cycle assessment principles. This aligns with the concept of data modelling within qualitative and environmental analyses of prototypes.

The proposed model allows for the dynamic selection of a product variant that ensures the desired quality and environmentally friendly materials. The proposed solution can be applied to any material, and with minor adjustments, it can be used for various types of products. It may serve as an effective and simple alternative to more complex quality and environmental analyses, particularly in small and medium enterprises (SMEs). These enterprises often lack that resources available to large companies, such as access to life cycle assessment software. Therefore, the model can support early decision making in product development, especially in material selection and modifying quality criteria, without the need for prior physical prototyping. The model may also be useful for other products, such as electronics, composites, or modular systems. However, adapting it to such applications will require revising the assumptions, as the current ones concern final, commonly used products. Separate studies will be necessary, and these will be the subject of future research by the authors.