A Novelty Model Employing the Quality Life Cycle Assessment (QLCA) Indicator and Frameworks for Selecting Qualitative and Environmental Aspects for Sustainable Product Development

Abstract

The objective of this investigation was: (i) to develop a model that supports sustainable product development, considering the quality aspect and the environmental impact in the product life cycle, and (ii) to establish a framework to select the proportion of the share of these aspects during product development decisions. This research concentrates on achieving products that meet customer demand and have environmentally friendly life cycles. It also supports the implementation of design activities at an early stage of product development, positioning the share of quality in relation to environmental impact. The model is based on creating hypothetical prototypes of current products, and this approach concentrated on aggregating the quality (customer satisfaction) with life cycle environmental impacts (as in ISO 14040). The model was developed in five main stages, including: (i) defining product prototypes according to the modifications of quality criteria most desired by customers, (ii) assessing the quality of prototypes according to the Q quality index, (iii) prospective assessment of the environmental impacts of the life cycles of prototypes according to the LCA environmental index, (iv) methodical integration of the above-mentioned indicators into one quality and environmental indicator QLCA, and (v) analysis of possible production solutions and setting the direction of product development, taking into account both quality and environmental aspects. This research was extended with a sensitivity analysis of the QLCA indicator, after which a framework for selecting the proportion of the Q and LCA indicator’s share in product development decisions was established. The originality of this research is the ability of the developed model to facilitate eco-innovative product design and improvements while also selecting the share of qualitative and environmental aspects needed to develop sustainable products. The results provide a dynamic and effective tool for manufacturing companies; mainly designers and managers during qualitative and environmental prototyping of products commonly used by customers. The model will provide support in predicting a product that will be manufactured that will be satisfactory for customers and environmentally friendly based on LCA.

1. Introduction

The increasing pressure to achieve sustainable development goals has made the development of concepts and projects increasingly important [1]. Sustainable production is a process that creates products within economically justified processes [2], which at the same time reduces the negative impact on the natural environment by saving energy and natural resources [3,4]. It is also crucial to adapt the quality of products to expectations regarding their usability (functionality) [5]. However, making sustainable decisions is difficult, because they should involve both planning and decision-making in the early stages of product development as well as during product improvement [6]. Then, access to real and reliable data is difficult. In the context of sustainable development, there is a lack of methods that could support business practices in basic customer-oriented activities, including services, in the form of implementing, among others, the principles of the circular economy [7]. Key aspects for enterprises also include the so-called green innovations, which support the implementation of sustainable development strategies by ensuring the ability of green dynamics to examine the mechanisms driving eco-innovations in enterprises. The aforementioned green innovations include ecological products and innovations in the area of ecological processes [8]. As reported by ref. [9], enterprises are increasingly introducing eco-innovations in products and processes in order to meet customer requirements, but it is still difficult to account for environmental aspects at the same time. Therefore, tools are sought to improve this process, because these solutions are still an open topic. Therefore, a review of the literature was conducted, covering the improvement of products in terms of their sustainable development, taking into account the qualitative and environmental aspects, which were the subject of the presented research.

For example, efforts were made to ensure sustainable product development in terms of quality and environment by integrating the ecological quality function (QFD) [10] with life cycle assessment (LCA) [11]. The research was based on the development of an environmentally friendly product, as presented, for example, in refs. [11,12,13]. Similar approaches to the problem have also been developed using additional methods, e.g., eco-design and LCA [6] or the design process model (Eco-Process) [14], which involved combining production processes, the use process, and product end-of-life strategies. Furthermore, product quality analysis, life cycle evaluation, and life cycle cost assessment (LCC) [15] were combined to evaluate the product from functional, environmental, and economic perspectives, as presented in ref. [16]. A similar research concept was presented in ref. [17], where the authors developed a tool for reasoning about a favorable eco-design, taking into account the mentioned aspects of product development. Methodologies have also been developed to integrate methods and tools as part of the redesign of products in terms of their sustainable development. For example, in ref. [18], the following were used in a combined manner: the failure mode and effective analysis (FMEA) method [19], the QFD method, the LCA method, the theory of inventive problem solving (TRIZ) method [20,21], and the fuzzy technique for order of preference by similarity to the ideal solution (TOPSIS) method [22]. This approach was intended to support product innovation, including innovative redesign of existing products. The integration of similar methods was presented by the authors of ref. [23], which combined the quality matrix with the LCA method, the TRIZ method, and the analytical hierarchy process (AHP) method [24]. The idea of this combination additionally supported the classification of production solutions from the point of view of environmental protection and meeting the customer’s requirements regarding product quality. In turn, ref. [25], combined the QFD method, LCA, the FMEA method, and the LCC method, where production costs were additionally considered. There have also been studies that examined consumers’ approaches to the sustainable development of products; e.g., the authors of ref. [26] analyzed the actual results of product sustainability assessment and compared them with consumers’ opinions on the environmental sustainability of products, taking into account their quality. It is important to mention the authors of this article, who have already conducted research in the field discussed. These studies include, among others, the development of a procedure for aggregating a quality indicator with an environmental impact indicator in the product life cycle [27] and the methodical guarantee of the analysis of design solutions based on scenarios considering the quality of materials and products in their life cycle [28]. A summary of the main observations of this review is presented in

Table 1. Summary of the literature review on the subject.

Reference	Tools, Methods, Instruments	Idea of Research
[11,12,13]	QFD and LCA	Improving production processes and products in terms of quality and reducing negative environmental impact in the end-of-life strategy
[6]	QFD, LCA, and eco-design
[14]	QFD, LCA, and eco-process
[16]	Qualitative analysis, LCA and LCC	Improving product quality, reducing negative impacts in the life cycle, and adjusting financial requirements
[17]	QFD, LCA, and cost analysis
[24]	QFD, LCA, FMEA, and LCC
[18]	QFD, LCA, FMEA, TRIZ, and FTOPSIS	Redesigning products to improve quality, reduce negative environmental impacts during the life cycle, analyze the causes and effects of defects, and rank and analyze innovative design solutions
[22]	Quality matrix, LCA, TRIZ, and AHP	Ranking production solutions from the point of view of environmental protection and meeting customer requirements regarding product quality
[25]	Survey, LCA	Comparative analysis of product sustainability assessment results according to LCA and customer opinions towards these products in terms of environmental sustainability and quality
[26]	Formalized scoring method (PS) and LCA	Aggregation of the quality indicator with the environmental impact indicator in the product life cycle
[27]	Grey relational analysis (GRA) and LCA	Methodically ensuring the analysis of design solutions based on scenarios that take into account the quality of materials and products throughout their life cycle

.

Based on a review of the synthetic review of the literature, it was observed that there are studies in the area of sustainable product development in terms of quality, environmental, and quality aspects. Previous studies did not take into account the aggregation of quality criteria and sustainable development. These elements proposed have not been simultaneously considered so far. This is also a response to the need to support decision-makers in modern enterprises that want to create products with a high level of customer satisfaction while being consistent with the idea of sustainable development. However, these studies concern the assessment and analysis of products already on sale, where no methodology has been developed for the prospective assessment of prototypes of these products as part of their sustainable development. This would be based on the quality and environmental aspects, including showing the impact of the proportion of these aspects on the decision-making process regarding the direction of product development. This was considered a research gap that we intended to fill.

Therefore, the objective of this research was to develop a model that supports sustainable product development, taking into account the quality aspect and the environmental impact in the product life cycle, and to establish a framework for selecting the proportion of the share of these aspects in product development decisions. As part of this research, the following hypothesis was adopted:

Hypothesis 1.

Sustainable product development should aim to achieve customer satisfaction with product quality and ensure a low impact of the product throughout its life cycle (LCA), where the proportions of these aspects influence product development decisions at an early stage of its design.

The originality of this research is a model that supports the development of sustainable products in terms of quality and environment. The model provides a prospective assessment of prototypes of any product, which are assessed in terms of meeting customer expectations regarding product quality, as well as the environmental impact of the product throughout its life cycle (LCA).

Another novelty of this research is the QLCA indicator developed within the model, which consists of a separately proposed quality indicator Q, showing the fulfilment of customer satisfaction with the usability of the product, and the LCA indicator, presenting the environmental impact of the product throughout its life cycle. The use of QLCA helps us to combine the quality aspect with the aspect of the environmental impact of a product during its life cycle. On this basis, product prototypes are prospectively ranked in order to determine the direction of sustainable product development.

Furthermore, the originality of this research also focuses on the developed framework for selecting the proportion of the share of the quality indicator and the environmental burden indicator in the product life cycle in the QLCA indicator. The new framework for making product development decisions from a quality–environmental perspective can improve decision-making based on the importance of the quality and environmental aspects in the overall product improvement process.

The model presented with the QLCA indicator and its dedicated framework for making product development decisions can effectively support the sustainable development of any product. Hence, they can be used mainly by decision-makers, designers, and managers in manufacturing companies at the stage of designing new products or improving products already on the market. Following the design assumptions will enable the company to predict the direction of product development so that the product is as satisfying as possible for customers while at the same time being environmentally friendly throughout its life cycle.

2. Model with the QLCA Indicator

2.1. General Description

A model was developed to support decision-making regarding sustainable product development. The concept of the model includes the analysis of alternative product solutions (prototypes) [29] in terms of quality and environmental aspects. The model developed the QLCA indicator, as detailed in this section.

Quality aspects refer to customer satisfaction with the use of the product, where in the proposed model, they are assessed and processed based on the voice of the customer (VoC) [30]. Based on these results, the weighted criteria quality index and the product quality index (including its prototypes) are determined [31].

However, environmental aspects include the environmental impact of the product, which is considered in terms of life cycle assessment (LCA). Then, an indicator of the environmental impact of the product is obtained in its life cycle, which is modelled prospectively for product prototypes.

At the last stage of the proposed model, the indicators are combined into one quality and environmental indicator (QLCA). This is done by relativizing the quality indicator and the environmental impact indicator in the life cycle of the product and its prototypes. These indicators are methodically combined, and on their basis, a ranking of prototypes is created. According to this classification, it is possible to determine the direction of product improvement activities, where these activities will be consistent with the principles of sustainable development and will be able to support efforts to achieve the highest possible product quality while limiting the negative impact during its life cycle.

Furthermore, an analysis of the sensitivity of the QLCA indicator was carried out in relation to the proportion of the share of qualitative and environmental indicators, which constitutes an additional added value of the article. On its basis, a framework for prospective qualitative and environmental assessment of the product in terms of its sustainable development was established.

The model and the dedicated QLCA indicator were developed according to five main stages:

Stage 1. Selecting the subject of research and defining the purpose of the research.

Stage 2. Selecting product quality criteria and defining their alternative production solutions.

Stage 3. Obtaining customer requirements and determining the quality of the product and its prototypes.

Stage 4. Prospective life cycle assessment of the product and its prototypes.

Stage 5. Analysis of production solutions to meet customer expectations and reduce the negative impact of the product based on the LCA.

When creating the model, a set of complementary indicators was developed that support its implementation at a given stage of the model. At the same time, they constitute an original approach to prospective qualitative and environmental analysis. The model development diagram is shown in Figure 1.

The model implemented various quality management and decision support techniques. The main techniques were as follows:

• SMART(-ER) method (specific, measurable, achievable, reasonable, timely, evaluate-ate, readjust) [32]—its application can support detailed and precise development of the research objective according to dedicated elements. Then, it is possible to focus on a clearly defined objective, including its measurable verification during its achievement as part of the model implementation.
• Survey or interview–popular techniques for obtaining the customer’s voice, where in the proposed case, a 100-point assessment scale was used, which supports the achievement of various results from the survey, including their further interpretation in the most clearly differentiated way possible.
• Life cycle assessment (LCA) according to ISO 14040 [33]—a method for assessing the life cycle of a product, where the “cradle to grave” approach is used, i.e., taking into account the phases of extraction and acquisition of materials, production, use, and end of life. According to this method, the environmental burden of the product based on the LCA is developed for one burden criterion.
• The remaining methodologies are presented in more detail at individual stages of the model, where the idea and justification for their use are supported by adequate argumentation.

2.2. Characteristics of the Model Stages

The characteristics of the model stages and the procedure are presented in five main sections.

2.2.1. Stage 1. Selecting the Research Subject and Defining the Purpose of the Research

The research subject (product) is determined depending on the needs of the entity using the model. This is why it is chosen by an expert. The subject of the research is arbitrary, but it should be a product commonly used by customers. It may be a product in a phase of maturity or decline, where there is a need to improve it. At the same time, the analyzed product may require the introduction of innovation due to the high level of competition on the market for products of the same application.

Taking into account the selected research subject, the purpose of the analysis is determined. A precise and detailed definition of the goal may be possible using the SMART(-ER) method (specific, measurable, achievable, reasonable, timely, evaluate, readjust) [32]. In the proposed model, it is assumed that the goal is to predict the most advantageous product alternative that will meet quality requirements and limit the negative impact of the product on the natural environment during its life cycle. The purpose of the investigation can be adapted to the individual needs of the entity using the model.

2.2.2. Stage 2. Selection of Product Quality Criteria and Definition of Their Alternative Production Solutions

The product selected for research is considered in terms of meeting customer expectations (product quality in terms of its usability) [34] and the environmental impact of the product throughout its life cycle (LCA). The concept of the model assumes that this product will be modified depending on the dynamics of market changes and the needs of its sustainable development towards caring for the natural environment in the LCA of the product. At this stage of the model, aspects related to the quality of the product and its possible design changes are analyzed.

To characterize the product in terms of quality, it is necessary to define its quality criteria; i.e., those that influence the level of customer satisfaction with the use of the product. The quality criteria are selected by a team of experts (including designers). The team makes its selection based on the product catalogue and its own knowledge and experience. Following the authors of references [35,36,37,38], the number of criteria for a low-complex product should be in the range of 15 ± 10. This means that the criteria analysed should be the main criteria; i.e., those closely related to the level of customer satisfaction with the use of the product. The criteria can be measurable (e.g., weight) or non-measurable (e.g., color).

Then, alternative product solutions (prototypes) are determined. In this case, it comes down to determining the current state of the product criteria (i.e., currently on sale) and possible modifications of these criteria in the future (i.e., planned to be used in the product). The current states and modified (hypothetical) states are determined for all quality criteria. This is done by a team of experts [39]. It is advisable to use the product catalogue, which contains the current product parameters. On the basis of the catalogue, the parameters of the current criteria and possible changes to these parameters should be adopted as the so-called modifications (alternative solutions) of the criteria in future products. All criteria should be characterized by one current state and subsequently by at least five, but not more than ten, modified states [35,38]. The number of current and modified states results from the principles of effective multicriteria decision-making, which will support further implementation of the model. The criteria states expressed in the current and modified state can be expressed as a parameter, a value, or a range of values, as well as a verbal description. If appropriate, criteria states are adopted in accordance with international metric units. Possible modifications of the criteria states can be selected according to expert knowledge, or by increasing or minimizing them by 20% of the value, which ensures a noticeable change according to the Pareto principle [40]. The set of modified criteria states is called a product prototype.

2.2.3. Stage 3. Obtaining Customer Requirements and Determining the Quality of the Product and Its Prototypes

At this stage of the model, the current product and its prototypes are assessed by customers, and the quality of the product and the proposed prototypes is determined based on these assessments. This applies to the process of obtaining the voice of the customer (VoC) [41]. Research on customer expectations regarding the quality of the product and its prototypes should be carried out among customers who know the product, i.e., those who use it or have used it. This will ensure the reliability of the results and a more precise definition of the direction of product development to meet customer requirements. The number of customers participating in the investigation can be estimated based on the method of determining the sample size presented; for example, in ref. [34]. Customer requirements are obtained through surveys or in-depth interviews [42]. Customers evaluate the importance of product criteria and satisfaction with their states (current and modified), which were identified in the second stage of the model. In the case of assessing the importance of criteria, the scores are awarded by dividing 100 points between all criteria, where the higher the number of points, the more important the criterion [43,44]. However, when assessing satisfaction with the criteria states, the customer divides 100 points between the states of a given criterion, where the higher the number of points, the more desirable the criterion state is for the customer.

According to the ratings given by the quality index of the client, the weighted criteria (S) is determined, as shown in Formula (1) [45]:

$${\mathrm{S}}_{\mathrm{i}}={\mathrm{w}}_{\mathrm{i}}\times {\mathrm{c}}_{\mathrm{i}}$$

(1)

where S is the indicator of the weighted quality of the criterion, w is the weight of the criterion, c is the assessment of the fulfilment of customer satisfaction with the condition (modification) of the criterion, i is the modification of the criterion, and i = 1, 2, …, n.

The S index is estimated to make a preliminary selection of criterion modifications so that further data modelling includes only such criteria modifications expected by customers. This involves calculating the average weighted quality index (y) separately for each of the criteria, where modifications below the expected average satisfaction should be rejected, as shown in Formula (2):

$$\begin{array}{ccc}\mathrm{i}\mathrm{f} {\displaystyle \frac{\sum {\mathrm{S}}_{\mathrm{i}}}{\mathrm{n}}}=\mathrm{y}& \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{S}}_{\mathrm{i}}\ge \mathrm{y},& \mathrm{t}\mathrm{h}\mathrm{e}\mathrm{n} \mathrm{m}\mathrm{o}\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s} \mathrm{a}\mathrm{r}\mathrm{e} \mathrm{l}\mathrm{i}\mathrm{k}\mathrm{e}\mathrm{l}\mathrm{y} \mathrm{b}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{a}\mathrm{l}\\ \mathrm{i}\mathrm{f} {\displaystyle \frac{\sum {\mathrm{S}}_{\mathrm{i}}}{\mathrm{n}}}=\mathrm{y}& \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{S}}_{\mathrm{i}}\le \mathrm{y},& \mathrm{t}\mathrm{h}\mathrm{e}\mathrm{n} \mathrm{m}\mathrm{o}\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s} \mathrm{a}\mathrm{r}\mathrm{e} \mathrm{l}\mathrm{i}\mathrm{k}\mathrm{e}\mathrm{l}\mathrm{y} \mathrm{u}\mathrm{n}\mathrm{f}\mathrm{a}\mathrm{v}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\end{array}$$

(2)

where S is the index of the weighted quality of the criterion, n is the number of modifications of the criterion, y is the average index of the weighted quality of the criterion, I is the modification of the criterion, and i = 1, 2, …, n.

Selected modifications of product criteria for further analysis should be summarized, e.g., in a table. Then, the table columns list the quality criteria, while the rows list the current and modified states of the criteria identified after the initial selection. On this basis, it is possible to estimate the product quality index (Q) for the state of the criteria expressed as current and modified, which are defined as a weighted sum of points (3):

$$\left\{\begin{array}{ccc}\mathrm{Q}={\displaystyle \frac{{\mathrm{w}}_1{\mathrm{c}}_{\mathrm{a}1}+{\mathrm{w}}_2{\mathrm{c}}_{\mathrm{a}2}+\dots +{\mathrm{w}}_{\mathrm{n}}{\mathrm{c}}_{\mathrm{a}\mathrm{n}}}{{\mathrm{w}}_1+{\mathrm{w}}_2+\dots +{\mathrm{w}}_3}}& \mathrm{f}\mathrm{o}\mathrm{r}& \mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{s}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{c}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\\ {\mathrm{Q}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{w}}_1{\mathrm{c}}_{\mathrm{m}1}+{\mathrm{w}}_2{\mathrm{c}}_{\mathrm{m}2}+\dots +{\mathrm{w}}_{\mathrm{n}}{\mathrm{c}}_{\mathrm{m}\mathrm{n}}}{{\mathrm{w}}_1+{\mathrm{w}}_2+\dots +{\mathrm{w}}_3}}& \mathrm{f}\mathrm{o}\mathrm{r}& \mathrm{m}\mathrm{o}\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{e}\mathrm{d} \mathrm{s}\mathrm{t}\mathrm{a}\mathrm{t}\mathrm{e} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t} \mathrm{c}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\end{array}\right.$$

(3)

where Q I the quality indicator of the current product or its prototype, w is the weight of the criterion, c is the assessment of the fulfilment of customer satisfaction with the criterion state, a is the current state, m is the modified state, n is the criterion, i is the prototype, and i, n = 1, 2, …, n.

As a result, the quality index of the current product and the quality indexes of the prototypes are calculated. They depend on expected (customer-satisfying) changes in the product quality criteria. Their further analysis and interpretation are carried out in the next stage of the model.

2.2.4. Stage 4. Prospective Life Cycle Assessment of the Product and Its Prototypes

From the perspective of sustainable product development, the modification of product quality criteria should be considered, taking into account the impact of these changes on the environmental impact of the product during its life cycle (LCA) [46]. Therefore, the model concept includes the evaluation of the life cycle of the current (reference) product and the subsequent modelling of the results of this assessment as part of a prospective life cycle evaluation of product prototypes [47]. The environmental assessment of the product prototypes is performed depending on variables that are closely related to the change in environmental impact during the life cycle (LCA). These variables are the result of the nature of the analysis and the availability of data, and they are determined individually by experts [48].

The life cycle assessment is carried out according to the ISO 14040 standard [49], where the purpose and scope of the investigation, the inventory, the environmental impact assessment, and the interpretation of the results are determined. The LCA method supports the evaluation of the environmental impact of products and processes on the natural environment [50]. It is carried out on the basis of appropriately selected environmental impact criteria, usually according to the “cradle-to-grave” approach [51]; i.e., taking into account the phase of extraction and processing of materials, production, transport, distribution, use, and end of life. Life cycle assessments (LCAs) can be supported by computer programs, e.g., the GREET model, OpenLCA, SimaPro, or Gabi. After carrying out a life cycle assessment, an indicator of the environmental impact of a product or prototype is obtained throughout its life cycle for the analyzed environmental load [52]. This indicator is marked as the LCA. The higher the LCA value, the higher the environmental burden.

2.2.5. Stage 5. Analysis of Production Solutions to Meet Customer Expectations and Reduce the Negative Impact of the Product in LCA

The idea of the model concerns determining the direction of product development in terms of sustainable development. Therefore, production solutions are then analyzed in terms of meeting customer expectations of product quality while at the same time reducing the negative impact of the product on the environment throughout its life cycle (LCA). This concerns the comparison of the quality indicator (Q) of the product and prototypes with the environmental burden indicator for the product and its prototypes (LCA). To make this possible, it is necessary to relativize the quality indicator and the environmental impact indicator so that they can be comparable. For this purpose, Formula (4) is used, which presents the percentage of relative quality and environmental impact indicators:

$$\left\{\begin{array}{ccc}{\mathrm{r}\mathrm{Q}}_{\mathrm{i}}=\left({\displaystyle \frac{{\mathrm{Q}}_{\mathrm{i}}-\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{Q}}{\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{Q}}}\right)\times 100& \mathrm{f}\mathrm{o}\mathrm{r}& \mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{o}\mathrm{r} \mathrm{m}\mathrm{o}\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{e}\mathrm{d} \mathrm{q}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\\ {\mathrm{r}\mathrm{L}\mathrm{C}\mathrm{A}}_{\mathrm{i}}=\left({\displaystyle \frac{\mathrm{m}\mathrm{a}\mathrm{x}\mathrm{L}\mathrm{C}\mathrm{A}-{\mathrm{L}\mathrm{C}\mathrm{A}}_{\mathrm{i}}}{\mathrm{m}\mathrm{a}\mathrm{x}\mathrm{L}\mathrm{C}\mathrm{A}}}\right)\times 100& \mathrm{f}\mathrm{o}\mathrm{r}& \mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t} \mathrm{o}\mathrm{r} \mathrm{m}\mathrm{o}\mathrm{d}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{e}\mathrm{d} \mathrm{L}\mathrm{C}\mathrm{A} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{r}\mathrm{o}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\end{array}\right.$$

(4)

where Q is the quality indicator of the current or modified product, minQ is the minimum value of the product or prototype quality indicator among all those considered in a given analysis, maxLCA is the maximum value of the environmental impact indicator of the product or prototype in LCA for the analyzed environmental load, LCA is the product environmental impact indicator or prototype in LCA for the analyzed environmental load, i is the product or its prototype, and i = 1, 2, …, n.

In the case of the Q index, relativization is performed based on the percentage distance from the minimum value. This is due to the fact that the higher the value of the Q index, the better. However, relativization for the LCA index was performed in the case of the percentage distance from the maximum value, because the lower the value of the LCA index, the better.

The values of the relativized values of quality and environmental indicators are compiled into one quality and environmental indicator (QLCA). Formula (5) applies to this:

$${\mathrm{Q}\mathrm{L}\mathrm{C}\mathrm{A}}_{\mathrm{i}}={\displaystyle \frac{{\mathrm{r}\mathrm{Q}}_{\mathrm{i}}+{\mathrm{r}\mathrm{L}\mathrm{C}\mathrm{A}}_{\mathrm{i}}}{2}}$$

(5)

where rQ is the relativized quality indicator, rLCA is the relativized indicator of environmental impact in the life cycle, i is the product or prototype, and i = 1, 2, ..., n.

On this basis, it is possible to determine the direction of product development. QLCA values should be arranged in one ranking, where the maximum value of this indicator is the first position, and the minimum value is the last position. The maximum value of the QLCA indicator means that the product has the highest relative quality, so it will be satisfactory for customers in terms of use. At the same time, this product can have the lowest possible negative environmental impact throughout its life cycle. Therefore, it is considered environmentally friendly. The company should strive to undertake improvement activities appropriate to the prototype with the highest QLCA index. If this prototype is not possible at a given moment, e.g., for financial reasons, it is possible to choose another production solution based on the order in the ranking. These decisions are made by a team of experts, e.g., designers and company management.

3. Results

3.1. Model Test

The model test and its illustrations are presented in five main stages. In the first stage, the subject of the research was selected, and the goal was defined. The model test was carried out on the example of electric scooters. Electric scooters are electric vehicles that are used for passenger transport [53,54]. Electric scooters are gaining popularity, mainly because they are a more ecological form of vehicle compared to combustion scooters [55]. An electric scooter is considered a light vehicle; therefore, it has a special place among products that contribute to reducing global warming [56]. A summary of the data, including the expected number of sales, electricity consumption, greenhouse gas emissions, consumer costs incurred, and revenue from the sale of electric scooters, is presented in

Table 2. Comparison of data covering the main factors of the electric motorcycle sales market. Own study based on [57].

Year	Sale	Electric Energy Usage	Greenhouse Gas Emissions	Consumer Costs	Revenue
2010	38,419	655	229	79	5.3
2030	44,185	738	76	102	8.2

.

Furthermore, it is expected that by 2030, the market will reach USD 109.5 billion, compared to approximately USD 46 billion in 2021 [58].

Then, the purpose of the research was determined. In the proposed case, the aim of the research was to predict the most advantageous prototype of an electric scooter that would meet customer expectations in terms of its quality level and, at the same time, be environmentally friendly throughout its life cycle.

As part of the second stage, the quality criteria for the electric scooter were selected, and its alternative design solutions were determined. The criteria were selected based on the product catalogue, where, following the assumptions of the model, 10 criteria were defined:

• Weight (kg)—total weight of the vehicle without the driver;
• Dimensions (m)—length, width, height of the vehicle;
• Battery capacity (kWh)—time of current consumption by its value, which means the amount of energy that can be stored by the battery;
• Battery charging time (h)—time needed to fully charge the battery;
• Color—external color of the vehicle;
• Engine power (W)—maximum engine power that can be achieved at upper revolutions;
• Maximum range (km)—maximum distance;
• Maximum speed (km)—the highest speed that can be achieved by the vehicle in traditional (horizontal) motion;
• Energy consumption (kWh/km)—the amount of energy used to power the vehicle;
• Rated power (kW)—basic engine power.

The selected quality criteria were considered to be the main ones; i.e., having a significant relationship with the level of customer satisfaction with the use of the electric scooter. Subsequently, alternative design solutions were identified. All of the criteria were characterized according to the current state and the ten modified states. When determining the state of the criteria, the criteria were based on catalogues of electric scooters and a review of the literature on the subject; for example, refs. [55,59]. If it was justified, the criteria were modified according to the Pareto principle; i.e., by 20% of the value of the basic parameter. The set of state modifications denotes the electric scooter prototype, as shown in

Table 3. Electric scooter prototypes.

Prototype	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
P1	60.00	15.00 × 0.65 × 1.08	1.00	1.00	Black	1600	25	40	1.50	0.75
P2	66.00	16.50 × 0.87 × 1.45	1.20	1.30	White	1650	30	48	1.80	0.90
P3	72.60	18.00 × 0.78 × 1.30	1.44	2.00	Gray	1750	35	58	2.16	1.08
P4	79.86	19.80 × 0.87 × 2.38	1.73	2.30	Red	1700	40	70	2.59	1.30
P5	87.85	21.60 × 0.94 × 1.56	2.07	3.00	Green	1750	45	83	3.11	1.56
P6	96.63	23.76 × 1.03 × 1.71	2.49	3.30	Yellow	1800	50	100	3.73	1.87
P7	106.29	25.92 × 1.12 × 1.87	2.99	4.00	Purple	1850	55	120	4.48	2.24
P8	116.92	28.51 × 1.24 × 1.90	3.58	4.30	Blue	1900	60	140	5.37	2.69
P9	128.62	31.10 × 1.35 × 2.24	4.30	5.00	Silver	1950	65	150	6.45	3.22
P10	141.48	34.21 × 1.48 × 2.46	5.16	5.30	Golden	2000	70	160	7.74	3.87

C1—weight (kg), C2—dimensions (m), C3—battery capacity (kWh), C4—battery charging time (h), C5—color, C6—engine power (W), C7—maximum range (km), C8—maximum speed (km/h), C9—energy consumption (kWh/km), C10—rated power (kW).

.

Product P6 is a current product (on sale). The others are planned prototypes of an electric scooter. All of them were subjected to customer evaluation in the form of pilot studies. The research included ten customers who expressed their expectations about the quality of the scooter using a questionnaire. They distributed 100 points between the criteria, assessing their importance, and then among the alternatives of design solutions, assessing their quality levels. Due to the fact that the investigation covered more than one client, these ratings were averaged. On their basis, the weighted criteria quality index (S) was determined using Formula (1). The results are shown in

Table 4. Processed ratings of criteria validity and customer satisfaction with electric scooter prototypes presented with weighted criteria quality indicators.

Prototype	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
Weight	8.50	6.00	9.50	7.70	2.10	16.80	13.00	16.10	4.70	15.60
Average customer satisfaction ratings for electric scooter prototypes
P1	6.70	7.30	1.30	24.70	11.50	8.00	4.30	4.40	25.30	3.00
P2	6.20	8.30	1.60	17.70	11.10	8.90	5.00	5.10	13.70	4.80
P3	7.10	9.00	2.50	13.50	11.30	10.50	5.60	5.80	14.30	5.70
P4	8.90	12.10	3.50	10.20	10.70	10.70	6.20	6.30	10.50	6.90
P5	11.70	15.00	4.10	8.20	10.00	11.00	8.20	8.30	9.10	8.60
P6	13.10	15.70	4.50	7.20	7.80	13.70	8.90	8.80	8.80	9.40
P7	14.60	12.30	12.00	6.30	8.50	10.60	10.30	10.20	7.70	11.40
P8	11.00	9.60	10.00	5.50	6.20	8.80	12.50	12.40	5.70	11.80
P9	11.10	6.20	25.30	4.60	10.60	8.90	17.90	17.80	3.10	14.10
P10	9.60	4.50	35.20	2.10	12.30	8.90	21.10	20.90	1.80	24.30
Weighted quality criteria indicators for electric scooter prototypes (S)
P1	56.95	43.80	12.35	190.19	24.15	134.40	55.90	70.84	118.91	46.80
P2	52.70	49.80	15.20	136.29	23.31	149.52	65.00	82.11	64.39	74.88
P3	60.35	54.00	23.75	103.95	23.73	176.40	72.80	93.38	67.21	88.92
P4	75.65	72.60	33.25	78.54	22.47	179.76	80.60	101.43	49.35	107.64
P5	99.45	90.00	38.95	63.14	21.00	184.80	106.60	133.63	42.77	134.16
P6	111.35	94.20	42.75	55.44	16.38	230.16	115.70	141.68	41.36	146.64
P7	124.10	73.80	114.00	48.51	17.85	178.08	133.90	164.22	36.19	177.84
P8	93.50	57.60	95.00	42.35	13.02	147.84	162.50	199.64	26.79	184.08
P9	94.35	37.20	240.35	35.42	22.26	149.52	232.70	286.58	14.57	219.96
P10	81.60	27.00	334.40	16.17	25.83	149.52	274.30	336.49	8.46	379.08

C1—weight (kg), C2—dimensions (m), C3—battery capacity (kWh), C4—battery charging time (h), C5—color, C6—engine power (W), C7—maximum range (km), C8—maximum speed (km/h), C9—energy consumption (kWh/km), C10—rated power (kW).

.

Then, Formula (2) was used to calculate the average index of the weighted quality of the criteria, and on its basis, modifications that were probably beneficial were identified. The remaining ones were considered of little importance from the point of view of customer satisfaction. The results are shown in

Table 5. Choosing to modify the electric scooter criteria expected by customers.

Weight		Dimensions		Battery Capacity		Battery Charging Time		Color
$\overline{\mathrm{S}}$	85	$\overline{\mathrm{S}}$	60	$\overline{\mathrm{S}}$	95	$\overline{\mathrm{S}}$	77	$\overline{\mathrm{S}}$	21
P7	124.1	P6	94.20	P10	334.4	P1	190.19	P10	25.83
P6	111.35	P5	90.00	P9	240.35	P2	136.29	P1	24.15
P5	99.45	P7	73.80	P7	114	P3	103.95	P3	23.73
P9	94.35	P4	72.60	P8	95	P4	78.54	P2	23.31
P8	93.5	P8	57.60	P6	42.75	P5	63.14	P4	22.47
P10	81.6	P3	54.00	P5	38.95	P6	55.44	P9	22.26
P4	75.65	P2	49.80	P4	33.25	P7	48.51	P5	21
P3	60.35	P1	43.80	P3	23.75	P8	42.35	P7	17.85
P1	56.95	P9	37.20	P2	15.2	P9	35.42	P6	16.38
P2	52.7	P10	27.00	P1	12.35	P10	16.17	P8	13.02
Engine power		Maximum range		Maximum speed		Energy consumption		Rated power
$\overline{\mathrm{S}}$	168	$\overline{\mathrm{S}}$	130	$\overline{\mathrm{S}}$	161	$\overline{\mathrm{S}}$	47	$\overline{\mathrm{S}}$	156
P6	230.16	P10	274.3	P10	336.49	P1	118.91	P10	379.08
P5	184.8	P9	232.7	P9	286.58	P3	67.21	P9	219.96
P4	179.76	P8	162.5	P8	199.64	P2	64.39	P8	184.08
P7	178.08	P7	133.9	P7	164.22	P4	49.35	P7	177.84
P3	176.4	P6	115.7	P6	141.68	P5	42.77	P6	146.64
P2	149.52	P5	106.6	P5	133.63	P6	41.36	P5	134.16
P9	149.52	P4	80.6	P4	101.43	P7	36.19	P4	107.64
P10	149.52	P3	72.8	P3	93.38	P8	26.79	P3	88.92
P8	147.84	P2	65	P2	82.11	P9	14.57	P2	74.88
P1	134.4	P1	55.9	P1	70.84	P10	8.46	P1	46.8

Where: yellow background—modifications that are probably beneficial.

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Ultimately, forty-eight favorable modifications were identified for the ten main criteria of the electric scooter. On their basis, the quality index of product prototypes was estimated. For this purpose, Formula (3) and the assumptions adopted in the general model were used, where the sum of the criteria weights in each case is 100. The current quality index of the electric scooter was calculated. Based on possible modifications of the criteria, eight basic electric scooter prototypes were adopted, the results of which are presented in

Table 6. Quality indicator (Q) of the electric scooter and its prototypes.

Criteria	Qa	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8
C1	99.45	111.35	124.10	93.50	94.35	81.60	111.35	111.35	111.35
C2	72.60	90.00	94.20	73.80	57.60	94.20	94.20	94.20	94.20
C3	42.75	114.00	95.00	240.35	334.40	42.75	42.75	42.75	42.75
C4	190.19	136.29	103.95	78.54	55.44	55.44	55.44	55.44	55.44
C5	24.15	23.31	23.73	22.47	21.00	16.38	22.26	25.83	16.38
C6	176.40	179.76	184.80	230.16	178.08	230.16	230.16	230.16	230.16
C7	115.70	133.90	162.50	232.70	274.30	115.70	115.70	115.70	115.70
C8	141.68	164.22	199.64	286.58	336.49	141.68	141.68	141.68	141.68
C9	118.91	64.39	67.21	49.35	41.36	41.36	41.36	41.36	41.36
C10	146.64	177.84	184.08	219.96	379.08	146.64	146.64	146.64	146.64
Q	11.28	11.95	12.39	15.27	17.72	9.66	10.02	10.05	9.96

Yellow color—beneficial modification state of criteria, Q—quality indicator of the electric scooter or its prototype, C1—weight (kg), C2—dimensions (m), C3—battery capacity (kWh), C4—battery charging time (h), C5—color, C6—engine power (W), C7—maximum range (km), C8—maximum speed (km/h), C9—energy consumption (kWh/km), C10—rated power (kW).

.

It was observed that the offered modifications of the electric scooter, depending on the most important customer requirements, sometimes differ little in terms of the quality indicator, e.g., modifications 6 and 7. This is due to the single changes in the main criteria, which in general do not contribute to a noticeable change in the quality of the product. However, this situation can change if the changes in the environmental impact of the offered product are taken into account.

Therefore, a life cycle assessment of electric scooter prototypes was performed. The OpenLCA 2.0.0 program was used for this purpose, employing the Ecoinvent 3.10 database [60]. An approach was used that took into account the phases of production, transport, distribution, use, and end of product life. During the life cycle assessment, the ISO 14040 standard [48] was followed, where the results were modelled in a simplified way as part of the evaluation of environmental loads for the prototypes. In this case, the environmental loads selected for the analysis concerned the ecological footprint, which is one of the most popular types of emissions. Ecological footprint is an indicator that allows one to estimate the consumption of natural resources in relation to the Earth’s ability to regenerate them [61]. The categories of ecological footprint are carbon dioxide (CO₂) emissions, i.e., carbon footprint, nuclear energy emissions, and land development (including land development and modernization). The carbon footprint is the total amount of greenhouse gases produced over the life cycle of a product. The conversion unit is equivalent to carbon dioxide (CO₂e) [62,63]. Nuclear energy emissions include the energy released during nuclear transformation. Energy changes result from differences in individual atomic nuclei. However, land development includes activities related to the development or modernization of land as a result of processes that must be carried out during the product life cycle. Due to the fact that the three categories of impacts were considered within the ecological footprint, the conversion unit of these criteria includes a square meter of impact per year of a given impact (m²a).

Furthermore, as part of the life cycle assessment of an electric scooter, it was necessary to adopt a functional unit that would normalize the data and ensure comparison of the results. Following the authors of the studies, for example, refs. [64,65,66], this unit was taken as one passenger kilometer (1 p × km); i.e., one kilometer travelled by a passenger (driver) on an electric scooter. Later, the boundary of the system was adopted. In this case, the research covers one piece of an electric scooter, which is considered in the production, transport, distribution, use, and end-of-life phases. The production of the electric scooter takes place in Italy, and its components (battery and charger) are produced in China. The finished product is transported to Poland, where it is used by customers. The system boundaries are shown in Figure 2.

According to research by other authors, for example, refs. [55,59], it was assumed that the average lifespan of the scooters is 50,000 km. Referring to the functional unit that includes the scooter driver, 1.1 passengers can be assumed for each scooter model. According to these assumptions, the total service life of the scooter is 55,000 pkm.

Due to the boundaries of the adopted system, it is assumed that the production of electric scooters takes place in Italy [59]. Data on scooter production, excluding the battery, were taken from the Ecoinvent 3.10 database. In the case of transport of the final product, it was assumed that it was transported from the place of production to the destination of use, i.e., Poland (Europe). According to preliminary estimates, the distance is approximately 2100 km. Following the studies of other authors, for example, refs. [59,66,67], transport is most often carried out using an EURO5 truck, which is why this was adopted in this study.

The use of an electric scooter is possible using batteries and a charger produced in China [55]. It is a lithium-ion battery (LFP) weighing 25 kg [68,69,70] and a charger dedicated to this type of battery. Data on the production of these products were taken from the Ecoinvent 3.10 database. According to ref. [59], under ideal conditions of use, one battery can cover the total life of the scooter, which is 1000 cycles, each of which is 100 km. Usage concerns energy consumption; therefore, following the authors of ref. [59], the battery is charged approximately 500 times during the assumed vehicle life based on data from the Ecoinvent 3.10 database. It was assumed that it is a mix of low-voltage electricity from Poland (low voltage power). The same applies to the distribution process and use-related emissions such as brake and tire wear.

In the last phase, i.e., the end of the life of the electric scooter, research is focused on manual disassembly, which includes basic modules such as the drive system [59]. These materials are disposed of in appropriate plants, but their further analysis was not considered in this study, and transport was omitted. Additionally, the output included emissions arising from the use of brakes, tire wear, and road wear, and the amount of emissions was estimated according to ref. [59].

It was observed that it is possible to model the results according to the main quality criteria, i.e., vehicle weight, dimensions, battery capacity, and battery voltage. The adopted inventory data are presented in

Table 7. Inventory data. Own study based on [59].

Type	Flow	Amount	Unit
Input	Electric scooter without battery	${\mathrm{m}}_1÷$ m10	kg
	Li-ion battery, rechargeable, prismatic	25	kg
	Electric scooter charger	1	item
	Distribution network (electricity, low voltage)	${\mathrm{d}}_1÷$ d10	km
	Electricity (low voltage)	${\mathrm{e}}_1÷$ e10	kWh
	Maintenance of electric scooter (without battery)	1	item
	Manual dismantling of electric scooter	1	item
	Transport, 3.5–7.5 metric ton, EURO5	$({\mathrm{m}}_1÷$ m10) × 2100	kg × km
Output	Electric scooter without battery	1	item
	Brake wear emissions (passenger car)	0.007	kg
	Road wear emissions (passenger car)	0.077	kg
	Tire wear emissions (passenger car)	0.448	kg

m, d, e—depending on the reference product and its prototypes (modifications); m, d, e = 1, 2, …,10. m—mass, d—distribution network, e—electricity.

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To calculate the consumption of low-voltage electricity when using the vehicle, as well as to estimate the distribution network (electricity, low voltage), Formula (6) is used [55]:

$$\left\{\begin{array}{c}\mathrm{d}=\left(\mathrm{n}\times 8.75\times {10}^{-8}\times \mathrm{c}\times \mathrm{v}\times \mathrm{y}\right)/1000\\ \mathrm{e}=(\mathrm{n}\times \mathrm{c}\times \mathrm{v}\times \mathrm{y})/1000\end{array}\right.$$

(6)

where n is the number of passengers, c is the battery capacity, v is the battery voltage, and y is the number of battery charges.

According to refs. [55,59], the battery voltage (v) can be conventionally assumed to be 48 V for the reference scooter. Therefore, it was changed in proportion to the battery capacity. According to the assumptions and using Formula (6), calculations were made regarding the change in energy necessary to distribute the prototypes offered for electric scooters and the change in low-voltage electricity consumption related to the use of scooters. By changing the weight of the scooter and the estimated transport distance of the scooter from the place of production to the place of use, transport by the EURO5 truck was estimated. In accordance with the assumptions and data from the Ecoinvent 3.10 database in OpenLCA 2.0.0, a life cycle assessment of a reference electric scooter and its prototypes was carried out based on an ecological footprint assessment. The results are shown in

Table 8. Life cycle assessment results of an electric scooter and its prototypes.

Phases	LCAa	LCA1	LCA2	LCA3	LCA4	LCA5	LCA6	LCA7	LCA8
Production	2449.31	2294.13	2449.31	2620.06	2807.94	3014.74	3242.04	2449.31	2449.31
Transport and distribution	273.86	248.98	273.86	301.24	331.37	364.53	400.97	273.86	273.86
Use	236.78	236.78	219.36	255.65	271.69	255.65	236.78	236.78	236.78
End of life	0.02	0.02	0.00	0.02	0.02	0.02	0.02	0.02	0.02
Total	2959.98	2779.91	2942.53	3176.97	3411.02	3634.95	3879.82	2959.98	2959.98

LCA—result of the product or prototype life cycle assessment, a—reference product (current), prototype ∈ 1, ..., 8.

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The highest environmental burden in the life cycle of the electric scooter prototypes was observed to be modification 6 (LCA₆). The lowest environmental burden is expected for modification 1 (LCA₁). At this stage, it would be possible to conclude that improvement actions would need to be taken to match this modification. However, the qualitative aspect is then omitted. Therefore, further analysis was performed.

Due to the fact that the obtained quality and environmental rankings are different and the values of quality and environmental indicators are expressed on different scales, they have been relativized. Formula (4) was used for this purpose. Then, the relativized values of quality and environmental indicators were compiled into one quality and environmental indicator (QLCA), as given in Formula (5). Based on the QLCA indicator, a ranking of electric scooter prototypes was created, as shown in

Table 9. Relativized indicators and the quality and environmental indicator (QLCA).

Prototype	Pa	P1	P2	P3	P4	P5	P6	P7	P8
rLCA	23.71	28.35	24.16	18.12	12.08	6.31	0.00	23.71	23.71
Ranking	3	1	2	4	5	6	7	3	3
rQ	16.77	23.71	28.26	58.07	83.44	0.00	3.73	4.04	3.11
Ranking	5	4	3	2	1	9	7	6	8
QLCA	20.24	26.03	26.21	38.10	47.76	3.16	1.86	13.87	13.41
Ranking	5	4	3	2	1	8	9	6	7

P_a—current electric scooter (reference product), P₁–P₈—electric scooter prototypes, rLCA—relativized environmental load indicator in the product or prototype life cycle [%], rQ—relativized product or prototype quality indicator [%], QLCA—quality and environmental indicator of the product or prototype [%].

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Prototype P₄ was observed to be the most preferred (QLCA = 47.76). According to the assumptions adopted, it has the highest quality index (83.44) and a relatively high environmental burden index (12.08). In this case, it is expected to be the most advantageous production solution in terms of quality and environment. The second place was taken by the P₃ prototype (QLCA = 38.10), which had a quality level that was approximately 25% lower than the P₄ prototype and an environmental burden approximately 6% lower. In turn, the least favorable was the P₆ prototype (QLCA = 1.86). It had the greatest environmental impact, and its quality was low. The current electric scooter was characterized by a moderate level of quality and a moderate environmental impact, where it ranked fifth in every ranking. Therefore, the decision to determine the direction of development of the electric scooter should be based on the P₄ prototype. If this prototype could not be implemented at a given time, it would be good to consider the prototype P₃ or possibly P₂ or P₁ as a good one. The final decision is based on the production and economic capabilities of the company.

3.2. Sensitivity of the QLCA Indicator to the Proportion of the Share of the Quality Indicator and the Environmental Indicator in Product Improvement

The model presented includes the assumption that the product and its environmental impact have the same importance in the general meaning of the QLCA indicator. This means that the values of the quality indicator and the value of the environmental impact indicator were equivalent and had the same share in making decisions about the product improvement process. Therefore, the research was extended to include an analysis of the sensitivity of the QLCA indicator to the share of the quality indicator and the environmental load indicator in the life cycle of the product or prototype in the decision-making process about the improvement of the product. Based on the results obtained, the company will be able to determine, depending on the needs and individual practices, the appropriate ratio of the share of the quality indicator and the environmental indicator (environmental burden in the life cycle). As a result, it will be possible to direct improvement activities more precisely and thoughtfully, depending on the company’s business strategy.

Research in this area was based on modelling the sensitivity of the share of quality and environmental load in the life cycle of a product or prototype in various proportions to observe the sensitivity of the developed model as well as the QLCA indicator. The base data were the final values of the rQ, rLCA, and QLCA indicators (as in Table 8). The proportions of weights (share) of the relativized quality indicator and the relativized environmental indicator were changed proportionally by 5%, which ignores the relationship where one of the indicators is not taken into account at all. This is due to the nature of the model, which is dedicated to qualitative and environmental analyses. The adopted proportions of the share of model indicators in the final QLCA indicator are presented in

Table A1. Proportions of the share of relativized rQ and rLCA indicators in the final QLCA indicator.

Share Proportions [%]
rQ	rLCA
95	5
90	10
85	15
80	20
75	25
70	30
65	35
60	40
55	45
50	50
45	55
40	60
35	65
30	70
25	75
20	80
15	85
10	90
5	95

, where the 50%/50% proportion is the initial proportion (indicators are equally important).

In accordance with the proposed proportions of the share of the relativized quality indicator (rQ) and the relativized environmental load indicator in the product or prototype life cycle (rLCA), the change in the value of the QLCA indicator was simulated, the results of which are presented in

Table A2. Results of computational simulation presenting changes in the QLCA index depending on the change in the proportion of the qualitative index rQ and environmental rLCA.

No.	Share Proportions [%]		QLCA Indicator of Prototypes
	rQ	rLCA	Pa	P1	P2	P3	P4	P5	P6	P7	P8
1	95	5	8.56	11.97	14.03	28.04	39.93	0.16	1.77	2.51	2.07
2	90	10	8.73	12.09	13.93	27.04	38.15	0.32	1.68	3.00	2.58
3	85	15	8.91	12.20	13.82	26.04	36.37	0.47	1.58	3.49	3.10
4	80	20	9.08	12.32	13.72	25.04	34.58	0.63	1.49	3.99	3.61
5	75	25	9.25	12.43	13.62	24.04	32.80	0.79	1.40	4.48	4.13
6	70	30	9.43	12.55	13.52	23.04	31.02	0.95	1.30	4.97	4.64
7	65	35	9.60	12.67	13.41	22.04	29.23	1.10	1.21	5.46	5.16
8	60	40	9.77	12.78	13.31	21.05	27.45	1.26	1.12	5.95	5.67
9	55	45	9.95	12.90	13.21	20.05	25.66	1.42	1.02	6.44	6.19
10	50	50	10.12	13.01	13.10	19.05	23.88	1.58	0.93	6.94	6.70
11	45	55	10.29	13.13	13.00	18.05	22.10	1.74	0.84	7.43	7.22
12	40	60	10.47	13.25	12.90	17.05	20.31	1.89	0.75	7.92	7.73
13	35	65	10.64	13.36	12.80	16.05	18.53	2.05	0.65	8.41	8.25
14	30	70	10.81	13.48	12.69	15.05	16.74	2.21	0.56	8.90	8.76
15	25	75	10.99	13.59	12.59	14.05	14.96	2.37	0.47	9.40	9.28
16	20	80	11.16	13.71	12.49	13.05	13.18	2.52	0.37	9.89	9.79
17	15	85	11.33	13.83	12.39	12.05	11.39	2.68	0.28	10.38	10.31
18	10	90	11.51	13.94	12.28	11.06	9.61	2.84	0.19	10.87	10.82
19	5	95	11.68	14.06	12.18	10.06	7.83	3.00	0.09	11.36	11.34

. In turn, the changes in the position of prototypes in the ranking after changing the share of indicators in the QLCA are presented in

Table A3. Rankings of electric scooter prototypes obtained depending on the change in the proportion of the share of the rQ and rLCA indicators in the QLCA indicator.

No.	Share Proportions [%]		Ranking of Prototypes
	rQ	rLCA	Pa	P1	P2	P3	P4	P5	P6	P7	P8
1	95	5	5	4	3	2	1	9	8	6	7
2	90	10	5	4	3	2	1	9	8	6	7
3	85	15	5	4	3	2	1	9	8	6	7
4	80	20	5	4	3	2	1	9	8	6	7
5	75	25	5	4	3	2	1	9	8	6	7
6	70	30	5	4	3	2	1	9	8	6	7
7	65	35	5	4	3	2	1	9	8	6	7
8	60	40	5	4	3	2	1	8	9	6	7
9	55	45	5	4	3	2	1	8	9	6	7
10	50	50	5	4	3	2	1	8	9	6	7
11	45	55	5	3	4	2	1	8	9	6	7
12	40	60	5	3	4	2	1	8	9	6	7
13	35	65	5	3	4	2	1	8	9	6	7
14	30	70	5	3	4	2	1	8	9	6	7
15	25	75	5	3	4	2	1	8	9	6	7
16	20	80	5	1	4	3	2	8	9	6	7
17	15	85	5	1	2	3	4	8	9	6	7
18	10	90	3	1	2	4	7	8	9	5	6
19	5	95	3	1	2	6	7	8	9	4	5

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Due to the fact that a set of values was obtained that included changes in the QLCA index, it was statistically analyzed to determine statistically significant differences between these values. The STATISTICA 13.3 program was used for this purpose, as well as basic tools including ANOVA analysis of repeated measures systems. The choice of this type of analysis resulted from its applicability to the analysis of the values obtained from many dependent variables that correspond to measurements for different levels of one factor [67]. All QLCA index values obtained from the computational simulation for all electric scooter prototypes were selected as dependent variables. The confidence interval for the alpha value was set at 0.95, and the significance level was set at 0.05 [71,72]. The results of the statistical analysis are presented in

Table 10. Results of the ANOVA test, including the simulated change in the value of the QLCA index depending on the share of quality–environmental proportions.

Test	Value Effect	F	df Effect	df Error	p-Value	Effect Strength Measure	Non-Centrality	Power Observed
Shapiro–Wilk	0.009	2042.280	1.000	18.000	0.000	0.991	2042.280	1.000

F—coefficient of the estimated function.

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The results of the analysis confirmed that changes in the proportion of the share of the qualitative indicator (rQ) and the environmental indicator (rLCA) have a significant statistical impact on the quality–environmental indicator (QLCA), where p < 0.05. Therefore, they were further analyzed. After qualitative analysis of the data (from Table A2 and Table A3), it was observed that the changes in the value of the QLCA indicator that influence changes in the ranking of electric scooter prototypes concerned several selected proportions of the share of the qualitative (rQ) and environmental (rLCA) indicators:

• rQ index values $\in 〈0.95;0.65〉$ and rLCA index values $\in 〈0.05;0.35〉$;
• rQ index values $\in 〈0.60;0.25〉$ and rLCA index values $\in 〈0.40;0.75〉$;
• rQ index values $\in 〈0.20;0.15〉$ and rLCA index values $\in 〈0.80;0.85〉$;
• rQ index values = 0.10 and rLCA index values = 0.90;
• rQ index values = 0.05 and rLCA index values = 0.95.

Selected results of the computational simulations for the proportion of indicators for which different rankings of prototypes were observed are presented in

Table 11. Changes in the ranking of prototypes according to computational simulation of the impact of the proportion of quality and environmental indicators on the quality–environmental indicator (QLCA).

Prototype	Pa	P1	P2	P3	P4	P5	P6	P7	P8
rLCA = 50%: rQ = 50%
rLCA	23.71	28.35	24.16	18.12	12.08	6.31	0.00	23.71	23.71
rQ	16.77	23.71	28.26	58.07	83.44	0.00	3.73	4.04	3.11
QLCA	20.24	26.03	26.21	38.10	47.76	3.16	1.86	13.87	13.41
Ranking	5	4	3	2	1	8	9	6	7
rLCA = 25%: rQ = 75%
rLCA	5.93	7.09	6.04	4.53	3.02	1.58	0.00	5.93	5.93
rQ	12.58	17.78	21.20	43.56	62.58	0.00	2.80	3.03	2.33
QLCA	9.25	12.43	13.62	24.04	32.80	0.79	1.40	4.48	4.13
Ranking	5	4	3	2	1	9	8	6	7
rLCA = 80%: rQ = 20%
rLCA	18.97	22.68	19.33	14.49	9.67	5.05	0.00	18.97	18.97
rQ	3.35	4.74	5.65	11.61	16.69	0.00	0.75	0.81	0.62
QLCA	11.16	13.71	12.49	13.05	13.18	2.52	0.37	9.89	9.79
Ranking	5	1	4	3	2	8	9	6	7
rLCA = 90%: rQ = 10%
rLCA	21.34	25.51	21.74	16.30	10.87	5.68	0.00	21.34	21.34
rQ	1.68	2.37	2.83	5.81	8.34	0.00	0.37	0.40	0.31
QLCA	11.51	13.94	12.28	11.06	9.61	2.84	0.19	10.87	10.82
Ranking	3	1	2	4	7	8	9	5	6
rLCA = 95%: rQ = 5%
rLCA	22.52	26.93	22.95	17.21	11.48	6.00	0.00	22.52	22.52
rQ	0.84	1.19	1.41	2.90	4.17	0.00	0.19	0.20	0.16
QLCA	11.68	14.06	12.18	10.06	7.83	3.00	0.09	11.36	11.34
Ranking	3	1	2	6	7	8	9	4	5

P_a—current electric scooter (reference product), P₁-P₈—electric scooter prototypes, rLCA—relativized environmental load indicator in the product or prototype life cycle [%], rQ—relativized product or prototype quality indicator [%], QLCA—quality and environmental indicator of the product or prototype [%].

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If the rLCA and rQ indicators are equally important (proportion: 50%/50%), the P₄ prototype takes first place in the ranking. It is characterized by the highest level of quality and a relatively high environmental burden (compared to the others, it ranks fifth). Subsequently, prototype P₃ takes second place in the ranking, P₂ takes third place in the ranking, and P₁ takes fourth place in the ranking. The rankings of these leading prototypes change only when the proportions are determined as rLCA = 80% and rQ = 20%. Then, the P₄ prototype moves from first place to second, the P₃ prototype moves from second to third, the P₂ prototype moves from third to fourth, and the P₁ prototype moves from fourth to first. Then, for the most advantageous prototype (P₁), a very low environmental burden in the life cycle and a low quality level prevail. Subsequently, the greater the proportion of the rLCA index in relation to rQ, the greater the changes observed in the ranking of prototypes; e.g., when the quality index is already very low (which is unfavorable) but beneficial from the point of view of environmental impact because the environmental burden index is small.

Therefore, it was concluded that with a greater the share of the environmental burden indicator rLCA in relation to the quality indicator rQ, there are changes in the ranking of the prototypes, characterized by a relatively low environmental burden and a low quality level. In turn, with greater shares of the quality indicator rQ in relation to the environmental load indicator rLCA, the first positions are occupied by prototypes with a high level of quality and for which the environmental load is relatively high.

Based on the results of the computational simulation, the basic proportions for selecting the share of the rQ and rLCA indicators were developed as part of the determination of the direction of product development in terms of quality and environment. This is shown in

Table 12. Results of computational simulation and method of selecting the proportion of the share of model indicators.

Proportions of the Share of Model Indicators	Observations	Conclusions
$\mathrm{rQ}\in 〈95\%;25\%〉$ $\mathrm{rLCA}\in 〈5\%;75\%〉$	Assuming the proportions of indicators in the given value ranges, no significant changes are observed in the ranking of prototypes according to the QLCA indicator. The ranking presents the order of prototypes, where the first prototypes are characterized by a high quality index and average environmental load in LCA.	Proportions are determined when the design of a new product or the improvement of a product already on the market is conditioned by the assumption that the quality of the product is much more important than the environmental impact of the product throughout its life cycle.
$\mathrm{rQ}\in 〈20\%;5\%〉$ $\mathrm{rLCA}\in 〈80\%;95\%〉$	Assuming the proportions of indicators in the given value ranges, changes in the ranking of prototypes according to the QLCA indicator will be observed. This ranking presents the order of prototypes, where the first prototypes are characterized by a low quality index and low environmental load in LCA.	Proportions are dedicated when the design of a new product or the improvement of a product already on the market is conditioned by the assumption that the quality of the product is much less important than the environmental impact of the product throughout its life cycle.

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The results of the computational simulation for various proportions of the model indicators and the observations and conclusions can be used by the expert to adopt an appropriate product development strategy. The decision regarding the proportion of share of the quality and environmental indicators depends on the individual preferences and predispositions of the production company.

The results confirmed that the model is sensitive to changes in the proportion of the share of the quality indicator and the environmental burden indicator in the life cycle. Therefore, it was considered effective and adaptable to quality–environmental analyses of product prototypes in terms of prospective assessments of quality and environmental burden over their life cycle.

4. Discussion

Sustainable product development should be supported by appropriate tools, but there are no such tools to predict product prototypes in terms of quality and environment [43,73]. Although various studies have been conducted that combine qualitative and environmental aspects, for example, refs. [11,12,13,74], techniques are still being sought for the prospective evaluation of product prototypes so that they meet customer expectations about product quality and are environmentally friendly throughout their life cycle (LCA). Therefore, the objective of this research was to develop a model that supports the sustainable development of products, taking into account the quality aspect and the environmental impact during the product life cycle, and to establish a framework for selecting the proportion of the share of these aspects during product development decisions.

As part of the research and the hypothesis adopted, it was shown that sustainable product development should focus on achieving customer satisfaction with product quality and ensuring a low impact of the product throughout its life cycle (LCA), where the proportions of the share of these aspects influence the development decisions undertaken at an early stage of product design.

The main benefits of the presented model, the QLCA indicator, and sensitivity analysis include the following:

• methodically determining the product modifications most desired by the customer;
• improving the process of evaluating product prototypes based on the main quality criteria to ensure customer satisfaction with the use of the product;
• supporting the process of assessing the environmental impact of product prototypes throughout their life cycle in order to reduce environmental burdens;
• ensuring methodical integration of quality and environmental aspects (covering the entire product life cycle);
• supporting the process of ranking product prototypes based on integrated indicators regarding quality and environmental aspects as part of determining the direction of product development;
• supporting the process of selecting the proportion of quality and environmental aspects according to the product development strategy.

The results of the conducted research also have business implications, e.g., predicting favorable production solutions in terms of meeting customer expectations and producing environmentally friendly products throughout their life cycle, determining possible improvements for the product, and the possibility of providing planning ahead of the competition.

Some limitations include modelling the results of the assessment of quality and environmental aspects in a simplified way due to the lack of access to real data regarding the environmental impacts of the prototypes in their life cycles. At the same time, this research focuses on two key aspects of sustainable development, ignoring, for example, the costs of purchasing products, which may also affect the ranking of prototypes and the chosen direction of their development. A certain limitation is also its applicability in other product categories, including industries. In particular, it will be problematic in the case of products with specialized applications that are not commonly used by customers, e.g., machine elements, tools, etc. This is a barrier to achieving customer expectations regarding their quality. Therefore, the model will be effective in the case of products with traditional use among customers.

Therefore, future research will focus on extending this research to include the financial aspect, mainly predicting the costs of purchasing the final product by customers. Additionally, it is planned to expand the research to analyze a greater number of possible modifications to the product criteria. Also, it is planned to create new assumptions and approaches to aggregate quality and life cycle assessment indicators by extending model to include VoC in other its stages. This will be adequate for the design-thinking approach, where VoCs are processing as part of non-linear, iterative processes that teams use to understand users, challenge assumptions, redefine problems, and create innovative solutions to prototype and test. It is most useful to tackle ill-defined or unknown problems and involves five phases: empathize, define, ideate, prototype, and test.

5. Conclusions

The need to meet customer requirements while reducing negative environmental impacts is a key challenge in sustainable development. Therefore, the objective of this research was to develop a model that supports the sustainable development of products, taking into account the quality aspect and the environmental impact in the product life cycle, and to establish a framework for selecting the proportion of the share of these aspects during product development decisions.

The model was developed in five main stages and tested on an electric scooter. The quality criteria assessed by the customer included weight, dimensions, battery capacity, battery charging time, color, engine power, maximum range, maximum speed, energy consumption, and rated power. Ten electric scooter prototypes were defined using these criteria. After a pilot study, the modifications of the quality criteria most desired by the client were selected, and eight prototypes of electric scooters were developed based on these criteria. They were rated according to the quality index (Q). The environmental impact of the electric scooter was then examined throughout its life cycle, where the ecological footprint was the impact criterion. The LCA results were modelled for the electric scooter prototypes using a dedicated environmental indicator (LCA). Quality and environmental indicators were methodically combined into one QLCA indicator, which ensured the classification of scooter prototypes in terms of meeting customer requirements regarding product quality and limiting the negative environmental impact of the product in its life cycle. Furthermore, this research was extended to analyze the sensitivity of the QLCA indicator to the proportion of the share of the quality indicator and the environmental indicator. The results of this analysis provide a framework for selecting these proportions for sustainable product development.

Therefore, the model presented with the QLCA indicator and its dedicated framework to make product development decisions will support the sustainable development of any product. The results of the presented research can serve as a tool supporting decision-making by decision-makers, designers, and managers in manufacturing companies. The area of application of the model, the QLCA indicator, and the offered decision-making framework can be effectively used already in the early stages of product development, e.g., designing new products, but it can also successfully contribute to the improvement of products already on the market.

Following the assumptions offered will be useful in predicting the direction of product development so that the product is both as satisfying as possible for customers and environmentally friendly throughout its life cycle. Additionally, the product development process can be individually adapted to the company’s strategy by ensuring the selection of the proportion of the share of quality and environmental aspects in decisions regarding the adoption of the direction of improvement activities.