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.
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.
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.
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.
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.
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 (CO
2) 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 (CO
2e) [
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 (m
2a).
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.
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]:
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.
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.
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, 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. 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.
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.
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 and rLCA index values ;
rQ index values and rLCA index values ;
rQ index values and rLCA index values ;
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.
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.
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.