Investment Decision-Making to Select Converted Electric Motorcycle Tests in Indonesia

Abstract

The issue of carbon emissions can be addressed through environmentally friendly technological innovations, which contribute to the journey towards achieving net-zero emissions (NZE). The electrification of transportation by converting internal combustion engine (ICE) motorcycles to converted electric motorcycles (CEM) directly reduces the number of pollution sources from fossil-powered motors. In Indonesia, numerous government regulations support the commercialization of the CEM system, including the requirement for conversion workshops to be formal entities in the CEM process. Every CEM must pass a test to ensure its safety and suitability. Currently, the CEM testing process is conducted at only one location, making it inefficient and inaccessible. Therefore, most conversion workshops in Indonesia need to take investment steps in procuring CEM-type test tools. This research aims to determine the best alternative from several investment alternatives for CEM-type test tools. In selecting the investment, three criteria are considered: costs, operations, and specifications. By using the investment decision-making model, a hierarchical decision-making model is obtained, which is then processed using the analytical hierarchy process (AHP) and the technique for order of preference by similarity to the ideal solution (TOPSIS). Criteria are weighted to establish a priority order. The final step involves ranking the alternatives and selecting Investment 2 (INV2) as the best investment tool with a relative closeness value of 0.6279. Investment 2 has the shortest time process (40 min), the lowest electricity requirement, and the smallest dimensions. This research aims to provide recommendations for the best investment alternatives that can be purchased by the conversion workshops.

1. Introduction

The transportation sector is a fairly high contributor to CO₂ [1,2,3]. Indonesia is one of the developing countries in the ASEAN region with more motorcycle users than cars [4]. The number of motorcycles in Indonesia is shown in

Table 1. The population of vehicles in Indonesia 2018–2022.

Vehicles	2018	2019	2020	2021	2022
Buses	222,872	231,569	233,261	237,566	243,450
Trucks	4,797,254	5,021,888	5,083,405	5,299,361	5,544,173
Cars	14,830,698	15,592,419	15,797,746	16,413,348	17,168,862
Motorcycles	106,657,952	112,771,136	115,023,039	120,042,298	125,305,332
Total	126,508,776	133,617,012	136,137,451	141,992,573	148,261,817

, reaching 84% of the total vehicle population and increasing every year [5,6,7,8]. This fact shows that conventional vehicles contribute to increasing carbon emissions [9]. Therefore, switching from conventional vehicles to electric vehicles (EVs) can help reduce carbon emissions and air pollution, as well as improve air quality [10]. The government issued Presidential Regulation Number 55 of 2019 concerning the acceleration of the battery-based electric motorcycle (EM) program [11].

Electric vehicle innovation is a solution for transportation that is environmentally friendly, energy-efficient, and has low operational and maintenance costs [12]. In this case, two-wheeled electric vehicles are the object of study. EMs consist of two types: new-design electric motorcycles and converted electric motorcycles (CEMs) [4,13]. Newly designed electric motorcycle is assembled by factories into electric vehicles, while CEM is a conventional motorcycle that has engine components replaced with conversion kits [14]. CEMs as a solution to carbon emissions are a new technology in Indonesia. People do not need to purchase a new electric motorcycle, but they can change their conventional motorcycle to a CEM. CEMs have been through a process of commercialization, adoption, and use by the public even though the numbers are still minimal [15].

In order to increase the adoption of EVs, the government provides incentives for the purchase of CEM amounting to IDR 7 million per unit, as regulated in the Minister of Energy and Mineral Resources Regulation Number 3 of 2023. As time goes by, the incentive for CEM increases to IDR 10 million per unit to increase the uptake of CEM adhering to the aforementioned regulation. However, in its implementation, there are obstacles from the customer side. The obstacle from the technical perspective was low mileage [16,17,18], from the perspective of facilities was rare charging stations [19,20,21,22], and from an economic perspective was the high cost compared to conventional vehicles [23].

Viewed from the perspective of CEM manufacturing, the obstacles encountered include the testing mechanism for each vehicle, which can only be carried out at the testing center, causing it to take a long time and be expensive. CEM will gain legal status once it passes the CEM-type test for component and vehicle physical suitability. According to the Minister of Transportation Regulation Number 39 of 2023, testing can be conducted at the Land Transportation Management Center, accredited private testing units, accredited public service testing agencies, and type A conversion workshops. As previously mentioned, to become a type A conversion workshop, certain requirements must be fulfilled, one of which is the availability of CEM-type testing or roadworthiness testing tools. Therefore, this study presents an investment decision-making model for selecting CEM-type test tools for conversion motorcycles.

Of the seven types of tests for conversion motorcycles, five require special tools: brake testers, headlight testers, sound level testers, weight testers, and speedometer accuracy testers. In Indonesia, these testing tools must be imported from abroad, such as from Europe and China, which requires significant time and investment costs.

Multi-criteria decision-making (MCDM) is the most widely used method for addressing decision-making problems. The purpose of MCDM is to determine the best alternative among several mutually beneficial options based on the performance of various criteria set by the decision-makers [24]. There are several MCDM methods, including the Analytic Hierarchy Process (AHP) [25,26], Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [27], Simple Additive Weighting Method (SAW) [28], Analytic Network Process (ANP) [29], and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) [30].

The purpose of selecting CEM-type test tools is to convince owners, investors, partners, and other stakeholders to maintain a certain point of view regarding productivity, efficiency, income generation, and total investment costs. This research integrates the Analysis Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. AHP is one of the most widely applied methods to solve decision-making problems such as electronic vehicle selection [31], welding process selection [32], and strategy selection [33] because of its ease of use and strong mathematical foundation. Meanwhile, TOPSIS is a method that refers to the premise of the best solution, which has the smallest distance from the positive ideal solution and the furthest distance from the negative ideal solution. TOPSIS is also widely used because it is easy and is not limited to the many criteria and alternatives. Some examples of using TOPSIS for decision-making problems are assessing renovation solutions [34], material selection [35,36], implementation strategy selection [37], and financial performance analysis [38,39,40].

The research aimed to select a CEM-type test tool by considering several criteria. In selecting the CEM-type test, the decision-maker needs to determine meaningful criteria and have special knowledge regarding vehicle inspection tests. The selected criteria must consider the benefits obtained by the company. The selection was carried out using the AHP method to determine the weight of each criterion based on the opinion of the decision-maker. TOPSIS is an MCDM method to overcome brightness in decision selection and to select alternatives based on weight criteria, where the alternative is selected.

This paper is presented in five sections. The first section, the introduction to the study, contains the research background. A literature review and basic theory of AHP and TOPSIS are provided in Section 2. In Section 3, we provide the material and methods used in this paper. Section 4 proposes a hybrid MCDM method based on AHP and TOPSIS, financial analysis, and comprehensive assessment. Conclusions and future research directions are provided in Section 5.

2. Literature Review

2.1. Investment Decision-Making

Investment decision-making is the process of making a decision that involves evaluating potential investments based on various criteria related to risk, return, liquidity, and strategy. Investment decision-making is a more specific type of MCDM problem [41]. Based on Markowitz’s Modern Portfolio Theory, diversification is the key to optimizing profits while minimizing risk [42]. Making investment decisions is risky and uncertain [43].

2.2. Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) is an MCDM method developed by Thomas L. Saaty around 1970 [44]. AHP is a methodology often used in various MCDM problems because it is understandable and applicable to a wide range of complex decision-making problems [45]. The AHP method is used to assist decision-makers in calculating the weight of each criterion using pairwise comparison assessment [46]. The AHP method is based on a comparative assessment of alternatives and criteria [47]. Saaty scale is illustrated in

Table 2. Saaty scale.

Math. Representation	Meaning
1	Both are equally important.
3	One is moderately more important than the other.
5	One is strongly more important than the other.
7	One is very strongly more important.
9	One is extremely more important than the other.
2, 4, 6, 8	Values between moderate and strong importance.

.

The AHP data processing procedure is shown as follows:

Step 1: Build a structural hierarchy.

Step 2: Create a pairwise comparison matrix A. The pairwise comparison matrix A is expressed as Equation (1);

$$A=\left[\begin{array}{cccc}{a}_{11}& a_{12}& \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & a_{2n}\\ \cdots & \cdots & \cdots & \cdots \\ a_{n1}& a_{n2}& \cdots & a_{nn}\end{array}\right]$$

(1)

where a_ij represents the relative importance value of a_i to a_j, a_i and a_j (i, j = 1, 2,…, n) represent criteria.

Step 3: Build a normalized decision matrix utilizing Equation (2). Normalization starts by adding up the weights in each column j. S_j represents the number of weights in each column.

$$S_j={\sum }_{i=1}^n{a}_{ij}$$

(2)

Afterward, each column value is divided by the sum of the weights for each column, which is represented by V_ij using Equation (3).

$$V_{ij}={\displaystyle \frac{a_{ij}}{S_j}}$$

(3)

Step 4: Create a weighted and normalized decision matrix using Equation (4);

$$W_i={\displaystyle \frac{{\sum }_{i=1}^n{Q}_i}{n}}$$

(4)

where W_i is the weight of criteria i, Q_i is the normalized comparison value of criteria i to criteria j.

Step 5: Calculate the maximum Eigenvalue, λ_max utilizing Equation (5);

$$Ax={\lambda }_{max}\times x$$

(5)

where Ax is the consistent matrix, x is the eigenvector, λ_max is the maximum eigenvalue.

Step 6: Calculate the consistency index and consistency ratio using Equations (6) and (7). The consistency index (CI) is used to check the consistency of the judgments, where λ_max is the largest eigenvalue of the pairwise comparison matrix and n is the number of classes.

$$\mathrm{C}\mathrm{I}={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(6)

After the calculation of CI, the next task is to calculate the consistency ratio (CR), where CI is the consistency index and RI is the ratio index.

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}}$$

(7)

2.3. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

TOPSIS is a decision-making method developed by Hwang and Yoon in 1981 [48]. This method was developed based on the premise that the best solution has the smallest distance from the positive ideal solution and the furthest distance from the negative ideal solution [49]. TOPSIS is widely used in research addressing MCDM issues. This is because TOPSIS has a strong, simple, and applicable mathematical basis [50]. TOPSIS is considered one of the fundamental methods of MCDM due to its popularity as a basis for new methods and comparative analysis [51]. The steps for calculating the TOPSIS alternative ranking are as follows [52].

Step 1: Build a normalized decision matrix from each criterion. Determine the normalized assessment matrix using Equation (8) to reduce the subjectivity and errors from the original values.

$$y_{ij}={\displaystyle \frac{Xij}{\sqrt{{\sum }_{i=0}^m{x}_{ij}^2}}}$$

(8)

Step 2: Compute the weighted normalized matrix using Formula (9) to weight the normalized values and balance the matrix by multiplying the weights W_j of the evaluation criteria with y_ij.

$$v_{ij}=y_{ij}\times W_j;\left(i=1,2,\dots ,I;j=1,2,\dots ,J\right)$$

(9)

Step 3: Determine positive ideal solution (PIS), $A^{*}$ as the most desirable alternatives and negative ideal solution (NIS), $A^{-}$ as the least desirable alternatives using Equations (10) and (11) to obtain the desired best and worst values

$$A^{*}=\left[v_1^{*},v_2^{*},\dots ,v_J^{*}\right]maximumvalues$$

(10)

where $v_i^{\mathrm{*}}=\left\{max\left(v_{ij}\right)ifj\in J;min\left(v_{ij}\right)ifj\in J^{-}\right\}$.

$$A^{-}=\left[v_1^{-},v_2^{-},\dots ,v_J^{-}\right]minimumvalues$$

(11)

where $v_i^{-}=\left\{min\left(v_{ij}\right)ifj\in J;max\left(v_{ij}\right)ifj\in J^{-}\right\}$.

Step 4: Calculate the distance parameters in Euclidean space $S_i^{*}$ and $S_i^{-}$ utilizing Formulae (12) and (13). The Euclidean distance of every option from the best and worst ideal options is used to find an optimal option.

$$S_i^{*}=\sqrt{\sum _{j=1}^J{\left(v_{ij}-v_j^{*}\right)}^2,}j=1,2,\dots ,J$$

(12)

$$S_i^{-}=\sqrt{\sum _{j=1}^J{\left(v_{ij}-v_j^{-}\right)}^2,}j=1,2,\dots ,J$$

(13)

Step 5: Calculate the coefficient of closeness relative to the ideal solution for each alternative using the TOPSIS approach (14). It is calculated to determine an efficient option among several available options.

$$C_i^{*}={\displaystyle \frac{S_i^{-}}{S_i^{-}+S_i^{*}}}$$

(14)

Step 6: Based on decreasing values of the closeness coefficient, the alternatives are ranked from most valuable to least valuable. The alternative that has the highest closeness coefficient is selected.

The AHP is used in this study to assign weights to criteria. The following evaluation steps are considered for the selection of the best fuel:

• Developing an evaluation criteria hierarchy model.
• Using the AHP approach to calculate the weights of these criteria.
• Using the TOPSIS approach to produce ultimate ranking outcomes as assessment procedures.

Based on the literature review, we define the following research hypothesis:

The application of hybrid MCDM in the selection process of CEM-type test investments will result in a more accurate prioritization of investment alternatives based on the criteria selected.

3. Materials and Methods

In this section, materials and methods are pivotal to introducing the model used. The conversion workshop was intended to purchase CEM-type test tools. Currently, three alternatives have been obtained to be selected as the best one. The decision-makers or experts have more than 2 years of experience in their respective domains. The experts were given the list of selection criteria and were requested to develop a pairwise comparison matrix of the selection criteria based on Saaty’s 1–9 scale [53,54,55,56]. The ideal scheme for modeling MCDM problems requires a small group of three to five experts. Experts were selected from the association of conversion workshops in Indonesia related to the EV industry. A total of five experts in the field of CEM and vehicle tests were involved in these surveys. We have gained years of experience since 2019, when a program of battery-based electric motorcycles started. A two-part questionnaire was sent to these experts. There are three main criteria, nine sub-criteria, and three investment alternatives. The criteria and investment alternatives were selected from a literature review and market research. Offering three alternatives for selecting investments provides a balanced, manageable, and strategic approach to decision-making. This ensures that each alternative is thoroughly evaluated on all relevant criteria, leading to more informed and reliable decisions. Too many choices can lead to decision paralysis, where decision-makers find it difficult to make a choice at all. The first part was related to the weight of the criteria in this model, while the second part of the questionnaire was related to ranking the alternatives. The experts provide an assessment of the proposed criteria, after recapitulating the questionnaire results. AHP is formed to calculate the weight of each criterion. Next, an assessment of the consistency of the criteria was carried out to ensure the reliability of the criteria. Once the relative weights of selection criteria were established, the MCDM method of TOPSIS was used for comparing and selecting the best investment decision that satisfied the selection criteria optimally. To accomplish this, all alternatives were evaluated by experts based on the criteria and sub-criteria. At this stage, positive ideal and negative ideal values were identified to determine the best alternative. Moreover, financial analysis using the cost of goods sold (COGS), breakeven point (BEP), and equivalent annual cost (EAC) was obtained to validate the result. In the final stage, an analysis of the selected investments to be implemented was carried out.

Based on the aforementioned description, the hybrid MCDM method, which consists of AHP and TOPSIS, was used to calculate criteria weights and alternative rankings. Finally, the MCDM model was developed for investment decision-making to choose the best investment alternative. Figure 1 depicts the entire process in the form of a flow diagram.

4. Result and Discussion

4.1. Data Collection and Scenarios Setting

This section presents a case study of CEM-type test investment decision-making. The name of the company where the study took place is not disclosed due to confidentiality. This company is a workshop that provides the process of converting conventional motorbikes into electric motorbikes. Researchers communicated with several experts to find out what problems and challenges were encountered by conversion workshops in the CEM testing process; thus, they had to choose an alternative investment in CEM-type test tools.

In general, individuals involved in the investment selection process for electric motorcycle-type test tools work in the vehicle testing and converted electric motorcycle sectors. Five experts were selected as respondents, whose backgrounds and years of experience are depicted in

Table 3. Detailed expert information.

Expert	Background	Years of Experience
E1	Engineering Manager	5
E2	Operational Manager	3
E3	Operational Manager	5
E4	President Director	4
E5	Quality Manager	2

.

4.2. Criteria Definition

Based on the literature studies and discussions with several experts, decision-making criteria are determined: costs, operations and specifications. Each criterion is further explained into sub-criteria as shown in

Table 4. Decision criteria.

No.	Criteria	Sub-Criteria	Definition	Ref.
1	Cost (C1)	Investment Cost (C11)	Total purchase costs, installation costs, and training costs for using vehicle test equipment	[53,54]
		Maintenance Cost (C12)	Costs that must be incurred for maintenance and upkeep of vehicle test equipment	[53,54]
		Operational Cost (C13)	Costs associated with the use of vehicle test equipment	[53,54]
2	Operational (C2)	Time Process (C21)	The amount of time required to complete a vehicle testing process using vehicle test equipment	[55]
		Electrical Requirement (C22)	Electrical power required in selecting vehicle test equipment	[56]
3	Specification (C3)	Dimension (C31)	Physical dimensions of vehicle test equipment	[53]
		Load (C32)	The maximum load that the test equipment can measure	[57]
		Light Intensity (C33)	The range of light intensity that the test equipment is capable of measuring	[58]
		Sound Noise (C34)	The range of sound noise that the test equipment is capable of measuring	[59]

.

4.3. Weight of Criteria Calculation

The weights of the main criteria and the sub-criteria that consider the experts’ subjective judgments are estimated using AHP. A pairwise comparison matrix of the main criteria and sub-criteria is shown in

Table 5. Main criteria pairwise comparison matrix.

	C1	C2	C3
Cost (C1)	1.00	0.76	1.08
Operational (C2)	1.32	1.00	0.75
Specification (C3)	0.92	1.33	1.00

,

Table 6. Evaluation of sub-criteria related to cost (CR = 0.08).

	C11	C12	C13
Investment Cost (C11)	1.00	0.65	0.94
Maintenance Cost (C12)	1.55	1.00	0.58
Operational Cost (C13)	1.06	1.72	1.00

,

Table 7. Evaluation of sub-criteria related to operations (CR = 0.00).

	C21	C22
Time Process (C21)	1.00	1.21
Electrical Requirement (C22)	0.82	1.00

and

Table 8. Evaluation of sub-criteria related to specification (CR = 0.01).

	C31	C32	C33	C34
Dimension	1.00	2.46	2.54	2.75
Load	0.41	1.00	1.74	1.19
Light Intensity	0.39	0.57	1.00	1.15
Sound Noise	0.36	0.84	0.87	1.00

, and the calculation of the weights is depicted. Table 5 shows the result of the main criteria pairwise comparison matrix. Table 6 shows an evaluation of sub-criteria related to cost. Table 7 shows an evaluation of sub-criteria related to operational. Table 8 shows an evaluation of sub-criteria related to specification.

Table 9. Weights of criteria and sub-criteria.

No.	Name	Weight	No.	Name	Weight
C1	Cost	0.3121	C11	Investment Cost	0.0877
			C12	Maintenance Cost	0.0995
			C13	Operational Cost	0.1249
C2	Operational	0.3320	C21	Time Process	0.1820
			C22	Electrical Requirement	0.1499
C3	Specification	0.3559	C31	Dimension	0.1629
			C32	Load	0.0776
			C33	Light Intensity	0.0580
			C34	Sound Noise	0.0575

shows the recapitulation weights of criteria and sub-criteria, and Figure 2 shows the results of the hierarchy model.

4.4. Assessment of Alternatives and Determination Final Result

After calculating the weights of the criteria and sub-criteria, the next stage is to choose the best investment among the three alternatives (cf. Table 9 and

Table 10. Specific information about all alternatives.

Criteria	Alternatives
	Investment 1	Investment 2	Investment 3
Investment Cost (IDR)	460,979,000	685,039,000	907,767,000
Maintenance Cost (IDR per year)	15,200,000	14,000,000	17,500,000
Operational Cost (IDR per year)	870,468,114	837,519,448	875,588,514
Time Process (Minute)	55	40	60
Electrical Requirement (V; Hz)	230/400; 50	380; 50	220/380; 50
Dimension (mm)	750 × 1220 × 1370	6170 × 820 × 385	3260 × 750 × 470
Load (kg)	1020	2000	3000
Light Intensity (cd)	0–120,000	0–120,000	0–120,000
Sound Noise (dB)	30–80	30–130	35–130

). TOPSIS is used to select alternatives and the same expert was asked to assess the three alternatives based on the importance of each sub-criteria. The weights given to each alternative are then carried out by matrix calculations.

Table 11. Input values of the TOPSIS analysis.

No.	C11	C12	C13	C21	C22	C31	C32	C33	C34
INV1	4.9190	5.3783	5.7203	6.5814	7.5289	6.8751	6.1278	6.3458	6.0000
INV2	4.4777	5.7852	6.3816	8.5858	6.4907	6.2738	6.4907	5.9663	8.3859
INV3	5.5016	5.5780	5.9328	6.5814	6.7869	6.3196	6.8535	6.0933	7.7892
Weight	0.0877	0.0995	0.1249	0.1820	0.1499	0.1629	0.0776	0.0580	0.0575

lists all of the assessments from the five experts.

Table 12. The weighted normalized decision matrix.

No.	C11	C12	C13	C21	C22	C31	C32	C33	C34
INV1	0.0500	0.0553	0.0686	0.0946	0.0938	0.0996	0.0422	0.0346	0.0267
INV2	0.0455	0.0595	0.0765	0.1234	0.0808	0.0909	0.0447	0.0325	0.0373
INV3	0.0559	0.0574	0.0711	0.0946	0.0845	0.0915	0.0472	0.0332	0.0346

displays the normalized decision matrix. Next is determining the positive and negative ideal solutions, as illustrated in

Table 13. Positive ideal solution and negative ideal solution.

No.	C11	C12	C13	C21	C22	C31	C32	C33	C34
A+	0.0559	0.0595	0.0765	0.1234	0.0938	0.0996	0.0472	0.0346	0.0373
A−	0.0455	0.0553	0.0686	0.0946	0.0808	0.0909	0.0422	0.0325	0.0267

. The final step is ranking the alternatives shown in

Table 14. Ranking of the investment selection.

Alternatives	${\mathit{S}}_{\mathit{i}}^{\mathit{*}}$	${\mathit{S}}_{\mathit{i}}^{-}$	${\mathit{C}}_{\mathit{i}}^{\mathit{*}}$	Rank
INV1	0.0329	0.0164	0.3319	2
INV2	0.0190	0.0321	0.6279	1
INV3	0.0320	0.0149	0.3177	3

; Investment 2 was selected as the best alternative to be implemented. The real performance data for all criteria of the three alternatives are presented in Table 10.

4.5. Financial Analysis

This step aimed to validate the outcomes of data processing using a hybrid MCDM approach that included TOPSIS and AHP in an MCDM model. These three options calculate vehicle inspection test depreciation, cost of production, BEP, and EAC using the data collected in this study. The result is shown in

Table 15. Financial evaluation.

Criteria	Alternatives
	Investment 1	Investment 2	Investment 3
Depreciation (IDR per year)	69,146,850	102,755,850	136,165,050
Operational Cost (IDR per unit)	201,497	193,870	202,683
Production Cost (IDR per unit)	215,820	204,287	218,308
Price (IDR)	424,000	424,000	424,000
BEP (Unit)	379	507	694
BEP (IDR)	81,813,600	103,644,535	151,575,185
EAC (IDR)	992,073,212	1,018,231,011	1,115,055,162

.

It is evident from the economic feasibility analysis of the three investment options that option 1 has the lowest EAC and BEP. Accordingly, the economic feasibility study determines the order to choose investment alternatives: Investment 1 > Investment 2 > Investment 3.

4.6. Comprehensive Assessment

Based on the MCDM and calculation explained earlier, Investment 2 is chosen as the best investment in converted electric motorcycle-type test tools consisting of brake testers, headlight testers, sound level testers, weight testers, and speedometer accuracy testers. Based on the total processing time, this option can complete a vehicle test in 40 min. The total electrical power required to activate the test equipment is 380 V at 50 Hz. The area required to place the test tool is 6170 mm × 820 mm × 385 mm. The estimated annual operational costs and maintenance costs for the electric motorcycle type test are IDR 837,519,448 (USD 51,000) and IDR 14,000,000 (USD 860). The investment or purchase cost of this tool is IDR 685,039,000 (USD 42,000). The selected weight test equipment has a maximum test load limit of 2000 kg, making it effective for weighing vehicles, especially two-wheeled vehicles that have been converted. The light intensity range that can be measured by the lamp test equipment is 0–120,000 cd, while the noise range is 30–130 dB. The chosen weight test apparatus is useful for weighing vehicles, particularly modified two-wheeled vehicles, due to its maximum test load limit of 2000 kg. The lamp test apparatus has a measurement range of 0–120,000 cd for light intensity and 30–130 dB for noise.

Meanwhile, Investment 1 was determined to be the best option to be adopted by the conversion workshop based on the economic feasibility analysis. Investment 1 was selected due to its lowest EAC and BEP numbers compared to the other options. However, all other factors are not taken into account, and the assessment of each option is only dependent on financial estimates. As a result, the outcomes of the two computations differ.

Subsequent investigation revealed that variations in the standards applied to assess investment options were the cause of the discrepancies in the decision outcomes. The MCDM methodology weighs a number of factors while assessing potential options. In order to determine the weight of each criterion, the decision-maker offers an evaluation of the criteria that were utilized as evaluation parameters. Processing time is widely recognized as the primary factor taken into account by decision-makers when evaluating investment options for vehicle testing equipment. Meanwhile, acquisition, maintenance, and operating costs rank sixth, fifth, and fourth, respectively, according to the cost criterion. This demonstrates that it is impossible to consider only one aspect when analyzing investing options. Nevertheless, the decision-maker’s validation is required in order to validate the assessment criteria and make it official.

In discussing the environmental and economic benefits of implementing Alternative 2 at the conversion workshop, some points focus on improving the adoption of converted electric motorcycles. Environmental benefits include reduced carbon emissions, improved air quality, and reduced noise pollution. By converting the conventional motorcycle that runs on gasoline, a fossil fuel that releases significant carbon dioxide (CO₂) when burned, the reliance on gas is reduced, leading to lower CO₂ emissions. Converted electric motorcycles can be powered by renewable sources such as solar, wind, or hydroelectric power. As a result, this transition helps reduce the carbon footprint further. Even when considering the entire lifecycle of converted electric motorcycles, including the battery and disposal, the total emissions are typically lower than those of conventional motorcycles. If a conventional motorcycle emits harmful pollutants such as nitrogen oxides (NO_x), particulate matter (PM), and volatile organic compounds (VOC), converted electric motorcycles produce no tailpipe emissions, thereby significantly improving air quality. Improved air quality leads to better public health outcomes, reducing respiratory and cardiovascular diseases caused by air pollution. In urban environments, converted electric motorcycles are incredibly beneficial, where vehicle emissions contribute significantly to air pollution and smog. Converted electric motorcycles operate more quietly compared to conventional motorcycles. Reduced noise pollution contributes to a more pleasant and less stressful urban environment.

Economically, improving the widespread adoption of converted electric motorcycles and investing in converted electric motorcycle tests provides significant cost savings, enhances operational efficiency in conversion workshops, and contributes to job creation and local economic growth. Converted electric motorcycles have lower operating costs due to the cheaper price of electricity than gasoline. Maintenance costs are also reduced because electric vehicles have fewer moving parts and do not require oil changes. Thus, users can save significantly on fuel costs over the lifespan of the battery of a converted electric motorcycle. Many governments offer incentives such as tax credits, rebates, and grants for purchasing and converting electric vehicles, further reducing the financial burden on consumers and businesses. For conversion workshops, improving the tools for converted electric motorcycle tests can standardize and streamline the conversion process, leading to increased workshop efficiency and productivity. Workshops can handle a higher conversion volume with efficient processes and skilled staff, leading to increased revenue. It also creates new job opportunities in manufacturing, conversion, maintenance, and infrastructure development. Conversion workshops can stimulate local economies by providing jobs and supporting ancillary businesses, such as electric components and charging infrastructure suppliers. The move towards converted electric motorcycles encourages investment in green technologies, thereby fostering innovation and sustainable economic growth.

4.7. Implementation Recommendation

The conversion workshop can implement this recommendation based on their needs, including performance goals, budget constraints, and operational requirements. It includes thorough preparation, practical training, and continuous monitoring. The preparation step includes timeline planning and vendor selection based on predefined criteria, including cost-effectiveness, quality, and after-sales support. Installation and setup include site preparation, adequate space, electrical outlets, and necessary infrastructure. Delivering and installing the tools, as well as ensuring all components are correctly installed and configured, are administered by coordinating with the selected vendor. Moreover, conducting initial testing aims to verify that all the tools operate as expected and meet performance standards. The critical steps are training and capacity building. In this stage, training is carried out by developing a comprehensive training program for workshop staff focusing on operation, maintenance, and troubleshooting. A continuous learning and skill development system should also be implemented, including refresher courses and access to updated manuals and resources. After the installation and training step, adjusting existing workflows and processes to incorporate the CEM-type test tools is carried out to ensure minimal disruption to ongoing operations. In the end, monitoring and evaluation should be conducted. Regular performance reviews should be made to assess the impact of CEM-type test tools on operations and identify areas for improvement. After implementing this alternative, potential challenges arise, e.g., high initial costs, technical difficulties, and downtime during installation. However, it can be solved by negotiating favorable payment terms with the vendor or exploring financing options or grants to offset initial costs. Thus, it ensures that the vendor provides robust technical support and maintenance service and plans the installation during off-peak hours or scheduled maintenance periods to minimize disruption.

To ensure that the selected converted electric motorcycle type test remains up-to-date and well maintained in the face of evolving standards and technological advancements, conversion workshops can establish the following. These are the following: (1) a dedicated maintenance team; (2) vendor partnerships and support agreements with the vendor to ensure timely assistance with troubleshooting, repairs, and updates; (3) continuous training programs to keep the technicians and staff updated on the latest advancements and proper maintenance procedures; (4) monitoring technological trends; (5) implementing a feedback loop for technicians and operators to report issues, suggest improvements, and highlight areas requiring updates; (6) documentation and knowledge management by keeping detailed logs of all updates, maintenance activities, and issues resolved to ensure a comprehensive record of tool performance and modifications.

A comprehensive training and support plan was implemented in conversion workshops. This plan will encompass various training modules, support mechanisms, and continuous learning opportunities. Specific training programs include initial comprehensive training, advanced technical training, and certification programs. Support mechanisms include technical support (24/7 helpline and email), on-site support, online resources, and continuous learning opportunities. By enhancing the competence and confidence of technicians, improving operational efficiency, and encouraging wider adoption, these training and support initiatives will significantly contribute to the overall success and sustainability of the recommended investment.

4.8. Comparative Discussion

In comparing research findings on the use of Alternative 2 as an investment in converted electric motorcycle test tools with the existing scientific literature, we highlight several contributions. Pal [60] focuses on the performance metrics and environmental benefits of electric motorcycles compared to conventional motorcycles, and key metrics include energy efficiency, carbon emissions reduction, and overall environmental impact. We build on these findings by providing specific converted electric motorcycle test tools for conversion workshops to ensure that performance metrics are consistently achieved. The tools are designed to standardize the testing process, ensuring that converted electric motorcycles meet the performance metrics highlighted in the study. So, while those studies emphasize the theoretical benefits, our research offers a tangible solution to achieve these benefits through standardized type testing and conversion processes.

Meanwhile, Mandys [61] examines the barriers to the adoption of electric vehicles, including cost, lack of infrastructure, and technological limitations. It suggests that widespread adoption requires addressing these barriers through policy changes, incentives, and technological advancements. We align with these findings by addressing the technological limitations through the introduction of converted electric motorcycle test tools. By improving the reliability and performance of converted electric motorcycles, we help mitigate one of the key adoption barriers. Additionally, our research supports the idea of policy changes and incentives by demonstrating the effectiveness and necessity of standardized testing tools for achieving reliable performance metrics.

Unlike many studies that focus on theoretical benefits or barriers, our research provides a concrete solution to standardize the testing process for converted electric motorcycles. This standardization ensures reliability and consistency in performance metrics. Practical implementation includes training, maintenance, and support mechanisms, ensuring that the tools are effectively integrated into workshop operations. We not only highly value the environmental benefits but also address the economic impact and operational efficiency of conversion workshops. The introduction of Investment 2 as the selected converted electric motorcycle test tool is scalable and can be adapted to various conversion workshop sizes and capacities. This scalability ensures that the benefits of our research can be widely adopted across different regions and workshop setups.

5. Conclusions

This paper addresses the investment selection problem of choosing an electric motorcycle test for a conversion workshop to improve its competitiveness. This investment selection problem is discussed considering all the relevant risks associated with the investment decision. The investment selection criteria for the vehicle inspection test consist of two levels: main criteria and sub-criteria. The main criteria consist of costs, operations, and specifications. There are nine sub-criteria: purchase costs, maintenance costs, operational costs, processing time, electrical power requirements, dimensions, test load, light intensity, and sound noise.

Based on the results of calculating the weights of each criterion and sub-criteria using the AHP method, the following are the weights of the nine criteria used in making decisions on investment selection for vehicle test equipment. The nine criteria are as follows: purchase costs (0.088), maintenance costs (0.099), operational costs (0.125), processing time (0.182), electrical power requirements (0.150), dimensions (0.163), test load (0.078), light intensity (0.058), and sound noise (0.057).

The evaluation and selection of investment alternatives for vehicle testing equipment using the TOPSIS method produces a ranking and relative closeness value for each alternative. The relative closeness value for each alternative is Investment 1 (0.3319), Investment 2 (0.6279), and Investment 3 (0.3177). The alternative with the highest relative closeness value is Investment 2, while the alternative with the lowest relative closeness value is INV 3. Based on the relative closeness value, it can be concluded that the ranking order of vehicle test equipment investment alternatives is Investment 2 > Investment 1 > Investment 3. This research aims to provide recommendations for the best investment alternatives that can be purchased through the conversion workshops.

Implementing Alternative 2 at the conversion workshop offers compelling environmental and economic benefits, driving the adoption of converted electric motorcycles. By transitioning from gasoline-powered to electric models, there is a notable reduction in carbon emissions, improving air quality, and minimizing noise pollution. Electric motorcycles can be powered by renewable energy sources, further lowering their carbon footprint throughout their lifecycle. This shift not only enhances public health by reducing harmful pollutants but also contributes to a quieter and more sustainable urban environment.

Investing in the widespread adoption of converted electric motorcycles and enhancing the efficiency of conversion workshops not only delivers substantial economic benefits but also contributes significantly to environmental sustainability. The cost savings associated with lower operating and maintenance expenses for electric vehicles, coupled with government incentives, make them financially attractive for consumers and businesses alike. Improving conversion processes through advanced testing tools not only boosts workshop productivity but also generates employment opportunities across various sectors, thereby fostering local economic growth. Moreover, this transition promotes innovation in green technologies, supporting a sustainable future while mitigating environmental impact.

Implementing the recommendation for CEM-type test tools at the conversion workshop requires careful planning, comprehensive training, and ongoing monitoring to optimize operational efficiency and achieve performance goals. Despite potential challenges such as initial costs and technical adjustments, proactive management strategies can mitigate these obstacles, ensuring smooth integration and long-term benefits for the workshop and its stakeholders.

The implementation of a robust training and support plan in conversion workshops promises to elevate technician competence, streamline operations, and foster a broader acceptance of converted electric motorcycles. By providing comprehensive training modules and robust support mechanisms, this initiative not only enhances efficiency but also ensures long-term sustainability and success in adopting innovative green technologies.

This research has several limitations. Firstly, data collection only involved five experts, who possibly may not provide a comprehensive representation of broader industry perspectives. Additionally, the research focused exclusively on quantitative criteria, ignoring qualitative factors that could significantly influence investment decisions. The study employed AHP and TOPSIS for MCDM models; however, it did not analyze the relationships between the criteria, which could offer deeper insights into the decision-making process. These limitations suggest that future research should consider a larger and more diverse group of experts, incorporate qualitative criteria, and explore the interrelationships between different criteria to provide a more holistic understanding of the investment decision-making process. Similar research can be conducted by adopting a different combination of MCDM tools to augment the research findings. By addressing these issues, we provide more actionable and reliable recommendations for stakeholders considering investments in converted electric motorcycle tests.