Systematic Analysis of Packaging Production in the Electric Motors Industry: A Multi-Criteria Approach through the SAPEVO-M Method

Abstract

Market competitiveness drives the electric motors industry, which in turn necessitates the selection of optimal production scenarios, particularly in the context of packaging. This is crucial for maintaining competitiveness and meeting the rigorous quality and logistical demands that are characteristic of this industry. This paper presents a systematic analysis of the packaging production chain for electric motors, employing the SAPEVO-M method as a decision aid tool. The study examines various strategic options, including outsourcing and internalizing processes, with a particular focus on their impacts on logistics, quality control, and overall supply-chain efficiency. The research conducts a comprehensive evaluation of these strategies to ascertain the most effective approach for managing the complexities of packaging production. The SAPEVO-M method facilitated a structured decision-making process, allowing for the aggregation and prioritization of diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. A sensitivity analysis was performed to validate the robustness of the decision-making outcomes under varying alternatives. The findings highlight the benefits of internalizing certain processes, particularly the assembly (with a score of 43.27%), to gain direct control over production variables, leading to enhanced operational efficiency and product competitiveness. This paper contributes to the literature by demonstrating the application of MCDA in enhancing strategic decisions within the electric motors industry, providing insights for analyzing other manufacturing factors in the improvement of supply-chain processes.

1. Introduction

The industrial sector for electric motors plays a pivotal role in modern production, serving as a cornerstone for various applications, including industrial machinery and electric vehicles [1]. In this dynamic environment, operational efficiency and strategic decision-making have emerged as critical factors for competitive success. Innovative supply-chain strategies, such as logistics optimization, inventory management, and supplier collaboration, are essential to sustain this efficiency and competitiveness [2].

Concurrently, there is a substantial demand within the production chain for the manufacturing of crates for packaging, which is indispensable to the logistics, storage, and transportation of these motors and drives [3]. Consequently, it is imperative for companies to promptly adapt their processes to optimize the efficiency of the production chain and deliver more competitive products to the market, improving the supply-chain strategies [2].

In the contemporary business environment, the prevalent practice of outsourcing non-core processes, while focusing on core product development and expertise, enables companies to leverage specialized external capabilities and scale operations effectively. However, companies increasingly re-evaluate this approach when faced with complex logistical challenges, stringent quality standards, and a rapidly evolving market landscape [4,5]. These pressures prompt a growing number of organizations to internalize processes, thereby gaining greater control over production, quality, and logistics. This shift not only enhances their ability to respond swiftly to market demands but also facilitates tighter integration and coordination within the supply chain. By internalizing, companies can better align their operational strategies with overall business objectives, ensuring consistent product quality and more efficient supply-chain management [4,5].

In the electric motors industry, where the transportation of heavy components is a fundamental aspect of the production process, improvements in packaging technology not only contribute to a reduction in carbon emissions but also enhance logistical efficiency by improving the utilization of space and minimizing the risks of damage to the components. These factors highlight the significance of sustainable packaging practices in the pursuit of operational efficiency and environmental responsibility [6,7].

Given the challenges presented by the complexity of supply chains in the motor and drive sector, it is essential to employ complex mathematical models for the evaluation and selection of alternative strategies that can favor both efficiency and operational sustainability. Nevertheless, a research gap persists with regard to the deployment of the SAPEVO-M approach for decision-making in the internalization of manufacturing processes and the assessment of packaging production strategies. The current approaches presented in the literature often fail to provide a comprehensive analysis that integrates both operational and strategic factors, particularly within the context of the electric motors industry. The existing studies lack a structured framework for addressing the unique logistical and quality demands inherent to packaging production, which are critical for maintaining competitiveness in this sector. This gap underscores the necessity of employing more robust decision-making methodologies to better inform process selection and internalization strategies.

In this context, this study proposes a comprehensive analysis employing Multi-Criteria Decision Analysis (MCDA) methodologies to examine the processes associated with the internalization of crate manufacturing in the electric motors and drives industry.

Despite these advancements, there remains a gap in the comprehensive evaluation of packaging production strategies within the electric motors industry. The complexity of supply chains, combined with the necessity for high standards in packaging quality and logistical efficiency, highlights the need for a more structured approach to decision-making. This study addresses this gap by employing MCDA methodologies to systematically analyze and optimize the processes associated with the internalization of crate manufacturing. By integrating analytical tools with decision-making strategies, this research aims to enhance operational processes in line with the industry’s requirements, providing solutions that align with the company’s vision.

MCDA provides a structured framework for decision-making in complex scenarios involving multiple criteria and alternatives, making it particularly suitable for addressing the multifaceted challenges of packaging production. The application of MCDA in this context allows for a systematic evaluation of various strategic options, incorporating diverse criteria such as cost, quality, flexibility, environmental impact, and supply risk. The systematic analysis presented in this manuscript considers a range of aspects related to the production process, including production flow, time analysis, layout, and the necessary equipment required for different scenarios. This approach not only aids in identifying the most efficient and sustainable packaging strategies but also enhances decision-making transparency and robustness.

This study will investigate and analyze the process of internalizing crate manufacturing in the electric motors industry. It will identify viable alternatives and provide a theoretical foundation for these options. Our goal is to provide strategic support to decision-makers, enabling them to make well-informed choices. The study will then apply an MCDA methodology to streamline the decision-making process and select the most suitable scenario based on established criteria and the preferences of the decision-makers.

The article is structured into five sections. Section 2 provides a comprehensive literature review, which forms the basis for the subsequent analysis and discussion. Section 3 develops the case study; Section 4 provides a detailed analysis and discussion of the results. In conclusion, Section 5 of this article presents a summary of the key findings and their implications for the industry.

2. Material and Methods

The utilization of electric induction motors in industrial applications is pervasive due to their versatility and efficiency. They are particularly adept at providing robust and reliable electrical drives in industrial systems [8]. The inherent structural simplicity of induction motors and their high operational reliability result in low maintenance costs, rendering them economically attractive in the contemporary global landscape [1].

Consequently, there is an increasing demand for more efficient storage and transportation methods for these products, driven by the necessity to cope with a growing volume of movements. The strategic employment of wooden pallets and crates effectively organizes, stores, and moves goods on a large scale [3].

The manufacturing process of crates and pallets is complex and involves numerous stages. It begins with the selection and preparation of wood, continues with the assembly of boards, and culminates in the final wooden structure [9]. Despite the apparent simplicity of the production process, companies are compelled to optimize their production chains due to the pressures exerted by the scarcity of natural resources and the intensifying competition in the market. Consequently, a pressing need arises for precise decision-making to sustain competitiveness in the market.

The influence of heuristics and cognitive biases impedes the application of rational decision-making. These factors have the potential to directly influence choices, leading to judgment errors, particularly in uncertain circumstances [10]. Therefore, it is essential to acknowledge that environmental, contextual, and social factors directly influence decisions [11]. Recognizing the interwoven intricacies of decision-making allows for the application of effective methods and strategies in the decision-making process [12].

MCDA models comprise the following three principal components in their analysis: diverse evaluation criteria; a range of plausible alternatives for selection; and a comparison between these options within each criterion [13]. This framework enables a comprehensive and detailed evaluation, considering the complexity of decisions involving multiple factors and variables. Such problems encompass qualitative and quantitative objectives that may even conflict, posing challenges in analysis [14].

2.1. Multi-Criteria Decision Analysis (MCDA)

While processes based on multi-criteria choice enhance decision-making consistency, it is of the utmost importance to structure and select options and criteria with precision to guarantee informed choices by decision-makers [15]. MCDA comprises a series of constructive stages, the number and sequence of which may vary depending on the scenario and problem context [16]. In general, these stages encompass the following: problem identification; criteria determination; criteria weighting; alternative evaluation; aggregation and combination of alternatives; sensitivity analysis; and decision-making.

The criteria serve as the parameters and standards with which to guide decision-making and facilitate a comparison of alternatives from diverse perspectives, and can be hierarchically structured [17]. The process of weighing these criteria represents a complex and foundational step in the field of MCDA, as it significantly influences the resulting outcomes [18]. To ensure logical and sustainable outcomes, criteria in multi-criteria modelling must meet basic requirements such as being unambiguous, intelligible, operational, measurable, and homogeneous, as well as having access to information availability [17].

During the development of a model, it is paramount to analyze the relationships between different criteria to identify preference relations, which will then be used to determine the respective weights applied in the modelling process. These relations are classified as follows: indifference; weak preference; strict preference; incompatibility; and outranking [19].

Multi-criteria methods serve as comprehensive frameworks that streamline decision-making considering various perspectives and criteria [20]. These methodologies play a pivotal role in aiding decision-makers in analyzing alternatives and categorizing them based on predefined weights. MCDA methods enhance decision-making to be more rational, objective, and efficient [21]. The primary objective of these methods extends beyond simply presenting the best and worst options to facilitate the identification and exploration of alternative sets. This fosters a deeper understanding of the interrelationships among options, enriching the analysis and offering a holistic view of the decision-making landscape.

A pivotal aspect of the process involves selecting the appropriate analysis method [22]. A lack of knowledge or uncertainty in describing the problem can influence the selection process to be developed. According to Sałabun et al. [18], the method selection should prioritize objectivity and consider various properties related to the decision problems, comparison of variants, characterization of weights, performance of alternatives, and representation of fuzzy data and imprecise preferences.

The plethora of available models provides a plethora of options for method selection, enabling the choice of the most suitable one aligned with the decision-making context at hand. Despite the prevalence of widely applied methods, each situation warrants a specific approach. Identifying the decision dilemmas inherent in each context is crucial for determining the most appropriate method to address specific requirements [23]. A meticulous analysis of the issues allows for an informed and efficient selection of the most appropriate method, thereby aligning with the needs of each alternative.

2.2. Method Selection

The selection of the non-dominance MCDA method must be tailored to the unique context in which the decision-making process unfolds. Numerous factors merit consideration in this regard. First, the overarching objective of the decision must be considered. These objectives shape the pertinent criteria and determine how these criteria should be weighed. For instance, if the aim is profit non-dominance, MCDA methods adept at handling quantitative objectives may be more suitable. Conversely, if the objective is to enhance customer satisfaction, methods better attuned to qualitative criteria might be preferred.

Moreover, the decision context assumes a pivotal role. Specific constraints, decision-maker preferences, and data availability vary, depending on the context. For instance, in time-sensitive scenarios, the feasibility of implementing a model may emerge as the deciding factor in method selection. Furthermore, the magnitude of criteria and alternatives involved in the decision warrants consideration. In the event of a multitude of criteria or alternatives, it becomes imperative to select an MCDA method capable of navigating this complexity. Certain methods exhibit greater scalability and proficiency in managing extensive sets of criteria and alternatives.

Finally, the number of decision-makers involved in the decision-making process is relevant. Certain MCDA methods are tailored for individual decisions, while others are designed to facilitate collaboration and consensus among multiple decision-makers. It is important to consider the specific context and requirements of the situation when selecting an MCDA method. In this context, SAPEVO-M was selected due to its unique suitability for group decision-making, which sets it apart from other methods, such as AHP, TOPSIS, and PROMETHEE, which typically necessitate adaptations to effectively accommodate multiple decision-makers. SAPEVO-M permits a more streamlined and practical approach in collaborative settings, rendering it highly adaptable to industrial applications.

Furthermore, SAPEVO-M is particularly adept at managing ordinal inputs, thereby facilitating the effective processing of subjective preferences. This feature is in contrast to those of other methods, which rely on cardinal inputs and thus require more rigid, quantitative data.

Moreover, the decision-making factors are further enhanced by a digital platform, which serves as an additional facilitating factor for the method’s application. The digital platform facilitates the seamless visualization of results, rendering the foundational aspects of the model transparent and easily comprehensible. This attribute is of significant value in industrial settings, enabling stakeholders to grasp and validate the outcomes effectively.

In group decision-making scenarios where a mediator facilitates the method and constructs scenarios without direct participation in decisions, it is of the utmost importance for them to accurately convey and proficiently manage the methodology. This ensures that participants can comprehend the weighting process with clarity.

A structured process was devised in two stages to implement the SAPEVO-M method and elicit decision-makers’ preferences. In the initial stage, a collective meeting was convened with decision-makers via a digital platform. The primary objective of this session was to introduce the concept of the MCDA model and present the scenarios crafted as potential solutions.

These meetings were conducted individually and in person to reduce the influence of herd mentality on decision-making. This created an environment conducive to candid opinions, with reduced susceptibility to biases and preconceived judgments. During these sessions, preferences were documented in the SAPEVO-M model and organized on the dedicated digital platform SAPEVO-M (v.1) (www.sapevo-m.com, accessed on 20 March 2024) , which was developed by Moreira et al. [24].

The temporal distance between the inaugural assembly, which contextualized the scenarios, and the subsequent session, during which the actual weighting was conducted, was meticulously considered. Given the industry’s pressing demands, the objective was to minimize the interval between meetings. A prolonged interval between meetings may result in the loss of information over time, which could diminish the retention of critical information by decision-makers.

During individual sessions, it is common to encounter difficulties distinguishing between criteria and alternatives. It is not uncommon for decision-makers to link criteria with alternatives during the weighting process, potentially bypassing crucial steps in the evaluation process. This presents a significant concern, as it can potentially influence decision-makers in an unintended manner.

It is, therefore, of the utmost importance to emphasize and highlight the significance of a two-stage evaluation process. In the initial phase of the evaluation process, decision-makers should focus exclusively on the criteria, reserving considerations on how these criteria intersect with alternatives for a subsequent phase. This approach allows for a comprehensive assessment of each criterion’s merits and drawbacks before comparative analyses of alternatives are conducted. Such deliberation is inherent in individuals who are accustomed to decision-making and are driven by a desire to foresee the future implications of their present choices.

Consequently, the mediator must ensure that thoughts are structured and articulated methodically, adhering to defined steps. This fosters a more robust consensus-building process in decision-making. Through interviews, pairwise comparison matrices were formulated, providing detailed insights that will be discussed in the subsequent section.

2.3. Literature Review

The pursuit of improving decision-making processes across industries has resulted in a multitude of studies that have explored a variety of MCDA methods in different contexts. An integrated approach has been proposed, combining data envelopment analysis (DEA), the spherical fuzzy analytic hierarchy process (SF-AHP), and the spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) for supplier selection in the steel industry [25]. This model effectively addresses both quantitative efficiency and qualitative criteria, with a particular emphasis on sustainability, offering valuable insights for industry leaders.

In another study, the FAQT-2 method was introduced for the selection of industrial robots, with the method being applied in the context of a group-based multi-criteria decision-making analysis. The method demonstrated its effectiveness in weighting criteria and conducting sensitivity analyses, representing a significant advance in robot selection for the pharmaceutical industry [26].

A novel fuzzy–rough multi-criteria decision-making (MCDA) model for green supplier selection was also proposed that integrated the SWARA and ARAS methods with fuzzy–rough numbers [27]. This model offers a robust and flexible solution for supplier selection, validated through comparative and sensitivity analyses. It is particularly effective in addressing uncertainty challenges while offering practical applicability across industries.

Subsequent studies have employed the SAPEVO method in particular contexts. One study effectively demonstrated the use of SAPEVO-M for evaluating future scenarios for the treatment of produced water in the oil industry [28]. This was achieved by utilizing the Delphi method to illustrate the significance of risk assessment in evaluating sustainable technological alternatives. Another study proposed the application of the SAPEVO-H² method to the development of defense strategies against remotely piloted aircraft systems (RPAS) for the Brazilian Navy [29]. This study employed a hierarchical approach that integrated qualitative and quantitative data and utilized causal maps to analyze performance objectives and provide a structured solution to complex problems.

The SAPEVO-M-NC method was also successfully employed in the selection of regions suitable for African mahogany plantations. By employing data from INPE and conducting interviews with agricultural experts, this study facilitated more efficient decision-making processes and has provided support for the implementation of sustainable commercial forestry practices [30]. Moreover, the efficacy of the SAPEVO-M method in supplier selection has been substantiated, thereby enhancing procurement and maintenance processes by improving transparency in decision-making. Recommendations have been put forth for the expansion of its use in diverse economic contexts [31].

2.4. The SAPEVO-M Method

The SAPEVO-M methodology, developed by Gomes et al. [32], is a multi-criteria model designed to streamline group decision-making processes. The SAPEVO-M model is designed to integrate multiple criteria and alternatives, enabling the evaluation of preferences concerning the criteria and subsequently facilitating the comparison of criteria preferences for each presented alternative [33].

This model represents a significant evolution of the SAPEVO method, designed to offer greater versatility in decision-making contexts involving multiple decision-makers [24]. This proposal represents an advancement over the previous model and expands its capabilities to meet the demands of complex scenarios [33].

This method is distinguished by its practical characteristics, which facilitate its application in the field of MCDA. The model’s capacity for asynchronous group decision-making is a notable feature, allowing each decision-maker to express their preferences individually. It is only during the axiomatic phase of the model that opinions are integrated, thus providing a flexible and adaptable approach to group dynamics [34].

Furthermore, integrating agility and transparency in the axiomatic process ensures high traceability in decision modelling, thereby facilitating the identification of pivotal elements in the final selection. This, in turn, enables the selection of the optimal decision and the subsequent analysis of alternative options [35]. This quality is particularly significant in industrial settings, where agility in action is paramount.

This methodology’s distinguishing feature is its ability to integrate ordinal and cardinal models. By translating linguistic terms into numerical methodology, SAPEVO-M overcomes the limitation of expressing the relative importance of points that previously could not be measured [33]. This approach is essential for determining preference relations between alternatives within a specific criterion and calculating the degrees of importance associated with each of them, resulting in the assignment of corresponding weights [32].

By transforming initially ordinal methods into cardinal values, the SAPEVO-M methodology enables the application of axiomatic calculations to obtain the most appropriate decision. Axiomatic characterization plays a pivotal role in ensuring the scientific validity of a model, necessitating meticulous and comprehensive consideration from its perspective [35]. This methodical approach is essential to ensure the integrity and validity of the process, thereby contributing to the reliability of, and confidence in, the results achieved.

The axiomatic stage for the SAPEVO-M method is subdivided into two distinct steps. The process’s initial stage entails converting ordinal preferences into cardinal values, which facilitates the mathematical comprehension of these criteria. In the second step, the transformation from the ordinal order between alternative preferences to cardinal values is also performed.

To evaluate each level of variable and criterion sets, we employ an approach that transforms qualitative expressions into ordinal values, as shown in Table 1.

A pairwise evaluation method between variables is employed to analyze a set of variables. Each decision-maker contributes to forming a matrix reflecting their preferences among the variables. The initial step involves the utilization of Equation (1), which is then used to calculate the sums of the quantities, assigning a degree of importance to each variable as perceived by the decision-maker, as follows:

(1)${\upsilon }_i={\displaystyle \frac{\sum a_i-\mathit{min}{a}_i}{max a_i-\mathit{min}{a}_i}}$

Subsequently, the sums (2) are calculated for each decision-maker $w$, and the results are normalized (3) to indicate the relative importance of the variables to the higher-level attribute, as follows:

(2)$e_i={\displaystyle \frac{{\sum }_{m=1}^n{e}_{iw}}{w_i}}$

(3)${\upsilon }_i={\displaystyle \frac{e_i}{\sum e_i}}$

If any criterion yields a final weight of zero, we adhere to the practice of assigning to this criterion the value of 1% of the smallest non-zero value. This approach is endorsed by Gomes et al. [32], who emphasize the significance of not disregarding any criterion in an evaluation.

The final consolidation of data entails calculating the weighted sum of criterion weights $w_i$ with the performance of each alternative $a_{ij}$. This is represented by the following Equation (4):

(4)$v_i={\sum }_{j=1}^n{w}_i{a}_{ij}$

The SAPEVO-M model employs consistency calculation to validate the logic of comparisons between criteria and alternatives. This is achieved using an n-dimensional matrix, with the upper diagonal dedicated to this validation process.

Constructed based on the principles of transitive preference models, this matrix provides a benchmark for comparison and facilitates the consistency test. The calculation of $a_{ij}$ is based on the Equation (5), as follows:

(5)$a_{ij}=\left\{\begin{array}{c}-3se\left(a_{ij}+a_{i1}\right)\le -3\\ a_{ij}+a_{i1}se-3<(a_{1j}+a_{i1})<3\\ 3se(a_{ij}+a_{i1})\ge 3\end{array}\right.$

After comparing the input matrix with the ideal matrix, a third matrix, known as the consistency matrix, is generated. This matrix comprises binary entries (0, 1). The matrices are then compared. A difference calculation is performed if the value falls within the range of {−1; 1} and $a_{ij}$ is set to 0; otherwise, it is set to 1. This process is illustrated in Table 2.

Following a comparison between the input matrix and the ideal, a binary consistency matrix is generated. The sum of these scores results in the consistency index λ, which is calculated using Equation (6), as follows:

(6)$\lambda ={\displaystyle \frac{bp}{(\frac{n\left(n-1\right)}{2})}}$

Consistency values are evaluated within specific intervals, as illustrated in Table 3, enabling decision-makers to consider the subjectivity inherent in the decision-making process. This aids in determining whether to proceed with the current assignments or to revise the preference assignment process.

3. Case Study

To comprehensively analyze the packaging alternative, it is essential to understand the factors that shape this landscape. A preliminary on-site evaluation of the packaging process was initiated, covering everything from box assembly intricacies to operational bottlenecks. Subsequently, a survey was conducted using the SAP (System Applications and Products in Data Processing) platform to extract essential insights. The most significant discovery was the identification of three primary packaging modalities employed by the company for electric motors: boxes, pallets, and crates. Given the prevalence of crate utilization, the study concentrated on this packaging type, which consists of a crate, sides, and top, as depicted in Figure 1.

Moreover, the acceptance of wooden products procured from suppliers is contingent upon their compliance with stringent minimum quality standards. These standards encompass both physical and dimensional parameters and are non-negotiable. Any deviation from these criteria results in the material being rejected, necessitating its return to the manufacturer and the requisition of a replacement shipment.

Furthermore, a comprehensive review of European regulations governing box manufacturing, notably ISPM15, was undertaken. This standard is paramount in ensuring compliance with international packaging norms, particularly for export-bound shipments. A collaborative analysis with the quality department yielded a commendable quality index of 98.03%. However, to attain the status of a world-class enterprise, adhering to the principles of World Class Manufacturing (WCM) standards, achieving the 99.9% benchmark is imperative. This discernible gap underscores an inherent opportunity for refining manufacturing processes, with a dual focus on curtailing raw material wastage and augmenting operational efficiency across the production continuum. Additionally, adherence to European logistical regulations, such as ISPM15, alongside quality considerations, assumes the utmost significance. Notably, a substantial 93.39% of expenses are attributed to crate procurement. This analytical insight demonstrates the potential for procedural adjustments for cost optimization, given that a significant proportion of expenditure is tied to material acquisition.

3.1. Scenario Proposals

During the search, seven proposals were put forth to address concerns within the packaging process. Among these, three were discarded, while two underwent revisions. These proposals span a spectrum from maintaining the current process to partially internalizing packaging assembly alongside implementing ISPM15 phytosanitary treatment. Certain options, like fully internalizing the sawmill and production chain, were dismissed from further consideration. After careful consideration, the following four alternatives remained; each was meticulously explored in subsequent sections of the study:

1. Maintain Current System: The electric motor company currently relies on pre-assembled crates procured from external suppliers for packaging its products. Although functional, this approach exhibits limitations, notably high costs, and overreliance on suppliers. Nevertheless, maintaining the status quo is deemed feasible if the incremental expenses associated with internalization or supplier changes do not outweigh the anticipated benefits. Operationally, the company receives pre-assembled crates ready for final assembly, obviating the need for additional investments in infrastructure or staff training. However, supplier dependence poses potential risks for the company’s future. Maintaining the current process reflects a conservative stance, prioritizing stability and continuity. This option becomes more compelling if the potential merits of internalization or supplier alterations lack clear justification;

2. Supplier Exchange: The potential for process enhancement via supplier change is a pivotal consideration for companies aiming to optimize their products or procedures. Following a rigorous selection process, prospective suppliers were identified based on criteria such as quality, delivery capacity, costs, and geographical proximity. The anticipated cost benefits were not fully realized while seeking new suppliers, which was a strategic manoeuvre to mitigate dependence on a single source and diversify procurement channels. Despite the creation of a more robust supply chain, logistical and cost challenges arising from the geographic shift of new suppliers impact delivery timelines and necessitate adjustments in inventory and production management. In summary, while seeking new suppliers may bolster the supply chain, the complexities surrounding costs, logistics, and delivery times underscore the challenges inherent in strategic decision-making;

3. Assembly Line: The company opts to internalize the crate assembly exclusively in one alternative. This involves receiving pre-cut boards and conducting assembly on dedicated lines outfitted with moulds and nailers. This move eradicates reliance on suppliers for final crate assembly, affording greater control over production. Post assembly, crates are transferred to the packaging area for finalization alongside motors. This streamlines internal logistics and curtails production times. To mitigate the risks associated with wood dust contamination, it is recommended that the company consider leasing an external pavilion for the assembly zone. To comply with the ISPM15 guidelines, shifting the company’s inventory management strategy from assembled crates to pre-cut boards is necessary. Introducing specialized assembly lines may require the company to recruit additional personnel or reallocate existing human resources. Internalizing assembly aims to bolster the company’s autonomy, trim costs, and optimize operational efficiency, with a potential gain of 3.09%. The projected payback period is 0.63 years, owing to the substantial crate expenditure;

4. Sawmill: The company must integrate the wood-cutting process and assembly line within its facilities in another proposed internalisation alternative. This process begins with receiving wooden boards cut and utilized in crate assembly. To mitigate potential environmental and health hazards posed by wood dust, a separate pavilion is designated for this purpose, equipped with two dust-control exhausts. Boards must adhere to HT safety and quality regulations. With this alteration, the inventory model shifts to solid wooden boards, necessitating a dynamic and flexible approach to ensure continuous raw material availability. Process integration also warrants an evaluation of requisite manpower. A numerical analysis indicates a potential gain of 3.10%, with a payback period of 2.01 years.

3.2. Selection of Criteria and Decision-Making

Initially, a comprehensive literature review was conducted to identify potential factors relevant to the selection criteria. Following this, a brainstorming session was held involving key stakeholders and experts in the field. This session aimed to identify and outline potential factors by discussing the primary problems encountered during the construction of alternatives and the decision-making process. The identified factors were then rigorously filtered to ensure they met specific criteria: unambiguous, intelligible, operational, measurable, homogeneous, and accessible. Consequently, seven criteria were delineated for the application of MCDA.

Cost: This criterion encompasses the expenses associated with each alternative, including the costs of internalizing the sawmill and assembly, supplier transition costs, and ongoing operational expenditures. It is of the utmost importance to evaluate the costs associated with each option to ascertain its financial feasibility.

Quality: The term “quality” describes the excellence of the crates produced or supplied, encompassing aspects such as durability, strength, safety, and adherence to established standards. This criterion is paramount in enhancing product quality through internalization or external sourcing.

Flexibility: The flexibility of each alternative is gauged by its adaptability to fluctuations in market demands and customer preferences. Does internalization offer enhanced flexibility concerning delivery schedules, customization capabilities, and responsiveness to seasonal fluctuations?

Supply Risks: This criterion entails evaluating the potential risks associated with reliance on external suppliers. These risks include delivery delays, supply interruptions, and quality fluctuations.

Environmental Impact: The environmental impact criterion assesses the potential effects of each alternative on the environment, encompassing resource usage, carbon emissions, waste generation, and pollution levels.

Social Impact: The social impact criterion assesses the consequences of each alternative on local communities and the workforce engaged in the process. This criterion encompasses local employment, working conditions, community relations, and the company’s reputation.

Improvement Capacity: This criterion delineates the potential of each alternative to foster innovation and drive the development of new processes or product enhancements. Does internalization have the potential to stimulate innovation and future cost reductions, thereby becoming a pivotal factor in decision-making over the medium to long term?

Once the meticulous process of selecting criteria and consolidating alternatives has been completed, appointing decision-makers becomes imperative. This entailed a thorough analysis of individuals intricately linked to the production process who possess the acumen to evaluate diverse nuances and potential impacts associated with alterations in the production chain. Another pivotal factor influencing the selection of decision-makers was the quest for diverse perspectives pertinent to the process. In essence, decision-makers spanning various organisational spheres were chosen to ensure a decision grounded in multifaceted viewpoints and balanced weighting across departments.

The study was conducted with the participation of specialists from the following four key departments within the company: Production; Quality, Procurement; and Industrial Engineering. The specialists were selected based on their direct responsibilities within the value chain, which included resource management, quality assurance, material acquisition, and process optimization. The Industrial Engineering department was represented by two individuals, reflecting the department’s pivotal role in the study. By including these four departments, the company ensured that decisions were made with a comprehensive understanding of the production process, incorporating multiple perspectives. Consequently, the decision-making process involved five key decision-makers, providing a robust and balanced analysis for the application of the MCDA method.

The Figure 2 delineates the construction of the decision-making model, which encapsulates the involvement of the five decision-makers, the seven criteria, and the consideration of four alternatives.

3.3. Numerical Application

To initiate the SAPEVO-M method, it is essential to construct the weight matrix of the criteria to understand which criteria are most important according to Decision-Maker A’s evaluation.

The application of the SAPEVO-M method and the elicitation of decision-makers’ preferences were conducted in two discrete stages. In the initial phase, a joint meeting was conducted via a digital platform. During this meeting, the concept of the MCDA model, the methodology to be employed, the phases of the process, the alternative scenarios, and the necessary data were presented. Subsequently, a pertinent set of criteria for the decision-making context was delineated, and individual meetings with each decision-maker were scheduled. The objective of these in-person meetings was to minimize the influence of the group and allow the decision-makers to express their preferences in an independent manner. It was considered essential to limit the time interval between these meetings in order to prevent the loss of information over time.

The data collection process entailed individual meetings with pivotal decision-makers from the electric motor company in which the study was conducted. The objective of each meeting was to obtain insights and preferences from the decision-makers without external influence, thereby ensuring that their input reflected the specific context and challenges of the company. This approach facilitated a more precise and concentrated comprehension of the company’s requirements and strategic priorities.

This matrix, shown in Table 4, includes the evaluation, exploring the comparison matrix from the ordinal scale (Table 1), then presenting the punctuation and normalized values that provide a clear indication of the prioritization of criteria from the perspective of Decision-Maker A. The process of analysis is the same for all decision-makers; thus, Table 4 works as an example of how the axiomatic procedure was realized.

The normalized values in this matrix served as a foundation for the subsequent integration of weights from other decision-makers. After evaluating and consolidating the criteria weights from all decision-makers, we derived the final combined criteria weights matrix, as shown in Table 5. This final matrix represents the collective weighting of criteria, combining the perspectives of all involved decision-makers.

For instance, Decision-maker A emphasized the significance of social impact while downplaying supply risk. Conversely, Decision-maker B diverged by assigning greater weight to flexibility and supply risk while downplaying environmental and social impacts. Decision-maker C aligned with the prevailing trend, prioritizing cost and treating quality, flexibility, and supply risk as equivalent. Decision-maker D maintained a balanced stance, prioritizing cost while closely considering quality, flexibility, and supply risk. Similarly, Decision-maker E accentuated cost and viewed quality, flexibility, and supply risk as equally pertinent. The data synthesis underscored cost as the paramount criterion, trailed by quality and flexibility, with environmental and social impacts deemed less critical.

Then, considering the inter-criteria, with the first being cost relative to alternatives, the decision-makers exhibited a consensus favouring the assembly alternative, except for decision-maker B, who advocated for the sawmill scenario. Decision-makers C and D advocated for supplier substitution, citing uncertainties and the potential for long-term cost savings arising from increased supplier competition. Conversely, Decision-maker E raised concerns about escalated costs attributed to the distance from new suppliers. The MCDA analysis revealed that decision-makers were dissatisfied with the current cost structures, as evidenced by the low or negligible scores assigned to the present scenario. The significant weighting assigned to the cost criterion profoundly influenced the analysis outcome, with scenario 03 commanding a weight of 57.64%, as shown in the Table 6.

In the quality criterion, the assessments of decision-makers lacked a consistent pattern. Decision-makers A and E advocated that internalization would yield greater control over the production process, enhancing product quality and facilitating fault rectification. Conversely, decision-maker B contended that the supplier possesses the requisite expertise to ensure quality and suggested that internalization could compromise this aspect. Decision-maker C expressed uncertainty, viewing all scenarios as comparable. Decision-maker D acknowledged the possibility of quality deterioration from internalizing the sawmill due to a lack of expertise. However, he posited that internalizing only the assembly process could mitigate non-conformities.

Furthermore, decision-maker B vehemently opposed this change due to quality concerns already experienced with the current supplier. Despite attempts to mitigate uncertainties, such as procuring samples from new suppliers and researching manufacturing methods, the decision-makers presented divergent perspectives on quality. Table 7 indicates a slight preference for the assembly scenario and a certain degree of reluctance toward changing suppliers.

In evaluating the third criterion, flexibility, which accounted for 17.50% of relevance, the decision-makers exhibited a pronounced inclination toward the sawmill scenario, except for Decision-maker C. They underscored that integrating a wood-cutting line would enhance flexibility, enabling swift product adjustments and substitutions within the factory premises. Additionally, all decision-makers viewed the assembly scenario favorably, asserting that having an in-house carpentry facility would streamline packaging alterations. There was a divergence of opinion regarding the retention of the current supplier versus transitioning to new suppliers. While some decision-makers argued that relying solely on one supplier constrains flexibility, others perceived supplier diversification as costly and preferred a more limited supplier pool. The amalgamated decision-maker weighting matrix reflects this diversity of perspectives. The analysis in Table 8 reveals a distinct preference among decision-makers for internalization regarding flexibility.

In the context of the supply risk criterion, the perspectives of decision-makers diverged. Decision-makers B and C argued that the sawmill scenario offered enhanced security and reduced dependence on the supplier. They proposed that shifting back in the supply chain would facilitate the identification of suppliers less vulnerable to supply crises. In contrast, Decision-makers A, D, and E dissented, asserting that sourcing wood for crates from new suppliers would entail heightened exposure to risk. They advocated for the addition of new suppliers to mitigate this risk. Despite differing viewpoints, all decision-makers concurred that the assembly scenario was not the least favorable option, as it signified an improvement over the current crate supply. Furthermore, they collectively acknowledged the vulnerability of the existing board supply system and agreed on the desirability of supplier replacement. Table 9 reveals a clear preference for the supplier exchange scenario, with the current scenario identified as the most susceptible to supply crises or abrupt price escalations by the supplier.

In terms of environmental impact, each decision-maker presented unique perspectives. Table 10 reveals that Decision-maker A regarded the first three scenarios equally favorably, with the sawmill scenario perceived as the least favorable due to concerns about excessive waste generation and inefficiency. Decision-maker B identified supplier change as the least favorable scenario, citing concerns about supplier distance, while endorsing the sawmill scenario as the most favorable, emphasizing reduced transportation and improved carbon emissions. Decision-maker C favored the presence of multiple suppliers while viewing the sawmill scenario as the least desirable option due to wood waste generation. Decision-maker D suggested that internalizing only the assembly process would be the optimal option, given that it would result in reduced transportation.

Similarly, Decision-maker E considered the sawmill scenario the least favorable option, expressing a preference for the current scenario due to its proximity. The analysis highlights the intricate nature of environmental considerations in the decision-making process regarding the internalization of crate manufacturing. A consensus emerged regarding the disadvantage of the sawmill scenario, which was attributed to wood waste generation.

Regarding the social impact, Decision-makers A and B underscored the importance of considering scenarios that promote employment and enhance community relations, particularly favouring the assembly scenarios and, notably, the optimal sawmill scenario, as beneficial contributions. Conversely, Decision-maker C espoused a more cautious stance, prioritizing the preservation of the company’s image and avoiding scenarios that may result in internal waste generation, preferring the supplier exchange scenario to mitigate potential adverse impacts on the company’s reputation. Decision-maker D underscored the importance of waste reduction to safeguard the company’s image, proposing that internalizing assembly represented a viable option to create jobs while minimizing waste generation. Lastly, Decision-maker E adopted a neutral stance toward the proposed scenarios, indicating a lack of definitive preference regarding social impact. The assembly scenario, shown in Table 11, was identified as the most socially beneficial of the proposed options, with the increased workforce, the introduction of a new role within the factory, and the avoidance of waste generation associated with the sawmill scenario all contributing to this conclusion.

A comparison of decision-makers’ perspectives regarding the potential for improvement in various internalization scenarios reveals several commonalities and disparities. Decision-makers A and B identified the sawmill as the scenario with the greatest potential for improvement, followed by assembly and supplier exchange. They viewed the maintenance of the current state as the least favorable option. Decision-maker C considered assembly the scenario with the highest improvement potential while considering the current scenario the least conducive to improvement. Decision-maker D emphasized assembly as the most promising scenario regarding improvement potential, followed by the sawmill, while rating the other two scenarios unfavorably. Decision-maker E concurred that assembly holds the most improvement potential but favoured supplier exchange over the sawmill as the next favorable scenario. The analysis demonstrates a preference among decision-makers for assembly, followed by the sawmill and the current scenario. Table 12 reflects the collective confidence in the company’s capacity to advance processes; however, there are reservations about the efficiency of the wood-cutting process.

Thus, to facilitate a clearer interpretation of the findings, Figure 3 presents a polar graph depicting the resulting preferences across each criterion and alternative.

Upon comprehending the decision-makers’ inter-criteria preferences and subsequently conducting a comparative analysis of each criterion across the alternatives, a decision table, labelled Table 13, was derived. This table offers insights into the preferences of each decision-maker, culminating in the final weighting outcomes.

3.4. Sensitivity Analysis

A sensitivity analysis was conducted to assess the robustness of the decisions made concerning variations in input data. The objective was to evaluate the influence of each criterion on the final decision and to determine the stability of the decision in the event of changes in the preferences of the involved decision-makers. Additionally, this evaluation can identify alternatives and criteria that exhibit weaknesses, necessitating further investigation.

To accomplish this, variations of the parameters were made and identified with the codes S1 to S9. Consequently, new weights were assigned to the criteria outlined, as shown in Table 14.

To ensure a coherent sensitivity analysis, it was conducted as follows:

• ▪ S1: The actual values obtained during the MCDA were used as parameters for the other values;

• ▪ S2: A scenario was simulated where all criteria had equal weight;

• ▪ S3 to S9: 40% of the weight was assigned to one criterion, with 10% of the weight assigned to each of the other six criteria, to evaluate the predominance of any criterion over the others.

Following the assignment of new weights to the criteria, the final preferences of the decision-makers were recalculated based on the established alternative preferences. The outcomes for each alternative are presented in Table 15.

Then, the graph depicted in Figure 4 was generated, visually illustrating the response of each alternative to the new changes in values and showcasing their respective interactions.

The results depicted in Figure 4 consistently affirm the assembly scenario’s position as the optimal alternative across all variations, underscoring its robustness and appropriateness as the preferred choice. This consistent performance underscores the reliability of the decision made in favour of this scenario, even amidst varying conditions and scenarios.

Conversely, the sawmill scenario, initially ranked as the second-best option, displayed notable sensitivity to variations in evaluation criteria. Its relative position fluctuated at times, potentially losing its standing as the second-best option, depending on the predominant criterion.

As a result, the scenario involving a change in suppliers, which also exhibited a degree of sensitivity, emerged as a viable alternative to contend for the position of the second-best option. This shift in ranking emphasizes the significance of considering scenario sensitivity when making decisions, particularly in uncertain environments.

It is worth noting that maintaining the status quo consistently ranked as the least favored choice throughout all variations. However, the margin between the status quo option and the third- and second-place alternatives was minimal. This suggests that alterations in evaluation criteria can lead to the reordering of non-selected scenarios, resulting in a more competitive landscape among alternatives.

4. Discussion

The primary reason for the superior performance of the assembly criterion compared to other criteria is its cost-effectiveness. This criterion was the most crucial for decision-making, demonstrating the highest performance among others and accounting for 57.64% of the decision distribution. This stems from the calculated payback of 0.63, indicating a swift return on investment. Furthermore, in addition to cost, the assembly line offers enhanced flexibility within the production process compared to the current scenario. Moreover, the assembly scenario offers improved quality. Unlike the current scenario, where expertise in working with wood is limited, the assembly line represents the factory’s specialization in production lines. This justifies the advantage of improving capacity, enabling the company’s engineers to implement enhancements on the production line.

The social impact of internalization was assessed as positive, as it generates additional internal job opportunities and underscores the company’s capability to add value across all stages of the product. From an environmental perspective, the impact was also favorable. Internalization would increase the capacity for transporting wood in cubic meters from suppliers, thereby reducing carbon emissions.

The primary concern of the decision-makers is the raw material supply, despite its moderate weighting in the evaluation. This scenario poses a certain risk due to the necessity of altering the supplied products. Implementing the assembly scenario would entail contacting current suppliers to transition to boards, potentially impacting the supplier’s margin unfavorably. This could strain relationships with suppliers and create an unfavorable situation.

One potential solution is to identify new suppliers, although this entails risks associated with establishing new commercial relationships for a specific raw material. Moreover, reducing the number of crates ordered might dissuade suppliers from doing business, as they typically prefer large-scale production to maximize efficiency and profit margins. Furthermore, an overlooked aspect that warrants deeper evaluation is the responsibility for changes in the phytosanitary treatment control of the boards.

It is paramount to recognize that MCDA methods are not merely designed to identify the optimal scenario but also to facilitate an understanding of the least favorable alternatives. The current scenario is unsatisfactory due to its high cost, quality concerns, and limited flexibility. This has led decision-makers to express a strong inclination for change.

An investigation into the potential for changing suppliers reveals opportunities for improvement, yet it also presents challenges such as increased capital immobilization and compromised flexibility. Consequently, as presented, the scenario does not appear to be favourable. However, addressing specific issues, such as distance and trust in new suppliers, could significantly enhance its viability.

These diverse perspectives are inherent in decision-making and are sometimes guided by qualitative empirical data that cannot be quantified. However, such data are crucial in the decision-making process. This exemplifies why MCDA tools serve as decision support models rather than definitive and absolute tools, given that subjectivity in choice is always present. It is evident that the objective is to address uncertainty, transforming decision-making into something more rational and traceable. Nevertheless, it is unfeasible to disassociate decision-making from subjectivity, given that humans are constrained by their cognitive limitations and the influence of personal experiences.

The SAPEVO-M method is particularly suited for group decision-making problems involving ordinal inputs, a feature that sets it apart from many other MCDA methods, such as AHP, TOPSIS, or PROMETHEE, which typically handle cardinal inputs. Moreover, SAPEVO-M was developed to allow for the transformation of ordinal preferences into cardinal values, facilitating rigorous axiomatic calculations and ensuring scientifically valid results. The ability of SAPEVO-M to efficiently handle group decision-making, considering the complexities and dynamics of multiple decision-makers, along with the possibility of asynchronous use, offers a flexibility that is not commonly found in other MCDA methods. This flexibility and precision make SAPEVO-M especially valuable in industrial contexts, where decisions need to be fast, well-informed, and based on ordinal data.

Nevertheless, despite this perspective, it is essential to recognize that using tools like MCDA is not negated. In fact, due to the high level of subjectivity combined with the real world, humans require tools that can clarify and explain their choices. This allowed for the understanding and achievement of clearer and justifiable results and the comprehension of the motivators that led to the outcome. Eloquently, it can be posited that MCDA is not merely concerned with delineating decision-making processes but also with elucidating the constructive decision-making process by which agents arrive at outcomes. Thus, the use of structured criteria and a robust application method enabled a more informed decision-making process.

Finally, a sensitivity analysis was conducted on the results obtained, and the final choice was confirmed. By employing structured criteria and a robust application method, making a more informed decision was possible.

The assembly line scenario helps the industry transition towards automation and specialization, which are key to competing globally. Internalization benefits job creation and environmental responsibility, aligning with corporate social responsibility. As companies prioritize sustainability and adaptability, pursuing internalization strategies meets current demands and positions firms to respond to future market fluctuations. This illustrates how inferences from this analysis have broader implications. They offer valuable insights for industry-wide practices and future decision-making. It should be noted, however, that the present paper does not primarily address the issue of sustainability. This topic may be addressed in future research to facilitate a more comprehensive exploration of the sustainability implications of the findings.

Given that the favourable scenario is one of convergence, this discussion concludes with a clearly delineated strategy for the company. This outcome is the result of a comprehensive investigation, which considered a range of criteria involved in the process. Once the optimal scenario has been selected, the subsequent step is to implement the identified strategies with the utmost precision, with the objective of achieving the established organizational objectives.

5. Conclusions

This study proposes a practical method for applying an MCDA model in an industrial setting, specifically focusing on the internalization of crate manufacturing in an electric motor company. It begins with understanding the manufacturing process, analyzing potential solutions, and identifying weaknesses and challenges.

The SAPEVO-M method, selected for its flexibility and ability to manage qualitative data, guides the decision-making process by carefully defining criteria and preferences through mathematical models and narratives. This method fosters the consideration of multiple perspectives and resolves conflicting opinions. A sensitivity analysis was conducted to consolidate the final choice. The discussion acknowledges the diversity in decision-making, influenced by qualitative data that cannot always be quantified, and views MCDA tools as supportive aids rather than absolute solutions. It recognizes the inevitable subjectivity due to human cognitive constraints and personal experiences but emphasizes that MCDA remains valuable for clarifying and justifying decisions, enhancing understanding, and achieving informed outcomes through structured criteria and robust methods.

In examining the potential constraints of this study, it is imperative to recognize the absence of a comparative analysis with other MCDA methodologies. Although the SAPEVO method offers a framework for integrating decision-makers’ preferences and handling complex criteria, it is essential to compare its performance with alternative MCDM approaches. For instance, the Analytic Hierarchy Process (AHP) utilizes a geometric mean for group evaluations [34], which, although effective, may not provide the same degree of integration and flexibility as the SAPEVO-M. Similarly, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is another widely utilized MCDA method that employs a distance-based approach to rank alternatives relative to an ideal solution and a negative ideal solution. TOPSIS focuses on the proximity to ideal solutions and may not fully account for the complexities of integrating diverse qualitative and quantitative criteria, where all inputs are required to be cardinal. In contrast, the SAPEVO-M offers a more nuanced approach by incorporating decision-makers’ preferences in handling both quantitative and qualitative data, exploring the transparency of subjective information.

One of the primary limitations of this study is its focus on a specific industry, which may restrict the generalizability of the findings to other sectors. The unique characteristics and dynamics of the packaging production industry, including specific operational, economic, and regulatory factors, mean that the results and recommendations may not be directly applicable to different industries with distinct contexts and challenges. Future research could explore the application of the SAPEVO-M method in various other sectors to validate its versatility and adapt the methodology to broader contexts, enhancing its robustness and generalizability across diverse industrial landscapes.

The findings of this study contribute to the existing body of knowledge in several ways. Methodologically, by applying the SAPEVO-M method in an industrial context, this study demonstrates its efficacy in handling group decision-making and ordinal data, making a significant methodological contribution, which is particularly valuable for industries facing complex decision-making scenarios. Additionally, the structured approach and findings can serve as a basis for future research in similar industrial settings, offering a framework that researchers can build upon to explore other applications of MCDA methods or to refine the SAPEVO-M method further.

As the decision-making process progresses towards the ideal scenario, this article concludes with a clear strategy for the company. This outcome results from a comprehensive research process and the consideration of several criteria, which have culminated in the identification of strategies to effectively achieve the organization’s objectives during implementation.

This study, while being a case conducted in collaboration with the factory floor, ultimately selects the solution for implementation. Therefore, as a continuation of the research, it is proposed to implement the final decision of internalizing the crate assembly line. With the internalization of this process, it would be possible to validate the data collected and identify potential opportunities for improvement. Future research should consider expanding the scope of data collection to encompass a broader range of examples and technological advancements, thereby enhancing both the study’s applicability and relevance. Furthermore, an investigation into the sustainability implications of internalization, encompassing its long-term environmental and social impacts, would facilitate a more comprehensive understanding of the decision’s broader implications. Further validation of the method’s versatility and effectiveness across various contexts could be achieved through comparative analyses with other industries and the application of SAPEVO-M in different markets, particularly in group decision-making scenarios. Such comparative studies would offer valuable insights into the efficacy of the SAPEVO-M method and contribute to the refinement of decision-making techniques in diverse industrial settings.

Author Contributions

Conceptualization, C.E.L.M. and M.Â.L.M.; methodology, C.E.L.M., M.Â.L.M., C.F.S.G., and M.d.S.; software, C.E.L.M., M.Â.L.M., C.F.S.G., and M.d.S.; validation, C.E.L.M., M.Â.L.M., and M.T.R.P.; formal analysis, C.E.L.M. and M.Â.L.M.; investigation, C.E.L.M.; resources, C.E.L.M.; data curation, M.Â.L.M. and M.T.R.P.; writing—original draft preparation, C.E.L.M. and M.Â.L.M.; writing—review and editing, C.E.L.M., M.Â.L.M., M.T.R.P., and F.J.G.S.; visualization, M.T.R.P., C.F.S.G., and M.d.S.; supervision, M.T.R.P., C.F.S.G., and M.d.S.; project administration, M.T.R.P., C.F.S.G., M.d.S., and F.J.G.S.; funding acquisition, F.J.G.S. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Representation of a crate.

Figure 2. Structure of decision.

Figure 3. Polar graph of preferences for each criterion.

Figure 4. Score levels in each scenario.

Table 1

Verbal descriptions.

Verbal Descriptions	Score
Absolutely worse/less important	3
Much worse/less important	2
Worst/less important	1
Equivalent	0
Better/more important	1
Much better/more important	2
Absolutely better/more important	3

Table 2

Example consistency matrix.

	Y1	Y2	Y3	Y4	Yn
Y1	-	0	1	1	0
Y2		-	0	1	1
Y3			-	0	0
Y4				-	0
Yn					-

Table 3

Table consistency relations.

Verbal Consistency	Inconsistency Rate
Consistent	0–10%
Average consistency	11–20%
Low inconsistency	21–30%
Inconsistency	31–40%
Very inconsistency	41–100%

Table 4

Evaluation of criteria: Decision-Maker A.

Decisor A	Cost	Quality	Flexibility	Supply	Environment	Social	Improvement	0% (Consistent)
								Punctuation	Normalization
Cost	0	1	2	3	3	−1	2	10	0.262
Quality	−1	0	1	2	2	−1	1	4	0.191
Flexibility	−2	−1	0	1	1	−2	0	−3	0.107
Supply	−3	−2	−1	0	−1	−3	−2	−12	0.001
Environment	−3	−2	−1	1	0	−3	−1	−9	0.036
Social	1	1	2	3	3	0	2	12	0.286
Improvement	−2	−1	0	2	1	−2	0	−2	0.119

Table 5

Criteria weighting.

Criteria	A	B	C	D	E	Results
Cost	0.262	0.298	0.321	0.241	0.338	0.2920
Quality	0.191	0.179	0.167	0.223	0.182	0.1884
Flexibility	0.107	0.214	0.167	0.205	0.182	0.1750
Supply Risk	0.001	0.131	0.167	0.179	0.182	0.1318
Environmental	0.036	0.024	0.071	0.001	0.026	0.0314
Social	0.286	0.001	0.001	0.036	0.001	0.0646
Improvement	0.119	0.155	0.107	0.116	0.091	0.1176

Table 6

Cost criterion weighting.

Scenarios	A	B	C	D	E	Results
Current	0	0	0	0.125	0.1	0.045
Supplier	0.1	0	0.313	0.25	0	0.1326
Assembly	0.65	0.438	0.469	0.625	0.7	0.5764
Sawmill	0.25	0.563	0.219	0	0.2	0.2464

Table 7

Quality criterion weighting.

Scenarios	A	B	C	D	E	Results
Current	0	0.65	0.25	0.25	0	0.23
Supplier	0	0.35	0.25	0.25	0	0.17
Assembly	0.333	0	0.25	0.5	0.438	0.3042
Sawmill	0.667	0	0.25	0	0.563	0.296

Table 8

Flexibility criterion weighting.

Scenarios	A	B	C	D	E	Results
Current	0	0	0	0.219	0.125	0.0688
Supplier	0.167	0.188	0.15	0	0	0.101
Assembly	0.292	0.313	0.6	0.344	0.313	0.3724
Sawmill	0.542	0.5	0.25	0.438	0.563	0.4586

Table 9

Supply risk criterion weighting.

Scenarios	A	B	C	D	E	Results
Current	0.393	0	0	0.214	0.25	0.1714
Supplier	0.536	0.278	0	0.5	0.583	0.3794
Assembly	0.071	0.333	0.35	0.286	0.167	0.2414
Sawmill	0	0.389	0.65	0	0	0.2078

Table 10

Environmental impact criterion weighting.

Scenarios	A	B	C	D	E	Results
Current	0.333	0.286	0.344	0.25	0.417	0.326
Supplier	0.333	0	0.438	0.25	0.25	0.2542
Assembly	0.333	0.286	0.219	0.5	0.333	0.3342
Sawmill	0	0.429	0	0	0	0.0858

Table 11

Social impact criterion weighting.

Scenarios	A	B	C	D	E	Results
Current	0	0	0.281	0.25	0.25	0.1562
Supplier	0.219	0	0.469	0.25	0.25	0.2376
Assembly	0.344	0.438	0.25	0.5	0.25	0.3564
Sawmill	0.438	0.563	0	0	0.25	0.2502

Table 12

Improvement capacity criterion weighting.

Scenarios	A	B	C	D	E	Results
Current	0	0	0	0	0	0
Supplier	0.194	0.25	0.15	0	0.25	0.1688
Assembly	0.361	0.321	0.6	0.75	0.65	0.5364
Sawmill	0.444	0.429	0.25	0.25	0.1	0.2946

Table 13

Decision table.

Scenarios	A	B	C	D	E	Results
Current	0.0120	0.1230	0.0247	0.1780	0.1127	0.09
Supplier	0.1416	0.1778	0.1979	0.2145	0.1352	0.1734
Assembly	0.4180	0.2973	0.4887	0.4887	0.4708	0.4327
Sawmill	0.4285	0.4019	0.2887	0.1188	0.2812	0.3038

Table 14

Different weights in each scenario.

Criteria	S1	S2	S3	S4	S5	S6	S7	S8	S9
Cost	0.292	0.143	0.4	0.1	0.1	0.1	0.1	0.1	0.1
Quality	0.188	0.143	0.1	0.4	0.1	0.1	0.1	0.1	0.1
Flexibility	0.175	0.143	0.1	0.1	0.4	0.1	0.1	0.1	0.1
Supply Risk	0.132	0.143	0.1	0.1	0.1	0.4	0.1	0.1	0.1
Environmental	0.031	0.143	0.1	0.1	0.1	0.1	0.4	0.1	0.1
Social	0.065	0.143	0.1	0.1	0.1	0.1	0.1	0.4	0.1
Improvement	0.118	0.143	0.1	0.1	0.1	0.1	0.1	0.1	0.4

Table 15

Different scores in each scenario.

Scenario	S1	S2	S3	S4	S5	S6	S7	S8	S9
Current	0.090	0.143	0.113	0.169	0.120	0.151	0.198	0.147	0.100
Supplier	0.173	0.206	0.184	0.195	0.175	0.258	0.221	0.216	0.195
Assembly	0.433	0.389	0.445	0.363	0.384	0.345	0.372	0.379	0.433
Sawmill	0.304	0.263	0.258	0.273	0.322	0.246	0.210	0.259	0.272

References

1. Choudhary, A.; Mishra, R.K.; Fatima, S.; Panigrahi, B.K. Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor. Eng. Appl. Artif. Intell.; 2023; 120, 105872. [DOI: https://dx.doi.org/10.1016/j.engappai.2023.105872]

2. Virmani, N.; Sharma, S.; Kurma, A.; Luthra, S. Adoption of industry 4.0 evidence in emerging economy: Behavioral reasoning theory perspective. Technol. Forecast. Soc. Chang.; 2023; 188, 122317. [DOI: https://dx.doi.org/10.1016/j.techfore.2023.122317]

3. Sellitto, M.A. Reverse logistics activities in three companies of the process industry. J. Clean. Prod.; 2018; 187, pp. 923-931. [DOI: https://dx.doi.org/10.1016/j.jclepro.2018.03.262]

4. Foerstl, K.; Franke, H.; Cataldo, Z. What drives managers to insource production? Evidence from a behavioural experiment. J. Purch. Supply Manag.; 2021; 27, 100715. [DOI: https://dx.doi.org/10.1016/j.pursup.2021.100715]

5. Hartman, P.L.; Ogden, J.A.; Hazen, B.T. Bring it back? An examination of the insourcing decision. Int. J. Phys. Distrib. Logist. Manag.; 2017; 47, pp. 198-221. [DOI: https://dx.doi.org/10.1108/IJPDLM-09-2015-0220]

6. Krauter, V.; Bauer, A.-S.; Milousi, M.; Dörnyei, K.R.; Ganczewski, G.; Leppik, K.; Krepil, J.; Varzakas, T. Cereal and Confectionary Packaging: Assessment of Sustainability and Environmental Impact with a Special Focus on Greenhouse Gas Emissions. Foods; 2022; 11, 1347. [DOI: https://dx.doi.org/10.3390/foods11091347] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35564070]

7. Šerešová, M.; Kočí, V. Proposal of package-to-product indicator for carbon footprint assessment with focus on the Czech Republic. Sustainability; 2020; 12, 3034. [DOI: https://dx.doi.org/10.3390/su12073034]

8. Choudhary, A.; Goyal, D.; Shimi, S.L.; Akula, A. Condition Monitoring and Fault Diagnosis of Induction Motors: A Review. Arch. Comput. Methods Eng.; 2019; 26, pp. 1221-1238. [DOI: https://dx.doi.org/10.1007/s11831-018-9286-z]

9. Bastos, S.L.S. Rendimento em Madeira Serrada, Produtividade e Custos de uma Serraria Automatizada. Bachelor’s Thesis; Universidade Federal de Santa Catarina: Curitibanos, Brazil, 2023.

10. Ng, N.L.; Luke, D.M.; Gawronski, B. Moral judgment under uncertainty: A CNI model analysis. Eur. J. Soc. Psychol.; 2023; 53, pp. 1055-1077. [DOI: https://dx.doi.org/10.1002/ejsp.2952]

11. Joseph, J.; Gaba, V. Organizational Structure, Information Processing, and Decision Making: A retrospective and Roadmap for Research. Acad. Manag. Ann.; 2020; 14, pp. 267-302. [DOI: https://dx.doi.org/10.5465/annals.2017.0103]

12. Kahneman, D.; Rosenfield, A.M.; Gandhi, L.; Blaser, T. Kahneman on ‘Noise’ DECISION MAKING Noise: How to Overcome the High, Hidden Cost of Inconsistent Decision Making. Harv. Bus. Rev.; 2016; 94, pp. 38-46.

13. Ayan, B.; Abacıoğlu, S.; Basilio, M.P. A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making. Information; 2023; 14, 285. [DOI: https://dx.doi.org/10.3390/info14050285]

14. Alvarez, P.A.; Ishizaka, A.; Martínez, L. Multiple-criteria decision-making sorting methods: A survey. Expert Syst. Appl.; 2021; 183, 115368. [DOI: https://dx.doi.org/10.1016/j.eswa.2021.115368]

15. Lee, H.C.; Ter Chang, C. Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renew. Sustain. Energy Rev.; 2018; 92, pp. 883-896. [DOI: https://dx.doi.org/10.1016/j.rser.2018.05.007]

16. Maghsoodi, A.I.; Riahi, D.; Herrera-Viedma, E.; Zavadskas, K. An integrated parallel big data decision support tool using the W-CLUS-MCDA: A multi-scenario personnel assessment. Knowl.-Based Syst.; 2020; 195, 105749. [DOI: https://dx.doi.org/10.1016/j.knosys.2020.105749]

17. Cinelli, M.; Kadziński, M.; Gonzalez, M.; Słowiński, R. How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega; 2020; 96, 102261. [DOI: https://dx.doi.org/10.1016/j.omega.2020.102261]

18. Sałabun, W.; Watróbski, J.; Shekhovtsov, A. Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry; 2020; 12, 1549. [DOI: https://dx.doi.org/10.3390/sym12091549]

19. Wątróbski, J.; Jankowski, J.; Ziemba, P.; Karczmarczyk, A.; Zioło, M. Generalised framework for multi-criteria method selection. Omega; 2019; 86, pp. 107-124. [DOI: https://dx.doi.org/10.1016/j.omega.2018.07.004]

20. Chai, J.; Liu, J.N.K.; Ngai, E.W.T. Application of decision-making techniques in supplier selection: A systematic review of literature. Expert. Syst. Appl.; 2013; 40, pp. 3872-3885. [DOI: https://dx.doi.org/10.1016/j.eswa.2012.12.040]

21. Jayant, A. A Comprehensive Literature Review of MCDM Techniques ELECTRE, PROMETHEE, VIKOR and TOPSIS Applications in Business Competitive Environment. 2018; Available online: http://www.journalcra.com (accessed on 7 March 2024).

22. Odu, G.O. Weighting methods for multi-criteria decision making technique. J. Appl. Sci. Environ. Manag.; 2019; 23, pp. 1449-1457. [DOI: https://dx.doi.org/10.4314/jasem.v23i8.7]

23. Marttunen, M.; Lienert, J.; Belton, V. Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations. Eur. J. Oper. Res.; 2017; 263, pp. 1-17. [DOI: https://dx.doi.org/10.1016/j.ejor.2017.04.041]

24. Tenório, F.M.; Moreira, M.Â.L.; Costa, I.P.d.A.; Gomes, C.F.S.; dos Santos, M.; Silva, F.C.A.; da Silva, R.F.; Basilio, M.P. SADEMON: The Computational Web Platform to the SAPEVO-M Method. Procedia Comput. Sci.; 2022; 214, pp. 125-132. [DOI: https://dx.doi.org/10.1016/j.procs.2022.11.157]

25. Nguyen, T.L.; Nguyen, P.-H.; Pham, H.-A.; Nguyen, T.-G.; Nguyen, D.-T.; Tran, T.-H.; Le, H.-C.; Phung, H.-T. A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry. Mathematics; 2022; 10, 1897. [DOI: https://dx.doi.org/10.3390/math10111897]

26. Soltan, H.; Janada, K.; Omar, M. FAQT-2: A customer-oriented method for MCDM with statistical verification applied to industrial robot selection. Expert. Syst. Appl.; 2023; 226, 120106. [DOI: https://dx.doi.org/10.1016/j.eswa.2023.120106]

27. Chen, X.; Zhou, B.; Štilic, A.; Stevic, Ž.; Puška, A. A Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production. Sustainability; 2023; 15, 10745. [DOI: https://dx.doi.org/10.3390/su151310745]

28. Macêdo-Junior, R.O.; Serpa, F.S.; Santos, B.L.P.; de Vasconcelos, C.R.; Silva, G.F.; Ruzene, D.S.; Silva, D.P. Produced water treatment and its green future in the oil and gas industry: A multi-criteria decision-making study. Int. J. Environ. Sci. Technol.; 2023; 20, pp. 1369-1384. [DOI: https://dx.doi.org/10.1007/s13762-022-04057-3]

29. Moreira, M.Â.L.; Silva, F.C.A.; de Araújo Costa, I.P.; Gomes, C.F.S.; dos Santos, M. SAPEVO-H2 a Multi-Criteria Systematic Based on a Hierarchical Structure: Decision-Making Analysis for Assessing Anti-RPAS Strategies in Sensing Environments. Processes; 2023; 11, 352. [DOI: https://dx.doi.org/10.3390/pr11020352]

30. do Nascimento Maêda, S.M.; de Arajo Costa, I.P.; Gomes, C.F.S.; dos Santos, M.; da Mota, I.S.; de Barros Teixeira, L.F.H.d.S. Economic and edaphoclimatic evaluation of Brazilian regions for African mahogany planting—An approach using the SAPEVO-M-NC ordinal method. Procedia Comput. Sci.; 2021; 199, pp. 323-330. [DOI: https://dx.doi.org/10.1016/j.procs.2022.01.196]

31. de Oliveira, P.L.; Baptista, I.d.S.; Moreira, M.Â.L.; dos Santos, M.; Gomes, C.F.S. Análise de Fornecedores Mediante Aplicação do Método Multicritério SAPEVO-M. Proceedings of the XI Simpósio de Engenharia de Produ; Campina Grande, Brasil, 24–26 May 2023.

32. Gomes, C.F.S.; dos Santos, M.; de Souza de Barros Teixeira, L.F.H.; Sanseverino, A.M.; dos Santos Barcelos, M.R. Sapevo-m: A group multicriteria ordinal ranking method. Pesqui. Oper.; 2020; 40, pp. 1-23. [DOI: https://dx.doi.org/10.1590/0101-7438.2020.040.00226524]

33. Moreira, M.Â.L.; de Castro Junior, M.A.P.; de Araújo Costa, I.P.; Gomes, C.F.S.; dos Santos, M.; Basilio, M.P.; de Moura Pereira, D.A. Consistency Analysis Algorithm for the Multi-criteria Methods of SAPEVO Family. Procedia Comput. Sci.; 2022; 214, pp. 133-140. [DOI: https://dx.doi.org/10.1016/j.procs.2022.11.158]

34. Moreira, M.Â.L.; de Araújo Costa, I.P.; Pereira, M.T.; dos Santos, M.; Gomes, C.F.S.; Muradas, F.M. Promethee-sapevo-m1 a hybrid approach based on ordinal and cardinal inputs: Multi-criteria evaluation of helicopters to support Brazilian navy operations. Algorithms; 2021; 14, 140. [DOI: https://dx.doi.org/10.3390/a14050140]

35. de Assis, G.S.; dos Santos, M.; Basilio, M.P. Use of the WASPAS Method to Select Suitable Helicopters for Aerial Activity Carried Out by the Military Police of the State of Rio de Janeiro. Axioms; 2023; 12, 77. [DOI: https://dx.doi.org/10.3390/axioms12010077]