Integration of Reverse Logistics and Continuous Improvement in Portuguese Industry: Perspectives from a Qualitative Survey

Abstract

Reverse logistics lacks an exhaustive study on its impact on supply chain management and its integration with Continuous Improvement. Through a survey submitted to companies in the northern region of Portugal, this study shows how reverse logistics and Continuous Improvement have been considered and applied by companies. Microsoft Forms was used to properly administer the survey, which was submitted to 80 companies to collect data from a convenience sample. Yet, it was only possible to validate 60 responses, which, for a confidence level of 90%, represents a margin of error of 10.62%. Regarding the data analysis, descriptive statistics was used to present the main results. Nonetheless, normality tests were also carried out to understand if parametric or non-parametric methods could be applied to analyze the number of weekly hours dedicated to the reverse logistics process. The results show that most companies have informal reverse logistic management, being the biggest companies or those with higher turnover who apply a formal process. The main activity performed in the reverse logistics process is resale and remanufacture. For the interviewed companies, the main reason for having a reverse logistics process is to reduce materials’ or products’ costs and simultaneously increase profits by reusing products or materials. Regarding the factors that impact the performance of RL systems, the non-uniformity of the returned product is the most demandable. The most widely used continuous improvement tools in reverse logistics are 5S and the Kaizen. In sum, despite the benefits of improving customer satisfaction, reverse logistics should also be considered in enhancing sustainability and complying with regulations.

1. Introduction

Supply chain management (SCM) comprises all activities, from resource acquisition to the delivery of products or services to customers. The goal of SCM is to improve the flow of materials, information, and financial resources through the effective management of all process phases. The SCM starts with the acquisition of raw materials, consumables, and accessories, continuing through their production, storage, transportation, and distribution [1].

The companies’ ability to plan, control, and manage all operations is directly related to the efficiency of SCM. Inventory and information flow management, as well as the appropriate selection of suppliers and logistics partners, are critical points for the evolution of companies. The results achieved by organizations do not depend solely on themselves; their performance is influenced by the actions of the organizations that make up part of their supply chain [2].

Due to market globalization, SCM has become critical for companies that wish to remain competitive. Thus, the creation of value throughout the production chain depends on efficient SCM. This requires companies to identify customer needs, select the most suitable suppliers, and establish efficient and effective processes.

Effective SCM reduces costs and delivery times, increases operational flexibility, enhancing customer satisfaction [3]. Logistics is a component of SCM and has undergone significant evolution over time. Initially, logistics was considered an isolated function, focused on improving the company’s subsystems individually, creating intermediate stocks, and operating with limited communication between departments. However, over time, logistics evolved into an integrated approach, comprising a systemic view of the company, considering material and information flows and the integration of information systems [4]. This evolution also led to more complex supply chains involving an increased number of elements, including suppliers, production companies, warehouses, distribution centers, and retailers [5]. Logistics incorporates costs and flows of raw materials, Work in Process (WIP), finished products, and information. This integration has enabled improvements in customer service standards, making it a differentiating market factor [6]. This evolution has enabled more efficient and integrated management of operations, aiming to minimize costs, maximize profits/revenues, and offer high-quality services.

Furthermore, logistics has come to incorporate functions such as the selection of transportation modes, stock constitution and management, and coordination of the materials/products transportation, among others. In this way, logistics can be divided into the functions of supply logistics, internal logistics, and distribution logistics [7]. The implementation of advanced technologies, such as warehouse automation and the use of information systems, can increase efficiency and reduce logistical costs [5,8]. There are several practices and tools that can be used to improve logistics operations. For example, the use of technologies and material traceability systems or inventory management software is essential for monitoring and controlling operations [9]. The integration between the different segments of the supply chain and the close collaboration with suppliers and customers also play an important role in smoothing internal operations [2].

To understand the importance of internal logistics, it is necessary to identify the main challenges that the companies currently face. Globalization, armed conflicts, and increased market competition have driven organizations to constantly seek ways to reduce costs and improve the quality of products and services while improving delivery conditions. In this context, a well-structured internal logistics system is indispensable for companies to meet demand needs in a more effective way [10].

One of the main advantages of efficient internal logistics is the reduction in operational costs. By improving activities related to the supply and transport of materials, companies can lower expenses such as storage, inventory maintenance, and distribution, as well as avoid waste and losses caused by poor strategic management. Studies show that companies that invest in these logistical processes can achieve significant reductions in overall logistics costs [4]. By ensuring that materials are available at the right time, in the appropriate quantity, and in the desired condition, internal logistics enables the production of products with greater compliance in meeting customers’ specifications. Yet, in some contexts, it is necessary to return products or materials, requiring the implementation of reverse logistics (RL) activities.

The main objective of this study is to identify the types of RL models that are being implemented in companies in the northern region of Portugal and if those activities are being integrated with Continuous Improvement (CI) processes based on the application of a survey methodology.

In the context of the Portuguese industrial sector, the integration of RL and CI methodologies represents a strategic way to enhance the companies’ competitiveness. Organizations that adopt these practices are better positioned to meet the growing demand for sustainable and efficient operations, both within national markets and across global supply chains. Economically, RL enables the reduction in operational costs by allowing the recovery, refurbishment, and reintegration of returned products into the production cycle [11,12]. The application of CI frameworks—such as Lean, Six Sigma, and Kaizen—aims to optimize RL processes by minimizing waste, reducing lead times, and enhancing overall system performance [13]. The increasing prevalence of e-commerce within Portugal further underlines the importance of effective management of returns since the clients’ expectations shift toward rapid, flexible, and responsible service models. Also, significant benefits from structured RL systems can be associated with key Portuguese industries, including automotive, electronics, textiles, and agri-food, mostly in managing end-of-life products, defective items, and material recovery [14].

Thus, this study aims to contribute to the literature gap regarding the context of RL implementation in Portuguese industrial companies by analyzing the following: (a) the RL tools applied by the companies; (b) the reasons that lead companies to adopt an RL system; (c) the performance measures used to evaluate the RL system; (d) how RL systems allow companies to generate competitive advantages; and (e) the barriers to RL implementation. Lastly, this study intends to identify what CI practices are being applied within RL. When analyzing the literature, there is a reduced number of research works focused on the identification of RL models implemented in companies, much less so in the specific case of Portugal. Only two cases are reported. The first one regards a company that specializes in the reconditioning and recycling of industrial packaging. The collected used packaging materials, such as Intermediate Bulk Containers (IBCs), are refurbished and then reintroduced into the supply chain, promoting a circular economy [15]. The second study describes the case of L’Oréal Portugal, which focused on improving its RL processes by applying Lean methodologies, being the main objective of the reduction in collection requests for damaged and discontinued products [16].

2. Literature Review

By definition, RL represents “the process of planning, applying and controlling the effective material’ flows and costs, in-process inventory, finished goods and associated information from the point of final use to the point of origin for the purpose of recapturing value” [17]. Thus, RL in manufacturing refers to the process of managing the flow of materials or products after they have been produced, sold, or used. It involves moving these items back through the supply chain for purposes such as return, recycling, refurbishment, remanufacturing, or proper disposal. This process is critical for sustainability, cost efficiency, and customer satisfaction in the manufacturing industry [18].

2.1. Reverse Logistics Activities

Improving RL contributes to environmental sustainability, but significant challenges persist. These include additional costs and the need for deep collaboration with suppliers and customers. Other obstacles involve customer service issues and process inefficiencies [19]. Thus, it is of the utmost importance to understand the extension of RL systems and activities in an industrial context.

Notably, the RL activities identified in the literature include resale, remanufacturing, product return, product recalls for refurbishing or for repair, recovery, and end-of-life product management, which can include recycling or product disposal [20,21,22,23,24].

Resale in the RL context refers to a used product that is redirected to a new purpose, but normally in a context where it holds lesser value compared to its original use or primary application. With this RL process, companies aim to recover the products’ value by reselling them through different channels, such as direct resale, often at a discount, or through secondary markets [21].

Remanufacturing is the process of restoring used or defective products to a like-new condition through a more comprehensive industrial process involving taking pieces of the product into its main components, cleaning, repairing, or replacing damaged components, and then reassembling the product to its original specifications. The objective of this RL activity is to ensure that this remanufactured product’s performance matches new products [23]. Despite the resemblance, remanufacturing is different from refurbishment and repair logistics activities. In the context of RL, the three activities are processes aimed at restoring returned, used, or defective products to a functional state, but they differ in scope and depth [18]. Remanufacturing, as previously described, is a more comprehensive process, which is most common in the high-value products industry.

Refurbishing involves not only fixing defects but also promoting functional enhancements to improve overall product quality, being therefore, a more complete activity when compared to repairing. Thus, repairing is a process more focused on fixing specific faults or defects in a product to restore its functionality [21].

One of the most common activities of RL is the return to the supplier or the product recovery. In the context of RL, return/recover refers to the process of retrieving used or unsold products from customers, retailers, or other points in the supply chain, with the goal of recapturing value or properly disposing of them [25]. In the specific case of returning goods, several reasons might be identified. First of all, products are sent back due to defects, dissatisfaction, or warranty claims. Another reason is related to retail operations, whereas, unsold goods return to suppliers because they are seasonal products or because there is an excessive inventory. Both return and recover activities contribute to reducing waste by keeping products in circulation and by recapturing value from products instead of incurring losses, which supports the circularity of the economy [26].

Concerning recycling and disposal activities, both deal with the end-of-life phase of products. When returned, used, or defective products cannot be repaired, refurbished, or resold, businesses must choose between recycling valuable materials or disposing of waste to minimize environmental impact [27]. Recycling in the context of RL also means the process of breaking down products into raw materials that can be reused in manufacturing new goods [28]. This process also involves a complex chain of events from product collection, separating reusable components, extracting materials (e.g., metals, plastics, glass), and supplying raw materials to manufacturers. Recycling is a common process in different activity sectors such as electronics [29], automotive parts [7], packaging materials [17,30,31], and textiles/apparel [32,33]. Disposal is the last resort when recycling or reuse is not a viable option. By implementing responsible disposal strategies, companies can reduce costs, improve sustainability, and comply with environmental policies while managing end-of-life products effectively [28].

Nowadays, the main challenges in RL within industrial sectors are multifaceted, primarily driven by sustainability concerns, technological integration, and operational complexity. As the industrial sector attempts to adapt to a circular economy, it faces significant hurdles in managing returns, waste, and resource recovery [34]. For small-scale producers, financial feasibility and difficulties in obtaining returning packages pose important challenges despite potential cost reductions and socio-environmental benefits.

To address these challenges, organizations are exploring innovative tools like Radio-Frequency Identification (RFID) technology, Internet of Things (IoT), blockchain, or cloud computing for real-time visibility and collaboration [35]. Nonetheless, the initial investment and complexity of integrating these technologies can be daunting for the companies [34].

2.2. Reverse Logistics Conceptual Models

As known, Direct Logistics (DL) focuses on the movement of products from manufacturers to consumers, emphasizing efficiency in SCM (forward flows in the supply chain), whereas RL deals with the processes involved in returning products from consumers back to manufacturers or distributors (reversed flows). The latter aims to recapture value from returned products and manage waste effectively. Figure 1 shows a schematic representation of the flows involved in DL and RL. In the study [36], DL is defined as a more deterministic flow, focusing on the supply, manufacturing, and delivery of products, while RL is characterized by process uncertainties involving collecting, sorting, and processing the returned products.

While the DL typically results in increased sales and customer satisfaction through efficient delivery, the optimization of RL leads to cost savings and recovery of valuable materials, although it may also incur additional operational costs [6].

Several RL conceptual models have been described and applied to improve the flow of products, materials, and information back to the manufacturer or another point in the supply chain for purposes such as reuse, recycling, remanufacturing, or proper disposal, depending on the activities’ complexity.

Closed-loop Supply Chain (CLSC) models integrate direct and RL into a single system, with the purpose of minimizing costs and environmental impact. These models address multi-product systems, considering factors such as material procurement, production, distribution, recycling, and disposal of end-of-life items [37]. CLSC management has gained significant attention in the industry, focusing on themes like green operations, design, manufacturing, waste management, and product life cycle assessment [36,38].

Push and pull models are also applied in RL. The distinction between push and pull strategies in RL has been explored in different contexts. Push systems typically involve proactive disassembly and remanufacturing, whereas the products are returned based on company policies. Pull systems respond to market demand, e.g., customers return items voluntarily for refurbishment or resale. The choice between push and pull strategies can impact the optimal level of product modularity and RL decision-making.

Notably, the manufacturing and distribution systems often include both push and pull characteristics rather than being only one or the other. Thus, the push–pull frameworks serve as a classification scheme for material control systems, which impacts decision-making [39].

One particular common conceptual model described in the literature is the Reuse, Remanufacture, Recycle (3R) model. This model covers the direct use of the products with minimal processing, restoring products to like-new condition and breakdown materials. To implement the 3R model, two main strategies can be implemented. The first one regards partnering with third-party logistics providers (3PLs) for reverse SCM. The other one requires investing in technology for tracking, sorting, and optimizing resource recovery [40]. Other authors mention the lifecycle-based RL models, which incorporate RL planning for the complete product lifecycle, ensuring that products are designed with end-of-life recovery in mind [41].

Different mathematical models can be proposed. Those models include, for instance, methods such as Artificial Neural Networks (ANN), Fuzzy Logics, Analytic Hierarchy Process (AHP), dynamic regression models, statistical modeling, robust Bayesian belief networks with interval probabilities, engineering economics techniques, theory of production frontier, novel neighborhood rough set approach, and others [36,42,43,44].

All of the mathematical models share a multitude of fundamental characteristics. Firstly, the system’s description to be modeled facilitates an exact model formulation, which comprises system components (e.g., transportation methodologies or distribution centers), attributes (e.g., parameters such as transportation capacity and inventory capacity), operational regulations encompassing both formulas and algorithms (e.g., reverse logistics policies) and performance metrics (e.g., cost statistics over time). Stochastic approaches (e.g., consumer decisions and product return reoccurrence) are represented using probabilistic distributions, either derived from historical data or hypothesized. When randomness is present, which is often the case, output metrics are usually formulated in terms of probabilities or statistical measures [44].

In sum, RL models can be used to improve the efficiency and sustainability of supply chains. Reference models for RL processes improve the efficiency in supply chains, specifically in closed-loop systems, through the optimization of flows to returned products [45]. RL models also lower production costs [46] and quality costs [47]. Unlike DL, RL flows are complex and characterized by uncertainty, mostly due to the irregularity of return flows and variability of processing requirements. The cost evaluation must consider transportation, handling, reprocessing, quality control, and storage. Also, cost evaluation in RL involves assessing the efficiency and effectiveness of RL activities using key performance indicators (KPIs) such as return rate, recovery value, processing time, and customer satisfaction. Additionally, it should integrate quantitative and qualitative metrics to assess the financial viability and operational efficiency of RL activities to facilitate strategic decision-making and comprise broader sustainability objectives [46,47].

Environmentally, RL models’ adoption contributes to mitigating the negative impacts by reducing the volume of waste and conserving finite natural resources since products can have a second life-use [48]. By extending product life cycles and promoting material circularity, reverse logistics supports the transition toward a more sustainable economy, aligning industrial processes with current environmental objectives and regulatory frameworks [12,49].

2.3. Reverse Logistics and Continuous Improvement

RL not only simplifies the process of recovering and recycling products but also serves as a foundation for CI plans that drive operational excellence in the industry. This synergy can be explored through the integration of CI tools within the RL flows.

The integration of CI tools with RL highlights the importance of improving efficiency while identifying the key elements and conditions for a well-deployed reverse supply chain, mostly by eliminating unnecessary tasks and process duplications. Also, bottleneck identification allows to recognize the limitations imposed by the weakest link in the supply chain [50]. The integration of RL and CI allows companies to systematically identify inefficiencies and enhance the overall value chain. This integration enables companies to respond more flexibly to customer needs and market changes [45]. Moreover, data generated through RL activities can be leveraged to apprise quality improvements and innovation, creating a feedback loop that strengthens product design, supply chain responsiveness, and service delivery [48].

Several studies identify the synergy between RL and CI. In [51], the author states that the analysis of return patterns, defect rates, and other RL activities contributes to the identification of systematic issues, allowing the implementation of corrective actions. This process aligns with the principles of CI, where companies try to make incremental changes to enhance quality, reduce costs, and, as the last milestone, improve customer satisfaction. As companies improve their RL strategies through CI, they not only enhance operational efficiency but also contribute to sustainability goals, creating long-term competitive advantages [52].

In the case study presented by [53], authors identified challenges in remanufacturing within RL, using Lean Six Sigma approaches for CI. The work emphasizes that system reconfiguration requires practical considerations to effectively manage returned products in an automotive context. Optimizing RL activities by enhancing information sharing and collaboration among businesses and consumers allows for improved recovery and reuse efficiency and, therefore, supports the sustainable development goals through CI in economic efficiency [54].

Tools such as Value Stream Mapping (VSM), Root Cause Analysis (RCA), Failure Mode and Effects Analysis (FMEA), and the Plan–Do–Check–Act (PDCA) cycle are particularly effective in optimizing RL operations. These tool applications contribute to reducing cycle times and improving the accuracy of returns handling, repair, and remanufacturing processes. For instance, applying VSM to RL leads to the identification of non-value-adding tasks in product return flows [55], while FMEA allows for the anticipation of potential failures in reverse operations, thus mitigating risks [56]. Additionally, tools such as DMAIC (Define, Measure, Analyze, Improve, Control) or PDCA allow companies to analyze return data to uncover systemic issues in product distribution and identify corrective actions [57]. By embedding these tools into RL workflows, companies can establish a feedback-oriented approach that fosters continuous learning and process optimization—ultimately strengthening the relationship between DL and RL supply chain activities.

3. Methodology

The use of CI techniques in the RL process enables an efficient implementation of product reuse/repair or the proper disposal of waste that has not met the expectations of the customer. This concept aims to achieve efficient and appropriate management of the processes involved in companies’ RL systems.

3.1. Research Design

The design of the survey is directly related to the research questions, the proposed objectives of the study, and the research context [58]. The main purpose of planning and designing this survey is to analyze and describe organizational practices in the RL process, taking into account the integration of CI to achieve operational excellency. To understand how companies in northern Portugal involve CI concepts in their RL processes, two main research questions were defined:

1.

What types of RL practices are implemented in companies in the northern region of Portugal?

2.

Which CI processes are used in the RL models implemented by companies in the northern region of Portugal?

Thus, the survey research methodology was divided into four distinct phases: planning, implementation, data collection, data analysis, and its processing, as schematically presented in Figure 2.

In the planning phase, the research objectives were defined, i.e., to understand whether the RL process is seen as a process of CI by companies. For this purpose, Portuguese companies from the north region of Portugal were selected as the target audience. No sector of industrial activity was particularized since the objective of the study is to understand the practices adopted by different companies regarding the implementation of RL models. In this way, it is possible to assess the practices presented in the different activity sectors.

To structure the hierarchical chain of research questions previously defined and outline related sub-questions (i.e., questions that measure enough detail to be included in the final survey), a Data Requirements Table (DRT) was developed. A logical sequence of sections and questions was then created, and each question was carefully planned, considering the focus on its simplicity and objectivity. Closed-response questions were used in qualitative data collection, whereas quick open-response questions were used in quantitative data collection.

For each question, the collected variable, its type and measurement scale, as well as its coding (when applicable) were defined in detail. In this process, it was also possible to transform theoretical concepts into variables or indicators. Another important aspect defined in the DRT was the logical sequencing of conditional answer questions. As an example, when the respondent representing the company was asked whether familiar with the concept of RL, a more detailed definition of RL was given when the answer was negative, and a briefer definition was presented when the answer was affirmative.

After refining the DRT, it was possible to proceed to the survey design and implementation phase, which was structured, according to Figure 3, contemplating forty questions. The main objectives and the anonymous nature of the survey were explained to the respondents, and by responding to the survey, the company agreed to consent to the use of the data for research purposes.

In the first section, the company’s organizational information was collected, such as the activity sector in which it operates and its size in terms of employees and turnover levels. Section 2 included specific questions about aspects of how CI is integrated by companies in the RL process. The third section presented some questions about how the RL process operates, such as the existence of a formal RL model. In Section 4, the focus was to identify the metrics of the business model, such as the volume of returns taken into the product with the highest turnover. Finally, some final comments were collected in order to understand the most relevant challenges and barriers to RI implementation.

Microsoft’s Forms tool was used to properly implement and administer the online survey. Despite the tool’s potential, some limitations were encountered, particularly in the implementation of conditional questions, which were overcome by slightly rewording some of the questions. To validate the developed survey, a pilot test with twelve participants was conducted, involving companies of different sizes, six at an early stage and another six after some adjustments suggested by the respondents. The pre-test was used to make small amendments since, at a more initial stage, and according to some comments, it was found that some smaller companies were having difficulty answering the survey. Some of the answer options did not appear to be appropriate, as they did not allow for an answer to some particularities inherent to small businesses. It was therefore necessary to adjust some of the questions to cover not only larger companies but also smaller ones. An example was the question “What do you do with the product with the highest turnover that is returned?” where one company mentioned that a large part of its returns included equipment, so it was necessary to readjust the question to “What do you do with the product or item associated with the service (e.g., machine breakdown management and maintenance) with the highest turnover that is returned?” and two answers were added, namely “Re-enter the company’s process for use as components” and “Re-enter the company’s process with verification and changes”. In addition to the overall improvement of the survey, it was possible to find out other important information, such as the average response time. The estimated average time to respond to the survey was around nine minutes, a time that proved to be adequate for the type of response given by the companies.

The average effective response time was around six minutes. As previously mentioned, this study targets companies in the north region of Portugal, which, according to the Portuguese National Statistics Institute (INE—Instituto Nacional de Estatística), represent a total of 483,345 companies (Figure 4) in different areas of activity in the year 2022 (last known information).

Not only due to the size of the population but also due to the difficulty in accessing companies’ information, the data collection phase was one of the most difficult phases. Thus, a convenience sample was used to survey the companies. To maximize response rates, it was defined the strategy presented in Figure 5 to obtain responses.

At an early stage, telephone contact was made to request a contact to send the survey. Once the contact had been obtained, an email with the survey link was sent to the collaborator in the company’s department responsible for RL.

If it was not possible to contact a collaborator recommended by the company to answer the survey, an email was sent to the company’s general email address. With the impossibility of having an answer via digital means, face-to-face visits were made to companies, where it was provided a presentation of the survey and the reason for administering. This strategy was implemented during a full month, July 2023, in order to collect data from a convenience sample. The use of a convenience sampling method was primarily driven by practical considerations related to time constraints, resource limitations, and accessibility of participants. Also, this methodology was applied because the primary goal was to perform exploratory research, rather than results generalizability. The study concerns a specific context (companies located in the northern region of Portugal), so the population of interest naturally limits access to a random or stratified sample.

The survey was submitted to 80 companies, but it was only possible to validate 60 responses. Based on the population described (483,345 companies), for a confidence level of 90%, a sample size of 60 companies has a margin of error of 10.62%.

In the final phase, data from the survey were extracted, processed, and analyzed. In the preliminary analysis of the data, some limitations were identified, such as questions answered without the necessary assertiveness. For instance, in one of the questions requesting the number of hours made available by the company in the RL process, it was noted that most of the answers included not only the number of hours but also the word “hours” or “h” (example: 4 h).

To make observations and draw conclusions about RL as a process, an exploratory analysis of the data has been conducted. Firstly, the companies surveyed were characterized through their organizational information, followed by an analysis of CI integration in the RL process.

Descriptive statistics was used in this study to summarize and present the collected data clearly and concisely. Since the primary objective of this research was to provide a broad overview of the participants’ responses, descriptive statistics can be used as an efficient analytical tool, allowing data interpretation to support evidence-based remarks. While it is acknowledged that descriptive statistics may not capture the depth, this limitation is mitigated by the nature of the study, which focuses on quantifiable trends and patterns of the topic under study [60].

Yet, to analyze the number of weekly hours that companies dedicated to the RL process, normality tests were also carried out to understand if parametric or non-parametric methods could be applied.

3.2. Characterization of Inquired Companies

The aim of this section is to describe and characterize the sample under study, i.e., indicate the type of companies that completed the survey. As Figure 6 presents, different activity sectors were surveyed to understand their behavior in terms of the practices implemented in the RL process. In this study, there is a clear predominance of the manufacturing industry with 35 cases (58.33%), followed by textile with 14 cases (23.33%). Together, the metal-working industry (4), wholesale trade (3), engineering activities (2 cases), and the automotive industry (2) represent a total of 18.33% of the surveyed companies. The identification of the industrial sector was considered by grouping companies based on their own activity code, taking into account the Portuguese economic activity classification system (CAE).

Besides the inherent limitations of using a convenience sample, the distribution of responses obtained by the industrial sector is related to the great impact that the manufacturing sector has on the target region of this study.

Comprising cities like Porto, Braga, and Guimarães, the north region of Portugal is the country’s leading industrial hub, characterized by a strong tradition in textiles, footwear, furniture, and metal-working, alongside growing sectors such as technology.

The manufacturing industry in the northern region of Portugal is the most robust and diversified in the country, accounting for an important share of national manufacturing output and exports.

The companies’ size characterization was performed taking into account the number of employees. Small companies (with less than 50 employees) represented the lowest share of responses, with 14 (23.33%), followed by large companies (more than 250 employees), with 22 responses (36.67%).

The highest number of responses was received from medium-sized companies (between 50 and 249 employees), with 24 responses (40.00%). Despite these differences, the distribution of the sample by number of employees is balanced. It is possible to denote that, for the different company sizes, there is great variability in the number of employees in relation to the average, as presented in Figure 7.

The wider the interval of employees’ number, the higher the average value observed, as well as the standard deviation and confidence interval. The coefficient of variation measures the relative dispersion of the employees’ number in relation to the average, allowing the variability comparison between the analyzed classes of companies’ size.

The last class, 250 or more employees, is the one with the highest coefficient of variation since it is the set with the greatest amplitude range. In this class, minimum values of 250 and maximum values of 3400 can be observed, confirming the wide dispersion of the number of employees. This class is still the one with the biggest difference between the average and the median.

The activity sectors with the most responses are the manufacturing and textile industry sectors, with 58.33% and 23.34%, respectively (Figure 8). In the manufacturing sector, companies in the two largest classes represent 48.33%.

Companies were characterized by taking into account the annual turnover (Figure 9). Companies with the highest annual turnover are those with a greater number of employees; however, it was observed that 5.00% of companies with less than 50 employees have an annual turnover of more than EUR 5 billion, and 1.67% of companies with 250 or more employees have an annual turnover of between EUR 250,000 and 500,000.

Based on the distribution of the surveyed companies in the supply chain (Figure 10), it is possible to highlight that 80% of the surveyed companies are positioned in the supply chain as “Manufacturer”, representing 80.00%. The “Retailers” represent 11.66% of the responses, and “Wholesaler providers” correspond to 6.67% of the surveyed companies.

The “Service Providers” represent only 1.67% of the responses. There is a clear dominance of responses from respondents who position themselves in the supply chain as “Manufactures”, as they correspond to 48 responses of the 60 obtained in the survey.

RL models are more frequently integrated by manufacturers and retailers, though wholesalers may also participate depending on the industry. This outcome is corroborated by the literature. According to [23], manufacturers often need to deal with several RL activities, such as product returns, warranty claims, remanufacturing, and recycling. RL helps them recover valuable materials and reduce waste. In turn, the literature also mentions the retailers, since they need restocking, being responsible for collecting defective or unsold products and returning them to manufacturers. Wholesalers may engage in RL, usually for bulk returns or redistribution, but less frequently [17].

In sum, the collected data are relatively well distributed in terms of companies’ size, but there is a clear higher representativeness of some activity sectors and positions in the supply chain, particularly manufacturing areas.

4. Results and Discussion

In this section, the main results from the survey are presented and discussed. First of all, it analyzed the adoption of RL and the formality level of its implementation in the companies. Afterward, the types of RL implemented by the inquired companies are also identified. The last two topics concern the CI practices, its integration with RL, and the RL assessment metrics. The impact, challenges, and barriers of RL implementation are analyzed and discussed.

4.1. Extent of Reverse Logistics Adoption by Companies

Most of the surveyed companies (95.00%) are familiar with the concept of RL, whereas only 5.00% are not aware of the concept, demonstrating that companies are already mindful of the importance of practicing RL. When RL practices are applied, they can be formalized in the company, i.e., they can be well structured and planned, or they can be applied through common practices without any formal process associated.

Table 1. Department responsible for managing the RL process considering the process formality.

Department	Formal	Informal
Quality	6.67%	63.33%
Management	0%	18.33%
Logistics	1.67%	5.00%
CI	0%	3.33%
Sales	0%	1.67%

highlights that 91.66% of the companies state that the management of the RL system is informal, whereas only 8.34% have formal management of RL. The three most common departments for companies with formal or informal management of RL processes are the Quality (70.00%), Management (18.33%), and Logistics (6.67%) departments.

The departments responsible for the formal management of RL processes are the Quality and Logistics departments, with 6.67% and 1.67%, respectively. Companies implement RL processes, but there is still a low level of formalization of processes in the departments responsible for RL practices.

Figure 11 highlights that the majority of companies (93.34%) manage their own RL processes, only 3.33% use subcontracted companies, and 3.33% use both management systems, their own and subcontracted management. This information reveals that companies still prefer to manage their RL processes rather than outsourcing them.

Companies were asked about how the logistics cycle is accomplished in the company. Through Figure 12, it is possible to verify that 68.33% reuse the goods within the production cycle, whereas 26.67% of the goods return from the customer to the company (e.g., return and warranty policies) and 3.33% of the goods return to the company after going back to the supplier. Finally, in 1.67% of the cases, the goods travel from the company to the customer and return to the company (e.g., rental of machinery or service provider).

The RL cycle presents particularities that are strongly related to the type of service that the company provides. In fact, the literature categorizes those particularities in product type/industry, service-based versus product-based business, regulatory issues, customer involvement, and logistics complexity [40].

4.2. Time Spent with Reverse Logistics by the Different Industry Sectors

To enable a more detailed analysis of the RL systems’ operation across the different industrial activities, it has been analyzed the mean number of weekly hours that companies dedicated to this process for the product with the higher turnover, hereinafter referred to as X. In the first step, normality tests were performed to understand what type of tests, parametric or non-parametric, should be employed on the collected data.

For the selected variable, one company did not fill the value (answering with “Don’t know”). For the remaining analysis it is considered 59 valid cases, representing 98.3% of the collected answers. To provide a deeper analysis of the data, it has been used as an auxiliary tool, the IBM SPSS Statistics Software (Version 29.0.1.0 (171)). For the variable under analysis, a skewness of 3.32 and a kurtosis of 14.07 were identified (

Table 2. Statistics of average time dedicated to the RL process per week for the product with the highest turnover.

Descriptives			Statistic
Average Time Dedicated to the RL (Hours)	Mean		2.98 (Std. Error 0.319)
	95% Confidence Interval for Mean	Lower Bound	2.34
		Upper Bound	3.62
	5% Trimmed Mean		2.63
	Median		2.00
	Variance		6.017
	Std. Deviation		2.453
	Minimum		1
	Maximum		16
	Range		15
	Interquartile Range		1
	Skewness		3.321 (Std. Error 0.311)
	Kurtosis		14.074 (Std. Error 0.613)

), which indicates that there are deviations in relation to the normal distribution. The distribution is right-skewed and leptokurtic.

To confirm the result, the normality test was applied considering the following hypotheses:

H₀. 

X follows a normal distribution.

H₁. 

X does not follow a normal distribution.

As the proof value (p-value or sig) is less than 0.001, for a significance level of 5%, the null hypothesis is rejected for both the Kolmogorov–Smirnov and Shapiro–Wilk tests, which indicates that the data do not follow a normal distribution (

Table 3. Tests of normality regarding tested hypotheses over the time dedicated to the RL process.

Tests of Normality
Kolmogorov–Smirnov a			Shapiro–Wilk
Statistic	Df.	Sig.	Statistic	Df.	Sig.
0.294	59	<0.001	0.632	59	<0.001

^a Lilliefors Significance Correction.

).

Considering a sample of 59 values, it would be possible to use a parametric test because, according to the central limit theorem, the distribution of the sample mean tends toward a normal distribution. However, as most of the subgroups to be analyzed are smaller than 30, it was decided to verify the normality of the data in each group (

Table 4. Tests of normality for industry subgroups over the time dedicated to the RL process.

Industry	Kolmogorov–Smirnov			Shapiro–Wilk
	Statistic	Df.	Sig.	Statistic	Df.	Sig.
Automotive industry	0.0	2	0.0
Engineering activities	0.0	2	0.0
Manufacturing industry	0.253	34	<0.001	0.882	34	0.002
Metal-working industry	0.229	4	0.0	0.962	4	0.792
Textile industry	0.406	14	<0.001	0.729	14	<0.001
Wholesale trade	0.385	3	0.0	0.750	3	<0.001

).

Of the set of subgroups considered, two have insufficient data, and only the metal-working industry presents a proof value greater than the significance level, so it would be the only subgroup that follows a normal distribution (however, with only four observations). Due to the violation of normality assumptions, it was decided to use non-parametric tests. Furthermore, the small sample size in each group may compromise the robustness of parametric tests, especially in asymmetric distributions. Therefore, non-parametric tests offer greater suitability to the data profile.

Considering all these factors, the Kruskal–Wallis non-parametric test was used to understand whether there are significant differences in the weekly time spent by different sectors of the industry for RL. Before applying the test, the median value was analyzed by industry sector, obtaining the results shown in Figure 13. The metal-working sector presents the median highest value (9 h), while sectors such as engineering activities and wholesale trade present the lowest values (1 h).

For the Kruskal–Wallis test, the following hypotheses were formulated:

H₀. 

There are no significant differences in the median of X for the different industry sectors, ${\tilde{X}}_1={\tilde{X}}_2={\tilde{X}}_3={\tilde{X}}_4={\tilde{X}}_5={\tilde{X}}_6$.

H₁. 

There are significant differences between, at least, a pair of industry sectors, $\exists i,j that {\tilde{X}}_i\ne {\tilde{X}}_j, for i\ne j$.

As the test value is less than 0.001, the null hypothesis is rejected for a significance level of 5%, meaning there are at least two sectors of activity in which there are significant differences in the medians of the average time spent on the RL of the product with the highest turnover (

Table 5. Results of the independent-samples Kruskal–Wallis test.

Variable	Value
Total N	59
Test Statistic	21.240 a
Degree Of Freedom	5
Asymptotic Sig. (2-sided test)	<0.001

^a The test statistic is adjusted for ties.

).

To understand between which type of industry sector these differences exist, a pairwise comparison was executed considering the following hypotheses:

H₀. 

There are no significant differences in the median of X between different industry sectors i and j, ${\tilde{X}}_i={\tilde{X}}_j, i\ne j$.

H₁. 

There are significant differences of the median of X between different industry sectors i and j, ${\tilde{X}}_i\ne {\tilde{X}}_j, i\ne j$.

In Figure 14, the lines represented in blue indicate the rejection of the null hypothesis. As depicted in the figure, the original values are converted into ranks to allow for a more adequate comparison between industry sectors. The Kruskal–Wallis test analyses the average rank of each industry sector, which indicates possible variations in group distributions.

For the two most representative sectors of the data, it is concluded that there are no significant differences between the X medians, i.e., the textile and manufacturing industry sectors do not have significant differences.

By including the third most representative sector, there are significant differences between metal-working and textile or between metal-working and manufacturing industry sectors. Considering the number of cases obtained and the most representative sectors between the two most representative types of industry, metal-working and textile, there are no significant differences in the median of the mean number of weekly hours that companies dedicated to the RL process for the product with the higher turnover.

4.3. Types of Reverse Logistics Activities

RL systems comprise several types of activities. Companies may remanufacture or resell their products, recycle or discard components that are no longer usable in the production cycle, or assist in the services’ provision. A company can implement more than one RL activity, so each company has identified all the activities it usually performs.

The three most cited activities are “Resale”, “Remanufacture”, and “Return to supplier”, with shares of 45.00%, 38.33%, and 36.67%, respectively. As shown by Figure 15, the first two activities demonstrate the growing concern with maintaining the value associated with the asset. The third activity demonstrates that the communication process with the supplier in terms of RL is frequent, which may indicate some inaccuracies in the ordering process or defective goods, but also the return of excess or experimental materials often contracted in the supply processes.

The “Recycle” activity proves to be a relevant activity with 20.00% of cases, while “Dispose” is rarely carried out (1.67%), demonstrating a growing concern with sustainability principles. Activities such as “Refurbish” and “Recover” are both indicated in 10% of cases, which shows that companies are willing to perform more in-depth interventions to recover some more value from their assets, thus reducing recycling and disposal rates.

To understand the volume of returns that companies face and in order to extract reliable information, the volume of returns was collected for the month immediately before the survey was answered in June 2023 and for the product with the highest turnover.

The “No Knowledge” option was still displayed. Most companies (78.33%, 47 cases) state that returns for the highest turnover product in the company are less than 20%, while 20% of respondents (12 cases) recognize a range between 20% (inclusive) and 40% of returns. Only 1.67% (1 case) claim to have no knowledge.

Through this analysis, it is possible to observe that the percentage of returns is less than 40% for all surveyed companies that were aware of the number of returns for the product with the highest turnover in the company (

Table 6. Returns (June 2023) for the product with the highest turnover.

Interval	Relative Percentage of Returns
<20%	78.33%
[20–40%]	20.00%
[40–60%]	0%
[60–80%]	0%
≥80%	0%
No Knowledge	1.67%

).

Computer tools are often used to plan activities; however, when planning is informal, work instructions or other documents in paper format are sometimes used. The main tools used by companies in RL planning are spreadsheets (80%), 15% of companies use other computer applications, and only 5% claim not to use any computer tool (Figure 16).

The majority of the companies use spreadsheets or commercial software to manage RL processes. Companies with 250 or more employees already show greater use shares of commercial IT tools (31.82%), but the majority prefer to use spreadsheets (68.18%). Smaller companies (with less than 249 employees) mostly use spreadsheets (relative percentages higher than 85%). Only 8.33% of companies with less than 249 employees do not require any type of IT solution to manage the RL activities. Thus, the size of the company can influence the decision on the most appropriate IT tools to carry out RL management. It can be seen that the greater the number of employees, the greater the need to opt for other types of informatic tools over spreadsheets.

4.4. Analysis of Continuous Improvement Practices and Its Integration with Reverse Logistics

CI practices, combined with Lean principles, allow companies to become more competitive in the market through better management of their resources in the direct or RL process. In order to understand how CI practices can be integrated with RL, firstly, the definition of CI was presented to respondents with the purpose of classifying the activities developed by the company in a specific department. Figure 17 reveals that 78.33% (47) of the companies have a CI department or some department responsible for the CI process, while 21.67% (13) of the surveyed companies do not have a CI department or some department responsible for it. When evaluating which department is dedicated to the practice of CI, it can be observed that 48.33% (29) have a “Quality Department” dedicated to the practice of CI, while 28.33% (17) have a “Continuous Improvement Department”. Only one company states that production is the department responsible for CI practices (1.67%).

It is worth highlighting that around 22% of the surveyed companies still do not have any department dedicated to the practice of CI, which may indicate that these companies do not yet have a formal system for implementing consistent CI practices.

In contrast, a higher percentage of companies (around 78%) are already concerned and are trying to differentiate themselves by investing in CI practices.

When asked about the existence of a formal department responsible for CI, 57.14% of companies with less than 50 employees have no formal department to address issues related to CI. This value decreases to 16.67% for companies with between 50 and 249 employees and reaches 4.55% in larger companies (250 or more employees). The reasons why the company had a department responsible for CI were also assessed. For this analysis, it was possible to select more than one option from those available and to specify another option if the company considered it relevant.

All selected or suggested options are presented in Figure 18. The three main reasons identified by companies that led them to have a department responsible for CI are “Production cost reduction” with 89.36%, “Sales improvement” with 68.09%, and “Client engagement” with 31.91%. The “Ahead of competition” (6.38%) and “Process improvement” (2.13%) do not prove to be the strongest reasons to have a department responsible for CI.

Evaluating whether the surveyed companies use any CI tool as a process to improve RL, it can be observed that 37% (22 companies) of the surveyed companies already use CI methodologies or tools in RL. However, 63% (38 companies) of the surveyed companies do not use any CI methodology or tool as a process for improving RL.

The three CI methodologies most adopted tools by companies in the RL process are “the 5S Technique” (57.14%), “Kaizen/PDCA” (39.29%), and “Six Sigma” (3.57%). Although companies could select more than one option or suggest another method, the “Kanban” and “VSM” options were not selected (

Table 7. CI methodologies and tools used in the RL process.

CI Methodologies	Relative Percentage of Use
5S Technique	57.14%
Kaizen/PDCA Cycle	39.29%
Six Sigma	3.57%
Kanban	0%
VSM	0%
Other	0%

).

There is now some tendency to take advantage of the benefits that CI processes can add to the process of improving the RL. Lean tools are being used to reduce waste, improve efficiency, and optimize resource use while handling returns, refurbishments, recycling, and disposal processes. According to [50], the most common CI and Lean tools used in RL include 5S, Kaizen, and Six Sigma, as selected by the respondents. While 5S can be applied to organize return and recycling areas, improving visibility and efficiency, Kaizen fosters a culture of continuous, small-scale improvements (quick wins) to eliminate waste and streamline workflows.

The simplicity and the visual management of 5S technique implementation allow companies to improve efficiency in inspecting, refurbishing, and redistributing returned goods. Kaizen contributes to identifying and eliminating unnecessary steps in RL workflows, allowing incremental improvements in return processing. Six Sigma, through its methods, allows companies to enhance the quality and reliability of refurbished goods.

4.5. Importance and Impact Assessment of Reverse Logistics

To evaluate the RL practices, it is necessary to understand which performance metrics are most valued by companies. In this sense, each respondent was asked to select the performance metric they considered most relevant for evaluating the RL system. According to Figure 19, “Quality” (80%) is the most used performance measure to evaluate the RL practices (48 cases), followed by “Cost” with 11.66% (7 cases). This suggests that there is already a greater concern for quality over cost. Metrics such as “Productivity”, “Measuring financial assets”, and “Customer service” make up a total of 8.34%, with 5.00%, 1.67%, and 1.67%, respectively.

The reasons for return (

Table 8. Return reasons that imply RL flow.

Reasons Implying RL Flows	Frequency of Responses
Defective products	48
Expired products	27
Products damaged during transport	25
Damaged products	14
Shipping errors	7
Obsolete products	3
Seasonal products	0
Other	0

) for a good may vary within the same company, but they all generate RL flows. In this way, respondents were allowed to select more than one reason for returning a good. The main reason for returns is “Defective items” (48 responses), followed by “Expired products” (27 responses) and “Products damaged during transport” (25 responses). Products damaged but with no evidence of having been damaged during transport, as well as shipping errors, are other reasons for returns and had 14 and 7 responses, respectively.

Obsolete products seem to be the lowest reason for returns (3 responses), which may indicate that companies are taking greater precautions when it comes to long-term stock management. No seasonal or other factors were mentioned as motives for returning.

A well-implemented RL system can generate competitive advantages. These were verified using an agreement scale where it can be observed that the options “Important” and “Very important” were the most selected for all the advantages identified (Figure 20).

The three reasons considered most important are “Reduction of logistic costs”, “Recover the value of returned products” and “Customer satisfaction”. The percentages of 93.34%, 93.34%, and 78.33% are obtained cumulatively, considering the percentage of opinions that consider this reason as, at least, “Important” in the respective order.

The three reasons considered slightly less important are “Strategic objectives relating to increasing competitiveness” (66.67%), “Satisfy legal requirements” (63.33%), and “Reduction of stock” (55.00%).

There are some implementation barriers or factors that impact the performance of RL systems. Factors such as “Financial constraints” (85%), “Insufficient training” (68.33%), and “Lack of strategic planning related to reverse logistics” (66.66%) are the main barriers identified in the implementation of RL systems, considering responses such as “Important” and “Very important” (Figure 21).

Companies tend to focus more on forward flows, hesitating to adopt RL due to unclear economic benefits/financial constraints, which is corroborated by the study presented by [61]. The lack of information and the lack of strategic planning can be related to the limited forecasting capacity of companies due to the number of stakeholders involved in the SCM [62].

In addition to barriers, there are factors that impact system performance (

Table 9. Factors that impact the performance of RL systems.

Impact Factors on RL Implementation	Frequency of Responses
Non-uniformity of the returned product	48
Cost viability	15
Difficulty in forecasting returns	7
Location and transportation problems	6
Lack of information about disposal options	3

). One of the main difficulties encountered is the “non-uniformity of the returned product” (48 responses). The “Cost viability” (15 responses), “Difficulty in forecasting returns” (7 responses), “Location and transportation problems” (6 responses), and “Lack of information about disposal options” (3 responses) are other factors with impact identified by companies. In RL systems, companies face the unpredictability of return phenomena, and when they receive a return, they have the added difficulty of identifying the viability of the costs of goods that are not returned uniformly; that is, different goods may be returned in different versions of development.

Despite the barriers and factors that impact the performance of RL systems, companies identify the main reasons that led them to implement an RL system as cost reduction with materials or products (96.67%), profits from the reuse of products or materials (95%) and sustainability (88.34%), considering the opinions that considered reason at least as important (Figure 22).

Companies demonstrate a clear perception of the added value to the product when using RL systems. This added value also allows companies to be more sustainable, which is an increasingly growing concern. Compliance with laws and public policies, brand image, and competitive advantage are mainly identified as important factors (more than 50% in each factor). However, a percentage of neutral opinions is observed between 26.67% and 35.00%.

In sum, companies adopt RL systems to reduce costs, recover value from returned products, and comply with social and environmental regulations. By reusing, refurbishing, or recycling materials, companies can lower production costs while minimizing disposal costs and the environmental impact. Regulatory compliance enhances waste management, while sustainability initiatives enhance organizations’ reputation and customer trust. Also, RL improves customer satisfaction through efficient return practices, giving companies a competitive edge and increased opportunities for long-term profitability.

5. Conclusions

This research work aimed to explore the current state of RL practices across different activity sectors in the northern region of the Portuguese industry. To this end, the main RL practices were identified, analyzing RL implementation, its integration with CI, its impact metrics, challenges encountered in their implementation, and the reasons why RL systems should be implemented.

The goal was to deepen knowledge in this area to promote the implementation of CI processes for effective RL management, reinforcing the recognized benefits of RL since little research has been conducted on this topic in Portugal. Additionally, the study examined how the RL cycle is carried out, the departments responsible, and the CI tools used in the implementation of the LI system. To obtain these findings, a survey was designed, validated, and submitted to a convenience sample of 60 companies from different sectors.

The two most representative sectors involved in the study were the manufacturing industry (58.33%) and the textile (23.33%). Generally, companies with the highest annual turnover are those that have a greater number of employees.

Regarding the RL adoption, 95% of the companies surveyed are familiar with the term RL and only 5% of them are not familiar with the term, which shows that companies are already aware of the importance of implementing RL processes.

The informal RL system is the most used by companies, and the departments responsible for the formal management of RL processes are the Quality and Logistics departments. To answer the first research question, “What type of RL practices are implemented in companies in the Northern region of Portugal?”, companies were asked about their RL practices. The most commonly used RL practices in companies are “Resale” and “Remanufacture”. The main factors that lead companies to implement RL systems are “Reduction of costs of products through RL” and “Profits from reusing goods”.

The main reason for reverse flows is product quality problems. The return of materials that involve RL flows have problems associated with defective products, products with expired validity, and products damaged during transport.

The survey also analyzed CI practices and their integration with RL. The results show that 78% of companies have an IC department or some other department responsible for the IC process, whereas the majority of respondents (48%) reported that the department dedicated to CI practice is the “Quality Department”. The most widely used CI tools are 5S and the Kaizen. The integration of CI with RL has the main purpose of reducing costs. Integrating RL systems in industrial companies enhances sustainability and resource recovery, while CI tools like 5S and Kaizen allow optimizing processes, materials, and information flows. Together, these tools support a more efficient, responsive, and sustainable RL operation, aligning operational goals with environmental and economic benefits. This finding answers the second research question, “Which CI processes are used in the RL models implemented by companies in the Northern region of Portugal?”

Concerning the benefits associated with the implementation of RL practices in order to generate competitive advantages, the companies surveyed highlighted one more representative aspect, “Reduction of logistics costs”, which leads to an improvement in logistics efficiency, thus reinforcing the importance of implementing RL practices. “Financial constraints” and “Lack of training” were identified as the main barriers to the implementation of good practices in RL. The non-uniformity of the returned product appears to be the main reason that affects the proper management of RL systems.

Based on the literature review and the survey findings, it is possible to conclude that companies need to consider RL model implementation to reduce costs, enhance sustainability, comply with regulations, improve customer satisfaction, obtain a competitive advantage, and strengthen supply chain resilience. A well-structured RL system might also represent new revenue sources by refurbishing or remanufacturing products, recovering value from defective goods, and re-enforcing the economy’s circularity.

This study presents some limitations that need to be addressed. Since the project was very time-limited (6 months to collect and analyze the data), a convenience sampling method was chosen. Although convenience sampling may introduce bias and limit the generalizability of results, the fast data collection from readily available and willing participants was essential to ensure the feasibility and timely completion of the study. These limitations are acknowledged yet addressed through a careful interpretation of findings and clear communication of the results.

Based on the obtained results, as future work, the survey could be improved, and questions could be refined, allowing a more in-depth discussion of the topic. To overcome the main limitation of the current study, the survey should be submitted to a larger number of companies, perhaps extending it to the entire Portuguese territory, in order to carry out a comparison by region and considering the type of industry sector.

Also, by collecting more data, new research lines may be considered, namely, the use of Machine Learning algorithms to predict the number of hours that a company of a given industry sector should be using for the RL process. Regression models like Decision Tree, Random Forest, or Gradient Boosted Trees could be used to further analyze collected variables.