The development of manufacturing technologies has led to a revolution in the most diverse areas of modern society and sustainable development, with advances in manufacturing, infrastructure, and technologies [1
]. This has caused competition between companies to grow, with manufactures continually seeking higher productivities, product quality, and manufacturing efficiency [3
]. For example, in Brazil, where agribusiness represents about 22.54% of GDP (and 36% of total exports), it is estimated that the agricultural machinery industry will grow by an average of 5.8% per year in the next years [4
]. To avoid the impact of falling sales, the agricultural machinery industry has manufactured several product models (product mix) to make the production line more flexible. This has made it possible to maintain activity during seasonal periods, as 20.1% of the country’s workforce is employed in various segments of agribusiness [6
In addition to the need for the industry with a broad product mix, which makes production planning more complex, there are still problems facing the daily planning of orders for the shop floor [7
]. The production planning and control (PPC) sector is generally concerned with two criteria: (1) sequencing of the production line, and (2) balancing the production line. These two criteria seek only to make better use of working time on the production lines and do not analyze the environmental impact of manufacturing technology. In this sense, important criteria in planning the production mix can be focused on, namely: working time, productivity, and profitability.
Several studies have been conducted on the importance of good planning for the production mix. For example, configuring the production line according to the products to obtain good synchronization, allocating the appropriate amount of human resources to the process to avoid delays [8
]. In addition, the formulation of problems for the planning of aggregated production in the automotive industry makes the workforce more flexible, with changes in costs and the workload of operators [9
]. Improving the allocation and distribution of labor in the industry, for the purpose of improving the productivity of operators and the hours worked, can be cited as the task carried out in the automotive, plastic, and service industries [9
However, the industry seeks greater efficiency in production processes, where increased productivity and profitability are considered the primary criteria behind decision making. Improvements in the industry generally focus on reducing waste and process variability, while markets demand greater flexibility and lower product costs [13
]. However, along with technological advances, increased productivity, and competitiveness, there are also environmental issues that need to be accounted for. In this sense, a strong relation can be observed between components of the built environment and climate change, with agriculture and industry being some of these components [14
]. Unlike the natural environment, the built environment is comprised of manmade components. The built environment influences human choices, which in turn affect global climate and human health [15
It has been noticed that the monitoring of sustainability becomes important for decision making and management of activities in organizations [16
]. As for technological advances, in a study carried out in Ireland on the use of Smart Farming Technologies (SFT), it was observed that the cost and high initial investment are the factors that inhibit the adoption of new technologies by farmers. Another important aspect is the lack of infrastructure, such as the absence of the internet. However, the use of Cloud Computing technology among young farmers is higher compared to older farmers [17
]. Another study in Australia’s rice industry sought to understand the barriers to broader adoption of smart agriculture technologies. The study concluded that agricultural consultants and extension agents play an important role in assisting the farmer and encouraging smart agriculture [18
To assess the best production mix considering the production planning alternatives for various criteria, multi-criteria decision-making methods (MCDM) can be used. For example, the application of the analytical network process (ANP) for product mix selection by a semiconductor manufacturer [19
], the development of a model to optimize the selection of suppliers for the apparel industry, using sustainability criteria [20
], and the selection of suppliers using economic and environmental criteria in a technology company [21
]. Decision making is also an important component for smart agriculture in the use and encouragement of new technologies [17
A growing number of publications can be observed focusing on sustainability in the agricultural machinery industry, such as: the tractor engine load mode to determine fuel consumption and exhaust emissions [19
]; in agriculture, the work in the rotary harrowing operation and comparative evaluation of the life cycle [20
]; the tractor productivity evaluation that evaluated the efficiency in energy use, fuel consumption, and gas emissions for plowing work in fields with different lengths [21
]; the evaluation of sustainability indicators focused on productivity and operational performance [22
]. Nevertheless, there are still limitations in the analysis of the production mix, especially in the agricultural machinery industry, including the aspect of environmental impact. Several studies have been conducted on the environmental impact of various industries, such as choosing outsourced logistics providers [24
], vehicle engine technologies [25
], and strengths and weaknesses in the photovoltaic industry [26
]. However, there is a lack of studies to identify the best product mix in the agricultural industry that considers the environmental impact as one of its criteria. Based on this, this study aims to apply two multi-criteria methods for decision making, the analytic hierarchy process (AHP) and data envelopment analysis (DEA), for the definition of planning regarding the best production mix, with the environmental impact being one of the criteria. To achieve this goal, seven production mix configurations were evaluated. The products that make up the mix are grain trailers and machines used for transporting and storing fertilizers and fertilizers. Products were evaluated considering the following quantitative criteria: working time on the production line, productivity, profitability, and environmental impact which was measured with SimaPro software. After the results were obtained in each of the methods, AHP and DEA, a comparison was performed to assess the relationships and implications in the use of the methods, from the point of view of managerial implications, of the agricultural machinery industry for decision making.
The remainder of this article is structured as follows: The second section briefly describes the multi-criteria decision-making methods that are the focus of this study: AHP and DEA. The third section presents the methodology used. The results and discussion are presented in the fourth section. Finally, the conclusions and final considerations are presented.
2. Multi-Criteria Decision-Making
There is an increase in the amount of available information, which contributes to the complexity of decision-making. Generally, decisions are made based on the experience of the decision-maker, and the divergences between the analysis of decision-makers can be observed; there may be biased opinions that negatively influence the result [27
]. In this context, the MCDM aims to support decision-makers in making the best choice, enabling the evaluation of various criteria [28
Two decision-making methods were used for this study: the analytic hierarchy process (AHP) and data envelopment analysis (DEA). AHP is a multi-objective decision-making method that enables the analysis of qualitative and quantitative data [29
] whilst DEA categorizes the criteria used as inputs and outputs, where the factors that need to be minimized are placed as inputs and the factors to be maximized are placed as outputs [31
A literature review covering the years 1999 to 2017, in the area of mining engineering and mining processes, identified that the AHP method is the most used, both individually and in a hybrid manner [33
]. More studies can be cited with the use of AHP in the automobile industry for manufacturing performance and production flow [15
]. In the best use of equipment, eliminate bottlenecks, and enable training of operators [34
AHP is one of the most powerful multi-criteria techniques, which was originally proposed by Saaty in 1980 and applied to a variety of uses, measures intangibles with the assistance of expert judgments through peer comparisons [35
]. Once the criteria are selected, a paired comparison is performed, with the criteria weights calculated within the established hierarchy. First, a qualitative value is assigned to the criterion, and then a numerical value is assigned. Thus, the score is assigned in a way that looks reasonable, and the reciprocal pair comparisons performed in a carefully designed manner [36
DEA aims to benchmark the performance of decision-making units (DMUs). Units that use the same inputs and outputs are evaluated and compared, where the calculated efficiency is the maximum value, making this simplification effective in avoiding subjective assumptions. Judgment takes place objectively, and DMUs that fall outside the efficient boundary can be considered underperforming and further analyzed to determine what can be done to improve their efficiency [37
A layout study for a precision part machining industry can be cited as an example of a combination of the AHP and DEA methods. Qualitative performance data were obtained by applying AHP, and DEA was applied to identify the efficiency scores considering the quantitative and qualitative performance data. This enabled determining the best global alternative [38
]. The combination of methods was also used to assess the facility layout design, where AHP was applied to assess qualitative data for quality and flexibility [32
]. Another study combined AHP and DEA methods to evaluate the performance of companies in the PV energy sector. The AHP was applied to collect expert opinions and the DEA to measure which companies are the most efficient [26
], to evaluate the road safety performance of a set of European countries (or DMUs), combining the AHP and DEA method [39
], and to classify organizational units, where each unit has multiple inputs and outputs [40
This article proposes the application of multicriteria methods to define the best product mix in the production planning of the Brazilian agricultural machinery industry. Seven alternatives and four criteria were evaluated based on the environmental impact of the decision criteria. In this practical case, the AHP and DEA methods identified the best production mix, and a second production mix was also identified by the DEA as being equally efficient. In this sense, it is possible to assess that the criteria are conflicting, because selecting a lower environmental impact leads to lower profitability.
From a managerial point of view, the AHP method identified a best alternative, which facilitates decision-making. However, for the elaboration of the matrix, paired evaluations are necessary, and this study required two rounds of evaluations with experts. Furthermore, the AHP method allows the use of qualitative criteria for a future evaluation, in the interest of the management. The DEA method identified two alternatives as being the most efficient, wherein the manager needs to choose a mix that generates less environmental impact or greater profitability. Because of the conflicting criteria, decision making requires further analysis of the final result. However, the positive aspect of applying the DEA is a global view, with benchmarking of the performance of the DMUs, making it possible to obtain improvement with less efficient alternatives.
Although applied to agricultural industry, the presented methodology can be easily adapted to other products and activities related to the built environment, such as the construction industry.