Marian Carneiro
Pará State University - UEPA, Belém, PA, Brazil.
Tassy Alves
Pará State University - UEPA, Belém, PA, Brazil.
Mariana da Silva Monteiro
Pará State University - UEPA, Belém, PA, Brazil.
Nyna Arisa Ueoka
Pará State University - UEPA, Belém, PA, Brazil.
Giovanna Moreira Silva
Pará State University - UEPA, Belém, PA, Brazil.
The commercialization of the açaí fruit has increased considerably since the 1990s, boosting demand and the price of the fruit as well as attracting new investments in the açaí pulp processing industries to the northeastern region of the State of Pará. Therefore, this study aims to use the principles and methods of Lean Six Sigma to propose improvements to the açaí pulp processing process. These improvements aim to reduce machine downtime to provide the company with greater profitability and the development of continuous flow. First, a literature study was carried out to understand the methodologies of Lean Manufacturing and Six Sigma and how they complement each other. Then we used the Six Sigma method, DMAIC, with the Define, Measure, and Analyze phases. In this phase, the main reasons were identified and the percentage of stoppages that each one represents on the line, such as lack of product in the process area (33.75%), lack of production order (23.91%), change of laminates and coils (12.7%), cleaning before production (7.49%), and mechanical breakdown or failure (7.35%), which together account for 85.2% of the line's total stoppages. Therefore, an action plan was proposed for each reason identified to reduce or eliminate it. The main limitations of the research were the lack of a qualified team and a centralized organizational culture that would allow faster data analysis. Some of the main practical implications include statistical methods for decision-making in the açaí agro-industry, a sector that has sought to professionalize its management and improve the supply of its products.
Keywords: Açaí industry; Lean Six Sigma application; Downtime reduction; Flow.
In an increasingly competitive environment, organizations in various sectors constantly seek to improve their processes, making them more efficient. This continuous search for improvement has forced companies to find methods capable of managing and guaranteeing the quality of their products and/or services. This scenario is no different for the food industry. According to Costa et al. (2018), the global food industry faces several challenges that force companies in this sector to improve their productivity and quality strategies to remain competitive. In this context, açaí fruit is one of the most relevant products of national extractivism and one of the main contributors to highlighting the biodiversity of the Amazon rainforest. In terms of the national market, the country's northern region concentrates most of the açaí production, with Pará and Amazonas accounting for 87.5% of the total, with the state of Pará being the world's largest producer, having doubled its production in the last ten years, and Brazil's largest exporter, followed by Amazonas (Conab, 2019). Researchers and operators in the sector point to critical factors for the competitiveness of the Amazon in açaí production and processing, especially considering the emergence of plantations in other tropical areas. According to Fernandes and Almeida (2022), balanced production combined with sustainable planning is the key to açaí becoming one of the most commercialized fruits in the country. Therefore, companies in the segment need to invest in verticalization and crop expansion strategies.
Lean Manufacturing, also known as the Toyota Production System, aims to reduce waste in the production process, thus improving quality and reducing production time and, consequently, production costs. Lean uses several tools such as 5S, Bottleneck Analysis, Kaizen (Continuous Improvement), PDCA, Poka-Yoke, Root Cause Analysis, SMART objectives, Just-in-time, and Takt Time. The tools can be combined according to business type, such as lean startup, lean healthcare, and lean Six Sigma (Ferreira, 2018).
In this way, lean provides a relevant framework for improving efficiency by reducing waste, i.e., operations that are unnecessary, excessive setup times, unreliable machines that can be made more reliable, rework that can be eliminated, and others (Costa et al., 2018). Six Sigma is a statistical approach that seeks to identify and eliminate defects, reduce process variability, reduce production costs, improve product quality, and reduce defects (Cruz, 2021) using the DMAIC methodology. This methodology can be improved and structured as a diagnostic model for problem solving (Bugor and Lucca Filho, 2021). Lean Six Sigma combines the speed strategy and cultural and organizational processes of Lean aligned with Six Sigma statistical tools. The consequence of this fusion is that processes have higher quality and speed, avoiding waste and resulting in lower cost production (Silva and Gonzalez Junior, 2022). For Costa et al. (2018), the main studies encompassing the two philosophies, L and SSi, in the context of the food industry are mainly driven by six different factors: reducing process variation, reducing waste, improving competitiveness, reducing cost, reducing inventory, and increasing process efficiency.
This study aims to propose improvements in the açaí pulp processing process using Lean Six Sigma principles and methods.
The project was carried out in an açaí pulp processing company located in Brazil, in the municipality of Castanhal, PA. Its name or any information that could compromise it will not be mentioned during the work.
To conduct the study, information was initially sought from primary sources through unstructured interviews with the company's board of directors and management, as well as on-site observations. The aim was to define the study's context and purpose, focusing on the characteristics of this type of industry. By analyzing the processes and identifying the relationships with customers (as they are the crucial link in the business), the aim was to identify the main flaws that occur in the process and provide solutions.
The work in question was carried out using the DMAIC cycle, executing the Define, Measure, and Analyze steps with the construction of the entire action plan. This can be implemented later, as well as a brief description of what needs to be done to control the process.
A bibliographic survey was carried out to identify the set of activities that make up the DMAIC method using theoretical references. This provided the basis for the work, from how its structure to using the main tools to conduct it.
To achieve the proposed objective, the following stages were followed:
It was found that the main problem is related to the downtime of labor, parts, equipment, products, and information throughout the process, resulting in an inefficient flow. Specifically, the project scope is centered on the machine stoppages that prevent the factory from reaching its daily production targets. Currently, production occurs without rhythm and in a pushy manner, with no standard setup time or maximum machine downtime. As a result, many stoppages occur during production, preventing the main process—filling—from taking place on time.
The project aims to raise the level of utilization of the factory's production capacity, identify the activities that do not add value to the product, and, with this, draw up an action plan to minimize machine downtime, increasing productivity, providing the shop floor with a continuous production flow, and reducing costs.
Thus, the Project Initiation Agreement (PIA) was initially developed, establishing the commitment of the implementation team to the company and vice versa, as shown in Chart 1.
It is worth noting that the TAP has undergone some changes during the project: due to difficulties in accessing certain information, some variables could not be quantified more precisely, and the contract was realigned as the Measure phase was carried out.
Next, to better understand the process, the SIPOC Diagram (Figure 1), which outlines the basic process elements, was drawn up.
As the company's estimates reflect an increase in its production and sales volumes over time, it is necessary to analyze and understand the general flow of the process to subsequently establish the flow of the value chain and eliminate and/or reduce activities that do not add value to the product. A process flowchart (Figure 2) was drawn up for a better understanding of the stages following the process.
During this process, many problems influence the operation's low efficiency, especially concerning machine downtime. To quantify this problem, we analyzed data on machine downtime from April 2018 to October 2019.
Upon realizing that the factory cannot meet its daily targets due to the large number of stoppages during the process, the company has collected data to discover the reason. However, the data is not transformed into information to visualize and understand what is happening during the production process. Using statistics, we hope to understand the behavior of the variables that impact the high rates of machine downtime.
The graph in Figure 3 was plotted to better visualize the downtime behavior over the period analyzed.
As you can see, the graph shows a peak in August in both years compared to the previous months, showing a significant rise in 2019. In the period in question (the start of the harvest), the machines should be working at full capacity, as this is when demand is highest and the raw material price falls. A total of 2,582,227 minutes of downtime were recorded.
This data refers to specific machines in certain sectors, listed in the steering diagram illustrated in Figure 4. A relevant factor to take into account is that the machines that produce mix and the icebreaker were considered separately because they do not have a specific sector.
The downtime survey is presented by sector in the Pareto Diagram in Figure 5, allowing us to identify where the most machine downtime occurs.
A Pareto Diagram (Figure 7) was then drawn up to prioritize the line that will be the focus of the work, i.e., the one with the most downtime records.
The graph shows that the 100 g single line is the one with the longest downtime, accounting for 36.5% of the sector's total. In a general analysis, this means that this line accounts for 24.06% of the total downtime recorded in the period, as shown in Figure 8.
The single 100g line consists of seven machines in the filling sector, as seen in Figure 9.
For the analysis, the ideal would be to identify whether the machines show variations in downtime between them. However, as the line analyzed already corresponds to the percentage of improvement aimed by the study, all the machines in the sector were analyzed, regardless of their behavior. The graph in Figure 10 shows the distribution of total downtime in minutes by machine.
In this case, it can be seen that machine 3 has the highest downtime, and machines 1 and 2 have close values. On average, each machine has a total of 88,762.72 minutes of downtime. Considering the number of days each machine is down, this means an average of 3 hours and 42 minutes per day.
The data collected over the period was grouped by month and plotted on a control chart (Figure 11) to analyze the stability of the downtime.
The graph considered the harvest and off-season periods in 2018 and 2019.
Analyzing the two harvests and the two off-seasons separately, the average downtime per month is 32,831.63 and 32,607.82 minutes, respectively. These figures are very close, given that they are completely different periods in terms of the number of orders and production volume. This creates the false impression that the machines are being used more during the harvest period when, in fact, the average idle time is close to that of the off-season.
First, brainstorming was carried out to identify the possible causes, according to material, method, machine, and workforce.
When a machine stops, the employee records the downtime and specifies the reason. It is worth noting that a stoppage may not only be related to mechanical and/or electrical faults or defects. Various factors can influence this interruption, especially those related to the process. The various reasons recorded were grouped and/or consolidated for better analysis and are shown in the Ishikawa Diagram in Figure 12.
Figures 13, 15, and 17 show the Pareto Charts for prioritizing the reasons for downtime in general, in the harvest, and in the off-season, respectively.
A multiple regression analysis was carried out to validate the data and check whether the variables really do affect the problem. According to Figures 14, 16, and 18, the analysis was carried out for the five priority causes found in Figures 13, 15, and 17, and the —H0 (there is no relationship) — and alternative — H1 (there is a relationship) — hypotheses were defined.
The reasons that have an influence on the periods analyzed are those highlighted in gray since the F significance value and the P value are lower than 0.05. Therefore, the null hypothesis (that no relationship exists) should be rejected with a 95% confidence level.
The same analysis will be made for the harvest and off-season graphs in Figures 15 to 18.
When only the harvest period is analyzed (Figure 15), the graph shows that the five main causes of downtime are lack of product in the process area (31.39%), change of laminates and coils (24.11%), breakage due to mechanical failure (12.07%), lack of production order (7.87%), and cleaning before production (7.75%). Together, they accounted for 83.19% of the downtime in the period.
The regression analysis shows that only the lack of product in the process area, the change of laminates and coils, and breakage due to mechanical failure have an influence.
When the off season period is examined separately (Figure 17), the graph shows that the five main causes of downtime are lack of a production order (52.70%), lack of product in the process area (18.44%), cleaning before production (7.30), changing laminates and coils (4.35%), and breakage due to mechanical failure (3.89%). Together they account for 86.68% of the downtime in the period.
In summary, the diagram in Figure 19 shows the analysis discussed above, in which the single 100 g line is responsible for 24.06% of the total downtime. Of this, 42.27% (262,640 min) corresponds to the harvest period, and 57.73% (358,699 min) corresponds to the off-season.
To minimize and/or eliminate the five problems highlighted, action plans were drawn up for each one.
Given the data collected and the identification of the problems highlighted, action plans were developed for lack of production order (Chart 2), change of laminates and coils (Chart 3), cleaning before production (Chart 4), breakage due to mechanical failure (Chart 5), and lack of product in the process area (Chart 6). Current and future VSMs were also made for this problem, as shown in Figures 20 and 21.
This study's overall goal was to propose improvements in the açaí pulp beneficiation process using Lean Six Sigma principles and methods. The main reasons for downtime were identified, and an action plan drawn up, considering its application in the short, medium, and long term, with a view to implementing it for the following harvest. Therefore, the work's objective was achieved. Thus, knowledge was gained not only of the reasons that most influence stoppages on the line analyzed, which are possibly the same as those that affect other lines, but also of the time that each one represents in the total number of stoppages recorded over the months analyzed.
This was the company's first project focused on improvement using statistical tools. It should be noted that it was only possible to carry it out on the basis of the records collected and after implementing a system of indicators that allows information to be entered in real time on both productivity and machine and equipment idleness.
However, although the company records information daily, it does not use data control and management methods such as statistical processing, but rather uses forms. On the other hand, major factors limited the research: the lack of incentive from top management to carry out projects to improve processes in line with customer requirements, the lack of a qualified team to process the information, the application of tests to prove improvements and the organizational culture of centralized decision-making.
It is thus worth highlighting the importance of carrying out work that statistically proves the real effectiveness of the proposed actions. As a suggestion for future work, the real impact on reducing downtime could be demonstrated quantitatively through experimental tests. Another suggestion is a project aimed at implementing a data control or measurement system that provides the company with plausible results from its processes, serving as a basis for decision-making.
A culture of quality and process improvement should be encouraged and seen as a key to success, making the company more professional and competitive. By using the lean method combined with the Six Sigma tools, it can develop projects that add value to it and its stakeholders, in addition to identifying points of inefficiency in the process and eliminating them.
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Received: July 14, 2023
Approved: November 20, 2023
DOI: 10.20985/1980-5160.2023.v18n3.1890
How to cite: Carneiro, M., Alves, T., Monteiro, M.S., Ueoka, N.A., Silva, G.M. (2023). Proposing improvements in the açaí pulp processing process: an application of Lean Six Sigma. Revista S&G 18, 3. https://revistasg.emnuvens.com.br/sg/article/view/1890