Headnote
ABSTRACT
Objective: The objective of this study is to investigate the design of a statistical control model for the panela manufacturing process in the trapiches of San Marcos - Sucre, with the aim of improving production processes and product quality. This will help reduce variability in manufacturing and ensure a higher-quality final product.
Theoretical Framework: This section presents the fundamental concepts and theories supporting the research on statistical quality control and panela manufacturing in trapiches, providing the necessary information for understanding this study.
Method: The research was exploratory, descriptive, and applied, with a mixed approach. The studied population consisted of 17 trapiches in San Marcos, Sucre. Data were collected through surveys, interviews, direct observation, and collection sheets, allowing for an analysis of panela production and process control.
Results and Discussion: The results indicate that the artisanal panela production process is out of control due to high temperature variability. Factors such as bagasse quality, operator skill, lack of precise thermometers, and environmental conditions affect heat uniformity. This negatively impacts the texture, color, and consistency of the product, reducing its quality and market acceptance.
Research Implications: The implications of statistical quality control are analyzed, providing insights into how the results can contribute to process improvement, highlighting their impact on efficiency and competitiveness in the panela sector.
Originality/Value: This study contributes to the literature by applying statistical quality control to artisanal panela production, enhancing the economy in San Marcos, Sucre.
Keywords: Quality Control, Process Improvement, Artisanal Production, Variability.
RESUMO
Objetivo: O objetivo deste estudo é investigar o design de um modelo de controle estatístico para o processo de fabricação de rapadura nos engenhos de San Marcos - Sucre, com o propósito de melhorar os processos de produção e a qualidade do produto. Isso permitirá reduzir a variabilidade na fabricação e garantir um produto final de maior qualidade.
Marco Teórico: São apresentados os conceitos e teorias fundamentais que sustentam a pesquisa sobre controle estatístico da qualidade e fabricação de rapadura nos engenhos, fornecendo as informações necessárias para a compreensão deste estudo.
Método: A pesquisa foi de caráter exploratório, descritivo e aplicado, com uma abordagem mista. A população estudada foi composta por 17 engenhos de San Marcos, Sucre. Os dados foram coletados por meio de questionários, entrevistas, observação direta e fichas de coleta, permitindo a análise da produção de rapadura e o controle dos processos.
Resultados e Discussão: Os resultados indicam que o processo artesanal de produção de rapadura está fora de controle devido à alta variabilidade da temperatura. Fatores como a qualidade do bagaço, a habilidade do operador, a falta de termômetros precisos e as condições ambientais afetam a uniformidade do calor. Isso impacta negativamente a textura, a cor e a consistência do produto, reduzindo sua qualidade e aceitação no mercado.
Implicações da Pesquisa: São analisadas as implicações do controle estatístico da qualidade, fornecendo informações sobre como os resultados podem contribuir para a melhoria dos processos, destacando seu impacto na eficiência e competitividade do setor de rapadura.
Originalidade/Valor: Este estudo contribui para a literatura ao aplicar o controle estatístico da qualidade à produção artesanal de rapadura, promovendo o desenvolvimento econômico em San Marcos, Sucre.
Palavras-chave: Controle de qualidade, Melhoria de processos, Produção artesanal, Variabilidade.
RESUMEN
Objetivo: El objetivo de este estudio es investigar el diseño de un modelo de control estadístico para el proceso de fabricación de panela en los trapiches de San Marcos - Sucre, con el propósito de mejorar los procesos de producción y la calidad del producto. Esto permitirá reducir la variabilidad en la fabricación y garantizar un producto final de mayor calidad.
Marco Teórico: Se exponen los conceptos y teorías fundamentales que respaldan la investigación sobre el control estadístico de la calidad y la fabricación de panela en los trapiches, proporcionando la información necesaria para la comprensión de esta investigación.
Método: La investigación fue de tipo exploratoria, descriptiva y aplicada, con un enfoque mixto. La población estudiada estuvo conformada por 17 trapiches de San Marcos, Sucre. Se recopilaron datos mediante encuestas, entrevistas, observación directa y hojas de recolección, permitiendo analizar la producción de panela y el control de procesos.
Resultados y Discusión: Los resultados indican que el proceso artesanal de producción de panela está fuera de control debido a la alta variabilidad en la temperatura. Factores como la calidad del bagazo, la habilidad del operario, la falta de termómetros precisos y las condiciones ambientales afectan la uniformidad del calor. Esto impacta negativamente la textura, color y consistencia del producto, reduciendo su calidad y aceptación en el mercado.
Implicaciones de la Investigación: Se analizan las implicaciones del control estadístico de la calidad, se proporciona información sobre cómo los resultados pueden contribuir a la mejora de los procesos, destacando su impacto en la eficiencia y competitividad del sector panelero.
Originalidad/Valor: Este estudio aporta a la literatura al aplicar el control estadístico de la calidad a la producción artesanal de panela, mejorando la economía en San Marcos, sucre.
Palabras clave: Control de Calidad, Mejora de Procesos, Produccion Artesanal, Variabilidad.
1 INTRODUCTION
Panela is a natural sweetener widely consumed in Latin America, especially in Colombia, where according to the , Colombia is the second largest producer of panela in the world, only behind India, and represents 16% of world production, in addition to being the country with the highest per capita consumption of this sweetener. In addition, its production represents a source of employment and livelihood for thousands of families in rural areas of the country, becoming a fundamental axis of the peasant economy (Farfán et al.. 2015).
In Colombia, panela production is distributed across several regions, in departments such as Cundinamarca and Antioquia, both with 17% of national production, followed by Santander with 10% and Boyacá with 8%. These four departments together represent 52% of the country's total production. In addition, other departments with a significant share in panela production are Cauca and Nariño, each with 7%, and Tolima with 6%. This distribution reflects the importance of these regions in the panela production chain at a national level .
A field survey conducted in July 2024 revealed that panela production in the study area has a low technological level. Structural problems were identified, such as the artisanal and empirical nature of the processes, lack of maintenance in machinery, deficiencies in industrial hygiene and safety, and a negative environmental impact due to inefficient waste management. These conditions affect the quality of the product, as well as the productivity and competitiveness of producers. In the sugar mills of San Marcos, Sucre, where this research was carried out, producers face great difficulties in maintaining efficient control at different stages of the production process. The absence of advanced technologies and adequate monitoring systems prevents ensuring product uniformity, which generates variations in the process. This lack of control directly impacts the profitability of the business and the ability of producers to compete with other regions of the country that have more technological processes.
Given this situation, the objective of this study was to design a statistical control model to improve the production processes in the sugar mills of San Marcos - Sucre, in order to reduce variability in manufacturing and improve the quality of the final product, based on Statistical Process Control (SPC) tools, which allow monitoring and maintaining product quality within the desired standards. In addition, the study proposes complementary strategies for the improvement of the sector, including the strengthening of infrastructure and the training of operators in quality control techniques.
The importance of this study lies in the possibility of generating a positive impact on the panela industry in the municipality, providing practical tools to improve production and guarantee a higher quality product. Through the application of statistical process control, the aim is not only to improve efficiency in panela production, but also to lay the foundations for the modernization of the sector, promoting its sustainability and growth over time .
2 THEORETICAL FRAMEWORK
Physicochemical characteristics of panela: Panela is a product derived from sugar cane, which can be used as food in its natural form, also, as a sweetener, it can be dissolved as a drink "Panela water", also, it is used as an ingredient for desserts and sauces. It is a highly beneficial product since its production process is different from normal refined sugars, that is, it does not have additional sugars, so it maintains its natural flavor, thus maintaining all its nutrients including essential minerals and vitamins. Panela is commonly sold in solid blocks or sometimes in powder, which leads to making an economical product, thus being affordable for consumers . Panela is an unrefined food product, obtained by the evaporation and crystallization of sugar cane juice. It is characterized by its light brown color, sweet flavor and solid texture. Its chemical composition varies according to the variety of sugar cane, the climate and the manufacturing process .
Importance of panela production in Colombia: Initially, to produce panela, the entire process of cutting and transporting the sugar cane takes place, where the cane is cut when it reaches its point of maturity, this point is generally reached between 12 and 30 months, that depends on the area and its location above sea level, a timely cut must be made to guarantee the effective proportion between reducing sugars and pH. The sugar cane is generally transported to the mills in tractors, horses and trucks. Next, it goes to the milling or extraction, where the juice of mature sugar cane is extracted, which must have a high content of saccharose and be free of impurities. This juice is subjected to intense milling, which in addition to being useful for combustion in the boiler or for sale in the production of alternative paper, is beneficial for the plants. Then, the juice is heated at high temperatures, which allows obtaining the clarified juice, which is the first purification of the liquid. Subsequently, to achieve the desired consistency, the juice goes through a series of evaporation processes in the evaporators, until it becomes raw molasses, which has a dense texture and a darker color .
Importance of Statistical Process Control in Production Improvement (SPC): It helps maintain stability throughout the process and allows detecting possible irregularities. SPC, works as a valuable tool to identify and find solutions to production problems, ensuring product quality, improving efficiency and minimizing waste, usually classifies variations into two categories: Common causes and special causes, variations created by common causes are predictable and can be solved more easily, for example, by equipment maintenance or staff training. On the other hand, variations generated by special causes are unpredictable and often need external assistance, as in the case of a server outage. When applied correctly, Statistical Process Control has the ability to significantly improve manufacturing procedures, thus reducing waste generation, promoting greater productivity and creating constant improvement
Control charts: Control charts are a key tool within CEP. These charts allow you to visualize how the characteristics of a process vary over time, establishing upper and lower limits to determine if the process is under control. The most common charts are averages (X) and ranges (R), which allow you to analyze the mean and dispersion of the data. Their use in panela production will allow you to standardize the process and ensure that the products meet the established quality standards .
3 METHODOLOGY
The target population of the study comprised the 26 sugar mills located in San Marcos - Sucre. For the sample, a representative portion of this population was selected, maintaining the diversity and relevance of the context. This research was exploratory, descriptive and applied, with a mixed approach. In its first exploratory stage, theoretical information was collected on the available statistical models and those that could be relevant to address the proposed research problem, taking into account different applications carried out in similar research. Likewise, it was descriptive because the process of identifying the different factors or indicators that affected the production process was carried out, considering qualitative and quantitative variables, and the variables directly related to the quality and improvement of panela were defined. Finally, it was applied, since this research focused on solving a specific problem, helping to find concrete solutions that were applied .
This research focused on designing a statistical process control model to monitor and optimize panela production, ensuring consistency in product quality. A series of structured steps were followed that allowed identifying and eliminating variations out of control, improving process affinity and stability.
In these mills, panela production is an artisanal process, it is subject to high variability, especially because sugarcane bagasse is used as a source of energy to generate candle, which significantly influences the final quality of the product. Therefore, the control and monitoring of this variable becomes crucial to identify its causes and make informed decisions. During the analysis, four critical stages of the production process were taken into account: 1. Pre-cleaning, where the guarapo is filtered to eliminate impurities; 2. From guarapo to honey, in which the guarapo 1$ concentrated by evaporation; 3. From honey to melcocha, stage in which the adequate consistency for the formation of panela is reached; and 4. From melcocha to panela, where the final product is molded and solidified .
Control charts and measurements were performed independently for each of these stages. This separation was necessary due to significant differences in the operating conditions of each stage, such as required temperature, cycle times, and specific process parameters. Performing a single control chart for all stages would have generated inaccurate results, as the combined values would have indicated that the process was out of control, even though each stage met its specific parameters. Working with independent control charts allows for accurate monitoring. Each stage presents unique dynamics that affect the final quality of the product, and only through separate analysis is it possible to identify and correct possible deviations at each critical point in the process. This ensures a better understanding of the system's behavior, thus improving the efficiency and quality of the final product. The activities performed to achieve this goal are detailed below.
To define the control limits, the corresponding formulas were applied to calculate the range and standard deviation, adjusting the values according to specific factors of the X-R and X-S charts, taking into account the construction factors of the control chart, which depend on the sample size and are documented in Annex 3. Below are the equations used to calculate the limits in each of these charts defined by .
For the X - R graph,
... (1)
... (2)
... (3)
Where:
X: Es la media de las medias de la muestra.
R:Es el promedio de los rangos de la muestra.
A7: es un factor específico que depende del tamaño de la muestra.
For the X - $ graph
... (4)
... (5)
... (6)
X: Es la media de las medias de la muestra.
S: Representa el promedio de las desviaciones estándar de las muestras.
Az: es un factor específico que depende del tamaño de la muestra.
4 RESULTS AND DISCUSSIONS
This section presents the results, which seek to develop a statistical control model for the panela production process, with the purpose of maintaining and improving product quality. To achieve this objective, the data collection began, focusing on temperature measurement, a fundamental quality characteristic in the process.
For data collection, eight samples were taken per stage, each with 15 observations, completing a total of 120 data per stage. These observations were recorded during the cycle time corresponding to each stage, ensuring representativeness in the process conditions. Subsequently, calculations were performed to determine the control limits using formulas 1, 2 and 3 for the XR charts, and equations 4, 5 and 6 for the XS charts. The results obtained for each of the stages are described below, highlighting the calculated control limits and their interpretation based on the process conditions.
a) Stage 1: Pre-cleaning
Calculation of control limits for the XR chart
... (7)
... (8)
... (9)
The XR control chart for the pre-cleaning stage shows high variability over time, although most measurement points are within the control limits. While the process appears to be under statistical control, the high variability reflects significant fluctuations, possibly due to the manual nature of the process and the lack of precise temperature regulation equipment, common in artisanal production.
Calculation of control limits for the XS chart
... (10)
... (11)
... (12)
When analyzing the graph, one can notice considerable variability in the temperature measurements. Most of the data is within the control limits, indicating that the process is generally under control. However, there are times when the temperature approaches or touches the upper and lower limits, which may represent risks of the process going out of control if adjustment actions are not taken. Therefore, the oscillating behavior and the amplitude of temperature variation could indicate a lack of consistency in the process, perhaps due to external factors such as changes in the bagasse used to generate the candle, variations in the size of the cane charge or manual adjustments to the heat.
b) Stage 2: From guarapo to honey
Calculation of control limits for the XR chart
... (13)
...(14)
... (15 )
The XR control chart of the transformation stage of guarapo into honey in the mills of San Marcos, Sucre, shows a notable variability in temperature throughout the process, characteristic of artisanal production. Although the data remain mostly within the control limits, the dispersion of temperatures reflects fluctuations, possibly caused by the lack of precise regulation systems and by the variability inherent to manual operation.
Calculation of control limits for the XS chart
The XS control chart for the guarapo to honey transformation process reveals considerable variability in the quality characteristic. Although the central line remains relatively stable, the data points are widely dispersed above and below this line, sometimes reaching the upper and lower control limits. This high variability suggests that the process is not under statistical control, indicating the presence of special or assignable factors that influence the quality of the final product.
c) Stage 3: From honey to mercocha
Calculation of control limits for the XR chart
... (16)
... (17)
... (18)
This XR control chart shows the behavior of the temperature variable during the stage, From honey to taffy. It is observed that the temperature measurements fluctuate significantly, leaving the upper limit on several occasions, however, deviations suggest that the process presents variability that exceeds acceptable limits.
Calculation of control limits for the XS chart
... (22)
... (23)
... (24)
In this graph, freguent departures from the ULC may indicate problems related to inadeguate control of the thermal process, fluctuations in heat supply or lack of homogeneity in the handling of the cane juice. Drops towards values close to the LLC may be related to interruptions in heating during the stage.
d) Stage 4: From mercocha to panela
Calculation of control limits for the XR chart
... (25)
... (26)
... (27)
This XR control chart corresponds to stage 4, it shows that the temperature measurements present notable oscillations, with several observations that exceed both the upper and lower limits, this behavior indicates that the process is not under statistical control, since there are several fluctuations outside the limits.
Calculation of control limits for the XS chart
... (28)
... (29)
... (30)
After constructing the XR and XS control charts and the analysis carried out at each stage of the artisanal panela production process, it was determined that the process is out of control, mainly due to the high variability in temperature during the process.
This variability is due to multiple factors, such as the lack of precise regulation of heat sources, since in this case sugarcane bagasse is used as firewood to generate heat, with heat intensities that depend on the quality of the bagasse and the operator's skill to maintain constant combustion, the absence of reliable thermometers or their incorrect calibration, which generates inaccuracies in the measurement of this critical parameter, also, changes in environmental conditions, such as variations in wind and humidity, which directly affect the intensity and uniformity of the heat applied, the inadequate design or wear of the kettles, which can cause an uneven distribution of heat in the cane juice .
On the other hand, there is a lack of technical training for operators to identify and adjust temperature fluctuations in a timely manner. These conditions are aggravated by the artisanal nature of the process, where there are no automatic regulation systems to ensure the stability of the variables. As a consequence, variations in temperature negatively affect the characteristics of the final product, such as texture, colour and consistency, reducing its quality and market acceptance.
5 CONCLUSION
Regarding the statistical control model, it was designed based on XR and XS control charts, which proved to be effective tools for monitoring the panela production process. The application of these charts allowed the identification and analysis of variability in critical stages of the process, providing results aligned with the stated objectives. The incorporation of this model is essential for the continuous monitoring of the process, since it facilitates the detection of deviations, informed decision-making and the implementation of timely corrective actions. It also contributes to continuous improvement by generating objective data that allow optimizing product quality and reducing costs associated with lack of control.
Based on the results obtained, it is concluded that the implementation of a statistical control model in the mills represents a viable and necessary strategy to improve production processes. Although artisanal conditions can limit precision, the implementation of strategies aimed at optimizing production processes has also generated substantial improvements in the organization and execution of activities within the mills. The application of the statistical control model facilitates the identification of critical points in the process and allows informed decision-making to reduce waste, improve product quality and increase operational efficiency. In addition, the results obtained in this research lay the foundation for future initiatives that promote the technical and organizational development of the mills, benefiting both producers and consumers through a more consistent, efficient and high-quality panela production.
Finally, it is highlighted that the San Marcos sugar mills operate under a traditional production model that, while representing a key element of the region's cultural heritage, faces significant challenges in terms of quality, efficiency and sustainability. This study made it possible to identify and address these deficiencies through the characterization, standardization and development of a statistical control model, a fundamental tool for monitoring the production process over time, detecting variations and ensuring more stable and predictable production.
References
REFERENCES
Albert, S. (30 de Enero de 2013). ¿Que es panela? . Fonte: ¿Que es panela? https://www.verema.com/blog/productos-gastronomicos/104923 1-que-panela Variabilidad de procesos: donde la encontramos y cómo reducirla: https://www.atlasconsultora.com/como-controlar-la-variabilidad/#google vignette
Contreras, J. (2022). Variabilidad de procesos: dónde la encontramos y cómo reducirla. Fonte: https://www.atlasconsultora.com/como-controlar-la-variabilidad/#google vignette
Cristancho, A. L. (27 de Junio de 2012). Proceso de produccion de la panela. Fonte: Proceso de produccion de la panela: //es.slideshare.net/slideshow/proceso-de-produccion-de-lapanela-13475336/13475336
Duarte, M. A. (2010). Gestion tecnologica en el sector de la produccion de panela. Bogota: (Tesis. Universidad de Javeriana}. Fonte: https://repository.javeriana.edu.co/bitstream/handle/10554/9230/tesis318.pdf;jsessionid=
Echeverri, S. A. (2014). Estandarizacion de los procesos mediante la aplicacion del modelo Toyota a la produccion de panela la "Reina". Pereira : {Tesis. Universidad Tecnologica de Pereira}.
FAO. (2007). La Organización de las Naciones Unidas para la Agricultura y la Alimentación. .
Farfán, K., Vega, I., Guerrero, S., Morales, W., & Sánchez, К. (2015). Diseño de una línea de producción de ¡panela granulada. Universidad de Piura. Fonte: https://pirhua.udep.edu.pe/bitstream/handle/11042/2344/4. PYT%20%20%20%20Inform
GOV. (07 de Mayo de 2021). Portal Unico del Estado Colombiano. Fonte: Portal Unico del Estado Colombiano: https://cvn.com.co/la-panela-colombiana/
Group, S. (06 de Febrero de 2013). Graficos de control de calidad . Fonte: Graficos de control de calidad : https://spcgroup.com.mx/grafica-de-control/
Karl, L. (13 de Junio de 2023). Control estdistico de procesos - guia del fabricante. Fonte: Control estdistico de procesos - guia del fabricante: https://www.mrpeasy.com/blog/es/control-estadistico-deprocesos/#:~:text=E1%20Control%20Estad%C3%ADstic0%20de%20Procesos%20es%2
Sanchez, O. E. (2022). Modelo de Control Estadistico en el Proceso de Fabricacion de panela . Pereira: {Tesis. Universidad Tecnológica de Pereira}.
Shewhart, W. (1992). Gurus de la calidad. Fonte: https://blogdelacalidad.com/gurus-de-lacalidad-walter-shewhart/
Y.P, L.F, & J.A. (2016). Caracterización fisicoquímica y sensorial de panela granulada . Revista de Investigación e Innovación , pp. 111-122.
