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Full Research Article

Implementation of Supply Chain Performance Analysis: A Case of Sugar Factory in Indonesia

I
Ida Bagus Suryaningrat1,*
B
Bambang Herry Purnomo1
S
Shofiyan Tito Abadi1
M
Miftahul Choiron1
N
Nidya Shara Mahardika1
H
H.S. Shinta Syafrina Endah1
1Study Program of Agroindustrial Technology, Faculty of Agricultural Technology, University of Jember, Jln. Kalimantan 37, Jember, Indonesia.

ABSTRACT

Background: Sugar factories in Indonesia need to measure their supply chain performance due to several significant problems, including obstruction of sugarcane supplies, substandard quality of sugarcane supplies and excessive soil transportation during sugarcane delivery. The purpose of this research is to identify the activities, measure the performance level and provide improvement recommendations to increase the supply chain performance.

Methods: The method used in this research is the supply chain operation reference (SCOR) 14. The two main parts of SCOR 14, Processes and Performance, can be used to identify the activities and measure the performance level. Processes are used as work processes standard in identifying the activities, while Performance is used as performance indicators standard in measuring the performance level.

Result: The results from identifying the activities include several activities, such as delivery of sugarcane, processing of sugarcane into sugar, storing of sugar, purchasing of sugar and distributing of sugar. The results from measuring the performance level are included in the excellent category with a value of 91,050. Improvement recommendations include improving the coordination of harvest schedule, mechanizing the sugarcane harvesting process, enhancing the quality of sugarcane cultivation and increasing the minimum requirements for sugarcane yield.

INTRODUCTION

Supply chains play a vital role in the modern economy as a link between suppliers, producers, distributors and consumers in a complex network. The resilience and efficiency of the supply chain can affect product availability, costs and customer satisfaction. The supply chain competency also has a positive impact on those aspects (Sinha and Mishra, 2023). However, external factors such as the COVID-19 pandemic has also severely impacted the global food supply chain and its impact is devastating across countries (Sharma and Sinha, 2020). A deep understanding of the elements and interactions in the supply chain is essential for companies to gain a competitive advantage in business sustainability. Therefore, research and innovation in supply chain optimization continue to develop to address existing problems. One area of   research and innovation in supply chain optimization that can be done is by measuring supply chain performance. Supply chain performance measurement is the process of measuring the effectiveness and efficiency of activities or strategies used in the supply chain (Arif Uz-Zaman and Ahsan, 2014). Supply chain performance measurement plays a fairly important role for companies in the manufacturing industry because it can help ensure that every performance target previously set by the company has been achieved (Ilmiyati and Munawaroh, 2016). All of this is done to manage risks, which is very important to do so as to minimize disruptions in the supply chain (Puryantoro et al., 2025).
       
Sugar factories in Indonesia which are one of the manufacturing industries, produce sugar from sugarcane because it is one of the main sugar crops in the world (Hazarika and Phukon, 2025). Sugar is a very important commodity in Indonesia because many processed food products use sugar as an ingredient (Kurniawati, 2017). Measuring the performance of the supply chain at the sugar factory in Indonesia is important because there are several significant problems. The problems are obstruction of sugarcane supplies caused by rain which can stop the delivery of sugarcane from the plantation to the factory. This is a fairly important problem because the cessation of sugarcane supplies can cause the entire production process to stop. In addition, another problem is that several sugarcane supplies have substandard quality and a lot of soil is transported with them. The substandard quality is caused by the low awareness and consistency of farmers in managing their sugarcane plantations (Anriza, 2018). A poor harvesting process for sugarcane can also result in low-quality sugarcane produced (Chauhan et al., 2024). The substandard quality and a lot of soil can hamper the sugar production process. This is supported by the results of previous studies which state that the decline in sugar production can be caused by a decrease in the area of   sugarcane land, rainfall and yield levels (Apriawan and Mulyo, 2015). It shows that the partnership process between partner farmers and factory is still not running optimally and it’s still a common thing because partnership is one of the most frequently debated topics in SCM literature (Mappigau et al., 2025). Preventive actions taken by the company regarding these problems include holding socialization through the Regional Partnership Meeting Forum with partner farmers to improve the quality of the sugarcane produced. However, supply chain performance measurement using SCOR 14 which is the latest version of the method has never been conducted at the sugar factories in Indonesia.
       
The purpose of this research is to identify the supply chain activities, measure the supply chain performance level and provide improvement recommendations to increase the supply chain performance at the sugar factories in Indonesia.

MATERIALS AND METHODS

The site chosen for this research was the Prajekan Sugar Factory in East Java, Indonesia. This location was chosen because it is the largest sugarcane cultivation area and has a large capacity, making it representative of other sugar factories in Indonesia (Artikanur et al., 2024).
       
The tools used in this study are work process standards and performance indicators based on SCOR 14, questionnaires and the SuperDecision application. The work process standards used are Processes Level 1 and Level 2 SCOR 14. These standards are used as references in determining supply chain models and activities. The performance indicator standards used are the Performance Level 2 SCOR 14 metrics (ASCM, 2022). These performance indicator standards are used as KPIs (Key Performance Indicators) in measuring supply chain performance. Questionnaires are used to obtain primary data from experts or stakeholders who have been selected. The questionnaires consist of three types such as interview questionnaire, pairwise comparison questionnaire and actual data questionnaire. The SuperDecision application is used to calculate KPI weight values   based on the results of the pairwise comparison questionnaire.
       
The materials used in this study consist of primary data and secondary data. Primary data were obtained from the results of questionnaires such as information on supply chain models and activities based on the results of interview questionnaires, the importance level between KPIs based on the results of the pairwise comparison questionnaire and actual performance achievement data based on the results of the actual data questionnaire. Secondary data were obtained from previous research literature studies related to data collection and analysis methods. The research procedure was carried out through several stages (Fig 1).

Fig 1: The research procedure.


       
The data collection methods used in this study were questionnaires and interviews. Questionnaires and interviews in this study were used to obtain information related to the supply chain model and activities, the importance level of KPIs and actual performance achievement data. The respondents selected were stakeholders in the sugar factories in Indonesia who understood the entire supply chain flow, understood the performance indicators used and knew the actual performance achievement data obtained. Supply chain information of models and activities and the importance level between KPIs, were obtained from stakeholders through an interview questionnaire and a pairwise comparison questionnaire, while actual performance achievement data was obtained from departments or sections that had the data through an actual data questionnaire.
       
The data analysis methods used in this study involve carrying out several stages such as Identification and Determination of KPIs, Calculation of KPI Weight Values, Calculation of Actual Performance Achievements Data and Calculation of Final Supply Chain Performance Values. Identification and Determination of KPIs used in measuring supply chain performance is the Performance Level 2 SCOR 14 metrics which are validated by stakeholders so that the performance indicators used are by the company’s conditions and needs. The calculation of the KPI weight value using the SuperDecision application is carried out based on the results of the importance level determination. Determination of the importance level is done by using Saaty’s comparative assessment scale from 1 to 9 (Mandi et al., 2025) (Table 1). The comparative assessment is carried out on each KPI with other KPIs to determine the relationship between the importance level of KPIs. The calculation of actual performance achievement data is done by using data that has been obtained using the actual data questionnaire. Snorm De Boer normalization is a calculation using formulas to equalize the parameters to obtain the same scale of size and weight of the actual performance achievement data of each KPI (Sriwana et al., 2021) (Table 2). Calculation of final supply chain performance value is done by calculating the multiplication of the weight value of each KPI with the actual KPI value resulting from Snorm De Boer normalization. The final value is then monitored using performance indicators (Table 3).

Table 1: Scales in pairwise comparison.



Table 2: Snorm de boer normalization formula.



Table 3: Supply chain performance value standards.

RESULTS AND DISCUSSION

The results obtained from the collection and calculation of data based on the research procedures (Table 4).

Table 4: Data collection and calculation results.


       
The supply chain model and activities of the sugar production process at the sugar factories in Indonesia are identified using Processes Level 1 and Level 2 SCOR 14, which consist of Orchestrate, Plan, Order, Source, Transform, Fulfill and Return. The supply chain model is identified using the Orchestrate work process which orchestrates the entire supply chain process. One of the activities in Orchestrate is Network Design, which describes the location of facilities and resources, distribution networks, suppliers, customers, materials, products, capacities and capabilities to those locations. The implementation of Network Design activities at the sugar factories in Indonesia involves determining the supply chain model to be used (Fig 2). The supply chain explains the distribution network of materials, finance and information between suppliers, producers, distributors and consumers (Khaqim, 2024). Supply chain activities are identified using the Plan, Order, Source, Transform, Fulfill and Return work processes. These work processes are level 1 work processes in SCOR 14. Each level 1 work process has a level 2 work process to identify supply chain activities more specifically. The supply chain activities in sugar factory from those work process such as delivery of sugarcane, processing of sugarcane into sugar, storing of sugar, purchasing of sugar and distributing of sugar.

Fig 2: Supply chain model of sugarcane.


       
Identification and determination of KPIs used in measuring supply chain performance at the sugar factory is by using the Performance Level 2 SCOR 14 metrics which are validated by stakeholders. Validation is carried out by determining KPIs that follow the company’s conditions, have actual performance achievement data and can be used to measure supply chain performance. The results of the validation of the Performance Level 2 SCOR 14 metrics (Table 4) have 16 key performance indicators.
       

The calculation of KPI weight value using the Super Decision application is carried out based on the results of the importance level determination. Determination of the importance level is done by using Saaty’s comparative assessment scale from 1 to 9 (Mandi et al., 2025). The importance level represents the relationship between the importance level of a KPI and other KPIs.
       
The calculation of actual performance achievement data is done by using data that has been obtained using the actual data questionnaire. The calculation is using formula and justification scale. KPIs with numbers 1, 2, 3, 4, 5, 6, 11, 12, 13, 14, 15 and 16 are calculated using the formula calculation, while KPIs with numbers 7, 8, 9 and 10 are calculated using the scale justification. The use of the formula in calculating performance is because the data needed is actual data or data in the form of numbers, while the use of scale justification in calculating performance is because the data needed is an assumption or estimate from stakeholders. The formula and justification scale are used to obtain performance achievement results based on the KPI parameters used. The results of actual performance achievement data calculation of each KPI are then normalized using the Snorm De Boer (Table 4). Snorm De Boer normalization is a calculation to equalize the parameters of the actual performance value of each KPI. This is done to obtain the same scale of size and weight (Sriwana et al., 2021). Thus, the actual performance value of each KPI can be compared and measured. The scale of size and weight of Snorm De Boer normalization is 0 to 100. In the KPI normalization, there are several KPIs where the bigger the performance achievement value, the better the performance and there are several KPIs where the smaller the performance achievement value, the better the performance. The KPIs with the bigger performance achievement value, the better the performance, include the KPI with the numbers 1, 2, 3, 4, 7, 8, 9, 10, 11, 13 and 16. The KPIs with the smaller performance achievement value, the better the performance, include the KPI with the numbers 5, 6, 12, 14 and 15. The differences in the characteristics of the KPI need to be adjusted to the normalization formula used.
       
The calculation of final supply chain performance values is done by calculating the multiplication of the weight value of each KPI with the KPI value resulting from Snorm De Boer normalization. The results of the final supply chain performance obtained various values   from each KPI assessment (Table 4). The KPI with the highest value is direct material cost with an assessment result of 21.634, while the KPI with the lowest value is non-renewable energy consumed with an assessment result of 0.701. The total of the final supply chain performance value obtained from the sum of all the final values results above is 91.050. If this value is monitored using performance indicators (Table 2), it is included in the excellent category (Listiyono et al., 2024).
       
The final supply chain performance value at sugar factories in Indonesia, when compared with the results of supply chain performance measurements at sugar factories in other countries and at one of the sugar factories in Indonesia based on previous studies has different values. The results of supply chain performance measurements at sugar factory in other countries such as Thailand are 55,61 for manufactures, 80,97 for suppliers and 75,00 for customers (Sopadang and Wichaisri, 2017). The results of supply chain performance measurements at one of the sugar factories in Indonesia based on previous studies such as Madukismo Sugar Factory are 93.32 (Anindita et al., 2020). The difference of the results can be caused by the level of sugar factory performance and the selection of performance indicators used.
       
This research was conducted by addressing several weaknesses or deficiencies in the methodology or tools used in previous studies. First, this research uses the Performance Level 2 SCOR 14 metrics in determining performance indicators to address the weaknesses of previous research that do not use performance indicator standards. Second, this study does not use AHP (Analytical Hierarchy Process), while previous research uses it by creating a hierarchy between work processes and performance attributes in calculating supply chain performance. Third, the use of SCOR 14 is still rarely used in measuring supply chain performance.
       
Improvement recommendations are important for companies to improve the performance of the supply chain so they can compete and achieve the desired targets. The improvement recommendation or practical recommendation method is carried out by determining KPIs that have criteria for priority improvement and providing appropriate practical recommendations based on the cause of the problem from the KPI. The KPI criteria for priority improvement are KPIs that have the lowest normalized KPI value and are included in the marginal and poor categories or those with values   below 51. In addition, improvement priorities are also sorted from KPIs with the smallest assessment results to KPIs with the larger assessment results (Chotimah et al., 2018) (Table 5).

Table 5: Practical recommendation.


       
Improving the coordination related to the harvest schedule can help reduce excess sugarcane shipments to factories due to pressure from farmers. Mechanizing the the sugarcane harvesting process can help farmers to carry out the harvest process faster so that they can meet the needs of the sugarcane supply because post-harvest handling is a crucial phase in the food supply chain (Pundir et al., 2025). Improving the quality of sugarcane cultivation by partner farmers and increasing the minimum requirements for sugarcane yield to be accepted into the factory can help reduce the frequency of repairs to the refining station due to the buildup of non-sugar solids or impurities (Magfiroh and Wibowo, 2020).

CONCLUSION

The level of supply chain performance in the sugar production process at the sugar factories in Indonesia based on the sum of all final values results is included in the excellent category with a value of 91.050. Improvement recommendations or practical recommendations to maintain and improve the supply chain performance can be carried out based on previously identified activities. These recommendations for improvement consist of improving the coordination of harvest schedule, mechanizing the sugarcane harvesting process, enhancing the quality of sugarcane cultivation and increasing the minimum requirements for sugarcane yield to be accepted by the factory. Suggestions that can be given for further research are to develop the KPIs used in the Performance Level 3 SCOR 14 metrics so that supply chain performance evaluation can be carried out more completely.
 

ACKNOWLEDGEMENT

Sincere thanks are extended to all participants who contributed to this study. Special gratitude goes to the Prajekan Sugar Factory in East Java, Indonesia, PT. Sinergi Gula Nusantara and technical staff at the Laboratory of Industrial Systems Management and Engineering, Department of Agricultural Industrial Technology, Faculty of Agricultural Technology, University of Jember, for supporting facilities and their support in this study. We also thank the anonymous reviewers for their constructive feedback.

CONFLICT OF INTEREST

All authors declare that they have no conflicts of interest.

REFERENCES


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    Full Research Article

    Implementation of Supply Chain Performance Analysis: A Case of Sugar Factory in Indonesia

    I
    Ida Bagus Suryaningrat1,*
    B
    Bambang Herry Purnomo1
    S
    Shofiyan Tito Abadi1
    M
    Miftahul Choiron1
    N
    Nidya Shara Mahardika1
    H
    H.S. Shinta Syafrina Endah1
    1Study Program of Agroindustrial Technology, Faculty of Agricultural Technology, University of Jember, Jln. Kalimantan 37, Jember, Indonesia.

    ABSTRACT

    Background: Sugar factories in Indonesia need to measure their supply chain performance due to several significant problems, including obstruction of sugarcane supplies, substandard quality of sugarcane supplies and excessive soil transportation during sugarcane delivery. The purpose of this research is to identify the activities, measure the performance level and provide improvement recommendations to increase the supply chain performance.

    Methods: The method used in this research is the supply chain operation reference (SCOR) 14. The two main parts of SCOR 14, Processes and Performance, can be used to identify the activities and measure the performance level. Processes are used as work processes standard in identifying the activities, while Performance is used as performance indicators standard in measuring the performance level.

    Result: The results from identifying the activities include several activities, such as delivery of sugarcane, processing of sugarcane into sugar, storing of sugar, purchasing of sugar and distributing of sugar. The results from measuring the performance level are included in the excellent category with a value of 91,050. Improvement recommendations include improving the coordination of harvest schedule, mechanizing the sugarcane harvesting process, enhancing the quality of sugarcane cultivation and increasing the minimum requirements for sugarcane yield.

    INTRODUCTION

    Supply chains play a vital role in the modern economy as a link between suppliers, producers, distributors and consumers in a complex network. The resilience and efficiency of the supply chain can affect product availability, costs and customer satisfaction. The supply chain competency also has a positive impact on those aspects (Sinha and Mishra, 2023). However, external factors such as the COVID-19 pandemic has also severely impacted the global food supply chain and its impact is devastating across countries (Sharma and Sinha, 2020). A deep understanding of the elements and interactions in the supply chain is essential for companies to gain a competitive advantage in business sustainability. Therefore, research and innovation in supply chain optimization continue to develop to address existing problems. One area of   research and innovation in supply chain optimization that can be done is by measuring supply chain performance. Supply chain performance measurement is the process of measuring the effectiveness and efficiency of activities or strategies used in the supply chain (Arif Uz-Zaman and Ahsan, 2014). Supply chain performance measurement plays a fairly important role for companies in the manufacturing industry because it can help ensure that every performance target previously set by the company has been achieved (Ilmiyati and Munawaroh, 2016). All of this is done to manage risks, which is very important to do so as to minimize disruptions in the supply chain (Puryantoro et al., 2025).
           
    Sugar factories in Indonesia which are one of the manufacturing industries, produce sugar from sugarcane because it is one of the main sugar crops in the world (Hazarika and Phukon, 2025). Sugar is a very important commodity in Indonesia because many processed food products use sugar as an ingredient (Kurniawati, 2017). Measuring the performance of the supply chain at the sugar factory in Indonesia is important because there are several significant problems. The problems are obstruction of sugarcane supplies caused by rain which can stop the delivery of sugarcane from the plantation to the factory. This is a fairly important problem because the cessation of sugarcane supplies can cause the entire production process to stop. In addition, another problem is that several sugarcane supplies have substandard quality and a lot of soil is transported with them. The substandard quality is caused by the low awareness and consistency of farmers in managing their sugarcane plantations (Anriza, 2018). A poor harvesting process for sugarcane can also result in low-quality sugarcane produced (Chauhan et al., 2024). The substandard quality and a lot of soil can hamper the sugar production process. This is supported by the results of previous studies which state that the decline in sugar production can be caused by a decrease in the area of   sugarcane land, rainfall and yield levels (Apriawan and Mulyo, 2015). It shows that the partnership process between partner farmers and factory is still not running optimally and it’s still a common thing because partnership is one of the most frequently debated topics in SCM literature (Mappigau et al., 2025). Preventive actions taken by the company regarding these problems include holding socialization through the Regional Partnership Meeting Forum with partner farmers to improve the quality of the sugarcane produced. However, supply chain performance measurement using SCOR 14 which is the latest version of the method has never been conducted at the sugar factories in Indonesia.
           
    The purpose of this research is to identify the supply chain activities, measure the supply chain performance level and provide improvement recommendations to increase the supply chain performance at the sugar factories in Indonesia.

    MATERIALS AND METHODS

    The site chosen for this research was the Prajekan Sugar Factory in East Java, Indonesia. This location was chosen because it is the largest sugarcane cultivation area and has a large capacity, making it representative of other sugar factories in Indonesia (Artikanur et al., 2024).
           
    The tools used in this study are work process standards and performance indicators based on SCOR 14, questionnaires and the SuperDecision application. The work process standards used are Processes Level 1 and Level 2 SCOR 14. These standards are used as references in determining supply chain models and activities. The performance indicator standards used are the Performance Level 2 SCOR 14 metrics (ASCM, 2022). These performance indicator standards are used as KPIs (Key Performance Indicators) in measuring supply chain performance. Questionnaires are used to obtain primary data from experts or stakeholders who have been selected. The questionnaires consist of three types such as interview questionnaire, pairwise comparison questionnaire and actual data questionnaire. The SuperDecision application is used to calculate KPI weight values   based on the results of the pairwise comparison questionnaire.
           
    The materials used in this study consist of primary data and secondary data. Primary data were obtained from the results of questionnaires such as information on supply chain models and activities based on the results of interview questionnaires, the importance level between KPIs based on the results of the pairwise comparison questionnaire and actual performance achievement data based on the results of the actual data questionnaire. Secondary data were obtained from previous research literature studies related to data collection and analysis methods. The research procedure was carried out through several stages (Fig 1).

    Fig 1: The research procedure.


           
    The data collection methods used in this study were questionnaires and interviews. Questionnaires and interviews in this study were used to obtain information related to the supply chain model and activities, the importance level of KPIs and actual performance achievement data. The respondents selected were stakeholders in the sugar factories in Indonesia who understood the entire supply chain flow, understood the performance indicators used and knew the actual performance achievement data obtained. Supply chain information of models and activities and the importance level between KPIs, were obtained from stakeholders through an interview questionnaire and a pairwise comparison questionnaire, while actual performance achievement data was obtained from departments or sections that had the data through an actual data questionnaire.
           
    The data analysis methods used in this study involve carrying out several stages such as Identification and Determination of KPIs, Calculation of KPI Weight Values, Calculation of Actual Performance Achievements Data and Calculation of Final Supply Chain Performance Values. Identification and Determination of KPIs used in measuring supply chain performance is the Performance Level 2 SCOR 14 metrics which are validated by stakeholders so that the performance indicators used are by the company’s conditions and needs. The calculation of the KPI weight value using the SuperDecision application is carried out based on the results of the importance level determination. Determination of the importance level is done by using Saaty’s comparative assessment scale from 1 to 9 (Mandi et al., 2025) (Table 1). The comparative assessment is carried out on each KPI with other KPIs to determine the relationship between the importance level of KPIs. The calculation of actual performance achievement data is done by using data that has been obtained using the actual data questionnaire. Snorm De Boer normalization is a calculation using formulas to equalize the parameters to obtain the same scale of size and weight of the actual performance achievement data of each KPI (Sriwana et al., 2021) (Table 2). Calculation of final supply chain performance value is done by calculating the multiplication of the weight value of each KPI with the actual KPI value resulting from Snorm De Boer normalization. The final value is then monitored using performance indicators (Table 3).

    Table 1: Scales in pairwise comparison.



    Table 2: Snorm de boer normalization formula.



    Table 3: Supply chain performance value standards.

    RESULTS AND DISCUSSION

    The results obtained from the collection and calculation of data based on the research procedures (Table 4).

    Table 4: Data collection and calculation results.


           
    The supply chain model and activities of the sugar production process at the sugar factories in Indonesia are identified using Processes Level 1 and Level 2 SCOR 14, which consist of Orchestrate, Plan, Order, Source, Transform, Fulfill and Return. The supply chain model is identified using the Orchestrate work process which orchestrates the entire supply chain process. One of the activities in Orchestrate is Network Design, which describes the location of facilities and resources, distribution networks, suppliers, customers, materials, products, capacities and capabilities to those locations. The implementation of Network Design activities at the sugar factories in Indonesia involves determining the supply chain model to be used (Fig 2). The supply chain explains the distribution network of materials, finance and information between suppliers, producers, distributors and consumers (Khaqim, 2024). Supply chain activities are identified using the Plan, Order, Source, Transform, Fulfill and Return work processes. These work processes are level 1 work processes in SCOR 14. Each level 1 work process has a level 2 work process to identify supply chain activities more specifically. The supply chain activities in sugar factory from those work process such as delivery of sugarcane, processing of sugarcane into sugar, storing of sugar, purchasing of sugar and distributing of sugar.

    Fig 2: Supply chain model of sugarcane.


           
    Identification and determination of KPIs used in measuring supply chain performance at the sugar factory is by using the Performance Level 2 SCOR 14 metrics which are validated by stakeholders. Validation is carried out by determining KPIs that follow the company’s conditions, have actual performance achievement data and can be used to measure supply chain performance. The results of the validation of the Performance Level 2 SCOR 14 metrics (Table 4) have 16 key performance indicators.
           

    The calculation of KPI weight value using the Super Decision application is carried out based on the results of the importance level determination. Determination of the importance level is done by using Saaty’s comparative assessment scale from 1 to 9 (Mandi et al., 2025). The importance level represents the relationship between the importance level of a KPI and other KPIs.
           
    The calculation of actual performance achievement data is done by using data that has been obtained using the actual data questionnaire. The calculation is using formula and justification scale. KPIs with numbers 1, 2, 3, 4, 5, 6, 11, 12, 13, 14, 15 and 16 are calculated using the formula calculation, while KPIs with numbers 7, 8, 9 and 10 are calculated using the scale justification. The use of the formula in calculating performance is because the data needed is actual data or data in the form of numbers, while the use of scale justification in calculating performance is because the data needed is an assumption or estimate from stakeholders. The formula and justification scale are used to obtain performance achievement results based on the KPI parameters used. The results of actual performance achievement data calculation of each KPI are then normalized using the Snorm De Boer (Table 4). Snorm De Boer normalization is a calculation to equalize the parameters of the actual performance value of each KPI. This is done to obtain the same scale of size and weight (Sriwana et al., 2021). Thus, the actual performance value of each KPI can be compared and measured. The scale of size and weight of Snorm De Boer normalization is 0 to 100. In the KPI normalization, there are several KPIs where the bigger the performance achievement value, the better the performance and there are several KPIs where the smaller the performance achievement value, the better the performance. The KPIs with the bigger performance achievement value, the better the performance, include the KPI with the numbers 1, 2, 3, 4, 7, 8, 9, 10, 11, 13 and 16. The KPIs with the smaller performance achievement value, the better the performance, include the KPI with the numbers 5, 6, 12, 14 and 15. The differences in the characteristics of the KPI need to be adjusted to the normalization formula used.
           
    The calculation of final supply chain performance values is done by calculating the multiplication of the weight value of each KPI with the KPI value resulting from Snorm De Boer normalization. The results of the final supply chain performance obtained various values   from each KPI assessment (Table 4). The KPI with the highest value is direct material cost with an assessment result of 21.634, while the KPI with the lowest value is non-renewable energy consumed with an assessment result of 0.701. The total of the final supply chain performance value obtained from the sum of all the final values results above is 91.050. If this value is monitored using performance indicators (Table 2), it is included in the excellent category (Listiyono et al., 2024).
           
    The final supply chain performance value at sugar factories in Indonesia, when compared with the results of supply chain performance measurements at sugar factories in other countries and at one of the sugar factories in Indonesia based on previous studies has different values. The results of supply chain performance measurements at sugar factory in other countries such as Thailand are 55,61 for manufactures, 80,97 for suppliers and 75,00 for customers (Sopadang and Wichaisri, 2017). The results of supply chain performance measurements at one of the sugar factories in Indonesia based on previous studies such as Madukismo Sugar Factory are 93.32 (Anindita et al., 2020). The difference of the results can be caused by the level of sugar factory performance and the selection of performance indicators used.
           
    This research was conducted by addressing several weaknesses or deficiencies in the methodology or tools used in previous studies. First, this research uses the Performance Level 2 SCOR 14 metrics in determining performance indicators to address the weaknesses of previous research that do not use performance indicator standards. Second, this study does not use AHP (Analytical Hierarchy Process), while previous research uses it by creating a hierarchy between work processes and performance attributes in calculating supply chain performance. Third, the use of SCOR 14 is still rarely used in measuring supply chain performance.
           
    Improvement recommendations are important for companies to improve the performance of the supply chain so they can compete and achieve the desired targets. The improvement recommendation or practical recommendation method is carried out by determining KPIs that have criteria for priority improvement and providing appropriate practical recommendations based on the cause of the problem from the KPI. The KPI criteria for priority improvement are KPIs that have the lowest normalized KPI value and are included in the marginal and poor categories or those with values   below 51. In addition, improvement priorities are also sorted from KPIs with the smallest assessment results to KPIs with the larger assessment results (Chotimah et al., 2018) (Table 5).

    Table 5: Practical recommendation.


           
    Improving the coordination related to the harvest schedule can help reduce excess sugarcane shipments to factories due to pressure from farmers. Mechanizing the the sugarcane harvesting process can help farmers to carry out the harvest process faster so that they can meet the needs of the sugarcane supply because post-harvest handling is a crucial phase in the food supply chain (Pundir et al., 2025). Improving the quality of sugarcane cultivation by partner farmers and increasing the minimum requirements for sugarcane yield to be accepted into the factory can help reduce the frequency of repairs to the refining station due to the buildup of non-sugar solids or impurities (Magfiroh and Wibowo, 2020).

    CONCLUSION

    The level of supply chain performance in the sugar production process at the sugar factories in Indonesia based on the sum of all final values results is included in the excellent category with a value of 91.050. Improvement recommendations or practical recommendations to maintain and improve the supply chain performance can be carried out based on previously identified activities. These recommendations for improvement consist of improving the coordination of harvest schedule, mechanizing the sugarcane harvesting process, enhancing the quality of sugarcane cultivation and increasing the minimum requirements for sugarcane yield to be accepted by the factory. Suggestions that can be given for further research are to develop the KPIs used in the Performance Level 3 SCOR 14 metrics so that supply chain performance evaluation can be carried out more completely.
     

    ACKNOWLEDGEMENT

    Sincere thanks are extended to all participants who contributed to this study. Special gratitude goes to the Prajekan Sugar Factory in East Java, Indonesia, PT. Sinergi Gula Nusantara and technical staff at the Laboratory of Industrial Systems Management and Engineering, Department of Agricultural Industrial Technology, Faculty of Agricultural Technology, University of Jember, for supporting facilities and their support in this study. We also thank the anonymous reviewers for their constructive feedback.

    CONFLICT OF INTEREST

    All authors declare that they have no conflicts of interest.

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