Yani Nur Rachmawati (1), Enny Aryanny (2)
General Background: Warehouse material handling is critical for maintaining product quality, distribution continuity, and operational performance in the animal feed industry. Specific Background: PT XYZ experienced defects in animal feed material handling, including torn sacks, loose stitches, and contamination, which reduced process performance and caused operational losses. Knowledge Gap: Previous studies rarely focus on defect reduction in animal feed material handling, especially sack-based packaging, and the integration of Lean Six Sigma with Fuzzy FMEA in this context remains limited. Aims: This study aimed to identify waste levels, analyze defect causes, and propose priority improvements using Lean Six Sigma with the DMAIC approach, Process Cycle Efficiency, Pareto analysis, fishbone diagrams, and Fuzzy FMEA. Results: Defect waste ranked first with a weight of 3.25, followed by transportation, motion, overproduction, inventory, overprocessing, and waiting. The average defect rate was 0.58%, with DPMO of 1,943 and a sigma level of 4.4. Improvements reduced activities from 43 to 36 and lead time from 418 to 314 minutes, while PCE increased from 26.65% to 35.35%, producing a 24.88% efficiency improvement. Novelty: The study integrates waste identification and Fuzzy FMEA-based priority setting in animal feed material handling. Implications: The proposed actions support pallet standardization, stack height adjustment, forklift SOPs, operator training, 5S implementation, FIFO control, and defect reduction.
Highlights:
Keywords: DMAIC, F-FMEA, Lean Six Sigma
Lean Six Sigma Reduces Defects and Improves Animal Feed Handling Efficiency
Yani N. Rachmawati 1) , Enny Aryanny *,2)
1) Industrial Engineering Study Program, National Development University “Veteran” East Java, Indonesia
2) Industrial Engineering Study Program, National Development University “Veteran” East Java, Indonesia
*Corresponding Author Email: 22032010094@student.upnjatim.ac.id
Abstract : PT XYZ is a company engaged in the animal feed industry that plays a crucial role in maintaining product quality and distribution. However, defects in the material handling process still occur, reducing operational performance and causing losses. Previous studies rarely focus on reducing product defects in animal feed material handling, indicating a research gap. This study aims to identify waste levels and propose improvements using Lean Six Sigma with the DMAIC approach and Fuzzy FMEA to determine risk priorities. Cause analysis used Pareto and fishbone diagrams. The results show an average defect rate of 0.58%, with a DPMO value of 1,943 and a sigma level of 4.4. Process improvements reduced activities from 43 to 36 and time from 418 to 314 minutes, increasing PCE from 26.56% to 35.35% with an efficiency improvement of 24.88%. This study combines Lean Six Sigma and Fuzzy FMEA to reduce defects and improve efficiency .
Keywords - DMAIC, F-FMEA, Lean Six Sigma
The feed industry is a strategic sector that plays an important role in supporting the country's economic development, particularly in the livestock sector 1. The availability of high-quality and affordable feed is crucial for the sustainability of livestock businesses, whether small, medium, or large scale 2. In supporting the smooth distribution of these feed products, logistics and storage systems play a crucial role. The warehouse storage process includes various aspects that contribute to the efficiency and safety of the storage process 3. Therefore, companies must maintain products in the warehouse area from receipt, storage, to delivery of goods to customers so that they have guaranteed quality 4. PT XYZ is a leading entity involved in the largest and most integrated agri-food industry in Indonesia. This company is engaged in the production of animal feed (poultry, ruminants, and birds), pet food , and fish feed. One of its products is animal feed with sack packaging. In the animal feed storage area at PT XYZ, waste is still found which impacts the quality and efficiency of the process.
Based on field observations, there are several types of waste in the material handling process, where the dominant waste is defects with a defect rate of 0.58% which includes torn sacks, loose stitches, and contamination. In addition, motion waste was found due to repeated operator and forklift movements and transportation in the form of delays during the picking process . Other waste includes waiting due to the rework process , inventory due to stock buildup, overproduction due to production planning errors, and overprocessing that occurs due to the re-repair of defective products. Disability rate the is in the range of 0.39% to 0.97%. The highest value occurred in February 2026, where increase in production volume followed by an increase amount product defects . This indicates that the handling capacity in the warehouse is not optimal in keeping up with the increasing workload, especially in the material handling process. This condition shows that the increase in production volume has not been balanced by the effectiveness of quality control in the warehouse area. Therefore, continuous improvement is needed to achieve zero defects through the implementation of Lean Six Sigma with a DMAIC to identify waste and reduce the defect 5.
The data collection technique to identify waste due to product defects in the material handling process of PT XYZ uses a quantitative approach, the data used consists of primary and secondary data. Primary data collection was carried out through direct observation, interviews, and distribution of Likert scale questionnaires to respondents who understand the process. Secondary data obtained include production data, defect data, types of waste, and material handling process flow. The method used is Lean Six Sigma with the DMAIC approach and Process Cycle Efficiency (PCE) measurement to determine the comparison of value-added and non-value-added activities. In addition, problem cause analysis was carried out using fishbone and supported by Fuzzy FMEA as a basis for preparing improvement proposals. The steps that need to be taken to solve the problems in this study can be seen in the following explanation:
This stage aims to observe and determine the company's condition. This involves creating a production process map that includes an information flow map and a physical flow map using Big Picture Mapping tools , so that Process Cycle Efficiency (PCE) is obtained. This PCE will later become the basis for identifying waste in the process 11.
(1)
Identifying the most dominant types of waste and prioritizing them for improvement. The analysis was conducted by processing observation and questionnaire data, which had been classified into seven types of waste .
Determining the most appropriate analysis tools for identifying critical waste . The analysis is carried out by mapping waste types into the VALSAT matrix, then assigning weights to each tool based on their level of relevance 12. The tool with the highest value is selected as the primary analysis tool.
Waste Weight X Correlation Value (H, L, M) (2)
Information:
Waste weight = Based on the waste weight value in the questionnaire summary calculation
Correlation Value =H: multiplier factor (9)
M: multiplier (3)
L: multiplier factor (1)
In the Define stage , data analysis focuses on identifying initial problems in the form of types of packaging defects that appear during the material handling process 13. Defect data is collected based on a certain period.
This stage measures and processes the data that has been obtained, focusing on calculating defects per million opportunities (DPMO), sigma values 14.
( 3)
( 4)
This calculation converts the sigma value from defects per million (DPMO) to a sigma value using Microsoft Excel with the formula:
( 5)
This stage involves analyzing and identifying waste and determining the potential causes of the problem using Pareto diagrams and cause and effect diagrams ( fishbone ) to design improvement solutions 15 16.
The improvement method used is Fuzzy FMEA. The creation of Value Stream Mapping (VSM) for future conditions ( future state ) will be carried out at this stage as a follow-up to the results of the improvement proposals that have been implemented. The Fuzzy RPN calculation method is as follows 17:
( 6)
Big Picture Mapping is a tool used to visualize an entire system and its value streams within a company 18. The value stream mapping of the material handling process at its initial state can be seen in Figure 2.
Based on big picture mapping, the total lead time is obtained. The total lead time for animal feed material handling was 418 minutes with a total value-added time of 111 minutes, a total non-value-added time of 104 minutes, and a total necessary non-value-added time of 203 minutes. Therefore, the problem that occurred in the animal feed material handling process can be determined, namely the total lead time is too long at 418 minutes, equivalent to 6.96 hours or 6 hours 58 minutes, so the Process Cycle Efficiency (PCE) value is calculated as follows:
Critical waste was determined based on the results of a distributed questionnaire to determine which waste occurs frequently and which are the main priorities for improvement, as shown in Table 2.
Based on the table, the weighting results are obtained with the order of Ranking 1 to 7. Waste with Ranking 1 is defect with a weight of 3.25; Ranking 2 is transportation with a weight of 3; Ranking 3 is motion with a weight of 2.75; Ranking 4 is overproduction with a weight of 2.5; Ranking 5 is inventory with a weight of 2.25; Ranking 6 is overprocessing with a weight of 2; and Ranking 7 is waiting with a weight of 1.75.
The VALSAT analysis was carried out based on the results of the weight calculations obtained from interviews in determining critical waste in Table 2. The results of the Value Stream Analysis Tools (VALSAT) analysis can be seen in Table 3.
Based on the data in Table 3, the tool with the highest ranking is Process Activity Mapping (PAM), so PAM was chosen as the tool to be used in the calculation process. Process Activity Mapping is used to identify activities that do not provide added value in the material handling process, as can be seen in Table 4.
Table4 .Initial Process Activity Mapping
Based on Figure 3. the histogram above, it can be seen that the highest number of defects is torn sacks amounting to 38,498 units with a percentage of 0.3238%, then followed by loose stitching defects amounting to 23,248 units with a percentage of 0.1955%, and contaminated amounting to 8,134 units with a percentage of 0.0684%. Therefore, reducing the percentage of defects to approach zero defects for animal feed products must be done and suggestions for improvement will be given.
The results above indicate that PT XYZ is at a sigma level of 4.4, or 4 sigma, with an average DPMO of 1,943 per 1,000,000 units. While this score is quite good compared to the Indonesian industry average, it still falls short of the US industry average. In this context, further evaluation is needed to understand the differences and potential improvements to achieve higher standards.
The CTQ that has been determined in the data in Table 1., then a defect analysis is carried out to determine the cause of the highest defects which can be seen in Table 7.
Based on the calculation results in Table 7 that have been carried out, a Pareto diagram of livestock feed product defects is depicted in Figure 4.
From the Pareto diagram, it can be seen that the highest order of defect types is torn sacks at 55.09%, loose stitching at 33.27%, and contamination at 11.64%.
An analysis was carried out using a fishbone diagram to analyze the cause and effect of the type of torn sack defect which can be seen in Figure 5.
Figure 5. FishboneTorn Sack
The causes of torn sacks in animal feed products are influenced by several factors, namely human, material, and method. From the human aspect, operators are less careful when operating forklifts due to rushing and improper fork height adjustment. From the material aspect, substandard sack quality makes the packaging more susceptible to tearing when subjected to friction or impact. Meanwhile, from the method aspect, the transfer process does not comply with standard operating procedures (SOPs) and improper product arrangement, such as contact with damaged pallets or those with sharp edges, also increase the risk of packaging damage.
This study used Fuzzy Failure Mode and Effect Analysis (F-FMEA) with the aid of Pareto diagrams and cause-and-effect diagrams to identify problem priorities. The analysis using the Lean Six Sigma approach showed that defects were the most dominant source of waste 17.
The first stage is to identify severity, occurrence, and detection in the material handling process, which can be seen in Table 8.
The final stage of Fuzzy FMEA is determining the Fuzzy Risk Priority Number (FRPN) value which can be seen in Table 15.
Table 15. Determination of Fuzzy Risk Priority Number Value
After calculating the FRPN in Table 15, to determine repair priorities, repair recommendations are obtained for each damage, as shown in Table 16.
Table 16. Repair Recommendations Based on FRPN Sequence
Table 18. Future Process Activity Mapping
Based on the future process activity mapping shown in Table 17, the material handling process can then be described with the future big picture mapping in Figure 8, where this improvement is aligned with the concept of Process Cycle Efficiency (PCE) in Lean, which emphasizes increasing the proportion of value-added activities in a process 11.
Figure 8. Big Picture Mapping ProposalAnimal Feed Product Material Handling Process
Based on the time calculation after the improvement, namely by eliminating NVA from the material handling process of animal feed products, the lead time obtained was 314 minutes or equivalent to 5 hours 14 minutes with a total value-added time of 111 minutes, the necessary non-value-added time of 203 minutes. This indicates that there is a reduction in lead time so that the material handling process of animal feed can be more efficient. Therefore, the value for the percentage increase in efficiency in the material handling process of animal feed can be described as follows:
Process Cycle Efficiency (PCE) value can be determined using the following formula:
From the calculation results of the proposed Process Cycle Efficiency (PCE) value, the result obtained was 35.35%, which means that the animal feed material handling process has improved.
control phase aims to monitor the sustainability of improvements to waste occurring in the material handling process , so that the proposed improvements can be implemented consistently over a longer period of time. However, in this study, the control phase could not be implemented directly because the decision regarding the implementation of the proposed improvements rests entirely with PT XYZ. Therefore, the process of controlling the results of the improvements could not be carried out in this study.
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A. Juwito and A. Z. Al-Faritsyi, “Analisis Pengendalian Kualitas untuk Mengurangi Cacat Produk dengan Metode Six Sigma di UMKM Makmur Santosa,” Jurnal Cakrawala Ilmiah, vol. 1, no. 12, pp. 3295–3315, 2022. [Online]. Available: http://bajangjournal.com/index.php/JCI
R. I. Liperda, N. R. Fatahayu, E. V. Khairunnisa, M. A. Logika, M. Hibatullah, and R. Fridayanti, “Simulasi Sistem Penggunaan Ruangan di Gedung Griya Legita Universitas Pertamina,” JISI: Jurnal Integrasi Sistem Industri, vol. 8, no. 2, pp. 65–75, 2021, doi: 10.24853/jisi.8.2.65-75.
Nurlaela, Implementasi Value Stream Mapping pada Perumahan Sederhana di Indonesia. Yogyakarta, Indonesia: Deepublish Publisher, 2023.
A. B. Rizkyllah and E. Aryanny, “Product Defect Level Analysis Bone Plate with The Six Sigma Method and Fuzzy Failure Mode and Effect Analysis (F-FMEA): Analisis Tingkat Kecacatan Produk Bone Plate dengan Metode Six Sigma dan Fuzzy Failure Mode and Effect Analysis (F-FMEA),” vol. 26, no. 4, pp. 1–15, 2025, doi: 10.21070/ijins.v26i4.1477.
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M. F. Fadilah and R. Wibero, “Rancangan Lean Manufacturing untuk Mengurangi Pemborosan Pada Proses Pembuatan Sepatu dengan Pendekatan Metode Value Stream Mapping (VSM) dan Root Cause Analysis (RCA) di Home Industry Sepatu,” Jurnal Greenation Ilmu Teknik, vol. 2, no. 1, pp. 16–25, 2025, doi: 10.38035/jgit.v2i1.230.