The oil palm independent smallholder (farmer) is one of the important actors in maintaining the continuity of the production cycle in the oil palm agro-industry supply chain in Indonesia. Various fundamental problems faced by the independent smallholder are related to land legality as well as their limited ability to manage good agricultural practices, access funds and information on current prices, and use of quality and agricultural production facilities. The institutional strengthening of the independent smallholder requires attention and support from government and other business actors in the supply chain of the oil palm agro-industry. This study aims to obtain a model of institutional strengthening through the application of the enrich seven steps of soft system methodology. The assumptions that must be fulfilled in strengthening this institution were described. The institutional strengthening model was developed in three stages, i.e., the establishment of smallholder corporations, the establishment of independent cooperatives, and the revitalization of the Indonesian oil palm independent smallholder association. Various individual strengthening actors were investigated and then integrated. The suggested institutional strengthening model involved cooperatives, smallholder farmers groups, and the oil palm mill, supported by regulatory, financial, and input provider agencies with a mutual partnership program.

The classification of the ripeness degree of oil palm fruit has attracted the attention of numerous researchers. However, there are still many challenges due to constraints in the dataset, methodologies used, and variations in the use of data categories. Detecting oil palm fruit bunches accurately is crucial, given their complex shape and characteristics, particularly when different ripeness categories are present in a pile of oil palm. Most studies utilize oil palm images or the color spectrum of oil palm fruit to classify the level of ripeness. However, these methods are not real-time and lack efficiency. This study proposes a real-time model for determining the ripeness degree of oil palm using a smartphone and video data as input, incorporating modifications to the object detection approach. The research process involves collecting videos of palm oil piles using smartphones in the grading area of the palm oil industry. The videos are then pre-processed and labelled for the object detection and classification process. A detection and classification model is developed using the YOLOv4 approach with several performance improvements, enabling implementation on smartphones. The best-performing model is tested for detecting and classifying the ripeness of fresh fruit bunches using an android-based smartphone. The testing results, based on the mAP value, demonstrate that the YOLOv4 model with 16 quantization performs 12% better than YOLOv4 Tiny. Based on the test results at the grading location, this model can efficiently detect fruit bunches that do not meet the quality standards.

Supply chain sustainability assessment is key to maintaining and improving the performance of agroindustry supply chains, particularly in sustainable agroindustry development. The assessment of agro-industrial supply chain performance is a complex and dynamic process. Hence, there is a need for an adaptive fuzzy multi-criteria sustainability assessment model as an alternative method of analysis and improvement. This study aimed to design an adaptive fuzzy multi-criteria sustainability assessment and improvement model for the sugarcane agroindustry supply chain. In this study: (1) a fuzzy inference system (FIS) was developed to assess the performance of the sustainability dimensions. This study proposed 24 indicators for four dimensions: economic, social, environmental, and resource. (2) An adaptive neuro-fuzzy inference system (ANFIS) is designed to aggregate the overall supply chain sustainability performance. (3) The proposed fuzzy multi-criteria assessment model was compared with the common multidimensional scaling (MDS) and linear models. This study proved that the proposed synthesis of the FIS and ANFIS models is powerful and adaptive for evaluating supply chain sustainability and providing accurate results. (4) The strategies to improve sustainability performance were developed using the cosine amplitude method (CAM). The proposed model determined that the overall supply chain sustainability value was 68.58%, which was almost sustainable. Several strategies have been suggested to improve sustainability performance, including maintaining the sugarcane supply by strengthening the partnership program and improving the mill’s overall recovery, followed by factory revitalization or new factory investment.

The purposes of this paper are to evaluate sugarcane supply chain performance, identify supply chain risks, and define mitigation to improve the performance. Supply chains were modeled through a Supply Chain Operation Reference (SCOR) and Fuzzy-Analytic Hierarchy Process (AHP) to evaluate the performance. This paper modeled fuzzy-House of Risk (HOR) to identify and assess supply chain risk. Expert judgment for supply chain risk assessment was aggregated using an Ordered Weighted Average (OWA) model. The supply chain performance of farmers was found to be average, while that of sugar mill was at below average. Our model succeeded in identifying and assessing 15 risk agents for farmer, seven risk agents for sugar mills, and four risk agents for distributors which had to be mitigated. This work offers meaningful guidance for decision-makers to compose strategies to mitigate risk and enhance performance of sugarcane agroindustry supply chains.

Industry 4.0 adoption in industry and business has grown extensively. Supply chain had an opportunity to adopt Industry 4.0 in improving flexibility, efficiency, and response to consumer demands. Considering Industry 4.0, adoption in supply chain operations must be realized as an opportunity and challenge for practitioners. Hence, this study conducted a systematic literature review with a qualitative and quantitative approach to formulate supply chain 4.0 dimensions and operations in assessing readiness for adopting industry 4.0. A Preferred Reporting Item for Systematic Review and Meta-analyses (PRISMA) and bibliometric analysis are performed to analyze the literature related to industry 4.0 adoption in supply chain operations. This research combined a qualitative and quantitative analysis in reviewing the literature to deliver a broader benefit of Industry 4.0 in supply chain operations. This study found 525 papers related to Industry 4.0. Moreover, only twenty papers discussed supply chain specifically. Literature analysis using quantitative and qualitative approach showed that industry 4.0 adoption in the supply chain is limited to the maturity model with digital transformation. This study proposed five comprehensive dimensions to capturing supply chain operations: technology and information technology (IT) infrastructure, supply chain integration and coordination, manufacturing operations and inventory, leadership and human resources, and sustainability. The proposed dimensions for supply chain operations are based on a conceptual analysis of literature. Further verification and empirical analysis is needed to gain deeper insights. While digital technology and transformation are the main focus of Industry 4.0 adoption in many sectors, supply chain management requires broader applications as it involves various sectors and stakeholders. To further analyze this, a readiness and maturity model needs to be developed to assess the adoption of Industry 4.0 in the supply chain, taking into consideration the proposed dimensions.

The sustainability of the sugarcane agro-industry supply chain plays a crucial role in providing economic benefits, minimizing social and environmental impacts, and optimizing resource utilization. This research aims to analyze the sustainability performance of the sugarcane agro-industry supply chain using multi-criteria assessment and formulate strategies for sustainability improvement. The study proposes a multi-criteria assessment model with twenty-eight indicators and four dimensions of sustainability: economic, social, environmental, and resources, which were developed based on previous research. The fuzzy inference system (FIS) and multi-dimensional scaling (MDS) methods were utilized to analyze the multi-criteria indicators of sustainability performance in each dimension and overall supply chain. The Adaptive Neuro Fuzzy Inference System (ANFIS) model was used to aggregate multi-dimension sustainability to achieve overall sustainability performance. A fuzzy cognitive map (FCM) framework was developed to formulate strategies for improving the sustainability performance of the supply chain. The research was verified at two sugar mill locations in Java Island, Indonesia. The FIS and MDS models successfully analyzed the sustainability performance of the two sugar agro-industries, showing an average value of “quite sustainable”. The overall sustainability performance using the ANFIS model for mill A and B were 57.2 and 61.9, respectively. Series of FGDs combined with the FCM model successfully formulated five clusters of strategies as initiatives in improving the sustainability performance, namely raw material provision, harvesting and post-harvest activities, production process optimization, IT-based technology implementation, and institutional aspects. This present work seeks to contributes to the development of multi-criteria of sustainability performance for the food industry's supply chain. It also proposes a comprehensive framework for analyzing and improving sustainable supply chain performance under uncertainty using a combination of conventional and fuzzy assessment modeling approach. A practical initiative strategy in sustainability improvement is revealed for the sugarcane agroindustry's supply chain.