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The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP) approach. The SIDSS was used to measure the productivity of rubber plantation and rubber agroindustry by GP approach, and select the best strategies for increasing the productivity of rubber agroindustry.

This system was developed by combining spatial analysis, GP, and fuzzy analytic network process (ANP) with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry. Rubber plantation productivity measurement model was used to find the productivity level of rubber plantation with fuzzy logic, and also to provide information and decision alternatives to all stakeholders regarding spatial condition of rubber agroindustry, production process flow, and analysis of the seven green wastes at each production process flow using the geographic information system. GP measurement model was used to determine the productivity performance of the rubber agroindustry with the green productivity index (GPI). The best strategy for increasing the productivity was determined with fuzzy ANP.

Rubber plantation measurement model showed that the average of plantation productivity was 6.25 kg/ha/day. GP measurement model showed that the GPI value of ribbed smoked sheet (RSS) was 0.730, whereas of crumb rubber (CR) was 0.126. The best strategy for increasing the productivity of rubber agroindustry was raw material characteristics control. Based on the best strategy, the GPI value of RSS was 1.340, whereas of CR was 0.228.

This decision support system is still limited as it is based on static data; it needs further development so that it can be more dynamically based on developments in the rubber agroindustry related levels of productivity and environmental impact. In addition, details regarding the decision to increase the productivity of the rubber section by benchmarking efforts should be studied further, both among plantation as well as among countries such as Thailand so that the productivity of rubber plantation and agroindustry can be integrated.

This research can help the planters to select superior clones for rubber trees, to improve the technique of tapping latex, and to use a better coagulant. The good quality and quantity of raw material is a key factor in increasing the productivity of rubber agroindustry; if the quality of latex is good then the resulting product will also have a good quality and production cost can be reduced. In addition, the application of GP through the calculation of GPI value using improvement scenarios can be used as a reference and comparison for evaluating the performance of rubber agroindustry to reduce the waste generated by the activities of rubber processing plant.

Reduction of waste generated by production activities can improve the quality of life of the workforce and the environment. The calculation of GPI value can also be used as a basis to use raw materials, water, and electricity more efficiently.

This system was developed by combining spatial analysis, GP, and fuzzy ANP with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry.

This study aims to construct models to classify independent smallholder farmers’ (ISFs) plantation suitability and its competitiveness index. It proposes the models with the objective to accommodate ISFs as the main source of raw material for the palm oil industry. It was predicted that the supply of oil palm fresh fruit bunch would depend on ISFs’ plantations due to the government policy that restricts the expansion of the plantations.

The study was designed using a geographic information system approach and analytical hierarchy process for mapping the suitability of smallholder farmers’ oil palm plantation in the Kampar Regency. The competitiveness index was measured using a modified Diamond Porter framework and competitiveness index quantification. The model was conducted on 177 respondents from five districts in Kampar Regency.

The results indicated that it mapped 128,936.759 hectares area of ISFs’ oil palm plantation in the Kampar Regency. The results of plantation suitability showed that only 13.88% of plantations owned by ISFs were potential, 71.21% of them were in the developing category and 14.91% of plantations were non-potential. The competitiveness index showed there were only 7.91% of them at the developed competitive position, 73.45% at developing a competitive position and 18.64% at the least-developed position.

The paper includes implications for developing ISFs’ capacity building and best practice management for oil palm optimization, resulting in bargaining positions and social well-being.

The study had succeeded to visualize ISFs’ plantation area suitability and competitiveness at Kampar Regency, Riau Province. The model provides a brisk understanding and valuable information about ISFs’ conditions spatially. It offers specific outcomes and becomes important in optimize and develop the existing plantations at the right time and exact location.

In this chapter, we consider the model of call center incoming call forecasting and staffing-level optimization. We first present the structure of the model and how an agent-based modeling technique could enrich the decision rule and the model. A matrix layout is introduced to present the model so that it can be understood in an efficient way from the perspective of a programmer. The agent-based queuing model will be used in forecasting. We then utilize the bisection method and stepwise method to optimize the staff level to satisfy a target range service-level criteria. Call center management could use the model in practice for their management forecasting and optimization decision-making process in terms of how many agents they need to achieve the target business efficiency goal.

The role of information technology is very important, especially in achieving related performance to optimize supply chain management as recognized by various findings literature. The existence of computer equipment is no longer sufficient to cope with the various industry issues, especially those requiring the latest information from the aspect of visibility and information accuracy. This study focuses on the importance of visibility and accuracy of the information on the coffee supply chain and aims to identify the use of information technology to eliminate problems in the coffee industry, especially in West Java. The study begins by compiling basic assumptions and rules of thought used in research, data collection, data reduction, categorization, analysis, interpretation, and data display, and the conclusion. The data analysis used a decision-making trial and evaluation laboratory (DEMATEL), which was used to determine the representation of critical factors contained in the research dimensions. As a finding the coffee supply chain in West Java has not been responsive and efficient yet, so improvements need to be made to get high added value for regional economic growth. The results of the study also identified several obstacles along the supply chain and solution based on a high level of information accuracy with several challenges to utilize it, namely (1) data and information sharing, (2) forecasting optimization, and (3) collaborative planning. As a suggestion to increase visibility in supply chain management, further research is needed to create relevant software and systems to trace the solutions to the causes and challenges faced to improve the accuracy of the information and its visibility.

In the previous decade, a substantial amount of research has been undertaken to measure the digitalized supply chain (DSC) performance. This paper aims to present a systematic literature review on DSC performance measurement metrics to apprehend current practices, recognize gaps and advocate future research itineraries.

To guarantee a replicable, rigorous and transparent research process, the authors used a systematic literature review methodology to synthesize the research. A combination of 25 keywords was used to obtain 248 scientific studies in the first step. The balance scorecard (BSC) model was used to categorize 299 gathered performance metrics into 4 BSC perspectives.

The review highlighted the need for qualitative performance measuring metrics for DSC. During the review, only one study was identified that primarily focused on developing performance metrics for DSC. Additionally, the review identified that metrics related to internal and financial perspectives received the most attention while the “growth and learning” perspective received the least attention. The review also identified that external partners, such as distributors and suppliers, were virtually ignored in previous literature.

Although numerous literature reviews have been conducted in the past on the performance measuring metrics for supply chain management, no literature review aiming to synthesize the measuring metrics for DSC has yet been undertaken.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

This study aims to analyze the productivity improvement strategies of the agricultural sector based on the problems faced by the agricultural sector in Central Java, Indonesia.

The analysis is done through interviews, literature studies and analytical hierarchy process data processing aids.

The results show that the strategy for developing the agricultural sector in Central Java needs to be focused on institutional factors to organize the field agricultural sector institutions. The ideal institution for the development of the agricultural sector in Central Java is cooperatives that can be accompanied by the Islamic Microfinance Institutions (LKMS).

This paper will contribute new knowledge specifically about the productivity improvement strategy through the cooperative institution.

This study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.

Data analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.

This study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.

This study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.

The paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.

Sustainable tourism development.

This study finds the expansion of new theory competitiveness of ecotourism destinations.

The purpose of this paper on the level of sustainability of the oxbow lake is to determine the sustainability status, leverage of attributes and driving factors in the management of the oxbow lake in Buluh Cina Village in Kampar, Riau, Indonesia.

The types of data collected include primary data and secondary data. The data were collected using the methods of surveys, library research, laboratory analysis and interviews. The analysis methods employed in this research were the multidimensional scaling (MDS) analysis and the prospect analysis. The sustainability analysis was undertaken using the approach of the MDS analysis with the assistance of software RapOxbow.

The current sustainability status of the management of Lake Baru ecosystems according to a number of dimensions is regarded as sustainable with a sustainability index value by 50.95. Meanwhile, analysis results of each dimension show that the sustainability index for the ecological dimension is 42.56 and the sustainability index for the economic dimension is 47.44, which means that they are less sustainable. While the sustainability index for the socio-cultural dimension is 54.81, the sustainability index for the technological dimension is 53.12 and the sustainability index for the legal-institutional dimension is 56.83, meaning that they are fairly sustainable.

The originality of this paper lies at the approaches of this research which are MDS analysis and a prospect analysis, and the research location in Buluh Cina Village in Kampar, Indonesia, that has not been researched before. This is one of few studies which investigate comprehensively the sustainability analysis of management, especially in ecological, economic and socio-cultural dimensions in Indonesia.

The purpose of this study at the level of sustainability of oxbow lake was intended to determine the status of sustainability, leverage attributes and driving factors in the management of oxbow lake in Buluh Cina village in Kampar, Riau, Indonesia.

The types of data collected include primary data and secondary data. Data was collected using survey methods, library research, laboratory analysis and interviews. The analytical method used this study is multidimensional scaling (MDS) analysis and prospect analysis. Sustainability analysis is done using the MDS analysis approach with the help of RapOxbow software and compared to Monte Carlo analysis results.

The current sustainability status of the management of Lake Baru ecosystem according to a number of dimensions is considered sustainable with a sustainability index value of 50.95. Meanwhile, the analysis results of each dimension shows that the sustainability index for the ecological dimension is 42.56 and the sustainability index for the economic dimension is 47.44, which means that the index is less sustainable.

The approach of this research is MDS analysis and prospect analysis and research locations in Buluh Cina village in Kampar, Indonesia which have never been studied before. This is one of few studies that investigates comprehensively the analysis of management sustainability, especially in the dimensions of ecology, economics and socio-culture, especially in Indonesia.