Spatial intelligent decision support system for increasing productivity on natural rubber agroindustry by green productivity approach

2017 ◽  
Vol 66 (6) ◽  
pp. 742-765 ◽  
Author(s):  
Yuliana Kaneu Teniwut ◽  
Marimin Marimin ◽  
Nastiti Siswi Indrasti
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Natural Rubber ◽  
Support System ◽  
Measurement Model ◽  
Raw Material ◽  
Rubber Plantation ◽  
Content Type ◽  
Green Productivity ◽  
Decision Alternatives

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications 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. Originality/value 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.

A decision support system for retail assortment planning

2017 ◽  
Vol 45 (7/8) ◽  
pp. 808-825 ◽  
Author(s):  
Alexander Hübner
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Solution Structure ◽  
Product Variety ◽  
Substitution Effects ◽  
Assortment Planning ◽  
Shelf Space ◽  
Content Type ◽  
Solution Approach

Purpose Because increasing product variety in retail conflicts with limited shelf space, managing assortment and shelf quantities is a core decision in this sector. A retailer needs to define the assortment size and then assign shelf space to meet consumer demand. However, the current literature lacks not only information on the comprehensive structure of the decision problem, but also a decision support system that can be directly applied to practice in a straightforward manner. The paper aims to discuss these issues. Design/methodology/approach The findings were developed and evaluated by means of explorative interviews with grocery retail experts. An optimization model is proposed to solve the problem of assortment planning with limited shelf space for data sets of a size relevant in real retail practice. Findings The author identifies the underlying planning problems based on a qualitative survey of retailers and relates the problems to each other. This paper develops a pragmatic approach to the capacitated assortment problem with stochastic demand and substitution effects. The numerical examples reveal that substitution demand has a significant impact on total profit and solution structure. Practical implications The author shows that the model and solution approach are scalable to problem sizes relevant in practice. Furthermore, the planning architecture structures the related planning questions and forms a foundation for further research on decision support systems. Originality/value The planning framework structures the associated decision problems in assortment planning. An efficient solution approach for assortment planning is proposed.

Network-aware credit scoring system for telecom subscribers using machine learning and network analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongming Gao ◽  
Hongwei Liu ◽  
Haiying Ma ◽  
Cunjun Ye ◽  
Mingjun Zhan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Scoring System ◽  
Credit Scoring ◽  
Prediction Performance ◽  
Content Type ◽  
Connected Subgroup ◽  
Source Data ◽  
Credit Scores

PurposeA good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.Design/methodology/approachRooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.FindingsThe distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.Originality/valueThis paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

scholarly journals Sistem Pendukung Keputusan Kelayakan Pemberian Asuransi Jiwa Untuk Nelayan dengan Menggunakan Metode Vikor (Studi Kasus: Dinas Kelautan dan Perikanan Medan)

2020 ◽  
Vol 1 (3) ◽  
pp. 220
Author(s):  
Amatillah Nasution ◽  
Kurnia Ulfa
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Management Decision ◽  
Main Function ◽  
System Method ◽  
Management Decision Making ◽  
Decision Alternatives ◽  
Alternative Solutions

Life insurance is a term used to refer to actions, systems, or businesses in which financial protection (or financial compensation) for life, property, health and so on gets reimbursed from unexpected events that can occur such as death , loss, damage or illness, which involves regular premium payments over a period of time in exchange for a policy that guarantees such protection. The term "insured" usually refers to everything that gets protection. Decision Support System is defined a system intended to support management decision making, Decision making is the main function of a manager or administrator. Decision making activities include identifying problems, finding alternative solutions to problems, evaluating these alternatives and choosing the best decision alternatives. The Vise Kriterijumska Optimazacija Kompromisno Resenje (VIKOR) method is one of the methods used in decision making. To use the decision support system method must have criteria that will be used in the determination, in addition it must determine the level of importance of each criterion. So the decision support system used must also have comprehensive and integrated planning to minimize the level of risk of failure and decision selection

scholarly journals A decision support system to assure high-performance maintenance service

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Giuseppe Aiello ◽  
Julio Benítez ◽  
Silvia Carpitella ◽  
Antonella Certa ◽  
Mario Enea ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Preventive Maintenance ◽  
Programming Model ◽  
Laboratory Method ◽  
Maintenance Management ◽  
Maintenance Policy ◽  
Content Type ◽  
Blockchain Technology

PurposeThis study aims to propose a decision support system (DSS) for maintenance management of a service system, namely, a street cleaning service vehicle. Referring to the information flow management, the blockchain technology is integrated in the proposed DSS to assure data transparency and security.Design/methodology/approachThe DSS is designed to efficiently handle the data acquired by the network of sensors installed on selected system components and to support the maintenance management. The DSS supports the decision makers to select a subset of indicators (KPIs) by means of the DEcision-MAaking Trial and Evaluation Laboratory method and to monitor the efficiency of performed preventive maintenance actions by using the mathematical model.FindingsThe proposed maintenance model allows real-time decisions on interventions on each component based on the number of alerts given by sensors and taking into account the annual cost budget constraint.Research limitations/implicationsThe present paper aims to highlight the implications of the blockchain technology in the maintenance field, in particular to manage maintenance actions’ data related to service systems.Practical implicationsThe proposed approach represents a support in planning, executing and monitoring interventions by assuring the security of the managed data through a blockchain database. The implications regard the monitoring of the efficiency of preventive maintenance actions on the analysed components.Originality/valueA combined approach based on a multi-criteria decision method and a novel mathematical programming model is herein proposed to provide a DSS supporting the management of predictive maintenance policy.

Virtual reality-based decision support model for interior design and decoration of an office building

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Kai Juan ◽  
Hao-Yun Chi ◽  
Hsing-Hung Chen
Keyword(s):  
Decision Making ◽  
Virtual Reality ◽  
Decision Support ◽  
Decision Support System ◽  
Interior Design ◽  
Support System ◽  
Decision Makers ◽  
Office Building ◽  
Design Decision ◽  
Content Type

Purpose The purpose of this paper is to develop a virtual reality (VR)-based and user-oriented decision support system for interior design and decoration. The four-phase decision-making process of the system is verified through a case study of an office building. Design/methodology/approach Different “spatial layouts” are presented by VR for users to decide their preference (Phase 1). According to the selected spatial layout, a “spatial scene” is constructed by VR and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to determine the spatial scene preference (Phase 2). Based on the binary integer programming method, the system provides the optimal preliminary solution under a limited decoration budget (Phase 3). Finally, the consistency between the overall color scheme and pattern is fine-tuned by VR in order to obtain the final solution (Phase 4). Findings The questionnaire survey results show that decision makers generally affirm the operation and application of VR, and especially recognize the advantages in the improvement of VR-based interior design feasibility, communication efficiency and design decision-making speed. The optimization of the costs and benefits enables decision makers to effectively evaluate the impact of design decisions on subsequent project implementation during the preliminary design process. Originality/value The VR-based decision support system for interior design retains the original immersive experience of VR, and offers a systematic multiple criteria decision- making and operations research optimization method, thus, providing more complete decision-making assistance. Compared with traditional design communication, it can significantly reduce cognitive differences and improve decision-making quality and speed.

scholarly journals An integrated decision support system for ERP implementation in small and medium sized enterprises

2014 ◽  
Vol 27 (4) ◽  
pp. 358-384 ◽  
Author(s):  
Ying Xie ◽  
Colin James Allen ◽  
Mahmood Ali
Keyword(s):  
Decision Support ◽  
Nonlinear Programming ◽  
Decision Support System ◽  
Support System ◽  
Regression Models ◽  
Programming Model ◽  
Resource Allocations ◽  
Erp Implementation ◽  
Content Type ◽  
Nonlinear Programming Model

Purpose – Implementing enterprise resource planning (ERP) is a challenging task for small- and medium-sized enterprises (SMEs). The purpose of this paper is to develop an integrated decision support system (DSS) for ERP implementation (DSS_ERP) to facilitate resource allocations and risk analysis. Design/methodology/approach – Analytical regression models are developed using data collected through a survey conducted on 400 SMEs that have implemented ERP systems, and are validated by a simulation model. The validated analytical regression models are used to construct a nonlinear programming model that generates solutions for resource allocations, such as time and budget. Findings – ERP implementation cost increases along the time horizon, while performance level increases up to a point and remains unchanged. To maximise or achieve a certain level of performance within a budget limitation, CSFs are prioritised as: project management (highest), top management, information technology, users and vendor support (lowest). SMEs are recommended to concentrate effort and resources on CSFs that have a greater impact on achieving their desired goals while optimising utilisation of resources. Research limitations/implications – DSS_ERP proves to be beneficial to SMEs in identifying required resources and allocating resources, but could be further tested in case studies for its practical use and benefits. Practical implications – DSS_ERP serves as a useful tool for SMEs to predict required resources and allocate them prior to ERP implementation, which maximises the probability of achieving predetermined targets. It also enables SMEs to analyse risk caused by changes to resources during ERP implementation, and helps them to be better prepared for the risks. Originality/value – The research contributes to the scarce research on ERP implementation using scientific methods. A novel nonlinear programming model is constructed for ERP implementation under time and budget limitations, facilitating resource allocations in an ERP implementation, which has not been reported in any previous research. The research offers a theoretical basis for empirical studies of resource allocations in ERP implementation.

scholarly journals Designing Decision Support System for Midwifery Students’ Tuition Fees Problem

2021 ◽  
Vol 5 (02) ◽  
pp. 157-171
Author(s):  
Chandrawati Putri Wulandari
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Decision Makers ◽  
Automated System ◽  
Decision Making Process ◽  
Tuition Fees ◽  
Process Information ◽  
Decision Alternatives

Education is an essential need and an important element to create a broad-minded youth. While in Indonesia, the rate of tuition fees is still imbalanced with the economic rate. Thus, many people decided to discontinue their higher education. Decision making plays an important role to manage organizations, including in educational institutions, as one of the main duties in managerial is making decisions. This study took place in a midwifery academy in Malang. In response to this situation, the academy has a policy to alleviate students by paying the tuition fees in instalments. However, many students are in arrears as the result of this policy. Thus, management needs to take some consideration before making a decision toward the problem. Those considerations require high accuracy and time consuming to process information that support decision making. Currently, the decision-making process in this academy is still semi-automated, in which some processes are still done manually, which affect longer time to make decisions, and the accuracy of calculations could not be fully guaranteed. According to this condition, the purpose of this research is to design a decision support system that enables us to process information for decision making and to offer decision alternatives for decision makers. This research employs Systems Development Life Cycle (SDLC) with a decision table as a method to create decision alternatives. The prototype was developed using Visual Studio C#.Net. The result shows that by using the proposed prototype of DSS, decision makers can reduce 5-10 minutes of decision making process compared to the old semi-automated system which still required manual calculation and data collection and analysis before making the decision. A complete data and more detailed parameters for decision criteria are required to implement the proposed prototype of DSS in the institution with more objective consideration in the decision making process as the future work.

Design and development of a novel knowledge-based decision support system for industrial safety management at drilling process

2019 ◽  
Vol 17 (4) ◽  
pp. 705-718 ◽  
Author(s):  
Muhammad Mujtaba Asad ◽  
Razali Bin Hassan ◽  
Fahad Sherwani ◽  
Muhammad Aamir ◽  
Qadir Mehmood Soomro ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Oil And Gas ◽  
Safety Management ◽  
Industrial Safety ◽  
Content Type ◽  
Gas Drilling ◽  
Oil And Gas Drilling ◽  
Offshore Oil And Gas

Purpose Annually, hundreds of drilling crew suffer from major injuries during performing oil and gas drilling operation because of the deficiency of an adequate hazard safety management system for real-time decision-making in hazardous conditions. According to previous studies, there is a sheer industrial need for an effective industrial safety management decision support system for accident prevention at oil and gas drilling sites at both drilling domains. Therefore, this paper aims to focus on the design and development of knowledge base decision support system (KBDSS) for the prevention of hazardous activities at Middle Eastern and South Asian origins’ onshore and offshore oil and gas industries during drilling operations. Design/methodology/approach In this study, data were gathered from safety and health professionals from targeted oil and gas industries in Malaysia, Saudi Arabia and Pakistan through quantitative and qualitative approaches. Based on identified data, KBDSSs (HAZFO Expert 1.0) were systematically developed and designed by adopting Database Development Life Cycle and Waterfall Software Development Life Cycle models. MySQL and Visual Studio 2015 software were used for developing and designing knowledge base and graphical user interface of the system. Findings KBDSS (HAZFO Expert 1.0) for accident prevention at onshore and offshore oil and gas drilling industries based on identified potential hazards and their suitable controlling measures aligned with international safety standards and regulations. HAZFO Expert 1.0 is a novel KBDSS that covers all onshore and offshore drilling operations with three and nine outputs, respectively, to achieve the current trend of Industry Revolution 4.0 and Industrial IoTs for workforce safety. Practical implications This industrial safety management system (HAZFO Expert 1.0) will be efficiently used for the identification and elimination of potential hazards associated with drilling activities at onshore and offshore drilling sites with an appropriate hazard controlling strategy. Originality/value Moreover, the developed KBDS system is unique in terms of its architecture and is dynamic in nature because it provides HAZFO Expert 1.0 data management and insertion application for authorized users. This is the first KBDSS which covers both drilling domains in Malaysian, Saudi Arabian and Pakistani industries.

Sistem Pendukung Keputusan Penentuan Kualitas Tempe Siap Jual Dengan Metode Weight Product

2020 ◽  
Vol 3 (2) ◽  
pp. 158-162
Author(s):  
Benyamin Sembiring ◽  
Sulindawaty Sulindawaty
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Raw Material ◽  
Final Decision ◽  
Traditional Methods ◽  
Calculation Process ◽  
Product Method

According to this SNI, soybean tempeh is packaged in well-closed packages, namely leaves and plastic. At MSME Latersia Berkat Tempe, it was found that information problems determining the quality of tempe were very diverse in determining the quality of tempe, generally using traditional methods. In this study, the calculation was carried out by applying the SPK with the Weighted Product (WP) method in determining the quality of ready-to-sell tempe and designing the Decision Support System (SPK) application with the Weighted Product (WP) method in determining the quality of ready-to-sell tempe using Visual Studio 2010. Analysis of the data obtained Tempe data as alternative data is taken from general packaging, namely Tempe Wrapped in Plastic and Tempe Wrapped in Leaves. The criteria for determining the quality of tempe production, in this study are influenced by Raw Material (C1), Type of Yeast (C2), Packaging (C2), Taste (C4), Color (C5), Typical Odor of Tempe (C6), Texture (C7), Sales (C8). In the application, the Alternative Data and Criteria Data are inputted, then the calculation process is carried out using the Weighted Product method. The result of the WP ranking calculation is determined to be the highest value to be the final decision of the system.

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