Decision support model for supplier selection in healthcare service delivery using analytical hierarchy process and artificial neural network

The disruption has been occurring in financial services. Rethinking new strategy of banking is needed to make a sustainable competitive advantage innovation in organizations. The four types of business strategy for banks are prospector, analyzer, defender, and reactor. Studies mentioned that formulating strategy is very costly, time consuming, and comprehensive analysis. The banks have to get rid of execution time inefficiency, lack of flexibility, and lack of ability to present several scenarios in the dynamic business environment. The purpose of this study is to present an integrated intelligence algorithm for estimating strategic resources of the bank strategy in Indonesia. The algorithm has two basic modules which are artificial neural network (ANN) and analytical hierarchy process (AHP). ANN is utilized as an inductive algorithm in discovering predictive strategy of the bank and used to explain the strategic resources which improved in forward. AHP has the capability to handle multi-level decision-making structure with use of expert judgments in pairwise comparison process. AHP is used to measure the weight of the resources and the score is used to determine the strategy. The empirical results indicate that ANN and AHP integration is proved to predict the business strategy of the bank. The strategy choice appropriate with the condition of bank's resources. This framework can be implemented to help banker for the decision making in bank operation. Keywords: business strategy, ANN, AHP, resources

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SUITABILITY INDEX FOR COLLECTION BIN ALLOCATION USING ANALYTICAL HIERARCHY PROCESS (AHP) CASCADED TO ARTIFICIAL NEURAL NETWORK (ANN)

Detritus ◽

10.31025/2611-4135/2019.13867 ◽

2019 ◽

pp. 38-49

Author(s):

Debasree Purkayastha ◽

Mrinmoy Majumder ◽

Sumanta Chakrabarti

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Waste Management ◽

Solid Waste ◽

Analytical Hierarchy Process ◽

Solid Waste Management ◽

Collection Efficiency ◽

Suitability Index ◽

Artificial Neural ◽

Hierarchy Process

Municipal solid waste is an inevitable outcome of anthropogenic activities. Proper sustainable solid waste management is the need of the hour. In this study, a Suitability Index (S.I) has been determined which can measure the relative importance of a district with regard to its necessity or requirement of collection bins in comparison to other districts in a municipality. The S.I was computed using Analytical Hierarchy Process cascaded to Artificial Neural Network. Four criteria viz. Demographic, Social, Economic and Technical considerations and seven factors viz. Population Density (P.D), Street Width (S.W), Waste Generation Rate (W.G.R), Income Group Distribution (I.G.D), Average Minimum Distance between the bins (MIN.D), Available Number of Bins (A.N.B) and Cost of Waste Bins (C.W.B) were considered for developing the model. Available Number of Bins was found to have the highest impact on the model followed by C.W.B, W.G.R, MIN D., I.G.D, P.D, and S.W. This index will particularly help developing countries with resource constraint and unskilled labor force in Solid Waste Management. It will help such countries to easily locate districts in urgent need of collection bins with an easily available set of data and will help in increasing collection efficiency.

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Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank

Summer - GATR Journal of Finance and Banking Review ◽

10.35609/jfbr.2021.5.4(1) ◽

2021 ◽

Vol 5 (4) ◽

pp. 01-09

Author(s):

Mochammad Ridwan Ristyawan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Analytical Hierarchy Process ◽

Financial Services ◽

Business Strategy ◽

Process Integration ◽

Integration Model ◽

Inductive Algorithm ◽

Artificial Neural ◽

Hierarchy Process

Objective – The disruption has been occurring in financial services. Thus, rethinking a new strategy for banking is needed to make a sustainable innovation in organizations. Studies mentioned that formulating strategy is a very costly, time-consuming, and comprehensive analysis. The purpose of this study is to present an integrated intelligence algorithm for estimating the bank’s strategy in Indonesia. Methodology – This study used the integration model between two modules. The algorithm has two basic modules, called Artificial Neural Network (ANN) and Analytical Hierarchy Process (AHP). AHP is capable of handling a multi-level decision-making structure with the use of five expert judgments in the pairwise comparison process. Meanwhile, ANN is utilized as an inductive algorithm in discovering the predictive strategy of the bank and used to explain the strategic factors which improved in forward. Findings and Novelty – The empirical results indicate that ANN and AHP integration was proved to predict the business strategy of the bank in five scenarios. Strategy 5 was the best choice for the bank and Innovate Like Fintechs (ILF) is the most factor consideration. The strategy choice was appropriate for the condition of the bank’s factors. This framework can be implemented to help bankers to decide on bank operations. Type of Paper: Empirical JEL Classification: M15, O32. Keywords: Bank’s strategy, ANN, AHP, BSC, Indonesia. Reference to this paper should be made as follows: Ristyawan, M.R. (2021). Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank, Journal of Finance and Banking Review, 5(4): 01 – 09. https://doi.org/10.35609/jfbr.2021.5.4(1)

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