The supply chains shaping their distribution networks become more diverse as companies respond to global markets’ stringent criteria. This is also counterproductive to the visibility of the supply chain within the company and can adversely affect the organization’s core business. This paper attempts to evaluate how organizations can benefit from introducing Digital Twins to enhance their logistics supply network visibility. Additionally, deployment issues and technologies supporting Digital Twins were reviewed. This study used ATLAS.ti 9 software tools to save, classify, and evaluate the data for this analysis to systematically review the literature. We reviewed, compiled, and sorted papers from 227 publications for this article and then recognized 104 as critical to the work scope; this analysis’ quest date was set from 2002 to 2021. This article represents the first attempt at dealing with the issue of supply chain visibility through the Digital Twins in the logistics field. The research outcomes found that Digital Twins would help companies develop predictive metrics, diagnostics, projections, and physical asset descriptions for their logistics. This study also suggested some steps to overcome the challenges in implementing a Digital Twins in the logistics industry. For researchers, this review offers the possibility to unify and expand existing solutions and to identify links and interfaces that are still needed. As for managerial implications, this study can be used to identify future strategies and technologies to fulfil certain logistics tasks and develop new technological solutions for current and future demands.
Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.
The development of small and medium enterprises (SMEs) becomes the benchmark and leading position for developing countries’ economies. The digital transformation demands strategies, desires, and awareness of Information Technology (IT)-based market players and investments. Despite the transformation of a digital business platform, many SMEs have stumbled in the middle road. Therefore, this study aimed to determine priority indicators in assessing SMEs’ readiness towards digitalization and evolving a readiness model for SMEs based on the Decision Support System (DSS) approach. Multiple stakeholders’ viewpoints, particularly regarding academicians, governments, investors, market places, and SMEs’ business actors as targeted respondents, were scrutinized quantitatively and qualitatively to verify the proposed factors. The priority weights of factors have been examined from economic and IT perspectives and derived through deploying the Fuzzy Analytical Hierarchy Process (F-AHP) method. This study reveals the rank of measures necessary to assess the readiness of the digital revolution of SMEs. Transaction preparedness in SMEs’ cultural, educational, financial, and technological infrastructure views grows into the principal components during this assessment with 0.30 of vector value, accompanied by marketing and micro-environment at 0.24, management at 0.20, macro-environment at 0.03 and business activities at 0.02, respectively. For the recommendation purposes, the rubric segmented SME fitness into three levels, low, middle, and high performance. The prototype system DSS-SMEsReadiness was then evolved in order to simplify the adoption of the DSS method in the SME performance measurement model. The software analysis demonstrates that this application would assist decision-makers to ascertain SMEs’ readiness to digitalize. The future recommendation provides SMEs and stakeholders with knowledge transfers and acclimatization for taking the appropriate option about their business strategy, management resources, skills, and assistance programs for SMEs. This model attempts to reduce SME digitalization disruptions and achieve a digital business’s growth and sustainability in a nutshell.
Measuring organizational performance is pivotal for a comprehensive understanding of strengths, weaknesses and to improve the quality of any organization's performance. Balanced Scorecard (BSC) is the strategic evolution tool that is widely used to measure the organizational performances, and achievements from various aspects, both financial and non-financial. In this research, BSC was not only a straight jacket concept but also a high potential tool for measuring and managing tangible and accurate data through the application of several methods. This research weighted the variables of BSC based on significance values of Analytical Hierarchy Process (AHP) and Optimization of Measurement with Objective Matrix (OMAX). Moreover, a recommendation analysis was given based on the cause and effect analysis of variables and the achievement of Key Performance Indicators (KPIs). The flow of information, data, and performance measurement processes were designed into Business Intelligence (BI) software development i.e. BI-MonevDash. The framework and software BI-MonevDash proposed can be used as a new chosen tool for measuring and monitoring organizational performance. Recommendations could facilitate the leaders in decision making to improve the organizational performance and reduce risks.
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