Assessment of Digitalized Logistics for Implementation in Low-Income Countries

Integration of digitalization and automation with logistics systems promotes effective and efficient flow of goods, information, and services, contributing to economic development. The level of implementation of digitalization and automation in low-income countries is still low, however. The aim of this study is to establish which digitalized logistics practices could best be adopted by firms in low-income countries. A systematic literature review was used to identify state-of-the-art digitalization and automation technologies in logistics chains. Criteria for adopting digitalized logistics practices were also identified in the literature review. An expert survey was conducted to identify criteria weights using analytical hierarchy process (AHP). Economic benefit, infrastructure, and affordability were the criteria that were given the highest weights by the experts. Case studies that applied state-of-the-art technologies such as internet of things (IoT), radio frequency identification (RFID), blockchain, big data analytics (BDA), and sensors mainly for traceability, production operation, and warehouse and inventory management were considered as recommended practices. Identification of suitable practices considering the local conditions in low-income countries could help logistics professionals and policymakers adopt enabling technologies in logistics chains

Analyzing the influential factors of industry 4.0 in precision machinery industry

Abstract. Nowadays the science and technology progresses not only create the change to have a big impact on various industries, but also stimulate Industry 4.0 being applied in the manufacturing industry to achieve manufacturing efficiency and to reduce its cost to increase additional values. This study uses the Analytical Hierarchical Process (AHP) evaluation method, which considers four criteria layers: Internet of things factors, Automationfactors, Intelligent factors, Big data factors, and twelve influence factors in sub-layer are: perceived layer, network layer, application layer, field layer, management layer, control layer, process control visualization, system supervisory and control omni bearing, green energy manufacturing production, variety, volume, and velocity. Then, the relative risk indicator (RRI) is obtained by the Analytical Hierarchical Process method, and the overall risk indicator (ORI) can be obtained after introducing the evaluation value of each impact factor through the case. The research results confirm that the risk assessment values obtained the hierarchical analysis method are consistent. This research through the Analytic Hierarchy Process, then discusses Industry 4.0 pair of Taiwan's precision machinery industry management pattern institute emphatically face with target, expected will provide the existing machine manufacture industry as well as the future wants to invest the precision machine industry the management policy-maker reference value, also might take the government policy consideration factors and the machine manufacture industry scholars study the academic for reference.Keywords. Industry 4.0, Precision machine industry, Analytic Hierarchy Process.JEL. L22, M11, O14

Assessment of Cyber Risks in an IoT-based Supply Chain using a Fuzzy Decision-Making Method

Purpose: The Internet of Things (IoT) is a relatively new paradigm that is growing rapidly in modern wireless communication scenarios. The main idea of this concept is the pervasive presence of all kinds of objects around us. This technology is the basis of today's intelligent life and is known as one of the most important sources of big data. Meanwhile, businesses are no exception to this rule and try to use the Internet of Things to make their business smarter. Supply chain management is a goal-based goal of linking business operations to provide a common view of market opportunity. Methodology: Using IoT technology, all major parts of the supply chain, including supply, production, distribution and sales, can be affected. Because this evolutionary technology is intertwined with Internet technology, the use of network-based tools can always create risks for business owners who use these technologies. Therefore, understanding and investigating a variety of cyber risks in this area can It is very important and by understanding their hands, we can prevent many future risks. Linear analysis based on hierarchical analysis is used. Findings: The results show that privacy is very important in interaction with suppliers as well as customers, and therefore those effective measures to deal with these risks can reduce many of the problems caused by this technology. Originality/Value: This paper attend to assessment of cyber risks in an IoT-based supply chain using a fuzzy decision-making method

A Multi-Criteria Framework to Assist on the Design of Internet-of-Things Systems

The Internet-of-Things (IoT), considered as Internet first real evolution, has become immensely important to society due to revolutionary business models with the potential to radically improve Human life. Manufacturers are engaged in developing embedded systems (IoT Systems) for different purposes to address this new variety of application domains and services. With the capability to agilely respond to a very dynamic market offer of IoT Systems, the design phase of IoT ecosystems can be enhanced. However, select the more suitable IoT System for a certain task is currently based on stakeholder’s knowledge, normally from lived experience or intuition, although it does not mean that a proper decision is being made. Furthermore, the lack of methods to formally describe IoT Systems characteristics, capable of being automatically used by methods is also an issue, reinforced by the growth of available information directly connected to Internet spread. Contributing to improve IoT Ecosystems design phase, this PhD work proposes a framework capable of fully characterise an IoT System and assist stakeholder’s on the decision of which is the proper IoT System for a specific task. This enables decision-makers to perform a better reasoning and more aware analysis of diverse and very often contradicting criteria. It is also intended to provide methods to integrate energy consumptionsimulation tools and address interoperability with standards, methods or systems within the IoT scope. This is addressed using a model-driven based framework supporting a high openness level to use different software languages and decision methods, but also for interoperability with other systems, tools and methods

An Integration of Project Management Body of Knowledge and Project Management Information System to Improve On-time Deliverable of Liquefied Natural Gas Station Construction Projects

The objective of this study is to improve the liquefied natural gas station construction project to achieve on-time delivery. Diverse tools and techniques are integrated to make various interrelated activities in the project occur effectively as planned with less cost, suggested by the Project Management Body of Knowledge (PMBOK) guideline and the Project Management Information System (PMIS). To implement the PMIS along with the PMBOK, the project management software and Internet of Things (IoT) are utilized for real-time long-distance monitoring and control of the project. The proposed approach is implemented at a real demonstration project. The results reveal that the proposed approach is quite effective, which help increase the number of projects completed on schedule from 75% in the last year to 100% this year. Moreover, the implementation of the PMIS also results in substantial reductions in the employment allowance for routine site inspections and the travel expense for round-trip vehicles travelling from the company to the site

Industry 4.0 enabling sustainable supply chain development in the renewable energy sector:A multi-criteria intelligent approach

The aim of this paper is to provide a multi-criteria decision-making intelligent approach based on Industry 4.0 and Triple Bottom Line principles for sustainable supply chain development in the renewable energy sector. In particular, the solar photovoltaic energy supply chain is used as a case study, encompassing the entire energy production process, from supply to disposal. An exhaustive literature review is conducted to identify the main criteria affecting social, economic and environmental sustainability in the photovoltaic energy supply chain, and to explore the potential impact of Industry 4.0 on sustainability. Subsequently, three Fuzzy Inference Systems combining quantitative and qualitative data are built to calculate the supply chain's social, economic and environmental sustainability. Experts' opinions are used to identify the impact of Industry 4.0 technologies on the three pillars of sustainability for each supply chain stage. Finally, a novel sustainability index, Sustainability Index 4.0, is formulated to compute the overall sustainability of the photovoltaic energy supply chain in seven countries. The results show the applicability and usefulness of the proposed holistic model in helping policy makers, stakeholders and users to make informed decisions for the development of sustainable renewable energy supply chains, taking into account the impact of Industry 4.0 and digital technologies

Prioritization of public services for digitalization using fuzzy Z-AHP and fuzzy Z-WASPAS

In this paper, public services are analyzed for implementations of Industry 4.0 tools to satisfy citizen expectations. To be able to prioritize public services for digitalization, fuzzy Z-AHP and fuzzy Z-WASPAS are used in the analysis. The decision criteria are determined as reduced cost, fast response, ease of accessibility, reduced service times, increase in the available information and increased quality. After obtaining criteria weights using fuzzy Z-AHP, health care services, waste disposal department, public transportation, information services, social care services, and citizen complaints resolution centers are compared using fuzzy Z-WASPAS that is proposed for the first time in this paper. Results show that health care services have dominant importance for the digitalization among public services.WOS:000604482500002Science Citation Index ExpandedQ2Article; Early AccessUluslararası işbirliği ile yapılmayan - HAYIROcak2021YÖK - 2020-2

Evaluation of Garbage Management Based on IoT

Smart Waste Monitoring: To track the amount of waste in bins and containers, IOT-enabled garbage management systems use sensors and connected devices. These sensors can communicate real-time data to a centralized monitoring system and can identify the fill level. This data aids in streamlining waste collection routes, cutting back on pointless pickups, and enhancing garbage management effectiveness as a whole. Effective Resource Allocation: By giving precise data on waste generation patterns and trends, IOT-based garbage management systems enable optimal resource allocation. This information can be used by municipal authorities to make well-informed decisions on waste collection schedules, resource deployment, and staffing levels. IOT-based waste management solutions have the potential to make trash management procedures more effective and efficient while also being more affordable. The best garbage collection routes, operational cost reductions, and resource utilization may all be achieved with the aid of research into the best deployment strategies for IOT sensors and devices. Environmental Impact and Sustainability: Research Objective: Clearly identify the research objective, for example, by assessing how well IOT-based garbage management systems gather waste and allocate resources. Data gathering: Compile pertinent information on the methods used for trash generation, collection, and resource use. On-site observations, employee interviews, and database access for waste management operations are all effective ways to accomplish this. Gather information on IOT sensor technologies and their capabilities as well. Taken As alternative for Smart Waste Bins, Waste Level, Sensors, AI Recycling, Robots, E-Waste Kiosks. Taken for Evaluation preference is Reliability, Mobility, Service Continuity, User Convenience., and Energy Efficiency. Smart Waste Bins has performed more when compare to with other Real-Time Monitoring: The Internet of Things (IOT) can be used in waste management to enable real-time monitoring of trash cans or bins can be used to enhance garbage sorting procedures. Smart bins with cameras and sensors can automatically recognize and sort various types of rubbish. These smart bins can identify and categorise rubbish by utilizing IOT technology.  on their material composition or recycling category