Search results for: Monte Carlo Markov Chain

Authors: Gabriel Calvo, Carmen Armero, Luigi Spezia

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A basketball player is said to have a hot hand if his/her performance is better than expected in different periods of time. A way to deal with this phenomenon is to make use of latent variables, which can indicate whether the player is ‘on fire’ or not. This work aims to model the hot hand phenomenon through a Bayesian hidden Markov model (HMM) with two states (cold and hot) and two different probability of success depending on the corresponding hidden state. This task is illustrated through a comprehensive simulation study. The simulated data sets emulate the field goal attempts in an NBA season from different profile players. This model can be a powerful tool to assess the ‘streakiness’ of each player, and it provides information about the general performance of the players during the match. Finally, the Bayesian HMM allows computing the posterior probability of any type of streak.

Keywords: Bernoulli trials, field goals, latent variables, posterior distribution

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Authors: Mehmet Şimşek

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In the rapidly changing market conditions, it is getting harder to survive without adapting new abilities. Technology and globalization have enabled foreign producers to enter into national markets, even local ones. For this reason there is now big competition among production companies for market share. Furthermore, competition has provided customer with broad range of options to choose from. To be able to survive in this environment, companies need to produce at low price and at high quality. The best way to succeed this is the efficient use of supply chain management that has started to get shaped by the needs of customers and the environment.

Keywords: cycle time, logistics, outsourcing, production, supply chain

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Authors: Busra Gulnihan Dascıoglu, Ozalp Vayvay, Zeynep Tugce Kalender

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Humanitarian supply chain management has gain popularity in recent years in research fields. The aim of this paper is to review the literature on humanitarian operations and crisis/disaster management from 2010 to latest researches in order to identify the current research and to provide direction for future research in this growing field. Researches are classified considering the research publication year, research fields. Articles from humanitarian supply chain management were reviewed, keywords were identified within a disaster management lifecycle framework. Research gaps are identified for future research areas.

Keywords: crisis, disaster, humanitarian supply chain management, relief operations

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Authors: Richa Grover, Rahul Grover, V. Balaji Rao, Kavish Kejriwal

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Selection of suppliers is a crucial problem in the supply chain management. On top of that, sustainable supplier selection is the biggest challenge for the organizations. Environment protection and social problems have been of concern to society in recent years, and the traditional supplier selection does not consider about this factor; therefore, this research work focuses on introducing sustainable criteria into the structure of supplier selection criteria. Sustainable Supply Chain Management (SSCM) is the management and administration of material, information, and money flows, as well as coordination among business along the supply chain. All three dimensions - economic, environmental, and social - of sustainable development needs to be taken care of. Purpose of this research is to maximize supply chain profitability, maximize social wellbeing of supply chain and minimize environmental impacts. Problem statement is selection of suppliers in a sustainable supply chain network by ranking the suppliers against sustainable criteria identified. The aim of this research is twofold: To find out what are the sustainable parameters that can be applied to the supply chain, and to determine how these parameters can effectively be used in supplier selection. Multicriteria decision making tools will be used to rank both criteria and suppliers. AHP Analysis will be used to find out ratings for the criteria identified. It is a technique used for efficient decision making. TOPSIS will be used to find out rating for suppliers and then ranking them. TOPSIS is a MCDM problem solving method which is based on the principle that the chosen option should have the maximum distance from the negative ideal solution (NIS) and the minimum distance from the ideal solution.

Keywords: sustainable supply chain management, sustainable criteria, MCDM tools, AHP analysis, TOPSIS method

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Authors: Mohammad Y. Badiee, Saeed Golestani, Mir Saman Pishvaee

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In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown.

Keywords: supply chain management, closed-loop supply chain, multi-objective optimization, goal programming, uncertainty, robust optimization

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Authors: Yuan-Jye Tseng, Yi-Shiuan Chen

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The paper presented a sustainable design model for integrated evaluation of the design and supply chain of a product for the sustainable objectives. To design a product, there can be alternative ways to assign the detailed specifications to fulfill the same design objectives. In the design alternative cases, different material and manufacturing processes with various supply chain activities may be required for the production. Therefore, it is required to evaluate the different design cases based on the sustainable objectives. In this research, a closed-loop design model is developed by integrating the forward design model and reverse design model. From the supply chain point of view, the decisions in the forward design model are connected with the forward supply chain. The decisions in the reverse design model are connected with the reverse supply chain considering the sustainable objectives. The purpose of this research is to develop a mathematical model for analyzing the design cases by integrated evaluating the criteria in the closed-loop design and the closed-loop supply chain. The decision variables are built to represent the design cases of the forward design and reverse design. The cost parameters in a forward design include the costs of material and manufacturing processes. The cost parameters in a reverse design include the costs of recycling, disassembly, reusing, remanufacturing, and disposing. The mathematical model is formulated to minimize the total cost under the design constraints. In practical applications, the decisions of the mathematical model can be used for selecting a design case for the purpose of sustainable design of a product. An example product is demonstrated in the paper. The test result shows that the sustainable design model is useful for integrated evaluation of the design and the supply chain to achieve the sustainable objectives.

Keywords: closed-loop design, closed-loop supply chain, design evaluation, supply chain management, sustainable design model

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Authors: Naveed Ahmed Khan

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The ever increasing importance of food industry cannot be denied and especially in the wake of escalating population and prices both in developing and developed nations. Thus, this issue demands the attention of researchers especially in the area of supply chain to identify cost diminution waste eliminating supply chain practices in the said industry. For such purpose the 'Dairy Division' of Engro Foods Limited, one of the biggest food companies in Pakistan was taken into consideration in a case study manner. Based on the literature review and interviews following variables were obtained: energy, losses, maintenance, taxes, and logistics. Having studied the said variables, it was concluded that management of relevant industries operating in a comparable environment need to efficiently manage two major areas: energy and taxes. On the other hand, similar kind of other organizations could be benefited by adopting the proficient supply chain practices being observed at dairy division of Engro foods limited.

Keywords: cost diminution, supply chain, dairy industry, Engro Foods Limited

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Authors: Roberto M. G. Velásquez, Jonas R. Gazoli, Nelson Ponce Jr, Valério L. Borges, Alessandro Sete, Fernanda M. C. Tomé, Julian D. Hunt, Heitor C. Lira, Cristiano L. de Souza, Fabio T. Bindemann, Wilmar Wounnsoscky

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Nowadays, society is working toward reducing energy losses and greenhouse gas emissions, as well as seeking clean energy sources, as a result of the constant increase in energy demand and emissions. Energy loss occurs in the gas pressure reduction stations at the delivery points in natural gas distribution systems (city gates). Installing pressure reduction turbines (PRT) parallel to the static reduction valves at the city gates enhances the energy efficiency of the system by recovering the enthalpy of the pressurized natural gas, obtaining in the pressure-lowering process shaft work and generating electrical power. Currently, the Brazilian natural gas transportation network has 9,409 km in extension, while the system has 16 national and 3 international natural gas processing plants, including more than 143 delivery points to final consumers. Thus, the potential of installing PRT in Brazil is 66 MW of power, which could yearly avoid the emission of 235,800 tons of CO2 and generate 333 GWh/year of electricity. On the other hand, an economic viability analysis of these energy efficiency projects is commonly carried out based on estimates of the project's cash flow obtained from several variables forecast. Usually, the cash flow analysis is performed using representative values of these variables, obtaining a deterministic set of financial indicators associated with the project. However, in most cases, these variables cannot be predicted with sufficient accuracy, resulting in the need to consider, to a greater or lesser degree, the risk associated with the calculated financial return. This paper presents an approach applied to the technical-economic viability analysis of PRTs projects that explicitly considers the uncertainties associated with the input parameters for the financial model, such as gas pressure at the delivery point, amount of energy generated by TRP, the future price of energy, among others, using sensitivity analysis techniques, scenario analysis, and Monte Carlo methods. In the latter case, estimates of several financial risk indicators, as well as their empirical probability distributions, can be obtained. This is a methodology for the financial risk analysis of PRT projects. The results of this paper allow a more accurate assessment of the potential PRT project's financial feasibility in Brazil. This methodology will be tested at the Cuiabá thermoelectric plant, located in the state of Mato Grosso, Brazil, and can be applied to study the potential in other countries.

Keywords: pressure reduction turbine, natural gas pressure drop station, energy efficiency, electricity generation, monte carlo methods

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Authors: Ma Chaoyang

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With the acceleration of urbanization, old communities are facing renewal problems such as an aging material environment, declining living quality, and insufficient resilience. The once glorious old communities have become the most vulnerable areas in the city. Through a re-understanding of the ‘disaster chain’ and resilient communities, it is believed that considering the construction of resilient communities during community renewal is of great significance for promoting the sustainable development of communities. This article proposes renewal strategies for old communities based on the concept of preventing the occurrence of disaster chains. After analyzing the main demand characteristics of old communities, it proposes a reflection on improving community spatial safety resilience based on the ‘broken chain’ concept. In the four stages of ‘pre-disaster, mid-disaster, and post-disaster’, it elaborates that considering the occurrence of disaster chain in community renewal is the main content of research on spatial safety resilience construction and clarifies that community resilience is the idea and principle of responding with the process of disaster chain. The study focuses on the four dimensions of ‘pre-disaster, mid-disaster, and post-disaster’. This can provide ideas and references for resilience construction in community updates.

Keywords: community updates, disaster chain concept, community resilience, space environment

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Authors: Roman Snytsar

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Pseudo-random numbers are often used in scientific computing such as the Monte Carlo Simulations or the Quantum Inspired Optimization. Requirements for a parallel random number generator running in the modern multi-core vector environment are more stringent than those for sequential random number generators. As well as passing the usual quality tests, the output of the parallel random number generator must be verifiable and reproducible throughout the concurrent execution. We propose a family of vectorized Permuted Congruential Generators. Implementations are available for multiple modern vector modern computer architectures. Besides demonstrating good single core performance, the generators scale easily across many processor cores and multiple distributed nodes. We provide performance and parallel speedup analysis and comparisons between the implementations.

Keywords: pseudo-random numbers, quantum optimization, SIMD, parallel computing

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Authors: Aleksandr Kavetskiy, Galina Yakubova, Nikolay Sargsyan, Stephen A. Prior, H. Allen Torbert

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The carbon sequestration question of modern times requires the development of an in-situ method of measuring soil carbon over large landmasses. Traditional chemical analytical methods used to evaluate large land areas require extensive soil sampling prior to processing for laboratory analysis; collectively, this is labor-intensive and time-consuming. An alternative method is to apply nuclear physics analysis, primarily in the form of pulsed fast-thermal neutron-gamma soil carbon analysis. This method is based on measuring the gamma-ray response that appears upon neutron irradiation of soil. Specific gamma lines with energies of 4.438 MeV appearing from neutron irradiation can be attributed to soil carbon nuclei. Based on measuring gamma line intensity, assessments of soil carbon concentration can be made. This method can be done directly in the field using a specially developed pulsed fast-thermal neutron-gamma system (PFTNA system). This system conducts in-situ analysis in a scanning mode coupled with GPS, which provides soil carbon concentration and distribution over large fields. The system has radiation shielding to minimize the dose rate (within radiation safety guidelines) for safe operator usage. Questions concerning the effect of neutron irradiation on soil health will be addressed. Information regarding absorbed neutron and gamma dose received by soil and its distribution with depth will be discussed in this study. This information was generated based on Monte-Carlo simulations (MCNP6.2 code) of neutron and gamma propagation in soil. Received data were used for the analysis of possible induced irradiation effects. The physical, chemical and biological effects of neutron soil irradiation were considered. From a physical aspect, we considered neutron (produced by the PFTNA system) induction of new isotopes and estimated the possibility of increasing the post-irradiation gamma background by comparisons to the natural background. An insignificant increase in gamma background appeared immediately after irradiation but returned to original values after several minutes due to the decay of short-lived new isotopes. From a chemical aspect, possible radiolysis of water (presented in soil) was considered. Based on stimulations of radiolysis of water, we concluded that the gamma dose rate used cannot produce gamma rays of notable rates. Possible effects of neutron irradiation (by the PFTNA system) on soil biota were also assessed experimentally. No notable changes were noted at the taxonomic level, nor was functional soil diversity affected. Our assessment suggested that the use of a PFTNA system with a neutron flux of 1e7 n/s for soil carbon analysis does not notably affect soil properties or soil health.

Keywords: carbon sequestration, neutron gamma analysis, radiation effect on soil, Monte-Carlo simulation

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Authors: Amirhossein Mahlouji

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In current business market, the concept of integrating environmental sustainability into long-term as well as routine operations is becoming a prevailing trend. Therefore, several stimuli are helping organization to move toward environmental sustainability. The concept of green supply chain management can help provide a strategic framework to develop a customized sustainability roadmap for each organization. In this regard, this paper is mainly focused on presenting a strategic decision making framework that will assist top level decision-making issues. This decision-making tool is based on literature and practice in the area of environmentally conscious business practices. The goal of this paper will be on the components and parameters of green supply chain management and how they serve as a baseline for the decision framework. Later, the applicability of a multi-input multi-output decision model (MIMO), will be analyzed as the analytical network process, within the green supply chain.

Keywords: Multi-attribute, Green Supply Chain, Environmental, Sustainability

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Authors: Mohsen Shirani, Micaela Demichela

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During the last decades, a number of food crises such as Bovine Spongiform Encephalopathy (BSE), Mad-Cow disease, Dioxin in chicken food, Food-and-Mouth Disease (FMD), have certainly inflicted the reliability of the food industry. Consequently, the trend in applying different scientific methods of risk assessment in food safety has obtained more attentions in the academic and practice. However, lack of practical approach considering entire food supply chain is tangible in the academic literature. In this regard, this paper aims to apply risk assessment tool (FMEA) with integration of Human Factor along the entire supply chain of food production and test the method in a case study of Diary production, and analyze its results.

Keywords: FMEA, food supply chain, risk assessment, human factor

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Authors: Nicole Virgili, Romolo Remetti

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The aim of this work is to characterize, from radiation protection point of view, the emission into the environment of air contaminated by argon-41. In this research work, 41Ar is produced by a TR19PET cyclotron, operated at 19 MeV, installed at 'A. Gemelli' University Hospital, Rome, Italy, for fluorine-18 production. The production rate of 41Ar has been calculated on the basis of the scheduled operation cycles of the cyclotron and by utilising proper production algorithms. Then extensive Monte Carlo calculations, carried out by MCNP code, have allowed to determine the absolute detection efficiency to 41Ar gamma rays of a Geiger Muller detector placed in the terminal part of the chimney. Results showed unsatisfactory detection efficiency values and the need for integrating the detection system with more efficient detectors.

Keywords: Cyclotron, Geiger Muller detector, MCNPX, argon-41, emission of radioactive gas, detection efficiency determination

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Authors: Benga Ebouele, Thomas Tengen

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Due to long lead time, work in process (WIP) inventory can manifest within the supply chain of most manufacturing system. It implies that there are lesser finished good on hand and more in the process because the work remains in the factory too long and cannot be sold to either customers The supply chain of most manufacturing system is then considered as inefficient as it take so much time to produce the finished good. Time consumed in each operation of the supply chain has an associated energy costs. Such phenomena can be harmful for a hybrid inventory system because a lot of space to store these semi-finished goods may be needed and one is not sure about the final energy cost of producing, holding and delivering the good to customers. The principle that reduces waste of energy within the supply chain of most manufacturing firms should therefore be available to all inventory managers in pursuit of profitability. Decision making by inventory managers in this condition is a modeling process, whereby a dynamical approach is used to depict, examine, specify and even operationalize the relationship between energy consumption and hybrid inventory level. The relationship between energy consumption and inventory level is established, which indicates a poor level of control and hence a potential for energy savings.

Keywords: dynamic modelling, energy used, hybrid inventory, supply chain

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Authors: Nitish Suvarna, Anjali Awasthi

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In an era marked by rapid technological advancements, the integration of Autonomous Vehicles into supply chain networks represents a transformative shift, promising to redefine the paradigms of logistics and transportation. This thesis delves into a comprehensive assessment of the impact of autonomous vehicles on supply chain performance, with a particular focus on network design, operational efficiency, and environmental sustainability. Employing the advanced simulation capabilities of anyLogistix (ALX), the study constructs a digital twin of a conventional supply chain network, encompassing suppliers, production facilities, distribution centers, and customer endpoints. The research methodically integrates Autonomous Vehicles into this intricate network, aiming to unravel the multifaceted effects on transportation logistics including transit times, cost-efficiency, and sustainability. Through simulations and scenarios analysis, the study scrutinizes the operational resilience and adaptability of supply chains in the face of dynamic market conditions and disruptive technologies like Autonomous Vehicles. Furthermore, the thesis undertakes carbon footprint analysis, quantifying the environmental benefits and challenges associated with the adoption of Autonomous Vehicles in supply chain operations. The insights from this research are anticipated to offer a strategic framework for industry stakeholders, guiding the adoption of Autonomous Vehicles to foster a more efficient, responsive, and sustainable supply chain ecosystem. The findings aim to serve as a cornerstone for future research and practical implementations in the realm of intelligent transportation and supply chain management.

Keywords: autonomous vehicle, agri-food supply chain, ALX simulation, anyLogistix

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Authors: Xiangrong Liu, Chuanhui Xiong

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With the concern of large pollution emissions from coal-fired power plants and new commitment to green energy, global solar power industry was emerging recently. Due to the advanced technology, the price of solar photovoltaic(PV) module was reduced at a fast rate, which arose an interesting but challenge question to solar supply chain. This research is modeling the inventory strategies for a PV supply chain with a PV manufacturer, an assembler and an end customer. Through characterizing the manufacturer's and PV assembler's optimal decision in decentralized and centralized situation, this study shed light on how to improve supply chain performance through parameters setting in the contract design. The results suggest the assembler to lower the optimal stock level gradually each period before price reduction and set up a newsvendor base-stock policy in all periods after price reduction. As to the PV module manufacturer, a non-stationary produce-up-to policy is optimal.

Keywords: photovoltaic, supply chain, inventory policy, base-stock policy

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Authors: Mrigank Sahai, R. Sri Raghu

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This paper presents the modelling of rocket exhaust plume flow field and exhaust plume impingement in vacuum for the liquid apogee engine and attitude control thrusters of the lunar lander. Analytic formulations for rarefied gas kinetics has been taken as reference for modelling the plume flow field. The plume has been modelled as high speed, collision-less, axi-symmetric gas jet, expanding into vacuum and impinging at a normally set diffusive circular plate. Specular reflections have not been considered for the present study. Different parameters such as number density, temperature, pressure, flow velocity, heat flux etc., have been calculated and have been plotted against and compared to Direct Simulation Monte Carlo results. These analyses have provided important information for the placement of critical optical instruments and design of optimal thermal insulation for the hardware that may come in contact with the thruster exhaust.

Keywords: collision-less gas, lunar lander, plume impingement, rarefied exhaust plume

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Authors: Chitrangi Gupta, Arvind Bhardwaj

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This study seeks to investigate and quantify the influence of various dimensions of supply chain management (namely, supplier relationships, compatibility, specifications and standards, delivery processes, and after-sales service) on distinct dimensions of healthcare service quality (specifically, responsiveness, trustworthiness, and security) within the operational framework of XYZ Superspeciality Hospital, situated in Delhi. The name of the Hospital is not being mentioned here because of the privacy policy of the hospital. The primary objective of this research is to elucidate the impact of supply chain management practices on the overall quality of healthcare services offered within hospital settings. Employing a quantitative research design, this study utilizes a hypothesis-testing approach to systematically discern the relationship between supply chain management dimensions and the quality of health services. The findings of this study underscore the significant influence exerted by supply chain management dimensions, specifically supplier relationships, specifications and standards, delivery processes, and after-sales service, on the enhancement of healthcare service quality. Moreover, the study's results reveal that demographic factors such as gender, qualifications, age, and experience do not yield discernible disparities in the relationship between supply chain management and healthcare service quality.

Keywords: supply chain management, healthcare, hospital operations, service delivery

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Authors: John Lorenzo Bautista, Yoon-Joong Kim

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This paper presents the development of a Filipino speech recognition tool using the HTK System. The system was trained from a subset of the Filipino Speech Corpus developed by the DSP Laboratory of the University of the Philippines-Diliman. The speech corpus was both used in training and testing the system by estimating the parameters for phonetic HMM-based (Hidden-Markov Model) acoustic models. Experiments on different mixture-weights were incorporated in the study. The phoneme-level word-based recognition of a 5-state HMM resulted in an average accuracy rate of 80.13 for a single-Gaussian mixture model, 81.13 after implementing a phoneme-alignment, and 87.19 for the increased Gaussian-mixture weight model. The highest accuracy rate of 88.70% was obtained from a 5-state model with 6 Gaussian mixtures.

Keywords: Filipino language, Hidden Markov Model, HTK system, speech recognition

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Authors: Emilia Segun-Ajao

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The strategy of relocation aimed at strengthening supply chain resilience in the United States is examined, taking into account recent global disturbances and vulnerabilities in offshore manufacturing. It explains the procurement challenges faced by enterprises and offers solutions to mitigate risks and improve resilience. Through the analysis of innovative approaches, including technological advances, policy considerations, and strategic frameworks, this study provides insights to decision-makers about the complexity of supply chain management. Reshoring has gained attention as a strategy to improve supply chain resilience in the face of global disruptions. This analysis focuses on the importance of relocating as a multifaceted approach to strengthening supply chains, advocating economic benefits, technological advances, and policy frameworks to create a more robust supply landscape in the United States.

Keywords: collaborative partnerships, supply chain resilience, procurement challenges, technology adoption

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Authors: Mohamed Barbary, Mohamed H. Abd El-azeem

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Maneuvering non-ellipsoidal extended object tracking (M-NEOT) using high-resolution inverse synthetic aperture radar (ISAR) observations is gaining momentum recently. This work presents a new robust implementation of the Jump Markov (JM) multi-Bernoulli (MB) filter for M-NEOT, where the M-NEOT’s ISAR observations are characterized using a skewed (SK) non-symmetrically normal distribution. To cope with the possible abrupt change of kinematic state, extension, and observation distribution over an extended object when a target maneuvers, a multiple model technique is represented based on an MB-track-before-detect (TBD) filter supported by SK-sub-random matrix model (RMM) or sub-ellipses framework. Simulation results demonstrate this remarkable impact.

Keywords: maneuvering extended objects, ISAR, skewed normal distribution, sub-RMM, JM-MB-TBD filter

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Authors: Ramin Rostamkhani, Thurasamy Ramayah

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One of the most important topics of today’s industrial organizations is the challenging issue of supply chain management. In this field, scientists and researchers have published numerous practical articles and models, especially in the last decade. In this research, to our best knowledge, the discussion of data modeling of supply chain management components using well-known statistical distribution functions has been considered. The world of science owns mathematics, and showing the behavior of supply chain data based on the characteristics of statistical distribution functions is innovative research that has not been published anywhere until the moment of doing this research. In an analytical process, describing different aspects of functions including probability density, cumulative distribution, reliability, and failure function can reach the suitable statistical distribution function for each of the components of the supply chain management. It can be applied to predict the behavior data of the relevant component in the future. Providing a model to adapt the best statistical distribution function in the supply chain management components will be a big revolution in the field of the behavior of the supply chain management elements in today's industrial organizations. Demonstrating the final results of the proposed model by introducing the process capability indices before and after implementing it alongside verifying the approach through the relevant assessment as an acceptable verification is a final step. The introduced approach can save the required time and cost to achieve the organizational goals. Moreover, it can increase added value in the organization.

Keywords: analyzing, process capability indices, statistical distribution functions, supply chain management components

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Authors: Nattida Wannaruk, Wasawat Nakkiew

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This research aims to study the factors that affect the performance of green supply chain management in the Lampang ceramics industry. The data investigation of this research was questionnaires which were gathered from 20 factories in the Lampang ceramics industry. The research factors are divided into five major groups which are green design, green purchasing, green manufacturing, green logistics and reverse logistics. The questionnaire has consisted of four parts that related to factors green supply chain management and general information of the Lampang ceramics industry. Then, the data were analyzed using descriptive statistic and priority of each factor by using the analytic hierarchy process (AHP). The understanding of factors affecting the green supply chain management of Lampang ceramics industry was indicated in the summary result along with each factor weight. The result of this research could be contributed to the development of indicators or performance evaluation in the future.

Keywords: Lampang ceramics industry, green supply chain management, analysis hierarchy process (AHP), factors affecting

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Authors: Biniam Bahiru Tufa, Hana Delil Tesfaye, Seife Demisse Legesse, Manaye Tamire

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The healthcare industry is witnessing a digital transformation, with artificial intelligence (AI) offering potential solutions for challenges in health supply chain management (HSCM). However, the adoption of AI in this field remains limited. This research aimed to assess the knowledge, attitude, and practice of AI among students and employees in the health supply chain sector in Ethiopia. Using an explanatory case study research design with a concurrent mixed approach, quantitative and qualitative data were collected simultaneously. The study included 153 participants comprising students and employed health supply chain professionals working in various sectors. The majority had a pharmacy background, and one-third of the participants were male. Most respondents were under 35 years old, and around 68.6% had less than 10 years of experience. The findings revealed that 94.1% of participants had prior knowledge of AI, but only 35.3% were aware of its application in the supply chain. Moreover, the majority indicated that their training curriculum did not cover AI in health supply chain management. Participants generally held positive attitudes toward the necessity of AI for improving efficiency, effectiveness, and cost savings in the supply chain. However, many expressed concerns about its impact on job security and satisfaction, considering it as a burden Graduate students demonstrated higher knowledge of AI compared to employed staff, while graduate students also exhibited a more positive attitude toward AI. The study indicated low previous utilization and potential future utilization of AI in the health supply chain, suggesting untapped opportunities for improvement. Overall, while supply chain experts and graduate students lacked sufficient understanding of AI and its significance, they expressed favorable views regarding its implementation in the sector. The study recommends that the Ethiopian government and international organizations consider introducing AI in the undergraduate pharmacy curriculum and promote its integration into the health supply chain field.

Keywords: knowledge, attitude, practice, supply chain, articifial intellegence

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Authors: Raja Kannusamy

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The research focuses on reducing food waste in the restaurant industry. A study has been conducted on the chain of retail restaurant outlets. It has been observed that the food wastages are due to the inefficient inventory management systems practiced in the restaurant outlets. The major food items which are wasted more in quantity are being selected across the retail chain outlets. A moving average forecasting method has been applied for the selected food items so that their future demand could be predicted accurately and food wastage could be avoided. It has been found that the moving average prediction method helps in predicting forecasts accurately. The demand values obtained from the moving average method have been compared to the actual demand values and are found to be similar with minimum variations. The inventory optimization technique helps in reducing food wastage in restaurant supply chain outlets.

Keywords: food wastage, restaurant supply chain, inventory optimisation, demand forecasting

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Authors: Dina Tamazin, Nicoleta Tipi, Sahar Validi

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The oil refining industry plays vital role in the world economy. Oil refining companies operate in a more complex and dynamic environment than ever before. In addition, oil refining companies and the public are becoming more conscious of crude oil scarcity and climate changes. Hence, sustainability in the oil refining industry is becoming increasingly critical to the industry's long-term viability and to the environmental sustainability. Mainly, it is relevant to the measurement and evaluation of the company's sustainable performance to support the company in understanding their performance and its implication more objectively and establishing sustainability development plans. Consequently, the oil refining companies attempt to re-engineer their supply chain to meet the sustainable goals and standards. On the other hand, this research realized that previous research in oil refining sustainable supply chain performance measurements reveals that there is a lack of studies that consider the integration of sustainability in the supply chain performance measurement practices in the oil refining industry. Therefore, there is a need for research that provides performance guidance, which can be used to measure sustainability and assist in setting sustainable goals for oil refining supply chains. Accordingly, this paper aims to present a comprehensive oil refining sustainable supply chain performance measurement theoretical framework. In development of this theoretical framework, the main characteristics of oil refining industry have been identified. For this purpose, a thorough review of relevant literature on performance measurement models and sustainable supply chain performance measurement models has been conducted. The comprehensive oil refining sustainable supply chain performance measurement theoretical framework introduced in this paper aims to assist oil refining companies in measuring and evaluating their performance from a sustainability aspect to achieve sustainable operational excellence.

Keywords: oil refining industry, oil refining sustainable supply chain, performance measurement, sustainability

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Authors: Jun Sung Go, Jong Kang Park, Jong Tae Kim

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As semi-conductor manufacturing technology evolves; the single event transient problem becomes more significant issue. Single event transient has a critical impact on both combinational and sequential logic circuits, so it is important to evaluate the soft error tolerance of the circuits at the design stage. In this paper, we present a soft error detecting simulation using scan chain. The simulation model generates a single event transient randomly in the circuit, and detects the soft error during the execution of the test patterns. We verified this model by inserting a scan chain in an 8051 microprocessor using 65 nm CMOS technology. While the test patterns generated by ATPG program are passing through the scan chain, we insert a single event transient and detect the number of soft errors per sub-module. The experiments show that the soft error rates per cell area of the SFR module is 277% larger than other modules.

Keywords: scan chain, single event transient, soft error, 8051 processor

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Authors: Johnson Joseph Kwabina Arhinful, Owusu-Ansah Emmanuel Degraft Johnson, Okyere Gabrial Asare, Adebanji Atinuke Olusola

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This paper proposes a model to describe the blood types distribution of new Coronavirus (COVID-19) cases using the Bayesian Poisson - Hidden Markov Model (BP-HMM). With the help of the Gibbs sampler algorithm, using OpenBugs, the study first identifies the number of hidden states fitting European (EU) and African (AF) data sets of COVID-19 cases by blood type frequency. The study then compares the state-dependent mean of infection within and across the two geographical areas. The study findings show that the number of hidden states and infection rates within and across the two geographical areas differ according to blood type.

Keywords: BP-HMM, COVID-19, blood types, GIBBS sampler

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Authors: Cormac Reale, Luke J. Prendergast, Kenneth Gavin

Abstract:

While significant improvements have been made in axial pile design methods over recent years, the influence of soils natural variability has not been adequately accounted for within them. Soil variability is a crucial parameter to consider as it can account for large variations in pile capacity across the same site. This paper seeks to address this knowledge deficit, by demonstrating how soil spatial variability can be accommodated into existing cone penetration test (CPT) based pile design methods, in the form of layered non-homogeneous random fields. These random fields model the scope of a given property’s variance and define how it varies spatially. A Monte Carlo analysis of the pile will be performed taking into account parameter uncertainty and spatial variability, described using the measured scales of fluctuation. The results will be discussed in light of Eurocode 7 and the effect of spatial averaging on design capacities will be analysed.

Keywords: pile axial design, reliability, spatial variability, CPT

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