Modelling the effects of emission control areas on shipping company operations and environmental consequences

In this study,two integrated game models are developed to explore the possible economic and environmental consequences of Emission control areas(ECA)regulations.Moreover,the analytical solutions compared with a benchmark case are derived.We find that vessel speed and SO_(2)emissions will decrease under the ECA regulations.However,shipping company’s level of competition has no effect on the equivalent speed.The equivalent freight volume to be reduced or increased is determined by the additional operational cost per voyage due to ECA regulations.Numerical study and sensitivity analysis reveal that the vessel speed and SO_(2)emission reduction are very sensitive to the inventory costs of intransit cargo.Furthermore,if low-sulphur marine gas oil is used throughout the voyage,the SO_(2)emission reduction may be greater than 80%,with a low impact on the shipping company’s profit.Thus,considering the environmental effects,much stricter limits can be set in the future.

Evolution of process capability in a manufacturing process

Before mass production of individual components industries may assess its capability to produce it according to specifications.This capability assessment is a common requirement in the automotive sector.This work shows a case study,providing an in-depth analysis,on a critical component and finds evidence of process degradation over two years of production,quantified through capability analysis.At pre-production,it was concluded that the process was capable and,thus,no statistical process control was done during its production.Over the months no defective units were detected but then its level began to increase and it was apparent that process variability had increased and the process was no longer capable.Process improvement activities were developed using known quality tools and methodologies.This work shows how the control plan initially defined became obsolete and discusses the need to periodically review quality control mechanisms.

The role of an integrated logistics and procurement service offered by a 3PL firm in supply chain

As an integrated part in supply chain,third-party logistics(3PL)has intrinsic connections with upstream manufacturer and downstream retailer.Using a Stackelberg game model consisting of a manufacturer,a retailer and a 3PL to explicitly capture the interaction of firms’operations decisions,this paper attempts to better understand the role of integrated logistics and procurement service(ILPS)provided by a 3PL firm in supply chain management.Compared with a supply chain without ILPS,a Pareto region,in which all the supply chain members benefit from working with a 3PL firm offering ILPS,is disclosed.We also show that the Pareto region is more likely to occur with higher demand uncertainty.Finally,we reveal that the manufacturer obtains the highest profit in the Pareto region,and that the retailer can improve his profit share as the standard deviation of demand increases.

A linked data-based approach for clinical treatment selecting support

Treatment plan selection is a complex process because it sometimes needs sufficient experience and clinical information.Nowadays it is even harder for doctors to select an appropriate treatment plan for certain patients since doctors might encounter difficulties in obtaining the right information and analyzing the diverse clinical data.In order to improve the effectiveness of clinical decision making in complicated information system environments,we first propose a linked data-based approach for treatment plan selection.The approach integrates the patients’clinical records in hospitals with open linked data sources out of hospitals.Then,based on the linked data net,treatment plan selection is carried on aided by similar historical therapy cases.Finally,we reorganize the electronic medical records of 97 colon cancer patients using the linked data model and count the similarity of these records to help treatment selecting.The experiment shows the usability of our method in supporting clinical decisions.

A decision support system based on machined learned Bayesian network for predicting successful direct sales marketing

This paper proposes a decision support system based on a machine-learned Bayesian network(BN)to predict the success rate of telemarketing calls for long-term bank deposits.Telemarketing is one of the most common interactive techniques of direct marketing,widely used by financial institutions such as banks to sell long-term deposits.In this study,we develop a BN model that predicts the likelihood that a potential client subscribes to a long-term deposit,which is considered an output variable.The causal relationship among client attributes and outcomes has been identified using the augmented Naïve Bayes approach,a well-known supervised learning algorithm.The impact of each client’s attribute on the likelihood of subscribing is predicted.Further,we carry out multiple simulation scenarios using BN’s unique features(forward and backward propagation)to provide more in-depth discussions and analysis on predicting the likelihood of subscription for clients with particular characteristics.

A comprehensive review from hyperlink to intelligent technologies based personalized search systems

In the present era of big data,web page searching and ranking in an efficient manner on the World Wide Web to satisfy the specific search needs of the modern user is undoubtedly a major challenge for search engines.Even though a large number of web search techniques have been developed,some problems still exist while searching with generic search engines as none of the search engines can index the entire web.The issue is not just the volume but also the relevance concerning the user’s requirements.Moreover,if the search query is partially incomplete or is ambiguous,then most of the modern search engines tend to return the result by interpreting all possible meanings of the query.Concerning search quality,more than half of the retrieved web pages have been reported to be irrelevant.Hence web search personalization is required to retrieve search results while incorporating the user’s interests.In the proposed research work we have highlighted the strengths and weakness of various studies as proposed in the literature for web search personalization by carrying out a detailed comparison among them.The in-depth comparative study with baselines leads to the recommendation of Intelligent Meta Search System(IMSS)and Advanced Cluster Vector Page Ranking(ACVPR)algorithm as one of the best approaches as proposed in the literature for web search personalization.Furthermore,the detailed discussion about the comparative analysis of all categories gives new opportunities to think in different research areas.

Express analysis for prioritization:Best-Worst Scaling alteration to System 1

The work considers modification of the Best–Worst Scaling(BWS)to the so-called System 1(S1)approach.S1 was described by D.Kahneman as a spontaneous and automatic reaction by an unconsciousway in which human decision-makers choose among multiple alternatives.Application of S1 can be seen as a simplified BWS for data eliciting and express analysis of prioritization between many compared items.In S1,a respondent picks the items with which she feels“happy”,and those can be one,several,all,or none items in a task.Estimation of utilities is performed by multinomial-logit modeling with different optimization criteria which produce parameters of the models and choice probabilities of the items.Numerical examples by marketing research data are encouraging and demonstrating that spontaneous choice decisions can make S1 approach very fast,efficient,and convenient for express analysis of items prioritization,especially for big data.

Analytics on medical records collected from a distributed system deployed in the Indian rural demographic

One of the standout problems in rural regions of India is the lack of a comprehensive medical record system.Medical records are a vital part of any diagnosis as they provide a glimpse into the patient’s past,which is influential to the current diagnosis.Medical records help practitioners spot anomalies and patterns in a variety of medical cases.It is also utilized to gain a better understanding of the health situation in certain demographics.In this paper,the authors have proposed a comprehensive distributed system which can be used to preserve the medical history of individuals,and also provide a valuable insight into the health situation of the rural populous.The distributed data are aggregated into a single entity from which observations are gathered.The data acquired could be of extreme importance to the government,as it can be utilized to determine the health issues which require immediate attention,and to evaluate possible mitigation plans.The data collected would be portable and easily accessible with the help of mobile devices.

Explore success factors that impact artificial intelligence adoption on telecom industry in China

As the core driving force of the new round of informatization development and industrial revolution,the disruptive achievements of artificial intelligence(AI)are rapidly and comprehensively infiltrating into various fields of human activities.Although technologies and applications of AI have been widely studied and factors that affect AI adoption are identified in existing literature,the impact of success factors on AI adoption remains unknown.Accordingly,this paper proposes a framework to explore the impacts of success factors on AI adoption in telecom industry by integrating the technology,organization,and environment(TOE)framework and diffusion of innovation(DOI)theory.Particularly,this framework consists of factors regarding external environment,organizational capabilities,and innovation attributes of AI.The framework is empirically tested with data collected by surveying telecom companies in China.Structural equation modeling is applied to analyze the data.The study provides support for firms’decision-making and resource allocation regarding AI adoption.

Classification methodology for spare parts management combining maintenance and logistics perspectives

Spare parts management is a function of maintenance management that aims to support maintenance activities,giving real-time information on the available quantities of each spare part and adopting the inventory policies that ensure their availability when required,minimizing costs.The classification of spare parts is crucial to control the vast number of parts that have different characteristics and specificities.Spare parts management involves mainly two areas,maintenance and logistics.Therefore,the integration of both input information is recommended to make decisions.This paper presents a multicriteria classification methodology combining maintenance and logistics perspectives that intends to differentiate and group spare parts to,subsequently,define the most appropriate stock management policy for each group.The methodology was developed based on a case study carried out in a multinational manufacturing company and is intended to be included in its computerized maintenance management system to support decision-making.

Authentic leadership and employees’job performance:mediation effect of positive employee health

In light of global competition and the COVID-19 pandemic,organizations are encountering an increasingly challenging and unpredictable environment.Consequently,employees are experiencing heightened levels of job strain.This study aims to explore the impact of various organizational mechanisms on promoting positive employee health within the organization,ultimately affecting employees’job performance.The findings of this study indicate that authentic leadership and the absence of organizational politics are significant predictors of positive employee health.Moreover,positive employee health has a positive influence on supervisor-rated job performance through its effect on job engagement.This study serves as a valuable resource for organizations,shedding light on the fundamental factors that contribute to positive employee health.It also raises managers’awareness of the importance of nurturing and sustaining employees’emotional and physical well-being to maintain competitiveness in the market.

Optimal promotional policy of an innovation diffusion model incorporating the brand image in a segment-specific market

This paper addresses the problem of determining the optimal promotional policy for a diffusion model in a segment-specific market under the assumption that the additional demand of the new product also improves brand image in the form of goodwill of the firm.The model is framed with the assumption that the firm uses the mass and differentiated promotion effort for each segment.The differentiated promotional efforts target each market segment independently and the mass promotional effort reaches different segments with a fixed spectrum.We derive the optimal promotional effort policy for each segment using maximumprinciple and also analyze the stability of the dynamical system by constructing a Lyapunov function through the graph theoretic approach.The analysis gives a deep insight into how the promotional effort should be planned by the decision makers keeping in mind the financial constrains without hindering the promotional effort at the end of the planning period.

The impact of Facebook on real estate sales

Businesses have been using social media to promote products and services to increase sales.This paper aims to study the impact of Facebook on real estate sales.First,we examine how realtors’activities on Facebook business pages are associated with real estate sales.Then,we include time lags in analysis because a time lag can be expected between activates on Facebook and a resulting real estate transaction.For the collected datasets,the results suggest that:(1)The total numbers of Facebook likes,links,and stories are positively associated with real estate sales;(2)The average sentiment score of Facebook posts is negatively associated with real estate sales;(3)The influence of activities on Facebook has a time lag effect on real estate sales.The research findings can be used by real estate stakeholders to promote and potentially forecast sales.

An application of local linear radial basis function neural network for flood prediction

Heavy seasonal rain makes waterway flood and is one of the preeminent reason behind flooding.Flooding causes various perils with outcomes including danger to human life,harm to building,streets,misfortune to horticultural fields and bringing about human uprooting.Thus,prediction of flood is of prime importance so as to reduce exposure of people and destruction of property.This paper focuses on applying different neural networks approach,i.e.Multilayer Perceptron,Radial Basis functional neural network,Local Linear Radial Basis Functional Neural Network and Artificial Neural Network with Whale Optimization to predict flood in terms of rainfall,gauge,area,velocity,pressure,average temperature,average wind speed that are setup through field and lab investigation from the contextual analysis of river“Daya”and“Bhargavi”.It has always been a troublesome undertaking to predict flood as many factors have influence on it although with this neural network models the prediction accuracy can be optimized using back propagation method which is a widely applied over traditional learning method for neural system because of its preeminent learning ability.The flood prediction system is built with the four models and a comparison is made which provides us the answer to which model is effective for the prediction.

Efficiency measurement of fertilizer-manufacturing organizations usingFuzzy data envelopment analysis

The aim of the paper is to benchmark the performance of the Indian fertilizermanufacturing organizations based on the ranking of efficiencies using a fuzzy data envelopment analysis(FDEA).FDEA has been used to find the relative efficiency and ranking of the fertilizer-manufacturing organizations.The last few years’data have been converted into the fuzzy inputs and outputs as minimum,mean,and maximum values,respectively.The performance of the fertilizer manufacturing organizations is based on the output maximization model of DEA.The frontier organizations set the benchmark for the lagging organizations for further improvement in the performance.This method can also be used to incorporate the data of the several years for multiple inputs and outputs instead of consideration of data of only one year.The proposed approach in this study may help organizations to improve its efficiency to fulfill its goal.

An evolving trajectory-from PD,logistics,SCM to the theory of material flow

The growing interest in the material flow(MF)theory has invoked much interesting research in recent years.Although the MF theory is relatively new,a review of the related literature from a historical perspective shows that MF theory represents a new stage of the evolutionary development of interrelated subjects such as Physical Distribution(PD),Logistics,and Supply Chain Management(SCM).The purpose of this paper is to provide a summative review of the evolution of the subjects of PD,Logistics,and SCM,and their new development,MF theory.The paper aims at tracing how concepts and findings in PD,Logistics,SCM,and MF have been developed and have evolved.The study shows that PD evolved to Logistics in middle of the 1980s;starting from the late 1990s,Logistics has evolved to SCM;and today PD,Logistics,and SCM can be considered to be under the umbrella provided by a new theory called MF theory.This paper points out that MF theory is a necessity to deal with the overwhelming complexity of material flow systems in the global economy of the twenty-first century.

Call for Papers Journal of Management Analytics(JMA)Special issue:artificial intelligence and management analytics

Guest Editors(in alphabetical order)Michael Haenlein,ESCP Europe Business School,Paris,France Andreas Kaplan,ESCP Europe Business School,Berlin,Germany Chee-Wee Tan,Copenhagen Business School(CBS),Copenhagen,Denmark Pengzhu Zhang,Shanghai Jiao Tong University,China Artificial Intelligence(AI),defined as“a system’s ability to correctly interpret external data,to learn from such data and to use those learnings to achieve specific goals and tasks through flexible adaptation”(Kaplan&Haenlein,2019).

Fuzzy analytic network process(FANP)approach in formulating infrastructural decisions of sustainable manufacturing strategy

This paper explores a fuzzy analytic network process(FANP)approach in identifying the content of manufacturing strategy infrastructural decisions that integrates sustainability and classical manufacturing strategy framework with firm size component.The findings are as follows:(1)firms must ensure highquality product requirements with a make-to-order production system;(2)large firms must prioritize decentralization of functional areas but they must also provide higher systems integration of all production-related information to mobilize better communication channels;(3)large firms must employ highly skilled human resources in order to provide strong support on functional areas;and(4)small and medium enterprises must focus on product introduction to the market as a strategy priority area.The main contribution of this paper is the decision-making framework under uncertainty that holistically identifies the decisions that comprise a sustainable manufacturing strategy which may serve as guidelines in improving existing sustainable manufacturing practices or in creating new ones.

Simultaneous settings of order quantity,wholesale price,production run length,process mean,and warranty period

In this article,the authors propose a modified version of S.L.Chen and Liu’s model with a two-stage production system.Assume that the retailer’s order quantity is concerned with the manufacturer’s selling price and the warranty period of product.The used cost of the customer is measured under the Taguchi’s quadratic quality loss function and concluded in the retailer’s profit function.The quality of the lot for the manufacturer is determined by adopting a two-stage single sampling rectifying inspection plan.The modified economic manufacturing quantity(EMQ)model is addressed in formulating the manufacturer’s expected profit.The retailer’s order quantity,manufacturer’s wholesale price,production run length,process mean,and warranty period of product will be jointly determined by maximizing the total expected profit of the supply chain system including the manufacturer and the retailer.Finally,the quality investment policy is introduced to illustrate the profit improvement for the supply chain system.

Systematic analysis of artificial intelligence in the era of industry 4.0

Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily life.With the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry 4.0.This study conducts a systematic literature review to study the state-of-the-art on AI in industry 4.0.This paper describes the development of industries and the evolution of AI.This paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry 4.0.The findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of AI.In the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.