Next generation smart manufacturing and service systems using big data analytics

© 2018 Elsevier Ltd This special issue explores advancements in the next generation manufacturing and service systems by examining the novel methods, practical challenges and opportunities in the use of big data analytics. The selected articles analyse a range of scenarios where big data analytics and its applications were used for improving decision making in manufacturing and services sector such as online data analytics, sourcing decisions with considerations for big data analytics, barriers in the adoption of big data analytics, maintenance planning, and multi-sensor data for fault pattern extraction. The paper summarises the discussions on the use of big data analytics in manufacturing and service sectors

Naval Aviation Squadron Risk Analysis Predictive Bayesian Network Modeling Using Maintenance Climate Assessment Survey Results

Associated risks in flying have resulted in injury or death to aircrew and passengers, and damage or destruction of the aircraft and its surroundings. Although the Naval Aviation\u27s flight mishap rate declined over the past 60 years, the proportion of human error causal factors has stayed relatively constant at about 80%. Efforts to reduce human errors have focused attention on understanding the aircrew and maintenance actions occurring in complex systems. One such tool has been the Naval Aviation squadrons\u27 regular participation in survey questionnaires deigned to measure respondent ratings related to personal judgments or perceptions of organizational climate for meeting the extent to which a particular squadron achieved the High Reliability Organization (HRO) criteria of achieving safe and reliable operations and maintenance practices while working in hazardous environments. Specifically, the Maintenance Climate Assessment Survey (MCAS) is completed by squadron maintainers to enable leadership to assess their unit\u27s aggregated responses against those from other squadrons. Bayesian Network Modeling and Simulation provides a potential methodology to represent the relationships of MCAS results and mishap occurrences that can be used to derive and calculate probabilities of incurring a future mishap. Model development and simulation analysis was conducted to research a causal relationship through quantitative analysis of conditional probabilities based upon observed evidence of previously occurred mishaps. This application would enable Navy and Marine Corps aviation squadron leadership to identify organizational safety risks, apply focused proactive measures to mitigate related hazards characterized by the MCAS results, and reduce organizational susceptibility to future aircraft mishaps

Interaction between Supply Chain Management and Management Accounting Practices for Preventing Fraud to Firm’s Performance

Although several studies have been carried out on Management Accounting Practices (MAP) and Supply Chain Management (SCM) but separately, without looking at their synergic role in an organization. Similarly, studies have examined impact of fraud and corruption on a firms performance but none has measured the impact of Management Accounting Practices or Supply Chain Management singularly or in a combined role on Firm Performance (FPP). This study aims at understanding the interaction of Management Accounting Practices and Supply Chain Management for preventing fraud for ensuring a firm performance. Hence, this study attempts to examine the mediating role of Supply Chain Management in explaining the relationship between Management Accounting Practices and Firm Performance for the purpose of preventing fraud. Data for the study was collected through a questionnaire distributed to 60 financial institutions in Indonesia which included banks, insurance, finance and leasing companies. The respondents were mainly manager level executives. The collected data was analyzed through SEM Smart PLS Package. Pearson correlation coefficients were obtained to test the hypotheses of the study. Results revealed positive relationship between Management Accounting Practices and Supply Chain Management and a positive relationship between Management Accounting Practices and Firm Performance. The study also revealed that Supply Chain Management could be used to mediate the relationship between Management Accounting Practices and Firm Performance and this interaction could be utilized to prevent fraud. These findings support the theoretically expected positive relationship between Supply Chain Management and Firm Performance. Additionally, the results of the study also reiterate the theoretical role of Supply Chain Management to consolidate Management Accounting Practices. The findings would be useful for financial managers in banking and insurance sector to consider Supply Chain Management in determining their key performance indicators (KPIs) of the firm

Fire Risk Assessment: A Systematic Review of the Methodology and Functional Areas

Fire is a physical and social phenomenon that affects both individuals and the environment. Fire risk assessment is a critical part of a fire prevention program. In this process, the fire risk associated with the possibility of occurrence and severity of damage resulting from the fire is estimated and calculated. In this paper, a classification scheme and a systematic literature review are presented in order to classify and interpret the current researches on fire risk assessment methodologies and applications. Based on the scheme, 93 scholarly papers from 13 journals are categorized into application areas and other categories. The application areas include the papers on the topics of environmental impact, production and industry, transportation, buildings, power industry, oil and gas industry, urban fires and other topics. Scholarly papers are also classified by (1) year of publication, (2) journal of publication, (3) year of publication and application areas and (4) authors’ nationality. The survey results show that the largest number of papers was published during the period 2010-2012 with 31 (33.33%), the most of the studies have been carried out on environmental impact (47.31%), the journal of Forest Ecology and Management had the highest percentage of articles with 26.88%. It is hoped that the paper can meet the needs of researchers for easy references of fire risk assessment methodologies and applications. Therefore, this work would be able to provide useful insights into the anatomy of the fire-risk assessment methods, and suggest academic researchers and experts a framework for future attempts and researches

BAYESIAN-INTEGRATED SYSTEM DYNAMICS MODELLING FOR PRODUCTION LINE RISK ASSESSMENT

Companies, across the globe are concerned with risks that impair their ability to produce quality products at a low cost and deliver them to customers on time. Risk assessment, comprising of both external and internal elements, prepares companies to identify and manage the risks affecting them. Although both external/supply chain and internal/production line risk assessments are necessary, internal risk assessment is often ignored. Internal risk assessment helps companies recognize vulnerable sections of production operations and provide opportunities for risk mitigation. In this research, a novel production line risk assessment methodology is proposed. Traditional simulation techniques fail to capture the complex relationship amongst risk events and the dynamic interaction between risks affecting a production line. Bayesian- integrated System Dynamics modelling can help resolve this limitation. Bayesian Belief Networks (BBN) effectively capture risk relationships and their likelihoods. Integrating BBN with System Dynamics (SD) for modelling production lines help capture the impact of risk events on a production line as well as the dynamic interaction between those risks and production line variables. The proposed methodology is applied to an industrial case study for validation and to discern research and practical implications

Supply chain integration model: practices and customer values

Dissertation to obtain PhD in Industrial EngineeringIn order to increase partnership efficiency and truly meet the customers' demands, in today's business environment companies are operating in supply chains. Integration of supply chains facilitates minimizing diferent types of wastes and satisfying needs of the end customer. The first step toward supply chain integration is to understandand the customer values, and to reconfigure supply chain to support those values. The current research addresses supply chain integration through quantifying relations between supply chain practice and customer values. It employs Bayesian network and analytic network process as tools to quantify comparative relations among entities. The proposed approach starts with identifying trade-offs along customer values using Bayesian network. In parallel supply chain practices are comparatively analyzed through interviews with experts which is technically quantified using analytic network process. Thereafter, these two parallel phases join together to form a network of customer values and supply chain practices. The network is able to quantitatively identify relations among nodes; in addition, it can be used to plan scenarios and handle senstitivity analyses. This model is expected to be used by supply chain decision makers to have a quantitative measure for monitoring the influence of practices on preferences of the end customer. A survey and two case studies are discussed which go through aforementioned phases. The survey identifies and analyzes six customer values namely quality, cost, customization, time, know-how and respect for the environment. It makes input for the two cases which develop supply chain integration model for fashion and food industry. Supply chain practices are categorized into two groups of manufacturing and logistics practices. The two case studies include five manufacturing practices as cross functional operations, decrease work in process, implement standards, mixed production planning, and use recyclable materials as well as four logistics practices namely visibility to upstream /downstream inventories, information sharing with customer, implement logistics standards, and just in time.Fundação para a Ciência e Tecnologia - (MIT Project: MIT-Pt/EDAM-IASC/0022/2008

Methodology for Detection and Assessment of the Impact Of Informal Processes On Organizational Output

This research focuses on the detection and assessment of informal processes within an organization. Informal processes are defined as activities that are not formalized with respect to the inputs, resources, and/or controls; or an activity that deviates from a formal process. Informal processes affect all aspects of an organization's business. Informal processes cannot be eliminated (nor should they necessarily be). The question becomes how can we identify the informal processes and assess their impact on our system/s safety? The research reported in this paper is aimed at providing an answer to this question. A theoretical foundation in the area of organizational culture, structures and practices culminating in the SoTeRiA (Socio-Technical Risk Analysis) framework provides the general model for this research. A comprehensive methodology for the detection, identification and assessment of informal processes is presented which will allow an organization to benefit from positive informal processes, while resolving detrimental informal processes to preclude their use. Two detection methods have been developed - an indirect detection method (questionnaire completed by a management representative) and a direct detection method (process audit). A methodology has been developed to be utilized as a guideline in the performance of process audits that encompasses process element identification, process interactions, and the usage of document trees. A methodology for the assessment of the impact of informal processes on an organization has been developed that will enable businesses and organization's to have more accurate and complete data from which to make their decisions regarding the state of the organization. To assess the impact of informal processes, Bayesian Belief Networks were utilized to determine the probability of the process output failure with the inclusion of informal processes and then after the informal processes were brought into the formal system. The application of this methodology has proven that when either informal processes that are beneficial to an organization are brought into the formal system, or detrimental informal processes are eliminated, the probability of the output failure decreases. The methodology presented provides a comprehensive approach to the understanding, detection, and assessment of informal processes in an organization

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Application of Bayesian networks in analysing tanker shipping bankruptcy risks

Purpose: This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms. Design/methodology/approach: This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks. Findings: The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments. Research limitations/implications: The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available. Practical implications: The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate. Originality/value: Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences