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

Big data characteristics (V’s) in industry

In the new digital age, Data is the collection of the observation and facts in terms of events, thus data is continuously growing, getting denser and more varied by the minute across multiple channels. Nowadays, consumers generate mass amounts of data on a daily basis. Hence, Big Data (BD) emerged and is evolving rapidly, the various types of data being processed are huge, and ensuring that this data is being used efficiently is becoming increasingly more difficult. BD has been differentiated into several characteristics (the V’s) and many researchers have been developing more characteristics for new purposes over the past years. Therefore, it is shown from observation that there is a clear gap between researchers about the current status of the BD characteristics. Even after the introduction of newer characteristics, many papers are still proposing the use of 3 or 5 V’s, while some researchers are far more progressed and has reached up to 10V’s. This paper will provide an overview of the main characteristics that have been added over time and investigate the recent growth of Big Data Analytics (BDA) characteristics in each industry sector which will provide some detailed and general scope for most researchers to consider and learn from

Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis

Evaluation of CO2 storage potential in offshore strata, mid-south Atlantic: Southeast Offshore Storage Resource Assessment (SOSRA)

Subsurface geological storage of CO2 has the potential to significantly offset greenhouse gas emissions for safe, economic, and acceptable public use of fossil fuels. Due to legal advantages and vast resource capacity, offshore CO2 storage provides an attractive alternative to onshore options. Although offshore Lower Cretaceous and Upper Jurassic reservoirs have a vast expected storage capacity, quantitative assessment of the offshore storage resource in the southeastern United States is limited. This work is a part of the Southeast Offshore Storage Resource Assessment (SOSRA) project, which presents quantitative evaluation of a high-quality potential geological repository for CO2 in the Mid- and South Atlantic Planning Areas. This is the first comprehensive investigation and quantitative assessment of CO2 storage potential for the outer continental shelf within the Lower Cretaceous and Upper Jurassic rocks, including the Southeast Georgia Embayment and most of the Blake Plateau. An interpretation of 200,000 km of legacy industrial 2D seismic reflection profiles and geophysical well logs (TRANSCO 1005-1, COST GE-1, and EXXON 564-1) are utilized to create structure and thickness maps for the potential reservoirs and seals. Three target reservoirs isolated by seals based on their effective porosity values are identified and assessed. A quantitative evaluation of CO2 Storage Potential in the Offshore Atlantic Lower Cretaceous and Upper Jurassic Strata is calculated using the DOE-NETL equation for saline formations. The prospective storage resources evaluation ranges between 450 and 4700 Mt of CO2 within the Lower Cretaceous and between 500 and 5710 Mt within the Upper Jurassic sandstone rocks at P10 to P90. The efficiency factor of the dolomite ranges from 0.64 to 5.36 percent at P10 to P90 for the formation scale. Facies classification of five offshore wells in the Southeast Georgia Embayment was applied to the Machine Learning approach using Support Vector Classifier (SVC) and Random Forest Classifier (RFC). As a result, the SVC and RFC algorithms were compared to evaluate facies classification accuracy; the RFC had the most accurate and effectively used outcomes to classify lithofacies. The Machine Learning approach resulted in reliable and accurate values of predicted facies classification to improve CO2 storage estimation