A back-end view to climatic adaptation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alolote I. Amadi
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Ordinary Least Squares ◽  
Humid Tropics ◽  
Construction Sites ◽  
Humid Climate ◽  
Content Type ◽  
The Real ◽  
Climatic Adaptation ◽  
Technological Bias

PurposeThis study investigates the level of variance in the real time demand for bagged cement, induced in response to the climatic sequence of the humid tropics, to support best practice calls for a weather-responsive supply chain strategy.Design/methodology/approachData on the consumption of cement and site works for 100 ongoing building construction sites were gathered for a period of 12 months. The variance partitioning capabilities of the Ordinary Least Squares and Hierarchical Linear Modelling forms of regression analysis are comparatively used to evaluate the sensitivity of cement demand to the meteorological profile of wet-humid climateFindingsThe study outcome provides statistical evidence demonstrating that the meteorological profile of wet-humid climate induces a significantly high percentage of the variance in the real-time demand for bagged cement on construction sites. However, nested within this variance, are the fixed effects of the cement footprint of the building architecture inherent in the locality. Particularly, positive changes to reduce the wet trade composition of buildings or compensating changes in technological bias, are necessary to combat weather interference in the humid tropics.Research limitations/implicationsThe findings are exploratory, and not for the purposes of holistically forecasting cement demand, and can therefore only form part of a more comprehensive decision support system, bespoke to the study area.Practical implicationsThe study outcome provides a back-end view to climatic adaptation in wet humid settings, making a compelling case for localized climate-risk adaptive supply chain strategies and policies geared towards sustainability in cement usage.Originality/valueThe study delineates the confounding impact of weather, distinct from local building architecture and technological bias, thus creating a methodological platform for replication and comparative productivity studies in diverse geographical areas.

Cassandra-based data repository design for food supply chain traceability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeep Kumar Singh ◽  
Mamata Jenamani
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Query Language ◽  
Data Repository ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Traceability System ◽  
Real Time Data

Purpose The purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system. Design/methodology/approach The real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data. Findings The proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries. Originality/value The proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

scholarly journals Modeling of an IoT-enabled supply chain for perishable food with two-echelon supply hubs

2017 ◽  
Vol 117 (9) ◽  
pp. 1890-1905 ◽  
Author(s):  
Yingfeng Zhang ◽  
Lin Zhao ◽  
Cheng Qian
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Food Supply ◽  
Time Data ◽  
Food Supply Chain ◽  
Content Type ◽  
The Real ◽  
Industrial Parks ◽  
Iot Devices ◽  
Real Time Information

Purpose The huge demand for fresh goods has stimulated lots of research on the perishable food supply chain. The characteristics of perishable food and the cross-regional transportation have brought many challenges to the operation models of perishable food supply chain. The purpose of this paper is to address these challenges based on the real-time data acquired by the Internet of Things (IoT) devices. Design/methodology/approach IoT and the modeling of the Supply Hub in Industrial Parks were adopted in the perishable food supply chain. Findings A conceptual model was established for the IoT-enabled perishable food supply chain with two-echelon supply hubs. The performance of supply chain has improved when implementing the proposed model, as is demonstrated by a case study. Originality/value By our model, the supply hubs which act as the dominators of the supply chain can respond to the real-time information captured from the operation processes of an IoT-enabled supply chain, thus to provide public warehousing and logistic services.

Hardware-in-the-loop simulation for real-time software verification of an autonomous underwater robot

2016 ◽  
Vol 4 (3) ◽  
pp. 163-181
Author(s):  
Pouria Sarhadi ◽  
Reza Nad Ali Niachari ◽  
Morteza Pouyan Rad ◽  
Javad Enayati
Keyword(s):  
Software Engineering ◽  
Real Time ◽  
Autonomous Systems ◽  
Systems Design ◽  
Development Stage ◽  
Underwater Robot ◽  
Real Time System ◽  
Content Type ◽  
The Real ◽  
Time Performance

Purpose The purpose of this paper is to propose a software engineering procedure for real-time software development and verification of an autonomous underwater robotic system. High performance and robust software are one of the requirements of autonomous systems design. A simple error in the software can easily lead to a catastrophic failure in a complex system. Then, a systematic procedure is presented for this purpose. Design/methodology/approach This paper utilizes software engineering tools and hardware-inthe-loop (HIL) simulations for real-time system design of an autonomous underwater robot. Findings In this paper, the architecture of the system is extracted. Then, using software engineering techniques a suitable structure for control software is presented. Considering the desirable targets of the robot, suitable algorithms and functions are developed. After the development stage, proving the real-time performance of the software is disclosed. Originality/value A suitable approach for analyzing the real-time performance is presented. This approach is implemented using HIL simulations. The developed structure is applicable to other autonomous systems.

Blockchain technology development and implementation for global logistics operations: a reference model perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rukshanda Kamran ◽  
Nasreen Khan ◽  
Balan Sundarakani
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Reference Model ◽  
Process Efficiency ◽  
Future Research ◽  
Logistics Industry ◽  
Content Type ◽  
Traceability System ◽  
Blockchain Technology ◽  
Global Logistics

Purpose Blockchain technology offers a lot of potential benefits in supply chain management. However, there is a need of a reference model which addresses the gaps in existing frameworks. This paper aims to propose a blockchain technology-based reference model which can be applied to global logistics operations. Design/methodology/approach The researchers have integrated the fit-for-purpose theoretical framework and prototyping methodology to design the reference model, a blockchain-based logistics, tracking and traceability system (BLTTS). The researchers demonstrated the application of the reference model through a health-care supply chain case study. The proposed BLTTS can be implemented across global logistics operations for business performance improvement. Findings The research provides a framework and recommendations for global companies to consider when adopting the blockchain technology for implementation. The researchers found that the Ethereum blockchain technology improves security of the data shared within the block through the secure hashing algorithm 1. The hash algorithm ensures anonymity of the involved parties. The model integrates blockchain with supply chain thus creating transparent process, efficiency and real-time communication. Research limitations/implications The reference model will offer a better solution to global logistics operations challenges. It provides recommendations to key stakeholders involved in logistics operations segment of the logistics industry while adopting blockchain technology. Apart from the methodological limitation of the study, the system compatibility and the layer configuration aspects might be posing potential challenges while upscaling the implementation. Originality/value The proposed reference model overcomes the drawbacks of existing models as it integrates Ethereum technology. In addition, the researchers have applied the model to demonstrate its functioning in real-time environment, which could guide for future research.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

Right-time vs real-time pro-active construction safety and health system architecture

2016 ◽  
Vol 16 (3) ◽  
pp. 253-280 ◽  
Author(s):  
Jochen Teizer
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Occupational Safety ◽  
Time Window ◽  
Data Gathering ◽  
Critical Time ◽  
Construction Safety ◽  
Content Type ◽  
Safety And Health ◽  
High Concern

Purpose The purpose of this paper is to investigate the critical time window for pro-active construction accident prevention and response. Large to small organisations throughout the entire construction supply chain continue to be challenged to adequately prevent accidents. Construction worker injuries and fatalities represent significant waste of resources. Although the five C’s (culture, competency, communication, controls and contractors) have been focusing on compliance, good practices and best-in-class strategies, even industry leaders have only marginal improvements in recorded safety statistics for many years. Design/methodology/approach Right-time vs real-time construction safety and health identifies three major focus areas to aid in the development of a strategic, as opposed to tactical, response. Occupational safety and health by design, real-time safety and health monitoring and alerts and education, training and feedback leveraging state-of-the-art technology provide meaningful predictive, quantitative and qualitative measures to identify, correlate and eliminate hazards before workers get injured or incidents cause collateral damage. Findings The current state and development of existing innovative initiatives in the occupational construction safety and health domain are identified. A framework for right-time vs real-time construction safety and health presents the specific focus on automated safety and health data gathering, analysis and reporting to achieve better safety performance. The developed roadmap for right-time vs real-time safety and health is finally tested in selected application scenarios of high concern in the construction industry. Originality/value A strategic roadmap to eliminate hazards and accidents through right-time vs real-time automation is presented that has practical as well as social implications on conducting a rigorous safety culture and climate in a construction business and its entire supply chain.

Real-time data processing in supply chain management: revealing the uncertainty dilemma

2019 ◽  
Vol 49 (10) ◽  
pp. 1003-1019 ◽  
Author(s):  
Sabrina Lechler ◽  
Angelo Canzaniello ◽  
Bernhard Roßmann ◽  
Heiko A. von der Gracht ◽  
Evi Hartmann
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Data Processing ◽  
Real Time ◽  
Data Science ◽  
Research Question ◽  
Time Data ◽  
Chain Management ◽  
Content Type ◽  
Real Time Data Processing

Purpose Particularly in volatile, uncertain, complex and ambiguous (VUCA) business conditions, staff in supply chain management (SCM) look to real-time (RT) data processing to reduce uncertainties. However, based on the premise that data processing can be perfectly mastered, such expectations do not reflect reality. The purpose of this paper is to investigate whether RT data processing reduces SCM uncertainties under real-world conditions. Design/methodology/approach Aiming to facilitate communication on the research question, a Delphi expert survey was conducted to identify challenges of RT data processing in SCM operations and to assess whether it does influence the reduction of SCM uncertainty. In total, 14 prospective statements concerning RT data processing in SCM operations were developed and evaluated by 68 SCM and data-science experts. Findings RT data processing was found to have an ambivalent influence on the reduction of SCM complexity and associated uncertainty. Analysis of the data collected from the study participants revealed a new type of uncertainty related to SCM data itself. Originality/value This paper discusses the challenges of gathering relevant, timely and accurate data sets in VUCA environments and creates awareness of the relationship between data-related uncertainty and SCM uncertainty. Thus, it provides valuable insights for practitioners and the basis for further research on this subject.

Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

2017 ◽  
Vol 10 (2) ◽  
pp. 130-144 ◽  
Author(s):  
Iwan Aang Soenandi ◽  
Taufik Djatna ◽  
Ani Suryani ◽  
Irzaman Irzaman
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Necessary Condition ◽  
Support Vector ◽  
Adaptive Selection ◽  
Content Type ◽  
The Real ◽  
Time Optimization ◽  
Starting Point ◽  
Real Time Optimization

Purpose The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process efficiency. An accurate monitoring and controlling of the process can improve production yield and efficiency. The purpose of this paper is to propose a real-time optimization (RTO) using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the reactor. Design/methodology/approach The integration of the esterification process optimization using self-optimization (SO) was developed with classification process was combined with necessary condition optimum (NCO) as gradient adaptive selection, supported with laboratory scaled medium wavelength infrared (mid-IR) sensors, and measured the proposed optimization system indicator in the batch process. Business Process Modeling and Notation (BPMN 2.0) was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual phase. Next, Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine (SVM) classification and Arduino microcontroller for implementation. Findings This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent, lower error measurement with percentage error 1.11 percent, reduced the process duration up to 22 minutes, with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210°C which was more efficient, as it consumed less energy. Research limitations/implications In this research the authors just have an experiment for the esterification process using glycerol, but as a development concept of RTO, it would be possible to apply for another chemical reaction or system. Practical implications This research introduces new development of an RTO approach to optimal control and as such marks the starting point for more research of its properties. As the methodology is generic, it can be applied to different optimization problems for a batch system in chemical industries. Originality/value The paper presented is original as it presents the first application of adaptive selection based on the gradient value of mid-IR sensor data, applied to the real-time determining control state by classification with the SVM algorithm for esterification process control to increase the efficiency.

Optimal combinations of factors influencing the sustainability of Taiwanese firms

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fang-Yi Lo ◽  
Wing-Keung Wong ◽  
Jessica Geovani
Keyword(s):  
Regression Analysis ◽  
Real World ◽  
Ordinary Least Squares ◽  
Ols Regression ◽  
Content Type ◽  
The Real ◽  
Internal Resources ◽  
Sustainability Index ◽  
Know How ◽  
Factors Influencing

PurposeThe authors aim to obtain the optimal combinations of factors from institutional environment adaptation mechanisms and internal resources or capabilities that influence the sustainability of a firm.Design/methodology/approachThe authors develop a new index, called the sustainability index, based on the stakeholder perspective by employing a corporate credit risk index, an evaluation of a firm's corporate governance, corporate financial performance and firm age. The authors then apply both Ordinary Least Squares (OLS) Regression Analysis and Fuzzy set Qualitative Comparative Analysis (FsQCA) to obtain the optimal models for firms' sustainability.FindingsThe OLS analysis shows that the variables including financial leverage, slack, innovation capability, manufacturing capability and human capital that have significant influences on the sustainability of firms. Our FsQCA analysis obtains configurations of several solutions for firm sustainability and concludes that the fit of combinations of institutional factors and/or internal resources and capabilities of a firm is related to its sustainability.Research limitations/implicationsThe limitations in our new index include these: first, one may add more key metrics to measure the index; second, the findings do not provide any necessary nor a sufficient condition to get sustainability for sure. The limitations of using multiple regression analysis are that it is not able to reveal the combinations of causal conditions that can lead to the outcome in the real world as well as to the sustainability of a firm in our study. To overcome the limitations, the authors apply fsQCA analysis to identify combinations of causal conditions to a firm's sustainability in our study.Practical implicationsIntroducing the sustainability index enables us to find out all factors influencing the sustainability of a firm. The authors’ analysis can be used to identify combinations of causal conditions to lead to outcomes in the real world. Their analysis enables managers to know how to predict the sustainability of the firm. For example, the authors’ fsQCA analysis shows that low marketing capability will lead to the high sustainability of the firm. This information helps managers to make the decision or plan to achieve good results toward their businesses and get better allocate their resources and get a better investment.Social implicationsThe authors’ analysis can be used to identify combinations of causal conditions to lead to outcomes in the real world and enable managers to know how to predict the sustainability of the firm. A correct prediction can assist companies in developing their future operations, which would enhance their competitiveness vis-à-vis rivals during this time of global economic volatility, which, in turn, enables firms to perform better and employ more employees that could help the entire society.Originality/valueThe sustainability index the authors developed in our paper is new in the literature and the findings obtained by both OLS Regression Analysis and FsQCA are new in predicting a firm's sustainability. The authors’ findings are useful for academics, managers and policymakers in predicting and maintaining a firm's sustainability.

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