scholarly journals Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1495
Author(s):  
Jamal Maktoubian ◽  
Mohammad Sadegh Taskhiri ◽  
Paul Turner
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Business Processes ◽  
Wood Quality ◽  
Weather Condition ◽  
Remaining Useful Life ◽  
External Factors ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Biomass Waste

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

scholarly journals Understanding the value of big data in supply chain management and its business processes

2018 ◽  
Vol 38 (7) ◽  
pp. 1589-1614 ◽  
Author(s):  
Morten Brinch
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Business Process ◽  
Business Processes ◽  
Value Theory ◽  
Value Capture ◽  
Chain Management ◽  
Content Type ◽  
Process Perspective

Purpose The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective. Design/methodology/approach A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles. Findings By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data. Research limitations/implications This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence. Practical implications Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data. Originality/value Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

Big data use and its outcomes in supply chain context: the roles of information sharing and technological innovation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hamza Saleem ◽  
Yongjun Li ◽  
Zulqurnain Ali ◽  
Muhammad Ayyoub ◽  
Yu Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Information Sharing ◽  
Technological Innovation ◽  
Dynamic Capabilities ◽  
Survey Methods ◽  
Organizational Outcomes ◽  
Production Costs ◽  
Content Type ◽  
Product And Process

PurposeThis paper aims to investigate the use of big data (BDU) in predicting technological innovation, supply chain and SMEs' performance and whether technological innovation mediates the association between BDU and firm performance. Additionally, this research also seeks to explore the moderating effect of information sharing in the association between BDU and technological innovation.Design/methodology/approachUsing survey methods and structural associations in AMOS 24.0., the proposed model was tested on SME managers recruited from the largest economic and manufacturing hub of China, Pearl River Delta.FindingsThe findings suggest that BDU is positively related to technological innovation (product and process) and organizational outcomes (e.g., supply chain and SMEs performance). Technological innovation (i.e., product and process) significantly mediates the association between BDU and organizational outcomes. Moreover, information sharing positively moderates the association between BDU and technological innovations.Practical implicationsThis research provides deeper insights into how BDU is useful for SME managers in achieving the firm’s goals. Particularly, SME managers can bring technological innovation into their business processes, overcome the challenges of forecasting, and generate dynamic capabilities for attaining the best SMEs’ performance. Additionally, BDU with information sharing enables SMEs reduce their risk and decrease production costs in their manufacturing process.Originality/valueFirms always need to adopt new ways to enhance their productivity using available resources. This is the first study that contributes to big data and performance management literature by exploring the moderating and mediation mechanism of information sharing and technological innovation respectively using RBVT. The study and research model enhances our insights on BDU, information sharing, and technological innovation as valuable resources for organizations to improve supply chain performance, which subsequently increases SME productivity. This gap was overlooked by previous researchers in the domain of big data.

A Big Data implementation of the MANTIS reference architecture for predictive maintenance

2019 ◽  
Vol 233 (10) ◽  
pp. 1361-1375
Author(s):  
Felix Larrinaga ◽  
Javier Fernandez-Anakabe ◽  
Ekhi Zugasti ◽  
Iñaki Garitano ◽  
Urko Zurutuza ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Reference Architecture ◽  
Implementation Support ◽  
Physical Systems ◽  
Root Cause ◽  
Hadoop Distributed File System ◽  
Useful Life

This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.

scholarly journals Efficiency of "lean management" application in business processes management of refrigeration equipment supply chain during the COVID crisis

2021 ◽  
pp. 40-52
Author(s):  
Mariia Hryhorak ◽  
◽  
Liliya Shevchuk
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Customer Service ◽  
Business Processes ◽  
Production Costs ◽  
Global Supply Chains ◽  
Open Market ◽  
Lean Management ◽  
Refrigeration Equipment

The article reveals the main trends in the functioning of global supply chains in the context of the COVID-19 pandemic and their impact on the activities of Ukrainian enterprises are identified. It is noted that the closure of borders between countries and the introduction of self-isolation regime caused a significant reduction in production capacity and the volume of international trade. Global supply chains have become very vulnerable and necessitate their revision and the search for alternative ways to deliver goods to end users. Since Ukraine is a country with an open market economy, this article summarizes the main challenges for import-dependent supply chains and makes proposals on ways to overcome them. The expediency of applying the concept of Lean-management in business processes management of refrigeration supply chains during a pandemic as a means of overcoming crisis situations and ensuring sustainable development are proved. The dynamics and structure analysis of the company LLC "Holod Engineering" income and expenses allowed to establish a tendency to reduce the profitability of the company's business and capital turnover, as well as increase the share of logistics costs in the production costs. The greatest impact on the growth of logistics costs have the inventory costs due to the processes of storage and orders completion and delivery delays, which lead to customer dissatisfaction and complaints. Methodical approaches to estimating the level of processes coordination in the supply chains of refrigeration equipment, calculation of supply lots optimal parameters, levels of raw materials and components stocks, production and storage capacity rationalization of the enterprise are substantiated. With the help of Shewhart's control charts, the coordination of business processes in the equipment supply chains were assessed and the sources of potential losses were identified. The technological and logistics processes optimization in terms of their cost, duration and quality of results takes into account not only individual processes of enterprises, but also interprocess connections between supply chain links. It is proposed to implement a number of organizational measures using the concept of lean management, which involves market integration, production process, procurement and sales in order to provide a high level of customer service. The efficiency of the proposed organizational changes and their impact on business profitability, inventory turnover, the amount of logistics costs and the quality of customer service are determined.

scholarly journals Achieving Resilience and Business Sustainability during COVID-19: The Role of Lean Supply Chain Practices and Digitalization

2021 ◽  
Vol 13 (22) ◽  
pp. 12369
Author(s):  
Matteo Trabucco ◽  
Pietro De Giovanni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Supply Chain ◽  
Customer Service ◽  
Mobile Apps ◽  
Production Costs ◽  
Sustainable Business ◽  
Business Sustainability ◽  
Logistic Regression Models

This paper investigates how firms can enjoy a sustainable business even during the COVID-19 pandemic. The adoption of lean coordination mechanisms over the supply chain (SC) and lean approaches in omnichannel strategies can guarantee the business sustainability and resilience. Furthermore, we investigate whether business sustainability, along with digitalization through mobile apps, Artificial Intelligence systems, and Big Data and Machine Learning enable firms’ resilience. We first explore the background on the subject, identify the research gap, and develop some research hypotheses to be tested. Then, we present the data collection process and the sample, which finally consists of firms from different sectors, including retailing, electronics, pharmaceutics, and agriculture. Several logistic regression models are developed and estimated to generate findings and managerial insights. Our results show that a lean omnichannel approach is an effective practice to preserve production costs, SC visibility, inventory available over the SC, and sales. Furthermore, lean coordination with contracts can make a business sustainable by preserving quality, ROI, production costs, customer service, and inventory availability. Finally, firms can be highly sustainable through resilience when they engage in sustainable ROI, SC visibility, and sales; in contrast, the adoption of mobile apps worsens firms’ resilience, which is not influenced by Artificial Intelligence and Big Data and Machine Learning.

scholarly journals FAKTOR EKSTERNAL DALAM PENENTUAN STRATEGI DASAR PROGRAM PENDIDIKAN BIDANG REKAYASA LOGISTIK DAN RANTAI SUPLAI

2018 ◽  
Vol 2 (2) ◽  
pp. 116-126
Author(s):  
Rio Aurachman
Keyword(s):  
Supply Chain ◽  
Industry 4.0 ◽  
English Language ◽  
Business Processes ◽  
Soft Skills ◽  
Language Skills ◽  
External Factors ◽  
Education Curriculum ◽  
Evaluation Approach ◽  
Data Object

Abstract In planning education strategies in the field of logistics and SCM, external factors evaluation approach can be implemented. This study proposes the approach of mass media sources and latest news as a basis for determining external factors, as a result, the formulation obtained is newest and more contextual. Those external factors then are going to be analyzed to formulate a strategy that is suitable for logistics education. The strategy emerges into several main things; although it is not time to lead fully to Industry 4.0, the Education curriculum should include knowledge that is aligned and supports the knowledge, Soft Skills that needed to be in logistics and supply chain education are English language skills and the ability to search for data. Object of the research in terms of logistics education which currently growing interests are namely logistics and SCM marine, e-commerce and international zones. Graduates need to be equipped with the ability to do business process efficiently and digitize all business processes.

scholarly journals Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective

Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 22
Author(s):  
Hisham Alidrisi
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Multicriteria Decision Making ◽  
Analytic Network Process ◽  
Assessment Model ◽  
Self Assessment ◽  
Interpretive Structural Modeling ◽  
Supply Chain Finance ◽  
Network Process ◽  
Order Preference

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.

Sign in / Sign up

Export Citation Format

Share Document