Digitalization and Big Data in Smart Farming – Bibliometric and Systemic Analysis

Agriculture has always had a great significance in the civilization development. However, modern agriculture is facing increasing challenges due to population growth and environmental degradation. Commercially, farmers are looking for ways to improve profitability and agricultural efficiency to reduce costs. Smart Farming is enabling the use of detailed digital information to guide decisions along the agricultural value chain. Thus, better decisions and efficient management control are required through generated information and knowledge at any farm. New technologies and solutions have been applied to provide alternatives to assist in information gathering and processing, and thereby contribute to increased agricultural productivity. Therefore, this article aims to gain state-of-art insight and identify proposed solutions, trends and unfilled gaps regarding digitalization and Big Data applications in Smart Farming, through a literature review. The current study accomplished these goals through analyses based on ProKnow-C (Knowledge Development Process – Constructivist) methodology. A total of 2401 articles were found. Then, a quantitative analysis identified the most relevant ones among a total of 39 articles were included in a bibliometric and text mining analysis, which was performed to identify the most relevant journals and authors that stand out in the research area. A systemic analysis was also accomplished from these articles. Finally, research problems, solutions, opportunities, and new trends to be explored were identified.

In the Era of Digitalising Shop Floor Management: In Blissful Ignorance of Superfluous Work

This study aims to understand the extent of superfluous work at shop floors and suggests some managerial opportunities for reducing superfluous work. Drawing on the abductive reasoning, the research systematically combines a theoretical conceptualisation of decision-making processes in a digitalised manufacturing with an empirical enquiry of a smart manufacturing. The paper reveals superfluous work if decision-making processes cross disciplinary and/or organisational boundaries. Superfluous work occurs because of lacking data and information to guide reflective thinking and knowledge sharing. In relation to high complex decision making the ongoing implementation of workarounds does also cause superfluous work. Prerequisites for reducing superfluous work are accessibility of applicable data to guide reflective thinking and knowledge sharing.

Stamping Tools for Sheet Metal Forming: Current State and Future Research Directions

Sheet metal forming tools play an important role in the manufacturing of many products. With shorter product life cycles and demand for shorter time to market for new products, the process for design and manufacturing of stamping tools becomes a critical part. Stamping dies are often designed and manufactured by smaller, specialized companies. For a tooling company, knowledge and experience is an important competitive advantage. Traditionally the design process has been characterized by being based on few key individuals with much experience and craftsmanship. To stay competitive in this market there is a need for more efficient processes, systems, tools and supports in order to become more industrialized. This paper presents results from a study of the state of practice in industry within progressive stamping tool design as well as a review of relevant literature. The design and manufacturing processes for stamping dies in six companies have been investigated through semi-structured interviews, from which the main challenges in the current state for the companies are identified. The results from the interviews was analyzed and compared to the established concepts and frameworks of methods found in the literature review. The results and analysis points in the direction of efforts needed in supporting the formalization and reuse of information and knowledge from previous tool projects and production, especially during the critical steps of tool process planning and creating the tool layout.

A Hybrid Heuristic Optimization Approach for Green Flatcar Transportation Scheduling in Shipbuilding

To increase efficiency and decrease energy in fierce competition, higher standard of transportation scheduling mode for shipbuilding is necessary and urgent. By analyzing the “one-vehicle and one-cargo” transportation scheduling problem in shipbuilding, this paper proposes a bi-objective mathematical model and design a Multi-Objective Tabu Search algorithm(MOTS) to minimize total carbon emission and transportation time cost. Further, to improve the computation performance of the solution method, we combined NSGA-âĚą and MOTS to design a hybrid heuristic algorithm. Computational experiments compare three optimizing approaches and reveal that MOTS and NSGAâĚą-MOTS have certain advantages in terms of solution effect and convergence speed in large-scale instances. The case shows the proposed optimization approach can reduce carbon emissions by 61.22% for daily transportation.

scone – A Requirements Management Tool for the Specification and Variability-Based Analysis of Product Lines

Nowadays, industrial products as well as software applications are expected to be tailored to the user’s needs in an increasingly distinct manner. This often makes it necessary to design a vast number of customized variants, which leads to complex and error prone analysis and development processes. Generally, requirements engineering is considered to be one of the most significant activities in software and system development. Variant management has proven to play an important role in handling the complexity arising from mass-customization of products. However, there are only a few, often rather complex-to-use, applications which allow adding variance information directly to requirements. Especially in case of small and medium sized enterprises, approaches to meet this challenge often result in isolated solutions that are not driven by state-of-the-art analysis methods and do not cope with future requirements. This paper introduces a lightweight requirements management tool called scone, which will be embedded into an overall variability management methodology. scone enables the user to create and manage requirement specifications and augment them with variability information. Based on this specification, the requirements can be analyzed in a formal way with respect to their variability using the variability management tool Glencoe. scone was created as a single-page web application to eliminate the need for installation and allow it to run on many devices, while offering the experience of working with a native application, rather than a website. Both tools are designed to provide a proof of concept for the seamless integration of variability information within a system development process as well as to show how variability can be handled in an easy-to-use way from the very beginning within this process.

Customized Product Development Supported by Integrated Information

Information systems are key enablers for the integration and reliable management of the product development process. Information systems are the backbone that connects various sub-processes and enables flexible product customization. Fast, robust, and cost-efficient product adaptation is especially important in one-of-a-kind production. This paper presents a transformation of the product development design process for large power transformers into a competitive and smartly supported process. One-of-a-kind production is specific, as each product must be customized, wherefore a robust design process well supported by IT plays a key role in creating a digital twin and the product’s final value. Based on a systematic analysis of the sample company, this paper proposes a model for the complete renewal of information systems and of working methodology, where reorganization is demonstrated in an increase of overall effectiveness.

Intelligent Identification of Trademark Case Precedents Using Semantic Ontology

A registered trademark distinctively identifies a company, its products or services. A trademark (TM) is a type of intellectual property (IP) which is protected by the laws in the country where the trademark is officially registered. TM owners may take legal action when their IP rights are infringed upon. TM legal cases have grown in pace with the increasing number of TMs registered globally. In this paper, an intelligent recommender system automatically identifies similar TM case precedents for any given target case to support IP legal research. This study constructs the semantic network representing the TM legal scope and terminologies. A system is built to identify similar cases based on the machine-readable, frame-based knowledge representations of the judgments/documents. In this research, 4,835 US TM legal cases litigated in the US district and federal courts are collected as the experimental dataset. The computer-assisted system is constructed to extract critical features based on the ontology schema. The recommender will identify similar prior cases according to the values of their features embedded in these legal documents which include the case facts, issues under disputes, judgment holdings, and applicable rules and laws. Term frequency-inverse document frequency is used for text mining to discover the critical features of the litigated cases. Soft clustering algorithm, e.g., Latent Dirichlet Allocation, is applied to generate topics and the cases belonging to these topics. Thus, similar cases under each topic are identified for references. Through the analysis of the similarity between the cases based on the TM legal semantic analysis, the intelligent recommender provides precedents to support TM legal action and strategic planning.

Transdisciplinary System of Systems Development in the Trend to X4.0

Since the announcement of Industry 4.0 in 2012, multiple variants of this industry paradigm have emerged and built on the common platform of Internet of Things. Traditional engineering driven industries such as aerospace and automotive are able to align with Industry 4.0 and operate on requirements of the Internet of Things platform. Process driven industries such as water treatment and food processing are more influenced by societal perspectives and evolve into Water 4.0 or Dairy 4.0. In essence, the main outcomes of these X4.0 (where X can be any one of Quality, Water or a combination of) paradigms are facilitating communications between socio-technical systems and accumulating large amount of data. As the X4.0 paradigms are researched, defined, developed and applied, many real examples in industries have demonstrated the lack of system of systems design consideration, e.g. the issue of training together with the use of digital twin to simulate operation scenarios and faults in maintenance may lag behind events triggered in the hostile real world environment. This paper examines, from a high level system of systems perspective, how transdisciplinary engineering can incorporate data quality on the often neglected system elements of people and process while adapting applications to operate within the X4.0 paradigms.

Text Analysis of Disciplinary Research Papers

Research literature terminology illustrates that publications claim to pertain to “disciplinary” approaches and researcher’s align themselves to specific, multi-, inter- or trans-disciplinarities. Ambiguity exists in definition and application of disciplinarity, hence there is need to establish a coherent application of disciplinarity. We present results of content analysis of research literature claiming to be inter-, multi-, or transdisciplinary to assist in ascertaining commonalities or differences for those disciplinarities. We analyse the abstracts and keywords of 8834 papers, using n-grams and bi-grams, dating from 1970 until 2018, extracting a list of 76,552 terms for comparison. The top 15 most frequent terms characterise each disciplinarity and Venn diagrams of the top 15 features illustrate differences and overlap. A total of six terms appear common to all approaches in the abstracts, with four shared by multi- and inter-, two between inter- and trans-, and none common to multi- and trans-. The term “social science(s)” appears to be a unique feature in the trans- abstracts and our findings identify common text terms such as the “research” feature, common to all disciplinarities. This supports characterising the nature of transdisciplinarity and its unique differences from other approaches such as inclusion of social science(s).

Using AutomationML and Graph-Based Design Languages for Automatic Generation of Digital Twins of Cyber-Physical Systems

The interdisciplinary development of smart factories and cyber-physical systems CPS shows the weaknesses of classical development methods. For example, the communication of the interdisciplinary participants in the development process of CPS is difficult due to a lack of cross-domain language comprehension. At the same time, the functional complexity of the systems to be developed increases and they act operationally as independent CPSs. And it is not only the product that needs to be developed, but also the manufacturing processes are complex. The use of graph-based design languages offers a technical solution to these challenges. The UML-based structures offer a cross-domain language understanding for all those involved in the interdisciplinary development process. Simulations are required for the rapid and successful development of new products. Depending on the functional scope, graphical simulations of the production equipment are used to simulate the manufacturing processes as a digital factory or a virtual commissioning simulation. Due to the high number of functional changes during the development process, it makes sense to automatically generate the simulation modelling as digital twins of the products or means of production from the graph-based design languages. The paper describes how digital twins are automatically generated using AutomationML according to the Reference Architecture Model Industry 4.0 (RAMI 4.0) or the Industrial Internet Reference Architecture (IIRA).