Data Analytics Strategies to Exploit Historical Databases for Process Optimization and Innovation in Digitalized Industry 4.0

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

Cloud computing offers a cost-efficient data analytics platform. This is enabled by constant innovations in tools and technologies for analyzing large volumes of data through distributed batch processing systems and real-time data through distributed stream processing systems. However, due to the sensitive nature of data, many organizations are reluctant to analyze their data in public clouds. To address this stalemate, both software-based and hardware-based solutions have been proposed yet all have substantial limitations in terms of efficiency, expressiveness, and security. In this thesis, we present solutions that enable practical and expressive confidentiality- preserving batch and stream-based analytics. We achieve this by performing computations over encrypted data using Partially Homomorphic Encryption (PHE) and Property-Preserving Encryption (PPE) in novel ways, and by utilizing remote or Trusted Execution Environment (TEE) based trusted services where needed.We introduce a set of extensions and optimizations to PHE and PPE schemes and propose the novel abstraction of Secure Data Types (SDTs) which enables the application of PHE and PPE schemes in ways that improve performance and security. These abstractions are leveraged to enable a set of compilation techniques making data analytics over encrypted data more practical. When PHE alone is not expressive enough to perform analytics over encrypted data, we use a novel planner engine to decide the most efficient way of utilizing client-side completion, remote re-encryption, or trusted hardware re-encryption based on Intel Software Guard eXtensions (SGX) to overcome the limitations of PHE. We also introduce two novel symmetric PHE schemes that allow arithmetic operations over encrypted data. Being symmetric, our schemes are more efficient than the state-of-the-art asymmetric PHE schemes without compromising the level of security or the range of homomorphic operations they support. We apply the aforementioned techniques in the context of batch data analytics and demonstrate the improvements over previous systems. Finally, we present techniques designed to enable the use of PHE and PPE in resource-constrained Internet of Things (IoT) devices and demonstrate the practicality of stream processing over encrypted data.

Employees’ Reactions to IT-enabled Process Innovations in the Age of Data Analytics in Healthcare

In times of digital transformation and often-disruptive markets, companies have to continuously optimize their existing business models and at the same time promote new products and services as well as organizational structures and working processes. Human creativity in combination with artificial intelligence and cognitive systems are key enablers for organizations to optimize their business and better predict and control their innovation processes. Optimized symbiotic interaction between humans and machines needs a methodological approach in order to design and evaluate enhanced innovation processes for generating new ideas and implementing innovative hybrid working scenarios. In this paper, the authors present key elements for the optimization of innovation processes based on established creativity techniques and potentials of cognitive systems.

The effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.

Lasting development entails a balance between environmental protection, economic growth, and social cohesion, which is ensured by constantly finding alternative solutions. Sustainability along the whole food chain, by engaging all stakeholders “from farm to fork” in this highly competitive market, involves innovation of processes, products, or development of new technologies, which is crucial for food quality and safety (incl. shelf life), diversifying of range of food commodities, process optimization and a better allocation of all resources aiming at securing, and growth businesses, as well as environmental protection.Novel thermal and nonthermal food processing technologies have already proved their efficiency, e.g., in terms of food safety and shelf life of agro-food goods compared to conventional technologies. Due to the adverse effect of thermal technologies on food quality, sometimes question marks about consumer acceptability have arisen. So, nowadays, consumer-oriented innovation focuses on the “hurdle concept” and emerging nonthermal technologies. An overview of applications of the innovative nonthermal and minimal processing technologies on the value food chain is presented.KeywordsNonthermal processingHurdle conceptSustainabilityFood chainApplicationsEmerging technologies

In 2014, China launched an incentive-based policy of Tax Credit Rating Disclosure (TCRD). The objective of this policy is to reform the tax administration and facilitate enterprise digitisation. Using a difference-in-differences approach, this study finds that TCRD significantly promotes enterprises’ digital transformation (DT), with stronger effects for state-owned enterprises, large firms, and those in high-tech and manufacturing industries. The mechanism analysis highlights a dual pathway. Under dynamic capability, TCRD enhances enterprises’ DT by strengthening innovation resources, supporting strategic adjustments, and improving resource allocation. The policy fosters forward-looking innovation, sustainability, technological innovation, R&D personnel and investment, strategic focus, business strategy and resource allocation, thereby accelerating the enterprises’ DT. However, under organisational inertia, weaknesses in digital infrastructure, planned investment, digital talent recruitment, business and process innovation, process optimisation and corporate culture limit the firms’ responsiveness and offset part of the policy’s positive impact.

This study takes the technological innovation of pharmaceutical process as the starting point to explore its importance in the current pharmaceutical industry background. The purpose is to analyze the key problems of pharmaceutical process optimization and put forward the corresponding technological innovation strategies. In terms of methods, this study analyzed the characteristics and challenges of pharmaceutical process in detail from three dimensions of intelligent control technology, green production technology and process integration and optimization technology, and discussed how to improve process efficiency and product quality through technological innovation. The results show that the application of intelligent control technology, green production technology and process integration and optimization technology can effectively improve the automation level, environmental protection standard and production efficiency of pharmaceutical process. It is concluded that the technological innovation combined with the characteristics of the process is the key to promote the sustainable development of the pharmaceutical process, and has great significance to enhance the overall competitiveness of China's pharmaceutical industry.

Bismuth-based nanomaterials-assisted photocatalytic water splitting for sustainable hydrogen production

This study's goal was to determine how business data analytics strategies affected the performance of banks in the Ndola district's banking sector. A descriptive research design was used with banks in the banking industry which served as the study's target demographic. To ensure that every bank in the population had an equal chance of being chosen, stratified and purposeful sampling was used. 50 employees were selected on purpose as the study's sample size. A questionnaire was used as a primary data gathering tool and was given to employees from at least 5 different departments, particularly in Information Technology, Finance, Operations, Marketing and Customer Service departments. The survey responses were reviewed and divided into a number of categories to make it easier to use descriptive statistics for analysis. The information collected from respondents was summarized using frequency distribution tables. To make the data easier to interpret and study, it was presented as frequency distribution tables, pie charts, and bar charts and in addition data narration was also used. The study found that banks in Ndola City's banking industry used reasonable data analytics techniques and tools to aid them in maintain and attaining a competitive advantage, as well as mitigate financial risk and enhance customer experience. The research also showed that banks encountered many difficulties in the usage of business data analytics which includes organizational culture, human resource expertize, High Data Volume and Velocity, Support from top management, Customer trust and Transparency, Data Security and privacy. If performance of their banks were to remain competitive, the study advises banks in in Ndola District and other companies to implement good business data analytics practices. They should constantly make sure that the process is as inclusive as feasible while implementing data analytics practices. According to the study, commercial banks in Zambia's Ndola district should focus on business data analytics to identify patterns and correlations, improve resource management, and reduce bank hall congestion. The report also suggests that in order for Zambia's listed commercial banks to benefit as much as possible from business analytics technology, they should invest in the appropriate data analytics tools and enable their employees to use them and take appropriate action based on the knowledge they acquire.

This Special Issue collects eight peer-reviewed papers showcasing recent developments in design and manufacturing technologies, with a particular focus on the integration of advanced materials, process innovation, human-centered design, and digitalization. The contributions reflect how modern manufacturing is increasingly multidisciplinary, merging design, production, and system intelligence. The main topics covered include the following:Four-dimensional printing and smart structures, introducing self-folding mechanisms enabled by pre-stressed materials;Optimization of additive and hybrid processes, including parameter tuning for FFF with PVA supports and design strategies for directed energy deposition;Precision machining studies applied to aerospace alloys, analyzing hole quality and tool wear;Human-centric manufacturing, with on-field ergonomics assessment and human-error analysis in collaborative robotics;IoT-based monitoring and scheduling optimization for flexible manufacturing systems, improving responsiveness and reliability.

Process industries represent a significant share of the European industry in terms of energy consumption and environmental impact. In this area, data analytics techniques can prove effective when applied to the optimization of production processes and can lead to significant savings, both economic and environmental. However, application of these techniques is not straightforward. In many cases, process industries must invest in the monitoring and data integration as well as in the development and maintenance of the underlying infrastructure for data analytics, often capable of handling big data. In this talk, we present the architecture of a cross-sectorial Big Data platform for the process industries. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of large volumes of data from multiple industry domains. Such platform should be able to connect to the existing environment in the plant and use the data gathered to build predictive functions to optimize the production processes. The analytical platform will contain a development environment enabling the data scientists to build these functions and a simulation environment to evaluate the models. The platform will be shared among multiple sites from different industry sectors. Cross-sectorial sharing will enable the transfer of knowledge across different domains. The deployed architecture was tested in two process industry domains, one from the aluminium production and the other from the plastic moulding area.

Automated data collection of social networking websites plays an important role in decision making. It is very important to know that the websites like Twitter, Facebook, YouTube, Pin interest, etc. are becoming vital components of social life at present. In any research problem the mass impact on various issues can be analyzed by analyzing the data generated from these websites. Moreover, these social platforms are open and widely used for view sharing. Here various tools and strategies have been evaluated to collect the data from these websites and the analytic perspectives have also been shown. The tools and strategy shared here can prove very helpful for those who opt this open area of research. The capabilities of sentiment analysis extend to the number of real life decisions like health issues in society, or the customer reactions, etc. In this paper data collection techniques have been illustrated with the help of live implementation.

Data Analytics Strategy and Internal Information Quality

Data science aims to describe methods, techniques, data analytics strategies, mathematical, statistical, and computational modeling for engineering and data science, for timely decision-making, and also in the interaction of these areas and the tools that result from it, through the transversal and applied nature of the program, with the ability to address and solve complex problems in an innovative way. Planning, designing and executing data science projects, aligning them with accepted regulations, good practices, and IT governance, this study aims to demonstrate the relevance of the creation of the Data Science engineering career, specifically in Ecuador, obtaining as a result, a favorable response to relevance, with high scores of interest and acceptability in studies and relevance in the area of data sciences, including the study also reflects a global trend in this area.KeywordsData scienceRelevance studyData science engineeringBig data