In process A you can work in the situation of the optimal production batch BA* calculated in section 5.1.1. Finally, the user interface in the DSS should present the conclusions of the analysis to the user.
Introduction
This chapter presents one of these evolutions with a current transducer (CT), which can measure this strength, perform an initial processing of the signal and send it to a panel or control center. In addition, this current transducer does not require a power source to operate, as it is self-powered by the current it measures. Since it is cheap, it can be distributed throughout the facilities and provide the power at different points of the observed electrical network.
With signal processing, useful information can be inserted into this device, informing already pre-processed elements to reading devices, becoming part of the IoT world. In Section 3, the basic components used in the implementation of the proposed current transducer are presented.
Overview of the current transducer
Measuring CT modeling
In accordance with recent trends, this chapter proposes a wireless self-powered current transducer (CT) as an IIOT application for induction motors. There are arrangements with a better input impedance, but it is worth bearing in mind that any minor transformation ratio errors arising from the interaction of shunt resistance and input impedance can be compensated for by software in the microcontroller.
Modeling and simulation of the power extraction CT
The tendency of the double-core CT to saturate with a higher current in the primary is perceived. In any case, according to the rated currents and measurements in the cable, the CT is operating far from the saturation point of the core. It should also be noted that core saturation plays an important role in helping to protect the extraction electronics.
In this model a CT inductance compensating capacitor, a full wave rectifier to produce a continuous voltage in the load, and a voltage ripple capacitor. It was previously estimated that the final prototype would be equivalent to a maximum load of 1.1 W, operating at 5 VDC.
Implementation of the current measurement module
This power is equivalent to a resistive load of 22.72 Ω, which has been approximated to a 25 Ω load for availability, using three 75 Ω resistors in parallel. This figure shows a rectifier with ripple filtering capacitors, the inverter itself, and a resistive load equal to the rated application load.
Wireless interface specifications
UDP checks the integrity of the data with "checksums" and uses a system of multiple ports for different functions, both in the destination and in the source. The hardware of the Wi-Fi communication interface module prototype consists of the CC3200-LaunchXL board. This board is composed of peripherals circuitry for the CC3200 microcontroller, circuitry for debugging functions, and the antenna itself for the wireless communication system.
In the case of the personal security standard, only the use of a password is required. The basic flow of connecting, sending, and receiving data using a UDP socket from the SimpleLink CC3200 application programming interface (API).
Assembly of the current transducer
The open device case exposing (a) the electronics and (b) the CTs for measuring and extracting power.
Results of tests
Tests in the laboratory
The assembly for simulating the initial situation is shown in Figure 17; Meanwhile, the starting curve of the motor is shown in figure 18. During the tests, the main subsystem verified was the protection circuit of the buck converter, which prevents the input voltage from being greater than the allowed limit, 40 VDC. In this case, the protection is specified to harvest the voltage to about 36 Vcc, keeping the application running and dissipating the excess power to the transistor's protection buffer.
Motor starting test showing starting current reached about 344 peak (243 arms) - current transducer ratio equal to 1 MV/a.
Operation at the Pimental hydraulic power plant
Thermal image of the buck converter transistor/protection sink, in the state of starting current, 602 A, after approx. 60 s operation. In the field measurements, it was found that in the BB-ORV motors, which have a soft starter device, the starting state takes about 4 s and reaches a current of 400 A. The aim is not that the prototype can measure these currents, since they are far above the nominal CT, which is 200 A, but can withstand them without failure.
Therefore, it is normal, in this condition, for the prototype to exhibit saturated or distorted currents, as shown in Figure 23.
Considerations about the power harvesting capability of the prototype The power harvesting capability of the prototype reaches 2.5 W in the condition
Conclusions
In the analyzed cases, IIoT is produced using and integrating various digital services and software in the enterprise. The data produced by IIoT can be raw data or pre-analyzed by the IIoT service provider according to the needs of the enterprise. IIoT solutions provide many benefits to an enterprise, but they also push the enterprise to redesign its operations into data-driven processes.
This requires the company to make strategic but also organizational changes in order to succeed with the change [1]. In some cases, the Industrial Internet can affect a company's entire strategy by reshaping or changing its operations.
Research approach
This chapter presents a multiple case study (MCSR) investigation of three change projects in which new IIoT solutions have been deployed at three different Finnish companies. Both the individual case and the multiple result should be the focus of a summary report. In Figure 1, the feedback with the dotted line represents a discovery situation, where one of the cases does not fit the original design of the multiple case study.
The sources of evidence used in the individual case studies consist of documentation, archival records, interviews with top management of enterprises and IIoT solution providers, direct observations, participant observation, and physical artifacts. Each individual case was reported separately to the top management of the company in question.
Industrial Internet of Things in service business
The data is collected in a certain period of time (cross-sectional), the largest part of the data is qualitative (empirical) and involves purposive sampling and a specific selection of a phenomenon (case studies). As the trial period for IIoT solutions has been exceeded in the last few years, companies today are looking for sustainable solutions to support their operational processes and add real value to the business. Data can not only be used to guide operations, but also display real-time data.
This encourages companies to place customers and services at the center to not only achieve higher customer satisfaction but also increase service sales. Because the volume of data is growing at an unprecedented scale and depth with the proliferation of smart and sensor devices, big data analysis has emerged as a key initiative in the IIoT field [6-8].
Empirical cases
Case II: Pohjolan liikenne
Oy Pohjolan Liikenne Ab (later Pohjolan Liikenne) has been active in the transport sector since 1949. Telia's solution means that bus vehicle actions are monitored and optimized based on real-time data. That said, the fuel economy data is accurate, which is why the company has been able to find the best driving mode for drivers.
Together with the service, Pohjolan Liikenne is able to measure the driver's driving index and thereby develop better driving performance. In addition, the company can gain insight into drivers' driving time, breaks and working hours.
Case III: Delete
Cross-case conclusions
However, the essential prerequisite for success is the commitment of top management. The change project itself includes, among other things, a clear definition of the cause and objectives of the change, communication, staff involvement and evaluation. The data provided by IIoT is a valuable asset compared to the competitors of the business enterprises.
In addition, the individual case studies showed that motivation for each organizational level is essential to succeed in the implementation of the change project. However, the success factor for data-driven digital transformation depends on the business strategy and the commitment of top management, who should carry the business strategy into practice.
TOP 1%
BDA capabilities
Big data specifically refers to large data sets whose size is so large that the amount can no longer fit in memory. As the amount of data has grown, so has the need to renew the tools used to analyze it. This data should not be put into neat columns and rows like traditional data sets to be analyzed with today's technology, not at all like in the past.
As another categorization, big data consists of numerical data, image data, voice, text and discourse data. However, the combination of big data and analytics makes various tools that help decision makers obtain valuable meaningful insights and turn information into business intelligence.
Supply chain analytics
- Statistical analysis
- Simulation
- Optimization
Big Data Analytics and Its Applications in Supply Chain Management DOI: http://dx.doi.org/10.5772/intechopen.89426. The various potential benefits to be gained from data-enabled decision-making have prompted academics and researchers to pay attention to the potential integration of big data into SCM. The results indicated that big data has a positive and significant effect on social and environmental components of sustainability [15].
For example, the research presents a parallel statistical algorithm for performing sophisticated statistical analysis of big data. This algorithm uses special methods such as Mann-Whitney U testing, conjugate gradient, and ordinary least squares to model and compare the densities and distributions of big data [2].
Application of BDA in SCM areas
- BDA and supplier relationship management
- BDA and supply chain network design
- BDA and product design and development
- BDA and demand planning
- BDA and procurement management
- BDA and customized production
- BDA and inventory management
- BDA and logistics
- BDA and agile supply chain
- BDA and sustainable supply chain
Designing the supply chain according to product design creates competitive advantage and flexibility in the supply chain [38]. One study has used external and internal big data to quickly identify and manage supply chain risk [51]. Schlegel [52] also provided a big data predictive analytics framework to identify, evaluate, mitigate, and manage supply chain risk.
Supply chain visibility and BDA are complementary in the sense that each supports the other [66, 67]. Therefore, BDA techniques must be applied throughout the supply chain to achieve full benefits [79].
Application of BDA in different types of supply chain
- Application of BDA in manufacturing
- Application of BDA in finance
- Application of BDA in healthcare
To achieve sustainable competitive advantage and stay afloat in the industry, these institutions must continuously use big data and appropriate analytical techniques in their business strategy. After the 2008 global financial crisis, financial institutions must use big data and analytical techniques to gain competitive advantage [2]. Big data” in the healthcare industry includes all data related to wellness and patient healthcare.
Big data in the healthcare industry includes these characteristics of high-dimensional, diverse, heterogeneous, velocity. Big data can be used for population health management and preventive care as a new application of Huge Data in the future [106].
Analytics in supply chain
There are also other challenges in using big data in healthcare, including continuity of data acquisition, ownership, standardized data, and data cleansing [109]. For example, retailers' point of sale (POS) data provides real-time demand data with pricing information. RFID data provides an automated replenishment signal, automated receipt and storage information, and automated checkout data, informing real-time inventory status.
Manufacturing sensor data provides real-time monitoring of manufacturing equipment and identifies an unavoidable problem. During the delivery process, GPS data provides real-time inventory location data and helps to find optimal routes and reduce inventory learning times and fulfillment [110].
Conclusion and managerial implications
Big data for supply chain management in the services and manufacturing sector: challenges, opportunities and future perspectives.
