Decentralized Machine Autonomy for Manufacturing Servitization

Blockchain ecosystems are rapidly maturing and meeting the needs of business environments (e.g., industry, manufacturing, and robotics). The decentralized approaches in industries enable novel business concepts, such as machine autonomy and servitization of manufacturing environments. Introducing the distributed ledger technology principles into the machine sharing and servitization economy faces several challenges, and the integration opens new interesting research questions. Our research focuses on data and event models and secure upgradeable smart contract platforms for machine servitization. Our research indicates that with the proposed approaches, we can efficiently separate on- and off-chain data and assure scalability of the DApp without compromising the trust. We demonstrate that the secure upgradeable smart contract platform, which was adapted for machine servitization, supports the business workflow and, at the same time, assures common identification and authorization of all the participants in the system, including people, devices, and legal entities. We present a hybrid decentralized application (DApp) for the servitization of 3D printing. The solution can be used for or easily adapted to other manufacturing domains. It comprises a modular, upgradeable smart contract platform and off-chain machine, customer and web management, and monitoring interfaces. We pay special attention to the data and event models during the design, which are fundamental for the hybrid data storage and DApp architecture and the responsiveness of off-chain interfaces. The smart contract platform uses a proxy contract to control the access of smart contracts and role-based access control in function calls for blockchain users. We deploy and evaluate the DApp in a consortium blockchain network for performance and privacy. All the actors in the solution, including the machines, are identified by their blockchain accounts and are compeers. Our solution thus facilitates integration with the traditional information-communication systems in terms of the hybrid architectures and security standards for smart contract design comparable to those in traditional software engineering.

Theory of Constraints and Human Resource Management Applications

This purpose of this article is to assess constraints and suggest a theory that can improve Human Resource Management Systems (HRMS's). It investigates the relationship between Theory of Constraints (TOC) and operation management, which is based on the Critical Chain PMs book entitled The Goal. In 1984, the author, Goldratt, introduced an entire management philosophy about the TOC. The mental process and improvement theory tools discussed in his book mainly focus on manufacturing environments. However, the practice and examples in the book helped many organizations succeed, even in the private sectors. This study offers definitions of throughput, operating expense, and inventory measurements, and uses the principle of TOC to identify bottlenecks and constraints in every business process. By using these analyses, the study remodels the system to increase performance measurements of HRMS.

Knowledge Management of Work Stress in Mexican Manufacturing Environments

This chapter presents a knowledge management perspective to propose a pair of structural models to determine the relationship between burnout syndrome (BS) and body mass index (BMI) among high and middle managers of Mexican maquiladoras. In developing countries like Mexico there are opportunities to expand burnout study on diversity of contexts and occupations. The Maslach burnout general inventory questionnaire (MB-GI) was used, and sociodemographic data were collected as well as the weight and size of respondents. Instrument shows an acceptable reliability index. Structural equation models are used to determine relationship among variables. From a sample of 361 people, using segmentations of BMI, two segments are distinguished: normal weight and overweight. Model using normal weight participants shows more explanatory qualities about the relationship among burnout dimensions than the one using overweight participants. One can conclude that more factors and variables are needed to explain overweight Mexican managers.

A branch and bound method in a permutation flow shop with blocking and setup times

In this paper it is presented an improvement of the branch and bound algorithm for the permutation flow shop problem with blocking-in-process and setup times with the objective of minimizing the total flow time and tardiness, which is known to be NP-Hard when there are two or more machines involved. With that objective in mind, a new machine-based lower bound that exploits some structural properties of the problem. A database with 27 classes of problems, varying in number of jobs (n) and number of machines (m) was used to perform the computational experiments. Results show that the algorithm can deal with most of the problems with less than 20 jobs in less than one hour. Thus, the method proposed in this work can solve the scheduling of many applications in manufacturing environments with limited buffers and separated setup times.

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

New and Emerging Hazards for Health and Safety within Digitalized Manufacturing Systems

The Fourth Industrial Revolution is radically reshaping the procedures and the manufacturing environments through the digitalization process. The digitalization process can change according to the context and to specific solutions, and it is able to modify manufacturing systems and production areas. All the employees are directly affected by the transformation of the working environment, manufacturing tools, and working conditions and by the increasing need for new competencies. In this context, it is crucial to identify new and emerging hazards concerning the health and safety of the employees to ensure a conscious and safe digital transformation for everyone involved. In this regard, the paper presents the state of the research and defines seven areas of interest for a safe and harmless digital transformation for the employees, drawing attention to the hazards in the different technological areas. The state of the research unveils the absence of detailed analysis to identify specific hazards of 4.0 technologies. Therefore, every specific 4.0 technologies is analyzed by an extensive review to provide a comprehensive matrix of new and emerging hazards for health and safety within digitalized manufacturing systems. The results can help manufacturing organizations to perform robust risk assessments for worker when introducing specific 4.0 technologies.