SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field

Adding Digital Forensic Readiness as a Security Component to the IoT Domain

The unique identities of remote sensing, monitoring, self-actuating, self–adapting and self-configuring “things” in Internet of Things (IoT) has come out as fundamental building blocks for the development of “smart environments”. This experience has begun to be felt across different IoT-based domains like healthcare, surveillance, energy systems, home appliances, industrial machines, smart grids and smart cities. These developments have, however, brought about a more complex and heterogeneous environment which is slowly becoming a home to cyber attackers. Digital Forensic Readiness (DFR) though can be employed as a mechanism for maximizing the potential use of digital evidence while minimizing the cost of conducting a digital forensic investigation process in IoT environments in case of an incidence. The problem addressed in this paper, therefore, is that at the time of writing this paper, there still exist no IoT architectures that have a DFR capability that is able to attain incident preparedness across IoT environments as a mechanism of preparing for post-event response process. It is on this premise, that the authors are proposing an architecture for incorporating DFR to IoT domain for proper planning and preparing in the case of security incidents. It is paramount to note that the DFR mechanism in IoT discussed in this paper complies with ISO/IEC 27043: 2015, 27030:2012 and 27017: 2015 international standards. It is the authors’ opinion that the architecture is holistic and very significant in IoT forensics

Enabling effective operational decision making on a Combined Heat and Power System using the 5C architecture

The use of Cyber Physical Systems (CPS) to optimise industrial energy systems is an approach which has the potential to positively impact on manufacturing sector energy efficiency. The need to obtain data to facilitate the implementation of a CPS in an industrial energy system is however a complex task which is often implemented in a non-standardised way. The use of the 5C CPS architecture has the potential to standardise this approach. This paper describes a case study where data from a Combined Heat and Power (CHP) system located in a large manufacturing company was fused with grid electricity and gas models as well as a maintenance cost model using the 5C architecture with a view to making effective decisions on its cost efficient operation. A control change implemented based on the cognitive analysis enabled via the 5C architecture implementation has resulted in energy cost savings of over €7400 over a four-month period, with energy cost savings of over €150,000 projected once the 5C architecture is extended into the production environment

The Change Management Process for Automation Implementations

The objective of this thesis is to identify change management processes in manufacturing and, if they exist, identify challenges and opportunities for improvement. There are many changes encountered in manufacturing as the advances of automation are integrated within production. For this reason, a change management process is required to effectively and efficiently implement these changes. To research this, a case study was conducted at a large manufacturing firm (more than ten-thousand employees). The facility studied produces low volume (~one per week), high complexity (~million components) products. The case study spanned six months, in which sixteen interviews were conducted with nine people from three different functional groups. The case study focused on a change to production, which was an automated machine that was implemented in the facility. This was not a change to the product, but a newly configured production station resulting in a decrease in automation level (bringing more manual activity into the task). The previous manufacturing method was fully automated but was not robust. Therefore, the change was to increase the human-robot cooperation in the robotic system. This study investigated the change process for this newly implemented automation. This was identified as a good case example to study due to several reasons. First, this was implemented within the past five years, which meant that people involved in the change process were still present. In addition to this, since the machine was still in operation it meant the propagation effects were stable and the changes were kept. Another reason this was a good example, was because this was a large-scale investment (~million dollars). This meant the return on investment (ROI) was high, leading to more attention to detail and higher resource allocation. From a research perspective, these reasons ensure the process was a critical case for study. Many change management processes align with the following high-level process: identify opportunity, gather approval to find a solution, form teams to solve, discover a solution, review, deploy a solution, and measure the solution. The change management process identified through the interviews followed this general pattern. In this model, thirty-four tasks were identified. Through a series of follow-up interviews, the process model was validated. However, obstacles were identified throughout some of the tasks in the process that encountered many changes. To explore this, a collaborative design resistance model was applied to see whether the model could accurately identify the tasks of highest resistance. The resistances were applied to the objective data from the interviews, such as team size and communication, and then compared to the subjective obstacles. From this, it was determined that the resistance model accurately predicted the challenges throughout the process. This research resulted in a mapped change management process for typical automation implementations. It additionally helped discover opportunities for making these implementations more efficient by mitigating the resistances. Motivated from this study, the following are some opportunities that were discovered for future work: conducting workshops to have participants build the change process model, studying the process at a small-medium enterprise, studying the process at a company with product change (high volume, low complexity)