Human versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design

2021 ◽  
pp. 1-38
Author(s):  
Joshua Gyory ◽  
Nicolas F Soria Zurita ◽  
Jay Martin ◽  
Corey Balon ◽  
Christopher McComb ◽  
...  
Keyword(s):  
Real Time ◽  
Design Process ◽  
Process Management ◽  
Superior Performance ◽  
Team Members ◽  
Engineering Teams ◽  
Complex Design ◽  
Data Driven Approach ◽  
Complex Engineering ◽  
Process Managers

Abstract Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an Artificial Intelligent (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams' actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers' intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from the both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.

The Influence of Process Management: Uncovering the Impact of Real-Time Managerial Interventions via a Topic Modeling Approach

2021 ◽  
pp. 1-46
Author(s):  
Joshua Gyory ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Problem Solving ◽  
Real Time ◽  
Process Management ◽  
Topic Model ◽  
Design Team ◽  
Design Cognition ◽  
Team Members ◽  
Conceptual Engineering ◽  
Before And After ◽  
The Impact

Abstract Computationally studying team discourse can provide valuable, real-time insights into the state of design teams and design cognition during problem-solving. The particular experimental design, adopted from previous work by the authors, places one of the design team conditions under the guidance of a human process manager. In that work, teams under this process management outperformed the unmanaged teams in terms of their design performance. This opens the opportunity to not only model design discourse during problem solving, but more critically, to explore process manager interventions and their impact on design cognition. Utilizing this experimental framework, a topic model is trained on the discourse of human designers of both managed and unmanaged teams collaboratively solving a conceptual engineering design task. Results show that the two team conditions significantly differ in a number of the extracted topics, and in particular, those topics that most pertain to the manager interventions. A dynamic look during the design process reveals that the largest differences between the managed and unmanaged teams occur during the latter half of problem-solving. Furthermore, a before and after analysis of the topic-motivated interventions reveals that the process manager interventions significantly shift the topic mixture of the team members’ discourse immediately after intervening. Taken together, these results from this work not only corroborate the effect of the process manager interventions on design team discourse and cognition but provide promise for the computational detection and facilitation of design interventions based on real-time, discourse data.

scholarly journals DECOMPOSITION AND RECOMPOSITION STRATEGIES OF PROFESSIONAL ENGINEERING DESIGN TEAMS

2021 ◽  
Vol 1 ◽  
pp. 871-880
Author(s):  
Julie Milovanovic ◽  
John Gero ◽  
Kurt Becker
Keyword(s):  
Design Process ◽  
Design Theory ◽  
Design Teams ◽  
Theoretical Understanding ◽  
Design Problems ◽  
Engineering Teams ◽  
Complex Design ◽  
Decomposition Strategies ◽  
Design Proposal ◽  
Empirical Foundation

AbstractDesigners faced with complex design problems use decomposition strategies to tackle manageable sub-problems. Recomposition strategies aims at synthesizing sub-solutions into a unique design proposal. Design theory describes the design process as a combination of decomposition and recomposition strategies. In this paper, we explore dynamic patterns of decomposition and recomposition strategies of design teams. Data were collected from 9 teams of professional engineers. Using protocol analysis, we examined the dominance of decomposition and recomposition strategies over time and the correlations between each strategy and design processes such as analysis, synthesis, evaluation. We expected decomposition strategies to peak early in the design process and decay overtime. Instead, teams maintain decomposition and recomposition strategies consistently during the design process. We observed fast iteration of both strategies over a one hour-long design session. The research presented provides an empirical foundation to model the behaviour of professional engineering teams, and first insights to refine theoretical understanding of the use decomposition and recomposition strategies in design practice.

Bringing Ergonomics to the Design of a Behavioural Care Unit

2000 ◽  
Vol 44 (8) ◽  
pp. 8-11
Author(s):  
Jacqueline B. Barnett
Keyword(s):  
Design Process ◽  
Iterative Process ◽  
User Needs ◽  
Work Processes ◽  
Team Members ◽  
Early Design ◽  
Design Changes ◽  
Final Design ◽  
The Individual ◽  
The Cost

The application of ergonomics is important when considering the built environment. In order to create an environment where form follows function, a detailed understanding of the tasks performed by the individuals who will live and work in the facility is required. Early involvement in the project is key to maximizing the benefit of ergonomics. At Sunnybrook and Women's College Health Sciences Centre in Toronto, Canada, this early intervention was embraced during the design process of a behavioural care unit for aggressive patients. The ergonomist was involved in three phases of design; user needs analysis, block schematics and detailed design. The user needs and characteristics were established using a combination of focus groups, interviews, direct observation, task analysis and critique of current working environments. The challenge was to present the information to the design team in a useful manner. The format chosen was a modification of Userfit (Poulson 1996) that outlined the various characteristics of the patient group and the design consequences with “what does this mean for me” statements. During the block schematics phase an iterative design process was used to ensure that the ergonomic principles and the user needs were incorporated into the design. Ergonomic input was used in determining the room sizes and layout and to ensure work processes were considered. Simple mock-ups and anthropometric data assisted in illustrating the need for design changes. Examples that highlight the areas of greatest impact of ergonomic intervention include the patient bathrooms, showers and tub room. Significant changes were made to the design to improve the safety of the work and living space of the end users. One of the greatest challenges was having an appreciation for the individual goals of the team members. Ensuring there was adequate space for equipment and staff often resulted in recommendations for increased space. This in turn would increase the cost of the project. The architect and, later in the project, the engineer had goals of bringing the project in on budget. The final design was very much a team effort and truly die result of an iterative process. The sum of the individual contributions could not match the combined efforts. It was only through the ergonomic contributions in this early design phase that the needs of the staff, patients and families could be so well represented. The success of the iterative process provides the foundation for bringing ergonomics considerations into the early design stages of future projects.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1995 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Abstract Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

A Novel Corrosion Monitoring and Prediction System Utilizing Advanced Artificial Intelligence

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Waleed Dokhon ◽  
Fahmi Aulia ◽  
Mohanad Fahmi
Keyword(s):  
Real Time ◽  
Oil And Gas ◽  
Salt Water ◽  
Oil And Gas Industry ◽  
Training Dataset ◽  
Metal Loss ◽  
Corrosion Monitoring ◽  
Prediction System ◽  
Well Performance ◽  
Data Driven Approach

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.

A Process Management Strategy for Re-Design: An Anchoring Adjustment Approach

1998 ◽  
Author(s):  
Lisa A. Dixon ◽  
Jonathan S. Colton
Keyword(s):  
Design Process ◽  
Management Strategy ◽  
Process Management ◽  
General Pattern ◽  
Evaluation Metrics ◽  
Anchoring And Adjustment ◽  
Adjustment Model ◽  
Design Activities ◽  
Design Process Model ◽  
Process Strategy

Abstract Preceding research on the re-design process focused on the development and verification of an Anchoring and Adjustment design process model. Compared to the existing, predominantly top-down, models, this new model was tailored specifically to describe designers’ approaches to re-design tasks. Building upon that work, this paper presents an evaluation of a re-design process strategy that is based on the key elements identified in the Anchoring and Adjustment model (a general pattern for re-design activities and two evaluation metrics). The overall goal was to formulate an efficient and effective process management strategy unique to re-design activities. Data were collected from three industry re-design projects for the evaluation. First, an analysis of the data confirmed that the pattern of design activities and evaluation metrics used by the company’s designers could be mapped onto those that comprise the Anchoring and Adjustment model. Second, the analysis of the data suggested that with additional formalization — based on an anchoring and adjustment approach — the company’s current process management technique could provide more accurate feedback to the designers for the more efficient and effective management of their re-design processes. One of the industry case studies is detailed to illustrate the research results and conclusions.

scholarly journals Complex Event Processing for Sensor Stream Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3084 ◽  
Author(s):  
Kyoungsoo Bok ◽  
Daeyun Kim ◽  
Jaesoo Yoo
Keyword(s):  
Real Time ◽  
Complex Event Processing ◽  
Superior Performance ◽  
Event Processing ◽  
Stream Data ◽  
Internet Of Things Environment ◽  
Processing Cost ◽  
Single Operator ◽  
Long Time ◽  
The Cost

As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by combining primitive events through a number of operators. However, the existing complex event-processing methods take a long time because they do not consider similarity and redundancy of operators. In this paper, we propose a new complex event-processing method considering similar and redundant operations for stream data from sensors in real time. In the proposed method, a similar operation in common events is converted into a virtual operator, and redundant operations on the same events are converted into a single operator. The event query tree for complex event detection is reconstructed using the converted operators. Through this method, the cost of comparison and inspection of similar and redundant operations is reduced, thereby decreasing the overall processing cost. To prove the superior performance of the proposed method, its performance is evaluated in comparison with existing methods.

Investigating the Use of Large-Scale Immersive Computing Environments in Collaborative Design

2014 ◽  
Author(s):  
Meisha Rosenberg ◽  
Judy M. Vance
Keyword(s):  
Design Process ◽  
Collaborative Design ◽  
Large Scale ◽  
Design Teams ◽  
Team Members ◽  
3D Cad ◽  
Cad Models ◽  
Computing Environments ◽  
Design Ideas ◽  
Human Motions

Successful collaborative design requires in-depth communication between experts from different disciplines. Many design decisions are made based on a shared mental model and understanding of key features and functions before the first prototype is built. Large-Scale Immersive Computing Environments (LSICEs) provide the opportunity for teams of experts to view and interact with 3D CAD models using natural human motions to explore potential design configurations. This paper presents the results of a class exercise where student design teams used an LSICE to examine their design ideas and make decisions during the design process. The goal of this research is to gain an understanding of (1) whether the decisions made by the students are improved by full-scale visualizations of their designs in LSICEs, (2) how the use of LSICEs affect the communication of students with collaborators and clients, and (3) how the interaction methods provided in LSICEs affect the design process. The results of this research indicate that the use of LSICEs improves communication among design team members.

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