seeking out and resolving misalignment of beliefs. Open communications were the norm where identifying and sharing risks, discrepancies, and differing opinions were seen as a positive behavior
Overall, for Project One, eroding belief alignment was evident over the life of the program as the stakeholders did not learn from previous activities and outcomes and instead hardened their own beliefs. Once these patterns were set, the culture of the project made it hard to change the patterns of behavior and project structure. As can be seen, the project had a consistent trajectory leading from initial confidence to the reality of a poorly run project. Conversely, the culture of Project Two employed open communications across all stakeholders and maintained the idea of being a learning organization that actively sought to improve throughout the project life cycle. Continuous alignment and high stability in the project are evidenced by the low variation in measurements for Project Two over time.
While this is an initial study, indications are that the constructs of Epoch 4 will improve success in quality project deployment. As more projects follow these constructs, it is hoped that future studies will prove the value of employing HSE and AI into the development environment.
3.13 Summary
As SE enters Epoch 4, the complexity and scale of projects have increased dramatically. While the advent of MBSE has brought a level of rigor and repeatability to the practice, formal methods that recognize the importance of the sociotechnical aspects of system development are lagging. However, quantitative real‐time analysis of the sociotechnical space has yet to be widely employed. Historically, the difficulties in using sociotechnical models could be traced to a lack of data and computational power. With the proliferation of digital communications, natural language processing, and methodologies, such as SAFe, these fundamental barriers are falling. Yet even with agile approaches, over half of projects fail to be delivered within the time, cost and performance constraints, or fail outright (Mersino, 2018).
This chapter explored how a quantitative model of the sociotechnical space can be used to identify incongruities between stakeholders and highlight risks. While SAFe provides a methodology for continuous communication and data exchange, in most implementations, the data is informal or at best captured in meeting notes. With improvements in natural language processing, text processing, and uncertain reasoning as well as machine learning, there is an opportunity for both model stakeholder belief systems to predict their actions and train the models by analyzing the correlation between text communications, e‐mail, and other sources.
The chapter also illustrated how the conceptual architecture described in Section 3.3.1 can be readily instantiated as a tool to identify incidental and emerging risk. Results indicated promise that sociotechnical risks can be identified early on in the development effort and dealt with. The case study presented illustrated the possible positive effects of employing this approach.
The quest to find more effective methodologies for system development efforts continues to be a research challenge across numerous industries. While improving digital representation can result in faster, more accurate system development, it still does not address the most frequent reason for project failures, the misalignment between stakeholders’ beliefs and expectations. In addition to the obvious challenge of mismatched expectations, misalignment can have a more insidious risk. When belief structures are misaligned, noncomplementary actions, and even actions that are cross‐purposes, can be taken. Quantitative sociotechnical modeling and risk analysis have the potential to significantly improve successful development by explicitly monitoring and analyzing belief structures and making proactive predictions of emerging risks. Mature sociotechnical modeling as part of the SE capability development is the realization of the promise of Epoch 4.
The authors’ affiliation with The MITRE Corporation is provided for identification purposes only and is not intended to convey or imply MITRE’s concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author. This paper has been approved for Public Release; Distribution Unlimited; Case Number 20‐1208.
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