The term technical debt (TD) is no longer limited to software engineering but can be applied to the full product development lifecycle. Technical debt is particularly relevant to systems engineers because it impacts product development as well as program execution, resulting in lower productivity and increased risk. Although TD has its benefits, Leading Indicators have traditionally been used in Systems engineering to help prevent surprises during system development by providing timely information about potential problems, improve cost estimating by providing more accurate information about the system under development, and provide information about which activities are most likely to impact the schedule.
The use of leading indicators (LI) supports the effective management of systems engineering by enabling predictions of expected project performance and potential future states. Moreover, leading indicators aid leadership in delivering value to customers and end users, while facilitating interventions and actions to avoid rework and wasted effort1.
This paper examines how different technical debt types can be linked to leading indicators in systems engineering. It also provides a simple introduction of technical debt to systems engineers and technical program managers presenting leading indicators in systems engineering as an established methodology with relevant metrics.
The vision of the Army Installations Strategy (AIS) and the Installations of the Future (IotF) program is to enhance mission effectiveness and resilience in a prudent, efficient, and forward-thinking manner, including Army installations and contingency bases, energy, and environmental programs. Modernization of the nation’s installation portfolio is a challenge, due in part to the lack of a framework for acquiring and transmitting data to and from government networks and systems for implementation of modernization efforts. This research effort supports AIS and IotF by contributing to the modernization of installation decision-making processes through providing a data-driven platform for applying complex computational analytics and high-performance computing assets. This approach can inform installation decision makers in making holistic installation tactical and operational decisions. Current decision processes are predominantly manual in nature and require extensive human interactions, while lacking relevant data. This research integrates various analytical methods such as machine learning (ML) in conjunction with real-time data, to power a decision dashboard that can more effectively communicate the impact of risks to an installation. The desired outcome will create a sustainable and translational data-driven decision framework that will inform leadership for installation operations decision process. This paper explores the ML analytics applied to historical data that provide insight to support the modernization of the weather-impacted installations operations decision flow process.
Measurement may seem a rudimentary concept: temperature can be measured in degrees using a thermometer; length can be measured in distance using a ruler. Measurement for system design is more complex, given that measures are implicitly intended to assess the “goodness” of a design. This research proposes that measures in systems design can direct a design away from its goals through measurement challenges. Three types of measurement challenges are considered: challenges from the measures themselves, challenges from weighting the measures, and challenges from the use of the measures. This research illustrates measurement challenges for system design mathematically, graphically, and through real-world case study examples. The goal of this research is to demonstrate through multiple forms of evidence that measurement can misdirect system design. Establishing that measures in system design can influence a design away from its goals enables future research to investigate why these measurement challenges occur and how to prevent them.
While there are numerous products on the market to help prevent flat tires from occurring, none have proven to be 100% effective. Airless tire products have been proposed in the past and the research behind such technologies is ongoing. However, the envisioned product designed, discussed, and analysed in this paper (an airless tire insert for mountain bikes), addresses the issue with a radically different perspective - and proposes a potential solution that not only can eliminate flat tires from occurring altogether, but can also improve ride feel. The contents of this paper include the underlying derivations used to model a “Multi-Spring” model of an airless mountain bike tire, benchmark Force vs Displacement testing data of pneumatic tires inflated to varying psi’s, and physical test data obtained from the team’s patent pending experimental prototype. In addition, this paper also discusses the feasibility of using Shape-Memory-Alloy (SMA) material (used by NASA on the wheels of the Mars rover) as the primary structural component for a tire insert. The findings obtained and discussed verified the feasibility of the proposed product and presented exciting areas of further research going forward.