This role also involves risk assessment on various system elements and overall risk management during the system creation. Leading, coordinating and managing. In addition to being a technical manager, the systems engineer must be a manager of activities and leader and coordinator of people. The job includes dealing with work plans, schedules, and budgets, but also working with people — organising their work, motivating them, communicating with them, and dealing with their needs.
Logistics and operations management. Information management.
To see the overall information needs of the system, plan the forms and means in which information will be created, disseminated and stored, and direct the process of information sharing and configuration control. The technical and the managerial have to be seen within the context of the whole that is systems engineering. Its two components rely symbiotically on each other and both are systems engineering specific.
The inextricably linked relationship of the technical and managerial are shown at the centre of Figure 49 , which also indicates that the two components are special subsets of the wider general subjects of engineering and management. As you work through this course you will need various resources to help you complete some of the activities.
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Course content. Stage 1: Problem definition what is the problem? Stage 2: Analysis of the existing situation where are we now? Stage 3: Identification of objectives and constraints where would we like to be? Stage 4: Generation of routes to objectives how could we get there?
Stage 5: Formulating measures of performance how will we know when we have arrived? Stage 6: Developing the options what would the options be like? Stage 7: Option testing how well will each work?
Collaborative Tools For Model-Based Systems Engineering
Stage 6: Debate on feasible and desirable changes. The career of a concept. About this free course 25 hours study. Level 3: Advanced. Many of these originate from the discipline of Industrial Engineering, which is often synonymous with Systems Engineering. These models capture task dependencies and timing in the execution of a large project. Engineering economics and financial engineering models are used to make cost-effective decisions on capital investments and portfolio optimization. As an example, Steenstra et al.
3 What is Systems Engineering?
Statistical modeling is used to capture relationships, patterns, correlations, and probabilistic structure in data. In systems engineering, statistical methods are essential for input modeling and analysis in step 2, for any required experimental design and analysis in step 4, and for many quality control applications where a performance or quality characteristic can be monitored and controlled over time.
Specific techniques include regression, design of experiments, and statistical quality control. Stochastic processes model the random nature of complex systems and processes. These types of models can be used to derive expected values and variances of performance measures under a variety of conditions.
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They can also be used to develop optimal decision policies, that is, decision-making rules that optimize the expected system performance while minimizing risk. Queuing models, Markov chains, Brownian motion, and Markov decision processes MDP are common types of stochastic models. Hauskrecht and Fraser 19 apply MDP to the treatment of heart disease.
Operations research models are well suited for optimal resource allocation, determining how to cost effectively distribute resources. Operations research methods also provide the analytical foundation for important systems applications such as patient flow, inventory control, and scheduling. Common techniques include linear programming, network flow analysis, and dynamic programming. As an example, Brennan et al. Human factors models can be used to optimize human performance in complex systems. These models can capture both cognitive and ergonomic concerns and include the area of human computer interactions.
As an example, Koubek et al. Process flow models capture how work tasks need to be sequenced, coordinated, and synchronized. Interleaving of the work processes of system components allows the modeling of resource competition and delays. As an example, Gupta et al. Discrete event simulation is perhaps the most commonly applied systems engineering tool. In essence, these models mimic system behavior in accelerated time. Simulation models can easily capture a plethora of operational detail and are easily used in experimentation.
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Hung et al. We now provide two examples of how the tools discussed above are being applied in health care.
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The first focuses on medical decision making whereas the second focuses on hospital management. We now briefly discuss several examples. Hemodialysis is a method for removing waste products from the blood stream of renal failure patients. Maintaining AV access is a significant problem for many hemodialysis patients because of progressive stenosis.
Studies show that early stenosis detection and treatment intervention e. AV patency can be modeled as an optimal stopping time problem a common problem in stochastic processes in which, the pressure in the access represents the state of the system. Decisions include when to intervene with treatment, when to prepare a new AV site, and when to abandon the current site and move to the next, all with the objective of optimizing patency and expected patient life.
Therapeutic optimization is being used in liver transplantation. This decision can be modeled using Markov decision processes MDP.
Chapter 2: Systems Engineering (SE) – The Systems Design Process
These models can be solved to find an optimal decision rule that maximizes the expected transplant success. The MDP approach is also useful for timing liver transplants from living donors to maximize patient life expectancy. Therapeutic optimization is being used to optimize radiation therapy in cancer treatment.
In this treatment, a specified amount of radiation needs to be applied in a localized cancerous region. To achieve this, the radiation must pass through healthy tissue, which it can damage. Radiation treatment technologies have evolved significantly and are now highly computerized so that very fine beam modulation is possible. Optimization models have been developed for controlling the modulation mechanism so that the cancerous region receives the prescribed dose while collateral damage to surrounding health tissue is minimized.