Methodology of Systems Analysis

1. Identification of objectives

• very important; If the correct objectives are not identified, the correct problem will not be solved!
• consult others
• use multi-disciplinary team
• may have multiple objectives
• determine your client - usually person paying the bill!
• establish the needs of the client - sometimes difficult to establish
• identify the clients single most important objective
• choose a measure of effectiveness
• discuss the project objective with the client
• insure that the client clearly understands and agrees with the project objective

2. Quantification of objectives

• Identify and write objective function - this is a quantitative expression of the goals or objectives of the project
• objective function might take on the form F=G(X1, X2, X3, ..., Xn) where Xi's are independent variables and represent values of parameters under the control of the systems analyst
• constraint set should be identified; The constraint set consists of equations that define the domain of feasible solutions. For example, in determining the optimum mix of corn and soybeans to plant on a 450 hectare farm, a constraint on the amount of land that can be used might be written as: Corn Hectares + Soybean Hectares <= 450.

3. Development of a system model

• most often this is the responsibility of the systems analyst or engineer
• keep in mind that the model is an abstraction of the system
• a two stage process is sometimes used:
  • model decoupling - simplifying step where system components are modeled and analyzed as subsystems. This can be helpful in better understanding the system.
  • model integration - entire system is modeled (e.g., the subsystem components are integrated)
• delicate balance exists between model detail and the ability to effectively and efficiently analyze the mode. Modeling detail may offer better reality at increased computational expense. Under certain circumstances, a simple model may prove more valuable than a more complex model. The project objectives should dictate the level of detail required.
• many types of models are available for use
• the type of model chosen depends on system, the objectives, perspective (time scale) of models
• one should select the most "appropriate model" - by the end of the semester you should have a better feel for this
• why model?

4. Evaluation of alternatives

• goal is to find an optimum solution
• identify alternative solutions
• gather as much information about alternative solutions as possible - may require searching the literature, obtaining technical and cost data on equipment, operation, maintenance, and other pertinent information
• perform sensitivity analysis to determine response to change in model parameters
• verification - computer code reproduces model chosen
• validation - model of system faithfully reproduces the actual system

5. Detailed design and development

• complete the design and necessary actions

Optimum solution - the combination of resources that best meets the stated objective(s) and satisfies all constraints.

Types of models:

• iconic - physical models that are images of the real world; dimensions are usually scaled up or down; for example, models of cars might be constructed and tested in a wind tunnel
• analog - model that substitutes one set of properties for another; may be iconic or mathematical; electric resistance often used as an analog of the friction of a fluid flowing in a pipe; this approach is not as widely used as at one time - digital computers have allowed the development of other modeling techniques that have replaced analog models
• stochastic - probabilistic model that uses randomness to account for unmeasurable factors (e.g., weather)
• deterministic - model that does not use randomness but uses explicit expressions for relationships that may or may not involve time rates of change
• discrete - model where state variables change in steps as opposed to continuously with time (e.g., number of cattle in a barn); may be deterministic or stochastic
• continuous - model whose state variables change continuously with time (e.g., biomass in a field); usually sets of differential equations used; initial conditions required (can be difficult to obtain for some systems!)
• combined - model where some state variables change continuously and others change in steps at event times; for example, a field of hay might be modeled using a combined approach with the biomass modeled continuously during growth and then as a discrete event when harvested
• mathematical - abstract model usually written in equation form
• object-oriented - use objects that are abstractions of real world objects and develop relationships and actions between objects; comes from field of artificial intelligence
• heuristic - heuristics (rules) are used to model the system; comes from field of artificial intelligence