System Lifecycle Models

The life cycle model is one of the key concepts of systems engineering (SE). A life cycle for a system generally consists of a series of stages regulated by a set of management decisions confirming that the system is mature enough to leave one stage and enter another.

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Topics

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A Generic System Life Cycle Model

A life cycle model for a system identifies the major stages that the system goes through, from its inception to its retirement. The stages are culminated by decision gates, at which the key stakeholders decide whether to proceed into the next stage, remain in the current stage, or terminate or re-scope related projects. Its inception begins with a set of stakeholders agreeing to the need for a system and exploring whether a new system can be developed whose life cycle benefits are worth the investments in the life cycle costs.

As most organizations adapt to the need for “Competing on Internet Time” (Cusumano and Yoffee 1998), the need for rapid adaptation to unforeseen changes has caused many organizations to emphasize evolutionary development with emergent requirements as compared with traditional development to a fixed set of requirements. Yet, there are still significant areas in which development driven by fixed requirements is appropriate.

Thus, there is no single “one-size-fits-all” system life cycle model that can provide specific guidance for all project situations. Figure 1, adapted from (Lawson 2010, ISO/IEC 2008, and ISO/IEC 2010), provides a generic life cycle model that will then be used to describe the most common versions of prespecified, evolutionary, sequential, opportunistic, and concurrent life cycle processes.

    Figure 1.  A Generic Life Cycle Model (adapted from Lawson 2010, ISO/IEC 15288, and ISO/IEC 24748)

The Definition Stage adds the Exploratory Stage to the Concept Stage of ISO/IEC 15288 (2008); the Development Stage follows ISO/IEC 24748 (2010), and the combination of the ISO/IEC 15288 (2008) Production, Support, and Utilization Stages reflects their concurrent evolution between Development and Retirement. Elaborated definitions of these stages are provided below and in the Glossary, and combined in various ways in the System Life Cycle Drivers and Stages article to follow.

The System Definition Stage begins with a decision by a protagonist individual or organization to invest resources in a new or improved system. Its usually-concurrent activities include determining the system’s key stakeholders and their desired capabilities; developing the system’s concept of operations and business case; negotiating the system’s requirements among the key stakeholders; selecting the system’s non-developmental items (NDIs); developing the system’s architecture and systems-level life cycle plans; and performing system analysis showing evidence of the compatibility and feasibility of the resulting system definition. There may be one or more intermediate decision gates within the Definition Stage. The transition into the System Development Stage can lead to either single-pass or multiple-pass development.

Note. The System Definition activities above constitute the major portion of the activities performed by systems engineers when performing systems engineering. Other activities include prototyping or actual development of high-risk items to show evidence of system feasibility; collaboration with business analysts or performing mission effectiveness analyses to provide a viable business case for proceeding into development; and continuous improvement of the systems engineering process. These activities will generally continue through the remainder of the system life cycle to handle system evolution, especially under multi-pass development.

The System Development Stage begins when the key stakeholders decide that the system development elements and feasibility evidence are sufficiently low-risk to justify committing the resources necessary to develop and sustain at least the initial operational capability (IOC), or for single-pass development of the full operational capability (FOC). Its usually-concurrent activities include construction of the developmental elements; integration of these with each other and with the non-developmental item (NDI) elements; verification and validation (V&V) of the elements and their integration as it proceeds; and preparing for the concurrent Production, Support, and Utilization activities.

The System Production, Support, and Utilization (PSU) Stage begins when the key stakeholders decide that the system life-cycle feasibility and safety evidence are sufficiently low-risk to justify committing the resources necessary to produce, field, support, and utilize the system over its expected lifetime. The lifetimes of production, support, and utilization are likely to be different. Aftermarket support will generally continue after production is complete, and users will often continue to use unsupported systems.

System Production includes the fabrication of system copies or versions, and of associated aftermarket spare parts. It also includes production quality monitoring and improvement, product or service acceptance activities, and continuous production process improvement. It may include low-rate initial production (LRIP) to mature the production process, or continuing preservation of the production capability for future spikes in demand.

Systems Support includes various classes of maintenance: Corrective (for defects); Adaptive (for interoperability with independently evolving co-dependent systems), and Perfective (for enhancement of performance, usability, or other key performance parameters). It also includes hot lines and responders for user or emergency support, and the provisioning of needed consumables (gas, water, power, etc.). Its boundaries include some gray areas, such as the boundary between small system enhancements and the development of larger complementary new additions; and the boundary between rework or maintenance of earlier fielded increments in incremental or evolutionary development. Systems support usually continues after System Production is terminated.

System Utilization includes the use of the system by operators, administrators, the general public, or systems above it in the system-of-interest hierarchy. It usually continues after Systems Support is terminated.

The Retirement Stage often executes incrementally as system versions or elements become obsolete or uneconomic to support, and undergo disposal or recycling of their content. Increasingly, affordability considerations make system repurposing an attractive alternative.

Figure 1 shows just the single-step approach for proceeding through the stages of a system’s life cycle. There are also several incremental and evolutionary approaches for sequencing the stages. Next are examples of how a system-of-interest’s value propositions may lead to different sequencing approaches, and a discussion of how aspects such as complexity, dynamism, new technologies such as 3-dimensional printing, and non-physical building materials (e.g., software) can introduce variations on the overall generic life-cycle theme. The subsequent article on Incremental and Evolutionary Development will summarize the primary approaches, including their main strengths and weaknesses, along with criteria for choosing the best-fit approach.

Type of Value Added Products/Services

Adding value, as a product, a service, or both, is the common purpose of all enterprises. This holds whether public or private, for profit or non-profit. Value is produced by providing and integrating the elements of a system into a product or service according to the system description, and transitioning it into productive use. These value considerations will lead to various forms of the generic life cycle management approach in Figure 1. Some examples are as follows (Lawson 2010):

A manufacturing enterprise, for example one producing nuts, bolts, and lock washer products, sells their products as value added elements to be used by other enterprises who integrate these products into their more encompassing value added system; for example, an aircraft or an automobile. Their requirements will generally be pre-specified by the customer, or by industry standards.

A wholesaling or retailing enterprise offers products to their customers. Its customers (individuals or enterprises) acquire the products and use them as elements in their systems. Its enterprise support system will likely evolve opportunistically, as new infrastructure capabilities or demand patterns emerge.

A commercial service enterprise such as a bank sells a variety of “products” as services to their customers; for example, current accounts, savings accounts, loans, and investment management. These services add value and are incorporated into customer systems of individuals or enterprises. The service enterprise’s support system will also likely evolve opportunistically, as new infrastructure capabilities or demand patterns emerge.

A governmental service enterprise provides citizens with services that vary widely, including health care, highways and roads, pensions, police, and defense. Where appropriate, these services become infrastructure elements utilized in larger encompassing systems of interest to individuals and/or enterprises. Major initiatives such as a next-generation air traffic control system or a metropolitan-area crisis management system (hurricane, typhoon, earthquake, tsunami, flood, fire) will be sufficiently complex to follow an evolutionary development and fielding approach. At the element level, there will likely be pre-specified single-pass life cycles.

For aircraft and automotive systems, there would likely be a pre-specified multi-pass life cycle to capitalize on early capabilities in the first pass, but architected to add further value-adding capabilities in later passes.

A diversified software development enterprise provides software products that meet stakeholder requirements (needs), thus providing services to product users. It will need to develop tailorable capabilities that can be used in different customers’ life-cycle approaches, along with product-line capabilities that can be quickly and easily applied to similar customer system developments. Its business model may also include providing the customer with system life-cycle support and evolution capabilities.

Within these examples, there are systems that remain stable over reasonably long periods of time and those that change rapidly. The diversity represented by these examples and their process needs makes it clear there is no one-size-fits-all process that defines the systems life cycle. Management and leadership approaches must consider the type of systems involved, their longevity, and the need for rapid adaptation to unforeseen changes, whether in competition, technology, leadership, or mission priorities. In turn, the management and leadership approaches impact the type and number of life cycle models that are deployed as well as the processes used within any particular life cycle.

Variations on the Theme

The Generic System Life Cycle Model in Figure 1 does not explicitly fit all situations. A simple, precedented, follow-on system may need only one phase in the Definition Stage, while a complex system may need more than two. With build-upon (vs. throwaway) prototypes, a good deal of Development may occur during the Definition Stage. System integration, verification, and validation may follow implementation or acquisition of the system elements. But particularly with software test-first and daily builds, integration, verification, and validation will be interleaved with element implementation. And with the upcoming Third Industrial Revolution of three-dimensional printing and digital manufacturing (Whadcock 2012), not only initial Development but also initial Production may be done during the Concept stage.

Several variations on the theme involve the special nature of software. Software is a flexible and malleable medium which facilitates iterative analysis, design, construction, verification, and validation to a greater degree than is usually possible for the purely physical components of a system. Each repetition of an iterative development model adds material (code) to the growing software base; the expanded code base is tested, reworked as necessary, and demonstrated to satisfy the requirements for the baseline.

Software can be electronically bought, sold, delivered, and upgraded anywhere in the world within reach of digital communication, making its logistics significantly different and cost-effective. It doesn’t wear out, and its fixes change its content and behavior, making regression testing more complex than with hardware fixes. Its discrete nature means that its testing cannot count on analytic continuity as with hardware. Adding 1 to 32767 in a 15-bit register does not produce 32768, but zero, as experienced in serious situations such as the use of the Patriot missile.

There are a large number of potential life cycle process models. They fall into three major categories:

primarily pre-specified and sequential processes (e.g., the single-step waterfall model);
primarily evolutionary and concurrent processes (e.g., Lean development, the Rational Unified Process, and various forms of the Vee and spiral models); and
primarily interpersonal and emergent processes (e.g., agile development, Scrum, extreme programming (XP), the dynamic system development method, and innovation-based processes).

The emergence of integrated, interactive hardware-software systems also made pre-specified processes often harmful, as the most effective human-system interfaces tended to emerge with use. This led to further process variations such as soft SE (Warfield 1976, Checkland 1981) and human-system integration processes (Booher 2003, Pew and Mavor 2007). However, until recently, process standards and maturity models have tried to cover every eventuality, and have included extensive processes for acquisition management, source selection, reviews and audits, quality assurance, configuration management, and document management, which often became overly bureaucratic and inefficient. This led to the introduction of more lean (Ohno 1988; Womack et al. 1990; Oppenheim 2011) and agile (Beck 1999; Anderson 2010) approaches, and concurrent hardware-software-human factors approaches such as the concurrent Vee models (Forsberg 1991; Forsberg 2005) and Incremental Commitment Spiral Model (Pew and Mavor 2007; Boehm and Lane 2007).

In the next article on System Life Cycle Process Drivers and Choices, these variations on the theme of life cycle models will be identified and presented.

Systems Engineering Responsibility

Regardless of the life cycle models deployed, the role of the systems engineer encompasses the entire life cycle for the System-of-Interest. Systems engineers orchestrate the development and evolution of a solution from requirements determination through operations, and ultimately system retirement. They assure that domain experts are properly involved, all advantageous opportunities are pursued, and all significant risks are identified and, where possible, mitigated. The systems engineer works closely with the project manager in tailoring the generic life cycle, including key decision gates, to meet the needs of their specific project.

Systems engineering tasks are usually concentrated at the beginning of the life cycle, but both commercial and government organizations recognize the need for SE throughout the system’s life cycle. Often this ongoing effort is to modify or change a system product or service after it enters production or is placed in operation. Consequently, SE is an important part of all life cycle stages. During the Production, Support, and Utilization (PSU) stages, for example, SE executes performance analysis, interface monitoring, failure analysis, logistics analysis, tracking, and analysis of proposed changes, all activities essential to ongoing support of the system.

All project managers must ensure that the business aspect (cost, schedule, and value) and the technical aspect of the project cycle stay synchronized. Often, the technical aspect drives the project, and it is the systems engineers’ responsibility to ensure that the technical solutions considered are consistent with the cost and schedule objectives. This can require working with the users and customers to revise objectives to fit within the business bounds. These issues also drive the need for decision gates appropriately spaced throughout the project cycle. Although the nature of these decision gates will vary by the major categories above, each will involve in-process validation between the developers and the end users. In-process validation asks the question: “Will what we are planning or creating satisfy the stakeholders’ needs?” In-process validation begins at the very outset of the project during user needs discovery and continues through daily activities, formal decision gate reviews, final product or solution delivery, operations, and ultimately to system closeout and disposal.

(End of introductory article)

References

Works Cited

Anderson, D. 2010. Kanban, Sequim, WA., Blue Hole Press.

Beck, K. 1999. Extreme Programming Explained, Addison Wesley.

Boehm, B. and J. Lane. 2007. “Using the Incremental Commitment Model to Integrate System Acquisition, Systems Engineering, and Software Engineering.” CrossTalk. October 2007: 4-9.

Booher, H. (ed.) 2003. Handbook of Human Systems Integration. Hoboken, NJ, USA: Wiley.

Checkland, P. 1981. Systems Thinking, Systems Practice. Hoboken, NJ, USA: Wiley (2nd edition 1999).

Cusumano, M., and D. Yoffie 1998. Competing on Internet Time, New York, NY, USA. The Free Press.

Forsberg, K. and H. Mooz, 1991. "The Relationship of System Engineering to the Project Cycle," Proceedings of NCOSE, October 1991 (first paper on the Vee model).

Forsberg, K., H. Mooz, H. Cotterman. 2005. Visualizing Project Management, 3rd Ed. J. Wiley & Sons.

Lawson, H. 2010. A Journey Through the Systems Landscape. London, UK: College Publications.

Ohno, Taiichi (1988). Toyota Production System. Productivity Press.

Oppenheim, B. 2011. Lean for Systems Engineering, Hoboken, NJ: Wiley.

Pew, R. and A. Mavor (eds.). 2007. Human-System Integration in The System Development Process: A New Look. Washington, DC, USA: The National Academies Press.

Warfield, J. 1976. Systems Engineering. Washington, DC, USA: US Department of Commerce (DoC).

Whadcock, I. 2012. “A third industrial revolution,” The Economist, April 21, 2012.

Womack, James P.; Daniel T. Jones, and Daniel Roos 1990. The Machine That Changed the World: The Story of Lean Production, New York, NY, USA: Rawson Associates.

Primary References

Blanchard, B. S., and W. J. Fabrycky. 2011. Systems Engineering and Analysis, 5th ed. Prentice-Hall International series in Industrial and Systems Engineering. Englewood Cliffs, NJ, USA: Prentice-Hall.

Forsberg, K., H. Mooz, H. Cotterman. 2005. Visualizing Project Management, 3rd Ed. J. Wiley & Sons.

INCOSE. 2011. Systems Engineering Handbook, version 3.2.2. San Diego, CA, USA: International Council on Systems Engineering (INCOSE). INCOSE-TP-2003-002-03.2.2.

Lawson, H. 2010. A Journey Through the Systems Landscape. London, UK: College Publications.

Pew, R. and A. Mavor (eds.). 2007. Human-System Integration in The System Development Process: A New Look. Washington, DC, USA: The National Academies Press.

Additional References

The following three books are not referenced in the SEBOK text, nor are they systems engineering "texts"; however, they contain important systems engineering lessons, and readers of this SEBOK are encouraged to read them.

Kinder, G. 1998. Ship of Gold in the Deep Blue Sea. New York, NY, USA: Grove Press.

This is an excellent book that follows an idea from inception to its ultimately successful conclusion. Although systems engineering is not discussed, it is clearly illustrated in the whole process from early project definition to alternate concept development to phased exploration and “thought experiments” to addressing challenges along the way. It also shows the problem of not anticipating critical problems outside the usual project and engineering scope. It took about five years to locate and recover the 24 tons of gold bars and coins from the sunken ship in the 2,500-meter-deep sea, but it took ten years to win the legal battle with the lawyers representing insurance companies who claimed ownership based on 130-year-old policies they issued to the gold owners in 1857.

McCullough, D. 1977. The Path Between the Seas: The Creation of the Panama Canal (1870 – 1914). New York, NY, USA: Simon & Schuster.

Although “systems engineering” is not mentioned, this book highlights many systems engineering issues and illustrates the need for SE as a discipline. The book also illustrates the danger of applying a previously successful concept (the sea level canal used in Suez a decade earlier) in a similar but different situation. Ferdinand de Lesseps led both the Suez and Panama projects. It illustrates the danger of not having a fact-based project cycle and meaningful decision gates throughout the project cycle. It also highlights the danger of providing project status without visibility, since after five years into the ten-year project investors were told the project was more than 50 percent complete when in fact only 10 percent of the work was complete. The second round of development under Stevens in 1904 focused on “moving dirt” rather than digging a canal, a systems engineering concept key to the completion of the canal. The Path Between the Seas won the National Book Award for history (1978), the Francis Parkman Prize (1978), the Samuel Eliot Morison Award (1978), and the Cornelius Ryan Award (1977).

Shackleton, Sir E.H. 2008. (Originally published in by William Heinemann, London, 1919). South: The Last Antarctic Expedition of Shackleton and the Endurance. Guilford, CT, USA: Lyons Press.

This is the amazing story of the last Antarctic expedition of Shackleton and the Endurance in 1914 to 1917. The systems engineering lesson is the continuous, daily risk assessment by the captain, expedition leader, and crew as they lay trapped in the arctic ice for 18 months. All 28 crew members survived.

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