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Time-Expanded Decision Networks: a Framework for Designing Evolvable Complex Systems*

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Time-Expanded Decision Networks: a Framework for Designing Evolvable Complex Systems* Regular Paper Time-Expanded Decision Networks: A Framework for Designing Evolvable Complex Systems* Matthew R. Silver and Olivier L. de Weck† Department of Aeronautics & Astronautics, Engineering Systems Division, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 TIME-EXPANDED DECISION NETWORKS Received 10 October 2006; Accepted 13 February 2007, after one or more revisions Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/sys.20069 ABSTRACT This paper describes the concept of Time-Expanded Decision Networks (TDNs), a new methodology to design and analyze flexibility in large-scale complex systems. This includes a preliminary application of the methodology to the design of Heavy Lift Launch Vehicles for NASA’s space exploration initiative. Synthesizing concepts from Decision Theory, Real Op- tions Analysis, Network Optimization, and Scenario Planning, TDN provides a holistic frame- work to quantify the value of system flexibility, analyze development and operational paths, and identify designs that can allow managers and systems engineers to react more easily to exogenous uncertainty. TDN consists of five principle steps, which can be implemented as a software tool: (1) Design a set of potential system configurations; (2) quantify switching costs to create a “static network” that captures the difficulty of switching among these configurations; (3) create a time-expanded decision network by expanding the static network in time, including chance and decision nodes; (4) evaluate minimum cost paths through the network under plausible operating scenarios; and (5) modify the set of initial design configurations to exploit high-leverage switches and repeat the process to convergence. Results can inform decisions about how and where to embed flexibility in order to enable system evolution along various development and operational paths. © 2007 Wiley Periodicals, Inc. Syst Eng 10: 167–186, 2007 * An earlier version of this paper was published and presented as follows: M. Silver and O. de Weck, Time-expanded decision network methodology for designing evolvable systems, AIAA-2006-6964, 11th AIAA/ISSMO Multidisciplinary Analysis Optim Conf, Portsmouth, VA, September 6–8, 2006. †Author to whom all correspondence should be addressed (e-mail: [email protected]). Systems Engineering, Vol. 10, No. 2, 2007 © 2007 Wiley Periodicals, Inc. 167 168 SILVER AND DE WECK Key words: changeability; flexibility; real options; time-expanded networks; decision theory; system optimization; lock-in NOMENCLATURE or for them to be changed by an external agent in the face of environmental change and uncertainty [Arthur, c = arc traversal cost, 1989; Simon, 1996; Cowan, 1990]. Lock-in and its cO = organizational premium for system relationship to technological evolution has been the change, subject of much research in various fields including CLCi = estimated lifecycle cost of the ith sys- engineering [Utterback, 1974; Silver 2005b], econom- tem configuration, ics [Arthur, 1989; Arrow, 2000; David, 2000], political CD = development cost (DDT&E), economy [Nelson and Winter 1982] and strategic inno- CF = fixed recurring cost per time period, vation management [Henderson et al., 1990; Christen- CV = variable cost as a function of demand son, 1997; Hax, Wilde, and Thurow, 2001]. The exact per time period, causes of lock-in are still debated [Liebowitz, 1985]. CSW(A, B) = cost of switching from A to B, They are generally acknowledged to be multidimen- ∆ ( ) CD B, A = incremental DDT&E of the new sys- sional, taking root in sociotechnical issues such as tem configuration B starting from A, network externalities [Katz, 1984; Witt, 1997], the cost CR(A) = cost of retiring system A, of learning new technologies [Utterback, 1974] and ∆ ( ) CR A, B = incremental retirement cost of system operating procedures, the risks associated with switch- A when switching to B, ing to new systems, the tradeoff between operating Dj = demand during time period j, slightly inefficient fielded technologies and developing i = number of system configurations con- new technologies [Sarsfield, 2001], and broader issues sidered, associated with political and organizational inertia m = number of arcs in the network, [Puffert, 2003]. The relative importance of these factors n = number of nodes in the network, in creating lock-in varies, depending upon the architec- N = number of people involved in operat- ture of the system and the nature of the system’s opera- ing the system, tions. N(n, m) = static network with a set of nodes, n, For large-scale technical systems, these factors can and set of arcs, m, be grouped into the general concept of switching costs, O(C) = “order of” computational cost for C which are weighed—whether formally or informally— configurations, against the benefits of switching to new systems, prod- O(Z) = “order of” computational cost for Z ucts or operating procedures. Switching costs can be time periods, real dollars, or quantifiable costs associated with per- Pi = point design, i, performance of the sys- sonnel considerations, political implications, or the tem when in the ith configuration, time it takes to switch to a new technology or system r = discount rate, configuration. They are further associated with what S = start node, might be called switching risk [Dixit, 1992]. t = arc traversal time, Increasing the adaptability or flexibility of a system T = number of steps considered, demands lowering future switching costs before the Ti = time period i, ∆ environmental changes which demand a switch arise. T = time step, Suh and de Weck have used the concept of switching Z = end node. costs to find where to embed flexibility in automotive platforms [de Weck and Suh, 2006]. Beyond cost, 1. INTRODUCTION adaptability is complicated by uncertainty in the oper- ating environment. More specifically, a large variance There is increasing recognition that adaptability between time-scales of environmental fluctuations and [Ashby, 1966] and more recently changeability [Fricke those associated with switching times and total system and Schulz, 2005] are critical to the long-term effective- life-cycle times greatly hampers operators’ ability to ness and, in relevant cases, profitability of complex react strategically to exogenous change.1 We argue that, technical products and systems. Many complex systems in order to avoid architectural lock-in and by extension exhibit high degrees of architectural lock-in. This allow operators to react strategically in the face of makes it difficult for these systems to adapt themselves environmental fluctuations, the causes of switching Systems Engineering DOI 10.1002/sys TIME-EXPANDED DECISION NETWORKS 169 costs and risks must be mitigated during the design using standard methods from time-expanded network stage, with explicit and rigorous consideration of uncer- theory [see Jarvis and Ratliff, 1982]. Once formulated, tainty in the future acquisition and operating environ- designers can use the time-expanded decision network ments. to conduct scenario planning and iteratively design Much recent research has addressed the need to more evolvable complex systems. design for flexibility and methods to design for it. Real As noted, while the sources of architectural lock-in Options Analysis, for example, is a method of valuing are multidimensional, from an economic perspective it system flexibility by framing system design in terms of stems from high capital expenses (capex), associated stock-option theory.2 Similarly, the concepts of Spiral with switching to new systems or system configura- or Evolutionary Development emphasize the need for tions. Framed this way, the problem becomes fairly continual development and improvement of large sys- simple: Lock-in will occur if the cost of switching from tems rather than all-at-once development and operation. one system configuration to another exceeds the ex- At their core, these methods seek either ways of system- pected benefits, discounted at some rate and assuming atically and strategically identifying the benefit of vari- a level of risk [Simon, 1996].4 In order to design for ous kinds of flexibility and introducing it at the design adaptability (changeability), we must lower switching stage, or delaying critical design decisions until exoge- costs. This insight leads directly to two important ques- nous uncertainties are resolved. The differences be- tions: (1) How can we quantify switching costs and (2) tween adaptability in the systems themselves versus once quantified, how can we design systems to diminish adaptability in the system development process have the appropriate elements of switching costs in order to recently been clarified by Haberfellner and de Weck improve life-cycle adaptability? The TDN methodol- [2005]. ogy described in the next section (Section 2) addresses Building on these studies, we introduce a new meth- these questions explicitly. Section 3 discusses how the odology and associated modeling tools to design method scales with problem size, and Section 4 then “evolvable” complex systems. Given their size and applies the method to the specific example of launch long-life cycles, complex technical systems are best vehicle development and operations under demand un- characterized as operational organizations evolving in certainty. The paper concludes with a summary, limita- the face of exogenous uncertainty, able to take various tions, and recommendations for future work in Section development paths when confronted
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