Soft Variables Applied to System Dynamics Modelling - Coursework Example

Chapter 4 Reviewing the Mystery - Soft Variables 4 Introduction From the discussions in previous chapters, this study advances towards the revelation of critical issues concerning soft variables. The purpose of dwelling on this particular chapter is to review the idea of soft variables since they do not emerge from void in any system dynamics model. In a very contrary approach, the impacts of soft variables majorly relies on the development of the previous structures, forming the fundamental conventional project models to bring about unexpected results, often counter-intuitively to the behaviour of the model. Soft variables can be considered as the fundamental element to build up the structure as well as decision rules in project models. Soft Variables are essential in system dynamic models, in which from the observation of McLucas (2001, p49), they demonstrate events in the real world systems. While admitting the limitations in the relevant literature, difficulty in conceptualizing the term has caused greater challenges to modellers. This makes them grab the opportunity to reveal the mystery of soft variables. 4.2. Background Simulation of systems in system dynamics makes use of mathematical models to process dummy information and provide interactive feedback on complicated challenges (Schmidt and Gary 2002, p47 - 54). The concept of soft variable was initially formulated in the 1950s. This was to enable corporate business managers to enhance their knowledge about industrial processes. It is presently being applied in system dynamics both in the private and in public sector in the design and analysis of industrial processes. Simulation of dynamic systems is done using special software packages such as Simulink, Vensim and Logisim. This led to development of convenient graphical interfaces in system dynamics in the 1990s, and subsequent application in various systems (Sterman 2002, p59). In system dynamics, soft variables exhibit dynamic attributes of complex systems. Forrester (1969, p68) argues that the basic principle of system dynamics is the recognition of the time-delayed links between soft variables and dynamic model (Sterman 2002, p59). In Systems Science, system dynamics is a method of studying the dynamic attributes of complex systems. According to Schmidt and Gary (2002, p47 – 5), the basic principle of system dynamics is the recognition of the time-delayed links among soft and hard variables. 4.2.1. Genesis of Soft Variables The invention of the soft variable concept is a contribution of Jay Forester, who developed the idea in the design of System dynamics in the middle of 1950s (Coyle 1999, p49). The goal of his original study was to use simulations to develop science and technology. This was essential in determining the application of his positive and negative feedback. Forrester identified the effects of the soft variables as the underlying component of systems in engineering. This idea eventually culminated in the formulation of system dynamics. Forrester made realization that omission of important variables for lack of numerical data or the data being less scientific leads to inaccurate values than the human judgment in estimating the values (Ford and Sterman 1998, p76). Omission of the concepts for lack of numerical data resulted into narrow boundaries of the dynamic model. In the first statement of ‘soft variables’: Forester (1961, pp59) suggested that omission of the term soft implies that they have absolutely no effect. This is – probably the only value that is known to be wrong as he proceeds to argue (Forrester, 1961, pp57) 4.2.2. Further Reinforcement of the Concept As the application of system dynamics increased from 1950s to the1960s, another modeller, John Collins made further broadening of the application of soft variables in system dynamics beyond the corporate system models. Having been a mayor in Boston, John Collins collaborated with Jay Forrester in their project of urban dynamics as a non-corporate way to apply system dynamics. Urban dynamics involved the use of manual simulations using soft variables in the contemporary corporate structures of decision-making. The soft variables were the characters of the employees being hired or fired in the various departments of the urban development authorities. Jay Forrester successfully showed the link between the instability in the capacity of employment and the organizational internal structure. This was contrary to the previous notion that the instability was because of external force including variations in business cycle. The manual simulation was the starting point of application of soft variables in the modern day system dynamics. In a meeting in Bern, Jay Forrester was challenged on whether system dynamics were able to solve the issues of the dilemma of humanity. Forrester proved that it was possible by developing a set of soft variables to create the pioneer model of system dynamics specially designed for social and economic system. 4.3. Detailed Interpretation 4.3.1. Derived Meaning Standard Definition There are various definitions of soft variables. Forrester (1969, p60) defines soft variables as a collection of qualitative parameters such as the quality of work, morale and productivity and intangible variables such as human behaviour and attitude. Soft variables are used in to describe intangible attributes of human characters. For example, in a model of human resource system, soft variables are the personal traits of the staff being taken through the recruitment process, disciplinary procedure, promotion. Since models are virtual representations of real systems, soft variables are intangible or virtual data, which cannot be omitted in simulation of dynamic models (Forrester 1969, p52). Further explanation on ‘soft variables’ The derived meaning of soft variables according to McLucas (2002, p53), is of course, human behavioural characters, as represented in system simulations. It is inevitable that very often; there is need in system dynamics to develop models that that do not omit human behaviours in simulation of various processes. This consideration of soft variables faces the challenge of incorporating the effects of soft variables to make them produce relevant, significant, reliable and replicable outcome. Modelling of system dynamics requires development of detailed understanding about the effects and responsibility of the human characters. It is therefore vital to minimize or eliminate frequent guessing concerning the effects of the soft variables on the simulations (Schmidt and Gary 2002, p48). In the contrary, it is important to create logical hypothesis and do repeated tests on the hypotheses regarding the soft variables. This requires a systematic documentation about the hypotheses on the effects and the production and combination of the effects. The documentation avails the results of the influences to the relevant parties. Consideration of soft variables in system dynamics enables the modellers to articulate and appreciate the nature and importance of soft variables in the contemporary system models as well as the real systems. As it occurs in the real world, Forrester (1969, p68) argues that errors are inevitable because of inefficiency of machines, compromise of processes and omission of important variables. Likewise, it is inevitable to face errors in simulated models, through omission of important soft variables or poor description and mapping of the effects and processes of the simulations. In system dynamics projects, loose definition of soft variables makes them loose meaning and their significance. According to (Roberts 1978, p52), soft variables are loosely defined when system dynamics projects use insufficient data or their models lack empirical support. Players of the real-life systems however, focus mainly on the effects and actions, instead of the mechanisms generating the effects. 4.3.2. ‘Soft Variables’ Recognized by Modellers The modellers of system dynamics refer to actual data attributes of real world systems such as financial data; personal information and physical object dimensional measurement before developing the concept into a project model. In the conceptualization, the modellers use the ‘hard’ variables to derive the concepts of soft variables including fatigue, congestion in communication traffic, personal competence and experience, morale, personal attitude and so on. In a general classification, the modellers identify the variables under two groups as applied in dynamic project models. These include exogenous variables and endogenous variables, which are described below. 4.3.2.1 Exogenous Variable These are the intangible attributes determining the interrelationship between an individual and other stakeholders of the organization. For example, the morale and outside image among the sales staff promotes the relationships between the employee and organization clients or suppliers. The modelers find it reasonable to choose exogenous variables because according to Sterman, (2000, p32), it is easier to determine their limitations and alternatives. From the limitations, the modelers are able to control the boundaries between these intangible variables and the endogenous variables. The modelers articulate the value of these exogenous variables depending on the influence of the organization in solving the uncertainties and issues of the clients. The soft variables reflect in the reputation and customer relations skills, which can be either good or bad, and can be transformed through proactive steps of motivation. 4.3.2.2 Endogenous Variables These are the soft variables identified by internal actions, which individuals in an organization direct internally. The soft variables in this category reflect in personal traits and behavior such as fatigue, personal morale, hope, internal knowledge, work experience, competence, productivity and quality of work. It also extends to organizational internal systems such as rules, principles and policies. Internal structure variables are partially independent on and reliant on individuals. Modelers prefer endogenous variables in situations where decision-making is involved and the model ought to generate feedback. They support a holistic practice of determining the boundaries between open and closed systems according to Sterman (2000, p34-35). 4.4. Measures of Soft Variables Values of variables in dynamic models are estimated using a special method when the data cannot be found. The process of estimating the soft variables involves consolidation of consensus on problem solving among the experts. The contents of the consensus are the essential tools for modelling and developing the toolbox for project models. The fundamental dimension of the present project has been to identify the empirical quantities of the soft relationships as used in the system dynamics models in the contemporary organizations. Before establishing the ways of estimating the variables, the previous studies had raised concerns on the relevance and effects of soft variables in the contemporary system dynamics and simulation modelling. The other concern was about the ways of handling the soft variables together with hard variables in developing the models of system dynamics and the validity testing of the models. In clarity, investigation and implementation of better approaches of quantifying the soft variables assists in the puzzle of the concerns addressed. For instance, the solution to the benefits and reliability of the system dynamics can benefit highly from preparing and maintaining a register or a catalogue of all intangible assets (Senge, 1990, p49 and Homer 1997, p30). This implies maintaining a database of information concerning the intangible objects, such as a set of relevant scales and metrics for gauging and quantifying the soft components of the dynamic models (Randers 1980, p36). The register of intangible objects (assets) will explain ways of measuring and comparing the measures consisting of individual motivation, skills, competence, knowledge, stress level and all intangible variables that affect the project’s performance and are applicable in the project dynamic models being developed. Other modellers address the issue of registers for intangible assets in the system dynamics, which demand the inclusion of intangibles (Meadows, 1972, 72). This leads to the development of detailed, practical, relevant and logical methods of quantifying the virtual assets. The intangible assets provide a significant ground for producing models for system project-related models, such as work force planning systems models. The development of the register for intangible assets and the dedication to the continuous validation of the quantification techniques as well as the eradication of the sources of disparities in the values set aside for the intangible variables in dynamic system modelling, would cause enormous significance (Coyle 2000, p56). More research is required to improve the ways of managing and operating the register for intangible assets. This has to be followed by the validation and subsequent publishing of the outcomes, along with the critical review. This ensures that valuable representation of the system dynamic models involving soft variables is built. After the building, the modeller presents them to the public for general use. The community of system dynamics would find it logical to adopt this research, as it exposes the significant failures in the present practice of modelling (Ford and Sterman 1998, p49). They all require registers for intangible assets, and detailed methods to combine the intangible assets as described briefly in the concerns addressed in the previous studies. 4.5. Debate: Application and Omission of Soft Variables 4.5.1. Debate 1: Omission of soft variables There is a controversial debate among many scholars on whether the idea of soft variable can be omitted in dynamic systems modelling. This follows the decision of Jay Forrester to pass a subjective measure on the soft variables, claiming that it is preferable compared to infinite quantification of the variables. Organizational behaviour and psychological aspect of the argument shows the demand of variables such as employee motivation and the involvement that can be subjected to various hypothetical test as had Forester proposes (Forrester 1969, p67 and p77). With the inclusion of dynamic system simulation in the production of graphical user interface, the availability and validity of the variables implies that they cannot be omitted as they play vital roles in the representation of real world measurable quantities. In that regard, even if the subjective quantification begins on certain variables, it will be important to refine them such that they can represent the structure of the system under critical review. Secondly, majority of the users of the models are conversant with the concept of quantification of every object they understand subjectively. In order to prevent such notions, the soft variable usage cannot be omitted in any simulation, so long as they are quantified in any manner. The second criteria of usage of the variables are the ease of mapping the effects of the simulation to their respective causes and the ability to vary the values of the soft variables. In System dynamics, the principal task of the modeller is to write critical functions and model equations in simple graphical form permit the users understand the complex internal structures and their interdependencies. 4.5.2. Debate 2: Omitting soft variables Forester (1969, p68) argues that omitting the soft variables in system dynamics is impossible if they lack quantitative measures. This implies that when omitted, the values of the omitted variables shifts to zero (Forester 1969, p68)). In another perspective, Soderlund (2004, p655) states that the omission is a possibility when their level of abstraction is reduced. Again, Sterman (2000, p94) notes that the main challenge with soft variables lies in the abstraction level, adding that Soft variables are concepts with high level of abstraction that cannot be measured. It means that the level of abstraction has to be reduced for the soft variables to be measurable. Forester uses a model containing soft variable as pollution and hard variable as the pollutant. The level of abstraction in Pollution has therefore been reduced in this model leading to a measurable variable- pollutant. Even though Forester maintained that the variables could not be omitted, Sterman found solutions for making the soft variables quantitative. The second question on omission of soft variables, the response to the debate is that the possibility of omitting soft variable depends on the system that is being modelled. For example, it argues that the model of pollution and pollutant cannot work because pollution and pollutant are two very different subjects. Logically, pollutants are agents of pollution. Secondly, the rate of pollution perpetually changes. It means that the measurement of pollution at one point does not equal the rate of pollution sometimes later. Additionally, the amount of pollutants can reduce or increase in the same manner as other favourable factors to pollution. This argument is supportive to the idea of finding alternative measures of variable and evaluating the relationship between the hard and soft variable. The omission depends on the role of the soft variable that has been quantified. This debate avoids integrating the concept of omission into the illustrated model since the models may be too complex and of little use. A second model example is that of pricing and investment, in which the impacts of pricing are dependent on many parameters. The model is therefore changed into smaller models to test the effect of each variable on the output and the model behaviour. Simple models are desirable such that there is no need to study the effects of variables on the model. Simulation of number of models provides better ideas about the real world. In this debate, models enable modellers to have a focused view on the real world; hence, there is no difference between omitting and accepting the soft variables. Another model is written quality (WQ) of dynamic patterns. Writing Quality is the soft variable represented as: WQ = 5C = Clear + Concise + Correct + Creative + Coherence All the 5 c variables on the right hand side are viewed as soft variables, which can be omitted by applying concrete theories including the number of complex words, unwanted words and other determining factors on the writing quality. It therefore possible to omit the soft variables but the question of whether it is good or bad remains debatable. In response, the model ought to use quantifiable variables in the writing quality model instead of the 5 c variables. In order to avoid the omission, this debate proposes two cases to increase the levels of clarity of the soft variables and to replace them with quantifiable variable to be used as proxy. It is suggested that modellers ought to delete the many the 5cs soft variables and replace them with quantifiable. This essentially is rejecting the original model. 4.6 Conclusion The concept of soft variables in system dynamics demonstrates the interoperability between soft and hard variables by varying the level of abstraction in each variable. Modelers therefore focus on various ways of quantifying the soft variables in realization that their omission causes project performance deficiency in system dynamics. The main function of the modeller in system dynamics is to develop the model equations for detailed understanding of the complex endogenous parameters and their interdependencies. References Belton, V. and Stewart, T.J. 2002. Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers, Boston. Brown, B., Cochran, S. and Dalkey, N.C. 1969. The Delphi Method II: Structure of Experiments. Rand Corporation RM-5957-PR, June 1969. Coyle, R.G. 1999. Qualitative modelling in system dynamics or what are the wise limits to quantification? Keynote address: Conference of the System Dynamics Society, Wellington, New Zealand. Coyle, R.G. 2000. Qualitative and quantitative modelling in system dynamics: some research questions. System Dynamics Review, vol. 16, no. 3, (Fall) 2000, System Dynamics Society, Wiley. Ford D.N. and Sterman J.D. 1998. Expert knowledge elicitation to improve formal and mental models. System Dynamics Review, Vol 14, No 4, pp 309-340. Forrester, J W. (1969). Urban Dynamics. Pegasus Communications. Homer, J.B. 1996. Why we iterate: Scientific modeling in theory and practice. System Dynamics Review, vol. 12, no.1, (Spring): 1-19. Homer, J.B. 1997. Structure, data and compelling conclusions: notes from the field (1997 Jay Wright Forrester Award Lecture). System Dynamics Review, vol. 13, no.4, (Winter): 293-309. McLucas, A.C. 2001. An Investigation Into The Integration Of Qualitative And Quantitative Techniques For Addressing Systemic Complexity In The Context Of Organisational Strategic Decision-Making. PhD Dissertation, University of New South Wales, Canberra, Australia. McLucas, A.C. 2002. Improving causal mapping practice using the System Dynamics ‘Front-End’ Tool. Proceedings of the 20th International Conference of the System Dynamics Society, July 28 – August 1, 2002, Palermo, Italy. Meadows, D H. (1972). Limits to Growth. New York: University books. Morecroft, J (2007). Strategic Modelling and Business Dynamics: A Feedback Systems Approach. John Wiley & Sons. Nuthman, C. 1994. Using human judgement in system dynamics models of social systems. System Dynamics Review, vol. 10, no. 1 (Spring 1994): 1-27. Randers, J (1980). Elements of the System Dynamics Method. Cambridge: MIT Press. Roberts, E B. (1978). Managerial Applications of System Dynamics. Cambridge: MIT Press. Schmidt, M.J. and Gary, M.S. 2002. Combining system dynamics and conjoint analysis for strategic decision making with an automotive high-tech SME. System Dynamics Review, vol. 18, no. 3, (Fall 2002): 359-379. Senge, P (1990). The Fifth Discipline. Currency. Sterman, J.D. 2002. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review, vol. 18, no. 4, (Winter 2002): 501-531. Sterman, J D. (2000). Business Dynamics: Systems thinking and modeling for a complex world. McGraw Hill. Sveiby, K., Linard K. and Dvorsky L. 2002. Building a knowledge-based strategy: A system dynamics model for allocating value-adding capacity. Proceedings of the 20th International Conference of the System Dynamics Society, July 28 - Aug 1, 2002, Palermo, Italy. Chapter 5 Soft Variables applied into different Project Models 5.1 Introduction After detailed discussion of soft variables concepts in the previous chapters, this study explores various ways of applying the soft variables in two real project models. The models have been designed to enhance the project management in system dynamics, trying to discover different types of soft variable. It also explores the implementation of these soft variables in different project models with different structures. It applies the concept of conventional project model, with reference to the fundamental structures or properties of project model including the rework cycle, control of the project, ripple effects and knock on effects (Repenning and Sterman 2001, p68). This forms the foundation for building the project models for simulating the behaviours of a complete project model. This chapter illustrates the application of soft variables in association with the ripple and knock-on effects. It presents the soft variables as policy resistance resulting from behaviour and psychology (Belton and Stewart 2002, p74). This chapter explores the fundamental structures of dynamic project models with a wide variation in the model structures in attempt to develop behaviours of real world project representing actual business or industrial processes. At every step, it considers soft variable as a reflection of resistance to the policy. 5.2 Soft variables applied into project models As Lyneis, Cooper and Sharon (2001, p79) suggests, soft variables are applied in system modelling based on fundamental features such as project feature, rework cycle, project control, ripple and knock-on effects. However, these features are applied on the extended structures, the multi-project and multi-tasks structures. 5.2.1. Background The application of soft variables in System Engineering projects is driven by practical failures in a number of large systems. From research in engineering firms, engineering experts have applied hard variables such as quantity of production, time spent, operational cost and speed of production. Regarding the systems engineering model, these hard variables can generate soft variables including quality of production, the brand name of the organization and the motivation of the workforce. The contemporary systems engineering combines various soft variables of significance to their operations (MacInnis 2004, p54). In sophisticated system engineering projects such as constructions, there are multiple variables generating multiple feedbacks. Failure reports include the project not being able to work within the time schedule or resources are insufficient. 5.2.2 Identification of Soft Variables The first model of system engineering has two underlying projects running in an organisation. Therefore, the modeller divides the model into 2 levels of causal relationship, the intra-project, which displays causal relation under individual project level. In addition, Inter-project, that displays causal relation between the two projects and the organisation itself. Figure 1: Intra-phase Systems Engineering Process Dynamics Obviously, in the above model, the causal loop diagram is sketched for individual project. It displays the dynamic behaviour under the project level. From this diagram, the feedback loops upon which the soft variables take effects within the project are, work harder – by increasing work intensity/efficiency, the productivity for that project improves. Skill dilution, Burnout, Cancellation Disrupts Design, Morale, Coordination and Meeting time are the other soft variables. Figure 2: Inter-Phase Dynamics In the inter-phase causal loop diagram, the elements presented include the two projects and the organisation. Soft variables in the inter-phase model are being conventionally used in the organisational level and the effects reflect in each project and the organisation. The feedback loop of the learning curve captures the soft variable of the work environments. It symbolizes the relation between the multiple tasks in Project 2, and the pressure faced in attempt to reach the targets. The repercussion is fatigue, which the loop provides suggestions leading to its resolutions. The soft variable effective communication and the movies to avoid improve productivity and personal development in Project 2. Figure 3: Project Approval Phase for new Project The Soft variables in the second project include Over-estimating, Lack of trust in decision bodies to estimate, Disagreement functional departments and client, Tendency toward optimistic estimates, Motives to avoid work pressure, Time concerns pre-study and Pressure to reach a compromise. Productivity of this project model positively depends on the motives to avoid work pressure, time concerns pre-study and the tendency towards optimistic estimates. In the contrary, other soft variables work against the productivity, including the departmental disagreements, lack of trust among the decision-making bodies and the pressure to reach a compromise. These pressures imply the possibility of the modellers to ignore key activities to save on the limited time. 5.2.3 Multi-Task and multi-project structure Figure 4: Single Project Model The diagram above is a traditional single project model described by Lyneis, Cooper and Sharon (2001, p77) and Ford and Ceylan (2002, p244). The model contains only 1 stock of ‘original work to do’ and 1 stock of ‘work done’, and tasks from original work to do is process through a series of processes determining the rates at which they are being completed. The model represents an organisation in multiple projects, with the resources available for optimizing performances. The effects of soft variables include aggressive goals, unrealistic expectations, bold promises, organization capability, resources, and customer satisfaction. Productivity of the product development model positively depends on the customer satisfaction, bold promises effective budget allocation and organization capability and resources. Figure 5: Dynamics Across Multiple Project Models The effects of soft variable in this model differ from those of the multi-project or the single-project model. The system-engineering model is constructed on a multi-project structure in the context of a project-based organisation. With multi-project structure in figure 5 above, the resources, budget, schedule pressure and quality of work for individual project are subject to availability of the organisation, and performances of all projects contribute simultaneously to organisation level performance and future determines the next step goals. Such type of organisation is mainly underpinned by multiple projects at hand, and tries to utilise resources among different projects in order to optimise performances on the single projects or tasks and the organisation itself. As the resources or project members for individual project are allocated from the organisational level, thus, the effect of soft variables attributed to employee traits and characters can apply to multiple projects within the organisation as the same time. Figure 6: Casual Relation To Maximize Resource Utilization The second application of soft variables is in the product development model based on multi-task structure. The model goes through transitions, exploring many stages toward the completion of the project. These include the initial design, the prototype, manufacturing, test and implementation in the performances of different tasks that a latter task may depend on the completion of the prior to start. In this case, the effects of soft variable can be somewhat different from that of the multi-project model or single-project model. The model in figure 6 above demonstrates a combination of a multi-task and a single project model that is meant to maximize the use of the limited resources and optimize the project efficiency and performance. For the project model, the resources for each task are subject to the availability of the entire project. Another task only commences upon the completion of the previous task. This implies that individual task performances may have influences on the latter ones and eventually the project. The soft variables affect the performance of the entire model instead of limiting its effect to its original model. This generates a long list of soft variables as follows: Project Delivery Over Estimating Lack of trust in decision bodies to estimate Disagreement functional departments and client Tendency toward optimistic estimates Motives to avoid work pressure Time concerns pre-study Pressure to reach a compromise 5.2.4 The Impacts of soft variables In the two models, the soft models operate as if they are in one model. In essence, all the processes form a sequence of tasks starting from projects in DB review, to the concepts study, then to the detailed development, then to the final development before the delivery of the product. As it can be seen, the soft variables are separated into two owing to this manner of interrelation. The first category of soft variables is the global variables, which operate and are applicable in the entire model structure. These variables include movies to learn from past, tendency towards optimistic estimates, learning obstacles, project costs, overtime working and exhaustion and motives to reduce the work pressure. The second category of soft variables is the local variables, whose scope of application is limited to the local (original) model. The soft variables the concerns over reaching the strategic targets, pressure to reach a compromise, Lack of trust in decision bodies to estimate, time concern for projects and the disagreement between functional departments and the clients (Roberts 1978, p64). The overall effect of the soft variables is synergy, since the limited resources serve two distinct dynamic models. The models demonstrate the efficiency of model performance by inter model relationship simulation. Secondly, soft variables enable the model to solve the time concern over the stages of models and the simulation shows time saving mechanism. Instead of the variable being applied twice for each project, as it would happen if the project were separate, each soft variable is used once. Figure 7: Closing the Loop – Relating Troubled Models The loop in the interrelated tasks and soft variables forms an endless sequence of tasks in the projects such that the dynamic model simulation can operate with a wide spectrum of parameters. Additionally, the dynamic model can be simulated as many times as possible to generate different results. 5.3 Conclusion As observed in the discussion, the development of soft variables requires consideration of the internal structures and the kind of model in which they are intended for use. Every researcher proposes unique methods of designing and implementing the variables, but they all operate in the standard conventional framework and the rework model. For further studies, project models give the structure for developing models for the project dynamics in every case. Control of the feedback is developed using the soft variables, the model and the effects of the policy from the analysis. References Howick S, Eden C. 2001. The impact of disruption and delay when compressing large projects: going for incentives? Journal of the Operational Research Society 52: 26–34. Joglekar N, Ford DN. 2005. Product development resource allocation with foresight. European Journal of Operational Research 160(1): 72–87. Lyneis, J., K. Cooper and A. Sharon (2001). "Strategic management of complex projects: a case study using system dynamics." System Dynamics Review 17(3): 77-88. Soderlund, J. (2004). "On the broadening scope of the research on projects: a review and model for analysis." International Journal of Project Management 22: 55-67. Ford DN, Ceylan K. 2002. Using options to manage dynamic uncertainty in acquisition projects. Acquisition Review Quarterly 9(4): 243–258. MacInnis DV. 2004. 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Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers, Boston. Roberts, E B. (1978). Managerial Applications of System Dynamics. Cambridge: MIT Press. Read More