Applying complexity science to new product development: Modeling considerations, extensions, and implications

Oyama, Kyle F.; Learmonth, Gerard P.; Chao, Raul O.

doi:10.1016/j.jengtecman.2014.07.003

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Studies taking a systems perspective consider that complexity is generated by the size and interdependence of the components of the system involved in NPD projects (Dooley & Van de Ven, 1999;McCarthy et al, 2006;Mihm et al, 2003). Consequently, complexity is suggested to be manageable by cutting interdependencies between problem-solving activities (Mihm et al, 2003) or by favouring complementary rather than conflicting dependencies (Oyama, Learmonth, & Chao, 2015).…”

Section: Complexity Of Npd Processesmentioning

confidence: 99%

Causal complexity of new product development processes: a mechanism-based approach

Sihvonen

Pajunen

2018

Innovation

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The outcomes of new product development (NPD) processes are dependent on the interplay of several interdependent activities. One product development activity can be dependent on the presence or absence of other activities, different kinds of NDP processes may lead to the same outcome, and specific kinds of activities may have a positive effect in one process but no effect in other processes. However, we currently lack means to examine and explain this causal complexity inherent in NPD processes. To address this issue, we introduce mechanism-based approach as a way to capture conjunctural and equifinal causal relations. We build this approach on the philosophical literature on mechanism-based explanations and the methodological opportunities provided by the qualitative comparative analysis (QCA) to identify how the activities of entities are configured together to generate outcomes. We elaborate this approach by presenting an in-depth historical analysis of the NPD projects of Vaisala, a meteorological instrument company. We discover and suggest that the company's NPD projects were driven by three mechanisms (ideation, evaluation and commercialisation) and that each of them were actualised by a set of different activity configurations. Accordingly, we contribute to the NPD and innovation literature by showing how mechanism-based explanations take into account both the abstract theorisation of NPD processes and their inherent causal complexity.

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Section: Complexity Of Npd Processesmentioning

confidence: 99%

Causal complexity of new product development processes: a mechanism-based approach

Sihvonen

Pajunen

2018

Innovation

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“…Therefore, after elimination of node ( ), the fuzzy duration resulting from (7) should be added to the duration of all output branches from node ( ), in accordance with (8): (ii) Computing the change of success probability in other separating branches from node ( ) by elimination of branch ( ) is as follows.…”

Section: Equivalent Making and Removing Of Reversible-to-itselfmentioning

confidence: 99%

“…Finally, the performance of models was compared with existing methods. The results showed proper and real performance of models in comparison with existing methods.Journal of Applied Mathematics methods, that is, CPM (Critical Path Method) and PERT (Program Evaluation and Review Technique), were introduced and developed [6].Project management, in particular, project planning, is a critical factor in the success or failure of new product development (NPD) [7] and research and development (R&D) projects [8]. Limited facilities of CPM and PERT methods for modeling of projects having complex and uncertain networks [6] caused the great scientists such as Pritsker and Whitehouse [9] and Pritsker and Happ [10] to introduce the probability GERT (Graphical Evaluation and Review Technique) which is a method consisted of flow graph theory, moment generation functions, and PERT to solve problems with probability activities.…”

mentioning

confidence: 99%

“…Project management, in particular, project planning, is a critical factor in the success or failure of new product development (NPD) [7] and research and development (R&D) projects [8]. Limited facilities of CPM and PERT methods for modeling of projects having complex and uncertain networks [6] caused the great scientists such as Pritsker and Whitehouse [9] and Pritsker and Happ [10] to introduce the probability GERT (Graphical Evaluation and Review Technique) which is a method consisted of flow graph theory, moment generation functions, and PERT to solve problems with probability activities.…”

mentioning

confidence: 99%

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Developing a Mathematical Model for Scheduling and Determining Success Probability of Research Projects Considering Complex-Fuzzy Networks

Norouzi

Heydari

Noori

et al. 2015

Journal of Applied Mathematics

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In project management context, time management is one of the most important factors affecting project success. This paper proposes a new method to solve research project scheduling problems (RPSP) containing Fuzzy Graphical Evaluation and Review Technique (FGERT) networks. Through the deliverables of this method, a proper estimation of project completion time (PCT) and success probability can be achieved. So algorithms were developed to cover all features of the problem based on three main parameters "duration, occurrence probability, and success probability." These developed algorithms were known as PR-FGERT (Parallel and Reversible-Fuzzy GERT networks). The main provided framework includes simplifying the network of project and taking regular steps to determine PCT and success probability. Simplifications include (1) equivalent making of parallel and series branches in fuzzy network considering the concepts of probabilistic nodes, (2) equivalent making of delay or reversible-to-itself branches and impact of changing the parameters of time and probability based on removing related branches, (3) equivalent making of simple and complex loops, and (4) an algorithm that was provided to resolve no-loop fuzzy network, after equivalent making. Finally, the performance of models was compared with existing methods. The results showed proper and real performance of models in comparison with existing methods.Journal of Applied Mathematics methods, that is, CPM (Critical Path Method) and PERT (Program Evaluation and Review Technique), were introduced and developed [6].Project management, in particular, project planning, is a critical factor in the success or failure of new product development (NPD) [7] and research and development (R&D) projects [8]. Limited facilities of CPM and PERT methods for modeling of projects having complex and uncertain networks [6] caused the great scientists such as Pritsker and Whitehouse [9] and Pritsker and Happ [10] to introduce the probability GERT (Graphical Evaluation and Review Technique) which is a method consisted of flow graph theory, moment generation functions, and PERT to solve problems with probability activities. Pritsker [11] developed GERT method and used it for different nodes and introduced use of simulation in that method. Of course it is pointed out that Pritsker and Whitehouse [9] used Maison's role which had been introduced in graph theory to solve probability networks. The pointed out probability methods are all based on probable distributions such as Beta ( ) or normal distribution which are used for estimation of project activities period. Thus, to use probable distributions, random samples, repeatability, and statistical deduction will be required.When fuzzy theory was introduced and developed by Zadeh [12], scientists' and researchers' attention in the field of network and science engineering was gradually caught by the issue that they will be able to solve the problem of uncertainty in problems and also expressed problems by a fuzzy approach. Thus, for the first time...

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“…Organisations performing research and development tasks with their teams and individuals interacting within them exhibit each of the characteristics of complex systems [3] such as dynamism, the interdependence of elements, emergent/self-organising behaviour, and non-linearity. Similarly, Oyama et al [4] argue that organisations can be seen as a complex socio-technical system. Complex systems display behaviour, which cannot be predicted by observing elements in isolation.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Modelling and Simulation of Product Development Teams

2018

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The success of product development highly depends on the quality of cooperation among members of a team involved in the process. Thus, a tool capable of simulating product development team may be beneficial for researchers interested in teamwork, as well as useful for managers struggling with team formation during process planning phase. This work aims at providing a detailed overview of agent-based simulators of product development teams. Specifically, the scientific databases Web of Science, Scopus, ACM DL, and IEEE were searched to extract relevant agent-based models of teamwork in mechanical engineering and aerospace context and obtained models were reviewed to identify their key advantages and limitations.

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Applying complexity science to new product development: Modeling considerations, extensions, and implications

Cited by 14 publications

References 38 publications

Causal complexity of new product development processes: a mechanism-based approach

Causal complexity of new product development processes: a mechanism-based approach

Developing a Mathematical Model for Scheduling and Determining Success Probability of Research Projects Considering Complex-Fuzzy Networks

Agent-Based Modelling and Simulation of Product Development Teams

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