Improving Collaborative Decision Making in New Product Development Projects Using Clustering Algorithms

Jaber, Hadi; Marle, Franck; Janković, Marija

doi:10.1109/tem.2015.2458332

Cited by 27 publications

(13 citation statements)

References 29 publications

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“…As a consequence, they have a significant impact on project performance and product complexity. Furthermore, the level of suppliers' involvement and the use of carryover parts influence the volume of engineering work to be done internally, then the project complexity [76]. As a result, this impacts profitability, lead time, and total product quality [85].…”

Section: Application To Vehicle Development Projectsmentioning

confidence: 99%

“…Moreover, requirement changes and a necessary degree of innovation do not only impact the product and its parts. Still, they may also lead to overhead costs and affect the coordination between project actors and suppliers [76]. Product complexity is considered the first significant source of complexity in the design and manufacturing or design and construction projects [77].…”

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confidence: 99%

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A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

et al. 2021

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This work aims to help managers anticipate, detect, and keep under control complex situations before facing negative consequences. This article explores complexity modeling theory and develops a framework and associated score sheet to measure project complexity. A framework comprising ninety factors is presented and divided into seven categories: stakeholders, project team, project governance, product, project characteristics, resources, and environment. For the project complexity assessment grid, the project manager prioritizes and weighs its factors using linguistic variables. The score sheet is customizable in its handling of the factors and their weights. A critical state of the art on multi-criteria methodologies is presented, as well as reasons for using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. This method provides early-warning signs with the possibility of comparing multiple projects. It also enables one to measure and prioritize areas and domains where complexity may have the highest impact. Practical applications on three projects within an automotive manufacturer highlight the benefits of such an approach for managers. Project managers could use both a project complexity rating system and a measure of risk criticality to decide on the level of proactive actions needed. This research work differs from traditional approaches that have linked proactive actions to risk criticality but not project complexity.

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Section: Application To Vehicle Development Projectsmentioning

confidence: 99%

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confidence: 99%

A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

et al. 2021

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“…Organizational clustering has also been examined in several studies, either as mitigating Complexity 3 communication risks or seizing creativity opportunities [86][87][88][89]. The clustering of specific multidomain elements, like decisions, deliverables, and risks has also been developed by Marle and coworkers [90][91][92][93][94][95]. The originality is then twofold: first, we consider interdependencies between elements that may be of different nature (a risk may be related to project objectives, to time, cost, to a product component, to an actor, to a company, to a project task or to an activity related to product recycling 20 years later, etc.).…”

Section: Related Workmentioning

confidence: 99%

Organizing Project Actors for Collective Decision‐Making about Interdependent Risks

2019

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The way project actors are organized is crucial in determining how they will be able to collectively cope with nontrivial complex problems and risks. Current project organizations are generally based on single-criterion decomposition, whether product, process, or organization based. The proposed approach forms complementary clusters of actors based on the interdependencies between the risks they manage. More precisely, distinction has been made between the interdependencies connecting two risks that are owned by different actors and those owned by the same actor. We argue that interdependency between two risks managed by the same actor is less dangerous, meaning that clustering algorithm is tailored to distinguish mono- and biactor risk interdependencies. The complementary structure offered by interdependency-based clustering tends to put together strongly interconnected actors, albeit they were often initially not grouped together. It increases the likelihood of a better communication, coordination, and collective decision-making in complex situations. Some risks remain out of proposed clusters and are declared transverse, which means that their owners act as information hubs and are not involved in a single cluster. An industrial application is presented with operational results and perspectives for further work are drawn from it.

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“…The main objective of this clustering is to find single partitioning results by combining the different results of similar or different methods. This method is popular because it finds quality clusters from the Clustering algorithms are used in many different fields [20]- [21]. The authors in the paper [14] present a brief study of dimensional reduction techniques in the k-means clustering.…”

Section: Statandard Validity Measuresmentioning

confidence: 99%

Optimized Time Efficient Data Cluster Validity Measures

Khandare¹,

Alvi²

2018

IJITCS

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The main task of any clustering algorithm is to produce compact and well-separated clusters. Well separated and compact type of clusters cannot be achieved in practice. Different types of clustering validation are used to evaluate the quality of the clusters generated by clustering. These measures are elements in the success of clustering. Different clustering requires different types of validity measures. For example, unsupervised algorithms require different evaluation measures than supervised algorithms. The clustering validity measures are categorized into two categories. These categories include external and internal validation. The main difference between external and internal measures is that external validity uses the external information and internal validity measures use internal information of the datasets. A well-known example of the external validation measure is Entropy. Entropy is used to measure the purity of the clusters using the given class labels. Internal measures validate the quality of the clustering without using any external information. External measures require the accurate value of the number of clusters in advance. Therefore, these measures are used mainly for selecting optimal clustering algorithms which work on a specific type of dataset. Internal validation measures are not only used to select the best clustering algorithm but also used to select the optimal value of the number of clusters. It is difficult for external validity measures to have predefined class labels because these labels are not available often in many of the applications. For these reasons, internal validation measures are the only solution where no external information is available in the applications.All these clustering validity measures used currently are time-consuming and especially take additional time for calculations. There are no clustering validity measures which can be used while the clustering process is going on.This paper has surveyed the existing and improved cluster validity measures. It then proposes time efficient and optimized cluster validity measures. These measures use the concept of cluster representatives and random sampling. The work proposes optimized measures for cluster compactness, separation and cluster validity. These three measures are simple and more time efficient than the existing clusters validity measures and are used to monitor the working of the clustering algorithms on large data while the clustering process is going on.

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Improving Collaborative Decision Making in New Product Development Projects Using Clustering Algorithms

Cited by 27 publications

References 29 publications

A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

A Framework to Evaluate Project Complexity Using the Fuzzy TOPSIS Method

Organizing Project Actors for Collective Decision‐Making about Interdependent Risks

Optimized Time Efficient Data Cluster Validity Measures

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