Scheduling new product development projects using simulation-based dependency structure matrix

Project Scheduling using Dependency Structure Matrix

To successfully develop a complex product, a firm must answer two critical questions: how to “develop the right product” and how to “develop the product right.” Motivated by the real practice of Xiaomi’s new product development (NPD) projects, this article responds to these calls in the following ways. To design the right product, using an enhanced PageRank algorithm to investigate customer needs, NPD managers can select appropriate function modules of the new product to meet customers’ demand. To develop the new product in the right way, NPD managers should optimize the NPD organization. This article applies the multi-domain matrix (MDM) to identify the technical coordination dependency strength among different teams and then to measure the NPD organization’s complexity according to its entropy. By proposing the External Entropy of Cluster (EEC) and Internal Entropy of Cluster (IEC), we develop an entropy-based two-stage clustering criterion of design structure matrix ( DSM) to optimize the NPD organization. The first-stage clustering criterion maximizes the added average dependency strength of DSM, and the second-stage clustering criterion minimizes the Weighted Total Entropy, including the IEC and EEC. An industrial example is provided to illustrate the proposed model. The results indicate that the clustered DSM can reduce the organization’s complexity.

With the growing complexity of the business context, companies are confronting more challenging Product Development (PD) projects because of the potential risks externally brought by the partners’ participation and internally accumulated through the activities’ execution. In this paper, we formulate and decompose a PD project in a hierarchical way, and the obtained end elements perceived as objects are built into a number of Design Structure Matrices (DSMs) and Domain Mapping Matrices (DMMs). We also propose a conceptual model of change occurring and change propagation, base on which the potential change propagating channels are discovered through observing and tracking change occurring and change propagation in DSMs and DMMs. Associated with the depth-first and breath-first search methods executed in matrices the critical objects during change propagation are discovered and the implicit change propagating channels cross multiple fields are identified. As the exploratory results, the critical objects taking account of change propagation are highlighted as the contribution to change management in PD project.

Many researchers have highlighted weaknesses of traditional approaches, such as PERT and CPM, in scheduling product development projects and in particular their failure to model iteration. The design structure matrix (DSM) method provides a practical and useful tool to deal with this issue. The advantages of DSM representation and analysis techniques have led to their increasing use in project planning, and have spawn dozens of research efforts on organizing design tasks. This paper explores the nature of DSM approach, and reviews its recent development in design project scheduling. The paper also discusses limitations of existing DSM-based models, and proposes future research directions.

This paper presents the results of a detailed quantitative analysis of performance metrics of a sample consisting of 89 transportation projects sponsored by the World Bank. The sample and a subset consisting of 65 projects were evaluated using the performance metrics of project cost, schedule and scope. The effect of project size (dollar value) and project duration on performance metrics was investigated. Also, the achievement of project goals and potential improvement in planning and estimating over time (the learning effect) was studied. It was found that, in general, in transportation projects sponsored by the World Bank, costs are overestimated and schedules are optimistic. The outcome with respect to cost seems counter‐intuitive because previous work by other researchers had shown a systematic underestimation of project costs. There is significant evidence that there are no efficient controls in place to predict or prevent schedule delays. The study also showed that during the past 15 years, no improvement (learning effect) was evident in project cost and duration estimation as the level of accuracy has not changed significantly. Further, it is observed that project duration did not affect the performance with respect to cost and delay.

Lean holistic fuzzy methodology employing cross-functional worker teams for new product development projects: A real case study from high-tech industry

Classical scheduling techniques are well-known to underestimate the average project duration, yet they remain widely used in practice due to their simplicity. In this paper, the new Critical Duration Index (CDI) is proposed. This index indirectly allows anticipation for the probability of a project ending late, as well as the average project duration extension compared with a deterministic project duration estimate. The accuracy of two simple regression expressions that use the CDI was tested on two representative data sets of 4,100 artificial and 108 empirical (real) projects. Results show that these regression expressions outperformed the only alternative index found in the literature. Besides allowing enhanced forecasting possibilities, calculating the CDI only requires basic scheduling information that is available at the planning stage. It can thus be easily adopted by project managers to improve their project duration estimates over prior deterministic techniques.

Construction design projects are multi-disciplinary requiring input from several players including planners and design professionals. Project managers have developed techniques for planning, organizing, monitoring, and optimizing complex design projects. The Critical Path Method (CPM) is traditionally the most commonly used to address these issues. Various techniques have been added to CPM like splitting the activities to optimize projects duration. However, the CPM approach is based on workflow and is not effective in handling iterations, which is a characteristic of design projects. Also, while CPM can effectively handle sequential and parallel activities, it cannot handle coupled activities. The Design Structure Matrix (DSM) approach represents information flows in addition to workflows in a project. The DSM method can handle sequential, parallel, in addition to coupled activities. The study reported here involves the application of DSM for design projects, and describes an algorithm to optimize planning of design processes. Using DSM and through finding critical activities, management focuses on the essential information transfer requirements in the project, thereby minimizing rework, and improving throughput and design quality.

The paper discusses the success factors influencing product development design and product launch projects. The lives of people and businesses have been threatened and affected by the global COVID-19 pandemic for more than two years. Knowing these factors can help us make the right decisions and develop an effective project plan and matrix. Nowadays, project management is a very important part of work in companies of different sizes. Project management can be seen as applying innovation processes in an enterprise. The authors conducted quantitative and qualitative research on 544 companies in the Slovak Republic. The research focuses on aspects of project management and individual success factors. The data obtained by the questionnaire survey were processed by statistical methods, where the authors chose the nonparametric Kruskal-Wallis test as the basic method and the Wilcoxon two-tailed test for independent sample data sets. An essential variable in the sample of enterprises surveyed was, in particular, the sector of the economy in which the enterprise surveyed operates. Two key factors were considered when developing the product – employee education and the implication of social responsibility. The study’s uniqueness lies in surveying all types of enterprises, irrespective of their size, line of business, and financial turnover. The percentage distribution of the maximum use of CSR among the surveyed companies in each sector was most positive in the Logistics industry. Training and certification of employees are primarily one of the most important factors to ensure an increase in the success of those projects aimed at launching new products on the market; in the case of the surveyed sample, this is more than 40% of enterprises. Based on the sub-results, we can conclude that enterprises place more emphasis on new product development projects and the environmental concept of CSR. Still, the launch of new products or services is underestimated by the project management of enterprises. After more than eight years, the research has shown an increase in the importance of using social responsibility in management processes for new product development. A breakthrough finding is comparing the use of CSR in small businesses in Slovakia to global trends in which we perceive the dominance of multinational corporations. Enterprises are advised to focus specifically on new product launches and detailed planning.

Overlapping between development activities stemming from concurrent engineering may not only reduce duration of a project but also create rework risk. This article presents a process modeling and analysis technique for managing complex product development projects using optimization and simulation techniques. In order to analyze the forced overlapped process with lead–risk, we propose evolution and sensitivity design structure matrix for reflecting overlapping and their impact on the degree of activity sensitivity and evolution in the process model. Using the design structure matrix, we present the schedule and cost model associated with iteration, overlapping, and rework by incorporating many more general characteristics of complex design processes. The model can be used for better project planning and control by identifying overlapping and risk for process improvements, and for evaluating alternative planning and execution strategies. An industrial example, a tactics aerocraft preliminary development design process at a company, is provided to illustrate the utility of the model using optimization and discrete-event simulation. The optimization model maximizes the compressed time associated with overlapping and yields several managerial insights, including optimization analysis of project activity sequence, how overlapping and rework risk impact on project schedule and cost, trading off schedule and cost risk.

This paper presents a new scheduling model for resource constrained projects with uncertain activity durations. The model produces ‘robust’ resource feasible schedules, which are totally immune against uncertainties in the activity durations. In the presented approach, it is assumed that activity durations are fuzzy variables with triangular membership functions. Theoretically the robust schedule searching process is formulated as a mixed integer linear programming problem. The proposed model is based on the so-called ‘forbidden set’ concept. The presented possibilistic (membership function oriented) model is a modification of a probabilistic (density function oriented) model. In order to illustrate the essence of the proposed approach detailed computational results for two problems are presented. The first problem is a small motivating example; the second problem is a larger project instance. To generate the optimal solutions a state-of-the-art MILP solver (CPLEX) was used.

Analogy based estimation framework is developed aiming to estimate design rework efforts which are unnecessary repetition design efforts. Design rework factors were captured from literature reviews and then they were developed to be design rework drivers by mapping with results from semi structure interviews with three companies in automotive industries. There are six design rework drivers for probability occurrence, and there is only one driver for efforts required. Design structure matrix is used to identify interactions and addressed a key critical component or subsystem. The analogy based estimation framework is applied with the water pump development project from an engine manufacturer. The initial statistical validation method called mean magnitude of relative error is implemented and the results are in acceptable range, while experts from company C agreed on the validity of design rework drivers and opportunity to improve the framework. The ultimate goal of this development is to develop a framework to estimate design rework efforts in the product design and development projects at the early stage.

Construction projects are data-rich environments. However, those data are usually captured for site-specific reasons, e.g., the filing and approval of inspection requests, with little regard to how they can be leveraged for improved project management. Typically, scheduling techniques rely on general probability estimates, which do not capture the details of the site processes causing schedule deviations. This paper illustrates how machine learning techniques can mine project data to forecast delay in the midst of the project. The proposed method uses concrete pouring requests as an example of a site data stream and implements a random forest predictive model to forecast the likelihood of acceptance for these requests. Embedded in the proposed approach is an analysis that allows for the addition of probabilistic time delays associated with the forecast of rejected requests. The methodology was tested on a real-world case study, allowing for the comparison between a project duration estimate based on critical path method (CPM) with static buffers and a project duration obtained using the proposed method. The results show a difference of 10% between the two durations. The paper shows how using data streams from a construction site with machine learning techniques can enhance project duration estimates in execution.

A robust optimization approach for the multi-mode resource-constrained project scheduling problem

Developing a complex product in a concurrent engineering environment requires managing information flow among ten or even hundreds of people of different specialties organized in a large number of product development (PD) teams. Managing these teams effectively requires understanding the level of information dependencies among them which are often vague and cannot be precisely predicted. Taking into account the limitations of relevant previous studies, this article proposes a fuzzy-social network analysis approach for modeling and analyzing the information flow among PD teams. The approach involves four major steps: mapping of dependencies, measuring the level of information dependencies based on the fuzzy set theory, visualizing the network, and performing quantitative analysis using three measures (network density, in-degree centrality, and out-degree centrality). To validate its practicality, the approach is used to model and analyze the dependencies among 22 PD teams in a real project adapted from the literature and involved developing an automobile engine. An advantage of the approach is that in addition to providing a holistic view of the interactions among PD teams, it permits PD project managers to identity the most important PD teams with respect to information flow control using a proposed new classification system that classifies PD teams under four categories: autonomous, receivers, transmitters, and transceivers.