Project dynamics and emergent complexity

This paper presents a theoretical analysis of project dynamics and emergent complexity in new product development (NPD) projects subjected to the management concept of concurrent engineering. To provide a comprehensive study, the complexity frameworks, theories and measures that have been developed in organizational theory, systematic engineering design and basic scientific research are reviewed. For the evaluation of emergent complexity in NPD projects, an information-theory quantity—termed “effective measure complexity” (EMC)—is selected from a variety of measures, because it can be derived from first principles and therefore has high construct validity. Furthermore, it can be calculated efficiently from dynamic generative models or purely from historical data, without intervening models. The EMC measures the mutual information between the infinite past and future histories of a stochastic process. According to this principle, it is particularly interesting to evaluate the time-dependent complexity in NPD and to uncover the relevant interactions. To obtain analytical results, a model-driven approach is taken and a vector autoregression (VAR) model of cooperative work is formulated. The formulated VAR model provided the foundation for the calculation of a closed-form solution of the EMC in the original state space. This solution can be used to analyze and optimize complexity based on the model’s independent parameters. Moreover, a transformation into the spectral basis is carried out to obtain more expressive solutions in matrix form. The matrix form allows identification of the surprisingly few essential parameters and calculation of two lower complexity bounds. The essential parameters include the eigenvalues of the work transformation matrix of the VAR model and the correlations between components of performance fluctuations.     

Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time

Shorter product life cycles, more rapid product obsolescence, and the increasing intensity of global competition have driven firms to strive for a more rapid introduction of new products to market. We introduce a normative model which yields insights concerning several key new product development (NPD) decisions. First, we examine investment strategies related to the timing and duration for investments in both design and process capacity over a given planning horizon. Second, the model offers guidance regarding the optimal time-to-market and ramp-up time necessary to meet peak demand for the new product. The model thus provides both theoretical and managerial insights into the crucial linkage between time-to-market and ramp-up time decisions. Finally, the implications of several specific NPD investment mechanisms on these NPD metrics are explored.     

Fuzzy linear programming models for NPD using a four-phase QFD activity process based on the means-end chain concept

Quality function deployment (QFD) is a customer-driven approach in processing new product development (NPD) to maximize customer satisfaction. Determining the fulfillment levels of the “hows”, including design requirements (DRs), part characteristics (PCs), process parameters (PPs) and production requirements (PRs), is an important decision problem during the four-phase QFD activity process for new product development. Unlike previous studies, which have only focused on determining DRs, this paper considers the close link between the four phases using the means-end chain (MEC) concept to build up a set of fuzzy linear programming models to determine the contribution levels of each “how” for customer satisfaction. In addition, to tackle the risk problem in NPD processes, this paper incorporates risk analysis, which is treated as the constraint in the models, into the QFD process. To deal with the vague nature of product development processes, fuzzy approaches are used for both QFD and risk analysis. A numerical example is used to demonstrate the applicability of the proposed model.     

Social media and sensemaking patterns in new product development: demystifying the customer sentiment

Artificial intelligence by principle is developed to assist but also support decision making processes. In our study, we explore how information retrieved from social media can assist decision-making processes for new product development (NPD). We focus on consumers’ emotions that are expressed through social media and analyse the variations of their sentiments in all the stages of NPD. We collect data from Twitter that reveal consumers’ appreciation of aspects of the design of a newly launched model of an innovative automotive company. We adopt the sensemaking approach coupled with the use of fuzzy logic for text mining. This combinatory methodological approach enables us to retrieve consensus from the data and to explore the variations of sentiments of the customers about the product and define the polarity of these emotions for each of the NPD stages. The analysis identifies sensemaking patterns in Twitter data and explains the NPD process and the associated steps where the social interactions from customers can have an iterative role. We conclude the paper by outlining an agenda for future research in the NPD process and the role of the customer opinion through sensemaking mechanisms.

     

Performance Variability and Project Dynamics

We present a dynamical model of complex cooperative projects such as large engineering design or software development efforts, comprised of concurrent and interrelated tasks. The model contains a stochastic component to account for temporal fluctuations both in task performance and in the interactions between related tasks. We show that as the system size increases, so does the average completion time. Also, for fixed system size, the dynamics of individual project realizations can exhibit large deviations from the average when fluctuations increase past a threshold, causing long delays in completion times. These effects are in agreement with empirical observation. We also show that the negative effects of both large groups and long delays caused by fluctuations may be mitigated by arranging projects in a hierarchical or modular structure. Our model is applicable to any arrangement of interdependent tasks, providing an analytical prediction for the average completion time as well as a numerical threshold for the fluctuation strength beyond which long delays are likely. In conjunction with previous modeling techniques, it thus provides managers with a predictive tool to be used in the design of a project's architecture. Bernardo A. Huberman is a Senior HP Fellow and Director of the Information Dynamics Laboratory. He is also a Consulting Professor of Physics at Stanford University. For the past ten years he has concentrated on understanding distributed processes and on the design of mechanisms for information aggregation and the protection of privacy as well as market-based distributed resource allocation systems. Dennis Wilkinson is a recent graduate of Stanford University with a doctorate in Physics, and has accepted a position in the Department of Defense. His research interests include dynamics of social networks and other stochastic systems, information extraction from large, complex networks, and techniques in distributed computing.     

Managing Portfolios of Projects under Uncertainty

Managers of projects and multi-project programs often face considerable uncertainty in the duration and outcomes of specific tasks, as well as in the overall level of resources required by tasks. They must decide, in these uncertain conditions, how to allocate and manage scarce resources across many projects that have competing needs. This paper develops a nonlinear mixed-integer programming model for optimizing the resource allocations to individual tasks to minimize the completion times of a collection of projects. The model contains a very flexible representation of the effects of changing resource allocations on the probability distribution of task duration, so it can accommodate a wide variety of practical situations. A heuristic solution procedure is proposed that works quite effectively. An illustration involving a collection of bridge construction projects is provided.     

Incentive schemes for resolving Parkinson’s Law in project management

Project management is a business process that supports about 30% of the world’s economic activity. Yet projects routinely suffer from the influence of Parkinson’s Law. This behavioural phenomenon routinely results in failure to deliver work that is completed early before its assigned deadline. As a consequence, the late completion of other work is not offset, and overall project performance suffers. Hence, project success rates below 40% are widely reported.Our work uses mechanism design within non-cooperative game theory. A particular issue in the design process is to eliminate the possibility that a project worker with multiple dependent tasks can improve their incentive payment by falsely reporting some of their task completion times. From our review of the academic and business literature of project management, no incentive scheme used in practice accomplishes this.Our results include the design of incentive schemes that eliminate or mitigate Parkinson’s Law. These schemes apply to projects designed under either traditional Critical Path Method (CPM) planning or modern Critical Chain Project Management (CCPM) planning, and are also invulnerable to group strategy. A large-scale computational study validates the resulting benefit to project performance as substantial and also robust across different project characteristics. We also provide what is apparently the first analytical comparison between traditional CPM and modern CCPM planning systems.The incentive schemes we propose are simple and easily implementable. We recognize that performance incentives are structured differently by each organization, but our work provides a flexible basis from which various practical schemes can be designed.     

Uncertainty Modelling in Software Development Projects (With Case Study)

A project scheduling model tailored specifically for software development projects is proposed in this study. The model incorporates uncertainties related to activity durations and network topology. The first type of uncertainty exists due to error-prone coding which might result in elongated task durations caused by validation and debugging sessions. Furthermore, in practice, macro-activities represent groups of sub-tasks in order to simplify the planning and monitoring of the project. Due to the aggregation, it is more difficult to be precise on the duration of a macro-activity.The uncertainty related to the network topology is due to common database design issues or program modules shared among parallel tasks in the project network. These tasks become associated with each other through uncertain Start-to-Start (SS) precedence relationships. On the other hand, SS lags may also be the outcome of technological precedence relationships among pairs of activities. However, the imprecision underlying the work content of a predecessor activity leads to uncertain SS lags.Software development projects are human-intensive projects and hence, the duration of a task depends on the skill of the person assigned to the job as well as his/her learning rate. Thus, a task may be realized by alternative staff members which results in different expected task durations. Hence, a realistic model proposed for software development projects should incorporate staff assignment features under the uncertainties discussed above. In this study, we develop a mathematical model for software development projects and propose heuristic solution methods to be used by the project co-ordinator in preparing the project plan. The heuristic algorithms developed here are tested on real data provided by a consulting firm undertaking software development projects from manufacturing companies in Turkey.     

Lagrangian heuristics for scheduling new product development projects in the pharmaceutical industry

To stay ahead of their competition, pharmaceutical firms must make effective use of their new product development (NPD) capabilities by efficiently allocating its analytical, clinical testing and manufacturing resources across various drug development projects. The resulting project scheduling problems involve coordinating hundreds of testing and manufacturing activities over a period of several quarters. Most conventional integer programming approaches are computationally impractical for problems of this size, while priority rule-driven heuristics seldom provide consistent solution quality. We propose a Lagrangian decomposition (LD) heuristic that exploits the special structure of these problems. Some resources (typically manpower) are shared across all on-going projects while others (typically equipment) are specific to individual project categories. Our objective function is a weighted discounted cost expressed in terms of activity completion times. The LD heuristics were subjected to a comprehensive experimental study based on typical operational instances. While the conventional “Reward–Risk” priority rule heuristic generates duality gaps between 47–58%, the best LD heuristic achieves duality gaps between 10–20%. The LD heuristics also yield makespan reductions of over 30% over the Reward–Risk priority rule.     

Methodological reflections on a three-step-design combining observation,stimulated recall and interview

In this paper a tripartite qualitative design combining abservation, stimulated recall and interview is presented and discussed. This three-step-design makes it possible to get insight into the interaction of internal and external processes when solving mathematical tasks. The data analysis depends on the research question and the methodological approach. In the light of two research projects in mathematics education two different methods of data analysis are presented and methodologically reflected.     

Community facilitation of problem structuring and decision making processes: Experiences from the EU LEADER+ programme

This paper reports on the work carried out supporting a rural community in Denmark under the LEADER+ programme. This is a programme that supports development in particularly vulnerable rural regions of the European countries members of EU. It supports creative and innovative projects that can contribute to long-term and sustainable development in these regions. The main tasks have been the organisation and facilitation of conferences and workshops to structure the problematic situation of identifying and designing innovative projects for the development of the community and to support decision making processes related to the agreement on action plans. Learning to design, plan, manage and facilitate conferences and workshops have also being another central activity. The main purpose of these conferences and workshops was not only problem structuring and decision making in connection with community development but also the transfer of facilitation skills and appropriate methods to the community.     

Designing Spreadsheet-Based Tasks for Purposeful Algebra

We describe the design of a sequence of spreadsheet-based pedagogic tasks for the introduction of algebra in the early years of secondary schooling within the Purposeful Algebraic Activity project. This design combines two relatively novel features to bring a different perspective to research in the use of spreadsheets for the learning and teaching of algebra: the tasks which are purposeful for pupils and contain opportunities to appreciate the utility of algebraic ideas, and careful matching of the affordances of the spreadsheet to the algebraic ideas which are being introduced. Examples from two tasks are used to illustrate the design process. We then present data from a teaching programme using these tasks to highlight connections between aspects of the task design and the construction of meanings for variable.     

协同设计资源的两级不确定调度问题研究及其求解

针对协同设计资源调度问题中存在的设计任务不确定、资源需求模糊以及任务进行过程中可能出现变化等情况,提出一种基于不确定规划和反应式调度的两级不确定调度模型,通过根据满足系数对最好及最坏规划进行折中的方法对模型进行转化,然后采用离散的自由搜索算法进行求解.通过实例,验证了该方法的有效性.     

The virtual design team: A computational model of project organizations

Large scale and multidisciplinary engineering projects (e.g., design of a hospital building) are often complex. They usually involve many interdependent activities and require intensive coordination among actors (i.e., designers) to deal with activity interdependencies. To make such projects more effective and efficient, one needs to understand how coordination requirements are generated and what coordination mechanisms should be applied for given project situations. Our research on the Virtual Design Team (VDT) attempts to develop a computational model of project organizations to analyze how activity interdependencies raise coordination needs and how organization design and communication tools change team coordination capacity and project performance. The VDT model is built based on contingency theory (Galbraith, 1977) and our observations about collaborative and multidisciplinary work in large, complex projects. VDT explicitly models actors, activities, communication tools and organizations. Based on our extended information-processing view of organizations, VDT simulates the actions of, and interactions among actors as processes of attention allocation, capacity allocation, and communication. VDT evaluates organization performance by measuring emergent project duration, direct cost, and coordination quality. The VDT model has been tested internally, and evaluated externally through case-studies. We found three way qualitative consistency among predictions of the simulation model, of organization theory, and of experienced project managers. In this paper, we present the VDT model in detail and discuss some general issues involved in computational organization modeling, including level of abstraction of tasks and actors' reasoning, and model validation.     

CIMS项目网络计划技术中成本优化算法研究

现有的网络计划在描述项目时已不能适应越来越大型和复杂的C IM S项目,为此,对现有网络计划中任务的约束条件及逻辑关系进行了扩充,并给出了扩充网络计划中时间参数的计算.在此基础上,提出了一种基于样本的成本优化算法,有效地解决了实际项目中的成本优化问题.     

Supporting disciplinary and interdisciplinary knowledge development and design thinking in an informal,pre‐engineering program: A Workplace Simulation Project

In this paper, we examined students’ engagement in an implementation of a Workplace Simulation Project (WSP). The WSP was designed to actively engage students in learning disciplinary content by inviting engineers from industry to have a physical presence within the school building to collaborate with teachers and students to complete projects which simulate the tasks authentic to their work. We focus on the first year implementation of the program that partnered a high school in the rural Midwest with an engineering unit of a government organization. Using a multiple methods study design, we analyzed disciplinary and interdisciplinary pre and posts test along with students’ interviews to determine learning gains as well as students’ interpretations of creative and critical thinking as experienced in the project and their knowledge of the engineering design process. Effect sizes showed that students in the WSP group had notable gains over the control group participants. Additionally, students’ knowledge of core elements of the design process were identified in inductive analyses of the interviews. Findings from this study will provide usable knowledge about effective ways to support systems and design thinking and ways to support expert‐novice collaboration to ensure success.     

Optimizing color picture tubes by high-cost nonlinear programming

In this paper we discuss the application of optimization techniques for the design of several parts of the color picture tube, the key component of television sets and computer monitors. These projects have been carried out for Philips Display Components in Eindhoven. Philips developed several computer simulation models of picture tube parts. Designers use these models to simulate the physical behavior of a particular part design. Depending on the amount of detail in the model, the running time of a typical simulation ranges from one up to 10 hours. Tube designers are confronted with the problem of finding settings for a large number of design parameters that are optimal with respect to several simulated tube characteristics. This problem can be modeled as a so-called high-cost nonlinear programming problem. This paper reports on the successful application of our four-step compact model approach to solve this problem. The presented results are based on four projects in which we optimized picture tube parts. Among the realized benefits for Philips are a design improvement of 30% and a time-to-market reduction of 50–60%.     

Performance Assessment in Science as a Tool to Enhance the Picture of Student Learning

The Iowa Assessment Project was funded by the National Science Foundation to explore the feasibility of combining the expertise of science teachers, science educators, and test developers to build innovative performance assessments that complement traditional, norm-referenced, multiple-choice science tests. The science teachers, graduate students, and science educators designed and tested performance assessment tasks to enhance the picture of science understanding in students through multiple points of evidence. This paper describes the design of four science performance tasks for Grade 9 students and the relationship between their performance on these tasks and multiple-choice items in the Iowa Tests of Educational Development. Students and schools used to develop the tasks were not included in the verification sample.     

动态多任务双边道德风险契约研究

针对管理活动的动态性与多任务性的特点,将解聘补偿与解聘倾向引入动态多任务契约设计中,构建了基于解聘补偿的动态多任务双边道德风险契约。通过数理推导分析的方法给出了最优契约设计,声誉效应和棘轮效应的度量,探讨了解聘倾向对于契约设计的影响。结果表明解聘倾向的引入对于委托人的道德风险约束是有效的,但是对于代理人的道德风险约束则取决于声誉效应与棘轮效应的大小。在第2期契约中,解聘倾向对固定支付的影响取决于代理人保留收入与解聘补偿的差额。而第1期的契约设计要受到解聘补偿,声誉效应与棘轮效应三者的综合影响。任务关联性对契约设计影响以及相应的实证分析是未来的研究方向。     

Step by step. The benefits of stage-based R&D licensing contracts

We examine how a licensor can optimally design licensing contracts for multi-phase R&D projects when he does not know the licensee’s project valuation, leading to adverse selection, and cannot enforce the licensee’s effort level, resulting in moral hazard. We focus on the effect of the phased nature typical of such projects, and compare single-phase and multi-phase contracts. We determine the optimal values for the upfront payment, milestone payments and royalties, and the optimal timing for outlicensing. Including multiple milestones and accompanying payments can be an effective way of discriminating between licensees holding different valuations, without having to manipulate the royalty rate, which induces licensees to invest less, resulting in lower project values and socially suboptimal solutions. Interestingly, we also find that multiple milestone payments are beneficial even when the licensor is risk-averse, contrary to standard contract theory results, which recommend that only an upfront payment should be used. In terms of licensing timing, we show that the optimal time depends on the licensor’s risk aversion, the characteristics of the licensee and the project value.