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The purpose of this paper is to present a process for managing project problem‐solving patterns. It focuses on shifting the emphasis of project teams toward a more collaborative and knowledge‐based style of dealing with challenges to project performance. The methods proposed in this paper encourage project managers to integrate processes for becoming more agile by tapping into lesson learned and knowledge gained to create higher quality solutions to problems.

This paper proposes a conceptual framework for recognizing problem‐solving patterns and transforming problem solving from an individual passive event to a more open, agile active, systemic process. Several actual case examples are provided to illustrate applications.

The paper examines how taking a more open approach to problem solving in projects leads to better solutions. The proposed method and lessons from actual cases offer support to these proposals.

The proposed models in this paper originate from the conclusions and observations drawn by the authors over many years of experience. However, they are not the product of a systematic research effort. This paper is intended to provide a new lens for project managers to view projects. It does not purport to declare findings of any research or analyze any sort of research.

The conceptual framework provided in this paper is a practical one derived from the practices used in leading companies. The paper provides practical guidelines to aid project managers in recognizing and managing problem‐solving patterns to create better solutions to problems.

Modern society is plagued by the effects of ineffective problem‐solving initiatives in business, government, and not‐for‐profit organizations. Flawed proposed solutions exact a toll on organizations, their members, and the constituents they serve. This paper proposes a way of improving the quality of problem‐solving processes that may benefit a broad scale of people.

The concept of a problem‐solving pattern and a typology of problem‐solving patterns presented in this paper, provide project managers with a new way of conceiving of how problem solving can be used to improve project performance and adaptability.

We propose that research on problem‐solving behavior can provide critical insight into mechanisms through which organizations resist learning and change. In this paper, we describe typical front‐line responses to obstacles that hinder workers’ effectiveness and argue that this pattern of behavior creates an important and overlooked barrier to organizational change. Past research on quality improvement and problem solving has found that the type of approach used affects the results of problem‐solving efforts but has not considered constraints that may limit the ability of front‐line workers to use preferred approaches. To investigate actual problem‐solving behavior of front‐line workers, we conducted 197 hours of observation of hospital nurses, whose jobs present many problem‐solving opportunities. We identify implicit heuristics that govern the problem‐solving behaviors of these front‐line workers, and suggest cognitive, social, and organizational factors that may reinforce these heuristics and thereby prevent organizational change and improvement.

This paper aims to present the evaluation results of a practice‐based research partnership to develop and implement a new police organizational model for crime reduction into one police agency which was implemented based on the best practices of problem‐oriented policing, hot spots policing, and Compstat.

A qualitative process evaluation of organizational changes and an impact evaluation examining specific crime types was conducted over seven years to determine increased efficiency, collaboration, and effectiveness of the police department's crime reduction strategies.

The process evaluation found that the agency improved its crime analysis capabilities as well as its coordination and communication, expanded its problem solving activities, and made a significant cultural shift towards incorporating problem solving and accountability throughout the organization. The impact evaluation found that the crimes addressed in the implementation – theft from vehicle – did decrease overall and when compared to other crime types and to neighboring jurisdictions.

The limitations of the research are that these results are from one agency and that the impact evaluation is not conclusive.

The work was carried out over seven years in which the collaboration between the researcher and the agency was seminal. The model developed can be used by other police departments, and a key finding was that strong leadership played the most important role in the implementation of the crime reduction strategies and accountability practices.

The purpose of this paper is to examine empirically why a systematic problem-solving routine can play an important role in the process improvement efforts of hospitals.

Data on 18 process improvement cases were collected through semi-structured interviews, reports and other documents, and artifacts associated with the cases. The data were analyzed using a grounded theory approach.

Adherence to all the steps of the problem-solving routine correlated to greater degrees of improvement across the sample. Analysis resulted in two models. The first partially explains why hospital workers tended to enact short-term solutions when faced with process-related problems; and tended not seek longer-term solutions that prevent problems from recurring. The second model highlights a set of self-reinforcing behaviors that are more likely to address problem recurrence and result in sustained process improvement.

The study was conducted in one hospital setting.

Hospital managers can improve patient care and increase operational efficiency by adopting and diffusing problem-solving routines that embody three key characteristics.

This paper offers new insights on why caregivers adopt short-term approaches to problem solving. Three characteristics of an effective problem-solving routine in a healthcare setting are proposed.

The purpose of this paper is to investigate the use of problem-solving approaches in maintenance cost management (MCM). In particular, the paper aims to examine characteristics of MCM models and to identify patterns for classification of problem-solving approaches.

This paper reflects an extensive and detailed literature survey of 68 (quantitative or qualitative) cost models within the scope of MCM published in the period from 1969 to 2013. The reviewed papers have been critically examined and classified based on implementing a morphological analysis which employs eight criteria and associated expressions. In addition, the survey identified two main perspectives of problem solving: first, synoptic/incremental and second, heuristics/meta-heuristics.

The literature survey revealed the patterns for classification of the MCM models, especially the characteristics of the models for problem-solving in association with the type of modeling, focus of purpose, extent and scope of application, and reaction and dynamics of parameters. Majority of the surveyed approaches is mathematical, respectively, synoptic. Incremental approaches are much less and only few are combined (i.e. synoptic and incremental). A set of features is identified for proper classification, selection, and coexistence of the two approaches.

This paper provides a basis for further study of heuristic and meta-heuristic approaches to problem-solving. Especially the coexistence of heuristic, synoptic, and incremental approaches needs to be further investigated.

The detected dominance of synoptic approaches in literature – especially in the case of specific application areas – contrasts to some extent to the needs of maintenance managers in practice. Hence the findings of this paper particularly address the need for further investigation on combining problem-solving approaches for improving planning, monitoring, and controlling phases of MCM. Continuous improvement of MCM, especially problem-solving and decision-making activities, is tailored to the use of maintenance knowledge assets. In particular, maintenance management systems and processes are knowledge driven. Thus, combining problem-solving approaches with knowledge management methods is of interest, especially for continuous learning from past experiences in MCM.

This paper provides a unique study of 68 problem-solving approaches in MCM, based on a morphological analysis. Hence suitable criteria and their expressions are provided. The paper reveals the opportunities for further interdisciplinary research in the maintenance cost life cycle.

An understanding of effective problem solving is essential for everyone involved in development teams. For most development projects, the design‐build‐test cycle is the fundamental building block of effective problem solving. Examines the design‐build‐test cycle and describes alternative modes of problem solving and their implications for organizational skills and capabilities. Examines how superior capabilities at conducting the cycle can be used to make dramatic improvements in individual product development efforts. Concludes by considering how a firm can leverage the problem‐solving ability into a competitive advantage.

The apparel design process involves gathering and analysing information on fashion trends, markets and past line sales and editing ideas for successful combinations of fabric, style and price. These ideas are the result of creativity. Creativity is most often modelled as a problem‐solving process involving complex chaotic systems. In the fields of architecture and software design, pattern languages have been developed to help understand the various fundamental components and dynamics of complex systems by using a series of related generic problem‐solving patterns empirically proven to be successful in a specified context of forces. Patterns record existing knowledge to make it rapidly and easily accessible and communicated between different users. A pattern language for the apparel design process could channel creative efforts and enhance communication between design team members by providing them with a common working language. Furthermore, pattern languages are increasingly used in the design of software and user‐interface programs (Booch 1996; Gamma et al. 1994). Preparing the ground by developing a pattern language for apparel products could save resources and time as well as improve the quality of future customised information.

This chapter adopts a problem-solving perspective to analyze the competitive dynamics of innovation ecosystems. We argue that features such as uncertainty, complexity, and ambiguity, entail different knowledge requirements which explain the varying abilities of focal firms to coordinate the ecosystem and benefit from the activities of their suppliers, complementors, and users. We develop an analytical framework to interpret various instances of coupling patterns and identify four archetypical types of innovation ecosystems.

Problem-solving” is the most crucial key insight of scientific research. This study focuses on constructing the “problem-solving” knowledge graph of scientific domains by extracting four entity relation types: problem-solving, problem hierarchy, solution hierarchy and association.

This paper presents a low-cost method for identifying these relationships in scientific papers based on word analogy. The problem-solving and hierarchical relations are represented as offset vectors of the head and tail entities and then classified by referencing a small set of predefined entity relations.

This paper presents an experiment with artificial intelligence papers from the Web of Science and achieved good performance. The F1 scores of entity relation types problem hierarchy, problem-solving and solution hierarchy, which were 0.823, 0.815 and 0.748, respectively. This paper used computer vision as an example to demonstrate the application of the extracted relations in constructing domain knowledge graphs and revealing historical research trends.

This paper uses an approach that is highly efficient and has a good generalization ability. Instead of relying on a large-scale manually annotated corpus, it only requires a small set of entity relations that can be easily extracted from external knowledge resources.