In this paper, we argue that ambiguity is an essential component of "fuzziness" in the Fuzzy Front End of New Product Development (NPD), and that a better understanding of how ambiguity emerges and is reduced, is called for. We explore the process by which ambiguity was reduced in four NPD projects, and propose a model that enhances our understanding of this process. Ambiguity arises as multiple interpretations, and interpretations can be understood as hypotheses, hence these can be tested by using the hypothetical-deductive method (HDM). We present a model showing that ambiguity in NPD projects is efficiently reduced by applying the HDM to test the multiple interpretations that give rise to ambiguity and the assumptions underlying these interpretations. We discuss theoretical implications and the usefulness of the model for practitioners of NPD.
Purpose -The "fuzzy front end" of new product development (NPD) is characterized by considerable uncertainty and ambiguity, but detailed studies of ambiguity specifically related to NPD are missing. This paper aims to establish a classification of ambiguity in NPD processes. Design/methodology/approach -The authors' research design is a holistic multiple-case-study design with the NPD project as the unit of analysis. A model is developed through a grounded theory approach, using qualitative analysis of case data from four medical-device companies. Findings -The authors present a model that classifies ambiguity along two dimensions: subject and source. The subjects of ambiguity include product, market, process, and organizational resources, whereas the sources of ambiguity include multiplicity, novelty, validity, and reliability. Research limitations/implications -As the study is based on just four case studies in a single industry segment, further research is needed to determine the model's wider applicability. Further research is also suggested, exploring how and in what contexts ambiguity should be managed as a balance between reducing or sustaining it. Practical implications -The model presented helps practitioners to better understand the origins and character of ambiguity in NPD, thereby improving their ability to manage it in their NPD projects. Originality/value -The model provides an improved theoretical understanding of ambiguity as a component of "fuzziness" in NPD by providing a detailed account of how ambiguity is related to specific elements of the NPD process in terms of where and why it occurs.
Emergency departments (EDs) had to considerably change their patient flow policies in the wake of the COVID-19 pandemic. Such changes affect patient crowding, waiting time, and other qualities related to patient care and experience. Field experiments, surveys, and simulation models can generally offer insights into patient flow under pandemic conditions. This paper provides a thorough and transparent account of the development of a multi-method simulation model that emulates actual patient flow in the emergency department under COVID-19 pandemic conditions. Additionally, a number of performance measures useful to practitioners are introduced. A conceptual model was extracted from the main stakeholders at the case hospital through incremental elaboration and turned into a computational model. Two agent types were mainly modeled: patient and rooms. The simulated behavior of patient flow was validated with real-world data (Smart Crowding) and was able to replicate actual behavior in terms of patient occupancy. In order to further the validity, the study recommends several phenomena to be studied and included in future simulation models such as more agents (medical doctors, nurses, beds), delays due to interactions with other departments in the hospital and treatment time changes at higher occupancies.
In this paper we challenge the traditional notion of ambiguity as an undesirable element in New Product Development (NPD) and explore how companies sometimes sustain or even increase ambiguity during their NPD projects. Based on qualitative analysis of case data from four NPD projects in the medical-device industry, we present a model by which this process can be better understood. We identify four ways that NPD projects can benefit from temporarily sustaining ambiguity: retaining fallback options, saving costs, saving time, and retaining ideas.
The COVID-19 pandemic required several interventions within emergency departments, complicating the patient flow. This study explores the effect of intervention policies on patient flow in emergency departments under pandemic conditions. The patient flow interventions under evaluation here are the addition of extra treatment rooms and the addition of a waiting zone. A predeveloped hybrid simulation model was used to conduct five scenarios: (1) pre-pandemic patient flow, (2) patient flow with a 20% contamination rate, (3) adding extra treatment rooms to patient flow, (4) adding a waiting zone to the patient flow, (5) adding extra treatment rooms and a waiting zone to the patient flow. Experiments were examined based on multiple patient flow metrics incorporated into the model. Running the scenarios showed that introducing the extra treatment rooms improved all the patient flow parameters. Adding the waiting zone further improved only the contaminated patient flow parameters. Still, the benefit of achieving this must be weighed against the disadvantage for ordinary patients. Introducing the waiting zone in addition to the extra treatment room has one positive effect, decreasing time that the treatment rooms are blocked for contaminated patients entering the treatment room.
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