Suchergebnisse

Intro -- Industriële statistiek als technologie voor het oplossen van problemen -- 1. Professionele probleemoplossers -- 2. Statistiek als methoden en technieken om problemen op te lossen -- 3. Competentie in het oplossen van problemen als onderdeel van professionaliteit -- 4. Industriële statistiek als wetenschap -- 5. Rijles en onderwijs in de industriële statistiek -- 6. De name of the game -- 7. Tot slot: erkenning en dank -- Noten -- Referenties.

The development of industrial statistics as a scientific discipline, as well as its contributions to improving quality and efficiency in modern industry are discussed. Furthermore, the nature, purpose, and research paradigm of scientific research in industrial statistics are profiled. These accounts form the basis for a discussion of the discipline's future and key elements therein.

ABSTRACTAppointment scheduling is generally applied in outpatient clinics and other healthcare services. The challenge in scheduling is to find a strategy for dealing with variability and unpredictability in service duration and patient arrivals. The consequences of an ineffective strategy include long waiting times for patients and idle time for the healthcare provider. In turn, these have implications for the perceived quality, cost‐efficiency, and capacity of healthcare services. The generation of optimal schedules is a notoriously intractable problem, and earlier attempts at designing effective strategies for appointment scheduling were based on approximation, simulation, or simplification. We propose a novel strategy for scheduling that exploits three tactical ideas to make the problem manageable. We compare the proposed strategy to other approaches, and show that it matches or outperforms competing methods in terms of flexibility, ease of use, and speed. More importantly, it outperforms competing approaches nearly uniformly in approaching the desired balance between waiting and idle times as specified in a chosen objective function. Therefore, the strategy is a good basis for further enrichments.

PurposeThe purpose of this study is to discover the learning mechanisms and temporal dynamics of implementing systems (Six Sigma) as it unfolds over time.Design/methodology/approachThe data come from a European engineering company that was implementing a Six Sigma-based quality management system (QMS) over a seven-year period. The analysis is based on an event-sequence reconstruction of the implementation process as it unfolded over time and discovers four different learning mechanisms that emerged: programmatic, persistent, adaptive and dialectical learning mechanisms. The research follows a process design study, where the authors study how the process unfolds over time.FindingsMuch of the literature on implementing management systems suggests that implementation follows a prescribed sequence of "turn-key" steps. However, the findings show that only 40% of all events were driven by prescribed "turn-key" generic practices, while 56% of events required constructing new practices via adaptive and dialectical learning. Moreover, the implementation process did not proceed in a linear programmatic fashion, but instead followed a punctuated equilibrium pattern, which alternated between periods of incremental change and major organizational change. The study also found that implementation required changing many complementary organizational structures and practices that were interdependent with the management system (i.e. Six Sigma). By understanding the implementation process, managers can better assess the time and effort involved, better adapt the system to their situated context and predict critical junctures where implementation could break down.Originality/valueThis research complements the few studies that have examined the process of implementing management systems. Most studies examine factors or conditions that result in implementation success (the what of implementing systems), but few examine the process of implementation and the learning that takes place during implementation (the how of implementing systems), which is a complex nonlinear process that involves different modes of learning.