Explanation of the professional development process of general surgery residents in the operating rooms: A situational analysis

Introduction: Numerous factors and elements are effective in the professional development of any field of study, including the educational structure, the individual characteristics of learners, and the educational atmosphere prevalent in the educational environment. Understanding each of these factors and elements and the relationships among them can guide educational system administrators in the direction of professional development. Surgical residents’ professional development is no exception to this rule. As a consequence, the present research sought to explain and suggest a model for surgical assistant professional growth in Iranian operating rooms.Methods: The present research was a grounded theory study based on a post-positivist approach, in which data analysis was performed using Clark’s situational analysis methodology by drawing three maps, situational map, social worlds/arenas map, and positional map.Results: In the presence of human and non-human factors, cultural, political, historical, and social components, the ordered situational map demonstrated the complexity of the operating room learning environment. The social worlds/arenas map confirmed the existence of several communities of practice wherein surgical residents were present with different power roles, and the positional map showed role of the educational level in the acquisition of thecompetence in the professional development pathway. Finally, the Triple Helix model of professional development was extracted, which has three components: psychological identity, social identity, and surgical competency.Conclusion: The surgical residents’ professional development in operating rooms occurs due to the acquisition of surgicalcompetency along with the growth of individuals and socialization. As a result, all factors and components impacting the residents’ competence development process in this learning environment must be identified and their linkages clarified

Quality management in heavy duty manufacturing industry: TQM vs. Six Sigma

‘Is TQM a management fad?’ This question has been extensively documented in the quality management literature; and will be tackled in this research though a critical literature review on the area. ‘TQM versus Six-Sigma’ debate, which has also been a fundamental challenge in this research filed, is addressed by a thematic and chronological review on the peer papers. To evaluate this challenge in practice, a primary research in heavy duty machinery production industry have been conducted using a case-study on, J C Bamford Excavators Ltd (JCB), the largest European construction machinery producer. The result highlights that TQM is a natural foundation to build up Six-Sigma upon; and not surprisingly the quality yield in a TQM approach complemented by Six-sigma is far higher and more stable than when TQM with no Six-Sigma focus is being put in place; thus presenting the overall finding that TQM and Six Sigma are compliments, not substitutes. The study will be concluded with an overview on quality management approaches in the heavy duty manufacturing industry to highlight the way forward for the industry

A strategic and global manufacturing capacity management optimisation model: A Scenario-based multi-stage stochastic programming approach

Large-scale multinational manufacturing firms often require a significant investment in production capacity and extensive management efforts in strategic planning in an uncertain business environment. In this research we first discuss what decision terms and boundary conditions a holistic capacity management model for the manufacturing industry must contain. To better understand how these decision terms and constraints have been employed by the recent model developers in the area of capacity and resource management modelling for manufacturing, 69 optimisation-based (deterministic and stochastic) models have been carefully selected from 2000 to 2018 for a brief comparative analysis. The results of this comparison shows although applying uncertainty into capacity modelling (in stochastic form) has received a greater deal of attention most recently (since 2010), the existing stochastic models are yet very simplistic, and not all the strategic terms have been employed in the current model developments in the field. This lack of a holistic approach although is evident in deterministic models too, the existing stochastic counterparts proved to include much less decision terms and inclusive constraints, which limits them to a limited applications and may cause sub-optimal solutions. Employing this set of holistic decision terms and boundary conditions, this work develops a scenario-based multi-stage stochastic capacity management model, which is capable of modelling different strategic terms such as capacity level management (slight, medium and large capacity volume adjustment to increase/decrease capacity), location/relocation decisions, merge/decomposition options, and product management (R&D, new product launch, product-to-plant and product-to-market allocation, and product phase-out management). Possibility matrix, production rates, different financial terms and international taxes, inflation rates, machinery depreciation, investment lead-time and product cycle-time are also embedded in the model in order to make it more practical, realistic and sensitive to strategic decisions and scenarios. A step-by-step open-box validation has been followed while designing the model and a holistic black-box validation plan has been designed and employed to widely validate the model. The model then has been verified by deploying a real-scaled case of Toyota Motors UK (TMUK) decision of mothballing one of their production lines in the UK after the global recession in 2010