This work tries to answer the following question: can healthcare be engineered using lean management tools? Lean is known to achieve successful results when implemented in the manufacturing sector. Typical results are operational cost reduction, cycle time reduction, and higher customer satisfaction. The service sector, however, has seen mixed results. For the last two decades, educators and healthcare professionals are trying to implement lean tools in healthcare. Some reported success and many did not, for variety of reasons. In this paper, we search the literature and reveal the special nature of healthcare services, success factors, and barriers facing implementation of lean in healthcare. We then conduct a survey of 18 elite Jordanian hospitals to study the case holistically. Statistical analysis of the survey results confirmed some of what the literature revealed; organizational leadership seems to be the most dominant factor, followed by knowledge of employees about lean, training, and patient satisfaction (customer focus). Another important finding, not captured by the literature, is that lean implementation success depends on educating physicians about continuous improvement and lean and ensuring they are part of the improvement team. Based on the revealed enablers and obstacles, we created a full lean implementation framework. This framework was then used along with selected engineering tools to implement lean in a major hospital successfully. Implementation results showed 60% of reduction in cycle time, 80% reduction in operational cost, and many other benefits.

Six Sigma is used heavily in various industrial sectors, yet no noticeable applications are seen in healthcare logistics. This paper reveals the special case of healthcare logistics where cost reduction is not the only factor considered in project selection; performance and criticality of each item in the logistics system are of high importance as well. This paper provides a proposed framework to apply Six Sigma in the area of healthcare logistics. It also presents a case study implementing the proposed framework at a Jordanian hospital. In the case study, the paper reveals how the modifications of the define phase to take into consideration the criticality, cost, and performance of items make typical Six Sigma methodology very beneficial for healthcare logistics. In addition, it shows how the Six Sigma project selection can be done to deal effectively with healthcare logistics issues. This paper paves the road for research to elaborate on ways to use Six Sigma in the area of improving healthcare logistics.

Purpose: Organizations have varied levels of success with Lean implementation and many did not see tangible results. Some scholars believe the reason has to do with weak Lean culture or Lean values (LVs). The purpose of this paper is to study the relationship between Lean implementation practices and LVs. The research goes further to study the nature of this relationship; does LVs affect Lean practices only or do they affect each other in a reciprocal manner? Design/methodology/approach: Literature regarding Lean implementation in various sectors was reviewed. Representatives from several organizations were surveyed. Results from both approaches are compared and presented to highlight the key challenges and drivers facing Lean implementation. Findings: Lean works well on enhancing organization performance (OP) but the implementation has to be preceded by careful nourishment of the proper Lean culture and LVs. The relationship between Lean implementation and LVs is of a complex nature and driven by OP. Practical implications: The study has important managerial implications that is if Lean is going to be sustained, continuous efforts has to be exerted by Lean professionals to engage leaders and decision makers in the organization and ensure proper values are nurtured. Originality/value: This is the first study to examine the reciprocal relationship between Lean implementation practices and LVs while focusing on OP by employing a structural statistical model.

Six Sigma is widely used in many industries for the purpose of improving process performance. Typical improvements sought in Six Sigma projects include decreased operational cost, increased customer satisfaction, decreased cycle time, and enhanced profits, but regardless of the nature of the improvement, all projects follow the “Define-Measure-Analyze-Improve-Control” (DMAIC) methodology. This paper proposes the use of simulation and multi response optimization in addition to other typical Six Sigma tools in order to arrive at optimum performance at the end of each project through an established optimization framework. The framework is used in a real case study performed at a global logistics company. The case study is concerned with an e-commerce logistics station located at John F. Kennedy airport (JFK) in New York; a station that suffers from low on-time delivery performance and high operational cost. As result, a Six Sigma project was implemented and the process was simulated to reveal various scenarios suggested by the Six Sigma team in order to optimize the process. The project resulted in 65 percent reduction in labor, 41 percent reduction in work in process (WIP) and 55 percent reduction in average time packages spend at the station. These improvements highly support the effectiveness of implementing Six Sigma projects in the format suggested.

Purpose: This study aims to help quality professionals in following a precise framework that maintains a company’s performance during health pandemics. In many companies, all quality methods seem to produce performance improvement at different levels, but lean management and kaizen were superior in preparing manufacturing companies to have high readiness levels when faced with a health pandemic.Design/methodology/approach: Which quality method can help businesses in performing well during a health pandemic? In order to answer this question and be able to provide specific recommendations toward performing well in all future health pandemics, 500 manufacturing companies were selected and surveyed. The selected companies are known to use at least one quality method such as lean management, ISO 9001, European foundation for quality management (EFQM) and Six Sigma.Findings: EFQM comes second, then Six Sigma and ISO 9001. Lean creates a healthy workplace and enhances worker's health and well-being. This research helps quality professionals in following a precise framework that maintains performance during health pandemics. Whether the company uses lean or other quality methods, the framework helps in achieving pandemic readiness status. The framework is implemented in a company that does not currently use lean. Implementation resulted in significant improvement in pandemic preparedness, corporate performance and employee well-being.Originality/value: After the coronavirus disease 2019 (COVID-19) pandemic, all companies should start thinking of creating a workplace that functions well during pandemics. To do that, companies should start including sanitation and health pandemic preparedness as part of their performance measurements. Using lean management and kaizen helps organizations to create a healthy workplace; clean and equipped with smaller number of workers. If organizations choose to use other quality methods, it is okay, but they should enhance these methods with lean tools such as elimination of the seven wastes, gemba, 5S and visual management.

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized overall total tardiness). Computational experiments showed that the heuristic part (DAS-h) of the proposed algorithm significantly enhanced the performance of the GA for large-size problems. The results indicated that the proposed algorithm should only be used for large-scale PMS problems because DAS-h trapped GA in a region of local optima, limiting its capabilities in small- and mainly medium-sized problems.