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Help Chain (HC) is a problem-solving practice underpinned by lean manufacturing principles and also adopted for health services, which have different complexity characteristics in relation to manufacturing. This study is based on the premise that Social Network Analysis (SNA) might be an effective analytical approach to account for the complexity of HCs in healthcare. Hence, two research questions are addressed: how to model and interpret HCs in health services as a social network and how SNA can either challenge or add to the lean assumptions of HC design. The use of SNA to model HCs was tested in a maternity hospital, where HCs related to five problems were analyzed, concerning the supply of instruments from the centre of sterilised materials to the surgical and obstetrics units. We analyzed the HCs of single problems and then combined the five problem-solving networks to produce a multilayered network that accounted for their interactions. The patterns of social interactions varied according to the problem and three dimensions of actors' performance (i.e. availability, reliability, and agility). SNA unveiled the complexity of HCs and provided guidance for revising the lean assumptions in their design, matching the realities of health services. [ABSTRACT FROM AUTHOR]

Information and communication technologies (ICTs) are known for supporting healthcare services in dealing with adverse situations. However, little is known on the contribution of ICTs in a prolonged crisis involving a new disease, such as the COVID-19 pandemic. In this study, we carry out an exploratory investigation of which ICTs contribute the most to the emergency care of patients diagnosed with COVID-19 according to healthcare technology experts and how physicians perceive these contributions. Initially, we applied an online survey to 109 healthcare technology experts. Then, we conducted 16 in-depth follow-up interviews with emergency medicine professionals from 10 countries to identify the ICTs contributing the most to treat COVID-19 patients. Results from the survey indicated four ICTs as the most useful to support the treatment of COVID-19 patients; they are remote consultations, digital platforms for data sharing, digital non-invasive care, and interconnected medical decision support. The interviews provided insight into the applicability of those ICTs for the studied context. The four main ICTs were also found to be logically compatible with the complexity of the pandemic, reducing undesirable complexity attributes (e.g. physical proximity between caregivers and infected patients) and amplifying desirable ones (e.g. interactions that support collaborative work and knowledge sharing). [ABSTRACT FROM AUTHOR]

This study examines the effect of Industry 4.0 (I4.0) technologies on the learning process of operators. We collected data from the training of new operators in a quality inspection workstation. Two distinct scenarios were considered: before and after the adoption of I4.0 technologies. Data from 10 operators were collected in each scenario; the quality inspection cycle was repeated by each operator 30 consecutive times. A 2-parameter hyperbolic learning curve model was used to assess the learning process in the two groups. Results indicated that operators supported by I4.0 technologies had a significantly higher learning rate than those performing the same tasks without I4.0 support. No significant difference was found in the final performance level between groups. Our study bridges a theoretical gap in the relationship between I4.0 and learning by directly comparing the effect of digital support on the training of new employees in a manufacturing environment. We also offer arguments to support managerial decisions with regards to I4.0 adopti-on at an operational level. That allows organisations to prioritise their digitalisation efforts so that the training of operators in workstations can be expedited. [ABSTRACT FROM AUTHOR]

This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that 'Analyze' is by far the best-supported DMAICs phase through techniques, such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context. [ABSTRACT FROM AUTHOR]

Sustainable operational excellence (OPEX) initiatives result in a competitive advantage by optimising the use of resources, contributing to profitability, and leading to expanded growth of organisations. OPEX being a practice-led initiative has no standard framework leading to sustainable OPEX efforts in organisations. In this study, we employ the Design Science Research (DSR) approach to develop a framework to assess the sustainability of OPEX initiatives in an organisation. The proposed framework evaluates OPEX initiatives based on operational, economic, environmental, and social sustainability criteria. Besides, the enablers for the sustainability of OPEX initiatives are identified. The enablers, as well as multidimensional assessment criteria of OPEX initiatives, help the organisation develop a methodological approach for implementing sustainable OPEX solutions. The current success rate for OPEX initiatives in organisations has been dismally poor, and the proposed framework will help improve the success of OPEX efforts. Practitioners can use the proposed framework first to assess their organisational readiness in terms of enablers before implementing OPEX initiatives and evaluate qualitatively and quantitatively to obtain a multidimensional perspective for sustainable solutions. The frameworks proposed in prior studies are predominantly conceptual. This study used the DSR methodology to demonstrate the validity of the proposed framework. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between organizational culture (OC) profiles and the adoption of Industry 4.0 (I4.0) design principles. We surveyed 153 middle and senior managers from Brazil and India, whose manufacturing companies have been implementing I4.0. Participants provided answers regarding the perceived OC characteristics that prevail in their companies, which were assessed based on the Competing Values Framework. We also collected data on the adoption levels of I4.0 design principles, whose analysis was conducted through the utilization of multivariate data analysis techniques. Our results indicated that, depending on the I4.0 design principle, OC profiles (Clan, Adhocracy, Market and Hierarchy) may either corroborate or impair its adoption, allowing the verification of the hypothesized relationships. The understanding of the association between OC profiles and I4.0 design principles provides practitioners with arguments to identify possible problems during their digital transformation. As changes in OC require significant efforts and are usually time-consuming, companies that can anticipate those issues might face a smoother transition towards the Fourth Industrial Revolution, hence, obtaining competitive advantages. [ABSTRACT FROM AUTHOR]

This study aimed at identifying applications of learning curve (LC) modelling at individual, group, and organisational levels in Industry 4.0 (I4.0) environments. For that, a scoping review on four databases was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Our results indicated that LCs are more prominently adopted in I4.0 to model learning at the individual level using technologies oriented to sensing and communication (e.g. big data, IoT, wireless sensors, cloud computing, remote control, or monitoring). However, the effect of a few processing and actuation technologies, such as augmented/virtual reality, collaborative robots, and machine learning/AI, on learning seems promising. Further, despite the number of studies investigated, few explicitly described the LC model used to represent the impact of I4.0 technologies on learning. Our findings allowed the proposition of five research directions. Literature on both LC and I4.0 is still fragmented, poorly addressing their relationship. As I4.0 is an innovative approach that allows more extensive information exchange and processing, new ways of using I4.0 technologies to expedite data collection, which has always constrained LC practical applications, should be devised to close the gap between I4.0 and learning. [ABSTRACT FROM AUTHOR]

This research aims to empirically examine how Lean Supply Chains (LSC) have coped with the disruptive effects of the COVID-19 pandemic from the normal accidents theory (NAT) perspective. Initially, a literature review was conducted to identify the main dimensions of LSC. Then, four experts were interviewed, offering insight into how each of the LSC dimensions copes with disruptions in general. Finally, three case studies of manufacturing shed light on the LSC implications for the pandemic context in particular. From the NAT viewpoint, the effects of the pandemic are analyzed according to two dimensions of the interactions that make up an LSC: linear to non-linear, and loosely to tightly-coupled. The more non-linear and tightly-coupled the interactions the more vulnerable the LSC is to disruptions. Results indicated that LSC might effectively and quickly adapt to keep interactions as linear as possible through transparent information flows and collaborative work while at the same time temporarily reducing tight-couplings through inventory increases. Some of the countermeasures to cope with the disruptions caused by the COVID-19 pandemic conflict with LSC principles, such as inventory reduction, developing a solid customer-supplier relationship, and long-term orientation. This points out the contingent nature of some lean principles as well as the meta principle that guided companies' behaviours in the case studies, namely adding value to internal and external customers. [ABSTRACT FROM AUTHOR]

Health organisations have adopted technologies since the 1960s, but only after the Industry 4.0 were such technologies systematized and organised under the H4.0 acronym. The pace at which digital information and communication applications have been developed in recent years challenge healthcare managers to choose assertively those with the largest potential impacts on their operations. In this paper, we propose using value stream mapping, a technique from the lean healthcare (LH) toolbox, to guide the choice of H4.0 digital applications that are more likely to support the improvement of value flows in healthcare organisations. We propose a three-step method, starting with mapping current and future value streams of the process under analysis, gathering data from team members on the indicated kaizen bursts and H4.0 digital applications, and finally assessing and ranking H4.0 digital applications that best support improvements and comply with attributes that characterise successful technological innovations. Our propositions are illustrated through a case study conducted in the sterilisation unit of a large public university hospital. Our findings indicate that three H4.0 digital applications should be prioritised to support the improvement of the value stream under analysis. Our method combines the simplicity of LH with more sophisticated solutions brought by H4.0. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the digitalization of operational processes and activities in lean manufacturing firms and explore the associated learning implications through the lens of cumulative capability theory. Design/methodology/approach: Adopting a multiple-case design, we examine four cases of digitalization initiatives within lean manufacturing firms. We collected data through semi-structured interviews and direct observations during site visits. Findings: The study uncovers the development of learning capabilities as a result of integrating lean and digitalization. We find that digitalization in lean manufacturing firms contributes to the development of both routinized and evolutionary learning capabilities in a cumulative fashion. Originality/value: The study adds nuance to the limited theoretical understanding of the integration of lean and digitalization by showing how it cumulatively develops the learning capabilities of lean manufacturing firms. As such, the study supports the robustness of cumulative capability theory. We further contribute to research by offering empirical support for the cumulative nature of learning. [ABSTRACT FROM AUTHOR]