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PurposeBuilding on the routine dynamics literature, this paper aims to expand our philosophical, practical and infrastructural understanding of implementing lean production. The authors provide a process view on the interplay between lean operating routines and continuous improvement (CI) routines and the roles of different actors in initiating and establishing these routines.Design/methodology/approachUsing data from interviews, observations and document analysis, retrospective comparative analyses of three embedded case studies on lean implementations provide a process understanding of enacting and patterning lean operating and CI routines in manufacturing SMEs.FindingsIncorporating the “who” and “how” next to the “what” of practices and routines helps explain that rather than being implemented in isolation or even in conjunction with each other, sustainable lean practices and routines come about through team leader and employee enactment of the CI practices and routines. Neglecting these patterns aligned with unsustainable implementations.Research limitations/implicationsThe proposed process model provides a valuable way to integrate variance and process streams of literature to better understand lean production implementations.Practical implicationsThe process model helps manufacturing managers, policy makers, consultants and educators to reconsider their approach to implementing lean production or teaching how to do so.Originality/valueNuancing the existing lean implementation literature, the proposed process model shows that CI routines do not stem from implementing lean operating routines. Rather, the model highlights the importance of active engagement of actors at multiple organizational levels and strong connections between and across levels to change routines and work practices for implementing lean production.

The aim of diagnostic point-of-care testing is to minimise the time to obtain a test result, thereby allowing clinicians and patients to make a quick clinical decision. Because point-of-care tests are used in resource-limited settings, the benefits need to outweigh the costs. To optimise point-of-care testing in resource-limited settings, diagnostic tests need rigorous assessments focused on relevant clinical outcomes and operational costs, which differ from assessments of conventional diagnostic tests. We reviewed published studies on point-of-care testing in resource-limited settings, and found no clearly defined metric for the clinical usefulness of point-of-care testing. Therefore, we propose a framework for the assessment of point-of-care tests, and suggest and define the term test efficacy to describe the ability of a diagnostic test to support a clinical decision within its operational context. We also propose revised criteria for an ideal diagnostic point-of-care test in resource-limited settings. Through systematic assessments, comparisons between centralised testing and novel point-of-care technologies can be more formalised, and health officials can better establish which point-of-care technologies represent valuable additions to their clinical programmes.

Purpose This study aims to examine interactions between multiple management accounting routines in integrating a new management accounting routine into a routine cluster. Design/methodology/approach This study uses a theoretical framework based on routine clusters, including routine complementarities. The authors use an in-depth case study to explore interactions of a management accounting routine integrating into a routine cluster. Findings The findings show that complementarity between an existing and a new management accounting routine facilitates integration of the new routine into a routine cluster. They also suggest that when an ostensive understanding of a routine exists, the integration of the new management accounting routine is stronger, as the new and existing routines in the routine cluster are more closely intertwined. Originality/value To the best of the authors’ knowledge, this paper is among the first to explore the role of intertwinedness of a new management accounting routine and existing organizational routines in integrating a new management accounting routine into a routine cluster. The findings imply that future management accounting research may need to distinguish between different forms of complementarity.

Author guidelines for journals could help to promote transparency, openness, and reproducibility Transparency, openness, and reproducibility are readily recognized as vital features of science ( 1 , 2 ). When asked, most scientists embrace these features as disciplinary norms and values ( 3 ). Therefore, one might expect that these valued features would be routine in daily practice. Yet, a growing body of evidence suggests that this is not the case ( 4 – 6 ).

Solving differential equations of fractional (i.e., non-integer) order in an accurate, reliable and efficient way is much more difficult than in the standard integer-order case; moreover, the majority of the computational tools do not provide built-in functions for this kind of problem. In this paper, we review two of the most effective families of numerical methods for fractional-order problems, and we discuss some of the major computational issues such as the efficient treatment of the persistent memory term and the solution of the nonlinear systems involved in implicit methods. We present therefore a set of MATLAB routines specifically devised for solving three families of fractional-order problems: fractional differential equations (FDEs) (also for the non-scalar case), multi-order systems (MOSs) of FDEs and multi-term FDEs (also for the non-scalar case); some examples are provided to illustrate the use of the routines.

Liquid chromatography–tandem mass spectrometry (LC–MS/MS) has been increasingly used in routine clinical laboratories during the last two decades. The high specificity, sensitivity, and multi-analyte potential make it an ideal alternative to immunoassays or conventional high-performance liquid chromatography (HPLC). It also provides higher throughput than gas chromatography–mass spectrometry (GC–MS). LC–MS/MS also offers higher flexibility than immunoassays because LC–MS/MS assays are typically developed in-house. In addition, abundant information can be obtained from a single LC–MS/MS run which can produce a large amount of quantitative or qualitative data. In this review, typical LC–MS/MS clinical applications are presented, personal experiences are shared, and strengths and weakness are discussed. It is foreseeable that LC–MS/MS will become a key instrument in routine clinical laboratories.

The promise of point-of-care medical diagnostics — tests that can be carried out at the site of patient care — is enormous, bringing the benefits of fast and reliable testing and allowing rapid decisions on the course of treatment to be made. To this end, much innovation is occurring in technologies for use in biodiagnostic tests. Assays based on nanomaterials, for example, are now beginning to make the transition from the laboratory to the clinic. But the potential for such assays to become part of routine medical testing depends on many scientific factors, including sensitivity, selectivity and versatility, as well as technological, financial and policy factors.

Fundamental algorithms such as sorting or hashing are used trillions of times on any given day 1 . As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved in the past 2 , making further improvements on the efficiency of these routines has proved challenging for both human scientists and computational approaches. Here we show how artificial intelligence can go beyond the current state of the art by discovering hitherto unknown routines. To realize this, we formulated the task of finding a better sorting routine as a single-player game. We then trained a new deep reinforcement learning agent, AlphaDev, to play this game. AlphaDev discovered small sorting algorithms from scratch that outperformed previously known human benchmarks. These algorithms have been integrated into the LLVM standard C++ sort library 3 . This change to this part of the sort library represents the replacement of a component with an algorithm that has been automatically discovered using reinforcement learning. We also present results in extra domains, showcasing the generality of the approach.  Artificial intelligence goes beyond the current state of the art by discovering unknown, faster sorting algorithms as a single-player game using a deep reinforcement learning agent. These algorithms are now used in the standard C++ sort library.

Purpose This study aims to investigate the impact of the innovative ritual-based redesign of a routine in the challenging context of the dining-out sector, characterized by low employee commitment and high turnover. Design/methodology/approach This study adopts a mixed methods experimental design. This study focuses on a field experiment in a real restaurant centered on the restaurant’s welcome entrée routine. The routine is first observed as it happens, after which it is redesigned as a ritual. Findings The ritual-based redesign of the routine enhances employee sharing of the purpose of the routine and reduces the variability of the execution time of the routine, which increases group cohesion among the restaurant staff. Besides the positive impact on the routine’s participants, the ritual-based redesign has a beneficial effect on the performance of the routine by increasing the enjoyment of the end-consumers at the restaurant. Research limitations/implications The ritual-based redesign of routines is a powerful managerial tool that bonds workers into a solidary community characterized by strong and shared values. This allows guidance of the behavior of new and existing employees in a more efficient and less time-consuming way. Originality/value Rituals have been traditionally analyzed from the customer perspective as marketing tools. This research investigates the employees’ perspective, leveraging ritual-based redesign as a managerial tool for increasing cohesion among workers.