Abstract:Purpose: Lean Manufacturing is widely regarded as a potential methodology to improve productivity and decrease costs in manufacturing organisations. The success of lean manufacturing demands consistent and conscious efforts from the organisation, and has to overcome several hindrances. Industry 4.0 makes a factory smart by applying advanced information and communication systems and future-oriented technologies. This paper analyses the incompletely perceived link between Industry 4.0 and lean manufacturing, and investigates whether Industry 4.0 is capable of implementing lean. Executing Industry 4.0 is a cost-intensive operation, and is met with reluctance from several manufacturers. This research also provides an important insight into manufacturers' dilemma as to whether they can commit into Industry 4.0, considering the investment required and unperceived benefits. principles prove the hypothesis that Industry 4.0 is indeed capable of implementing lean. It uncovers the fact that committing into Industry 4.0 makes a factory lean besides being smart. Originality/value: Individual researches have been done in various technologies allied with Industry 4.0, but the potential to execute lean manufacturing was not completely perceived. This paper bridges the gap between these two realms, and identifies exactly which aspects of Industry 4.0 contribute towards respective dimensions of lean manufacturing.

Considering the ongoing trend of digitalization of the manufacturing industry to Industry 4.0, this paper assists in the transformation. The research work is focused on studying the possible impacts of Industry 4.0 on lean management (LM) tools which play a vital role to foster quality and reliability of products and services that are delivered to the customers. The LM tools which are impacted by the advent of Industry 4.0 and assisting in successful implementation of future smart factory will be investigated in particular focus. An interaction plot matrix is established to quantify the influence of LM tools on Industry 4.0. Interaction between these Industry 4.0 design principles and LM tools reveal several opportunities for achieving synergies thus leading to successful implementation of future interconnected smart factories. Overall, the research work serve as a guideline for industries that are under the transformation phase towards future smart factory and offers space for further scientific discussion.

The purpose of this study was to determine the impact of pharmacist monitoring with a clinical decision support system (CDSS) on clinical outcomes related to intensive care unit (ICU) delirium. Methods: This was a single-center, before-and-after study. This study compares patient outcomes of the preintervention group, which is the standard of care of pharmacist rounding, and the intervention group of pharmacy rounding with the CDSS rules. Using a CDSS, specific delirium risk factor rules were created to alert pharmacists to patients who have an increased risk of developing ICU delirium. Patients were included in the study if they were ⩾18 years of age, admitted to the trauma intensive care unit (TICU), and had one of the CDSS rule alerts. The CDSS notified pharmacists in real time to patients in the intervention group that met these criteria to provide timely recommendations in an effort to prevent ICU delirium. Results: Compared with the preintervention group receiving the standard of care (n = 28), the intervention CDSS group (n = 33) had a nonsignificant trend in decreased incidence of delirium (33.3% vs 24.1%, P = .45), ICU length of stay (LOS) (10.11 vs 7.55 days, P = .26), and ventilator duration (7.11 vs 5.03 days, P = .26). The intervention group had a significantly shorter hospital LOS (14.74 vs 9.98 days, P = .04). There was a nonsignificant increase in mortality with the intervention group from nondelirium causes (24.2% vs 7%, P = .07). Conclusion: The utilization of a CDSS by clinical pharmacists to monitor for delirium-specific risk factors led to a significantly shorter hospital LOS. Further studies using this model are warranted to see the impact on the ICU population.

No abstract

Clinical and epidemiological studies in the field of periodontics and endodontics often utilize radiographs to monitor or measure the changes in bone structure and density. Periodontal bone loss or gain can be quantified on a radiograph by measurement of the distance between the bottom of the bony pocket and the apical contour of the involved tooth. The objective of this investigation was to study the accuracy of an image analysis system (IAS) to measure changes in height of the interproximal crest on radiographs. Artificial bone lesions were introduced in a dissectioned part of a human mandible. The distances between crest and apices were measured with a micrometer (MM). Radiographs were produced with horizontal and vertical deviations of 10 degrees. The radiographs were digitized and processed by computer. The landmarks in the digital image were enhanced mathematically and by histogram-based thresholding. The depth of the introduced defect was increased 6 times, followed by the measurement procedure. The IAS produced measurements of crown-apex distances with an accuracy of 0.066 to 0.358 mm. Repeated crest height measurements were recorded with an accuracy of 0.112 to 0.184 mm. Both the histogram-based binarization and the ellipse-fitting type of contour detection could be applied precisely. Misangulation errors during radiographic exposure of 10 horizontal or vertical did not statistically significant influence the IAS-measurements. The IAS can be applied in clinical trials and follow-up studies.