Just In Time (JIT) systems in the context of the relations between Japanese manufacturers in Europe and European suppliers of parts and materials have so far received little attention.
The focus on the sequential nature of the improvement initiatives has neglected the synergistic effects among practices and literature lacks research on complementarity between internal and external ...bundles of practices. The aim of this paper is to test the existence of complementarity among internal and external just-in-time bundles. We run statistical analysis using the third round of High Performance Manufacturing international research project data set and we find that upstream and downstream JIT are complements. This finding suggests the importance of managing the interdependencies both in designing and implementing upstream and downstream JIT in order to maximize operational performance.
Abstract
Background
The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s ...changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment.
Purpose
Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap.
Methods
Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration.
Conclusions
As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention
We clarify the scientific motivation for the Just-In-Time Adaptive Interventions, define its fundamental components, and discuss key design principles for each component.
Background and Aims
Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi‐factorial. Just‐in‐time adaptive interventions (JITAIs) aim to deliver tailored ...support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology‐mediated JITAIs for reducing harmful substance use.
Methods
Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist.
Findings
We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time‐invariant) decision rules to deliver support tailored to micro‐scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate‐to‐high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta‐analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security.
Conclusions
Current implementations of just‐in‐time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro‐scale changes in mood or urges. Studies on JITAI effectiveness are lacking.
The Covid‐19 pandemic and other recent disruptions in the early 2020s led to sections in the business press blaming just‐in‐time (JIT) practices for operational failings. Consequently, there are ...calls for moving away from JIT toward holding more inventory as preparation against future disruptions, which is referred to as just‐in‐case. The academic community is also divided. Some scholars argue that JIT is not resilient, while others maintain that JIT can continue providing superior performance even with disruptions. Motivated by this debate, we discuss various misconceptions about JIT that underlie this debate. Furthermore, we present different ways to adapt JIT for turbulent environments and argue that companies can improve their supply chain performance if JIT supply chain segments are chosen fittingly—even more so—during disruptions.
PurposeThis empirical study aims to explore the link between lean manufacturing practices (total quality management, just-in-time production, just-in-time purchasing, total productive/preventive ...maintenance), agile manufacturing, and operational and financial performance.Design/methodology/approachData were collected from 205 Tunisian manufacturing firms, and the results were analyzed using structural equation modeling.FindingsThe results indicate that (1) lean manufacturing practices have a direct positive relationship with agile manufacturing except for just-in-time production, (2) agile manufacturing has a positive impact on operational performance and (3) lean manufacturing practices did not seem to contribute directly to operational performance. However, this relationship is significant when it is mediated through agile manufacturing.Research limitations/implicationsThis paper shows practitioners the importance of lean manufacturing practices to support agile manufacturing and the key role of agile manufacturing to ensure operational performance.Originality/valueThis paper presents an innovative approach since it studies simultaneously the three dimensions of lean manufacturing and their relationship with agile manufacturing and organizational performance.
•Most criticality metrics are aggregate and thereby challenging for firms to integrate.•This work examines circular economy approaches to critical material risk mitigation.•Case studies highlight ...successful integration with quantifiable results.•Secondary benefits include reduced energy consumption, waste, pollution, and costs.
Raw materials deemed critical are defined as having potential issues in their supply, limited substitutes, and applications of importance, namely in clean energy, defense, healthcare, and electronics. Disruptions in supply of critical materials can have serious negative repercussions for firms, consumers, and economies. One potential set of mitigation strategies for firms dealing with criticality issues is the implementation of circular economy principles in their supply chain, operations, and end-of-life management. This work conducts a literature review combined with case study analysis to examine how certain firms assess and monitor their vulnerability to critical material supply chain issues and provides specific business examples for integrating circularity strategies. Results indicate the potential for risk reduction that could be gained from implementation of these strategies; specifically recycling, for example, can provide an in-house source (for prompt or fabrication scrap) or at least domestic source (for post-consumer scrap) for critical materials; up to 24% for the case of indium usage in China. Just in time manufacturing techniques have the potential to both exacerbate supply issues (by encouraging low inventory or needed resources for manufacturing) and improve supply issues by introducing resiliency in the supply chain indicating that approach of firms in undertaking these strategies is important. Many cases reviewed show other quantifiable secondary benefits beyond risk reduction, such as economic savings, reduction in energy consumption, and improved corporate social responsibility via enhanced supply chain oversight.
DR.JIT Jakob, Wenzel; Speierer, Sébastien; Roussel, Nicolas ...
ACM transactions on graphics,
07/2022, Letnik:
41, Številka:
4
Journal Article
Recenzirano
DR.JIT is a new just-in-time compiler for physically based rendering and its derivative. DR.JIT expedites research on these topics in two ways: first, it traces high-level simulation code (e.g., ...written in Python) and aggressively simplifies and specializes the resulting program representation, producing data-parallel kernels with state-of-the-art performance on CPUs and GPUs. Second, it simplifies the development of differentiable rendering algorithms. Efficient methods in this area turn the derivative of a simulation into a simulation of the derivative. DR.JIT provides fine-grained control over the process of automatic differentiation to help with this transformation. Specialization is particularly helpful in the context of differentiation, since large parts of the simulation ultimately do not influence the computed gradients. DR.JIT tracks data dependencies globally to find and remove redundant computation.
Defect prediction models are a well-known technique for identifying defect-prone files or packages such that practitioners can allocate their quality assurance efforts (e.g., testing and code ...reviews). However, once the critical files or packages have been identified, developers still need to spend considerable time drilling down to the functions or even code snippets that should be reviewed or tested. This makes the approach too time consuming and impractical for large software systems. Instead, we consider defect prediction models that focus on identifying defect-prone ("risky") software changes instead of files or packages. We refer to this type of quality assurance activity as "Just-In-Time Quality Assurance," because developers can review and test these risky changes while they are still fresh in their minds (i.e., at check-in time). To build a change risk model, we use a wide range of factors based on the characteristics of a software change, such as the number of added lines, and developer experience. A large-scale study of six open source and five commercial projects from multiple domains shows that our models can predict whether or not a change will lead to a defect with an average accuracy of 68 percent and an average recall of 64 percent. Furthermore, when considering the effort needed to review changes, we find that using only 20 percent of the effort it would take to inspect all changes, we can identify 35 percent of all defect-inducing changes. Our findings indicate that "Just-In-Time Quality Assurance" may provide an effort-reducing way to focus on the most risky changes and thus reduce the costs of developing high-quality software.
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