Just-in-time Research Articles

The Impact of Inventory Strategy on Operations Performance: Study on Bandung City Traditional Market Trader


 
 
 
 Traditional markets are trading activities between buyers and sellers as a process of interaction between communities to realize prosperity. However, the development of modern retailers has entered the suburbs since the issuance of the trade deregulation policy in 2008. If there is no systematic effort to understand the characteristics and existence of traditional markets and traders, the livelihoods of around 12.6 million traditional market traders will be threatened. On the other hand, the Covid-19 post-pandemic is a major event for the supply chain in almost any business. Therefore, one way for traders to overcome the impact of the risk of trade deregulation policies and post-pandemic conditions is to create an inventory strategy to improve the performance of their business operations. The results of research using SEM PLS in this study found that Economic Order Quantity (EOQ) and Strategic Supplier Partnership (SSP) did not affect Operations Performance (OP) at traditional market traders in Bandung City while Just-in Time (JIT) significantly and positively increased Operations Performance (OP) of traditional market traders in Bandung City.
 
 
 
 

Optimization-Aware Compiler-Level Event Profiling

Tracking specific events in a program’s execution, such as object allocation or lock acquisition, is at the heart of dynamic analysis. Despite the apparent simplicity of this task, quantifying these events is challenging due to the presence of compiler optimizations. Profiling perturbs the optimizations that the compiler would normally do—a profiled program usually behaves differently than the original one. In this article, we propose a novel technique for quantifying compiler-internal events in the optimized code, reducing the profiling perturbation on compiler optimizations. Our technique achieves this by instrumenting the program from within the compiler, and by delaying the instrumentation until the point in the compilation pipeline after which no subsequent optimizations can remove the events. We propose two different implementation strategies of our technique based on path-profiling, and a modification to the standard path-profiling algorithm that facilitates the use of the proposed strategies in a modern just-in-time (JIT) compiler. We use our technique to analyze the behaviour of the optimizations in Graal, a state-of-the-art compiler for the Java Virtual Machine, identifying the reasons behind a performance improvement of a specific optimization, and the causes behind an unexpected slowdown of another. Finally, our evaluation results show that the two proposed implementations result in a significantly lower execution-time overhead w.r.t. a naive implementation.

ANALISIS PENGENDALIAN PERSEDIAAN TEPUNG TERIGU DI UMKM CITRA MENGGUNAKAN METODE ECONOMIC ORDER QUANTITY (EOQ) DAN JUST IN TIME (JIT)

As a Micro, Small and Medium Enterprise that is engaged in the food business, namely crackers, the raw material used in Citra UMKM is wheat flour which is the main raw material in making crackers. raw material. In this study, the authors review controls to determine the size of the safety stock. the availability of raw materials for Citra UMKM by comparing the two availability systems. This study aims to determine the amount of safety stock and when to reorder and compare the availability control system that has been carried out by Citra UMKM using two availability control systems, namely the Economic Order Quantity (EOQ) method and the Just In Time (JIT) method. After further analysis showed that in controlling the inventory of raw materials, the costs incurred by Citra UMKM were greater than those using the Economic Order Quantity (EOQ) method and the Just In Time (JIT) approach. Meanwhile, if the Economic Order Quantity (EOQ) approach is compared with Just In Time (JIT) with each of the total inventory costs between companies, EOQ and JIT are IDR 10,000,000, IDR 1,109,995 and IDR 934,924, using the JIT approach is superior because the number of higher orders and lower total inventory costs.

Just-in-Time Defect Prediction for Self-driving Software via a Deep Learning Model

Edge computing is applied to various applications and is typically applied to autonomous driving software. As the self-driving system becomes complicated and the proportion of software increases, accidents caused by software defects increase. Just-in-time (JIT) defect prediction is a technique that identifies defects during the software development phase, which helps developers prioritize code inspection. Many researchers have proposed various JIT models, but it is difficult to find a case in which JIT defect prediction was performed on edge computing applications. In particular, due to the characteristic of self-driving software, which is frequently updated, there is a high risk of inducing defects into the update process. In this work, we propose a JIT defect prediction model via deep learning for edge computing applications called JIT4EA. Our research goal is to develop an effective model to predict defects in edge computing applications. To do this, we perform defect prediction on self-driving software, a representative edge computing application. We use pre-trained unified cross-modal pre-training for code representation (UniXCoder) to embed commit messages and code changes. We use bidirectional-LSTM(Bi-LSTM) for context and semantic learning. As a result of the experiment, it was confirmed that the proposed JIT4EA performed better than state-of-the-art methods and could reduce the code inspection effort.

Pre-trained Model-based Software Defect Prediction for Edge-cloud Systems

Edge-cloud computing is a distributed computing infrastructure that brings computation and data storage with low latency closer to clients. As interest in edge-cloud systems grows, research on testing the systems has also been actively studied. However, as with traditional systems, the amount of resources for testing is always limited. Thus, we suggest a function-level just-in-time (JIT) software defect prediction (SDP) model based on a pre-trained model to address the limitation by prioritizing the limited testing resources for the defect-prone functions. The pre-trained model is a transformer-based deep learning model trained on a large corpus of code snippets, and the fine-tuned pre-trained model can provide the defect proneness for the changed functions at a commit level. We evaluate the performance of the three popular pre-trained models (i.e., CodeBERT, GraphCodeBERT, UniXCoder) on edge-cloud systems in within-project and cross-project environments. To the best of our knowledge, it is the first attempt to analyse the performance of the three pre-trained model-based SDP models for edge-cloud systems. As a result, we can confirm that UniXCoder showed the best performance among the three in the WPDP environment. However, we also confirm that additional research is necessary to apply the SDP models to the CPDP environment.

Implementasi Sistem Just In Time (JIT) dalam Meningkatkan Produktivitas Perusahaan pada PT. Langgeng Makmur Utama Bangsalsari Jember

Just In Time is a model where companies only produce on demand without taking advantage of available inventory and without bearing inventory costs. Production will not occur until there is a signal from the next process that indicates a production request. Productivity is the ability of every individual, system or company to produce what they want by using resources effectively and efficiently and producing the highest possible goods and services. The purpose of this study was to determine the application of the just in time method at PT. Langgeng Makmur Utama and to find out the impact of applying the just in time method on the productivity of PT. Langgeng Makmur Utama. This research uses a qualitative approach, and the type of research is descriptive. By using data collection techniques, namely observation, interviews, and documentation. The selection of subjects used a purposive technique, the validity of the data used a triangulation technique. The research results show that: 1) the management of PT. Langgeng Makmur Utama has implemented the just in time method proven by eliminating some activities that do not add value which are considered waste such as over production, waiting time, transportation, processing, inventory levels, work movements and defective goods. 2) application of the just in time method at PT. Langgeng Makmur Utama, although not yet maximized, has been able to increase the productivity of the company, as evidenced by cost savings, cost control, increased profits, price competition, good delivery performance and improved quality. So that by implementing this just in time system, the company can increase its productivity which will make the company able to survive and be competitive in this globalization era.

Understanding Disengagement in Just-in-Time Mobile Health Interventions

Just-in-time (JIT) intervention aims to proactively detect a user's problematic behaviors and deliver interventions at an opportune moment to facilitate target behaviors. However, prior studies have shown that JIT intervention may suffer from user disengagement, a phenomenon in which a user's level of engagement with intervention apps and target behaviors declines over time. In this study, we aimed to deepen our understanding of disengagement in a mobile JIT intervention system. As a case study, we conducted a user study with college students (n = 54) for eight weeks to understand how disengagement appears over time and what factors influence user disengagement. Our findings reveal that personal traits, such as boredom proneness and self-control issues, are closely related to disengagement, with key factors including 1) boredom and habituation related to repetitive and monotonous JIT interventions, 2) inopportune alarm, 3) distrust for the JIT feedback mechanism, and 4) a lack of motivation due to low rewards. We provide theoretical and practical design guidelines for follow-up studies on JIT intervention system design.

Implementation of the Just In Time Method to Efficient Production Costs (Literature Review Study)

In production activities, the problem of inventory is one thing that must be considered in order to avoid waste in the production process. This study shows the control of raw material inventory in a production to get the optimal level of inventory. The just in time (JIT) method reduces the amount of inventory so that it can reduce costs as efficiently as possible. Inventory control with the just in time (JIT) method is the development of the economic order quantity (EOQ) method by reducing shipping lots. Based on this research, it is known that with the same data, using just in time (JIT) the costs incurred are smaller than the costs incurred previously. Costs previously incurred were Rp. 14,019,680.00 using economic order quantity (EOQ) the costs incurred were Rp. 8,497,593,00 while using just in time (JIT) the costs incurred were Rp. 3,800,239, 00 From these results it can be seen that with the just in time (JIT) method the production costs incurred are more efficient. The solution using the just in time (JIT) method is done with the help of MATLAB software.

Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients.

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

Dynamic cyclic kitting part-feeding scheduling for mixed-model assembly line by a hybrid quantum-behaved particle swarm optimization

PurposeThe purpose of this paper is to cut down energy consumption and eliminate production waste on mixed-model assembly lines. Therefore, a supermarket integrated dynamic cyclic kitting system with the application of electric vehicles (EVs) is introduced. The system resorts to just-in-time (JIT) and segmented sub-line assignment strategies, with the objectives of minimizing line-side inventory and energy consumption.Design/methodology/approachHybrid opposition-based learning and variable neighborhood search (HOVMQPSO), a multi-objective meta-heuristics algorithm based on quantum particle swarm optimization is proposed, which hybridizes opposition-based learning methodology as well as a variable neighborhood search mechanism. Such algorithm extends the search space and is capable of obtaining more high-quality solutions.FindingsComputational experiments demonstrated the outstanding performance of HOVQMPSO in solving the proposed part-feeding problem over the two benchmark algorithms non-dominated sorting genetic algorithm-II and quantum-behaved multi-objective particle swarm optimization. Additionally, using modified real-life assembly data, case studies are carried out, which imply HOVQMPSO of having good stability and great competitiveness in scheduling problems.Research limitations/implicationsThe feeding problem is based on static settings in a stable manufacturing system with determined material requirements, without considering the occurrence of uncertain incidents. Current study contributes to assembly line feeding with EV assignment and could be modified to allow cooperation between EVs.Originality/valueThe dynamic cyclic kitting problem with sub-line assignment applying EVs and supermarkets is solved by an innovative HOVMQPSO, providing both novel part-feeding strategy and effective intelligent algorithm for industrial engineering.

Just-in-time procedural training for pediatric emergency medicine trainees: A randomized educational interventional trial.

Pediatric requirements include procedural skills training such as peripheral intravenous (PIV) catheter placement and bag-mask ventilation (BMV). Clinical experiences may be limited and temporally remote from scheduled teaching. Just-in-time (JIT) training prior to utilization can promote skill development and mitigate learning decay. Our objective was to assess the impact of JIT training on pediatric residents' procedural performance, knowledge, and confidence with PIV placement and BMV. Residents received standardized baseline training in both PIV placement and BMV during scheduled educational programming. Between 3 and 6 months later, participants were randomized and received JIT training for either PIV placement or BMV. JIT training included a brief video and coached practice, totaling <5 min. Each participant was videotaped performing both procedures on skills trainers. Blinded investigators scored performance using skills checklists. Pre- and postintervention knowledge was assessed using multiple-choice and short-answer items, and confidence was reported using Likert scores. Seventy-two residents completed baseline training sessions: 36 were randomized to receive JIT training for PIV and 36 for BMV. Thirty-five residents in each cohort completed the curriculum. There were no significant differences between the cohorts with regard to demographics, baseline knowledge, or prior simulation experience. JIT training was associated with improved procedural performance for PIV (median 87% vs. 70%, p < 0.001) and for BMV (mean 83% vs. 57%, p < 0.001). Results remained significant after using regression models to adjust for differences in prior clinical experience. Improvements in knowledge or confidence were not associated with JIT training in either cohort. JIT training resulted in a significant improvement in resident procedural performance with PIV placement and BMV in a simulated environment. There were no differences in outcome with regard to knowledge or confidence. Future work might explore how the demonstrated benefit translates into the clinical setting.

Optimal and heuristic policies for production and inventory controls in dual supply chains with fluctuating demands

AbstractIn the context of fast‐fashion business, we study the joint control of production, inventory, and unilateral transshipment in dual supply chains in which one supply chain is a responsive supply chain for fashion products and the other is an efficient supply chain for basic products in a fluctuating demand environment (FDE). Our research focuses on: (1) formulating an appropriate model for this complex problem; (2) characterizing the structured optimal policy, which can be used to develop effective heuristic policies; and (3) generalizing our studies for more applications. To address these issues, we formulate a discrete‐time finite‐horizon stochastic dynamic program model in which FDE is represented by a world state evolving in accordance with the discrete‐time Markov chain. The supply chain for the fashion product is modeled by a two‐stage tandem production system with a make‐to‐stock (MTS) stage for the fabric and a subsequent MTS stage for finished fashions. In parallel, the supply chain for the basic products is also modeled by a two‐stage tandem production system with a MTS stage for the fabric and a subsequent make‐to‐order (MTO) stage for finished basics. Further, the two supply chains are coupled via the unilateral transshipment of fabric with random transshipment time. By value iteration, we characterize the optimal policy for the single‐product problem with a set of monotone switching surfaces. The results are extended to the multi‐product multi‐supplier case. Further, based on the structure of the optimal policy, we develop a heuristic policy for the multi‐dimensional dynamic program model. From a practice perspective, we demonstrate the value of transshipment and controlled transshipment, analytically and numerically, and therefore justify the establishment of two separate local manufacturing bases in dual supply chains. Additionally, to achieve both quick response and efficiency, we show that the total cost can be reduced significantly if sufficient fabric is prepared just in time (JIT) for in‐season production at the beginning of a season.

Sustainable Ready-Mixed Concrete (RMC) Production: A Case Study of Five RMC Plants in Nigeria

This study aims to examine whether ready-mixed concrete (RMC) production in Nigeria is sustainable. This study proposes that RMC production will be sustainable, assuming the RMC plant, RMC products, plant management, RMC supply, RMC quality, and demand for RMC are sustainable. Based on a constructivist worldview, the proposition of RMC production’s sustainability was assessed by conducting a contextual analysis of five RMC plants in Lagos State, Nigeria. It was observed that the RMC plants required sustainability in power supply, plant output, and plant capacity. The plants have a sustainable supply of raw materials. The management methods and product control strategies were found to be unsustainable. Fair supply time, supply methods, and quality control systems were established in the findings. Challenges, such as administrative issues, economic problems, poor technology, and the absence of an innovative business model, influenced the sustainable demand for RMC products. This study concludes that alternative power supplies and methods such as just-in-time (JIT) purchasing systems and learning frameworks ought to be considered for RMC plants. Likewise, improving the ease of doing business would significantly help the sustainability of RMC production. This study presumes that RMC production is, as of now, not sustainable in Nigeria. Still, the sustainability of RMC production could be ensured through measures such as the reuse of waste, the adoption of innovative RMC production and delivery, and technological development.

Just in time vessel arrival system for dry bulk carriers

Cargo conveyance onboard dry bulk carriers is contemporarily often affiliated to preoperational waiting times, which may affect the income situation of stakeholders and the sustainability of the sector. Therefore, repetitively occurring waiting problems, potentially paired with port congestion phenomena, indicating that just in time (JIT) arrival potential for a distinct or a combination of reasons has not been realised, can be frequently identified. Undesired increment of waiting times and development of port congestion is frequently responded to by an array of measures. JIT arrival concepts, vessel arrival systems (VAS) and virtual arrival (VA) agreements thereby do not strive to eliminate waiting times but facilitate their sensible transformation into additional navigation time. In practice, VAS applications may, however, only enfold their inherent sustainability potential within closely defined delimitations. At the same time, JIT mechanisms and VA agreements may lack acceptance due to impracticability or missing alignment to underlying trade requirements. Therefore, fair but environmentally inefficient arrival mechanisms like the first come first serve (FCFS) concept remain widely applied. As a remedy, a VAS has been conceptualised by diverting from a static to a dynamic time-, distance- and speed JIT concept wherein these parameters are defined by predicted berth and cargo operation availability. A circular based Reporting Line furnished with the functions attributable to the place where line up positions are customarily allocated is fluctuating in correspondence to the time to go until the nearest berthing opportunity becomes available. The concept does not only provide for a dynamically shifting line and corridor to obtain an often highly valued line up position, but for the distance and conditions where under a vessel is going to arrive JIT. The FCFS concept interwoven with unbiased allocating of line-up positions is being retained as an integral part while VA applications are supported.

A Just-in-Time Networking Framework for Minimizing Request-Response Latency of Wireless Time-Sensitive Applications

This article puts forth a networking paradigm, referred to as just-in-time (JIT) communication, to support client-server applications with stringent request-response latency requirement. Of interest is not just the round-trip delay of the network, but the actual request-response latency experienced by the application. The JIT framework contains two salient features. At the client side, the communication layer will “pull” a request from the client just when there is an upcoming transmission opportunity from the network. This ensures that the request contains information that is as fresh as possible (e.g., a sensor reading obtained just before the transmission opportunity). At the server side, the network ascertains that the server, after receiving and processing the request to generate a response (e.g., a control command to be sent to the client), will have a transmission opportunity at just this time. We realize the JIT system, including the protocol stack, over a time-division-multiple-access (TDMA) network implemented on a System-on-Chip (SoC) platform. We prove that a TDMA network with a power-of-2 time slots per superframe is optimal for realizing the server-side JIT function. Our experimental results validate that JIT networks can yield significantly lower request-response latency than networks without JIT support can.

Graph-based machine learning improves just-in-time defect prediction.

The increasing complexity of today's software requires the contribution of thousands of developers. This complex collaboration structure makes developers more likely to introduce defect-prone changes that lead to software faults. Determining when these defect-prone changes are introduced has proven challenging, and using traditional machine learning (ML) methods to make these determinations seems to have reached a plateau. In this work, we build contribution graphs consisting of developers and source files to capture the nuanced complexity of changes required to build software. By leveraging these contribution graphs, our research shows the potential of using graph-based ML to improve Just-In-Time (JIT) defect prediction. We hypothesize that features extracted from the contribution graphs may be better predictors of defect-prone changes than intrinsic features derived from software characteristics. We corroborate our hypothesis using graph-based ML for classifying edges that represent defect-prone changes. This new framing of the JIT defect prediction problem leads to remarkably better results. We test our approach on 14 open-source projects and show that our best model can predict whether or not a code change will lead to a defect with an F1 score as high as 77.55% and a Matthews correlation coefficient (MCC) as high as 53.16%. This represents a 152% higher F1 score and a 3% higher MCC over the state-of-the-art JIT defect prediction. We describe limitations, open challenges, and how this method can be used for operational JIT defect prediction.

Identifying critical challenges to lean construction adoption

PurposeThis study aims to set out to identify and evaluate potential obstacles to successfully implementing lean construction (LC) as a result.Design/methodology/approachSeveral indicators were recognized as major obstacles following an exhaustive assessment of the literature and a multicriteria decision analysis based on the analytic hierarchy process (AHP) of information obtained from a questionnaire survey that was directed to practitioners in the Indian construction industry.FindingsThe results of this AHP model suggest that “Managerial” and “Inadequate resources” categories with a priority weight of “0.361” and “0.309” have the highest levels of influence, respectively, while “Inadequate knowledge” and “just in time (JIT)” categories with a priority weight of “0.053” and “0.034” have the lowest levels of influence, respectively.Research limitations/implicationsConstruction companies can use the study’s findings as a guide to determine whether they are ready to embrace LC, learn more about the components needed for implementation or investigate any challenges that may arise. These businesses can then create plans to promote the adoption and application of the lean philosophy.Originality/valueThe Indian construction industry may see great success with LC management initiatives. LC concepts have been adopted by many nations, but during the past 20 years, there has only appeared to be a limited amount of lean implementation in the Indian construction industry. It seems that several structural and cultural barriers are preventing its effective implementation. Organizations will not be able to determine what improvement efforts are required, where these efforts should be directed or which initiatives could provide the best outcomes if they are unaware of the elements that influence the effective implementation of LC.

Perbandingan Efektivitas Metode EOQ dan JIT dalam Pengelolaan Persediaan pada PT XYZ

PT. XYZ is a manufacturing company engaged in bodywork sector. Every company surely expects high profit . O ne of the ways to achieve th e goal is by minimizing the expenses. Economic Order Quantity (EOQ) and Just In Time (JIT) methods are commonly used methods to save inventory costs. This study aims to compare which method is more effective and efficient in reducing inventory costs at the company. The results of the study show that the JIT method can reduce inventory costs by 91.14% for p late with 2mm 4x8 s tandard and 93.62% for p late with 3mm 4x8 s tandard, while the EOQ method can reduce inventory costs by 33.45% for both types of the raw materials. Based on the results above, it can be concluded that JIT method is more effective than EOQ method in reducing inventory costs in the company.

Small, sustainable, steps to success as a scholar in Health Professions Education - Micro (macro and meta) matters

Introduction: The aim of this short communication is to examine the journey of scholarship in health professions education (HPE). Methods: We will focus on tangible small steps to start, sustain, and succeed along this journey. Through a proposed innovation in scholarship – micro-scholarship – we will describe how this is similar to and an extension of bite-size or micro-learning, and workplace micro-practice related to just-in-time (JiT) learning settings. Results: We will demonstrate how the small steps for generating and engaging with micro-content can be similarly applied to micro-scholarship. Then, progressive and iterative refinement of output and practice of micro-scholarship can be combined and result in macro-scholarship after cycles of public engagement for final digital or print publication. This stepwise approach creates an accessible, sustainable strategy to achieve success as a scholar in HPE. We will elaborate on micro, macro, and meta matters and celebrate how these small steps encourage and allow broad participation in the creation, critique, and progressive refinement of scholarship. Conclusion: Small, sustainable, steps leads to success as a scholar in Health Professions Education - Micro (macro and meta) matters.

Digitalization of Multistep Chemistry Exercises with Automated Formative Feedback

For various reasons, students receive less formative feedback at post-secondary institutions compared to secondary school. Considering feedback as one of the most important influencing factors on learning processes, formative feedback is a promising approach to improving students’ performances. In this context, new technologies, such as learning management systems (LMS) or intelligent tutoring systems (ITS), can make a valuable contribution to improving higher education teaching by providing automated and individualized error-specific just-in-time (JIT) feedback. However, the digitalization especially of paper-based open-ended tasks that can be used by LMS is currently still associated with a loss of quality. In this paper, we present an approach that allows us to transfer open-ended paper-based tasks in the field of chemistry into online tasks without losing quality and provide large university courses with automated and individualized error-specific JIT feedback. Results of a study of 238 first-year chemistry students reveal that the automated individualized error-specific JIT feedback had a significant positive influence on students’ performance.