Just-in-time Research Articles

Environmental Monitoring and Assessment for Sustainable Construction Projects: Leveraging Lean Techniques

To increase productivity and avoid waste, the construction industry has started implementing Lean ideas and methodologies in construction projects. Due to a lack of awareness of lean practices in the preparation, design, and execution of building and infrastructure projects, lean practices are not very familiar among construction projects, which are most commonly used in the manufacturing industry. Hence, an effort has been made in this paper to provide a comprehensive review of the literature and case studies to analyze the suitability of lean practice in sustainable waste management, increased productivity, and on-time project delivery. It aims to explore the effect of improving communication and fostering collaboration among stakeholders on time, costs, and resource management. The review identified the most commonly applied lean practices, Just in Time (JIT) and Last Planner System (LPS), and linked the adoption of lean techniques within the construction sector to a total of sixteen distinct benefits for the economy, society, and the environment. According to this study, lean techniques have a strong chance of boosting productivity in the construction industry and developing a sustainable built environment, but they also need to be used widely and continuously to achieve these goals.

How action learning broke down silos, built referent power, and created sustainable capability building

This paper presents a case study of how technology support moved from being a just in time (JIT) help service to developing capability and capacity building through a process of building digital self-efficacy (DSE). By active membership of the UNSW Online Learning and Innovation Community of Practice (OLI CoP) a process of action learning cycles was undertaken in partnership to focus on pedagogy ahead of technology. By doing so, a scalable change process was initiated that brings the coalition of the willing into being core university influencers. As a result, the academics see technology become embedded into their pedagogical practice rather than a tool alone. This case provides insights from the key learnings which have, in turn, enabled a new model of technology support delivery. This model has also provided career growth opportunities for both academics and the educational technology services (ETS) team, while also supporting student learning.

PP456 Topic: AS16–Medical, Nursing and AHP Workforce & Education: Staff Education and Development/Equity, Diversity, Inclusion/Other: EFFECTS OF JUST IN TIME SIMULATION ON RESIDENT COMFORT AND PERFORMANCE DURING MOCK CODES

PP456 Topic: AS16–Medical, Nursing and AHP Workforce & Education: Staff Education and Development/Equity, Diversity, Inclusion/Other: EFFECTS OF JUST IN TIME SIMULATION ON RESIDENT COMFORT AND PERFORMANCE DURING MOCK CODES

Identifikasi Added and Non-Value Added activities Melalui Analisis Just In Time Pada Pekerjaan Pondasi Bore Pile

A construction project is a complex job because it involves various resources to achieve the targets of the project. Management of these resources must be carried out with the aim of achieving high productivity. The aim of this research is to determine activities that have added value (Value Added activity) and activities that do not have added value (non-Value Added activity) as well as to evaluate the productivity efficiency of machines and tools in Bore Pile foundation work. The author uses the Just In method Time to identify the effectiveness and efficiency of Bore Pile foundation work. Just In Time will focus on reducing waste so that it can increase productivity. The application of the Just In Time method is only used for resources, namely machines and tools needed for a job. This method will be assisted by the Manufacturing Cycle Effectiveness concept to measure the percentage of Value Added activities. The research results show that there are still several machines or heavy equipment with a percentage of non-Value Added activity that is greater than Value Added activity. This is caused by quite a lot of waiting time and Moving time, resulting in smaller Value Added activity.

SISTEMA TOYOTA DE PRODUÇÃO

Although the Toyota System has many issues, it has still managed to establish itself by using a strategy to shorten production time and, more importantly, to be precise in terms of quantity while formulating a policy to prioritize waste reduction. In the Toyota Production System, its development is supported by two essential pillars in communication. Both systems are well-known and are called Just in Time and Autonomation, also known as Jidoka. The Toyota Production System developed a method for detecting seven types of waste that large industries face in the production system, which are Waiting, Defect, Transportation, Motion, Excess Inventory, Overproduction, and Over/Under Processing.

Improvement of Productivity by Using Means of Lean Manufacturing, Just in Time, and Production Technology in Thai SMEs Manufacturing Industry

This study investigated the current situations, opportunities, and obstacles in manufacturing industry, and to create a model for improving the efficiency while reducing the levels of inventory. This study gathered information from in-depth interview using snowball sampling from 20 manufacturing companies in Thailand. The results revealed that the organization has implemented lean production principles as a strategic initiative to mitigate losses. The implementation of comprehensive work manuals, offered in diverse languages such as Thai, Burmese, Japanese, and English, serves as an additional mechanism to mitigate losses by ensuring clarity and understanding among the workforce. Furthermore, the integration of modern technologies, encompassing machine technology, robotics and artificial intelligence, is actively pursued to streamline production processes and consequently reduce losses. The results presented that companies refrain from adopting Just-In-Time production systems due to operational challenges associated with fulfilling customer orders. Specifically, frequent machine shutdowns for adjustments are necessitated, leading to elevated unit costs. Manufacturing companies in Thailand continue to harbor potential for growth, despite the inevitable rise in employee wages leading to increased operational costs. To navigate this challenging landscape, companies must remain adaptive, responding not only to shifts in customer preferences but also to advancements in technology, proactive examination of customer needs prior to initiating the production process, a paradigm shift from low-cost production to the premium-grade products addresses the demands of customers seeking superior product quality, and the process of broadening customer bases both overseas and globally, and the analysis of distinct customer groups in identifying new target demographics.

Just in Time Port Call Optimisation: Preliminary Regulatory Compliance Evaluation and Environmental Performance Assessment

Abstract Just in Time (JIT) arrival, as an operational measure towards shipping decarbonisation, promises increased energy savings and reduced emissions. Integrated digital systems targeting the optimisation of the port call process allow vessels to adapt their speed to avoid the “hurry-up-and-wait” syndrome, and thus, reduce their fuel consumption at a certain extend. However, there are a lot of concerns and challenges ahead for a wide adoption of such a solution, that are either related to the regulatory and standardisation frameworks, or to the realisation of the actual benefits of a JIT optimisation system. Thus, this paper attempts to shed light on some of these challenges, by focusing on the maritime regulatory framework around JIT operations and on emission reduction potential from speed adaptation. Results provide a baseline for regulatory compliance evaluation for such a system, and a preliminary environmental performance assessment from JIT arrival applied on an actual ship case. These insights and early results will be further explored within the EU-funded MISSION project towards a full appreciation of the JIT concept benefits and regulatory compliance, which will lead to its wide implementation.

A code change‐oriented approach to just‐in‐time defect prediction with multiple input semantic fusion

AbstractRecent research found that fine‐tuning pre‐trained models is superior to training models from scratch in just‐in‐time (JIT) defect prediction. However, existing approaches using pre‐trained models have their limitations. First, the input length is constrained by the pre‐trained models.Secondly, the inputs are change‐agnostic.To address these limitations, we propose JIT‐Block, a JIT defect prediction method that combines multiple input semantics using changed block as the fundamental unit. We restructure the JIT‐Defects4J dataset used in previous research. We then conducted a comprehensive comparison using eleven performance metrics, including both effort‐aware and effort‐agnostic measures, against six state‐of‐the‐art baseline models. The results demonstrate that on the JIT defect prediction task, our approach outperforms the baseline models in all six metrics, showing improvements ranging from 1.5% to 800% in effort‐agnostic metrics and 0.3% to 57% in effort‐aware metrics. For the JIT defect code line localization task, our approach outperforms the baseline models in three out of five metrics, showing improvements of 11% to 140%.

A Review of COVID-19s Impact on Just-in-Time Production in Automotive Industry

This paper reviews the impact of COVID-19 on Just in Time production in the automotive industry, focusing on the current situation and response in the field of supply chain management. The vulnerability of JIT systems has been exposed as supply chains have been disrupted due to the global lockdown policies brought about by the COVID-19 pandemic. The paper also discusses how automotive companies are coping during the pandemic and the possibility of transforming the production of medical devices. Drawing from various scholarly insights, this paper examines the transition from Just in Time to Just in Case, evaluates the effectiveness of this phenomenon in buffering supply chain disruption issues, and the results suggest that JIC may be the new popular trend in the future, providing more flexibility for businesses in an uncertain social environment. Finally, based on the experience of the automotive industry during the COVID-19 pandemic, this paper puts forward suggestions from three aspects: supply chain collaboration, information security protection, and seeking a balance between inventory and cost.

Dampak Just in Time pada Inventory Management Studi Kasus pada Industri Manufaktur Automotive

The purpose of this study is to determine the Impact of Just In Time on Inventory Management Case Study in the Automotive Manufacturing Industry by determining the key factors that are fundamental to just in time practices. This research was conducted in two automotive manufacturers serving the sector hereinafter referred to as Company A and Company B, as these companies cannot be named, for this reason, the identity of these companies cannot be revealed. The approach used here is a mixed approach of quantitative research and qualitative methodology. From the study it can be concluded that organizations can have lower performance or efficiency in managing or controlling inventory, when not implementing JIT or Just-Time practices. In addition, we can also see how JIT affects inventory management practices in organizations in a positive way. This further leads to a positive perception of business performance from both employees and management of the organization. The results for Company A show better performance with cost effective performance through the implementation of the JIT system, with employees being more aware of the inventory management practices in place and the changes and benefits being transmitted to the system

On enhancing memory forensics with FAME: Framework for advanced monitoring and execution

Memory Forensics (MF) is an essential aspect of digital investigations, but practitioners often face time-consuming challenges when using popular tools like the Volatility Framework (VF). VF, a widely-adopted Python-based memory forensics tool, presents difficulties for practitioners due to its slow performance. Thus, in this study, we evaluated methods to accelerate VF without modifying its code by testing four alternative Python Just In Time (JIT) interpreters - CPython, Pyston, PyPy, and Pyjion - using CPython as our baseline. Tests were conducted on 14 memory samples, totaling 173 GB, using a search-intensive VF plugin for Windows hosts. Employing our custom Framework for Advanced Monitoring and Execution (FAME), we deployed interpreters in Docker containers and monitored their real-time performance. A statistically significant difference was observed between the Python JIT interpreters and the standard interpreter. With PyPy emerging as the best interpreter, yielding a 15–20 % performance increase compared to the standard interpreter. Implementing PyPy has the potential to save significant time (many hours) when processing substantial memory samples. FAME enhances the efficiency of deploying and monitoring robust forensic tool testing, fostering reproducible research and yielding reliable results in both MF and the broader field of digital forensics.

Securing Your Eggs in Multiple Baskets — Assuring a Resilient and Secure Supply Chain

AbstractThe global supply chain is a complex system of systems made up of and relying on other complex systems of systems (SoS) to achieve its goals. To take a typical example, Enterprise A is supplied essential parts on a regular basis to manufacture its products. To place the order requires global financial systems, integrated email systems, the internet, multiple telecommunications systems, and supply software provided by large companies. To deliver the parts may require air and maritime transportation systems, the rail network, interstate highway systems, road haulage companies, state and local transportation systems and so forth. When any of these complex systems fail, the impact can be global, and the results catastrophic. Recent examples include the shortage of Personal Protective Equipment (PPE) during the COVID pandemic, computer chip shortages delaying the assembly and sales of cars, and, most recently, the baby formula shortage. These were due to disruptions in the supply chain caused by an overreliance on single sourced suppliers who failed to deliver, transportation disruptions, outsourcing of critical parts, supplies, medicines to distant countries, and/or an overreliance on “Just In Time” for inventory management. This is the case of placing too many eggs in too few baskets, and often just one basket. Counterfeit or substandard parts and products can enter the supply chain via graft, breaks in chain of custody, or carelessness. This has included critical mechanical parts on aircraft, chips containing spyware, and substandard or out of date medicines substituted for the real thing resulting in serious illness and death. This complex SoS needs to be examined, studied, and understood in the same way as a mission critical system; threats, vulnerabilities, and risks need to be identified and mitigated and assurance cases defined to ensure a solid and reliable supply chain. This paper will look at the supply chain of an example factory system to determine how some of these problems can be predicted, prevented, mitigated, and solved using the UAF, RAAML and assurance case techniques.

Pengendalian Persediaan Bahan Baku Bawang Merah Untuk Produksi Bawang Goreng Dengan Metode Economic Order Quantity (EOQ) Dan Just In Time (JIT)

Effective planning to manage the procurement of raw materials is crucial for maintaining productivity in production activities and controlling costs. Indonesian enterprise UD. XYZ is dedicated to the manufacturing of fried onions. However, between April 2022 and June 2023, there have been regular fluctuations in the raw material availability for the production process, which has resulted in subpar output levels and unmet demand. To solve this problem, this study will use time series forecasting techniques to estimate the demand for fried onions and the Economic Order Quantity method to control raw material supply. Furthermore, this study uses the Just In Time approach to determine the minimum inventory cost. Both systems are anticipated to lessen the effects of changes in raw material availability. Based on the research results, the linear regression method was used for forecasting, which predicted an average demand of 1354 Kg of fried onions with a MAPE value of 2,79%, MAD 27,59, and MSD 1317,81. Then, an EOQ value of 1992,85 kg was calculated using the Economic Order Quantity (EOQ) and Just In Time (JIT) methods. This resulted in a total minimum storage cost of Rp. 2.989.271.

Pengoptimalan Persediaan Bahan Baku Utama Untuk Mengatasi Tantangan Industri Menggunakan Metode Just In Time (JIT) Dan Silver Meal Heuristic Pada PT. Indonesia Plafon Semesta

The inventory problem that still occurs at PT Indonesia Plafon Semesta is the accumulation of resin and calcium raw materials which results in considerable losses. PT Indofon began to experience a buildup of resin raw materials in August amounting to 16,100Kg and for calcium amounting to 15,200Kg so that each month it soared until its peak in October, namely a buildup of 23,500Kg of resin, and 23,200Kg of calcium. The use of the right method for inventory control problems is Just In Time , which is a management strategy used to optimize the production process and product delivery by buying and producing only when needed and at the right time. Silver Meal Heuristic, a heuristic approach used in inventory control to determine the optimal lot size. The quantity of raw material orders using JIT is relatively more than the silver meal, but with JIT the supplier only needs to send 4-5 times, meaning that the company can save costs. The quantity and frequency obtained from data processing using silver meal is more optimal. However, the cost of raw material inventory incurred is quite large. For a decrease in efficiency costs by implementing a policy of making purchases with Just In Time (JIT) and is expected to save on raw material inventory costs. So that these cost savings can be allocated by the company to other costs.

Impact of Lean Manufacturing Practices on Waiting Time of Patients in Outpatient Department (OPD): A Study in the Sri Lankan Public Healthcare Industry.

This study aims to explain the impact of lean manufacturing practices on waiting time of patients at the outpatient department (OPD) in the Public hospitals. Cross-sectional data using a survey sample of 384 patients in the Kandy public hospitals with a satisfied random sampling were used. Data were analyzed using SPSS software. This study covers the four independent variables of lean manufacturing practices namely: 5S, visual management, kaizen, and Just in Time. The dependent variable is waiting time. The measurement instrument for lean manufacturing practices and waiting time was developed from comprehensive literature reviews. Results indicate that there is a negative significant impact of lean manufacturing practices on waiting time of patients in outpatient department (Beta = -0.626 & p=0.000). Therefore, these findings suggest that the theories of lean manufacturing and the knowledge-based view providing implementation of lean manufacturing practices can reduce waiting time for patients in outpatient department.

Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency

This is manufacturing, efficient production scheduling is crucial for maximizing output and reducing costs. This abstract describes strategies and technologies to achieve this efficiency. It covers aspects like demand forecasting, resource allocation, optimizing task sequencing, and leveraging real-time tracking. By implementing these methods, manufacturers can streamline workflow, minimize idle time, and ensure timely deliveries. This report explores the challenges faced in production scheduling in manufacturing and examines strategies and technologies that can be used to optimize scheduling processes. The report discusses the importance of production scheduling in achieving operational efficiency and meeting customer demands. Various strategies, such as lean manufacturing, just-in- time (JIT) production, and advanced planning and scheduling (APS) systems, are analyzed for their effectiveness in improving production scheduling. Additionally, the report explores technologies such as artificial intelligence (AI), machine learning, and the Internet of Things (IoT) that can enhance production scheduling capabilities. Case studies and examples are used to illustrate how these strategies and technologies have been implemented successfully in real-world manufacturing settings. The report concludes with recommendations for manufacturers looking to optimize their production scheduling processes.

Penerapan Supply Chain Management di PT Nippon Indosari Corpindo Tbk

This research discusses the application of Supply Chain Management (SCM) at PT Nippon Indosari Corpindo Tbk., one of the leading companies in the bakery industry in Indonesia. Focusing on enhancing operational efficiency and reducing production costs, this study explores how PT Nippon Indosari Corpindo Tbk. integrates advanced technology and Supply Chain Management strategies to maintain product quality while minimizing losses. Through the use of the Just In Time (JIT) concept, Advanced Planning & Optimization (APO) tool, and Radio Frequency Identification (RFID), the company has successfully improved inventory management, expedited distribution, and maximized customer satisfaction. The research findings show that effective SCM implementation has reduced the bullwhip effect, increased transparency in the flow of raw materials and finished products, and supported the sustainability of the company's operations. Thus, this study provides important insights into the benefits of SCM in the food and beverage industry, especially for companies operating on a large scale with dynamic market demands

Estimating Uncertainty in Labeled Changes by SZZ Tools on Just-In-Time Defect Prediction

The aim of Just-In-Time (JIT) defect prediction is to predict software changes that are prone to defects in a project in a timely manner, thereby improving the efficiency of software development and ensuring software quality. Identifying changes that introduce bugs is a critical task in just-in-time defect prediction, and researchers have introduced the SZZ approach and its variants to label these changes. However, it has been shown that different SZZ algorithms introduce noise to the dataset to a certain extent, which may reduce the predictive performance of the model. To address this limitation, we propose the Confident Learning Imbalance (CLI) model. The model identifies and excludes samples whose labels may be corrupted by estimating the joint distribution of noisy labels and true labels, and mitigates the impact of noisy data on the performance of the prediction model. The CLI consists of two components: identifying noisy data (Confident Learning Component) and generating a predicted probability matrix for imbalanced data (Imbalanced Data Probabilistic Prediction Component). The IDPP component generates precise predicted probabilities for each instance in the training set, while the CL component uses the generated predicted probability matrix and noise labels to clean up the noise and build a classification model. We evaluate the performance of our model through extensive experiments on a total of 126,526 changes from ten Apache open source projects, and the results show that our model outperforms the baseline methods.

A Proposed Costing System for Agile Just-In-Time Supply Chains with Back-Flush and throughput Accounting

According to the new advances in the form of new firms, including JIT and Agile, further advances in technologies, computer programs, and the systems of official accounting all become necessary to be modified. Also, the considerations of kaizen costing systems, in addition to the new movements towards the concern of the customer, should come first as a result of the new movements towards the customer profit analysis. In a JIT supply chain, suppliers are responsible for informing their cus-tomers of their products. Within the context of the current research, it was suggested to use the sys-tems of Back-flush costing as well as throughput accounting systems to simplify the application of just-in time systems and agile systems to satisfy the requirements of achieving the policy of continu-ous improvement. Furthermore, the continuous debate between the different parties of the game supposed here will be realized and considered. The integration of competitors into various aspects of the supply chain has been made possible by the emergence of intelligent supply chains. Additionally, the rapid advancements in the industrial and technological landscape have placed a significant bur-den on the movement of goods and information, requiring efficient and cost-effective solutions to meet time-sensitive requirements and customer demands. It is even more promising to consider the potential for surpassing competitors in terms of achievements and capabilities. The continuous advancements in artificial intelligence have further emphasized the need for progress in both production quality and cost optimization. The rapid progress in both the just-in-time/agile and resilient systems has led to additional requirements for the implemented accounting system and the selected level of automated accounting system. Extensive analysis of various arguments within the scope of the present study has been conducted, promoting the adoption of a more reality-sensitive model for the application.

Developing multifruit global near‐infrared model to predict dry matter based on just‐in‐time modeling

AbstractModeling near‐infrared (NIR) spectral data to predict fresh fruit properties is a challenging task. The difficulty lies in creating generalized models that can work on fruits of different cultivars, seasons, and even multiple commodities of fruit. Due to intrinsic differences in spectral properties, NIR models often fail in testing, resulting in high bias and prediction errors. One current solution for achieving generalized models is to use large calibration sets measured over multiple cultivars and harvest seasons. However, current practice primarily focuses on calibration sets for single fruit commodities, disregarding the rich information available from other fruit commodities. This study aims to demonstrate the potential of locally weighted partial least‐squares an example of just‐in‐time (JIT) modeling to develop real‐time models based on calibration sets consisting of multiple fruit commodities. The study also explores JIT modeling for leveraging relevant information from other fruit commodities or adapting the model based on new samples. The application demonstrated here predicts the dry matter in fresh fruit using portable NIR spectroscopy. The results show that JIT modeling is particularly effective for multiple fruit commodities in a single calibration set. The JIT models achieved a root mean squared error of prediction (RMSEP) of 0.69% fresh weight (FW), while the traditional partial least squares (PLS) modeling RMSEP was 0.93% FW. JIT modeling can be particularly beneficial when the user has multiple fruit datasets and wants to combine them into a single dataset to utilize all the relevant information available.