Improved Lead Time Research Articles

The Effect of Lean Manufacturing on Production / Operation for the Small and Medium Enterprise in Malaysia

At the start of 2022, the government announced its focus on supporting the development of small and medium enterprises (SMEs) to improve living standards following the COVID-19 pandemic. SMEs contribute significantly to the national economy, with the clothing industry playing a major role. This research applies lean manufacturing techniques to enhance the performance of SMEs in the clothing sector, specifically in T-shirt printing. Therefore the first objective was to collect, analyze processing times and recording of all issues encountered during production. The second objective involved using value stream mapping (VSM) to analyze the T-shirt printing process from raw material supply to finished product packaging. We also employed a why-why analysis to pinpoint the underlying causes of inefficiencies and devise specific solutions. The last objective focused on process improvement using VSM, along with the Kanban system, 5S and Make to Stock (MTS) to optimize block preparation and T-shirt printing. We selected Flexsilk Printshop as the case study. The findings showed a 66.59 % reduction in waiting time, a 30.77 % improvement in lead time, an increase in overall equipment effectiveness (OEE) from 93.3 to 97.45 % and a 50.41 % increase in efficiency.

The Impact of Lean Management Implementation on Inpatient Discharge Prescription Services at Pertamina Central Hospital

Background: The lead time of inpatient discharge prescription services affects the overall discharge process. In 2022, lead times at Pertamina Central Hospital's Inpatient Pharmacy varied, requiring continuous efforts to meet target times and avoid disrupting discharge plans. This research aims to evaluate the impact of lean management on reducing these lead times. Methods: The study employs a combined quantitative and qualitative research design with an action research approach. It was conducted from March 4 to May 8, 2023, involving pharmacists, pharmaceutical technical personnel, compounders, and pharmacy couriers. Data analysis included measuring lead time changes pre- and post-lean management implementation and narrative analysis of qualitative findings. Results: After implementing lean management, the lead time for non-compounded prescriptions decreased from 42.6 to 25 minutes, with value added ratio (VAR) increasing from 18.9% to 27%. For compounded prescriptions, lead time dropped from 67 to 45.7 minutes, and VAR rose from 25.2% to 37.9%. The Mann-Whitney U test confirmed the significant difference in average lead time. Conclusion: The implementation of lean management in inpatient discharge prescription services has been proven to improve lead time and increase process efficiency (VAR).

Improving Lead Time through Lean Manufacturing: A Case Study

Lean manufacturing is a well-established methodology aimed at optimizing production by eliminating waste, enabling industries to thrive in a globally competitive environment. This paper presents a case study of a well-known automotive manufacturing industry on the axle process. This article aims the Value Stream Mapping (VSM) methodology was used in the axle process to reduce lead time by producing quality products with a reduction in non-value-added activities. The current state of the axle process was created using industry data from the past six months. Significant improvements were realized following the successful implementation of VSM, including a reduction in the lead time from 89.50 hours to 50.55 hours. The new state map was also created after implementing the improvements. The results illustrated that the VSM increased the effectiveness by 56.48% in the axle process.

A complete lean system design for team-oriented product development projects

PurposeThe inherent variability on process times and demand are the factors that prevent the efficient application of lean philosophy in multi-project product development (PD) environments. Considering this variability, a hybrid push–pull project control system is developed, and value stream costing (VSC) analysis is performed to reflect the relation between project lead time, capacity and project cost. The assessment of the push/pull project control on lead time improvement and long-term savings on capacity have been aimed with the proposed complete design structure.Design/methodology/approachIn a team-based structure, formed through clustering, push control techniques for planning tasks within cross-functional teams and pull control techniques for planning tasks between cross-functional teams are developed. The final step evaluates the proposed structure through VSC and long-term savings have been pointed out, especially in terms of freed-up capacity. For the validation of the proposed methodology, an office furniture manufacturing firm’s PD department has been considered and the performance of the hybrid system has been observed through simulation experiments and based on the simulation results, the lean system is evaluated by VSC.FindingsThe results of simulation experiments show a superior performance of the proposed hybrid push/pull project control mechanism under different settings of cycle time between projects or shortly project cycle time, dispatching rules within teams and variability levels. The results of the Box-Score (tool to apply VSC) indicate increased capacity in the long term to add extra projects during the planning period with the same project lead time and without additional cost.Research limitations/implicationsAlthough extensive simulation experiments have been performed to quantify the effect of project control structure and positive results have been reported on lead time and cost, the proposed design structure has not been tested in all existing PD environments.Originality/valueTo the best of authors’ knowledge, the quantification of the effect of hybrid project control with VSC is the first attempt to be applied in lean PD projects.

The predicting role of sustainable supplier selection on lead-time performance in public procurement: relational capability as a moderator

PurposeWith increasing concerns regarding the performance of public procurement, especially concerning timely delivery, this study aims to investigate the predictive role of sustainable supplier selection (SSS) in enhancing lead-time performance in public procurement, while also examining the moderating effect of relational capability.Design/methodology/approachThe study collected primary data from 179 accredited public organizations in Tanzania through structured questionnaires and used partial least squares structural equation modeling for data analysis.FindingsThe results reveal that SSS and relational capability influence lead-time performance in public procurement. Regarding the moderating effect of relational capability, the study establishes that relational capability strengthens the influence of SSS on lead-time performance. Therefore, based on the collected data from surveyed public organizations, relational capability positively moderates the relationship between SSS and lead-time performance in public procurement.Social implicationsThe study offer valuable implications for public procurement practitioners, academics and other stakeholders. Public organizations should consider contemplate integrating sustainability aspects into supplier selection processes. Furthermore, the study contributes to advancing efforts toward achieving sustainable development goals (i.e. SDG12) within Tanzania, particularly by advocating for sustainable public procurement practices aligned with national policies and priorities.Originality/valueThis study not only enhances the understanding of public procurement performance by investigating lead-time improvement, incorporating considerations of SSS and relational capability, but it also offers empirical evidence of the moderating role of relational capability within the unexplored Tanzanian context.

Reduction of Lean Wastes by Using Value Stream Mapping: A Case Study of Textile Company in Pakistan

Purpose: Lean manufacturing, originating from the Toyota Production System (TPS), aims to reduce waste and optimize resources. Developed countries must adopt Lean practices for performance improvements while developing countries often use just-in-case approaches. Value Stream Mapping (VSM) is a crucial tool for diagnosing, implementing, and maintaining Lean Manufacturing, helping identify improvement opportunities and eliminating waste. The research at the company aims to eliminate Lean wastes and line unbalancing issues to improve lead time and the value-added ratio (VAR), enhancing production efficiency. The research aligns with Sustainable Development Goal 12 (Responsible Production & Consumption) by focusing on waste reduction.
 Methodology: The research uses time study, Edaw max, and Visio software to analyze tasks, create VSMs of current and future states, and control charts to examine data variations over time. Data collection involves cycle time, batch size, packet size, and the number of workers required for each activity.
 Findings: The research objectives include studying the existing scenario of production units via VSM, identifying, and eliminating Lean wastes, and comparing proposed and existing scenarios for improvement opportunities. The literature review highlights the importance of Lean manufacturing in eliminating unnecessary processes, reducing lead time, and fostering positive stakeholder relationships.
 Unique Contribution to Theory, Practice and Policy: This study suggests the better way to reduce the lean wastages and balance the line by using VSM technique. The future state VSM is created to conceptualize potential improvements and gather feedback, focusing on reducing non-value-adding tasks, work-in-process inventory, workforce, and overall process time.

Assessing probiotic viability in mixed species yogurt using a novel propidium monoazide (PMAxx)-quantitative PCR method.

Viability is a prerequisite for any therapeutic benefits associated with the ingestion of probiotic bacteria. Current culture-based techniques are inadequate for the enumeration of probiotics in mixed-species food products. This study utilized a quantitative PCR (qPCR) method coupled with propidium monoazide (PMAxx), and novel species-specific tuf gene primers to selectively enumerate Lacticaseibacillus rhamnosus, Bifidobacterium spp., and yogurt starter cultures in mixed-species probiotic yogurt. The method was optimized for PMAxx concentration and specificity and evaluated for efficiency and applicability. PMAxx-qPCR showed high specificity to the target organisms in mixed-species yogurt, quantifying only viable cells. The linear dynamic ranges were established over five to seven orders of magnitude. The assay was reliable with an efficiency of 91-99%, R2 values > 0.99, and a good correlation to the plate count method (r = 0.882). The results of this study demonstrate the high selectivity, improved lead time, and reliability of PMAxx-qPCR over the culture-dependent method, making it a valuable tool for inline viability verification during processing and improving probiotic quality assurance for processors and consumers.

Power-from-Shore Optioneering for Integration of Offshore Renewable Energy in Oil and Gas Production

Despite the widespread usage of high-voltage alternating current (HVAC) for the connection of offshore wind farms (OWF), its use to power-from-shore (PFS) offshore oil and gas (O&G) production sites is often not feasible. Its limitations for long-distance subsea transmission are usually found at 50–70 km from shore and might be even shorter when compared commercially to a direct-current (DC) alternative or conventional generation. Therefore, this research paper aims to address the standardization of offshore transmission with a particular focus on the high-voltage direct current (HVDC) alternative. While the distance is typically not a limiting factor when using DC, and the voltages used are rather standard, the concept of power envelopes can be quite useful in addressing the high variability of offshore site power requirements and setting a design baseline that would lead to improved lead time. In this article, a full back and front-end genetic optioneering model purposely built from the ground up in Python language is used to #1 define up to three DC power envelopes that would cater to most of the candidate’s requirements and #2 provide the lowest cost variance. The results will demonstrate that this can be achieved at a minor overall cost expense.

The Role of Supply Chain Integration and Agile Practices In Improving Lead Time

Lead time is one of the main factors which always significantly affects supply chain competitiveness. The recent COVID-19 pandemic has harmed the lead time, and an exceptional increase in the lead time has been experienced because of it. However, there are a few variables which can have a positive effect on the lead time. Aim: This research study was aimed at how the variables like Agile Practices and Supply Chain Integration may influence lead time. Methods: Research data was collected from the respondents, who were supply chain executives and managers, through a closed-ended questionnaire, and the Statistical Package for Social Sciences (SPSS) was employed to drive the results. Results: The findings of this study indicates that Supply Chain Integration and Agile Practices contribute to the reduction of lead time. Conclusion: Findings also suggest that the lead time can be significantly reduced with the employment of Supply Chain Integration and Agile Practices.

Improving Lead Time Forecasting and Anomaly Detection for Automotive Spare Parts with A Combined CNN-LSTM Approach

Improving Lead Time Forecasting and Anomaly Detection for Automotive Spare Parts with A Combined CNN-LSTM Approach

Lead time improvement through the nationwide Dutch Head and Neck Audit

Lead time improvement through the nationwide Dutch Head and Neck Audit

A Combined Lean & PCMM-based Process Improvement Framework for Better Human Capital / Resource Management in Mining Industry

In today’s competitive business environment, even small improvements in productivity and efficiency can have a huge impact on company’s profitability. Companies can improve operations and quality by controlling / improving lead time and reducing waste. Lean is an operational management technique used to systematically improve the efficiency and quality of processes / services. The concept is being used in mining organizations across the globe and resulted into improved productivity, safety & morale of the employees. PCMM is maturity model used for HR / people related process improvement. PCMM consists of best of the HR & People management practices used in IT & Technology industry. This paper explores the possibility of implementation of a new lean & PCMM-based framework in mining industries that will help in optimal utilization of human capital. This framework will have positive business impact and result into benefits. Companies that excel at implementing such best practices throughout the organization will find themselves at a great advantage in a world where humans and machines working together outperform either humans or machines working on their own.

Effects of used sand recycling on the foundry properties of three-dimensional printed sand moulds and cores

Sand casting is one of the important metal casting processes that involves hard labour and patternmaking to produce sand moulds. However, it has evolved considerably since the adoption of rapid sand casting, a type of additive manufacturing. This advancement has reduced costs and improved lead time by eliminating tooling. Nevertheless, there are still some challenges that are being experienced, such as the recyclability of the waste sand produced by the foundry. With high costs of virgin sand and sand disposal, reusing the used sand is important. The objective of this paper is to evaluate the feasibility of adding new sand to the used sand during rapid sand casting. The results indicate that the semi-activated sand collected from the hopper can be reused to produce sand moulds and cores if new sand is added to it.

Measurement of maintenance planning and control effectiveness using the Lean approach

Ineffective maintenance management can result in a backlog of maintenance work orders, inefficient activity prioritisations, and unnecessary outages. Making a strategic decision is challenging due to traditional procedures that only treated maintenance at a tactical level. Usually, the key performance indicator (KPI) measured for maintenance management is related to work order compliance. However, there is still less work focusing on measuring the effectiveness of Maintenance Planning and Control (MPC) processes. This paper aims to showcase a measurement of the maintenance effectiveness at offshore facilities using Lean methodology by measuring the lead time of sub-processes of MPC activities to evaluate existing practices, identify gaps and develop key recommendations. To this end, the lead time of the baseline MPC process is analysed and benchmarked with that of the improved MPC where new parameters to monitor and verify the process efficiency are included. The results show that a lead time improvement of close to 60% was achieved with the proposed methodology.

Can a Machine Learning–Enabled Numerical Model Help Extend Effective Forecast Range through Consistently Trained Subgrid-Scale Models?

Abstract The development of machine learning (ML) techniques enables data-driven parameterizations, which have been investigated in many recent studies. Some investigations suggest that a priori-trained ML models exhibit satisfying accuracy during training but poor performance when coupled to dynamical cores and tested. Here we use the evolution of the barotropic vorticity equation (BVE) with periodically reinforced shear instability as a prototype problem to develop and evaluate a model-consistent training strategy, which employs a numerical solver supporting automatic differentiation and includes the solver in the loss function for training ML-based subgrid-scale (SGS) turbulence models. This approach enables the interaction between the dynamical core and the ML-based parameterization during the model training phase. The BVE model was run at low, high, and ultrahigh (truth) resolutions. Our training dataset contains only a short period of coarsened high-resolution simulations. However, given initial conditions long after the training dataset time, the trained SGS model can still significantly increase the effective lead time of the BVE model running at the low resolution by up to 50% relative to the BVE simulation without an SGS model. We also tested using a covariance matrix to normalize the loss function and found it can notably boost the performance of the ML parameterization. The SGS model’s performance is further improved by conducting transfer learning using a limited number of discontinuous observations, increasing the forecast lead-time improvement to 73%. This study demonstrates a potential pathway to using machine learning to enhance the prediction skills of our climate and weather models. Significance Statement Numerical weather prediction is performed at limited resolution for computational feasibility, and the schemes to estimate unresolved processes are called parameterization. We propose a strategy to develop better deep learning–based parameterization in which an automatic differentiable numerical solver is employed as the dynamic core and interacts with the parameterization scheme during its training. Such a numerical solver enables consistent deep learning, because the parameterization is trained with a direct target of making the numerical model (dynamic core and parameterization together) forecast match observations as much as possible. We demonstrate the feasibility and effectiveness of such a strategy using a surrogate model and advocate that such machine learning–enabled numerical models provide a promising pathway to developing next-generation weather forecast and climate models.

Creation of standard work for the management of outpatient oral antineoplastic therapy.

369 Background: Recently, there has been a dramatic increase in the use of oral antineoplastics for the treatment of various malignancies. The development of these agents has been so rapid that the infrastructure needed for their appropriate supervision has trailed their availability. Previously at Virginia Mason, there was no standard work established for the management of patients starting these agents and consequently there were defects noted, including delays in the medications’ delivery to the patient, lack of appropriate teaching regarding potential medication toxicities and even delayed provider follow-up. The goal of this project was to create standard work around the treatment initiation and follow-up of all patients starting outpatient oral antineoplastic therapy. Methods: A multi-disciplinary team of providers, nurses, and pharmacists held a flow mapping session to identify defects and waste points in the process between the prescription being written until the first provider follow-up visit after medication delivery. The team focused on decreasing the amount of time between prescription initiation and first clinic follow up, as well as increasing the percentage of RN teaching sessions performed and documented. This involved the creation of a dedicated inbox in the Electronic Medical Record (EMR) to which pharmacy and care manager nurses had exclusive access to facilitate rapid communication between team members. The work also resulted in the creation of an antineoplastics teaching template which was used to document teaching on potential treatment toxicities. Results: Standard work was created for the outpatient oral antineoplastic administration process and teaching documentation. Using the new tools, lead time was improved by 21.7% and teaching documentation was improved by 185% at the 30-day mark. These metrics were monitored over a 3 month period, and at the end of 90 days the lead time improvement was maintained at 21.2% and teaching documentation improvement at 110% (Table). Conclusions: The rapid development of oral antineoplastic medications has required Oncology practices to develop more robust workflow infrastructure to administer these medications properly and safely. We created standard work for the proper initiation and follow-up of oral antineoplastic therapy. In doing so we identified specific defects and instances of waste and were able to improve the workflow process to address these issues, resulting in measurable quality improvements and overall better patient care.[Table: see text]

Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis

Foresight of crop yield is fundamental to producers and industry to better manage climate risks and mitigate ebbs and troughs in crop production. Rain-fed grain production in Australia is highly volatile and producers and industry are progressively confronted with projected uncertainties due to climate variability and change, input costs and market prices. Thus, having advance knowledge of the likely impact of the coming season's climate on crop yield and production is critical for decisions across the supply chain. Here we explore and analyse the lead time and skill of a wheat yield forecasting system using a biophysical crop yield simulation model connected to either a statistical ENSO-analogue climate forecasting system or a dynamic general circulation model (GCM) derived climate forecasting system. The comparative skill was investigated for 16 wheat producing districts (shires) of the broad Australian winter cropping region, each containing 9–35 irregularly-spaced simulation points associated with climate stations. Both the ENSO-analogue and GCM-derived systems produced reliable wheat yield forecasts with the GCM-based approach having general improved skill, and particularly during the early months of the season (March to May) before sowing. The shift in the forecast yield distributions relative to the climatology-based yield distribution were dependent on location and time in the season, with the GCM-derived forecast shifts more widespread and earlier in the season. Overall, the GCM-based climate/crop forecasting system showed a significant improvement in lead time (greater than two months before the normal planting time of wheat), across the Australian wheat belt. This result demonstrates an avenue for improved efficacy in future commodity forecasting frameworks via likely enhanced relevance and utility to industry associated with the use of GCM-derived approaches.

Implementation of Lead Time Improvement in the Cutting Production Process using Clustering Data Mining and Lean Manufacturing

In steel companies, there are some processes in the production such as cutting, machining and heat treatment. The production process begins with cutting process and then machining process and the last one is the heat treatment process. It becomes a background that gives the challenge and needs continuous improvement on all aspects, mainly in the cutting process. The research is conducted at one of steel companies located in Pulogadung Industrial Estate. The purpose of the research is to know the factors which cause process lead time cannot be achieved. The methods used are clustering data mining and lean manufacturing. Clustering method can be used to focus on big data and find out clusters or the same pattern. Those clusters are processed with Weka software and using K-means algorithm. Improvement ideas will be implemented to the formed clusters using lean manufacturing such as Single Minute Exchange of Dies (SMED) which have been mapped through value stream mapping, 5S, and Kanban beforehand. The materials’ dimension on the production process is affecting the cutting process lead time. The thicker material diameters will need a longer time to process. With the methods used, lead time of cutting process increased from 3449 minutes to 2165 minutes (3 days to be 2 days). Meanwhile if the “SMED” activities are implemented, the cutting process lead time increased from 187 minutes to 136 minutes.
 Keywords: Clustering; SMED; Lean Manufacturing; Process Lead Time; Value Stream Mapping

Hybrid Make-to-Stock and Make-to-Order (MTS-MTO)Scheduling Model in Multi-Product Production System

In production process, scheduling isan important role in meeting the orders and reducing the cost. This process becomes more complicated when the factory produces various products. Scheduling model in the production process can be divided into two groups: make-to-stock (MTS) and make-to-order (MTO). General constraint in the MTSmodel is limited production capacity and inventory cost. This work aims to propose hybrid MTS-MTO model that can improve lead time and maintain low inventory. In this work, we propose three hybrid MTS-MTO scheduling models for the multi-product production system. In these models, we modify several scheduling algorithms that are used in computer system, such as shortest remaining time(SRT), shortest processing time(SPT), and Round Robin(RR). These models are hybrid (s, S)-first-come-first served (FCFS)model, hybrid modified (s, Q)-SPT model, and modified (s, Q)-SRT model. This model then is implemented into production simulation. In this simulation process, we compare the proposed models with the existing item-by-item based (s, S)model. Based on the simulation result, these proposed models perform better than the basic MTS model. The hybrid (s, Q)-modified SPTmodel performs as the best model in creating high completion ratio, low lead time, and low inventory ratio. In certain condition, our proposed model performs 344 percent in completion ratio, 19.8 percent in lead time, and 3 percent in inventory ratio compared with the existing model.

Optimization of Production Schedule Using Advanced Scheduling Tools for Green Production

Significant number of producers and manufacturers are realizing substantial financial and environmental benefits from sustainable/ greener business practices. Sustainable production and manufacturing are the creation of manufactured products through economically-sound processes that minimize negative environmental impacts while conserving energy and natural resources. Sustainable manufacturing also enhances employee, community, and product safety. The aim of the paper is to provide the right information about using scheduling tools such as Advanced Planning and Scheduling tools to improve lead times, optimize constraints by this reduce waste time and lower emissions produced by producing the waste. This paper deals with problematic of production optimization and its impact on production efficiency.