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Supply chain resilience assessment and strengthening has become a strategic topic in supply chain management. Extant literature on the subject of supply chain resilience assessment mainly focuses on evaluating the operational performance (e.g., inventory, capacity, lead time) while overlooking aspects of the financial performance. This article presents an event-based Bayesian approach as an effective tool for modeling the causal relationships among variables at different time intervals to evaluate resilience across operational and financial criteria. The model focuses on two key elements of resilience—reliability (the ability of the system to sustain an original state) and recoverability (the ability of a system to recover following a disruption), which allows us to investigate how different operational and financial factors affect the network reliability, recoverability, and resilience. This article contributes to the literature in three ways—first, we incorporate a financial perspective to the quantitative study of supply chain resilience; second, we evaluate both the financial and operational aspects from a longitudinal perspective; third, we show that the financial performance of individual supply chain entities has a strong influence on the overall supply chain resilience. From a practical standpoint, the article presents a model that can help managers identify the weakest nodes in their supply chain, considering operational and financial aspects over time, allowing them to design and operate more resilient supply networks.

Value stream mapping (VSM) is a vital lean manufacturing tool accustomed to identify and reduce the process lead time and the work in process inventory for improvement of the overall efficiency of an organization. This paper aims to investigate and implement value stream mapping in water distillation plant. The data is collected from the various production processes carried out in plant after analyzing the all collected data. We had prepared the current state map and value-added activities and non-value-added activities had found. After conducting an impressive investigation on non-value-added activities, suggestions are made to minimize the process lead time and work in process inventory. The lean techniques like Kaizen, 5S are used to make necessary changes to improve the overall efficiency and after considering the proposed changes future state value stream map was drawn. After successful implementation of VSM Technique in water distillation plant the improvements are, the processing time is reduced from 171 s to 143 s and lead time is decreased from 8057 s to 6344 s.

The manufacturing world had undergone four separate industrial revolts. Industry 4.0 is a most recentchapter in the Industrial revolt story that pivotedmainly on manufacturing automation, live data capturing, smart machines, and internet connectivity with key objectivesof productivity rise, optimization of available working hours, and increased organization competitiveness. This research paper studiesthe industry 4.0 through the survey of available literature in a sequential manner that started with understanding the development of the manufacturing industry from industry 1.0 to industry 4.0, the nine key foundation blocks of the industry 4.0, the cyber-physical systems, the Smart factory concept,industry 4.0 application strategy guide step by step, Industry 4.0 benefits, risk, and challenges. We applied the Industry 4.0 concept step by step as a pilot project as per the Industry 4.0 application strategy guide atthe bus bar trunking production factory with the target of delivering the demand of customerswithin committed or shorter lead time from the bus bar manufacturing shop and discussed the outcomes briefly. We concluded that Industry 4.0 helps to improve operator work-life, overall productivity, and to reduce the operating cost of production drastically in mass manufacturing processes. Industry 4.0 is also a boon in today’s changing work culture after the CORONA pandemic through its core concepts of digitization, paperless work, networking, and maximum internet usage. It also guides to adopt automation and smart machines that are communicating in real-time with its customer. We also observed that its application is costly in terms of investment to deploy for small or middle scale manufacturing plants, but they shall implement partly to get the benefit from it.

High-precision electronic maps are required to provide more detailed and accurate information than traditional maps. With the rapid development of high-resolution remote sensing technology, it has become possible to extract fine-grained road scene information such as vehicles, road lines, zebra crossings, ground signs, and lane widths of roads from unmanned aerial vehicle (UAV) remote sensing images, which opens up opportunities for automatic mapping high-precision maps. The traditional method of deciphering remote sensing images is often obtained through manual visual interpretation. Due to the high cost and long lead time of this method, it leads to inefficiencies in updating large amounts of information. To address this problem, this letter models the fine-grained road scene understanding task as an image semantic segmentation problem and innovatively proposes a semisupervised fully convolutional neural network to extract the information efficiently at a low cost. Compared with the traditional supervised full convolutional neural network, this method can simultaneously optimize the standard supervised classification loss on labeled samples and the unsupervised consistency loss on unlabeled samples by using an integrated prediction technology and then input them to the end-to-end semantic segmentation network for training. This method is designed to effectively improve the classification accuracy of the semantic segmentation network and validly alleviates overfitting problems in the case of small numbers of labeled samples. In order to verify the effectiveness of this method, we constructed a data set for experimental, which is used to verify the effect of a variable number of unlabeled samples on model performance. Experimental results show that our method can efficiently complete the extraction of fine-grained road scene information such as vehicles, road lines, zebra crossings, ground signs, and lane widths of roads with a small number of labeled samples.

Geothermal reservoir characterization, construction, and operations are technology-intensive activities that contribute significantly to the cost of delivering renewable electricity. The technologies involved, such as downhole tools and drilling equipment, are similar to those used in oil and gas exploration and production must often be adapted for use in the corrosive, high-temperature geothermal reservoir environment. Low production volume of geothermal tools presents a major challenge in meeting the industry's technology needs. Production of specialized tools for geothermal subsurface applications is often cost-prohibitive. Reduced inventory of subsurface well construction, characterization, and production tools causes geothermal reservoir development efficiency and sophistication to lag behind that of the oil and gas industry. Advances in additive manufacturing provide opportunities to advance geothermal technology while reducing lead time and costs associated with production of low-volume, complex parts. This paper performs an initial techno-economic analysis comparing the cost of conventional production techniques and additive manufacturing for geothermal downhole applications. An analysis of representative downhole tools is used to create a framework for estimating fabrication costs of subtractive and additive techniques, including post-print machining required to meet final tolerances. The framework is used to explore several manufacturing scenarios and identify the dominant factors driving manufacturing time and cost. The current feasibility of additive manufacturing for geothermal downhole tool applications is assessed and issues for future development to better meet the needs of the geothermal industry are identified.

Motivated by recent advances in Internet-of-Things (IoT) technology for household appliances, we analyze a Smart Replenishment system that leverages point-of-consumption (POC) information at end consumers to decide on deliveries of consumables. As such, we extend the classic Vendor-Managed Inventory (VMI) concept to end consumers. We model the system for a single manufacturer who directly serves N end consumers with uncertain demand. End consumers partially adopt the new Smart Replenishment mode, which results in a mix of VMI and non-VMI customers. We assume that unfulfilled demand is lost and that the manufacturer’s dispatch capacity is constrained. Customers compete for the same capacity while featuring different out-of-stock risks and service-level expectations, both of which are costly to the manufacturer. Considering various adoption levels, we decide on the design of such a system and focus on (i) inventory control, (ii) customer prioritization, and (iii) degree of smart, integrated decision-making. Using discrete-event simulation and a full-factorial experiment, we show that replenishment decisions can be significantly enhanced with POC information. It leads to substantial improvements in service levels and capacity utilization without loading customers with inventories. This improvement potential is highest for a low demand coverage of the replenishment quantity, a high gap in the ordering behavior of manufacturer and end consumers, and a long lead time. To realize this improvement potential, we propose a flexible reorder corridor to manage inventories at VMI customers that balances the trade-off between out-of-stock risk and service-level expectation inherent in the system.

Purpose The application of lean thinking in the transportation industry provides opportunities to streamline operations with a value-added orientation. Prior literature shows evidence of limited application of lean thinking in the transportation operations of the pharmaceutical industry. This study aims to close this research gap by investigating the application of lean thinking for improving pharmaceutical transportation performance. Design/methodology/approach This study conducted an action research approach at an Indonesian pharmaceutical distribution company. One cycle in one year was analyzed; empirical data were collected and analyzed through direct observations, interviews and the study of company data and documentation. Findings The application of lean thinking in waste elimination allowed the delivered project to achieve a remarkable 40% reduction in overall transportation costs, 75% reduction in total lead time, 200% improvement in truck productivity and 100% improvement in truckload capacity utilization. Practical implications This study can guide the pharmaceutical industry toward achieving excellence in transportation operations through lean thinking implementation. Originality/value There has been limited research on this topic, and this study is the first attempt to generate new and significant evidence of a real-life application of lean thinking within the field of pharmaceutical transportation.

Purpose This study aims to integrate social capital theory and the knowledge-based view of firms to develop and test a theoretical framework of the mechanisms that explain the influence of relational governance (RG) on lead-time performance (LTP) of developing-country suppliers in global value chains (GVCs). The authors distinguish between the relational and structural dimensions of RG to explain how they interact to create knowledge and leaning opportunities that transform into performance outcomes. Design/methodology/approach Hypotheses were tested using data collected from a sample of 321 Bangladeshi suppliers embedded in apparel GVCs by using structural equation modeling. Findings RG creates knowledge and learning opportunities that transform into LTP. Relationship quality (RQ) is found to contribute positively to knowledge learned (KL) contingent on relationship structure. KL is found to fully mediate the influence of buyer-supplier RQ on LTP of suppliers. Research limitations/implications The dyadic nature of buyer-supplier interactions calls for multisource approaches in measuring buyer-supplier RQ. The authors also recommend future research to test the model in other industries for greater generalizability. Practical implications Developing-country suppliers in low-technology labor-intensive GVCs can improve their LTP by strengthening their capabilities for internalizing external knowledge and developing and maintaining high quality relationships with wide networks of buyers. Originality/value The conceptualization of RG that explicitly distinguishes between the relational and structural dimensions is novel and should inspire future theoretical and empirical research on GVC governance and performance.

Aging asset management requires careful consideration of end-of-life of any asset. This is particularly serious with aging infrastructure which may require complete replacement of a large asset which has a long lead time. In many current End-of-Life analysis approaches to calculate unavailability, the period of interest is sub-divided into yearly cycles with the assumption that the asset is available at the start of the year. Although this assumption is adequate if the replacement time for the asset occurs within the year, with longer lead times this can create excessively optimistic availabilities. This paper presents an improved probabilistic tool for risk assessment due to End-of-Life failure unavailability which overcomes this deficiency. The End-of-Life methodology proposed in this paper is required when aging assets have long lead times to replacement and so the capital investment decision must be made several years ahead. The method is applied to evaluate the End-of-Life unavailability of Manitoba Hydro Bipole II HVdc converters. The results are corroborated using Monte Carlo simulation. Finally, the risk of End-of-Life is incorporated into an economic cost-benefit analysis corresponding to the presented unavailability evaluation method.

This paper is being developed in Blazer on industrial engineering. In the last few decades, the global economy, in particular in international commerce and manufacturing organizations, has evolved substantially. The technical framework of today is thus distinguished by productivity-oriented, which can be accomplished by industrial engineering. This paper presents the different principles & approaches used in industrial engineering, such as time analysis, work studies, line balancing and the outcome of organizational disintegration tests in Blazer. This research has focused on several kinds of machinery to produce a Blazer. The traditional manufacturing industry has issues such as low efficiency, longer lead time, high rework & re-work, poor line balance, low style shift flexibility, etc. The implementation of Industrial Engineering correctly resolved these issues. This study looks at how to develop Blazer's processes by using Industrial Engineering, wasting less resources, time, raw materials, human capital and electricity.

PurposeThis study evaluated the influence of the coronavirus pandemic on the healthcare and non-cold pharmaceutical care distribution supply chain.Design/methodology/approachThe model involves four objective functions to minimize the total costs, environmental impacts, lead time and the probability of a healthcare provider being infected by a sick person was developed. An improved version of the augmented e-constraint method was applied to solve the proposed model for a case study of a distribution company to show the effectiveness of the proposed model. A sensitivity analysis was conducted to identify the sensitive parameters. Finally, two robust models were developed to overcome the innate uncertainty of sensitive parameters.FindingsThe result demonstrated a significant reduction in total costs, environmental impacts, lead time and probability of a healthcare worker being infected from a sick person by 40%, 30%, 75% and 54%, respectively, under the coronavirus pandemic compared to the normal condition. It should be noted that decreasing lead time and disease infection rate could reduce mortality and promote the model's effectiveness.Practical implicationsImplementing this model could assist the healthcare and pharmaceutical distributors to make more informed decisions to minimize the cost, lead time, environmental impacts and enhance their supply chain resiliency.Originality/valueThis study introduced an objective function to consider the coronavirus infection rates among the healthcare workers impacted by the pharmaceutical/healthcare products supply chain. This study considered both economic and environmental consequences caused by the coronavirus pandemic condition, which occurred on a significantly larger scale than past pandemic and epidemic crises.

Long lead-time is hurting the competitiveness of the supply chain, while the shorter is considered to be better in order to add value to the customer and to improve the supply dependability. Supply chain integration increases the coordination and shortens the processes. Agile practices also enable the supply chain to be more responsive to market variability. The aim of this research is to investigate the impact of supply chain integration and agile practices on lead-time reduction at Dubai HealthCare City. An empirical survey has been conducted in which the data were retrieved from 150 respondents and then analyzed using SPSS to examine the model. The result also showed a positive relationship between supply chain integration and lead-time reduction. Meanwhile, results indicated a positive relationship between agile practices on lead-time reduction. The study recommends promoting companies’ capabilities through flexibility and information sharing to achieve supply chain integration and agile practices such as responsiveness and alertness.

Purpose Building on the expectancy value theory, the purpose of this paper is to examine the effects of word-of-mouth (WOM) and customer value (i.e. functional value and ease-of-doing-business value) on B2B business performance in the B2B sales process. Design/methodology/approach The authors develop a framework to understand how B2B WOM and customer value influence supplier sales performance. This model was tested using structural equation modeling with a sample of 220 suppliers on Alibaba.com. Findings The empirical findings demonstrate that B2B WOM valence and volume have positive influences on the number of quotations and the number of transactions, respectively. Additionally, B2B WOM volume mediates the relationship between operational performance and the number of transactions. Response rate mediates the relationships between response time and both the number of quotations and the number of transactions, respectively. Originality/value This study contributes to the industrial marketing of B2B sales on the digital platform by investigating two influencers on sales performance: WOM and customer value.

Purpose The governments of different nations implemented various policy measures in response to the COVID-19 outbreak. These policy measures had a negative impact towards freight transportation. Further, the shock and ripple effect on the other supply chain complicates the recovery process of freight transportation. The transformation of freight transportation in the post-COVID-19 world was reported to be unsustainable. Thus, emerged the requirement to formulate the recovery measures in the context of freight transportation. This study aims to identify and model the recovery measures for sustainable freight transportation (SFT). Design/methodology/approach In this study, 13 critical recovery measures are established from the literature and finalized with the help of an expert panel. An integrated Grey Decision-Making trial and evaluation laboratory is used to prioritize and establish the cause-effect relationships among the identified critical recovery measures. Further, sensitivity analysis is performed to ensure the robustness of the proposed model. Findings The present study reflects that Efficient traffic management (M13), sustainability focused policies (M2), sensitization of stakeholders (M10), financial support (M9) and adoption of 4 R practices (M12) are the top five critical recovery measures for SFT. The results highlight that the transport sector needs to retain the learning from the COVID-19 period to operate under low workforce availability. Further, the emerging economies are suggested to promote local manufacturing to reduce the lead time and risk of unavailability. The study findings reflect that attaining sustainability without considering the social dimension of sustainability is impossible. Also, the results shed light on the controllable and uncontrollable recovery measures. Originality/value The findings from the study would assist policymakers and practitioners in re-formulating the recovery measures for freight transportation considering the aspect of sustainability.

This paper constructed a biobjective model based on the total cost and time satisfaction to provide a desirable solution to the distribution and inventory cooperation of agricultural means supply chain. The model simulated how the distribution center and retailers collaborate to meet the needs of the order customer in the random lead time and out-of-stock loss costs. By the features of the model, the biobjective genetic algorithm was improved based on elitism selection, aiming to improve the quality of noninferior solution in biobjective model. Finally, the influence degree of the lead time of delivery, unit inventory cost, and unit transport cost on the total cost of the system was quantified through the analysis of examples and sensitivity of model parameters. This research has provided valuable new insights into the distribution and inventory coordination of supply chain.

BackgroundUnreliable supply systems have plagued the provision of an uninterrupted supply of life- saving medicines in many developing countries, with antiretroviral (ARV) medicines having the worst repercussions.ObjectiveTo identify the inventory management practices used, evaluate the supply chain performance, and determine the challenges affecting inventory management of ARV medicines in public hospitals.MethodsThe study used a descriptive cross-sectional design, gathering snapshot data on inventory management practices happening in all the 8 public hospitals across Nyamira County. Data collection was done using structured questionnaires, key informant interviews, checklists and data from the national health information system.ResultsThe response rate was 97.3% for the questionnaires and 100% for the key informant interviews. The prevailing inventory management practices were: use of scheduled inventory control model (80.95%), forecasting demand using previous consumption data (100%), keeping accurate and updated stock records for each commodity (92.31%), having essential logistical data in reports (100%), including safety stock (61.54%) when ordering and keeping ARV medicines in dedicated stores (75%). With the exception of order lead time (17.98 days), the other supply chain performance metrics namely stock out rate (52.12%), stock wastage rate (43.2%), and reporting rates (70.84%) were found to be deficient. The challenges mostly affecting inventory management included inadequate staff and training, lack of proper storage, and unreliable supply of medicines.ConclusionInventory management practices were according to the recommended best approaches despite various challenges. The supply chain performance metrics evaluated, with the exception of order lead time, were all found to be unsatisfactory owing to the high stock out rates, below par reporting rates and high stock wastage rate due to expiries discovered. Adoption of an electronic inventory system, use of data for decision making, dedicated storage of ARV medicines, and inclusion of buffer stock, are some strategies to improve inventory management.Rwanda J Med Health Sci 2021;4(2): 257-268

Heavy rainfall occurs twice a year in the country and lately, thousands of people are always left homeless and hundreds lose life due to floods and landslides where rivers, dams, lakes and sewages overflow enhancing the spread of corona virus in slums. Agricultural products in the farms are also destroyed by floods, affecting agricultural performance to decline as it the key driver of the economy growth. Therefore we used inter-crossed model which was the combination of autoregressive moving average and artificial neural network. Zebiak cane model was also used for selection of variables that were associated to physical processes and testing the network variables. Climate networks were found to be effective tool for more qualitative El Nino Southern Oscillation prediction, by looking at a warning of the oncoming of El Nino when a predestined network attribute surpasses some critical value and also feed forward artificial neural network structures were found to be the first performing structure in terms of normalized root mean squared error at a three month head time prediction. By adding the network variable, we came up with a twelve month lead time prediction with same skill to the predictions at lower set times.

The El Niño-Southern Oscillation (ENSO) is one of the main drivers of the interannual climate variability of Earth and can cause a wide range of climate anomalies, so multi year ENSO forecasts are a paramount scientific issue. However, most existing works rely on the conventional iterative mechanism and, thus, fail to provide reliable long-term predictions due to error accumulation. Although methods based on deep learning (DL) apply the parallel modeling scheme for different lead times instead of a single iteration model, they leverage the same DL model for prediction, which can not fully mine the variability of different lead times, resulting in a decrease of prediction accuracy. To solve this problem, we propose a novel parallel deep convolutional neural network (CNN) with a heterogeneous architecture. In this study, by adaptively selecting network architectures for different lead times, we realize variability modeling of different tasks (lead times) and thereby improve the reliability of long-term predictions. Furthermore, we propose a relationship between different prediction lead times and neural network architecture from a unique perspective, namely, the receptive field originally proposed in computer vision. According to the spatio-temporal correlated area and sampling scale of lead times, the size of the convolution kernel and the mesh size of sampling are adjusted as the lead time increases. The Coupled Model Intercomparison Project phase 5 (CMIP5) from 1861 to 2004 and the Simple Ocean Data Assimilation (SODA) from 1871 to 1973 were used for model training, and the GODAS from 1982 to 2017 were used for testing the forecast skill of the model. Experimental results demonstrate that the proposed method outperforms the other well-known methods, especially for long-term predictions.

A total minimum commitment contract is a supply contract under which a firm commits to buying a minimum quantity of a product from its supplier during the contract duration (e.g., one year). Such contracts are widely used in industries, because they provide the buyer with flexibility in terms of the timing and size of each individual order and the supplier with a guaranteed total order volume. Previous studies on such contracts are primarily concerned with the firm's inventory decisions, but none of them has considered the coordination of inventory and pricing decisions. In this paper, we fill this gap by studying dynamic inventory and pricing problems under a commitment contract. Under a general demand model with backlogging and zero lead time, we prove that the optimal policy is a committed-inventory-position-dependent base-stock list-price policy and characterize its structural properties. We also conduct an extensive numerical study to derive further managerial insights. In particular, we find that dynamic pricing can substantially improve the firm's profitability and enables it to select contracts with large committed quantities and price discount rates, and ignoring the commitment in joint pricing and ordering decisions can result in substantial profit loss. Finally, we partly extend our results to a lost-sales model and a backlogging model with positive lead times.

This paper considers a retailer who sells perishable fresh products directly to customers through an online channel and encounters a transportation disruption. Products shipped during the disruption period come with an uncontrollable delivery lead time, resulting in product quality degradation. To balance the compensation price provided to customers because of quality losses, the retailer might employ freshness-keeping efforts to reduce the quality loss during transportation. Therefore, it raises several fundamental questions for the retailer in mitigating the disruption. Is it always optimal to satisfy those customers who are willing to purchase during disruption? If it is profitable to fulfill orders along with an extra delivery lead time, and with a quality loss compensation, what is the optimal freshness-keeping effort? If it is preferable to deliberately create unsatisfied demand by announcing shortages (rationing) to customers, when is the optimal time to do so? To answer these questions, we first present the dynamics of post-disruption inventory and demand, taking into account the demand learning effect facilitated from negative word-of-mouth during disruption and the demand recovery after disruption ends. Afterward, we develop a model to achieve the optimal selling strategy for maximizing post-disruption profit, identifying the joint decision of the rationing period and freshness-keeping effort. Finally, by numerical analysis, three types of selling strategies are visually provided to hedge against disruptions of different lengths.

Companies aiming to upgrade their supply chains to meet operational and sustainability objectives are often required to redesign their supply networks. However, there is scarce empirical evidence o...

While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs), we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most "informative" data collection, and improve production stability.

Drought forecasting can effectively reduce the risk of drought. We proposed a hybrid model based on deep learning methods that integrates an autoregressive integrated moving average (ARIMA) model and a long short-term memory (LSTM) model to improve the accuracy of short-term drought prediction. Taking China as an example, this paper compares and analyzes the prediction accuracy of six drought prediction models, ARIMA, support vector regression (SVR), LSTM, ARIMA-SVR, least square-SVR (LS-SVR) and ARIMA-LSTM, for SPEI. The performance of all the models was compared using measures of persistence, such as the Nash-Sutcliffe efficiency (NSE) and so on. The results show that all three hybrid models (ARIMA-SVR, LS-SVR and ARIMA-LSTM) had higher prediction accuracy than the single model. (ARIMA, SVR and LSTM), for a given lead time, at different scales. the NSEs of the hybrid ARIMA-SVR, LS-SVR and ARIMA-LSTM models for the predicted SPEI1 are 0.043,0.168 and 0.368, respectively, and the NSEs of SPEI24 is 0.781, 0.543 and 0.93, respectively. This finding indicates that when the lead time remains unchanged, the prediction accuracy of the hybrid ARIMA-SVR, LS-SVR and ARIMA-LSTM models for the SPEI at various scales is gradually improved with increasing time scale, and the prediction accuracy of the model with a one-month lead time is higher than that of the model with a two-month lead time. In addition, the ARIMA-LSTM model has the highest prediction accuracy at the 6-, 12-, and 24-month scales, indicating that the model is more suitable for the forecasting of long-term drought in China.

Value Stream Mapping (VSM) is a technique used in the lean principle that helps analyze the flow of materials and information needed to bring a product or service to the customer. VSM has received a lot of attention in the industrial sector from both the manufacturing industry and the service industry. The development of VSM tools has grown rapidly. Much of the research using VSM tools is carried out in industry and universities. This literature review aims to provide an overview of the implementation of Value Stream Mapping in the service industry and to find out its implications. This paper involves a review study of 45 papers related to a case study of VSM implementation in the service industry. The study was conducted using the Google Scholar database, Researchgate, ProQuest, Elsevier. This paper provides an advantage for the service industry to find problems related to waiting times. This paper extends the knowledge and study of the application of VSM as an unexplored tool in several branches of industry. VSM can also serve as a complementary approach, capable of increasing outcomes in many areas other than services. This paper also provides benefits for future researchers to add insights and references for studies related to VSM in the service industry sector.

Purpose The purpose of this paper is to develop a model based on the total interpretive structural modeling (TISM) approach for analysis of factors of additive manufacturing (AM) and industry 4.0 (I4.0) integration. Design/methodology/approach AM integration with I4.0 is attributed due to various reasons such as developing complex shapes with good quality, real-time data analysis, augmented reality and decentralized production. To enable the integration of AM and I4.0, a structural model is to be developed. TISM technique is used as a solution methodology. TISM approach supports establishing a contextual relationship-based structural model to recognize the influential factors. Cross-impact matrix multiplication applied to classification (MICMAC) analysis has been used to validate the TISM model and to explore the driving and dependence power of each factor. Findings The derived structural model indicated the dominant factors to be focused on. Dominant factors include sensor integration (F9), resolution (F12), small build volumes (F19), internet of things and lead time (F14). MICMAC analysis showed the number of driving, dependent, linkage and autonomous factors as 3, 2, 12 and 3, respectively. Research limitations/implications In the present study, 20 factors are considered. In the future, additional factors could be considered based on advancements in I4.0 technologies. Practical implications The study has practical relevance as it had been conducted based on inputs from industry practitioners. The industry decision-makers and practitioners may use the developed TISM model to understand the inter-relationship among the factors to take appropriate measures before adoption. Originality/value The study on developing a structural model for analysis of factors influencing AM and I4.0 is the original contribution of the authors.

The existing literature on make-to-order firms typically supposes that the unit production cost is a constant. In contrast, in this study, this cost is a variable that depends on the lead time. Under this assumption, we investigate the pricing problem for both a monopoly firm and two competing firms. Specifically, we develop a queueing-game-theoretic model to capture the interaction between customers and the firm’s manager and further solve the pricing problem. The results illustrate that, for a monopoly firm, there exists a service rate threshold, and for two homogeneous firms, there is a unique symmetric equilibrium. However, for two heterogeneous firms, the equilibrium may not exist, and if it does, it may not be unique. In this case, the equilibrium is characterized analytically if it is unique and explored numerically if not. Finally, a specific cost function is adopted to analyse the sensitivity of the optimal decisions. When the firms have this variable cost, compared with those with constant unit production cost, customers’ waiting time might be shorter, and the competition between firms might be fiercer. Also, for these firms, increasing the service rate or decreasing the cost parameter does not always help to increase their market share or profit.

PurposeImplementing just-in-time (JIT) practices on the supplier side is a common practice to streamline procurement and production planning in realization of cost efficiency and lead time reduction. However, it is little known how supplier JIT is related to firm flexibility performance and what factors may facilitate the relationship. This study aims to investigate the effects of supplier JIT on flexibility performance and explore the moderating impacts of advanced manufacturing technology (AMT) and human resource empowerment (EMP).Design/methodology/approachThis study applies hierarchical linear regression analysis to test the proposed model based on data collected from 213 manufacturing firms in China.FindingsThe results revealed a negative influence of supplier JIT on flexibility performance. However, the adoption of AMT and EMP reconciles the conflict between supplier JIT and flexibility performance. Besides, the results indicate the positive effects of AMT and EMP on flexibility performance.Originality/valueThis study contributes to supply chain JIT literature by providing a holistic view to understand the structural relationships between supplier JIT, AMT, EMP and flexibility performance.

Electron-beam melting (EBM) is a rapidly developing metal additive manufacturing (AM) method. It is more effective with complex and customized parts manufactured in low volumes. In contrast to traditional manufacturing it offers reduced lead time and efficient material management. However, this technology has difficulties with regard to the construction of overhang structures. Production of overhangs using EBM without support structures results in distorted objects, and the addition of a support structure increases the material consumption and necessitates post-processing. The objective of this study was to design support structures for metal AM that are easy to remove and consume lower support material without affecting the quality of the part. The design of experiment methodology was incorporated to evaluate the support parameters. The multi-objective optimization minimizing support volume, support removal time along with constrained deformation was performed using multi objective genetic algorithm (MOGA-II). The optimal solution was characterized by a large tooth height (4 mm), large tooth base interval (4 mm), large fragmented separation width (2.5 mm), high beam current (6 mm), and low beam scan speed (1200 mm/s).

A concorrência de mercado estimula as organizações a buscarem inovações, com o objetivo de alcançarem posição de destaque, trazendo diferenciais para o segmento em que atuam. Essa competitividade severa faz com que, também, seja necessário às empresas procurar formas de redução de custos, otimização de processos; a acuracidade na previsão de demanda é fator essencial para o aprimoramento da produtividade, a gestão de estoque, a redução de lead time, contribuindo para a melhora dos resultados da empresa. Este trabalho tem como objetivo utilizar modelos matemáticos de séries temporais e inteligência artificial para verificar qual método possui maior acuracidade nas previsões de demanda dos principais produtos de uma organização do segmento de sistemas de iluminação automotivo. Para o método de Redes Neurais Artificiais (RNA), buscou-se otimizá-lo, utilizando-se configurações em relação ao número de neurônios e diferentes algoritmos de treinamento para encontrar os modelos mais assertivos. Por meio da aplicação dos erros de previsão MAPE e MAE, constatou-se que determinadas configurações de RNA são os métodos de melhor acuracidade para realização das previsões dos produtos analisados, em que foi possível concluir que, em média, os erros de previsão da RNA otimizada são de 3,25 vezes (para o MAPE) e de 4 vezes (para o MAE) menor em relação ao método atual da empresa.

The purposes are to improve the accuracy of inventory demand forecast, balance the indexes of enterprises, and reduce the costs of human, material and financial resources of enterprises and suppliers, thus reducing the supply chain costs and meeting the actual needs of enterprises. In terms of training a large amount of data, deep learning is better than traditional machine learning. The sales demand time series data and the previous material demand time series data are input and trained by back propagation (BP) neural network, and then the material demand value is output. Therefore, the historical data of sales demand forecast and material information are input, and the model is established by BP neural network, which not only takes into account the decisive factor of sales demand forecast, but also considers the material consumption, achieving more accurate forecast. The material demand budget of enterprises is analyzed and a material forecast demand model based on deep learning algorithm is proposed. The model uses a neural network to input the sales demand forecast data, material inventory information and material attribute information into the model, and then the model is trained by the training set in accordance with the error back propagation algorithm. Finally, the training effect of the model is tested by the test set. The results show that when the independent variables include sales demand forecast, material consumption forecast and material attribute information, the forecast error of both models is lower and the effect is better, compared with the material consumption data only as an independent variable. The forecast method based on neural network proposed increases the lead time of the forecast, give the supplier a longer time to prepare goods, and reduce the shortage or surplus of supply caused by the short lead time. Therefore, the material demand forecast model based on convolution neural network (CNN) algorithm provides an important reference for the enterprises, helps them improve their work efficiency and promotes the development of enterprises. This model achieves a great improvement on the accuracy of material demand forecast, and has a certain guiding significance in relevant theory and practice.

This work explores the improvement of operational decision-making in a Fast Fashion manufacturing company, considering the Industry 4.0 era. The segment requires agile and flexible decision-making techniques to guarantee the companies survival in a high variety environment of products and demand. The proposed approach was based on three stages. First, we suggested changes and improvements in the system to adapt it to the Industry 4.0 principles. Then, we proposed a Digital Twin (DT) focused on operational resource planning (physical and human). The DT was composed of a Discrete Event Simulation model, an Artificial Intelligence model, and a decision dashboard that provides a user-friendly interface for the decision-maker. Finally, the last stage corresponds to cyclical and constant DT-based decision-making. The DT-based decisions helped to decrease the number of operators in the line reducing their idleness and, at the same time, the total lead time became shorter. Therefore, we highlight that the concepts and solutions of Industry 4.0 might be consistent with small companies without major structural changes, contributing to the evolution of the manufacturing systems.

Although deep learning has achieved a milestone in forecasting the El Nino-Southern Oscillation (ENSO), the current models are insufficient to simulate diverse characteristics of the ENSO, which depends on the calendar season. Consequently, a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times, thereby leading to arbitrary fluctuations in the predicted time series. To overcome this problem and account for ENSO seasonality, we developed an all-season convolutional neural network (A_CNN) model. The correlation skill of the ENSO index was particularly improved for forecasts of the boreal spring, which is the most challenging season to predict. Moreover, activation map values indicated a clear time evolution with increasing forecast lead time. The study findings reveal the comprehensive role of various climate precursors of ENSO events that act differently over time, thus indicating the potential of the A_CNN model as a diagnostic tool.

The purpose of this study is to analyze the adequacy of production capacity of the assembly process system of mobile bath vehicle’s top box panel and process design through a simulation analysis. Towards this end, the layout of the facility designed with pre-verification job using a simulation modeling and an experiment, and facility, logistics process, and personnel input method were made into a simulation model, and the design system’s adequacy was evaluated through an experiment. To produce 120 mobile bath vehicles annually, it was analyzed that 14 general workers and seven skilled workers were adequate through the experiment. It was also identified that three painting process lines carried out through outsourcing were adequate. Production lead time was 201.7 hours on average and it was 230 hours maximum. To meet customer delivery service level of 95% within the deadline when establishing a customer order and vehicle delivery plan, it was analyzed that more than 215 hours of lead time is needed minimum. If the process cycle time is reduced to 85% upon system stabilization and skillfulness improvement, it was analyzed that annual output of 147 vehicles can be achieved without additional production line expansion.

During the shift from gasoline vehicles to electric ones, auto parts manufacturing companies have realized the importance of improvement in the manufacturing process that does not require any layout changes nor extra investments, while maintaining their current production rate. Due to these reasons, for the auto part manufacturing company, I-company, this study has developed the simulation model of the PUSH system to conduct a process analysis in terms of production rate, WIP level, and logistics work’s utilization rate. In addition, this study compares the PUSH system with other three manufacturing systems -KANBAN, DBR, and CONWIP- to compare the performance of these production systems, while satisfying the company’s target production rate. With respect to lead-time, the simulation results show that the improvement of 77.90% for the KANBAN system, 40.39% for the CONWIP system, and 69.81% for the DBR system compared to the PUSH system. In addition, with respect to WIP level, the experimental results demonstrate that the improvement of 77.91% for the KANBAN system, 40.41% for the CONWIP system, and 69.82% for the DBR system compared to the PUSH system. Since the KANBAN system has the largest impacts on the reduction of the lead-time and WIP level compared to other production systems, this study recommends the KANBAN system as the proper manufacturing system of the target company. This study also shows that the proper size of moving units is four and the priority allocation of bottleneck process methods improves the target company’s WIP and lead-time. Based on the results of this study, the adoption of the KANBAN system will significantly improve the production process of the target company in terms of lead-time and WIP level.

Medicines must comply with quality, safety, and efficacy pillars. Nowadays, organizations seek to incorporate new management models encouraged by quality program following the world trend regarding the technological revolution. The present research aims to improve the sterile pharmaceutical product batches release process, using the Failure Mode Effects Analysis (FMEA) method. This study addresses the gap in literature on quality risk management during batch release. The methodology uses a form adapted to the process in order to systematize the information, improving its comparison and analysis, thus estimating, the identification of potential failure modes and their effects on their performance. Made it possible to assign values for the severity, occurrence, and failure modes detection, to then determine the risk level and the priority of risk level. The results obtained showed the mitigation and elimination of failures in the process, as well as opportunities for improvement and causes of failures identification, improvement in the process performance indicators, greater reliability, and reduction in batch release time. Keywords: good manufacturing practices pharmaceutical industry, risk management, risk management tools

São considerados serviços públicos aqueles que estão diretamente relacionados às necessidades da população, e se enquadram neste quesito atividades como capina, varrição, limpeza de terrenos públicos, remoção de móveis e eletrodomésticos inutilizados, coleta de resíduos, entre outros. Para melhor atendê-las, o modelo gerencial de administração pública prioriza a eficiência, agilidade, transparência, otimização de recursos e possui foco nos resultados, critérios estes nem sempre atendidos na prática e poucas vezes observados pelo cidadão. Tais critérios convergem com os objetivos do Lean Office, uma metodologia que objetiva analisar ambientes administrativos em busca de identificar e reduzir desperdícios como movimentos, espera, processamento e materiais, produzindo mais com menos recursos. Diante disso, o objetivo desta pesquisa consiste em analisar a contribuição desta filosofia de gestão, quando aplicada em uma secretaria municipal responsável pelo recebimento de demandas de limpeza pública, que possui contato direto com o cidadão. Em consequência, a pesquisa investigou o processo administrativo de solicitação de serviços deste órgão, com o intuito de identificar desperdícios e sua ocorrência, de modo a propor potenciais melhorias, que poderão contribuir agregando agilidade, eficiência e maior satisfação dos usuários. A metodologia da pesquisa consistiu em um estudo de caso, aliado ao levantamento bibliográfico, à observação sistemática planejada, à aplicação da metodologia do Lean Office e o auxílio da Modelagem de Processos, que juntos possibilitaram a realização da modelagem AS IS do mesmo, favorecendo o apontamento dos desperdícios existentes, a classificação das atividades quanto a frequência e agregação de valor e o cálculo do Lead Time. Essa análise favoreceu o mapeamento TO BE, agregando propostas com potencial de melhorias no tempo de execução e de resposta através da eliminação de cinco atividades e do ponto de decisão considerado o gargalo do processo. Os resultados obtidos demonstraram um grande acúmulo de desperdícios e pontos passíveis a aperfeiçoamentos, dentre estes os mais recorrentes foram os estratégicos, de estrutura e de processamento. Em caso de propostas de melhoria implementadas, potencialmente a secretaria ganhará em qualidade e confiabilidade das informações, facilidade de comunicação com o cidadão e com a empresa terceirizada, bem como irá dispor de um atendimento mais efetivo e rápido, culminando no atendimento das demandas do usuário e sua satisfação.

In this letter, we propose a novel approach to produce attenuation forecasts for microwave links using a probabilistic approach. It uses ensembles of forecast rainfall fields to easily derive attenuation forecasts for specific frequencies. The proposed approach uses the short-term ensemble prediction system (STEPS) to generate ensembles of high, spatial and temporal, resolution forecast rainfall fields based on observed weather radar fields with lead times of 15–90 min. Attenuation forecasts could eventually be used by telecommunication operators to drive the operation of wireless networks and ensure their maintenance during severe and extreme rainfall events. This study used 109 microwave links ranging from 15 to 40 GHz to verify the results of this probabilistic attenuation forecast. Results suggest that the STEPS-based attenuation forecasts were within the narrow span of the 90% confidence region for all microwave links tested up to 30 min lead time, decreasing for longer lead times. Examples of how the proposed approach can be used to derive a detailed probabilistic attenuation forecast for multiple lead times within a domain of few kilometers, as well as probability of attenuation maps for large areas are shown.

The business of buying and selling used cars is increasingly substantial in Indonesia, in the era of globalization progressively developing depends on demands a process that must be fast, precise, and economical. Repair to body, chassis, and engine are competency expertise in the field of Automotive Engineering that emphasizes automotive repair service skills. at now selling price of used cars, it is necessary to do repairs, the process of repairing used cars or better known as refurbishment work pre-owned cars is mostly done by small-scale used car buying and selling businesses. In order for used cars to return to their like-new condition, generally small-scale used car sellers carry out the process of repairing the vehicle themselves, especially repairs to the vehicle's body paint. This study aims to process of painting a Toyota Avanza car bodypaint against the thickness of the paint using a spray booth tool by using the model of central composite design (CCD). The effect of spray-on booth temperature, interstice size of the spray gun, and time according to the thickness of the paint will be analyzed using the CCD method. This spray booth painting technology is expected to help reduce bodypaint lead time. The results of the ANOVA (Analysis of Variance) regression analysis, the temperature of the spray booth, interstice size of the spray gun, and time are the factors that most significantly affect the thickness of the paint. The operating conditions to produce optimal paint thickness are temperature 55oC, interstice size of spray gun of 1.7 mm and time of painting and drying of 30 minutes, the resulted of a thickness of the paint for used and new cars are 130.2 μm and 81.84 μm., with a coefficient of determination for used and new cars of 90.78% and 96.19%.

Cast iron and steel weldments are the most common machine tool base elements. However, both construction methods have associated disadvantages for domestic machine tool manufacturers. This paper documents the investigation of an alternative method for machine tool base production using concrete to fill an additively manufactured polymer mold, where the motion components are attached to the concrete base after the initial concrete curing. Modal testing results for a three-axis, vertical spindle prototype indicate high damping and stiffness can be achieved using the concrete base construction. Advantages are reduced cost and lead time compared to traditional methods.

This study investigates the effects of inventory management on customers` satisfaction with lead time as a moderator variable in government-owned hospitals in Delta State, Nigeria. It aims to contribute to the extant literature on inventory management and customer satisfaction in developing countries, focusing on Delta State in Nigeria. Two hundred and sixty-five (265) questionnaires were distributed, comprising one hundred and five to measure inventory management variables administered among Medical Doctors, Nurses, Pharmacists, and Medical Laboratory Scientist. Similarly, one hundred and sixty questionnaires designed to measure customer satisfaction were administered to Patients. The study adopts multiple regression and structural equation modeling to analyze the data. Also, studying the impact of inventory management on customers` satisfaction some marketing analysis approaches were used. The obtained results support the appropriateness of the model as lead time possesses the qualities of a moderator between strategic supplier partnership, lean inventory, and information technology that are proxies of inventory management and customer satisfaction. Besides, the results record a positive and statistically significant relationship between strategic supplier partnership, lean inventory, and customer satisfaction at a 5 percent level of significance, respectively.In addition, lead time has a positive and statistically significant relationship with customer satisfaction. Impliedly, this study concludes that inventory management proxies and lead time drive customer satisfaction. Thus, the government is recommended to focus on the lead time to avert the dearth of basic inventories in the hospitals.

Graphical abstract Pharmaceutical production remains one of the last industries that predominantly uses batch processes, which are inefficient and can cause drug shortages due to the long lead times or quality defects. Consequently, pharmaceutical companies are transitioning away from outdated batch lines, in large part motivated by the many advantages of continuous manufacturing (e.g., low cost, quality assurance, shortened lead time). As chemical reactions are fundamental to any drug production process, the selection of reactor and its design are critical to enhanced performance such as improved selectivity and yield. In this article, relevant theories, and models, as well as their required input data are summarized to assist the reader in these tasks, focusing on continuous reactions. Selected examples that describe the application of plug flow reactors (PFRs) and continuous-stirred tank reactors (CSTRs)-in-series within the pharmaceutical industry are provided. Process analytical technologies (PATs), which are important tools that provide real-time in-line continuous monitoring of reactions, are recommended to be considered during the reactor design process (e.g., port design for the PAT probe). Finally, other important points, such as density change caused by thermal expansion or solid precipitation, clogging/fouling, and scaling-up, are discussed.

Urban flood inundation is worsening as the number of short-duration rainstorms increases, and it is difficult to accurately predict urban flood inundation over a long lead time; however, the traditional hydrodynamic-based urban flood models still have difficulty realizing real-time prediction. This study establishes a rapid forecasting model of urban flood inundation based on machine learning (ML) algorithms and a hydrodynamic-based urban flood model. The ML model is obtained by training the simulation results of the hydrodynamic model and rainfall characteristic parameters. Part of Fengxi New Town, China, was used to validate the forecasting model. A comparison of ML predictions and hydrodynamic model simulations shows that when using one ML algorithm (random forest (RF) or K-nearest neighbor (KNN)) for inundation prediction, the accuracy of the inundation water volume and area is insufficient, with a maximum error of 28.56%. Combining the RF and KNN models can effectively improve the prediction accuracy and overall stability, the mean relative errors of the inundation area and depth are less than 5%, and the mean relative errors of the inundation volume can control within 10%. The simulated time of a single rainfall event can be controlled within 20 s, which can provide sufficient lead time for emergency decision-making, thereby helping decision-makers to take more appropriate measures against inundation.

Advanced and accurate forecasting of COVID‐19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non‐linear problems. In the present study, the relationship between weather factor and COVID‐19 cases was assessed, and also developed a forecasting model using long short‐term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID‐19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID‐19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short‐term (1 day lead) forecast of COVID‐19 cases (relative error

Company YS participates in managing the natural gas network by creating a Metering Regulating System (MRS), which is a tool to measure gas usage and damage to the natural gas network. Based on the data obtained, the MRS delivery process was often not in accordance with the agreed schedule, there was a delay of up to 46%, namely 50 days late from the planned schedule. To overcome these delays, waste reduction is carried out by mapping the overall condition of the company in Value Stream Mapping (VSM) and mapping in detail with the Value Stream Mapping Analysis Tools (VALSAT). In the VSM method, a Current Value Stream Mapping is carried out which then identifies the waste in which the correlation score between the wastes is described in a matrix called the Waste Relationship Matrix then followed by the calculation of the Waste Assessment Questionnaire. Then in the VALSAT method, further waste identification was carried out using seven tools. The most influential percentage of waste that has been obtained is then searched for the root cause using a fishbone diagram and then eliminated and depicted in the Future Value Stream Mapping. The most influential waste in the fabrication process is Waiting and after repairs according to the recommendations, the lead time is obtained from 41,822.60 minutes or 99 working days to 35,055.60 minutes or 83 working days so that the fabrication process can be completed 3 days faster than scheduled.

Companies in manufacturing often find strategies to increase production efficiency and quality to be competitive in the long run. These strategies make companies remain profitable in a highly competitive market. Nonetheless, attempting to maintain a shorter production lead time is also vital as efficiency becomes a competitive priority. Whenever there are longer lead times, overtime is taken into account to meet the target. Overtime can be the most cost-effective way for companies to achieve their quality needs. Nevertheless, if poorly managed, overtime could quickly outstrip financial gains. This study aimed to establish the manufacturing industry model of non-value-added overtime (NAO) and formulate NAO equations. In this regard, the NAO equations were acquired from the critical factors of NAO. The vital aspects of NAO were then presented through the activities flow in the input/output manufacturing concept. The study results indicated that the highest critical factors contributed to the three processes: pre-process, in-process, and post-process.

Manufacturing sector has traditionally employed the concepts of lean manufacturing for production optimization and waste elimination. However, the looming danger of climate change demands responsible production and consumption; oftentimes the environmental aspects have been ignored for economic gains. The fourth industrial revolution brought about a paradigm shift in the manufacturing landscape, yet there is dearth of approaches that combine the concepts of lean manufacturing, environmentally conscious manufacturing and Industry 4.0 for improving the production and ecological performance of manufacturing sector. This study has fulfilled this research gap by developing a comprehensive implementation framework that combines lean manufacturing, green manufacturing and Industry 4.0 in a harmonious manner. Using a longitudinal case study approach, the developed framework has been applied to an auto-parts manufacturing firm to achieve reduced lead time, value added time, non-value-added time and greenhouse gases (GHG) emissions. The results revealed that the approaches of first-in-first out (FIFO), total productive maintenance (TPM), smart production control, cyberphysical systems, 6R and smart energy monitoring are extremely beneficial in achieving optimized and cleaner production, where the lead time, non-value added time and value added time has been reduced by 25.60%, 56.20% and 24.68% respectively, whereas the carbon dioxide, methane and nitrous oxide emissions were reduced by 55%. The theoretical contribution of this research work lies in the development of a novel integration framework that combines the concepts of lean manufacturing and green manufacturing with Industry 4.0 technologies, whereas practitioners can benefit from the synergistic combination to achieve automation-based fast and environmentally conscious manufacturing.