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The increasing penetration of highly intermittent wind generation could seriously jeopardize the operation reliability of power systems and increase the risk of electricity outages. To this end, this paper proposes a novel data-driven method for operation risk assessment of wind-integrated power systems. Firstly, a new approach is presented to model the uncertainty of wind power in lead time. The proposed approach employs k-means clustering and mixture models (MMs) to construct time-dependent probability distributions of wind power. The proposed approach can also capture the complicated statistical features of wind power such as multimodality. Then, a non-sequential Monte Carlo simulation (NSMCS) technique is adopted to evaluate the operation risk indices. To improve the computation performance of NSMCS, a cross-entropy based importance sampling (CE-IS) technique is applied. The CE-IS technique is modified to include the proposed model of wind power. The method is validated on a modified IEEE 24-bus reliability test system (RTS) and a modified IEEE 3-area RTS while employing the historical data of wind generation. The simulation results verify the importance of accurate modeling of short-term uncertainty of wind power for operation risk assessment. Further case studies have been performed to analyze the impact of transmission systems on operation risk indices. The computational performance of the framework is also examined.

The power prediction error of uncertain renewable energy sources (URESs) affects the power balance of a power grid. In the power systems with high proportion renewable power sources (PSHPRPSs), automatic generation control (AGC) cannot accommodate the day-ahead power prediction error. The dispatching control system (DCS) of PSHPRPSs adds real-time dispatching links to modify the day-ahead dispatching plan so that the grid power error is within the range of AGC accommodation. This paper proposes a critical time scale (CTS) selection algorithm based on time aggregation characteristics for real-time dispatching of PSHPRPSs and calculates the annual CTS of real-time dispatching of power grids. The uncertainty function is used to describe the relationship between the prediction error of the URESs and the prediction lead time. The total uncertainty function is calculated based on the time aggregation characteristics and is used to select the annual CTS of real-time dispatching. The proposed algorithm quantitatively describes the relationship between the CTS and the operation proportion of URESs and also AGC accommodation capacity. The calculated annual CTS not only ensures the power balance of the power grid, but also avoids the daily change of the CTS. Using the data of URESs in the Irish power grid, the feasibility of the proposed method was verified. The research results of this paper are helpful to accommodate the day-ahead power prediction error of URESs and maintain the safe operation of power grid.

The inclusion of artificial intelligence in both our daily and business life has created a new alternative to meet many different needs. In this study, the production time of a textile product was estimated by using the data generated from a textile company serving in the field of ready-made clothing. After determining the factors affecting the production time within the company, this paper aim to develop a decision support system with the model that gives the best results among a support vector regression and three ensemble learning methods (Random Forest, Decision Tree Regressor, and Bagging) based on an end-goal to predict the production time. The results show that the bagging and random forest yielded highest R2≥ 0.84 and with a minimal predictive error when compared with other approaches. A demo was prepared for the decision support system that can be used to predict the production time of new orders using the random forest model by developing an interface for users.

The expansion of renewable power generation, the increasing integration of European electricity markets, and the delays in grid expansion result in a high number of congestions in the European transmission grid. As a result, congestion management measures are increasingly planned under uncertainty, which poses challenges for the transmission system operators. In particular, the start-up decisions of power plants with a long lead time are made under great uncertainty. In today’s operational practice, transmission system operators often account for uncertainties and the worst case by adding safety margins to the results of deterministic redispatch simulations based on forecast data sets. This paper investigates the influence of different approaches to consider uncertainties implicitly in redispatch simulations, in order to gain knowledge about the impact of uncertainties in operational processes of transmission system operators. The exemplary results by means of grid reserve power plants show that additional safety margins to a deterministic approach to account for the worst case vary significantly depending on the situation. The probabilistic approach allows a more accurate estimation of the worst case, since it considers the influence of uncertainties on the start-up decision implicitly in the simulation. Finally, the comparison of these results with the stochastic approach shows that the worst case in the probabilistic approach can even underestimate or overestimate the impact of uncertainties and the stochastic approach prevents this.

Accurate and timely prediction of flash flooding events can be a very useful tool for stormwater officials and first responders. Having lead time with which to issue evacuation directives, to close flood prone roadways, to deploy rescue gear and personnel, and to fortify areas against flooding is essential to minimize property damage and risk of casualties. In this poster, we are presenting a flash flooding prediction workflow based on the Hydrology Lab-Research Distributed Hydrologic Model (HL-RDHM). This workflow leverages cloud computing and the Pegasus Workflow Management System to provide continuous high resolution flood predictions for the Dallas-Fort Worth Metroplex area in North Texas, and can be easily expanded to other regions.

The use of electromagnetic unipolar pulses, radiated spontaneously by the effect of enormous pressure in the subduction zone, for the study of the Earth's crust and eventually leading to the forecast of future earthquakes, has recently completed a decade of research at PUCP. At the beginning, with one, then two and followed by up to ten stations deployed in three distinct areas of the Peruvian coast, data gathering and especially related to nearby earthquakes, was a rather slow process. After all these years of reporting our achievements through various papers and presentations while dealing with the scarce data coming from our limited number of magnetometer stations in Peru, we have accumulated enough cases to start a more substantial statistical analysis. Many cases of successful forecast of seismic occurrences, allowed to build up software to automatically estimate the occurrence of small earthquakes in the Lima area with 10 to 18 days lead time and be off by a few hours and magnitudes within 0.1 magnitude. This presentation, with a video complement to illustrate better the process of the “building up” of energy that will end up, in a few weeks' time, in a future earthquake, gives a first idea of the consistency of the method, applied now so daily forecast reports are obtained on a regular basis. Over a time period of almost eight years, the system allows us to get a first glance of the statistics and helps us to better understand the role of electric charges in an early warning system.

Olkaria Wellheads has grappled with premature fracture of 1st stage blades on C50 geothermal steam turbines. On December, 29th 2018, KWG12 failed to progress beyond critical speed owing to high turbine vibrations after the turbine suffered two fractured 1st stage blades. The rotor needed re-blading and balancing. It was considered cost-effective to do in-house repairs using 1st stage blades recovered from a grounded C50 rotor. Repairs entailed static balancing of all 1st stage blade masses, simulation and re-blading. Success was epitomized by timely availing of the plant after 15 days outage, at 5000kW rated capacity and normal vibrations of 30µm. The initiative has kept the plant operational as new blades are procured, besides cushioning the plant from incurring downtime losses arising from long procurement lead time. Whereas the 15 days repairs incurred KShs. 16.9 million in direct and indirect costs, the plant accrues over KShs. 17.4 million monthly in revenue.

Convective storm nowcasting has long been an important issue and has attracted substantial interest. 3D radar images and 3D re-analysis data contain spatiotemporal information of the convective processes. This paper proposes a multi-channel 3D convolutional recurrent neural network (3D-CRN) for convective storm nowcasting, which aims to learn spatiotemporal information directly from these 3D radar and reanalysis data. 3D-CRN is composed of two sub-networks: three multi-channel 3D convolutional networks are used as the front-end spatial sub-networks, and the convLSTM encoder-decoder is constructed as a back-end temporal sub-network. By combing two subnets into one unified network, 3D-CRN can be jointly trained effectively. In order to give forecasts at different lead time simultaneously, we construct a many-to-many encoder-decoder structure to avoid tedious need to train several models respectively. Experimental results show the effectiveness of the proposed method.

Nowcasting of precipitation associated with landfalling hurricanes refers to the hurricane intensity analysis and forecast for the next few hours. It plays an important role in early weather warning system and real-time decision making for disaster management and risk reduction. However, the skill of traditional nowcasting techniques decreases rapidly in the first hour. In addition, numerical weather prediction (NWP) models have deficiency to provide accurate warning of heavy precipitation on the scale needed for hurricane nowcasting. Therefore, this paper proposes a deep learning-based system for precipitation nowcasting for landfalling hurricanes using multi-radar observations near the coast. The reflectivity imageries from previous 1-hr observations are used as input to the deep learning-based nowcasting model and the actual observations from “future” sequences are used as targets in the training stage. The performance and evaluation results of the nowcasting products with lead time up to 3 hours show that this deep learning system performs well in hurricane nowcasting.

Supply chains are of vital importance for all companies dealing with production and distribution of products. Major dealers and distributors of automobile manufacturers must manage their supply chain systems to meet the demand on time and minimize costs. In this study, we have considered a large automobile dealer and distributor company in the middle east. The company is selling luxurious cars in six countries in the middle east imported from Germany and the UK. One of the major problems they face is not being able to meet the demand on time due to storage limitations and delays in shipments received from the manufacturers. In many cases customers are lost and excess costs are incurred. The objective was to determine the numbers and locations of new storage and distribution centers so that enough quantity of automobiles was stored and distributed in the middle east to meet the customer demand during lead time. After analyzing the system and collecting all the necessary data, a linear programming model was formulated to optimize the locations, capacity, and the number of storages at minimum cost for the company’s supply chain system. The model proved to be very useful in optimizing the system.

To predict the future weather condition, the probability that the weather on the day of consideration will be least same as the previous day forecast but the chances of it becoming similar in the next two weeks are high. So, processing the weather data of two weeks from the last year slide window is required to choose a size equal to a week. Every quick window week coincides with the current year. Furthermore, the prediction is done based on a window algorithm slide. The results of the method suggest that, the utilization of proposed method to forecast the weather is effective with an average accuracy of 94.2%. Whereas, the radar remote-sensing arena is one of the most exciting and creative future technological enhancements for PWS. Also, the next-generation radar systems (dual-polarization radar, phased-array radar) will enhance the extreme weather detection, rainfall forecasts, and winter weather warnings, and at the same time it will improve the lead time for severe weather threats including tornadoes and heavy rain/flash flood events.

Identifying drivers' perception response time (PRT) is of utmost importance for the development of rear-end collision alarm systems. However, the effects of cognitive distraction on PRT under different levels of situational urgency are unclear. Therefore, the purpose of this study was to quantify and compare the effects of cognitive distraction for both high and low situational urgency. Participants (N = 45) were presented with a simulated car-follow scenario. The effects on both perception time and movement time were analyzed separately under headways of 1.5s and 2.5s using the Bayes factor approach, and a mixed-effects model was constructed to calculate the magnitude and significance of effects of cognitive distraction and situational urgency on PRT. The results revealed (1) the effect on perception time was smaller in the high situational urgency condition, and a high probability of distraction-related delay on perception time was found in both high (BF10 = 15.588) and low (BF10 = 23.203) situational urgency conditions; (2) the effect on movement time was larger in the high situational urgency condition, and the delay of movement time was more likely to occur in the high (BF10 = 19.642) situational urgency condition than in the low (BF10 = 0.493) situational urgency condition; (3) cognitive distraction increased driver's PRT by 1.556s, the average of drivers' PRT decreased by 0.241s for every 1s reduction in initial time headway. The results are beneficial for designing the lead time and the frequency of warning or intervention in rear-end collision alarm systems.

Production HPC clusters endure failures reducing computational capability and resource availability. Despite the presence of various failure prediction schemes for large-scale computing systems, a comprehensive understanding of how nodes fail considering various components and layers of the system is required for sustained resilience. This work performs a holistic diagnosis of node failures using a measurement-driven approach on contemporary system logs that can help vendors and system administrators support exascale resilience.Our work shows that external environmental influence is not strongly correlated with node failures in terms of the root cause. Though hardware and software faults trigger failures, the underlying root cause often lies in the application malfunctioning causing the system to fail. Furthermore, lead time enhancements are feasible for nodes showing fail slow characteristics. This study excavates such helpful empirical observations, which could facilitate better failure handling in production systems.

Modern Code Review (MCR) is the process of reviewing new code changes that need to be merged with an existing codebase. As a developer, one may receive many code review requests every day, i.e., the review requests need to be prioritised. Manually prioritising review requests is a challenging and time-consuming process. To address the above problem, we conductedan industrial case study at Ericsson aiming at developing a toolcalled Pineapple, which uses a Bayesian Network to prioritise code review requests. To validate our approach/tool, we deployed it in a live software development project at Ericsson, wherein more than 150 developers develop a telecommunication product.We focused on evaluating the predictive performance, feasibility, and usefulness of our approach. The results indicate that Pineapple has competent predictive performance (RMSE = 0.21 and MAE = 0.15). Furthermore, around 82.6% of Pineapple’s users believe the tool can support code review request prioritisation by providing reliable results, and around 56.5% of the users believe it helps reducing code review lead time. As future work, we plan to evaluate Pineapple’s predictive performance, usefulness, and feasibility through a longitudinal investigation. Index Terms—Modern Code Review, Prioritisation, open access

Due to the Novel Coronavirus (COVID-19) Pandemic, the demand for alcohol sanitizer as alcohol gel and alcohol liquid spray has been increasing rapidly. To facilitate its production, the Ministry of Public Health revised the regulation such that alcohol sanitizers can be produced by cosmetic manufacturers where all products must be formulated with Isopropanol or Ethyl Alcohol not lower than 70% V/V. This paper focuses on a cosmetics manufacturer whose 90% of revenue comes from facial skincare. With the new government regulation and the company's background on agility and flexibility in meeting market demands, the company is taking the opportunity to meet the demand for alcohol cosmetics products. Recently, it launched two standard formulations of alcohol sanitizers for private labeling. The objectives of this study are to identify the business processes and related activities in the sanitizer supply chain; to analyze problems, and to provide recommendations for improvement to be able to cope with supply chain disruption and demand spike. An in-depth interview of the owner and staff was conducted, and the sanitizer supply chain was analyzed using Integration Definition for Function Modeling (IDEF0). The results showed major problems from suppliers and brand owners. For suppliers, the lead time is very long, a 100% payment is required, and miscommunications happened. On the other hand, brand owners have very urgent orders due to unanticipated demand from the market. Solutions are suggested in this paper to address the issues on the cosmetic manufacturer's sanitizer supply chain and could serve as a guide for other cosmetics manufacturers.

In today’s competitive word, the automation of material handling, the manufacturing processes have replaced the mundane work in the various industries where the mass production of the part has taken place. The plastic product manufacturing is one of the mass production industry. A flow-packing machine is an essential equipment used for packing the final goods. It facilitates the material handling of bulk and unit material. Automation of flow-packing machine reduces labor requirement, lead time, workplace injuries, and by so allows maximum possible production with best quality. In this paper an automatic feeding mechanism is designed and fabricated for manually operated flow-packing machine. Before automation of the feeding mechanism, parts were placed on the conveyor manually. This results in a large waste of products and a rise possibility of workplace injury. Design of an automatic feeding mechanism overcome these problems. The proposed design is validated in real life scenario.

Technical debt (TD) is a metaphor that reflects the technical compromises that sacrifice the long-term health of a software product to achieve short term benefit. It is important to manage TD to avoid software degradation. In large-scale distributed projects, technical debt management (TDM) becomes more complex and challenging. There is a lack of empirical studies on the TD accumulation in large-scale distributed projects. Then, to address this gap, we conducted a case study in Ericsson (a European Telecom Company) to identify the relationship between TD accumulation and factors such as task complexity, lead time, total of developers, and task scaling. We used data from 33 projects extracted from managerial documents to conduct a regression analysis. We also conducted interviews with seniors developers of the team to interpret the results. We found out that Task Complexity has a strong relationship (p-value = 5.69 × 10-5) with Technical Debt, while Global Distance was mentioned by the interviewees as a relevant factor for TD accumulation (although not statistically significant in our regression analysis). Practitioners should consider avoiding complex/big tasks, breaking down big tasks into small ones (if possible). We also plan to analyze other projects in this company to confirm our findings further. © 2021 IEEE.

Artificial intelligence (AI) approaches have been used successfully in many recent studies showing their high capability of recognizing underlying pattern in complex processes. In this study, we aimed at the use of various AI approaches to model wind approach which may result in improving the stability and reliability of wind power systems. To this end, we investigate K-Nearest Neighbor (KNN), Support Vector Regression (SVR), Bagging Regression (BR), and Long Short Time Memory (LSTM) techniques in order to model and forecast wind speed (WS) at Basel weather station over the lead time of 3 hours. The forecasting accuracy obtained by the techniques was compared in both training and testing data sets. The results showed the LSTM is slightly superior to its counterparts in our case study.

Optimal order quantity at each period is an important issue in supply chain management. To avoid backorder or inventory holding costs, inventory models play key role in finding the optimal values at every time period. However, the inventory models fail to obtain the optimal solution when the sequence of ordering and receiving is different, which is called order crossover. Many factors bring to the order crossover problem. In this paper, we formulate a dynamic programming model for order crossover problem with lead time variation and propose a solution approach. The model provides the strategy to find the optimal solution when occurring the order crossover.

Background: Technical Debt is a consolidated notion in software engineering research and practice. However, the estimation of its impact (interest of the debt) is still imprecise and requires heavy empirical and experimental inquiry. Objective: We aim at developing a data-driven approach to calculate the interest of Technical Debt in terms of delays in resolving affected tasks.Method: We conducted a case study to estimate the Technical Debt interest by analyzing its association with the lead time variation of resolving related Jira issues.Results: Data-driven approaches could significantly change the Technical Debt estimation and improve the removing Technical Debt prioritization. Our case study shows that the presence of Code Technical Debt did not affect the lead time for resolving the issues.Conclusion: Future works include the further refinement of this approach and its application to a larger data-set and on different type of issues.

Nowadays, Hong Kong International Airport faces the issues of saturation and overload. The difficulties of selecting taxiways and reducing the lead time at the runway holding position are the severe consequences that appeared from increasing the number of passengers and increased cargo movement to Hong Kong International Airport but without constructing a new runway. This paper is primarily about predicting flight delays by using machine learning methodologies. The prediction results of several machine learning approaches are compared and analyzed thoroughly by using real data from the Hong Kong International Airport. The findings and recommendations from this paper are valuable to the aviation and insurance industries. Better planning of the airport system can be established through predicting flight delays.

Healthcare providers have begun providing care to patients via remote appointments using web-based, synchronous video visits. As this appointment modality becomes increasingly prevalent, decision-makers must consider how to incorporate patient preference for an in-person versus virtual care modality when scheduling future visits. We present a discrete-event simulation that models several potential policies that these decision-makers could use to schedule patients, and demonstrate this simulation in the clinical context of patients with gastroesophageal reflux disease. This simulation provides key metrics for decision-makers, including provider utilization, patient lead time, and proportion of appointments that satisfy patients’ preferences for appointment modality.

Space planning and management, as an important part of facility management in the hospital context, is closely related to patient flow and patient behavior. The lack of consideration of such interdependencies would complicate movement patterns of end-users during consultation and treatment process, and as a result, reduce the hospital operational efficiency. To facilitate better space planning and management in hospitals, this study integrates discrete-event simulation and agent-based simulation to examine and evaluate different layout designs. The constructed simulation models take into account the patient flow and patient behavior. At the same time, on-site surveying and monitoring data as well as realistic medical information are used as inputs for the simulation model. Simulation results including patient lead time and facility utilization were used for hospital layout selection. This research provides a new approach to layout design selection and contributes to more effective and efficient space planning and management in healthcare facility.

Faced with global competition and the great extent of market requirements, particularly in the automotive sector, companies are using Lean Manufacturing tools to meet customer requirements in terms of quality, cost and lead time. Companies are prioritizing the Technical aspect, which aims to reduce waste, produce better, faster and at lower cost. On the other hand, there are many companies that neglect the social and organizational side, which has a negative impact on safety, ergonomics and employee motivation. This document provides an overview of the conflicts between technical and social/organizational objectives, as well as an evaluation approach that values the social/organizational side to the industry. In addition, this article presents a sum up of existing information on Lean Management in the literature.

In continuous production enterprises, spare parts are the guarantee for the production and operation. However, the distribution of spare parts demand is uncertain due to the nature of spare parts demand, which is usually intermittent, slow-moving and erratic. This paper proposes a spare parts scheme based on probability distribution by combining spare parts distributional assumptions with spare parts classification, and uses the Kolmogorov-Smirnov (K-S) goodness-of-fit test to verify the validity of the scheme. After that, on the basis of classification and adopting the order-up-to-level policy, a cost-oriented inventory model with random lead time was constructed for spare parts based on different compound Poisson distributions. And through examples, the effectiveness of the spare parts classification scheme based on probability distribution in reducing costs is further verified.

The industrial sections have always been thirsty to predict events and be aware of future sudden changes. Where using time-series forecasting based on deep learning techniques can have a significant effect on addressing this issue. In this paper, we present Stacked Long Short-Term Memory (LSTM) networks to predict significant electric consumption changes when a group of devices is activated or deactivated randomly. Three Power quadratic phases are used as a dataset. The goal is to predict multi-steps in the future minutes. The supervised learning technique is used to transform series data to supervised data for training and prediction, and then the Walk-forward cross-validation is implemented to evaluate subsequent time steps prediction and output is a vector of prediction. Finally, the root-mean-square error (RMSE) method is employed as a metric for each lead time on an output vector. Furthermore, we compared our proposed model with the Vanilla and Bidirectional LSTMs architecture, in which the performance of our method shows that it was successful. Our experimental results support the view that enough data is crucial to event detection in time series data and suggest that Stacked LSTM is a good architecture with which to address it.

With the rapid development of aviation enterprises, the logistics system urgently needs to be improved through digitalization and intelligence. Most of the researches for logistics system focus on the buffer area or the second-level logistics system, which are insufficient to meet the multi-echelon inventory requirements in aviation enterprises. This paper presents a new method through the multi-echelon network model to solve the problems of inaccurate inventory and waste of resources in the logistics system. First, the optimization function is established considering the maximum inventory capacity, the safety stock and the replenishment lead time. Second, the genetic algorithm is used to minimize the cost and maximize the service level. At last, the feasibility of the proposed method is demonstrated through the numerical studies.

The resumption of work and production helps to avoid the short-term suspension of economic development caused by COVID-19 virus epidemic from becoming long-term economic depression. However, during the COVID-19 virus epidemic, the resumption of work may cause infection. Facing the uncertainty of external environment, the pressure of capital turnover and lead time of orders, the resumption of work and production is a severe problem. To judge the schedule of resumption, we analyze the electricity usage of enterprises in Shaoxing, select 200 enterprises in 4 industries, including heavy industries, general industry and commerce, agricultural production industries, agricultural irrigation and drainage industries. Using the electricity usage before and after the epidemic, we use STL decomposition and Mann Kendall trend test to intelligently judge the situation of resumption. At the same time, combined with the date of suspension and resumption of work, we determine the resumption threshold of enterprises and plot the resumption rate of industries. From the threshold and the plots, we can analyze the schedule of resumption one month before and after the Spring Festival to support the effect of epidemic on different industries.

Fall prediction for humanoid robots is a challenging problem. Increasing interest in the introduction of biped humanoids for diverse applications, e.g., search and rescue in a disaster scenario, necessitates endowing them with robust ways of handling environmental disturbances. We begin the paper with a brief review of previously proposed methods to predict an impending fall event for a biped perturbed by an unknown external force. Next, to improve on prior works, we consider a bidirectional long short-term memory (BLSTM) network, which makes use of historical measurements of system states as inputs, to effectively predict fall probability in real time. Through extensive simulation experiments, which utilize external forces with random magnitudes, directions, locations, and times of application, we demonstrate that the proposed BLSTM network can robustly predict fall events. In contrast to learning-based methods in prior literature, the technique of this paper is shown to reduce false positive rate (FPR) for fall prediction while achieving nearly the same level of lead time in predicting an approaching fall. To perform the sensitive tradeoff between the FPR and lead time, a tuning parameter α is included in our training process to permit a user control over the resulting prediction model. With these contributions, a biped can take advantage of the proposed BLSTM prediction model and tailor its effectiveness to the task at hand.

This paper presents a multi-stage optimal power flow (OPF) approach for efficient management of contingencies, in which dynamic thermal models of overhead lines and power transformers are used to evaluate maximum lead time (MLT) available to network operators to resolve violated post-contingency constraints. At the first stage, optimal settings of volt-var controls, such as transformers with on-load tap changers and shunt capacitors, are selected, taking into account voltage-dependent load models. The second stage provides optimal generation re-dispatch, including fast-start generators. At the final third stage, optimal load shedding is implemented to clear all remaining constraint violations. The OPF problem is solved by a hybrid metaheuristics method and illustrated on a modified IEEE 14-bus network. Obtained results demonstrate that presented method can help network operators to efficiently resolve all violated constraints within the available MLT.

This study presents proposed solutions for increasing the productivity of a production line in the perfumes industry in Egypt using lean manufacturing methodology. Enhancing efficiency is a major significant objective to consider in a typical manufacturing firm to improve the overall performance. Increasing productivity is achieved through applying an extensive lean program implementing appropriate lean tools to solve problems identified as wastage in materials and activities as well as bottlenecks increasing lead time. Information of current problems and gaps are gathered through visits and interviews. Problems are showed and analyzed using some lean tools and charts as bottleneck analysis, workflow sequence and fishbone diagrams. Lean methodology is selected to be applied due to its ability to achieve desired results, solve current gaps and maintain outstanding performance and continuous improvement enabling competitiveness within marketplace. Proposed lean tools and the fully lean manufacturing system are presented to increase efficiency and solve problems identified. Expected results showed decreased inventories by 20-30% as well as reduction in costs by 10-20%.

This paper aims to study how the agility concept is successfully applied in manufacturing facilities. The study is involved in selecting the industry and the machines for the novel Microfactory established at Nile Preneurs, Nile University, with focus on the sheet metal workshop located in the Microfactory. The recommendations of this work include the most suitable layout, scheduling technique according to the manufacturing lead time, utilization, work in process, tool change over and distance travelled by operators. Flexsim simulation software is used to determine the optimum combination of layout along with the most efficient scheduling technique.

The process mining allows to automatically discover process models from event logs. Those models provide information that allow to make many types of analysis based on models, such as: compliance verification, performance analysis and bottlenecks identification on the business process. The bottlenecks identification is highly relevant for process managers to suggest improvements. Here, the improvement which is interesting is the lead-time reduction. A double hypothesis was made. The first is that lead-time reduction goes through the identification and removal of bottlenecks at the business process. The second is that the focused intervention at the critical path allows to reduce lead-time causing small changes on the current process. The process mining allows, by using model process discovery, to obtain basic statistic information of a business process, as: minimal, maximum and average duration of each activity. The technique PERT/CPM allows to calculate for each activity the weighted average of the expected execution and the critical path. This helps to identify the relevant activity focusing on the bottlenecks of a business process. The experiments show that the method allows to reduce the lead-time of a process and indicate the degree of the resource's commitment in each of the method iterations, in terms of similarity among optimized model and original model. The experiments were conducted over real word "Hospital Billing" and "Sepsis cases" databases.

The role of IoT and related internet-based applications in otherwise mechanical devices to monitor, manage and enhance the performance of the same is quite widespread now. Almost all public cloud service providers provide scalable, fully managed and elastic IoT related services. The data flows from these services are essentially streaming and can be consumed for further use in various predictive, descriptive and visualization modules. The cloud platforms enable ingestion, transformation and usage of the data by providing streaming, machine learning and sharable visualization services. This ecosystem greatly reduces the time to create IoT based minimum viable product creation which in turn enhances the business value realization cycle. The effect of cycle time reduction to design, architect and develop IoT solutions leads to a rapid improvement of business lead time and makes it easier for businesses to gain from the data insights and plan the next course of action. In this paper, one such enterprise graded use case is explored, in which the Azure IoT platform in terms of the offerings and associated ecosystem of Azure Stream Analytics and Azure Machine learning services are explained. This paper covers design, architecture, development and deployment of the solution prepared and how the same is monitored once in production. Security is a very important aspect of the same and here the security architecture is being explored. A conclusion is presented with the scope of future enhancements using auto ML services in serverless platforms to enable real-time automated decision making augmented with human expertise and intelligence.

In modern homes, energy consumption accounts for most of the economic aspects and environmental sustainability. Intelligent energy management and its control play an important role in energy supply and demand; and it will change behavior and environmental changes. For energy management and its control, a hybrid Internet of Things (IoT) and personal wireless network-based devices have been developed. In terms of the need-side-management approach, the use of energy can be intelligently controlled by the device for greater durability. In this study, electricity consumption and utilization are categorized accurately based on data collected from consumer behavior in energy consumption and utilization. First, the data cut through the device is used to identify and summarize the power consumption patterns hidden in the data. Second, the different linear mode algorithms extracted from the Schick-Lear Python library will be used for energy consumption and its intelligent power control. By analyzing different algorithms, the predictive score is found to be sufficiently efficient for the recurrence prediction, while the multi-step and lead-time technique proved to be suitable for multidimensional energy prediction. Results show that root squared mean error (RSME) performance of the predictive model increased by 35% in the lead time approach. Similarly, in per day approach it is 33% more efficient than the recursive model when residual energy forecasting is utilized.

Many service-oriented systems (SoS) operate in uncertain and changing environments. Hence, SoSs should be able to adapt itself during runtime to ensure that they maintain user-expected quality indicators. In real-world environments, some adaptations may have non-negligible latency, and take some lead time to produce their effect. Adapting reactively is an after-the-fact approach, which starts when the system deviates from the expected indicators. It can result in inefficiency and instability due to without anticipating the subsequent adaptation needs. To solve this problem, we propose a novel proactive adaptation solution - LetPa, which makes decisions based on predictions about how adaptations will unfold up to its completion. LetPa divides control parameters into three levels according to the SoS architecture and rates the adaptations considering both goal satisfaction and action penalties. We design a dynamic programming based decision mechanism in LetPa that enables SoS to determine which adaptations need be performed that can prevent and mitigate upcoming problems in the near-future time series. Simulation result implies that LetPa shows good stability and efficiency in SoSs.

This paper reviews the Artificial Intelligence (AI) applications that help achieve water-related Sustainable Development Goals (SDGs). Current applications of AI in the water sector include i) predictive maintenance of water infrastructure, ii) forecasting water demand and consumption, iii) monitoring water reservoirs and dams, iv) tracking water quality, and v) monitoring and predicting water-related disasters. These applications contribute to achieving water-related SDG targets, specifically 3, 6, 11, and 15. The literature review shows that: i) the rate of adoption of AI-based solutions in predictive maintenance of water infrastructure has accelerated, as AI becomes increasingly accessible, and data analytics and smart sensors become more efficient and affordable; ii) deep learning technology has enabled a new generation of water management systems, which can generate short-term (daily) and long-term (annual) forecasts. iii) as Asia and South America experience an increase in water reservoir and dam construction, AI-based techniques are being successfully implemented in reservoir development and operation; iv) water quality monitoring has been the most significantly impacted by AI relative to other applications, as AI is used to examine small samples and large water bodies, and for real time water quality monitoring; v) AI can be used to forecast water-related disasters with higher accuracy, frequency and lead time, allowing for focused management of post-disaster activity. The paper ends by highlighting the challenges of adopting AI to achieve water-related SDGs.

Production facility design is one of the most important factors and affects to the company performance. The production process of production facility layout is incorrect cause the pattern of material flow is incorrect and the materials movement, products, information, equipment and labor becomes high relatively causes delay in the product lead time and increase of production costs. Based on this situation, it is necessary to design a production facility properly so that problems of production activities can be handled. Improvement of facility design is done using the BLOCPLAN algorithm. The BLOCPLAN algorithm is an effective method of production layout problems. The study was conducted the comparison results between the proposed layout using the BLOCPLAN algorithm and with the actual algorithm. The results of the study showed that the reduction of moment displacement after BLOCPLAN algorithm improvement, the moment displacement obtained in the initial layout of 69096101,32 and the moment displacement after improvement of 61862200. This means that there was a decrease in the moment displacement of 7233901.32.

With the emergence of e-commerce, the closed-loop supply chain composed of e-commerce direct sale channel and reverse logistics formed by return and exchange is becoming more and more complex. The lead time of each node in the supply chain increases the uncertainty of the system, making it more difficult to restrain bullwhip effect. The model predictive control (MPC) method is used to restrain the bullwhip effect. Firstly, based on the state-space model of the mixed recovery dual-channel closed-loop supply chain with lead time, an augmented state-space model is established through state reorganization to solve network complexity caused by the lead time; Secondly, a structural diagram is constructed in the SIMULINK environment. The prediction model, the state estimation, and the objective function are presented. Thirdly, the KWIK quadratic programming method is derived, and the solution steps of the method are given. Finally, by simulating with random demand and sinusoidal demand as system input, the effectiveness of the method is verified. The results show that this method can effectively restrain the bullwhip effect and make the production, ordering and inventory curves tend to be smooth.

Cable harness design for aerospace platforms has been, and continues to be, one of the most challenging and difficult areas for platform ElectroMagnetic Compatibility (EMC) integration. Multiple competing platform needs and requirements, all of which are diverse and are often mutually exclusive, drive cable harness design in often wildly oscillatory directions. Decisions on how cable harnesses are to be constructed must be hammered flat very early in the design process, and this leaves little room for debate or research to demonstrate one design is superior to another. Mass, volume, system complexity, change in process, component availability, manufacturing lead time, manufacturability, parallel hardware certification timelines, published documents and drawings impacted by engineering change orders, and of course cost and schedule, all compete in this fast changing and highly critical area. This presentation will attempt to highlight these competing areas to foment awareness.

Public procurement expenditure accounts for 45 to 65 percent of a national budget, often equating to at least 15-20 percent of a country’s gross domestic product and these estimates are even higher for developing countries like Pakistan. Due to weaknesses in current operational procedures of Public Procurement in Pakistan, it is approximately paying between US$ 31 to US$ 62 Billion more annually than it should be for procuring of goods, works and services. The inefficiency in operations of public procurement like long lead time of process results in funds lapse of allocated budget, which simply indicates the inefficient public funds utility. As the research scope is limited to public procurement of goods so the problem of inefficient public funds utilization will amplify in form of long term forecasts, high level of inventories, high costs, high cost and slow response to end user requirements. Since Public funds are scarce in developing country like Pakistan, so to warrant the efficient use of the resources this research work lays the basis for developing a technical and business model for optimizing the operational delay in public procurement process especially the phase of contracting, as in this phase most time is consumed and it’s the most critical of all other phases, especially in public procurement of goods. This will help in developing a model in a software application form, which will act as a decision support system to procurement management to optimize the contracting phase for reduction of wastes in public procurement process, saving millions in government expenditure, act as feedback on future evaluation and positively impact socioeconomic development in Pakistan.

Technology concepts are often focused on the technical aspects and parameters and fail to detail the need and business sense of the said concept. When a concept is envisioned, it stems from the contributor as a spark. However, detailing on three major aspects, namely Desirability (the need from an end user point of view), Feasibility (the approach from an engineer’s point of view) and Viability (the business case from an organization’s point of view) ensures that all major fronts are addressed, transforming an idea (the spark) into a fully mature Technology concept. In order to ensure this transformation, an idea must go through a series of 6 Phases, called the 6E Incubator Method. This method was applied in a satellite R&D Location in India on technological ideas, to create technical concepts with business cases. Using the method, the lead time for Management decisions to implement the concepts were significantly reduced and benchmarked for further application on future ideas.

The automatic parts feeding of multiple objects is an unsolved problem in the manufacturing industry. In this paper, we tackle the problem by proposing a multi-robot system. The system comprises three sub-components which perform bin-picking, regrasping, and kitting. The three subcomponents divide and conquer the automatic multiple parts feeding problem by considering a coarse-to-fine manipulation process. Multiple robot arms are connected in series as a pipeline. The robots are separated into three groups to perform the roles of each sub-component. The accuracy of the state and manipulation are getting higher along with the changes of the sub-components in the pipeline. In the experimental section, the performance of the system is evaluated by using the Mean Picks Per Hour (MPPH) metric and success rate, which are compared to traditional parts feeder and manual labor. The results show that the Mean Picks Per Hour (MPPH) of the proposed system is 351 with eleven various-shaped industrial parts, which is faster than the state-of-the-art robotic bin-picking system. The lead time of the proposed system for new parts is less than that of a traditional parts feeders and/or manual labor.

Longer lead time to setup new equipment for production versus time to react for spike demand, need a fast approach to squeeze existing equipment for more output. This paper presents the success story of the throughput gain for physical wafer deposition (PVD) tool by fixing the hidden capacity loss of the equipment for 200mm Al deposition process through Overall Equipment Effectiveness (OEE). PVD cluster equipment group has been forecasted to be number one bottleneck equipment due to new product mixed change and increase in volume. The capacity element for PVD equipment was setup based on its historical performance and performs accordingly. The new requirement needed additional 10 percent outputs and currently is not enough. The team has taken the challenges to beat the baseline data by listing down processes that limited the equipment output. The effort are being done through combination of field work observation studies, benchmarking PVD process with other FAB and vendors, re-generating OEE waterfall chart analysis process and further integration of equipment interfaces to understand equipment workflow. The root causes was later found that Ti (Titanium) pasting is one of the main contributors besides inefficiency of Work in progress (WIP) management, and lack of manpower allocation to handle high WIP. Improvement implementations have been made in the area of optimizing Ti pasting process with other equipment events, increasing systems flexibility, sequencing right WIP and adding dispatching procedure with real time dispatch (RTD) are the major events that successfully increased the equipment output and meet the company goal.