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In this paper, we study the implications of extending the balanced cost reduction property from queueing problems to general games. As a direct translation of the balanced cost reduction property, the axiom of balanced externalities for solutions of games, requires that the payoff of any player is equal to the total externality she inflicts on the other players with her presence. We show that this axiom and efficiency together characterize the Shapley value for 2-additive games. However, extending this axiom in a straightfoward way to general games is incompatible with efficiency. Keeping as close as possible to the idea behind balanced externalities, we weaken this axiom by requiring that every player's payoff is the same fraction of its total externality inflicted on the other players. This weakening, which we call weak balanced externalities, turns out to be compatible with efficiency. More speci fically, the unique efficient solution that satisfi es this weaker property is the proportional allocation of nonseparable contribution (PANSC) value, which allocates the total worth proportional to the separable costs of the players. We also provide characterizations of the PANSC value using a reduced game consistency axiom.

The article studies Automatic Oil Spraying Machine design and manufacture for baking device. In a current baking process, the workers manually spray the oil onto the trays. The spraying cycle increases the cost of man power in small scale industries. An automated oil spraying system is designed to solve these issues, where they can automatically spray the oil on the baking tray easily and effectively. The main components of this device are the mechanical and electrical components. The mechanical portion consists of conveyor belt, roller, bearing, gear, nozzle and tank, and mechanical structure. The electrical components include the IR sensor module, the dc motor, the dc pump, Arduino, and the source of the batteries. The key advantages of this approach are high precision oil spraying; improve efficiency, cost reduction and manpower. This model system's gross weight contains 15 kg (roughly). The efficiency of this device is checked for product production under different working conditions to track the performance of the work and the time durations.

How bridge refurbishment projects are performed requires a trade-off between the speed and cost of the project and the amount of traffic disturbances during the project. A possible way to help reach a better balance between these two extremes is the ASTRA Bridge developed in Switzerland. The ASTRA Bridge is a 236-meter long steel ramp system on wheels, which is placed on top of the bridge deck undergoing refurbishment to enable vehicles to continue to pass over the bridge while construction work progresses underneath. This study illustrates new refurbishment processes by using the ASTRA Bridge and presents the first quantitative analysis of the effects of using the ASTRA Bridge on the time, costs and traffic disturbances associated with bridge refurbishment. The bridge investigated is a short-span (50 m long) highway bridge requiring refurbishment of its superstructure. The analysis indicates that the use of the ASTRA Bridge resulted in reductions in duration and costs (14% and 3% for the example), and a substantial reduction in user costs (51% for the example). Although more analysis is required for different types of refurbishment projects, the initial results indicate that the ASTRA Bridge may become an integral part of future highway bridge refurbishment projects.

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

Mobile edge computing enables resource-limited edge clouds (ECs) in federation to help each other with resource-hungry yet delay-sensitive service requests. Contrary to common practice, we acknowledge that mobile services are heterogeneous and the limited storage resources of ECs allow only a subset of services to be placed at the same time. This paper presents a jointly optimized design of cooperative placement and scheduling framework, named JCPS, that pursues social cost minimization over time while ensuring diverse user demands. Our main contribution is a novel perspective on cost reduction by exploiting the spatial-temporal diversities in workload and resource cost among federated ECs. To build a practical edge cloud federation system, we have to consider two major challenges: user deadline preference and ECs strategic behaviors. We first formulate and solve the problem of spatially strategic optimization without deadline awareness, which is proved NP -hard. By leveraging user deadline tolerance, we develop a Lyapunov-based deadline-driven joint cooperative mechanism under the scenario where the workload and resource information of ECs are known for one-shot global cost minimization. The service priority imposed by deadline urgency drives time-critical placement and scheduling, which, combined with cooperative control, enables workloads migrated across different times and ECs.

Magnesium (Mg) alloys are extensively used in the automotive and aircraft industries due to their prominent properties. The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement. This decision entails the selection of suitable process parameters concerning various conflicting factors, so it has to be addressed with the Multiple Criteria Decision Making (MCDM) method. Therefore, this work addresses the MCDM problem through the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and COPRAS (COmplex PRoportional ASsessment) methods. The assessment carried out in the material Mg AZ91 with the Solid Carbide (SC) drill bit. The dependent parameters like drilling time, burr height, burr thickness, and roughness are considered with the independent parameters like spindle speed and feed rate. Drilling alternatives are ranked using the above said two methods and the results are evaluated. The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev. The identical sequencing order was observed in TOPSIS and COPRAS method. The empirical model was developed through Box-Behnken design for each response. Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation. The trends of various dependents based on the heterogeneity of various independents are not identical, these complex mechanisms are identified and reported. The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank. The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

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In a production process optimization is needed to increase the productivity of production results. This research was conducted on the Aida 800T Blanking machine, based on the results of field observations made on the Aida 800T Blanking machine at PT. XYZ there is frequent losstime, this hinders the production process which results in reduced production targets. Losstime often occurs during a production process, the factors that often occur are the environment, humans, materials, and machines. The purpose of this study is to find out how much the cost is due to loss time. The methods used to perform losstime analysis are 5 why analysis, Pareto diagrams and fishbone diagrams to identify problems that often occur in the production process. From the results of the research, it was found that the costs incurred were Rp. 6,509,220. By performing a losstime analysis, there is a cost saving of 3,797,736 during one month of production, so that the method is successfully used with the end result of doing kaizen., Pada suatu proses produksi dibutuhkan optimasi dalam meningkatkan produktivitas dari hasil produksi. Penelitian ini dilakukan pada mesin Aida 800T Blanking, berdasarkan hasil observasi lapangan yang dilakukan pada mesin Aida 800T Blanking di PT.XYZ terdapat losstime yang sering terjadi, hal tersebut menghambat proses produksi yang mengakibatkan hasil capaian target produksi berkurang. Losstime kerap terjadi ketika berlangsung sebuah proses produksi, faktor yang sering terjadi ialah lingkungan, manusia, material, dan mesin. Tujuan penelitian ini untuk mengetahui seberapa besar biaya cost yang terjadi karena adanya losstime. Metode yang digunakan untuk melakukan analisis losstime adalah 5 why analaysis, diagram pareto dan diagram fishbone untuk melakukan identifikasi masalah yang kerap terjadi pada proses produksi. Dari hasil penelitan di dapatkan hasil biaya cost dimana sebelum terjadinya perbaikan pada proses stamping menghabiskan biaya yang di keluarkan ialah Rp. 6.509.220. Dengan melakukan analisis losstime maka terjadi penghematan biaya cost sebesar 3.797.736 selama satu bulan produksi, sehingga metode tersebut berhasil di gunakan dengan hasil akhir melakukan kaizen.

The use of artificial intelligence in various areas of business has become the basis of successful competitive business. It is widespread in all spheres of economic activity, in various areas of business and serves as an indispensable assistant in the everyday life of every person. The prevalence of its use is so versatile that we do not always even realize who we are talking to and who performs certain functions. Chat-bots have become commonplace without which the activity of any company cannot be imagined. Artificial intelligence video games are an everyday entertainment tool. The face recognition function is used not only in business activities, but also in the everyday life of every person when using this function on the phone when identifying in various mobile applications. Self-driving cars, the work of which is based on artificial intelligence, trajectory planning, recognition of obstacles, development of the most optimal route - this is all a function performed by artificial intelligence. Work with voice applications (Alexa, Siri, Google Assistant, targeted advertising) is based on artificial intelligence. That is, the main function of artificial intelligence is the ability to generate processes performed by human intelligence. The teachings of John McCarthy, in the process of researching the ability of the computer to learn (1956), changed the course of human development, business development and transformed the era of industrialism into the era of knowledge and human intelligence (the era of postindustrial development). The functionality of artificial intelligence is so wide that it can also be used to work on the technological process of interaction with customers. Artificial intelligence performs audience segmentation, forms recommendations and optimizes production processes in marketing companies. In the process of analyzing the SEO strategy, artificial intelligence contributes to search optimization, the development of decision algorithms within the framework of the development strategy. When using artificial intelligence in the production security system, an array of data from cameras and sensors is analyzed, constantly compared with regulatory data, and an algorithm for the safest management solution is formed. Security against fraudulent actions in the financial and business spheres, customer loyalty research, risk classification and even court cases - all this can be done today by this unique, human-made development. HR specialists use artificial intelligence to select personnel for companies, and education specialists use it to develop training programs for people with special needs, in the training process, as training simulators. The field of hospitality is specific in the integration of artificial intelligence in technological processes, in the management system, in the communication system with customers and in the process of attracting guests. Only with the introduction of innovative digital technologies can hotel and restaurant businesses achieve the required level of competitiveness and economic efficiency., Використання штучного інтелекту у різних сферах бізнесу стало основою успішного конкурентоспроможного бізнесу. Він є поширеним в усіх сферах господарської діяльності, у різних сферах бізнесу та слугує незамінним помічником у повсякденному житті кожної людини. Поширеність його використання є настільки різнобічним, що ми не завжди навіть усвідомлюємо із ким спілкуємось та хто виконує ті чи інші функції. Чат-боти стали звичною справою без якої діяльність будь-якої компанії годі собі і уявити. Відеоігри із штучним інтелектом слугують повсякденним інструментом у сфері розваг. Функція розпізнавання обличчя використовується не лише у бізнесовій діяльності, але і у звичайному житті кожної людини при користуванні даною функцією на телефоні при ідентифікації у різних мобільних додатках. Безпілотні автомобілі, робота яких основана на штучному інтелекті, прокладання траєкторії, розпізнання перешкод, розробка найбільш оптимального маршруту – це все функція, яку виконує штучний інтелект. Робота із голосовими додатками (Alexa, Siri, Google Assistant, таргетингова реклама) функціонує на основі штучного інтелекту. Тобто, головною функціє штучного інтелекту є спроможність генерувати процеси, які виконує людський інтелект. Вчення Джона МакКарті, в процесі дослідження можливості комп’ютера навчатись (1956 р.) змінило хід розвитку людства, розвитку бізнесу та перетворило епоху індустріалізму у епоху знань та людського інтелекту (епоха постіндустріального розвитку). Функціонал штучного інтелекту є на стільки широким, що використовувати його можна також для роботи на технологічному процесі взаємозв’язку із клієнтами. У маркетингових компаніях штучний інтелект здійснює сегментування аудиторії, формує рекомендації та здійснює оптимізацію виробничих процесів. В процесі аналізу SEO-стратегії штучний інтелект сприяє пошуковій оптимізації, розробці алгоритмів рішень в межах стратегії розвитку. При використанні штучного інтелекту в системі безпеки на виробництві аналізується масив даних із камер і датчиків, співставляється постійно із нормативними даними та формується алгоритм найбільш безпечного управлінського рішення. Убезпечення від шахрайських дій у фінансовій і підприємницькій сфері, дослідження лояльності клієнтів, класифікація ризиків та навіть вирішення судових справ – усе це на сьогодні може виконувати ця унікальна, створена людиною розробка. HR-спеціалісти за допомогою штучного інтелекту підбирають кадри для підприємств, а спеціалісти сфери освіти за його допомогою розробляють програми навчання для осіб із особливими потребами, у навчальному процесі, як навчальні тренажери. Сфера гостинності є специфічною у інтегруванні штучного інтелекту в технологічні процеси, у систему управління, в комунікативну систему із клієнтами та в процес залучення гостей. Лише при впровадженні інноваційних цифрових технологій заклади готельного та ресторанного бізнесу можуть досягти необхідного рівня конкурентоспроможності та економічної ефективності.

Background: Implementation of an anesthesiology-led cardiac implantable electronic device (CIED) service can be viewed to have economic and efficiency challenges. This study evaluates the cost savings of an anesthesiology-led CIED service. Methods: A total of 830 patients presented in the pre-implementation period from 1 March 2016 to 31 December 2017, and 1981 patients presented in the post-implementation period from 1 January 2018 to 31 October 2021. Interrupted time-series analysis for single-group comparisons was used to evaluate the cost savings resulting from reduction in operating room (OR) start delays for patients with CIEDs. Results: OR start-time delay was reduced by 10.6 min (95%CI: −20.5 to −0.83), comparing pre- to post-implementation. For an OR cost of USD 45/min, we estimated the direct cost to the department to be USD 1.68/min. The intervention translated into a total cost reduction during the intervention period of USD 250,000 (USD 18,000 to USD 470,000) per year for the institution and USD 9800 (USD 730 to USD 17,000) per year for the department. The yearly cost of employing a full-time team of CIED specialists would have been USD 135,456. The service triggered electrophysiology consultation on 13 device malfunctions. Conclusions: An anesthesiology-led CIED service resulted in substantial cost savings, increased OR efficiency and patient safety.

Firms offer a variety of products to meet different customer needs. In many horizontally differentiated markets, prices are stable, and firms make infrequent adjustments to their product lines. Although prior research focused on product line design, we investigate how firms should allocate their marketing effort when their product lines are fixed. We propose a simple model to analyze product line marketing. Our model exhibits a flagship product effect in which the firm’s optimal marketing effort is concentrated, provided that the ratio between consumer tastes dispersion and the convexity of the cost of marketing effort is below a threshold. The flagship product is selected according to a marketing effort allocation index that measures the trade-off between a product’s markup and its potential market share. This result is robust with or without competition and whether prices are exogenous or endogenous. Firms often experience shocks to their marketing cost because of technological improvement or externalities. If a monopolist’s cost of marketing effort declines, she should place more emphasis on a low-utility, high-markup product. Conversely, if the cost increases, the monopolist may find it beneficial to focus her marketing effort on a high-utility, low-markup product. When multiproduct firms compete against each other, we show that if the opponent’s cost of marketing effort decreases, there can be a spillover effect, in which the firm benefits from the opponent’s cost reduction, thereby leading to a win-win situation. This paper was accepted by Dmitri Kuksov, marketing.

Nowadays, there is an increase in the demand for optimized services in urban environments. However, ground transportation in big urban centers has been facing challenges for many years (e.g., resilience and congestion) and new paradigms have been proposed, such as the Urban Air Mobility (UAM) concept. UAM aims to enhance the urban transportation system using manned and unmanned aerial vehicles (i.e., Electric Vertical Takeoff and Landing - eVTOL - vehicles). Although UAM offers many benefits (e.g., cost reduction and increase in transportation capacity), many challenges need to be faced to enable safe and efficient operations. Furthermore, trajectory planning is challenging in the National Airspace System (NAS) and UAM operations due to several factors. Finally, new initiatives concerning UAM trajectory planning can be accelerated with the support of an automatic what-if platform capable of evaluating trajectories feasibility and efficiency. This research aims to propose a simulation platform for enabling trajectory evaluation in UAM operations. This platform, named Trajectory-Based Urban Air Mobility Simulator (TUS), focuses on simulating trajectories in the urban aerial environment. TUS enables users to test new UAM algorithms (e.g., flow management strategies, real-time evaluation of maneuvers effectiveness, airspace configurations) and simulate both manned and unmanned vehicles. Furthermore, TUS operation relies on a set of inputs and evaluates the trajectories generated from efficiency and safety perspectives. This process is performed using a Discrete Event Simulation (DES) approach. The experiments performed showed that TUS can perform a realistic evaluation of UAM trajectories and can be effortlessly used to simulate hundreds of scenarios.

Suppliers often impose a minimum order quantity (MOQ) to ensure that production runs and shipping quantities of each specific item are economically viable. Additional economies of scale may arise as various items share high joint costs (e.g. the cost of an overseas container shipment) and require coordination of their replenishment policies. The buyer needs to find the joint ordering interval and replenishment policies for each of the individual items to minimize the system-wide ordering and inventory costs while satisfying the quantity restrictions. We focus on the case of constant demand without backlogging. Given a fixed joint reorder interval, we characterize the optimal inventory ordering strategy for each item, which we refer to as Multi-Replenishment (MR) inventory ordering policy, and derive a closed-form expression for the optimal average inventory costs per unit of time. In contrast to the previous literature, the MR inventory ordering policy does not follow the zero-inventory-ordering (ZIO) rule and allows for orders of different sizes over time to optimally accommodate the MOQ restriction. A numerical approach is used to determine the optimal joint reorder interval, and is later extended to account for the presence of empty replenishments. An extensive computational study shows 1) the total inventory and setup cost reduction associated with the MR inventory policies and empty replenishments; 2) the impact of various parameters on cost and policy performance; and 3) the loss associated with discretizing time at different levels of granularity (weeks, days, hours, minutes, seconds) to accommodate restrictions in the timing of item deliveries.

Cloud computing is gaining popularity among small and medium-sized enterprises. The cost of cloud resources plays a significant role for these companies and this is why cloud resource optimization has become a very important issue. Numerous methods have been proposed to optimize cloud computing resources according to actual demand and to reduce the cost of cloud services. Such approaches mostly focus on a single factor (i.e. compute power) optimization, but this can yield unsatisfactory results in real-world cloud workloads which are multi-factor, dynamic and irregular. This paper presents a novel approach which uses anomaly detection, machine learning and particle swarm optimization to achieve a cost-optimal cloud resource configuration. It is a complete solution which works in a closed loop without the need for external supervision or initialization, builds knowledge about the usage patterns of the system being optimized and filters out anomalous situations on the fly. Our solution can adapt to changes in both system load and the cloud provider's pricing plan. It was tested in Microsoft's cloud environment Azure using data collected from a real-life system. Experiments demonstrate that over a period of 10 months, a cost reduction of 85% was achieved.

This project involves the development of a program that utilizes expert systems to evaluate the effectiveness of production machines. The system will diagnose potential issues and provide recommendations for improving efficiency, {"references":["Muminjonovich, K. A. (2023). SUN'YIY INELLEKTNI RIVOJLANTIRISHDA DASTURLASH TILLARINING RO 'LI. Journal of new century innovations, 12(4), 159-161","Kayumov, A. (2023). THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE EDUCATIONAL PROCESS. Потомки Аль-Фаргани, 1(1), 35–38. извлечено от https://al-fargoniy.uz/index.php/journal/article/view/5","Olim, O., & Mokhichkehra, B. (2022). FEATURES OF MULTIPARTY SYSTEM IN UZBEKISTAN AND TURKEY: COMPARATIVE ANALYSIS. Web of Scientist: International Scientific Research Journal, 3(10), 1312-1321","Ionin A. A. et al. Lasers on overtone transitions of carbon monoxide molecule //Laser Physics. – 2010. – Т. 20. – С. 144-186","KONEV, Y., KOCHETOV, I., KURNOSOV, A., & MIRZAKARIMOV, B. (1994). CALCULATION OF CO LASER KINETICS WITH ALLOWANCE FOR MULTIPHOTON VV EXCHANGE. KVANTOVAYA ELEKTRONIKA, 21(2), 133-136","R. Zulunov. Sun'yiy intellekt texnologiyalarini ta'lim jarayonida qo\"llanilishi. Xorazm Ma'mun akademiyasi habarnomasi, 11/3 2022, 163-166 b","Асраев, М., Собир, Р., & Dadakhanov, M. (2023). ОСОБЕННОСТИ ОБРАБОТКИ И АНАЛИЗА ИЗОБРАЖЕНИЙ РУКОПИСНОГО ВВОДА. Потомки Аль-Фаргани, 1(1). извлечено от https://al-fargoniy.uz/index.php/journal/article/view/15","Musayev X.SH., Ermatova Z.Q., KOTLIN DASTURLASH TILIDA KORUTINLAR BILAN ISHLASHNI TALABALARGA O 'RGATISH //Journal of Integrated Education and Research. – 2022. – Т. 1. – №. 6. – С. 119-125","Ogli K. A. M. MODERN PROGRAMMING LANGUAGES: CLASSIFICATION AND CHARACTERIZATION //International Journal of Advance Scientific Research. – 2022. – Т. 2. – №. 11. – С. 108-111","Azizjon Mo'minjon o'g X. et al. The Importance of Mathematical Game and Methods in the Formation of Mathematical Concepts in Primary Schools //Journal of Pedagogical Inventions and Practices. – 2022. – Т. 8. – С. 208-211","Холматов А. А. У., Хайитов А. М. Ў. ИЗУЧИТЬ И ИЗУЧИТЬ СВОЙСТВА БАРИЯ И СТРОНЦИЯ-ТИТАНА, СИНТЕЗИРОВАННЫХ В БОЛЬШОЙ СОЛНЕЧНОЙ ПЕЧИ //Oriental renaissance: Innovative, educational, natural and social sciences. – 2021. – Т. 1. – №. 11. – С. 79-93","Xolmatov A. A., Karimov J. X., Xayitov A. M. Effect of crystallizer catalyst on properties of glass-crystalline materials //EPRA International Journal of Research and Development (IJRD). – 2021. – С. 273-275","Musayev, X., & Soliev, B. (2023). PUBLIC, PROTECTED, PRIVATE MEMBERS IN PYTHON. Потомки Аль-Фаргани, 1(1), 43–46. извлечено от https://al-fargoniy.uz/index.php/journal/article/view/17","Zulunov, R., & Soliev, B. (2023). IMPORTANCE OF PYTHON LANGUAGE IN DEVELOPMENT OF ARTIFICIAL INTELLIGENCE. Потомки Аль-Фаргани, 1(1), 7–12. извлечено от https://al-fargoniy.uz/index.php/journal/article/view/3"]}

Objective: The purpose of this study is to examine the impact of an activity-based costing system on cost reduction during the COVID-19 pandemic. An example of ASE. Theoretical framework: a 42-item work-related cost scale and a 10-item cost reduction scale were applied to a random sample of 261 workers working in ASE. Design/methodology/approach: The results show a positive impact and correlation between an activity-based costing system and cost reductions during the COVID-19 pandemic. Results and recommendations: The study recommends enhancing the professional qualifications of financial managers, heads of accounting departments and accountants in Jordanian industrial companies by attending various professional courses.

The implementation of a texturing pattern on the surface of a solar cell is well known for reducing reflection, thus increasing the absorption of sunlight by the solar cell. Nanowires (NWs) that are large in their height have been widely used for this purpose. Through rigorous numerical simulations, this work explores the benefits of short but index-matched NWs and how these designs are also affected by surface recombination. Additionally, this work further optimized power conversion efficiency (PCE) by placing two or three NWs of different heights and diameters on top of each other to mimic the performance of two-NW and three-NW ARC designs with PCEs of 16.8% and 17.55%, respectively, when a radial pn junction is considered. These are the highest reported so far for such a thin silicon solar cell. Furthermore, we also show how these designs were impacted by surface recombination velocity and compare these findings to simple NWs of different heights and diameters.

Purpose: This study investigates the effect of lean implementation on production cost reduction and the moderating role of lean manufacturing maturity. Design/methodology/approach: This study employed a cross-sectional survey, of which the samples were drawn using a cluster sampling procedure from 151 middle and top-level management of medium and large manufacturing companies gathered from the Federation of Manufacturers Malaysia (FMM). Data were analyzed using the PLS-SEM approach through SmartPLS4 software. Findings: The result revealed that lean implementation does not leverage production cost reduction as the lean journey requires long-term orientation, which finally leads to valid cost reduction. The moderating role of lean manufacturing maturity was significant and positive in the relationship between lean manufacturing implementation and production cost reduction. Research limitations/implications: Future research should include longitudinal settings as this study is cross-sectional, and future research might put the present model to the test in multiple industries and regions, including a cross-country comparison to improve the generalizability of the findings. Additionally, further studies could extend this study using a mixed-method approach. Practical implications: By offering a holistic perspective, this study expands the existing literature, contributes to knowledge of integrated lean implementation and supports the management in planning their path towards cost-cutting performance. Originality/value: This study answers the inconclusive finding between lean implementation and operational performance in terms of production cost reduction. The study contributes to the body of knowledge and, most importantly, to the practitioners in planning their lean journey

Automated driving technology is one of the emerging technologies which will change the transportation mode in the future. Distributing the automated driving vehicles from the automobile factory to dealers has a huge difference compared with traditional vehicles. This paper considers the variety of cases where the automated driving vehicles can be both distribution tools and distribution goods. Then establishes a new distribution model that integrates self-distribution of autonomous vehicles into the existing distribution rings form a complex network composed of ring-branches. This cooperative distribution model of automated driving and traditional vehicle transport to minimize transportation cost and maximize the overall efficiency. Furthermore, we design an improved adaptive large neighborhood algorithm (ALNS) to solve the model which combines the merger heuristics and splitter heuristics to promote generate the optimal solutions. The efficiency of our proposed method is proved by real-world data from an automobile enterprise. Additionally, the sensitivity analysis of the maximum distance for automated driving vehicles to be delivered by themselves suggests that the larger distance, the more outstanding of cost reduction and efficiency increase for our proposed model.

Optimizing wind turbine performance is still a challenge due to the dynamic interactions between the spatially-temporally stochastic wind fields and the wind turbine as a complex mechanical system. Recent cost reduction of remote sensing wind measurement technologies, such as light detection and ranging (LiDAR), has opened a new research area on the use of deep learning models for predicting wind turbine's responses. In this paper, a LiDAR-aided deep learning model is presented to learn the powerful spatial-temporal characteristics from the input wind fields. In the proposed method, the combination of 2-D convolutional neural networks (CNNs) and bidirectional long short-term memory (BLSTM) units is used to capture high levels of abstractions in wind fields concurrently, and thus forecasting wind output power and fatigue load as two representatives of wind turbine responses. The LiDAR wind preview information is used as the 2-D-images of wind fields for the CNN. Moreover, the BLSTM is incorporated with the proposed CNN to improve the forecasting accuracy further and learn deep temporal features. The aero-elastic 5-MW reference wind turbine of National Renewable Energy Laboratory (NREL) is used to evaluate the performance of proposed model compared to the state-of-the-art deep-learning based architectures in the recent literature.

A high-frequency dynamically coordinated Si/SiC hybrid continuous conduction mode (CCM) totem-pole bridgeless PFC (TPBPFC) converter is reported in this paper. The proposed hybrid TPBPFC converter is interleaved with a low-current high-frequency SiC phase and a high-current low-frequency Si phase, in comparison to the prior-art design of two half-capacity SiC phases, resulting in a considerable cost reduction. A coordinated Si/SiC control strategy is proposed to address the un-compensable range issue in hybrid-frequency interleaving topologies, and to dynamically distribute power load between the Si and SiC phases for optimal efficiency and performance. An average current mode control (ACMC) based control algorithms and sequences are discussed in detail to enable the unique operation. A 3.3 kW hybrid TPBPFC prototype made of Si IGBTs and SiC MOSFETs is built and tested to validate the proposed design concept. Compared to the prior art two-phase all-SiC interleaved TPBPFC design, the proposed hybrid TPBPFC has demonstrated nearly identical power efficiency, THD, power factor, and effective frequency, while offering 33.5% device cost and 15.1% total cost reductions.

Providing ancillary services through flexible electric vehicle (EV) charging has the potential to offer extra market benefit for EVs. EV aggregator controlling a fleet of EVs can play a significant role in managing the considerable EV charging demand and bid in the electricity markets. The increasing penetration of EVs has created the feasibility of participating in both the day-ahead energy market and frequency regulation market. This work presents a multi-market optimization model for minimizing the net operation cost of EV charging considering the benefit from performing frequency regulation. A two-level optimization algorithm for EVs controlled by the aggregator is proposed to determine optimal operation strategies of EV aggregations and the charging power of each individual EV. The optimization is able to merge revenue from frequency regulation with the cost reduction objectives of traditional EV charging management. The effectiveness of optimization algorithm is demonstrated by simulating EVs charged at the workplace and residential areas. The increased profitability of participation in the sequential electricity markets has been illustrated. Net operating cost of EV aggregations can be significantly reduced considering both capacity and energy remunerations in the regulation market and the charging demand in the energy market

In this paper, bulk water abstractions and a purification process in terms of water pumped energy consumption, is modelled and simulated. The study proposes an optimal energy consumption-rate management system, using real-time data. The aim is to achieve the goal of obtaining potable water by scheduling the pumping process, to obtain the cheapest energy-rate at any given load demand. Considering the loss due to the effect of evaporation, the effect of precipitation (or rainfall) and seepage losses. As a baseline, an electronic timer switching system for the powering of electrical pumps, used to pump water from one level to another, is incorporated. An optimal control system is included that looks at the energy cost before scheduling the pumping sequence in such a way that pumping occurs during the more affordable energy costing period and ensures that the water demand from consumers matches the water pump level in the storage reservoir. The configuration involved the time of use (TOU) tariff; in the context of the South African electricity tariff. The optimal control output, compared to an electronic timer switching system, shows an end-of-day cost reduction of 1994 ZAR in water-pumped energy consumption from the grid. This reduction in energy consumption is therefore a direct reflection of the reduction in the cost of water supply to consumers.

With the increasing demand for renewable energy integration and DC power consumption, the application and investigation of residential low voltage DC (LVDC) power system have attracted more attention in recent years. The residential DC power system has advantages on distributed generation integration and conversion cost reduction, which could provide high efficiency and high power quality power supply to consumers. However, the lack of voltage level standards challenges the commercial application of the LVDC power system. In this paper, a comprehensive study on the performance of different voltage levels is proposed, in order to provide a voltage level guidance for DC power supplier and appliance manufacturer. Existing voltage selections from applications and researches are compared with each other in terms of voltage compatibility, topology, power supply capability and electrical safety. Comparative simulation is conducted in the residential DC power system model of a community with four household subsystems. Based on the simulation and investigation, the comparison result indicates that: 240V and 380V is optimal for the unipolar residential system with low power demand and ±375V bipolar DC system should be used in heavy load scenarios.

The abundance and uneven distribution of renewable energy might cause congestion and curtailment in electric power systems. Transmission expansions can potentially alleviate transmission congestion and reduce renewable energy curtailment. On the other hand, with the substantial cost reduction of electrolyzer technology and the continuing rise of hydrogen demand, converting surplus renewable energy to hydrogen provides synergistic benefits to electric grid and hydrogen system, but consequently requires joint planning of the two systems. To this end, we propose a joint planning approach for power transmission and hydrogen transportation networks to coordinately optimize the investment and operation of power and hydrogen infrastructure. In our proposed model, detailed truck routing, pipeline, and hydrogen storage are formulated to quantify the hydrogen transportation system flexibility. A robust joint planning approach is also proposed to address various uncertainties from renewable energy, electric load, and hydrogen demand. Our numerical simulation shows the proposed joint planning model can save the total system cost, reduce renewable energy curtailment, and increase the utilization level of transmission lines.

OBJECTIVE A large proportion of healthcare expense is operating room (OR) costs. As a means of cost mitigation, several institutions have implemented surgeon education programs to bring awareness about supply costs. This study evaluates the impact of a surgical cost feedback system (surgical receipt) on the supply costs of endoscopic skull base surgery (ESBS) procedures. METHODS The supply costs of each ESBS surgical case were prospectively collected and analyzed before and after the implementation of a nonincentivized, automated, and itemized weekly surgical receipt system between January 2017 and December 2019. Supply cost data collected 15 months prior to intervention were compared with cost data 21 months after implementation of the surgical receipt system. Demographics, surgical details, and OR time were collected retrospectively. RESULTS Of 105 ESBS procedures analyzed, 36 preceded and 69 followed implementation of cost feedback. There were no significant differences in patient age (p = 0.064), sex (p = 0.489), surgical indication (p = 0.389), or OR anesthesia time (p = 0.51) for patients treated before and after implementation. The mean surgical supply cost decreased from $3824.41 to $3010.35 (p = 0.002) after implementation of receipt feedback. Usage of dural sealants (p = 0.043), microfibrillar collagen hemostat (p = 0.007), and oxidized regenerated cellulose hemostat (p < 0.0001) and reconstructive technique (p = 0.031) significantly affected cost. Mediation analysis confirmed that the overall cost reduction was predominantly driven by reduced use of dural sealant; this cost saving exceeded the incremental cost of greater use of packing materials such as microfibrillar collagen hemostat. CONCLUSIONS Education of surgeons regarding surgical supply costs by a surgical receipt feedback system can reduce the supply cost per case of ESBS operations.

Improving the performance and the production of renewable energy sources, especially the wind energy, is considered an attractive approach to reduce the Cost of Energy (COE) associated to the hydrogen generation process. In this context, flow control strategies have been object of detailed investigation during last years. Originally flow control devices were designed to be implemented in the aeronautical sector. Nonetheless, the optimization of the performance of the wind turbine blades with the introduction and implementation of these devices is being widely investigated these days. An analysis of the influence of implementing Vortex Generators (VGs) and Gurney Flaps (GFs) in Wind Turbine Blades (WTBs) on the Annual Energy Production (AEP) of large Horizontal Axis Wind Turbine (HAWT) is proposed in this paper. For that purpose, the Weibull distributions of annual wind speed data series of a real wind farm have been calculated, and Blade Element Momentum (BEM) based calculations have been performed to evaluate the effect of the flow control devices on the power curve and the AEP of the NREL offshore 5 MW Baseline Wind Turbine. The obtained results show an enhancement of the AEP of the turbine of 2.43% during year 2015 and 2.68% during year 2016. As a result, increments of the generated hydrogen volume larger than 130000 Nm3 are achieved during both years with no considerable additional cost in the design of the wind turbine.

At present, city logistics mostly adopts a two-echelon dispatching model which combines distribution centers located in suburbs and fixed satellites located in urban areas for distribution. However, both expensive rental fees and daily changes of customer demand in metropolitan areas make dispatching route generated by fixed satellites inefficient. Moreover, the existing mobile depot model needs a large investment for facilities. In this paper, we propose a two-echelon city dispatching model with mobile satellites (2ECD-MS) which locations of mobile satellites change according to demands of customers to ensure the efficiency of delivery routes in every day. A cluster-based variable neighborhood search scheduling algorithm is proposed to determine locations of mobile satellites and dispatching routes of trucks and tricycles. Then, the 2ECD-MS is extended to 2ECD-MS-TDD to allow trucks dispatching directly (TDD) for further cost reduction. Experimental results show that the 2ECD-MS significantly reduces the total cost against the model using fixed satellites mode by 3.5% while the 2ECD-MS-TDD further reduces the total cost against the 2ECD-MS significantly by 3.25% in 54 cases with different customer scales, geographical scopes, and distribution types. These show the superiority of the proposed methods in cost reduction for city logistics in comparison to the traditional fixed model.

The huge amount of energy consumed by the data centers around the world every year motivates the cloud service providers to operate data centers in a more energy efficient way. A promising solution is to turn off the idle servers, which, however, may be turned on later, incurring a significant startup cost. The problem turns to dynamically provisioning the workload, and cutting down the energy cost which includes the power to support the running of data center and the startup cost. Different from previous studies that usually consider the worst case performance guarantee when designing online algorithms, this paper considers the average case which is more practical. We propose a simple online algorithm based on the expectation of job interval of workload, which is proven to be optimal for exponential and uniform distributions and achieves tight competitive ratio $\frac{e}{e-1}$ and $\frac{4}{3}$ respectively for them. Simulations using the synthetic data verify our theoretical analysis. Numerical results employing Google's data center workload trace demonstrate that the proposed algorithm outperforms the worst case based algorithm in terms of operation cost reduction.

With continuously decreasing circuit scale, more and more test patterns (test contents) are added to test set to achieve acceptable defect levels, which seriously affect test time and, consequently, test cost. Theoretically, dropping invalid (pattern that can make the test pass) test patterns can reduce test cost. In fact, When the valid (pattern that can make the test fail) and invalid patterns overlap seriously, identifying effective patterns performs poorly, which will not achieve the expected results. This paper proposes a kind of eliminate redundancy method, so that this LDA-MRMR algorithm which can provide test cost reduction without increasing the defect level obviously. Experimental results demonstrate that the proposed method sacrifices 3% of the predictive accuracy in exchange for 3.7 times time saving compared with traditional methods. In addition, the algorithm is completely software-based and does not require any additional hardware overhead and is directly compatible with traditional integrated circuit testing process.

Peer-to-peer (P2P) electricity trading promotes the local consumption of renewable energy. However, it suffers from high transaction costs and mutual distrust among users. To address these issues, we propose a credit-based P2P electricity trading model in a blockchain environment. First, the P2P electricity trading process is introduced, which involves six stages: order generation, default query, order picking, trading execution, trading verification, and payment. Credit management is also introduced in this model to manage the default behavior of users. In particular, the default users are provided a waiting time in the default query stage or penalized in the payment stage. Finally, the model is simulated on the Hyperledger Fabric platform using Docker and Go. Experimental results show that the proposed model can facilitate cost reduction for users in the blockchain and realize credit management in P2P electricity trading, thereby enhancing trading stability and efficiency.

INTRODUCTION Integrated quality improvement (QI) and cost reduction strategies can help increase value in cancer care. Time-driven activity-based costing (TDABC) is a bottom-up costing tool that measures resource use over the full care cycle. We applied standard QI and TDABC methods to improve workflow efficiency and reduce costs for MRI-guided prostate brachytherapy. METHODS AND MATERIALS We constructed process maps of the baseline prostate brachytherapy workflow from initial consultation through one year after treatment. Process maps reflected resources and time required at each step. TDABC costs were calculated by multiplying each process time by the cost per min of the resource(s) used at that step. We then used plan-do-study-act methodology to identify workflow inefficiencies and implement solutions to reduce resource consumption. RESULTS The highest cost components at baseline were the operating room (OR) (40%), imaging (8.7%), and consultation (7.6%). Higher-than-expected costs (3%) were incurred during surgery scheduling. After targeted QI initiatives, OR time was reduced from 90 to 70 min, which reduced overall cost by 5%. Personnel task downshifting reduced costs by 10% at consultation and 77% at surgery scheduling. Re-engineering of follow-up protocols reduced costs by 8.4%. Costs under the new workflow decreased by 18.2%. CONCLUSIONS TDABC complements traditional QI initiatives by quantifying the highest cost steps and focusing QI initiatives to reduce costs and improve efficiency. As payment reform evolves toward bundled payments, TDABC and QI initiatives will help providers understand, communicate, and improve the value of cancer care.

Purpose We aim to assess the healthcare value achieved from a shared savings program for pediatric appendectomy. Methods All appendectomy patients covered by our health plan were included. Quality targets were 15% reduction in time to surgery, length of stay, readmission rate, and patient satisfaction. Quality targets and costs for an appendectomy episode in two 6-month performance periods (PP1, PP2) were compared to baseline. Results 640 patients were included (baseline:317, PP1:167, PP2:156). No quality targets were met in PP1. Two quality targets were met during PP2: readmission rate (−57%) and patient satisfaction. No savings were realized because the cost reduction threshold (−9%) was not met during PP1 (+1.7%) or PP2 (−0.4%). Conclusions Payer-provider partnerships can be a platform for testing value-based reimbursement models. Setting achievable targets, identifying affectable quality metrics, considering case mix index, and allowing sufficient time for interventions to generate cost savings should be considered in future programs.

Contemporary commercial building’s demonstrate performance from a sustainability and environmental perspective. Usually, an appropriate energy performance is achieved using efficient mechanical systems, however the operation method with smart control is significant in the overall energy consumption. The research follows a two-year design and construction process of a commercial building where the integration of smart technology was a mandatory requirement by the owner, alongside with the prerequisites of the LEED® green building certification. The goal was to design, build and optimize the performance of the new Innovation Campus commercial office building complex using smart building solutions. The research was performed with energy modeling and dynamic simulation until completion of the building, where the design and investment decisions were constantly evaluated. The new optimized building’s performance demonstrated 42% of annual energy reduction and 34% of cost reduction according to the “ASHRAE 90.1 Appendix G” baseline Benchmark building.

Outsourcing has long been a strategy to decrease cost. Increasingly firms recognize the value in their supply chains and call on suppliers to innovate, both in products and processes. Innovation to increase quality and demand or to reduce costs is critical to firm and supply chain success. In a two-stage supply chain, we investigate the impact of focal firm and supplier innovation costs (and capabilities) on the type of outsourcing chosen and the resulting investments in process and product innovation. The focal firm determines whether to perform design (including product innovation in the form of quality enhancement) and manufacturing (including process innovation in the form of cost reduction) in-house, to outsource manufacturing/process innovation while insourcing design/product innovation, to outsource both manufacturing/process innovation and design/product innovation, or to codevelop product innovation while outsourcing manufacturing/process innovation. We also examine the conditions under which codevelopment is favorable, given the supply chain faces potential positive and negative synergies from either the colocation of the innovation activities or costs of collaboration. After characterizing the optimal outsourcing decision, we find that the decision to outsource is more nuanced than simply which activities to outsource but must include options to collaborate on particular activities and specifically product innovation. We offer the insight to managers that codevelopment, despite the costs of collaboration, can benefit the firm and result in higher profits. This occurs through the improvement of demand via higher quality products.

Reductions in defense expenditure require holistic and coordinated planning of two critical and interconnected defense capabilities, namely a fleet of assets and the workforce required. In this paper, we model and solve a joint problem of strategic workforce planning and fleet renewal in a military context. The joint problem studied involves addressing a trade-off among several costs (e.g., workforce, maintenance, operating, etc.) and operational readiness (availability) of the fleet. To make such trade-offs, the decisions associated with workforce planning (i.e., the recruitment and the career progression of the workforce) and fleet renewal (i.e., the timing of asset replacements) strategies have to be simultaneously considered and optimized. We develop a simulation-optimization approach by coupling a system dynamics (SD) simulation model and a genetic algorithm (GA) to solve the joint problem. In the developed approach, the GA generates candidate workforce planning and fleet renewal strategies to find the best joint strategy. Then, the candidate workforce planning and renewal strategies are passed to the SD model which simulates both the career progression of the workforce and the life-cycle of assets to evaluate the total cost. We illustrate the applicability and effectiveness of the joint model on a realistic case study motivated by the recent modernization efforts of the Royal Australian Navy. The results obtained indicate that this approach leads to a considerable cost reduction and identifies the causes of inferior performance. We also test the robustness of the optimized strategies under uncertainty by sensitivity and scenario discovery analyses to infer further insights.

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing companies.To support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies.

A bstract The use of shared vehicle architectures with BEVs offers a high potential for FCEVs in terms of cost reduction. Therefore, the hydrogen must be stored in the box-shaped design space in the underbody of the vehicles, where the battery in BEVs is located. Box-shaped pressure vessels show the potential to store high amounts of hydrogen in this design space. An optimal design of a type I box-shaped pressure vessel with inner tension struts is investigated in literature. In this study, the relatively low efficiency of this type of pressure vessel is presented first. This motivates to work out the possibilities to increase the efficiency by reducing the total weight. Therefore, a numerical analysis is conducted to reinforce the vessel wall with fiber reinforced composite in the main section of this study. In a first step the possible measures to reinforce the pressure vessel are analyzed and evaluated. Then, a basic design, which serves as a baseline for the numerical studies, is determined analytically. Using a numerical model, the structure of the reinforcement and the acting loads are investigated with finite element method in parametric studies varying several parameters. Based on the knowledge gained, optimal design parameters are derived. These design parameters allow a comparison of the pressure vessel efficiency with other pressure vessel types and enable the evaluation of this vessel concept.

The landscape for cloud services and cyberinfrastructure offerings has increased drastically over the past few years. Initially, users moved their applications to the cloud to take advantage of a pay-per-usage model and on-demand access. However, as more cloud providers joined the market, users shifted their goals for using cloud computing from cost reduction to resilience, agility, and optimization. These goals can be achieved by dynamically combining services from multiple providers, for example, to avoid data center or cloud zone outages or to take advantage of extensive offerings with different price points. However, to efficiently support application deployment in this dynamic environment, new models and tools that can measure the application performance and the Quality of Service (QoS) of different configurations are required. The goal of this work is to evaluate the application performance and the QoS of a distributed Software-Defined Environment as well as calculate the QoS of alternative configurations from the underlying pool of services. In particular, we present a mathematical model and a tool for evaluating the performance and QoS of batch application workflows in a distributed environment. We experimentally evaluate the proposed model using a bioinformatics workflow running on infrastructure services from multiple cloud providers.

Recently, the exponential growth of the Internet of Things (IoT) services with heterogeneous requirements becomes a burden to the traditional cloud/datacenter platform. Edge computing is an emerging solution to gain business value of IoT services where real-time demands become satisfied by moving computing resources close to data sources. Nevertheless, edge resources are still limited to be able to fulfill all demands at the same time. Among new approaches, resource sharing between edge/cloud service providers has been considered as a promising mechanism to address resource scarcity and pursue cost reduction. In this paper, we propose an allocation and sharing model in the Edge Cloud network where providers team up to efficiently utilize resources, named the Share to Run IoT Services (SRIS). In particular, we formulate a resource allocation and sharing optimization model to implement IoT services of multiple edge/cloud providers that can maximize the providers’ utility while satisfying service constraints. We relax SRIS into a tractable form that can be solved efficiently using well-known distributed convex frameworks, such as the dual decomposition and alternating direction method of multipliers. Finally, we evaluate our methods by providing several simulation cases, in which our proposed mechanisms show outstanding outcomes by obtaining a faster convergence, increasing by 6.9% of utilization, and 16% of acceptance rate compared to the non-optimal approach.

Reliability requirement assurance is an important quality of service (QoS) for workflow execution in cloud-based services. For a workflow with a reliability requirement, the enough replication for redundancy minimization (ERRM) and quantitative fault-tolerance with minimum execution cost + (QFEC+) algorithms are state-of-the-art algorithms to reduce the redundancy and cost, respectively. In this work, we define the reliability increment ratio (RIR) and propose the redundancy minimization using RIR (R_RIR) algorithm. In addition, we introduce the geometric mean and propose the cost reduction using geometric mean (C_GM) algorithm based on redundancy minimization. Experimental results show the proposed R_RIR and C_GM algorithms are superior to state-of-the-art algorithms: (1) although both R_RIR and ERRM show the same redundancy results, R_RIR is proven to generate minimal redundancy, whereas ERRM cannot; (2) R_RIR only consumes a few seconds to achieve minimal redundancy for large-scale workflows, and it has much higher time efficiency than ERRM; and (3) C_GM generates less cost than QFEC+ in a large part of cases.

Background:: Indonesia has implemented National Health Insurance since 2014 through quality improvement and cost efficiency strategy. In the early implementation, many hospitals suffered a loss due to the absence of efficiency in operational health services. In the other hand, few hospitals have implemented lean as an attempt of quality improvement and cost efficiency in National Health Insurance era. There is not enough evidence in Indonesia to support management tools implementation such as Lean for improving efficiency. Aims:This study aims to develop scientific knowledge and goodwill of Lean implementation in healthcare services in many countries. This study also becomes evidence-based to implement lean in Indonesia. Methods:This study used a systematic literature review and searched PubMed, Scopus, Clinical key, Sciencedirect, EBSCO, and other related journals from 2009 to 2018 with syntax keywords for each database. Twenty-two articles were included in the reviewed. Results:The result showed that most journals discussed Lean reducing lead time and ALOS to improve the patient value and patient safety, as well as the amount of journal discussed about Lean made cost reduction in healthcare operational to improve efficiency. Conclusion:Lean implementation should be adopted by Indonesian hospitals as quality improvement and cost containment strategy and enhance patient safety under National Health Insurance era.

Airtificial Intelligent Robots es una compañía con sede central en Sant Fruitós de Bages, con operaciones en 5 países. Se dedica a diseñar, producir e instalar máquinas y líneas de montaje para el sector automoción. Es una compañía que hace uso de herramientas de Lean Six Sigma como parte de su estrategia operacional. El ciclo de mejora DMAIC es una de las más importantes. En todas las fases de dicho ciclo es importante contar con información suficiente y confiable para formular y hacer seguimiento a acciones de mejora. En este contexto, la Inteligencia de Negocios (Business Intelligence) cobra particular importancia. El presente proyecto tuvo como objetivo implementar un tablero de control de inteligencia de negocios que permitiera monitorear los principales indicadores relacionados con mejora continua. Para este propósito, en la primera fase del proyecto la mejor herramienta fue seleccionada. Teniendo en cuenta que una restricción por parte de la compañía era el uso de PowerBI de Microsoft, la herramienta pin live presentó los mejores resultados. Permitió el uso de filtros e interactividad, además de ser flexible en el uso de cualquier tipo de visualización dentro del tablero. Además, el mencionado tablero de control fue ocupado por secciones dedicadas a los diferentes proyectos de mejoramiento de la compañía. Para cada uno de ellos, los indicadores más relevantes fueron escogidos. En los casos en los que las fuentes de datos no estaban lo suficientemente organizadas, se dio la estructura necesaria. Una vez los datos tenían un formato adecuado, los campos adicionales y medidas fueron creados. De esta manera fue posible crear las visualizaciones necesarias en cada sección. Siguiendo esta metodología, ocho secciones fueron creadas en el tablero de control principal, cinco dedicadas a proyectos de mejora y tres enfocadas a seguimiento de eficiencias y costes de proyectos ejecutados por la compañía. Airtificial Intelligent Robots is a company with headquarters in Sant Fruitós de Bages, developing operations in 5 countries. It is dedicated to design, produce, and install machines and assembly lines for the automotive industry. It uses Lean Six Sigma tools as part of its operational strategy. The DMAIC improvement cycle is one of the most relevant. In every phase of this cycle, it is important to rely on a proper quantity of reliable data, to formulate and follow up improvement actions. In this context, Business Intelligence is particularly important. The objective of the present project was to implement a dashboard to monitor the main indicators related to continuous improvement. To fulfill this, the best tool was chosen. Considering that the project was constrained to the use of Microsoft PowerBI, the tool pin live exhibited the best results. It allowed using filters and interactions, besides flexibility when using any kind of visualization. The dashboard was filled by sections, each of them dedicated to one improvement project of the company. For each project, the most relevant indicators were chosen. For those cases in which the data sources were not correctly organized, proper data structure was created. With suitable data format, the required calculated field and measures were generated. This way, it was possible to develop suitable visualizations for each section. In addition to the visualization set to the main dashboard, each section on it had an associated support dashboard, with further visualizations that provided additional insights about the particular project. By following this methodology, eight sections were created in the main dashboard, five of them dedicated to improvement projects, and other three focused to follow up efficiencies and cost in projects executed by the company. All the components of the dashboard were validated along with the corresponding users, after which, they were put in operation.

Refereed/Peer-reviewed This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for a grid-connected house based on an energy-sharing mechanism. The grid-connected house, also mentioned as house 1 where it is relevant, shares electricity with house 2 under a mutually agreed fixed energy price. The objective is to minimize the cost of electricity (COE) for house 1 while decreasing the electricity cost of house 2. Practical factors such as real data for solar insolation, electricity consumption, grid constraint, ambient temperature, electricity rate, and battery degradation are considered based on actual data. The developed methodology is examined by taking the actual load data of two houses in South Australia. Different scenarios of contract years between the houses are investigated to make it more practical in real life. Sensitivity analyses are conducted for the sharing of energy between the houses and by changing parameters like export power limitation, load of houses, and costs of PV and BES. Likewise, operational analysis is done for two days of summer and winter. It is found that when energy sharing is applied, the optimal design of the PV-BES system will achieve lower COE for both houses.

Говорится о необходимости использования автоматизированного управления цепочками поставок для всех партнеров, принимающих участие в обеспечении различных этапов жизненного цикла товара (услуги) независимо от его специфики или назначения, The modern market dictates the need for automated supply chain management as the most effective tool for increasing company profits and product competitiveness. Requirements for supply chain security is the main objective of the international standard GOST R ISO 28000–2019. We have analyzed the features, methods, and stages of building a supply management system to form an effective chain with carefully planned management. They pointed out the important role of information support, which is necessary for high-quality interaction of all participants in the chain — from supply planning to analysis of consumer demand. Having studied the possibilities of modular supply chain management systems, we came to the conclusion about the key advantages of modular solutions. We believe, the ever-increasing increase in the impact of uncertainty factors on organizations requires the use of risk-based thinking. This is the only way companies will be able to prepare for emergency situations and minimize possible damage. Today there are many standards with recommendations on the use of risk management tools, but ultimately each organization must independently adapt solutions based on the specifics of its activities and ambition.

Denna studie undersöker utvecklingen av digitala tjänster och integrationen av innovativ teknik hos försäkringsförmedlingsföretaget Portfolio Försäkra. Under de inledande diskussionerna med VD, Andreas Adolfsson, noterades att företaget hade vissa luckor i sitt digitala tjänsteutbud och saknade användning av innovativ teknik. Bristerna är inte unika för Portfolio Försäkra utan potentiellt förekommande inom hela försäkringssektorn, vilket innebär en stor inverkan på marknadens konkurrens. I dagens digitala era är expertis inom teknik och innovativa metoder avgörande för försäkringssektorns framgång. Förbättrad digital tjänsteinnovation är nödvändig för att kunderna ska kunna använda information mer effektivt, vilket i slutändan leder till ökad kundlojalitet och kundnöjdhet. Därför är behovet av att förbättra de digitala tjänsterna inom försäkringssektorn absolut nödvändigt. Syftet med denna undersökning är att fördjupa oss i de digitala möjligheter som finns tillgängliga för Portfolio Försäkra och andra försäkringsbolag som för närvarande saknar de senaste tekniska lösningarna. Vi antar rollen som forskare, genomför intervjuer och kommunicerar med anställda på Portfolio Försäkra. Vår dataanalys och våra intervjuer kommer att hjälpa oss att identifiera problemområden som kan hindra företagets utveckling och kundnöjdhet. Resultaten tyder på att integrationen av innovation och digital teknik måste bli en integrerad del av Portfolio Försäkra företagskultur för att säkerställa en hållbar och långsiktig tillväxt. Resultaten av denna studie erbjuder värdefulla insikter för andra organisationer inom försäkringssektorn som vill förbättra sina digitaltjänsteinnovation och kundnöjdhet. This study explores the digital service development and innovative technology integration of Portfolio Försäkra, an insurance brokerage firm. During the initial discussions with the CEO, Andreas Adolfsson, it was noted that the company faced gaps in its digital service offerings and lacked the adoption of innovative technologies. The observed shortcomings are not unique to Portfolio Försäkra but potentially prevalent across the insurance sector, signifying a major impact on market competition. In today's digital age, expertise in technology and innovative practices is crucial to the success of the insurance sector. Improved digital service innovation is essential for the customers to utilize information more efficiently, ultimately leading to increased customer loyalty and satisfaction. Therefore, the need for digital service improvement in the insurance sector is imperative. The aim of this research is to delve deeper into the digital opportunities available to Portfolio Försäkra and other insurance companies that currently lack the necessary technological advancements. We adopt the role of researcher, conducting interviews and communicating with Portfolio Försäkra's employees. Our data analysis and interviews will help identify problematic areas that could hinder the company's development and customer satisfaction. The findings suggest that the integration of innovation and digital technology must become an integral part of Portfolio Försäkra's company culture to ensure sustainable and long-term growth. The results of this study offer valuable insights for other organizations in the insurance sector seeking to enhance their digital service offerings and customer satisfaction.

The article explores the main indicators and criteria for the economic efficiency of agricultural production as a multi-level socio-economic category. Increasing economic efficiency for all sectors of the agro-industrial complex is of particular importance, from the point of view of the peculiarities of the organizational form and its role in nature, as a factor in the growth of crop yields. The most important task is to effectively organize production, implement the achievements of scientific and technological progress, optimize production structures, increase the material interest of manufacturers in the final results, expand the product market, contributing to the growth of production volumes and product quality. The essence of the indicator of economic efficiency is to obtain the required quantity of products in the right assortment and high quality at the lowest cost of productive forces per unit of output.