Customer Side Research Articles

Evaluation Model of Front End System Implementation of Bank XYZ’s from Customer Side

Information Systems is used by banking companies to process and store financial data and transactions made by customers. The company strives to continuously improve its technological innovations in order to support internal business processes. With the implementation of information technology to contribute to the acceleration of internal business processes that are more efficient and effective internally, it is expected that companies can compete to be more competitive. The purpose of this study is to determine the main factors of the implementation of the existing front end system at bank XYZ, so that further models can be made that can be used to evaluate the performance of the system. This research method is carried out by collecting data from respondents or customers who are directly affected by the service from this front end system, then observations are made using the factor analysis method. The results of this study found that there were new factors formed by the presence of a number of clustered indicators which could then be represented as new factors that reflected the expectations of respondents in particular and bank customers in general. The conclusion obtained is that there are new factors formed: Automation, Information. Management Information Systems, Business Process, and Performance.

Evaluation of the Impact of the Lusaka Stock Exchange on the Financial Performance of Telecommunication Companies in Zambia: A Study of Airtel Zambia Limited (2012 – 2021).

This paper evaluates the impact of the Lusaka Securities Exchange (LUSE) on the financial performance of telecommunication companies in Zambia: a study of Airtel Zambia Limited (2012 – 2021). Using time series quantitative data and a combination of primary and secondary data obtained through structured questionnaires to meet the following specific research objectives: i) to analyse the effect of Airtel Zambia’s stock price on the annual net revenue of Airtel Zambia, ii) to determine the correlation between Airtel Zambia’s stock price and the annual net revenue of Airtel Zambia, iii) to determine management’s level of knowledge of the Securities Exchange Market and how the knowledge is being applied to influence revenue performance, iv) to examine the perception of employees on the effect of stock price on the revenue performance of Airtel Zambia Limited and v) to identify and examine factors that affect revenue performance of Airtel Zambia Limited. The results of the study show that there exists a statistically significant relationship, there is a low effect of stock prices on Airtel Zambia’s revenue and that there is a correlation between the stock price and revenue performance. It is recommended that more concerted efforts should be made toward stimulating participation on the stock exchange both from the companies’ side and the customers side.

Pemanfaatan Simulasi Monte Carlo Untuk Perhitungan Kinerja Gangguan Tenaga Listrik Pada PT PLN (Persero) UIW Suluttenggo

Objective: This study aims to determine the optimum number of electrical disturbances at PLN UIW Suluttenggo both in terms of frequency and duration of disruption to determine the level of quality of electric power reliability service on the customer side. Quality reliability or "reliability" is related to the ability of a system to deliver electricity to all points of its user in the appropriate standards and quantities. There are two things related to the reliability of electric power (1) Adequacy (adequacy) and (2) System security (security).
 Method: Monte Carlo simulation is a simulation model that includes a series of random sampling with a probability distribution that can be known and determined, so this simulation can be used. In this study, data was taken from the number of electrical disturbances that occurred at PLN UIW Suluttenggo in 7 service unit branches using SAIDI and SAIFI data in 2021 and 2022.
 Findings: The finding of this study is that the use of Monte Carlo simulations can be used to analyze ganggaun data where the calculation results compared to the calculation of the excell formula produce almost the same results.
 Results: The results of the interference performance calculation showed the best performance were UP3 Manado (1.65), UP3 Kotamobagu (1.55) and UP3 Gorontalo (1.05). As for each interruption performance for the shortest duration of outage is UP3 Kotamobagu (0.75) and the smallest interference frequency is UP3 Manado (1.0).

Fiber Optic Attenuation Analysis Based on Mamdani Fuzzy Logic in Gambir Area, Central Jakarta

In this study, the authors conducted an analysis of the quality of fiber optic network maintenance based on attenuation value and maintenance time using fuzzy Mamdani logic and simulated using Matlab software, to improve accuracy in drawing conclusions on maintaining quality. This study uses a quantitative method, in which the author obtains a summary of customer data from PT. Telkom Indonesia in a period of 4 months of observation from August to November 2021. In August there were 776 customers, in September there were 362 customers, in October there were 359 customers, and in November 445 customers who underwent Indihome fiber optic cable maintenance. The test results with the centroid method with an input Handling Time of 1.5 hours and an Attenuation of 15 dB, then the output Repair Quality is 5.5 or categorized as Good. The greater the attenuation value generated, the more time it takes to maintain the IndiHome internet network disturbance. This is due to the many technical maintenance of fiber optic cables carried out by technicians to adjust for damage/trouble in the field. It is expected that maintenance can be carried out routinely in order to avoid fatal internet disturbances on the customer's side, and maximize maintenance time according to the dosage determined by the company, which is less than 3 hours, taking into account the work performance of technicians and also the quality of maintenance.

A new multi-timescale optimal scheduling model considering wind power uncertainty and demand response

When wind power is connected to a power grid, intermittency and uncertainty increase the difficulty of power system dispatching and operation. A multi-timescale optimal scheduling model based on wind power uncertainty and demand response are proposed to address the uncertainty of wind power integration. The demand response program is first implemented on the customer side to adjust the electricity consumption pattern and reduce the peak-valley load difference. Then, the robust optimization based on extreme scenarios is used in the day-ahead scheduling stage according to the characteristic that the wind power prediction accuracy gradually improves step by step with the refinement of timescales so that the decision variable meets requirements in all scenarios. In the intraday scheduling stage, the method based on stochastic chance-constrained programming is used, and the out-of-bounds phenomenon is allowed in extreme cases. During real-time scheduling, the deviation remaining after intraday scheduling is corrected. Finally, a 10-unit system is used as an example to demonstrate the feasibility and effectiveness of the proposed scheduling model. The simulation results show that the total economic cost, reserve cost and wind power curtailments of the real-time scheduling model are reduced by 18.21%, 66.06% and 66.57%, respectively, compared with the day-ahead scheduling model. In addition, compared with the intraday scheduling model, the wind power curtailments of the real-time scheduling model are reduced by 20.00%, which proves the necessity of multi-timescale coordinated scheduling.

Compensation of Distributed Generations Outage Using Controlled Switched Capacitors

Researchers recently came up with innovative ways to generate enough electricity to meet the rising demand through establishing an electricity distribution system and enhancing power quality on the customer side. One of these innovative ways is the installation of distributed generation (DG). DG is widely used in modern networks due to its great benefits of improving the voltage profile and the system’s power quality. Additionally, DGs are ideally placed near the end user in distribution systems to improve the system’s performance while minimizing power losses and enhancing voltage profile in the grid. DG recently grew in importance, and its penetration increased in most distribution systems. Due to the spreading of DG in the power system, the dynamic performance of the system is affected. This paper studies the system’s performance and behavior under condition of DG outage from the system. The model of DGs in this study assumes two cases of the power provided to the network; the first case considered DG units injecting active power only to the grid (unity power factor). In the second case, DG injects both active and reactive power to the system. After that, outage of DG units is fixed by injecting a reactive power source using a capacitor with a controlled switch to compensate the outage of DGs from the distribution system and to reduce the outage’s negative effect on the network. The sizing of capacitors is optimized using a harmony search algorithm (HSA) in the same location of the DG units.

Prospective risk analysis of the online-adaptive artificial intelligence-driven workflow using the Ethos treatment system

PurposeThe recently introduced Varian Ethos system allows adjusting radiotherapy treatment plans to anatomical changes on a daily basis. The system uses artificial intelligence to speed up the process of creating adapted plans, comes with its own software solutions and requires a substantially different workflow. A detailed analysis of possible risks of the associated workflow is presented. MethodsA prospective risk analysis of the adaptive workflow with the Ethos system was performed using Failure Modes and Effects Analysis (FMEA). An interprofessional team collected possible adverse events and evaluated their severity as well as their chance of occurrence and detectability. Measures to reduce the risks were discussed. ResultsA total of 122 events were identified, and scored. Within the 20 events with the highest-ranked risks, the following were identified: Challenges due to the stand-alone software solution with very limited connectivity to the existing record and verify software and digital patient file, unfamiliarity with the new software and its limitations and the adaption process relying on results obtained by artificial intelligence. The risk analysis led to the implementation of additional quality assurance measures in the workflow. ConclusionsThe thorough analysis of the risks associated with the new treatment technique was the basis for designing details of the workflow. The analysis also revealed challenges to be addressed by both, the vendor and customers. On the vendor side, this includes improving communication between their different software solutions. On the customer side, this especially includes establishing validation strategies to monitor the results of the black box adaption process making use of artificial intelligence.

EFFICIENCY OF LAST MILE DELIVERY OF LOGISTICS SERVICE PROVIDERS (LSPS) IN MALAYSIA: POST-COVID

Several studies have researched on numerous areas about last mile delivery over the past years however, studies focusing on factors influencing efficiency of last mile delivery of logistics service providers (LSPs) is still fragmented and yet to be examined vigorously particularly during post-covid era. To fill the unanswered gaps of last mile delivery efficiency, this research aims to investigate the factors that influence efficiency of last mile delivery of logistics service providers (LSPs) in Malaysia in post-covid era. As a result, this research assists logistics service providers (LSPs) in identifying essential elements and focusing on these aspects in order to successfully improve and execute their last mile delivery services. Perhaps as a consequence, the authors think that this research will guarantee that the hurdles to achieving optimal last-mile delivery are addressed, ensuring the quality of transportation operations and a positive customer experience. This research was conducted in a quantitative approach and the method of data collection was conducted using the physical and online survey questionnaire to obtain responses from major LSPs involved in B2C e-commerce in Malaysia (Pos Malaysia, J&T Express, GDex, Ninjavan) using a non-probability sampling method. A total of 300 respondents participated in the survey from which all of them were taken from around LSPs situated in Malaysia. In accordance with analysis from regression, it is seemed issues from customer side factor seems the stronger influence in the efficiency of last mile delivery during the post covid era, followed by cost of last mile delivery and then by customer expectation. These variables should drive LSPs' strategies and dynamic attempts to enhance last mile delivery efficiency.

Behind-the-Meter Resources: Data-driven modeling, monitoring, and control

Behind-The-Meter (BTM) resources are distributed energy resources (DERs), such as rooftop solar photovoltaics (PVs), electric vehicles, and battery storage systems, located on the customer side of smart meters. Driven by monetary incentives, declining costs, and increasing electricity service interruptions, the penetration of BTM resources has been increasing exponentially in the past few years. For example, the small-scale BTM solar PV capacity in the United States has quickly increased from 7,642 MWac in September 2015 to 34,029 MWac in February 2022.

Optimal spare parts production–distribution scheduling considering operational utility on customer equipment

Integrated scheduling of production and distribution is conducive to saving transportation cost and improving customer satisfaction. However, in previous related literature, no optimization objective has been set to intuitively reflect the influence of the scheduling scheme on the customer side, ignoring the fact that: 1) the production and distribution of spare parts directly affect the performance of operation and maintenance and further affect the operational utility of customer equipment; 2) the delivery time of spare parts has a positive effect on optimizing the operational utility of customer equipment corresponding to the delayed spare parts. To fill this gap, in the paper, we construct an optimal spare parts production scheduling model integrating logistics distribution in flowshop environment, in which equipment operation utility on the customer side is set as an optimization objective and a speed adjustment strategy is introduced, so that the manufacturer can adjust the operation strategy of the equipment corresponding to the delayed spare parts and guide the equipment operation and maintenance in the customer side. There are two objectives in our model. The first is to maximize equipment operational utility on the customer side, and the second is to minimize the total cost on the manufacturing side. To solve the problem, a three-layer hybrid coding memetic algorithm (THMA) is constructed. Two initial heuristics, respectively named Heuristics 1 and Heuristics 2, are constructed. Two local search operators operated respectively on the production and distribution stages of spare parts are constructed. By performing extensive contrast experiments, the superiority of THMA and the effectiveness of all the above operators are verified.

Design and application of new power load management system

In order to improve the load management ability of the new power system, this paper proposes a design scheme of the new power load management system. The design scheme is based on the cloud platform and adopts the micro service and micro application architecture to build applications such as load control customer management, equipment file management, load monitoring, load characteristic analysis, load control scheme formulation, load regulation and implementation, load implementation monitoring, implementation effect analysis, operation management, power safety, early warning monitoring, command and drill, and realize the real-time monitoring and accurate control of load resources on the customer side. The practical application proves the effectiveness of this scheme in improving the management of customer side load resources.

Service design based on social simulation: An integrated experience mapping methodology considering customers and service providers

Approaches using both real data and social simulations have been proposed to address economic and social problems. Understanding the structure of models and the results of simulations may be used by not only model developers and analysts but also by decision-makers in management and administration as well as on-site managers. Therefore, methodologies for extracting knowledge and insights from simulation log data and a framework for propagating the extracted knowledge and insights to stakeholders are important. In this study, we compare log analysis frameworks and create a formal description of social simulations for benefit-delay products and services to determine their merits, shortcomings, and limitations. Then, by extending an experience mapping method from a previous study, we propose a method for also describing a service provider’s experience. We use this method to simulate pre- and post-retirement asset sustainability. The main findings are as follows: 1) an integrated experience description of the customer and provider sides of a benefit-delay service design is feasible, and 2) customer targeting based on this description is possible.

Power allocation model for residential homes using AI-based IoT

Conventional power transmission and distribution schemes have been completely transformed by the development of the Smart Grid (SG). Nearly every field has been impacted by technology and advancements, including smart grids. Electricity also isn't inexpensive to produce, so Smart Meters (SM) play a crucial role in controlling, managing, and performing efficiency to use the electricity successfully on the customer side, which would be referred to as demand side management (DSM). A Home Energy Management System (HEMS) has been proposed in the research study as a way of optimizing home appliances and saving as much money as possible. The Eagle Hard Optimization (EHO) method and our hybridization of the EHO technique have been implemented. EHO appears to be a Genetic Algorithm (GA) known as a Genetic EHO (GEHO). Compared to normal EHO and unplanned systems, simulation results showed that GEHO better planned the devices to lower the maximum price. Peak to Average Ration packs (PAR) has additionally been noted. Due to the planning of the largest amount of devices on both sides, GEHO& unforeseen has PAR equal, while EHO has a low PAR. Various Operation Time Intervals (OTI) were used to help in efficiency measures. With each of the three plans, capacity & costs money have indeed been thoroughly analyzed.

The impact of supply chain social capital on supply chain performance: a longitudinal analysis

PurposeDrawing on the resource-based view (RBV), this study aims to investigate the impact of social capital, namely, structural (information sharing), cognitive (shared value) and relational (relationship commitment) capital in the supplier and the customer side on supply chain performance in a longitudinal design. It further aims to examine the moderating effect of change in competition intensity.Design/methodology/approachBased on two-wave data collected from 203 manufacturers in China, this study uses the ordinary least square and first-difference regression methods to test the proposed relationships.FindingsThe results show the effect of social capital on supply chain performance and the dynamic nature of supply chain social capital. The causal analysis further reveals the significance of supplier-side structural and relational capital in improving supply chain performance. Moreover, competitive intensity plays an important moderating role.Originality/valueThis study, to the best of the authors’ knowledge, is one of the first to demonstrate the longitudinal effect of supply chain social capital on supply chain performance.

Reliability assessment of modern distribution networks embedded with renewable and distributed resources

With the rapid depletion of fossil fuel based resources and growing concerns related to climate change, significant development of renewable energy has been observed around the world. Reliability assessment of power systems is a key aspect to measure the impact of renewable distributed generation (DG) units on modern distribution networks. This paper proposes a Monte Carlo simulation based approach to investigate the influence of the stochastic nature of renewable and distributed resources including solar PV, battery energy storage, and electric vehicles on the reliability of distribution systems. A modified Roy Billinton test system (RBTS) is used to demonstrate the findings and calculate customer side reliability indices including SAIDI, SAIFI, and CAIDI with the consideration of capacity factors.

An Equivalent Circuit-Based Approach for Power and Emission Tracing in Power Networks

This article develops an equivalent circuit-based model to perform power and emission tracing in power grids. By relying on the proposed model, a power network can be readily converted into a series of equivalent acyclic circuits so that the contributions of generators to a specific load can be clearly and unequivocally traced from a circuit point of view without making any assumptions on how nodal power inflows contribute to the outflows. The effectiveness of the proposed method is discussed through both power flow and optimal power flow simulations on three case studies, including a simple two-generator network, and the IEEE standard 14-bus and 118-bus systems. Furthermore, the proposed power flow tracing method is used in estimating the electricity-associated emissions from the customer side.

A review of voltage sag control measures and equipment in power systems

In recent years, voltage sags are one of the most critical research issues in the field of power quality. With the all-embracing study of voltage sag mitigation measures and equipment, the classification of voltage sag mitigation measures and equipment is becoming more and more necessary. Reasonable classification methods not only can guide users to choose appropriate control measures and equipment, but also provide basis and research direction for new voltage sag control measures and equipment. Firstly, this study performs a detailed analysis of the current stage of voltage sag control measures and equipment, and proposes a classification method that divides the voltage sag control measures into three categories: the power supply side, the customer side and the equipment manufacturing company. Secondly, due to the difference between single power supply and dual power supply in voltage sag control equipment, this study proposes a classification method for voltage sag control equipment based on the number and type of power supply. Finally, in order to help users to select the voltage sag control equipment suitable for the actual situation, this paper summarizes the prevention and control ability of the main voltage sag control equipment.

Connected On-Board Inert Gas Generation System with Real-Time Data Access & Machine Learning Approach for Predictive / Prognostics Maintenance

<div class="section abstract"><div class="htmlview paragraph">The purpose of the OBIGGS is to reduce the amount of oxygen in the fuel tank to a 'safe' level to significantly reduce the possibility of ignition of fuel vapors. There are circumstances where equipment of OBIGGS like ASMs, Ozone Converter Catalysts, etc. gets degraded earlier than the provided MTBF.</div><div class="htmlview paragraph">This paper studies the present conventional systems limitations, like due to memory constraints only the faults and limited shop data are being recorded, hence there is no provision to store/report the stream of data margins with which we can pass/fail the performance tests.</div><div class="htmlview paragraph">This paper also explains how a new design of the Connected concept achieves access to real-time data from the system and how the data is pushed to the cloud network. A connected solution for the OBIGGS is the technology to access real-time data (Systems LRUs Performance data and Custom data Parameters) from the Systems controller data bus, this data is further applied to AI/ML methods for predictive/prognostics features to compare why the performance of the ASMs in some systems may degrade quicker than others and to inform of when equipment of OBIGGS may need to be inspected/tested/replaced.</div><div class="htmlview paragraph">VOCs related to ASMs degradation, FQIS field issues, sensors, valves, and other equipment's data parameters can be monitored over time that would be of value and an interest in more details for the Suppliers, Manufacturers, and Customers side.</div></div>

The multi-product vehicle routing problem with cross-docking: a novel strategy hybrid bat algorithm for Industry 3.5 in Thailand's food industry

ABSTRACT This paper presents a multi-product vehicle routing problem with cross-docking operations for food industry to ensure products which can be delivered in time and with minimum transportation and hiring costs to perform for both supplier side and customer side. The problem was formulated as a special multi-product vehicle routing problem with cross-docking and time window constraints (MPVRPCDTW). A novel strategy hybrid based on the Bat algorithm (NSHBA) is presented to solve the MPVRPCDTW for the sustainable food industry by minimising the transportation cost and the number of vehicles, using the poultry industry as a case study. Additionally, a new disturbance algorithm (DA) was developed to find the best neighbourhood strategy to increase the efficiency and quality of solutions in the NSHBA. The computational results revealed that the NSHBA-DA outperformed the Bat algorithm, the differential evolution algorithm, and the particle swarm optimisation algorithm. The MATLAB App architecture based on all eight algorithms was designed and developed. The overall result of this study should prove to benefit the logistics staff in making the right decisions on cross-docking transportation by employing real-time data.

Customer-Centric, Two-Product Split Delivery Vehicle Routing Problem under Consideration of Weighted Customer Waiting Time in Power Industry

This paper introduces a new model of the customer-centric, two-product split delivery vehicle routing problem (CTSDVRP) in the context of a mixed-flow manufacturing system that occurs in the power industry. Different from the general VRP model, the unique characteristics of our model are: (1) two types of products are delivered, and the demand for them is interdependent and based on a bill of materials (BOM); (2) the paper considers a new aspect in customer satisfaction, i.e., the consideration of the production efficiency on the customer side. In our model, customer satisfaction is not measured by the actual customer waiting time, but by the weighted customer waiting time, which is based on the targeted service rate of the end products. We define the targeted service rate as the ratio of the quantity of the end product produced by the corresponding delivery quantities of the two products to the demand of the end product. We propose a hybrid ant colony-genetic optimization algorithm to solve this model with actual data from a case study of the State Grid Corporation of China. Finally, a case study is explored to assess the effectiveness of the CTSDVRP model and highlight some insights. The results show that the CTSDVRP model can improve customer satisfaction and increase the average targeted service rate of the end products effectively.