Power Business Intelligence Research Articles

Optimizing mining projects: how business intelligence enhances data-driven decisions

The project management sector grapples with the challenge of handling a vast amount of information. Data compilation is essential to support effective decision-making processes by project managers. This paper emphasizes the significance of both project planning and execution monitoring. It concurrently explores the applicability of Business Intelligence (BI) software and its contributions to enhancing project management. The methodology employed a literature review combined with the authors' expertise in engineering projects. The findings reveal that BI offers substantial benefits. These include reducing the time required to transform large datasets into clear and concise graphical information that can be readily presented and dynamically adjusted to meet specific user needs. A study conducted in a mining company demonstrated a significant reduction in project management activities. By utilizing Power BI software, they achieved a 50% reduction in total time dedicated to project management activities. Additionally, there was a remarkable 83% reduction in the time spent on report issuance and updates. This outcome enables that project team members are aligned with consolidated information, facilitating real-time decision-making and fostering greater integration and productivity.

The Important Role of Microsoft Power Business Intelligence Tool for Analyzing Unit Cost

Problematic problems in calculating unit cost for institution can lead the wrong decision taken by management. Those problems such are lack data information for analyzing, difficulties in querying meta data, illiterates in using tools of technology, and still many other problems. Management’s leader will be difficult to make decision if the meta data as the sources of information cannot be crystalized as requested. This research is conducted for answering the curiosity regarding determination of unit cost that be mining from meta data sources. The originality value of this research is for answering the curiosity in determining unit cost. This research takes place in the higher institutional education along with the name of State University of Jakarta. As Educational Institution, State University of Jakarta is not fully funded by the government of Indonesian Republic. This institution is considered as public entity that part of its funding come from government and the rest of funding come from the ability of institution in generating income. Economics, efficient, and effective are the most determination that should be achieved by State University of Jakarta that is not fully funded by The Indonesian Government. For this reason, State University of Jakarta should be aware in managing available income for institution survival. In analyzing meta data for the consumption of decision making, the researcher has explored the usage of Microsoft Power Business Intelligence (BI) tool for determining unit cost each unit in this institution. Through this tool, then researcher analyzing and describing the crystalized meta data that supported calculation for unit cost. The value result of this research shows that almost 65% of the total units cannot surviving its own entity. In the conclusion, this institution should be able to querying cost in each unit. Determination of direct cost and indirect cost for each unit should be placed proportionally. Also, tuition fee should be able to cover the idle capacity of unit that in the previous time suffering deficit.

Implementasi Penggunaan Aplikasi Microsoft Power BI dalam Melihat Statistik Hutang Negara di Dunia

In the era of globalization, an in-depth understanding of sovereign debt statistics is crucial to ensure economic stability. One solution that can significantly contribute to the analysis of sovereign debt data is the application of Business Intelligence (BI) technology, such as Microsoft Power BI. The importance of in-depth understanding of sovereign debt statistics is crucial, considering that sovereign debt can affect economic policy, currency stability, and various other aspects. Therefore, an efficient approach is needed to manage and analyze data related to sovereign debt in a holistic manner. One of the solutions that can make a significant contribution to the analysis of data on data analysis is the application of Business Intelligence (BI) technology, such as Microsoft Power BI. Power BI enables effective data visualization that can be accessed by various stakeholders, making it easier to understand trends, patterns, and the impact of sovereign debt. This research will focus on the application of Power BI to analyze sovereign debt statistics in various parts of the world, with the aim of contributing positively to the understanding and management of sovereign debt globally through the perspective of advanced information technology

Power BI and SQL Server Dashboard Data for Monitor Transportation Presence Employees at PT PON Gresik

Employee presence is a crucial aspect in office management. It reflects the way employees work in a company and can impact their productivity. A high level of productivity can indicate achievement of company targets. However, this attendance data is often large and has not been processed. To turn it into valuable information, Business Intelligence (BI) software is needed that is able to visualize employee attendance and transportation data. This research aims to create a dashboard that shows employee attendance data, including entry hours and attendance status, as well as transportation data such as expenditure for each type of vehicle and travel destination code. Microsoft Power BI was used for data visualization, while Microsoft SQL Server Management Studio was used to create the database. Data is collected and processed so that it can become a source of information. The research results include a daily attendance dashboard with peak attendance at 08.00 and 21.00. The trip dashboard shows the highest fare with travel code "7AA0112" exceeding Rp. 12,000,000.00 from PT. PON Gresik. Travel information shows transportation use in Jakarta, Bandung and Surabaya with the largest total fare in Jakarta reaching Rp. 97,761,000.00

Quality Management in Pharmaceutical Development of Biotechnology-Derived Medicinal Products: Implementation and Evaluation

SCIENTIFIC RELEVANCE. One of the challenges associated with the development of medicines lies in creating a quality management system (QMS) and tools to assess its performance.AIM. The study aimed to propose a QMS model for the development of biotechnology-derived medicinal products and a performance evaluation tool for this QMS universally applicable in the research centre of the company regardless of the specific activities of its individual divisions.MATERIALS AND METHODS. The study analysed internal audit outcomes, noncompliance responses, and the document flow of the research centre. Parameter values for quality index (QI) calculations were entered into validated Microsoft Excel spreadsheets. Data analysis and visualisation involved using the Microsoft Power Business Intelligence (BI) business analytics platform (mainly, the Power BI Desktop and Power BI Service components).RESULTS. The QMS was implemented, and the authors proposed their QMS performance evaluation tool universally applicable to all the divisions of the research centre. CONCLUSIONS. The authors proposed their QMS model for the development of biotechnology-derived medicinal products and the QI tool for collecting digital data, carrying out standardised monitoring, and tracking QMS status changes. The QI tool is universal for all company departments regardless of their requirements, easy to use, and customisable by adding individual company-specific quality parameters. This makes the QI tool applicable not only to drug development departments but also to other research units.

Developing a data-driven operational guide for the texturized yarn production process: data mining and intelligence approach

PurposeThe textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.Design/methodology/approachThis research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.FindingsThe results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.Originality/valueThis paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Evaluating internet access and information needs in different age cohorts

The digital divide remains a notable hurdle to equitable development, with rural communities in particular, where there are high tendencies for poor access to information and communication technology (ICT). This study examines the availability and utilization of free internet services, the satisfaction of information needs, and the engagement with online information services across three age groups 15-19, 20-29, and 30-49 in Ulemon community in Edo state, Nigeria. Utilizing a structured survey and data analysis conducted via Microsoft Power BI (Business Intelligence), the research reveals critical disparities in digital access and usage. The results indicate that the 20-29 age group has the highest access to free internet at 60%, the highest utilization rate at 40%, the greatest satisfaction with their information needs at 55%, and the highest usage of online information services at 75%. These findings point out the urgency in the need for designated interventions to enhance digital inclusion in rural areas. Recommended actions include expanding broadband infrastructure, implementing subsidized internet plans, enhancing digital literacy through educational programs, and developing locally relevant online content. In conclusion, addressing the digital divide in rural communities is essential for promoting inclusive growth, improving access to education and healthcare, and empowering individuals to participate fully in the digital economy. This study provides valuable insights and actionable strategies to guide efforts toward achieving these objectives and to ensure that the general populace is included in the digital age.

Business Intelligence Applied in the Corporate Sector: A Systematic Review

Companies rely on technological tools like Business Intelligence (BI) to make efficient decisions and gain a competitive edge in the market. This study investigates the implementation of BI in various company areas, methodologies used, benefits obtained, tools applied, and elements of BI in the business sector. Following the Kitchenham and Charters methodology, a bibliographic search in 5 databases yielded 16 relevant articles. According to the main results of the work: logistics was one of the areas where BI was most implemented, followed by sales and finance; the most used methodology was Ralph Kimball; also, the most cited benefits were decision-making, cost optimization, and knowledge generation; the most used BI tool was Power BI, followed by QlikView; and the BI elements most applied in the business area were ETL and OLAP, closely followed by dashboards.

Predictive business intelligence dashboard for food and beverage business

This research was conducted to provide an example of predictive business intelligence (BI) dashboard implementation for the food and beverage business (businesses that sell fast-expired goods). This research was conducted using data from a bakery's transactional database. The data are used to perform demand forecasting using extreme gradient boosting (XGBoost), and recency, frequency, and monetary value (RFM) analysis using mini batch k-means (MBKM). The data are processed and displayed in a BI dashboard created using Microsoft Power BI. The XGBoost model created resulted in a root mean square error (RMSE) value of 0.188 and an R2 score of 0.931. The MBKM model created resulted in a Dunn index value of 0.4264, a silhouette score value of 0.4421, and a Davies-Bouldin index value of 0.8327. After the BI dashboard is evaluated by the end user using a questionnaire, the BI dashboard gets a final score of 4.77 out of 5. From the BI dashboard evaluation, it was concluded that the predictive BI dashboard succeeded in helping the analysis process in the bakery business by: accelerating the decision-making process, implementing a data-driven decision-making system, and helping businesses discover new insights.

Modelling business intelligence technologies framework for analyzing academic performance from learning management systems (LMS)

<p><strong>Background: </strong>Learning analytics (LA) has been utilized to measure, collect, analyze, and report data regarding learners and their contexts, with the aim of understanding and optimizing the learning process. However, most universities having difficulties in identifying students’ academic performance and weaknesses in each subject throughout every semester. <strong>Methods:</strong> Therefore, to address this issue effectively, the integration of result in learning management systems (LMS) with business intelligence (BI) has been proposed. In this case study, power BI tools are used in Universiti Sultan Zainal Abidin (UniSZA) LMS platform known as knowledge and eLearning integrated platform (KeLIP) to analyze student performance. The three-layer business intelligence framework is applied, data source layer, data analytics layer and presentation layer. <strong>Results</strong><strong>:</strong> The successful implementation of the integrated LA system with business intelligence tools yielded valuable insights into students’ academic performance, facilitating informed decision-making within the academic environment. <strong>Conclusions:</strong> In conclusion, this system will assist lecturers in guiding learners’ academic progress and provide them with da-ta-driven insights into their students’ performance each semester, enabling learners to enhance their academic achievements as well as their involvement in co-curricular activities. Furthermore, the integration of LA will contribute to optimizing the learning system, teaching methods, and educational management in this new era of education.</p>

Use of Elasticsearch-based business intelligence tools for integration and visualization of biological data.

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets.

Digital Health Dashboards for Decision-Making to Enable Rapid Responses During Public Health Crises: Replicable and Scalable Methodology.

The COVID-19 pandemic has reiterated the need for cohesive, collective, and deliberate societal efforts to address inherent inefficiencies in our health systems and overcome decision-making gaps using real-time data analytics. To achieve this, decision makers need independent and secure digital health platforms that engage citizens ethically to obtain big data, analyze and convert big data into real-time evidence, and finally, visualize this evidence to inform rapid decision-making. The objective of this study is to develop replicable and scalable jurisdiction-specific digital health dashboards for rapid decision-making to ethically monitor, mitigate, and manage public health crises via systems integration beyond health care. The primary approach in the development of the digital health dashboard was the use of global digital citizen science to tackle pandemics like COVID-19. The first step in the development process was to establish an 8-member Citizen Scientist Advisory Council via Digital Epidemiology and Population Health Laboratory's community partnerships. Based on the consultation with the council, three critical needs of citizens were prioritized: (1) management of household risk of COVID-19, (2) facilitation of food security, and (3) understanding citizen accessibility of public services. Thereafter, a progressive web application (PWA) was developed to provide daily services that address these needs. The big data generated from citizen access to these PWA services are set up to be anonymized, aggregated, and linked to the digital health dashboard for decision-making, that is, the dashboard displays anonymized and aggregated data obtained from citizen devices via the PWA. The digital health dashboard and the PWA are hosted on the Amazon Elastic Compute Cloud server. The digital health dashboard's interactive statistical navigation was designed using the Microsoft Power Business Intelligence tool, which creates a secure connection with the Amazon Relational Database server to regularly update the visualization of jurisdiction-specific, anonymized, and aggregated data. The development process resulted in a replicable and scalable digital health dashboard for decision-making. The big data relayed to the dashboard in real time reflect usage of the PWA that provides households the ability to manage their risk of COVID-19, request food when in need, and report difficulties and issues in accessing public services. The dashboard also provides (1) delegated community alert system to manage risks in real time, (2) bidirectional engagement system that allows decision makers to respond to citizen queries, and (3) delegated access that provides enhanced dashboard security. Digital health dashboards for decision-making can transform public health policy by prioritizing the needs of citizens as well as decision makers to enable rapid decision-making. Digital health dashboards provide decision makers the ability to directly communicate with citizens to mitigate and manage existing and emerging public health crises, a paradigm-changing approach, that is, inverting innovation by prioritizing community needs, and advancing digital health for equity. RR1-10.2196/46810.

POWER BUSINESS INTELLIGENCE DASHBOARD VISUALIZATION of LOSSES NUMBER of MOTOR VEHICLE in EAST JAVA, INDONESIA

East Java Province has a high number of motorized vehicle losses with various modes of crime. There needs to be a system that can process the data to assist the decision-making process for law enforcement officers in investigating the crime of motor vehicle theft and reducing the amount of loss. For this reason, in this research, data processing of motor vehicle theft was carried out using Business Intelligence (BI) and displayed on the Power BI dashboard. The research methodology used is literature study, interviews, data collection, implementation, evaluation, and documentation. The test results show the highest number of vehicle theft cases occurred in 2016 (1,635 cases) with the most modes using fake keys, and with the highest vehicle brand being Honda. The final result of the study shows that the presentation of data and information visually and interactively based on Power BI can help law enforcers to understand data, obtain information, and make decisions and policies to handle motorcycle theft cases. 

95 - Dashboard with Data Extracted from the Dbtc Using R Scripts Visual in Power Business Intelligence

95 - Dashboard with Data Extracted from the Dbtc Using R Scripts Visual in Power Business Intelligence

Data Visualization and Forecasting Domestic Component Level (TKDN) Indonesian Ministry of Industry Using Power Business Intelligence

The government is optimizing the Domestic Component Level (TKDN) for strategic projects funded by the State. Domestic products that must be used must have a TKDN value of at least 25%. According to the Head of LKPP, the target for procurement of government goods/services for domestic products (PDN) in 2023 is 90%. Ironically, products that have TKDN certificates are not comparable to the needs of government agencies. This is of particular concern, whether the targets declared in 2023 will be met or not. Therefore, a touch of technology is needed to be able to analyze TKDN data quickly and precisely. Power Business Intelligent is one of the tools that can be used to analyze data easily understood. Data is taken on the official website of TKDN Kemenperin which has a Microsoft excel file extension. The indicators observed are the trend of TKDN certificates per year, the trend of certificates per month, the trend of goods categories, regional demographic trends in Indonesia and forecasting TKDN certificates for the next 5 years. The overall results of TKDN certified products in 2019-2022 were 33,554. The average number of TKDN products is 49.15% with the highest content of 99.99%. On average, the Ministry of Industry issues TKDN certificates every day as many as 43 products. The number of products certified TKDN with the LKPP e-catalog website is not comparable. The results of the analysis showed that the number of new TKDN certified products was 34.6% and 65.94% had not been certified.

An exploratory cybercrime analysis and its impact on india

In the modern era, the rapid use of the internet has led to reports of cybercrimes. The number of cybercrimes is constantly increasing. The purpose of this paper is to identify the root causes of cybercrime and find insights that can be used by the administration to take fruitful decisions to overcome cybercrime offenses. It has collected data related to the registered cybercrime cases in various Indian states on different parameters for the years 2016 to 2019. The preprocessing was applied to individual datasets, and a common data set was prepared. Data analysis using Power BI (business intelligence) and Microsoft (MS) Excel tools was performed for the Indian states, and its impact was analyzed through year-wise analysis. The outcome of this paper can be utilized by the Indian administration, cyber experts, working professionals, and users to take care of the affecting factors and limit cybercrimes.

Retrospective analysis of selected aspects of public blood transfusion service activities as a starting point for assessment of the status of transfusion medicine in Poland. Part 3: Donations of blood and blood components in the period 1997–2017

Background: Blood donation is primarily an activity of collecting blood from healthy people for the benefit of those who require transfusion (e.g. surgery, oncology and hematology patients or those who suffer massive blood loss) or for the purpose of manufacturing blood products. The overall availability of blood in a country is reflected by the whole blood donation rate (WB) per 1000 inhabitants. It is accepted that the minimum of 30 WB donations/1000 inhabitants provides sufficient protection for the healthcare system, whereas blood supply is considered insufficient when the number of donors drops below 10/1000 inhabitants. A European country is at the basic level of self-sufficiency in terms of blood supply if approximately 2.5% of its population donates blood regularly. Poland has been self-sufficient for many years now, but there is growing awareness that the self-sufficiency may be disturbed mainly because of ongoing demographic changes. One such change is the growing number of elderly people which means an increase in the overall percentage of the sick. In the recent years, Poland has reached a certain level of blood donations (about 1.2 mil per year) and it may be difficult to improve the result without altering the approach to donor recruitment. Material and methods: Material for analyses were the available annual reports from 21 Polish Regional Blood Transfusion Centers (RBTCs) forwarded to the Institute of Hematology and Blood Transfusion (IHTM). The following tools were used for statistical analysis of the available data: Microsoft Office: Access and Excel, Microsoft Power Business Intelligence (Power BI) software and STATISTICA version 13.3 software (TIBCO Software Inc.). Results: In the years 1997–2017, a total of over 20.5 mil WB units were collected (mainly from voluntary non-remunerated donors > 99.7%). The largest number of voluntary nonremunerated donations was collected in RBTC in Warsaw and RBTC in Katowice (2.05 and 2.02 mil respectively), while the lowest number in RBTC in Słupsk and RBTC in Radom (0.43 and 0.39 mil respectively). Blood components were also obtained from apheresis: platelet cell concentrate (Aph. PC) — a total of 522.4 thous donations, plasma from automated plasmapheresis — a total of > 1.9 mil donations, plasma from manual plasmapheresis — a total of > 55.4 thous donations, granulocyte concentrate — 1.5 thous donations and red blood cells from apheresis (Aph RBCs) > 2.2 thous donations (in 2005–2017). Throughout the analyzed period, the indicator of the number of donations per one donor was within the 1.69–2.08 range, while in the last 6 years — at the level of approximately 2. In individual RBTCs the total indicator for the period 1997–2017 was calculated at the level of 1.74–2.16. Conclusion: The study analysis of the number of donations collected throughout the 1997–2017 period, indicates that whole blood was the most often collected component in all RBTCs, while the least frequent one were RBCs from apheresis and granulocyte concentrate (GC). The number of WB donations in RBTCs varied — most donations were collected in RBTC in Warsaw and Katowice, while the least in Słupsk and Radom, which is most certainly related to the number of inhabitants/potential donors in the area of activity of these RBTCs.

Imaging volumes during COVID-19: A Victorian health service experience.

BACKGROUNDThe World Health Organisation declared the coronavirus disease 2019 (COVID-19) a pandemic on March 11, 2020. While globally, the relative caseload has been high, Australia’s has been relatively low. During the pandemic, radiology services have seen significant changes in workflow across modalities and a reduction in imaging volumes. AIMTo investigate differences in modality imaging volumes during the COVID-19 pandemic across a large Victorian public health network.METHODSA retrospective analysis from January 2019 to December 2020 compared imaging volumes across two periods corresponding to the pandemic’s first and second waves. Weekly volumes across patient class, modality and mobile imaging were summed for periods: wave 1 (weeks 11 to 16 for 2019; weeks 63 to 68 for 2020) and wave 2 (weeks 28 to 43 for 2019; weeks 80 to 95 for 2020). Microsoft Power Business Intelligence linked to the radiology information system was used to mine all completed examinations.RESULTSSummed weekly data during the pandemic’s first wave showed the greatest decrease of 29.8% in adult outpatient imaging volumes and 46.3% in paediatric emergency department imaging volumes. Adult nuclear medicine demonstrated the greatest decrease of 37.1% for the same period. Paediatric nuclear medicine showed the greatest decrease of 47.8%, with angiography increasing by 50%. The pandemic’s second wave demonstrated the greatest decrease of 23.5% in adult outpatient imaging volumes, with an increase of 18.2% in inpatient imaging volumes. The greatest decrease was 28.5% in paediatric emergency department imaging volumes. Nuclear medicine showed the greatest decrease of 37.1% for the same period. Paediatric nuclear medicine showed the greatest decrease of 36.7%. Mobile imaging utilisation increased between 57.8% and 135.1% during the first and second waves. A strong correlation was observed between mobile and non-mobile imaging in the emergency setting (Spearman’s correlation coefficient = -0.743, P = 0.000). No correlation was observed in the inpatient setting (Spearman’s correlation coefficient = -0.059, P = 0.554).CONCLUSIONNuclear medicine was most impacted, while computed tomography and angiography were the least affected by the pandemic. The impact was less during the pandemic’s second wave. Mobile imaging shows continuous growth during both waves.

Innovation Crowdsourcing Mechanisms and Innovation Performance: An Empirical Study of a Business Intelligence Community

Purpose: This study aims to develop a research model to investigate how the structure and mechanisms of innovation crowdsourcing influence knowledge management and innovation performance, based on the perspectives of open innovation theory and the knowledge-based view (KBV) of the firm. Design/Methodology/Approach: The research model and associated hypotheses were tested using partial least squares structural equation modelling (PLS-SEM), based on a dataset from the Microsoft Power BI community of business intelligence (BI) and analytics tools. Findings: The results show that both organisational and technical mechanisms of the community positively influence the community structure. The community structure has a positive impact on knowledge acquisition, knowledge transformation and the size and diversity of crowd participation. The mechanisms of innovation crowdsourcing and knowledge transformation in turn have a strong influence on innovation performance. Originality: This study is among the first to provide analytical insights into the mechanisms of innovation crowdsourcing and their underlying impact on innovation performance in the context of BI and analytics tools that exhibit a multiplicity and complexity of functions and capabilities. It therefore provides strategic guidance on how to effectively stimulate crowd intelligence and maximise the collaborative and synergistic effectiveness of innovation crowdsourcing communities, focusing on knowledge management practices and user innovation behaviour and performance.

Problem‐based learning on household electricity consumption analysis using predictive models and tools

AbstractSmart grid (SG) is an emerging subject in power engineering that requires interdisciplinary skills. SG implementation has challenges in accurate prediction of electricity consumption, power stability, power quality, and critical infrastructure security. This study uses predictive modeling and different tools to introduce problem‐based learning techniques through household electricity consumption (HEC) analysis. The problem statement is to understand the effect of the weather conditioning data set on the accuracy of HEC prediction and select the most fitting predictive model for further decision‐making. Five predictive models, namely random forest, linear regression, support vector machine, neural network, and adaptive boosting, are utilized, and their prediction results are compared based on a performance matrix. The paper also briefs the usage of different tools, including Jeffrey's Amazing Statistics Program tool for finding the correlations within variables, the Power Business Intelligence tool for data visualization, and the Orange tool for building predictive modeling. This study focuses on three aspects. Firstly, to demonstrate energy predictive modeling analysis without knowing the coding knowledge. Secondly, to develop the creative thinking of engineering students to solve complex, interdisciplinary engineering problems using tools. Thirdly, to motivate students to be actively involved in their problem‐based research projects.