Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

2021 ◽  
pp. 036119812110190
Author(s):  
Hector Donaldo Mata ◽  
Mohammed Hadi ◽  
David Hale
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Ordinary Least Squares ◽  
Learning Tools ◽  
Least Squares Regression ◽  
Upper Level ◽  
Selection Operator ◽  
Transportation Agencies

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

scholarly journals IT Effect Assessment for Non-profit Organization

2019 ◽  
Vol 8 (4) ◽  
pp. 8854-8858
Keyword(s):  
Performance Management ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Federal State ◽  
Non Profit ◽  
Core Business ◽  
The Core ◽  
Commercial Activities ◽  
Non Profit Organizations

The article is devoted to assessing the effect of the implementation of information technologies in non-profit organizations. The purpose of the assessment is to evaluate the effect of IT implementation and its impact on key performance indicators of an organization. The indicators characterizing the results of the organization’s activities in accordance with the State Assignment and the results of commercial activities were used as the key performance indicators. For federal state budget NPOs, it has been shown that a positive IT effect for auxiliary business processes does not directly ensure positive performance indicators for the core business processes. Hidden effects of the use of IT were assessed by changes of the indicators of the core business processes. Performance indicators characterizing the results of commercial activities may demonstrate a negative effect. Understanding the specifics of non-profit organizations, as well as metrics and performance parameters characterizing the effectiveness of such organizations, is important to ensure a correct approach to the digitalization of business processes and their performance management.

scholarly journals A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

2021 ◽  
Vol 11 (19) ◽  
pp. 9296
Author(s):  
Talha Mahboob Alam ◽  
Mubbashar Mushtaq ◽  
Kamran Shaukat ◽  
Ibrahim A. Hameed ◽  
Muhammad Umer Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Feature Selection ◽  
Artificial Neural Networks ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Educational Institutions ◽  
Institutional Performance ◽  
Learning Models ◽  
Machine Learning Models

Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.

scholarly journals Review of Key Performance Indicators for Process Monitoring in the Mining Industry

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5169 ◽  
Author(s):  
Paulina Gackowiec ◽  
Marta Podobińska-Staniec ◽  
Edyta Brzychczy ◽  
Christopher Kühlbach ◽  
Toyga Özver
Keyword(s):  
Process Monitoring ◽  
Performance Indicators ◽  
Business Strategy ◽  
Lean Manufacturing ◽  
Process Management ◽  
Business Processes ◽  
Mining Industry ◽  
Key Performance Indicators ◽  
Sensor Data ◽  
Monitoring And Control

The sustainable development of an organisation requires a holistic approach to the evaluation of an enterprise’s goals and activities. The essential means enabling an organisation to achieve goals are business processes. Properly managed, business processes are a source of revenue and become an implementation of business strategy. The critical elements in process management in an enterprise are process monitoring and control. It is therefore essential to identify the Key Performance Indicators (KPIs) that are relevant to the analysed processes. Process monitoring can be performed at various levels of management, as well as from different perspectives: operational, financial, security, or maintenance. Some of the indicators known from other fields (such as personnel management, finance, or lean manufacturing) can be used in mining. However, the operational mining processes require a definition of specific indicators, especially in the context of increasing the productivity of mining machines and the possibility of using sensor data from machines and devices. The article presents a list of efficiency indicators adjusted to the specifics and particular needs of the mining industry resulting from the Industry 4.0 concept, as well as sustainable business performance. Using the conducted research and analysis, a list of indicators has been developed concerning person groups, which may serve as a benchmark for mining industry entities. The presented proposal is a result of work conducted in the SmartHUB project, which aims to create an Industrial Internet of Things (IIoT) platform that will support process management in the mining industry.

scholarly journals Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms

2021 ◽  
pp. 1-21
Author(s):  
Helper Zhou ◽  
Victor Gumbo
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Predictive Modelling ◽  
Sales Performance ◽  
Ordinary Least Squares ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection Operator

The emergence of machine learning algorithms presents the opportunity for a variety of stakeholders to perform advanced predictive analytics and to make informed decisions. However, to date there have been few studies in developing countries that evaluate the performance of such algorithms—with the result that pertinent stakeholders lack an informed basis for selecting appropriate techniques for modelling tasks. This study aims to address this gap by evaluating the performance of three machine learning techniques: ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), and artificial neural networks (ANNs). These techniques are evaluated in respect of their ability to perform predictive modelling of the sales performance of small, medium and micro enterprises (SMMEs) engaged in manufacturing. The evaluation finds that the ANNs algorithm’s performance is far superior to that of the other two techniques, OLS and LASSO, in predicting the SMMEs’ sales performance.

scholarly journals A Machine Learning Approach to the Residential Relocation Distance of Households Living in the Seoul Metropolitan Region

2018 ◽  
Author(s):  
Changhyo Yi ◽  
Kijung Kim
Keyword(s):  
Machine Learning ◽  
Residential Mobility ◽  
Ordinary Least Squares ◽  
Metropolitan Region ◽  
Learning Approach ◽  
Least Squares Regression ◽  
Mobility Patterns ◽  
Residential Relocation ◽  
Machine Learning Approach ◽  
Seoul Metropolitan Region

This study aimed to ascertain the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study involve the microdata of Internal Migration Statistics provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR by using machine learning techniques such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of the explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements have relatively longer distance. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information on the urban management of metropolitan residential districts and the construction of reasonable housing policies.

Machine learning product key performance indicators and alignment to model evaluation

2021 ◽  
Author(s):  
Ioannis Bakagiannis ◽  
Vassilis C. Gerogiannis ◽  
George Kakarontzas ◽  
Anthony Karageorgos
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Model Evaluation ◽  
Key Performance Indicators

Fuzzy set theory-based model for identifying the potential of improving process KPIs in production logistics area

2018 ◽  
Vol 4 (10) ◽  
pp. 327-336
Author(s):  
Denisa Hrusecka
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Performance Indicators ◽  
Business Processes ◽  
Fuzzy Model ◽  
Real Data ◽  
Key Performance Indicators ◽  
Production Logistics ◽  
Low Performance

The high complexity of today’s manufacturing environment brings many problems with planning and managing, especially production, logistic and other key business processes. In many cases, it is quite complicating to identify the real causes of problems that enterprises face or to decide which one of them should be solved first. Especially, in the case of large enterprises, it is complicating to access expertise among all departments and employed professionals in order to solve the problems most efficiently. Our fuzzy model provides a simple tool for easy identification of the most significant problems of observed processes that cause their low performance according to the measured values of their key performance indicators. The model is based on data gained through interviews with production managers, industry experts and other professionals, and verified by real data from a model company. The results are presented in the form of case studies in this contribution. Keywords: Production logistics, key performance indicators, KPI, productivity, problem identification, fuzzy set theory, process.

scholarly journals EVALUATION OF SERVICE STANDARDS IN THE INFORMATION AND COMPLIANCE SERVICE TAX OFFICE

2019 ◽  
Vol 1 (1) ◽  
pp. 55-66
Author(s):  
Agus Bandiyono ◽  
Shanti Dwi Aryani
Keyword(s):  
Internal Control ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Standard Operating Procedures ◽  
Service Guarantees ◽  
Standard Operating ◽  
Directorate General ◽  
Ministry Of Finance ◽  
Security Guarantees

This study aims to evaluate the service component standard of the Directorate General of Tax Information and Complaints Service Office and to identify the factors supporting the success of services at the Information and Information Services Office Complaints from the Directorate General of Tax. This study uses qualitative methods through interviews. The results show that there are several service standard components not included in the Information and Complaints Service Office's Standard Standards, namely cost/tariff requirements, infrastructure and/or facilities, implementing competencies, internal control, complaint handling, service guarantees, security guarantees, and performance evaluation executor. However, the Ministry of Finance has further stipulated which components must be included in the Standard Operating Procedures in the Regulation of the Minister of Finance of the Republic of Indonesia Number 131 / PMK.01 / 2015 concerning Guidelines for the Preparation of Business Processes, Decision Making Framework and Standard Operating Procedures in Ministry of Finance environment. Even though it does not meet the components in the Public Service Act it is not considered a problem as long as it fulfills the components in the PMK. Supporting factors for service success begins with determining Strategic Targets and Key Performance Indicators, then supported by activities needed to achieve the targets of each of the Key Performance Indicators.

scholarly journals THE MANAGEMENT SYSTEM OF BUSINESS PROCESS EFFICIENCY

2017 ◽  
pp. 66-74 ◽  
Author(s):  
Alina Igorevna Lykova
Keyword(s):  
Performance Management ◽  
Business Process ◽  
Balanced Scorecard ◽  
Performance Indicators ◽  
Business Process Management ◽  
Process Management ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Process Efficiency ◽  
Process Performance

Business process management and performance management merge with each other as business process management evolves. Efficiency is a characteristic of the system in terms of the ratio of costs and results of its functioning, ability to lead to given results. Efficiency in the concept of process management is the measurement of predefined operational characteristics of the process: qualitative and / or quantitative indicators that characterize the process. The main indicators of process efficiency are the process efficiency indices. In addition, in the theory of performance management key performance indicators are emitted. Although they have much in common (relatively constant, measurable, assess progress, etc.), the main difference between these indicators is that process performance indicators measure operational efficiency, while key performance indicators represent the business objectives that the company wants to achieve at a strategic level. There are different types of the process performance indicators: productivity and effectiveness, temporary, costly, high-quality; early and late. Process performance indicators are assigned to each process to monitor its effectiveness and to correlate the achievement of the process goal and the costs to achieve this goal. The establishment of key performance indicators depends on the organization's strategy and is implemented using methodologies developed and tested in practice, the most popular of which is the Balanced Scorecard. With the purpose of forming a control loop for the efficiency of business processes, the principles of managing the efficiency of processes are singled out: the level of development of performance management directly depends on the level of process maturity of the organization; when analyzing the process, performance indicators are primary, and then productivity; understanding customer motivation when evaluating the process; evolutionary measurement of effectiveness. The performance management framework of business processes consists of planning, execution (which also consists of performance monitoring processes for each selected process), verification and updating.

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