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

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 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 Defining business model key performance indicators using intentional linguistic summaries

2021 ◽  
Author(s):  
Rick Gilsing ◽  
Anna Wilbik ◽  
Paul Grefen ◽  
Oktay Turetken ◽  
Baris Ozkan ◽  
...  
Keyword(s):  
Decision Making ◽  
Business Model ◽  
Performance Indicators ◽  
Model Evaluation ◽  
Business Models ◽  
Real Life ◽  
Innovation Process ◽  
Key Performance Indicators ◽  
Business Model Innovation ◽  
Data Availability

AbstractTo sustain competitiveness in contemporary, fast-paced markets, organizations increasingly focus on innovating their business models to enhance current value propositions or to explore novel sources of value creation. However, business model innovation is a complex task, characterized by shifting characteristics in terms of uncertainty, data availability and its impact on decision making. To cope with such challenges, business model evaluation is advocated to make sense of novel business models and to support decision making. Key performance indicators (KPIs) are frequently used in business model evaluation to structure the performance assessment of these models and to evaluate their strategic implications, in turn aiding business model decision making. However, given the shifting characteristics of the innovation process, the application and effectiveness of KPIs depend significantly on how such KPIs are defined. The techniques proposed in the existing literature typically generate or use quantitatively oriented KPIs, which are not well-suited for the early phases of the business model innovation process. Therefore, following a design science research methodology, we have developed a novel method for defining business model KPIs, taking into account the characteristics of the innovation process, offering holistic support toward decision making. Building on theory on linguistic summarization, we use a set of structured templates to define qualitative KPIs that are suitable to support early-phase decision making. In addition, we show how these KPIs can be gradually quantified to support later phases of the innovation process. We have evaluated our method by applying it in two real-life business cases, interviewing 13 industry experts to assess its utility.

Optimalisasi Key Performance Indicators (KPI) Melalui Pendekatan Balance Scorecard Upaya Mengimplementasikan Performance Management System (PMS) Pada Perguruan Tinggi

CCIT Journal ◽  
2012 ◽  
Vol 6 (1) ◽  
pp. 17-34
Author(s):  
Untung Rahardja ◽  
Muhamad Yusup Eva ◽  
Rosyifa Rosyifa
Keyword(s):  
Performance Indicators ◽  
Management System ◽  
Educational Institution ◽  
Key Performance Indicators ◽  
Individual Performance ◽  
The State ◽  
Public Institution ◽  
Performance Measurement System ◽  
Educational Institutions ◽  
Successful Implementation

SQL Server Reporting Services is a way to analyze data, create reports using the indicators and gauges. Indicators are minimal gauges that convey the state of a single data value at a glance, and most are used to represent the state of Key Performance Indicators. Manage and harmonize the performance of an institution's educational institutions, especially universities with the performance of individuals or resources, no doubt is one of the essential elements for the success of an entity of the institution. Integrate the performance of an educational institution with individual performance is not an easy process, and therefore required a systematic approach to manage it. Implementation of a strategic management system based Balanced Scorecard can be used as a performance measurement system that will continuously monitor the successful implementation of the strategy of any public educational institution and measure the performance of its resources in a comprehensive and balanced, not the quantity but the emphasis is more concerned with the quality, so the performance of educational institutions at any time can be known clearly. Contribution of Key Performance Indicators to manage and harmonize the performance of any public institution is a solution in providing information to realize the extent of work that has set targets, identify and monitor measures of success, of course, with performance indicators show a clear, specific and measurable.

Strategic planning in grassland farming: Principles andapplications

1997 ◽  
pp. 191-197
Author(s):  
W.J. Parker ◽  
N.M. Shadbolt ◽  
D.I. Gray
Keyword(s):  
Strategic Planning ◽  
Performance Indicators ◽  
Cost Effective ◽  
Key Performance Indicators ◽  
Strategic Plans ◽  
Effective Manner ◽  
Farm Business ◽  
Pastoral Farming ◽  
Financial Environment

Three levels of planning can be distinguished in grassland farming: strategic, tactical and operational. The purpose of strategic planning is to achieve a sustainable long-term fit of the farm business with its physical, social and financial environment. In pastoral farming, this essentially means developing plans that maximise and best match pasture growth with animal demand, while generating sufficient income to maintain or enhance farm resources and improvements, and attain personal and financial goals. Strategic plans relate to the whole farm business and are focused on the means to achieve future needs. They should be routinely (at least annually) reviewed and monitored for effectiveness through key performance indicators (e.g., Economic Farm Surplus) that enable progress toward goals to be measured in a timely and cost-effective manner. Failure to link strategy with control is likely to result in unfulfilled plans. Keywords: management, performance

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