Number Of Key Performance Indicators Research Articles

A system for anomaly detection in reverse logistics: an application into an e-commerce company

Purpose This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company. Design/methodology/approach A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data. Findings The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators. Research limitations/implications The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics. Practical implications The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency. Originality/value The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.

EVALUASI KINERJA UTAMA PEMERINTAH KABUPATEN BEKASI PROVINSI JAWA BARAT

This research discusses the misalignment between strategic planning and annual planning in achieving key performance indicators (KPIs) at the national and regional levels, with a focus on Bekasi District, West Java. This alignment is critical to achieving community welfare. Gaps were found between the performance targets in the National Medium-Term Development Plan (RPJMN) and the Government Work Plan (RKP), especially on economic growth, poverty levels, and unemployment. An evaluation of the KPIs in Bekasi District also showed non-optimality, with a number of KPIs not achieved. Qualitative research methods were used to evaluate this. Clear KPI establishment, accurate data collection, regular monitoring, and root cause analysis are important steps in KPI evaluation. The results show that the KPI setting process in Bekasi District still does not fulfill the SMART (Specific, Measurable, Assignable, Realistic, Time Related) criteria thoroughly.There are weaknesses in senior management commitment, an unoptimized performance culture, and a lack of Critical Factor Success (CSF) approach in the formulation of performance indicators.In addition, the process of simulating the achievement of KPIs and midterm evaluation of the RPJMD was not adequately implemented; however, this process was not fully followed at both the national and regional levels. In addition, the process of simulating the achievement of KPIs and the midterm evaluation of the RPJMD were not adequately implemented. However, this process was not fully followed at both the national and regional levels.

The Impacts of Mobility-as-a-Service (MaaS) on a Diverse Society: A Framework for Advanced Assessment

With the latest developments in transportation technologies and services, the concept of MaaS has spawned its way, where consumers can purchase access to different modes and services owned and operated by several mobility service providers through a single integrated digital platform. Nonetheless, the MaaS ecosystem and benefits have not been fully understood yet, which varies case by case depending on the communities' behavioral responses. Qatar is known as one of the countries with a highly diverse population where many expatriate workers come from different cultures. This necessitates a comprehensive mobility arrangement given their various travel needs and diaries. To contribute to a comprehensive analysis of MaaS impact while taking into consideration societies' diversities, this paper aims to develop a framework to assist in evaluating and assessing MaaS design alternatives and their impact on different population segments. This study was conducted by utilizing existing literature findings about MaaS assessment supported by a stated preference (SP) survey from a Qatar case study. We found that there are a limited number of key performance indicators (KPIs) to comprehensively evaluate MaaS social impacts and mobility network efficiency. Therefore, as part of the assessment framework, additional KPIs were introduced for comprehensively evaluating MaaS's direct impact on transport networks and broader implications for social equity, economy, and human well-being. MaaS KPIs should consider reporting by age, monthly income, and ethnic socio-demographic factors. This can serve as a useful guide for diverse communities, particularly in Qatar. Furthermore, outputs from the assessment framework can be used as inputs to evaluate impacts on the MaaS provider's profit-making and business prospects.

GIANN—A Methodology for Optimizing Competitiveness Performance Assessment Models for Small and Medium-Sized Enterprises

The adoption of models based on key performance indicators to diagnose and evaluate the competitiveness of companies has been presented as a trend in the operations’ management. These models are structured with different variables in complex interrelationships, making diagnosis and monitoring difficult due to the number of variables involved, which is one of the main management challenges of Small and Medium-sized Enterprises. In this sense, this article proposes the Gain Information Artificial Neural Network (GIANN) method. GIANN is a method to optimize the number of variables of assessment models for the competitiveness and operational performance of Small and Medium-sized Enterprises. GIANN is a hybrid methodology combining Multi-attribute Utility Theory with Entropy and Information Gain concepts and computational modeling through Multilayer Perceptron Artificial Neural Network. The model used in this article integrates variables such as fundamental points of view, critical success factors, and key performance indicators. GIANN was validated through a survey of managers of Small and Medium-sized Enterprises in Southern Brazil. The initial model was adjusted, reducing the number of key performance indicators by 39% while maintaining the accuracy of the results of the competitiveness measurement. With GIANN, the number of variables to be monitored decreases considerably, facilitating the management of Small and Medium-sized Enterprises.

Optimization of Porosity Distribution in Gas Diffusion Electrodes for CO2 Electrolysis

The criteria dictating the performance of gas diffusion electrodes (GDEs) for CO2 electrolysis are not well understood, due to the complex and highly coupled relationships of the underlying physical and chemical phenomena. In addition, a number of key performance indicators (KPIs) have been identified (e.g., Faradaic efficiency, reaction selectivity, single-pass conversion, and productivity), and optimizing for any single metric often leads to inherent tradeoffs. Consequently, much recent work has focused on understanding how operational and architectural parameters control GDE performance. The porous catalyst supports and diffusion media in these devices are critical components as they mediate the transport and reactive processes. The microstructure of these components influences the balance between the electrochemical surface area, which dictates CO2 consumption, and mass transfer of the aqueous and gaseous species. Traditional porous media is often spatially homogeneous and provides limited opportunity to tailor this balance. In contrast, novel advanced manufacturing methods have now enabled researchers to create electrodes with variable porosity that can be tuned for optimal performance. To explore the impact of locally altering the porous media structure, we leverage previous modeling work by Weng et al. to allow for spatially varying porosity. Further, we couple the forward simulation to adjoint based optimization algorithms to determine optimal porosity distribution in the diffusion media and catalyst layer of a GDE. The cost functions for the optimization are derived from the previously mentioned KPIs. Finally, the performance of the resultant spatially varying porosity GDEs is compared to the performance of homogenous porosity GDEs, and we identify key features of the porosity distribution leading to improved performance. Weng, Lien-Chun, Alexis T. Bell, and Adam Z. Weber. "Modeling gas-diffusion electrodes for CO2 reduction." Physical Chemistry Chemical Physics 20.25 (2018): 16973-16984. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.LLNL-ABS-830082

Supply Chain Operational Reference untuk Perancangan Indikator Kinerja Rantai Pasok Farmasi

Warehouses have an important role in the supply chain. The pharmaceutical industry is no exception. Pharmacy Warehouse functions as a place to store drugs and medical devices before being distributed to health facilities such as community health centers, clinics, and hospitals. One of the Pharmacy Warehouse Technical Implementation Units located in Dairi Regency, North Sumatra, has several problems that occur along its supply chain. These problems, among others, are related to time, cost, and the number of products shipped. In addition, there was also an increase in aggregate demand for drugs and medical devices in 2018-2019. This increase in demand reinforces the need for designing pharmaceutical supply chain performance measurements. In designing the supply chain performance measurement, the framework used is the Supply Chain Operational Reference (SCOR) which includes the plan, source, make, delivery, and return processes, which are then translated into a number of criteria based on the attributes of reliability, responsiveness, agility, costs, and assets to a number of Key Performance Indicators (KPI). In weighting KPI, the method used is Analytical Hierarchy Process (AHP). Based on data processing and analysis, a supply chain performance measurement design was obtained with 11 performance indicators. Based on the AHP results, it was found that the highest weight value of 0.750 was obtained on the indicators of accuracy and delivery speed, while the lowest weight value of 0.117 was obtained on the order picking accuracy indicator.

Action plan for the mitigation of greenhouse gas emissions in the hospital-based health care of the Hellenic Army.

Climate change is a growing threat for human health and well-being, one that will seriously impact and potentially disrupt all economic sectors and supply chains, such as trade, tourism, agriculture, forestry, and fisheries. The environmental impact of the delivery of medical and hospital care, which generates its own greenhouse gas emissions, needs to be examined and analyzed in detail in order to design and implement effective mitigation actions and measures. Hospital internal energy use processes include the energy consumed for hospital operation, such as lighting, heating, cooking, waste treatment, and other functions associated with the logistical and operational support of hospitals. The present research work, which follows the assessment undertaken in a previous study of the transport activities of the 401 Military General Hospital of Athens (401 MGHA), focuses on the carbon footprint of the stationary emission sources of the 401 MGHA; it serves as a second step in the development of an action plan for the mitigation of greenhouse gas emissions in the hospital-based health care of the Hellenic (Greek) Army. A portfolio of energy saving and emission reduction actions is proposed and mapped according to their abatement cost and greenhouse gas (GHG) reduction potential. The highest decrease of GHG emissions is expected to be materialized by the decarbonization of the Greek power sector due to the lignite phase-out and increased share of low carbon fuels and renewable energy sources. Significant emission reduction potential could also be achieved by the replacement of face-to-face hospital visits by telemedicine, primarily by reducing transport-associated emissions. Furthermore, a number of key performance indicators (KPI) are proposed as simple and easily monitored metrics of the hospital's performance towards its sustainable low carbon objectives. Specific KPIs per mitigation action are presented, as well as a general KPI that covers all mitigation actions and sources of emissions in the form of "tCO2eq per patient" or "tCO2eq per hospitalization day."

Performance comparison of state-owned enterprises versus private firms in selected emerging Asian countries

PurposeThis policy paper compares the performance of state-owned enterprise (SOEs) versus private firms in selected emerging economies in Asia, focusing on a number of performance indicators. The indicators are internationally recognized quality innovation, product and/or service innovation, financing of operations, dealing with government regulations and labor performance. To the best of the authors’ knowledge, there has been no such comparative study for these indicators between SOEs and private firms and across countries. Most studies of SOEs have been national case studies. As such, they give us little knowledge of how a country compares with other countries at similar stages of economic development. A cross-country comparative analysis can help us identify broader trends and patterns.Design/methodology/approach The authors compare and discuss the performance of SOEs versus private firms in a number of emerging Asian countries, namely China, India, Indonesia, Malaysia and Vietnam. To do so, the authors use data from the 2018 World Bank Enterprise Survey (which is the latest available) for the period 2012–2015. The authors focus on a number of key performance indicators, namely internationally recognized quality innovation, product and/or service innovation, financing of operations, dealing with government regulations and labor performance.Findings The comparative analysis uncovers some interesting differences between the two types of firms. For example, somewhat surprisingly, SOEs tend to innovate more than private firms. However, the single most significant pattern the authors find is that in middle-income Asia both types of firms face formidable challenges with respect to doing business – e.g. scarcity of relevant training programs for employees. Therefore, the priority of policymakers must be to improve the overall business environment for all firms, regardless of their ownership structure.Research limitations/implicationsThe nature of this paper is a policy paper. This is because the data used in this study is survey data, conducted every four–five years (or more) for each country in the study and available for very few countries. As the data are not available for a continuous period of time, The authors could not conduct empirical research for this topic and thus made it a policy paper that presents a comparison across Asian countries as case studies.Originality/valueThe five selected Asian countries are interesting case studies for a comparative analysis since they are middle-income countries where SOEs play a significant role in the economy. Furthermore, state ownership is an important institutional dimension in emerging markets, and strong ties with the government can influence the performance of SOEs through various market and non-market channels. Despite the potential importance of the research theme, there is very little existing research on cross-country comparisons of the performance of SOEs vis-à-vis private firms. This could be explained by scarce data availability. With this in mind, the study attempts to shed some light on SOEs' performance and add to the rather limited literature.

Sustainable Development Model of EU Cities Compliant with UN Settings

Nowadays, the globally accepted UN concept of sustainable development (SD) is gradually transferred to the city level, including small and medium-sized cities. The implementation of SD settings requires regular measurement of developmental progress to monitor the level achieved in statics and dynamics, and to make strategic decisions for the next period. The existing urban SD indicator systems and indices are not well-suited for the monitoring of specific cities. Benchmarking algorithms and mathematical modelling procedures were applied to create a methodology and mathematical model for measuring the achieved urban SD level and to ensure the most objective selection and proportions of key performance indicators (KPIs) to be included in the model. The model (1) complies with the UN concept, (2) is usable for any EU city, (3) reflects the level of quality of life achieved, and (4) includes a limited number of KPIs related to municipal functionality. Mathematical computation of the (1) causality between the KPIs, (2) selection and proportions of KPIs, and (3) the general level of urban SD, as well as the reasonable combination of universality, accuracy, stability, and simplicity are strong advantages of the model. Using the published mathematical expressions of the model, calculation of the SD level does not require specific skills; the performed stability test also confirms that annual calibration of the model is not necessary. The index will help municipalities in planning and managing SD, and in the rational use of their usually limited resources. As a pilot project, SD level values are calculated for several cities.

Few-Shot Learning and Self-Training for eNodeB Log Analysis for Service-Level Assurance in LTE Networks

With the increasing network topology complexity and continuous evolution of the new wireless technology, it is challenging to address the network service outage with traditional methods. In the long-term evolution (LTE) networks, a large number of base stations called eNodeBs are deployed to cover the entire service areas spanning various kinds of geographical regions. Each eNodeB generates a large number of key performance indicators (KPIs). Hundreds of thousands of eNodeBs are typically deployed to cover a nation-wide service area. Operators need to handle hundreds of millions of KPIs to cover the areas. It is impractical to handle manually such a huge amount of KPI data, and automation of data processing is therefore desired. To improve network operation efficiency, a suitable machine learning technique is used to learn and classify individual eNodeBs into different states based on multiple performance metrics during a specific time window. However, an issue with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate data. To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using a dataset from a live LTE network that consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data.

Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems.

The strategic design of organizations in an environment where complexity is constantly increasing, as in the cyber-physical systems typical of Industry 4.0, is a process full of uncertainties. Leaders are forced to make decisions that affect other organizational units without being sure that their decisions are the right ones. Previously to this work, genetic algorithms were able to calculate the state of alignment of industrial processes that were measured through certain key performance indicators (KPIs) to ensure that the leaders of the Industry 4.0 make decisions that are aligned with the strategic objectives of the organization. However, the computational cost of these algorithms increases exponentially with the number of KPIs. That is why this work makes use of the principles of quantum computing to present the strategic design of organizations from a novel point of view: Quantum Strategic Organizational Design (QSOD). The effectiveness of the application of these principles is shown with a real case study, in which the computing time is reduced from hundreds of hours to seconds. This has very powerful practical applications for industry leaders, since, with this new approach, they can potentially allow a better understanding of the complex processes underlying the strategic design of organizations and, above all, make decisions in real-time.

Looking back and forging ahead: the weighting of ESG factors

The research for the investigation of the quality of the Corporate Annual Reports (CAR) and particularly the part of the narrative information contained in it has long been underestimated, mainly due to the lack of tools that allow an objective measurement and analysis of the narrative disclosed information of each company. The concept of narrative portion is extremely difficult and multidimensional, most studies use ESG composite scores to measure the CSP. The studies which investigate this issue are facing two major problems. The first problem related with the aggregation number of Key Performance Indicators while the second problem is that all ESG factors are not equal importance. This research focuses on the detailed presentation of a new method of reliable measurement of the quality of the narrative part of the CARs, which is greatly enhanced by using two composite indices, the unweighted index Ma.Co.I and the weighted one Ma.Co.Iw. These specific indices, for the first time, offer a comprehensive and coded checklist (for the measurement of the quality of the narrative information) which follows the guidelines set by the International Accounting Standards Board in 2010. Finally, this article proposes a novel weighting scheme of narrative portion and suggest specific minimum and maximum weights per ESG factors with purpose to contribute to a better understanding of the complexity of composite scores and to improve the quality of their outputs.

Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers.

Performance analysis is an essential task in high-performance computing (HPC) systems, and it is applied for different purposes, such as anomaly detection, optimal resource allocation, and budget planning. HPC monitoring tasks generate a huge number of key performance indicators (KPIs) to supervise the status of the jobs running in these systems. KPIs give data about CPU usage, memory usage, network (interface) traffic, or other sensors that monitor the hardware. Analyzing this data, it is possible to obtain insightful information about running jobs, such as their characteristics, performance, and failures. The main contribution in this paper was to identify which metric/s (KPIs) is/are the most appropriate to identify/classify different types of jobs according to their behavior in the HPC system. With this aim, we had applied different clustering techniques (partition and hierarchical clustering algorithms) using a real dataset from the Galician computation center (CESGA). We concluded that (i) those metrics (KPIs) related to the network (interface) traffic monitoring provided the best cohesion and separation to cluster HPC jobs, and (ii) hierarchical clustering algorithms were the most suitable for this task. Our approach was validated using a different real dataset from the same HPC center.

A multi agent-based approach for energy efficient water resource management

Water supply remains one of globally recognised challenging problems due to the scarcity in the water sources, the environmental concerns and hardness in access to clean and fresh water. Besides, energy consumption is also under focus as much as access to fresh water is. Substantial research efforts have been spent to produce good solutions for each of these global problems. However, there is not much investigated on considering both issues together in the same problems structure. This paper focuses on efficient planning and control of water supply in a middle-size water-rich city using a multi-agent approach to handle the problem with respect to a number of key performance indicators including energy efficiency, given a variety of water resources including constructed and natural reservoirs. A particular case scenario is considered and its mathematical model is developed in order to optimally plan and control fresh water supply to the metropolitan area of the city introduced in the scenario. The multi agent system has been used for production control of untreated water from different water resources. The evaluations have been done through episodes and the results are encouraging.

Sustainability Assessment for Manufacturing Operations

Sustainability is becoming more and more important as a decision attribute in the manufacturing environment. However, quantitative metrics for all the aspects of the triple bottom line are difficult to assess. Within the present paper, the sustainability metrics are considered in tandem with other traditional manufacturing metrics such as time, flexibility, and quality and a novel framework is presented that integrates information and requirements from Computer-Aided Technologies (CAx) systems. A novel tool is outlined for considering a number of key performance indicators related to the triple bottom line when deciding the most appropriate process route. The implemented system allows the assessment of alternative process plans considering the market demands and available resources.

Evaluating drivers of profitability for airlines in Latin America: A case study of Copa Airlines

Air Traffic demand in Latin America is expected to double over the next twenty years, yet airline profitability in the region remains highly problematic. The impediments challenging financial prosperity in the continent are numerous and have resulted in prolonged loss-making periods across most Latin American carriers. Breaking with this trend, Copa Airlines has been able to report double-digit net results for several years and recorded 56% of total profits earned by all Latin American carriers in 2016. This research has identified a number of Key Performance Indicators that have underpinned Copa Airlines' financial prosperity through a Product and Organisational Architecture (POA) framework analysis whose results were validated and elaborated upon by the CEO of Copa Airlines. Copa's sustained financial success was attributable to a number of factors. First, its geographical positioning has allowed it to engineer strong connectivity by coupling North and South America through its hub, which is reachable with narrowbodies to nearly all points in the Americas. Second, its low unit cost structure is akin to that of LCCs, operating a single aircraft type with high utilisation. Third, it has a uniquely low market concentration of competitors on its routes and capitalises on this by having a strong schedule with high frequencies together with outstanding punctuality. Fourth, it has a synergistic and fruitful cooperation with its hub airport at Tocumen. Finally it benefits from positive external factors such as a dollarized home economy with high GDP growth, exceptionally low unemployment and inflation rates ring-fenced with security. These pillars can be used as a reference for other Latin based airlines seeking to improve profitability.

Framework for Slice-Aware Radio Resource Management Utilizing Artificial Neural Networks

For accommodating the heterogeneous services that are anticipated for the fifth-generation (5G) mobile networks, the concept of network slicing serves as a key technology. Spanning both the core network (CN) and radio access network (RAN), slices are end-to-end virtual networks that share the resources of a physical network. Slicing the RAN can be more challenging than slicing the CN since RAN slicing deals with the distribution of radio resources, which have fluctuating capacity and are harder to extend. Improving multiplexing gains, while assuring the slice isolation is the main challenging task for RAN slicing. This paper provides a flexible and configurable framework for RAN slicing, where diverse requirements of slices are simultaneously taken into account, and slice management algorithms adjust the control parameters of different radio resource management (RRM) mechanisms to satisfy the slices’ service level agreements (SLAs). One of the proposed algorithms is based merely on heuristics and the other one utilizes an artificial neural network (ANN) to predict the behavior of the cellular network and make better decisions in the adjustment of the RRM mechanisms. Furthermore, a protection mechanism is devised to prevent the slices from negatively influencing each other’s performances. A simulation-based analysis demonstrates that in presence of local or global overload of one of the slices, the ANN-based method increases the number of key performance indicators (KPIs) that fulfill their defined SLA targets. Finally, we show that the proposed protection mechanism can force the negative effects of an overloading slice to be contained to that slice and the other slices are not affected as severely.

Machine learning models for predicting ship main engine Fuel Oil Consumption: A comparative study

As Fuel Oil Consumption (FOC) constitutes over 25% of a vessel’s overall operating cost, its accurate forecasting, and the reliable prediction of the relevant ship operating expenditures can majorly impact the ship operation sustainability and profitability. This study presents a comparison of data-driven, multiple regression algorithms for predicting ship main engine FOC considering two different shipboard data acquisition strategies, noon-reports and Automated Data Logging & Monitoring (ADLM) systems. For this, various multiple regression algorithms including Support Vector Machines (SVMs), Random Forest Regressors (RFRs), Extra Trees Regressors (ETRs), Artificial Neural Networks (ANNs), and ensemble methods are employed. The effectiveness of the tested algorithms is investigated based on a number of key performance indicators, such as the mean and median average error and the coefficient of determination (R2). ETR and RFR models were found to perform best in both cases, whilst the existence of an ADLM system increased accuracy by 7% and reduced the required period for data collection by up to 90%. The derived models can accurately predict the FOC of vessels sailing under different load conditions, weather conditions, speed, sailing distance, and drafts.

An exploratory analysis of the effects of ownership change on airport competition

Common or group ownership of airports poses a particular challenge for policy-makers, in that consumers (airlines and passengers) may not have access to benefits that stem from a more competitive system (e.g. lower prices, higher quality of service). However, whilst the arguments for and against group versus individual operations are well known, there are only limited practical cases when a change from common to individual ownership has occurred. One such case is in Scotland where the ownership of Edinburgh and Glasgow airports was separated in 2012. Therefore, the aim of this paper is to undertake a comparative assessment of the impact of this ownership change on the nature of competition between the two airports for the period 2006–2017. Catchment areas overlap, so it was hypothesised that separate ownership would lead to a more intense competitive rivalry with consequent effects on route development, traffic growth, the level/structure of aeronautical charges, financial performance, capital investment and quality of service. A number of key performance indicators covering these areas have been analysed, both before and after 2012 to assess whether there is evidence of a more competitive environment. The main findings are (i) traffic and routes have increased at both airports, although their relative roles appear to have changed; (ii) published charge levels have increased (iii) aeronautical yield has increased at Edinburgh but declined at Glasgow; (iv) prices have diverged reflecting differences in core market price elasticities; strategies have also been driven by a broader financial imperative around maximising EBITDA given declining unit costs and stagnation in non-aeronautical yields; (vi) certain performance indicators suggest that efficiency and service quality have improved. The implications for policy-makers are that airport market re-structuring and ownership change will most likely lead to divergence in pricing and route development strategies.

Global Process Effectiveness: When Overall Equipment Effectiveness Meets Adherence to Schedule

In recent decades, research has been done to better assess the effectiveness and efficiency of a production process. Therefore, some methods like Overall Equipment Effectiveness (OEE) or Overall Line Effectiveness (OLE) were proposed, however, none of the analysed methods has been able to illustrate accurate results in terms of adherence to a production plan. The purpose of this article is to present a metric, Global Process Effectiveness (GPE), based on OEE, that assesses effectiveness based on OEE factors and the schedule-adherence of a process to a pre-defined production plan, regarding product variety and quantity. If this new performance indicator replaces OEE, it will add one more goal to OEE without increasing the number of key performance indicators used in a company, giving a valuable assessment to operations planning experts.