# Overall Equipment Effectiveness: Systematic Literature Review and Overview of Different Approaches
## 1 Introduction  
Currently, various key performance indicators (KPIs) are used to make decisions at different organizational levels. [PERSON] and [PERSON] (2004) [1] considered KPIs as general indicators for identifying performance losses. [PERSON] et al. (2012) [2] reported that performance measurement has been developed in response to global and business trends. KPIs are used to measure process deviations to ensure that corrective action can be performed [3]; they are typically presented in dashboards and scorecards. Digital transformation has enabled information to be obtained quickly to accommodate the market changes. [PERSON] (2019) [4] suggested including digitalization as part of the business aspect of the evolution. In the Industry 4.0 framework, the digitalization of the production process in factories and data collection is important for improving business efficiency.  
Overall equipment effectiveness (OEE) is a KPI introduced by [PERSON] (1988) [5]; this metric was developed as part of the total productive maintenance (TPM) to measure the equipment productivity in a manufacturing system. OEE is a productivity ratio between real manufacturing and what could be ideally manufactured [6]. This indicator is widely accepted as a tool by some companies, e.g., when implementing lean manufacturing [7] or maintenance programs [5] to monitor the actualperformance of an equipment. OEE identifies six big losses comprising aspects of availability, performance and quality that reduce the equipment effectiveness. [PERSON] (2015) [PERSON] (2015) defined those three aspects as follows: (i) availability--Is the machine running or not?; (ii) performance--How fast is the machine running?; and (iii) quality--How many products satisfied the requirements?'.  
Availability measures downtime losses due to breakdowns or setup/adjustments; performance measures speed losses due to minor stoppages and reduced speed; and quality measures defect losses due to process defects or reduced yield [PERSON] (2015). Over time, OEE applications have been modified depending on industry needs; some authors have slightly modified the original formula, whereas others have proposed new formulas.  
The insufficiency of OEE as an indicator has resulted in its modification [PERSON] (2015). Many industries have customized it to fit to their particular requirements [PERSON] (2015). Based on the OEE structure, models have been developed for domains such as sustainability [PERSON] and [PERSON] (2016), line manufacturing [PERSON] and [PERSON] (2016), assets [PERSON] (2015), resources [PERSON] and [PERSON] (2017), transport [PERSON] (2018); [PERSON] (2019) and ports [PERSON] (2019).  
The objective of this research is to review and analyze the evolution of OEE. Hence, we conducted a systematic review. In this study, the chronology of the main contributions and modifications pertaining to OEE was analyzed to establish future trends.  
For a systematic review, we defined the research questions (RQs), selected and searched the database, applied inclusion and exclusion criteria, analyzed the results and answered the following (RQs).  
RQ (1) What is the focus of the current research effort in the OEE domain?  
RQ (2) What models based on OEE have been developed?  
RQ (3) What are the principal contributions in OEE and what are the future trends?  
This paper is structured as follows: Section 2 describes the methodology; Section 3 explains the systematic review process developed to obtain the information; Section 4 provides the results; and the final section presents the discussion and conclusions.