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A standard-aligned framework for energy KPI classification in discrete manufacturing : a systematic review

Zarenazhad, Parisa (2026)

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Mastersthesis_Zarenazhad_Parisa.pdf (1.073Mb)
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Diplomityö

Zarenazhad, Parisa
2026

School of Engineering Science, Tuotantotalous

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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2026060160664

Tiivistelmä

This thesis examines energy Key Performance Indicators (KPIs) in discrete manufacturing and develops a standard-aligned framework for classification and practical use. The main problem is inconsistent interpretation of energy-related indicators across studies. Similar KPI names can refer to different boundaries, calculation logic, and decision roles. This limits comparison and weakens the use of energy data as performance evidence. The study used a systematic literature review guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Scopus and Web of Science formed the main search path, and Google Scholar supported supplementary retrieval. The final review included 76 studies and 737 KPI entries. Each KPI entry was coded by energy status, KPI type, manufacturing level, measurement boundary, data type, calculation method, and standard relevance. International Organization for Standardization (ISO) 50001 was used as the main energy performance reference. ISO 22400 and ISO 20140 supported manufacturing operations and environmental manufacturing interpretation. The results show that direct energy KPIs dominate the reviewed literature. These indicators mainly measure energy consumption, power demand, efficiency, savings, and losses. Supporting KPIs explain output, machine state, downtime, and process context. Standard mapping showed that 78 percent of KPI entries aligned with ISO 50001, 15 percent with ISO 22400, and 29 percent with ISO 20140. The thesis contributes a KPI taxonomy with six groups and a decision framework. The framework starts from available operational data and checks level, boundary, data type, and required inputs before KPI type selection. The Hydroforming 8 application showed that power data, machine energy monitoring, and production output supported product level energy interpretation. The thesis gives a structured basis for moving from energy visibility to defensible energy performance evidence.
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