Bandingkan metode

	Analisis Kapabilitas Proses (Cp, Cpk)	Bagan Kendali Variabel Shewhart (X-bar / R)	Six Sigma DMAIC
Bidang	Statistika	Statistika	Manajemen Mutu
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1986	1931	2014
Pencetus	Victor Kane	Walter A. Shewhart	Motorola; Pyzdek & Keller
Tipe	Quantitative process evaluation index	Statistical process control chart for variables	Structured process improvement methodology
Sumber perintis	Kane, V. E. (1986). Process capability indices. Journal of Quality Technology, 18(1), 41–52.	Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company. ISBN: 978-0-87389-076-2	Pyzdek, T., & Keller, P. (2014). The Six Sigma Handbook (4th ed.). McGraw-Hill. ISBN: 978-0-07-184053-9
Alias	Process Capability Indices, Capability Study, Süreç Yeterlilik Analizi, Process Performance Analysis	X-bar and R chart, Shewhart chart, variables control chart, process control chart	DMAIC Framework, Six Sigma Process Improvement Cycle, Define-Measure-Analyze-Improve-Control, Altı Sigma DMAIC
Terkait	2	4	3
Ringkasan	Process Capability Analysis quantifies how well a manufacturing or business process produces output within specified tolerance limits. Introduced formally by Victor Kane in 1986, it summarises process spread and centering into dimensionless indices — most notably Cp and Cpk — allowing engineers and quality managers to judge whether a stable process is inherently capable of meeting customer or design specifications consistently.	The Shewhart control chart, invented by Walter Shewhart at Bell Labs in the 1920s and set out in his 1931 book, is the foundational tool of statistical process control. It plots a process statistic — typically the subgroup mean (X-bar) and range (R) — over time against a center line and three-sigma control limits, distinguishing the natural common-cause variation inherent in a stable process from special-cause variation that signals something has changed and warrants investigation.	Six Sigma DMAIC is a data-driven, five-phase process improvement methodology — Define, Measure, Analyze, Improve, and Control — used to reduce defects and process variation to fewer than 3.4 defects per million opportunities. Originating at Motorola in the 1980s and systematized by practitioners including Pyzdek and Keller, it is widely adopted in manufacturing, healthcare, finance, and service industries seeking sustained quality gains.