Книга Leveraging Digital Mixed Model Value Stream Maps Girish Hidaduggi

Leveraging Digital Mixed Model Value Stream Maps

Език: Английски език
Корици: С твърди корици
Издател: Taylor & Francis Ltd
Наличност: 50% вероятност
Ще претърсим света
220.32 430.90 лв
This guide serves as a roadmap for integrating digital value stream mapping (eVSM) into your lean de...

Информация за книгата

Език
Английски език
Корици
Книга - С твърди корици
Издадена
2025
страници
146
EAN
9781032891729
ISBN
1032891726
Enbook ID
46880269
Издател
Теглоt
420
Размери
210 x 280

Пълно описание

This guide serves as a roadmap for integrating digital value stream mapping (eVSM) into your lean deployment to improve production processes. Lean, with its mapping component, value stream mapping (VSM), has endured as a process improvement methodology for over 25 years. VSM is a metrics-based analytical mapping method with simple calculations for lean metrics such as Takt Time, VA, NVA, Capacity, OEE, etc.; however

• The calculations and charting associated with value stream maps can be tedious;

• It’s hard to keep updating the calculations for “what-if” studies;

• Manual calculations can be prone to error.

Digital VSM was introduced as a complement to the initial paper-and-pencil map. The eVSM software has been used to create Digital VSMs for many years, but it has also encountered a couple of hurdles:

• Lean application has expanded and is applied to increasingly complex value streams;

• Product variants (mixed models) have proliferated so that many variants are now made with shared resources.

Creating and analyzing mixed model value stream maps with only paper and pencil, however, is practically unfeasible. The eVSM program has been revamped for the mixed model production setting and is now accessible as eVSM Mix for creating and analyzing Digital Mix VSMs. The eVSM Mix software reintroduces VSM as a fundamental tool for lean practitioners working in mixed-model situations. This guide illustrates Digital Mix VSMs created with the eVSM Mix software, shows how to use them analytically for “what-if” studies, and highlights best practices for deployment.

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