Value stream mapping from the customer's perspective: expanding concepts, representations and key performance indicators based on a typical real case study

Autor: Guimarães, Leonardo de Aragão, Jardim, Eduardo Galvão Moura, Guimarães Marujo, Lino
Zdroj: International Journal of Lean Six Sigma; July 2022, Vol. 14 Issue: 2 p429-450, 22p
Abstrakt: Purpose: This study aims to improve the buying experience for both customers and providers by presenting a conceptual basis which seeks to expand the usual understanding, representation, mapping and measurements of the different value and non-value stages of a customer purchase journey (CPJ). Design/methodology/approach: Inspired by the precepts of lean thinking, with emphasis on the value stream mapping method, the approach is based on an in-depth analysis of a real and typical e-commerce acquisition of an electronic customised product (a mobile phone) during the COVID-19 pandemic. Findings: This study demonstrates different types of consumer stages, values and wastes for the CPJ. This allowed the development of a mathematical formulation – named customer journey engineering (CJE) – from which improvements of the different categories can be identified. Exemplifying with those whose implementations require no further efforts or costs, the following results could be readily obtained in the case studied: a reduction of 96 h of non-value activities, an improvement of approximately 15% of the established index for customer satisfaction and avoidance of loss worth US$50 for the analysed customer. Research limitations/implications: The consistency and applicability of the qualitative and quantitative findings presented here should be examined further in other customer purchase scenarios, allowing enhancements of the CJE approach. Originality/value: Regardless of the context in question, this investigation attempts to identify and precisely define any common universal elements, often overlooked, which constitute the structure of any CPJ and are crucial for its understanding and improvement.
Databáze: Supplemental Index