Value Stream Mapping : Introducing a Lean Production Method in Consulting Firms
Abstract
Value Stream Mapping (VSM) is a well-established Lean method for mapping flow of value and identifying waste in manufacturing contexts. The application of VSM within service and knowledge-intensive environment remains underexplored. Consulting processes are characterized by low standardization (due to case dependency), variability and heavy reliance on customer interaction. This poses a challenge for traditional VSM application. The purpose of this study is to explore how to apply VSM in the consulting context to identify the flow in value, improvement opportunities, inefficiencies of workflow and how a Future-state Value Stream Map (FVSM) can be established and juxtaposed with a current-state VSM. The study applies an exploratory research design that combines qualitative and quantitative collection and analysis methods through a case study. The quantitative data was collected through the business case using descriptive process analysis to examine different timestamps, distributions of outcomes and rates of success. The qualitative data was collected through semi-structured interviews with consultants and their managers. The qualitative data was analyzed using a deductive qualitative content analysis (DQICA) to adapt to analyzing the consulting work process through VSM. The results show that mapping of value creation (VC) in consulting is primarily in the delivery phase. The non-value adding (NVA) waste was identified to reside during the pre-contract phases, due to waiting times, rework and repeated customer interaction. The FVSM showcases how potential improvements can lower wastes by nearly 9% through estimations by consideration of previous findings. The study concludes that VSM possibly can be meaningfully applied in the consulting field through adaptations to service oriented conditions.
Fields of Study & Subjects
Quality and Supply Management
Management and Optimization Techniques
Digital Transformation in Industry
Publication details
- Journal / source
- KTH Publication Database DiVA (KTH Royal Institute of Technology)
- Publication date
- 1 January 2026
- Language
- EN
- Document type
- Thesis