Our experience is that, in general, the processes used for designing services are less well defined than those employed in manufacturing. Service processes are less tangible, both in the configuration and in the output they deliver. Consequently, this provides a huge opportunity to gain competitive advantage in innovation.
A scan of the market shows that the adoption of DFLSS is accelerating in the service sector, particularly in the financial services arena. We advocate that DFLSS is as effective for developing life insurance policies, credit cards, or IRAs as it is for designing tanks and braking systems. The emphasis on deep understanding of customer needs is the same, though the selection of design techniques naturally differs. From actuarial services to automobile manufacturing, innovative, leading firms use DFLSS as the means to reap the benefits of:
- Direct access to customer knowledge.
- Ownership and buy-in across functions.
- Earlier detection of changing customer needs.
- Broader perspective in understanding the market.
- Faster time to market.
And while Design for Lean Six Sigma (DFLSS) is a well-developed methodology used to guide the development of new products and services, it is also applied when existing processes are so dysfunctional that it makes sense to start from scratch.
VSATC has DFLSS Road Maps for both Service and Product design.
The DFLSS data-driven quality strategy and its methodologies are summarized under two widely used problem-solving, phased approaches:
IDOV: Identify, Design, Optimize, and Validate
DMADV: Define, messure, Analyze, Design, and Verify
Both use the DFLSS toolset, which guides you through the process of gaining a deep understanding of customer requirements, as well as to supporting the decision-making process of the team as it moves through the phases of the development process. The DFLSS toolset includes conjoint analysis, multigenerational product planning, concept selection methods, TRIZ, creativity tools, design scorecards, simulation techniques, QFD, reliability engineering and statistical engineering.