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Operations Article | "Relevance & Application of Six Sigma in Services Sector"

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Relevance & Application of Six Sigma in Services Sector

- by Aniket Raj Agarwal & Nikhil K. Jainender *

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Six Sigma methodology has a major role to play to pin-point the major problem areas and devise powerful strategies to tackle problems which improve the customer experience in these services. It follows the DMAIC (Define Measure Analyze Improve Control) approach
to problem solving and DMADV (Define Measure Analyze Design Verify) for developing new processes. It focuses on the following key issues: -

  • What core processes in our services business are critical to meet customer needs and expectations?

  • What are the critical-to-quality (CTQ) characteristics in our core processes, and how do we define and measure defects in these characteristics?

  • Why do such defects occur in our processes and at what frequency do they occur?

  • What is the impact of such defects on customer satisfaction?

  • What strategies should be developed and implemented to prevent the occurrence of such defects?

    Uniqueness of Services

    Most literature on quality management and improvement philosophies is originally designed to improve product quality in the manufacturing sector. In this section, we will briefly compare the manufacturing and service applications of Six Sigma.

    In the manufacturing sector, it is quite common to have some sort of measurements in place, which provides an indicator of process performance and product quality. In service industry, measurement is often an overlooked area and, therefore, improvement of quality is not adequately addressed.

    In the manufacturing processes, it is quite common to have process maps before a Six Sigma project is initiated. The use of flowcharts and process maps are uncommon in many service processes. In the manufacturing industries, the measurement system analysis (repeatability and reproducibility study) is explicitly defined, whereas in service industries, the measurement system analysis is often a more general problem of data quality and integrity.

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    * Contributed by: -
    Aniket Raj Agarwal & Nikhil K. Jainender,
    Symbiosis Institute of International Business, Pune.


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