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Quantitative quality control from qualitative data: control charts with latent semantic analysis

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Abstract

Large quantities of data, often referred to as big data, are now held by companies. This big data includes statements of customer opinion regarding product or service quality in an unstructured textual form. While many tools exist to extract meaningful information from big data, automation tools do not exist to monitor the ongoing conceptual content of that data. We use latent semantic analysis to extract concept factors related to service quality categories. Customer comments found in the data that express dissatisfaction are then considered as representing a non-conforming observation in a process. Once factors are extracted, proportions of nonconformities for service quality failure categories are plotted on a control chart. The results are easily interpreted and the approach allows for the quantitative evaluation of customer acceptance of system process improvement initiatives.

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Authors and Affiliations

  1. The University of Texas – Pan American, 1201 West University, Edinburg, TX, 78539, USA

    Triss Ashton

  2. University of North Texas, 1155 Union Circle # 311160, Denton, TX, 76203-5017, USA

    Nicholas Evangelopoulos & Victor R. Prybutok

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  1. Triss Ashton
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  2. Nicholas Evangelopoulos
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  3. Victor R. Prybutok
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Correspondence to Triss Ashton.

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Ashton, T., Evangelopoulos, N. & Prybutok, V.R. Quantitative quality control from qualitative data: control charts with latent semantic analysis. Qual Quant 49, 1081–1099 (2015). https://doi.org/10.1007/s11135-014-0036-5

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