Soft Computing

, Volume 21, Issue 7, pp 1895–1912

Predictive performance measurement system for retail industry using neuro-fuzzy system based on swarm intelligence

Methodologies and Application

DOI: 10.1007/s00500-016-2082-5

Cite this article as:
Holimchayachotikul, P. & Leksakul, K. Soft Comput (2017) 21: 1895. doi:10.1007/s00500-016-2082-5


Between 2011 and 2013, convenience store retail business grew dramatically in Thailand. As a result, most companies have increasingly been choosing the application of performance measurement systems. This significantly results in poor performance measurement regarding future business lagging measure. To solve this problem, this research presents a hybrid predictive performance measurement system (PPMS) using the neuro-fuzzy approach based on particle swarm optimization (ANFIS-PSO). It is constructed from many leading aspects of convenience store performance measures and projects the competitive level of future business lagging measure. To do so, monthly store performance measures were first congregated from the case study value chains. Second, data cleaning and preparations by headquarter accounting verification were carried out before the proposed model construction. Third, these results were used as the learning dataset to derive a predictive performance measurement system based on ANFIS-PSO. The fuzzy value of each leading input was optimized by parallel processing PSO, before feeding to the neuro-fuzzy system. Finally, the model provides a future performance for the next month’s sales and expense to managers who focused on managing a store using desirability function (\(D_{i})\). It boosted the sales growth in 2012 by ten percentages using single PPMS. Additionally, the composite PPMS was also boosted by the same growth rate for the store in the blind test (July 2013–February 2014). From the experimental results, it can be concluded that ANFIS-PSO delivers high-accuracy modeling, delivering much smaller error and computational time compared to artificial neural network model and supports vector regression but its component searching time differs significantly because of the complexity of each model.


Retailing value chain Predictive performance measurement system Neuro-fuzzy Swarm intelligence 

Funding information

Funder NameGrant NumberFunding Note
Thailand Research Fund
  • PHD/0090/2553
Excellence Center in Logistics and Supply Chain Management, Chiang Mai University

    Copyright information

    © Springer-Verlag Berlin Heidelberg 2016

    Authors and Affiliations

    1. 1.Department of Industrial Engineering, Faculty of EngineeringChiang Mai UniversityChiang MaiThailand
    2. 2.Excellence Center in Logistics and Supply Chain ManagementChiang Mai UniversityChiang MaiThailand

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