Soft Computing

, Volume 21, Issue 7, pp 1895–1912 | Cite as

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

Methodologies and Application

Abstract

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.

Keywords

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

References

  1. Almejalli K, Dahal K, Hossain MA (2008) Real time identification of road traffic control measures. Advances in computational intelligence in transport, logistics, and supply chain management, vol 144. Springer, BerlinGoogle Scholar
  2. Asiltürk I et al (2012) An intelligent system approach for surface roughness and vibrations prediction in cylindrical grinding. Int J Comput Integr Manuf 25:750–759CrossRefGoogle Scholar
  3. Bonabeau E, Meyer C (2001) Swarm intelligence. A whole new way to think about business. Harv Bus Rev 79:106–114, 165Google Scholar
  4. Castellano G, Castiello C, Fanelli AM, Mencar C (2005) Knowledge discovery by a neuro-fuzzy modeling framework. Fuzzy Sets Syst 149:187–207MathSciNetCrossRefMATHGoogle Scholar
  5. Cheng JH, Chen SS, Chuang YW (2008) An application of fuzzy delphi and Fuzzy AHP for multi-criteria evaluation model of fourth party logistics. WSEAS Trans Syst 7:466–478Google Scholar
  6. Costa HRN, La Neve A (2015) Study on application of a neuro-fuzzy models in air conditioning systems. Soft Comput 19:929–937CrossRefGoogle Scholar
  7. Deng W, Chen R, He B, Liu YQ, Yin LF, Guo JH (2012) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16(10):1707–1722CrossRefGoogle Scholar
  8. Department of Industrial Promotion (2012). http://www.dip.go.th/
  9. Du TCT, Wolfe PM (1997) Implementation of fuzzy logic systems and neural networks in industry. Comput Ind 32:261–272CrossRefGoogle Scholar
  10. Eberhart R, Kennedy J (1995) New optimizer using particle swarm theory. In: Proceedings of the international symposium on micro machine and human science, pp 39–43Google Scholar
  11. Efendigil T, Önüt S, Kahraman C (2009) A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis. Expert Syst Appl 36:6697–6707CrossRefGoogle Scholar
  12. Evans JR (2011) Retailing in perspective: the past is a prologue to the future. Int Rev Retail Distrib Consum Res 21:1–31CrossRefGoogle Scholar
  13. Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374:235–241CrossRefGoogle Scholar
  14. Fishburn PC (1997) Method for estimating addtive utilities. Manag Sci 13–17:435–453Google Scholar
  15. Fuzzy Logic Toolbox User’s Guide, MATLAB 7.9 (2015)Google Scholar
  16. Ganga GMD, Carpinetti LCR (2011) A fuzzy logic approach to supply chain performance management. Int J Prod Econ 134:177–187CrossRefGoogle Scholar
  17. Garcia Infante JC, Medel Juarez JJ, Sanchez Garcia JC (2010) Evolutive neural fuzzy filtering: an approach. WSEAS Trans Syst Control 5:164–173Google Scholar
  18. Gnana Sheela K, Deepa SN (2014) Performance analysis of modeling framework for prediction in wind farms employing artificial neural networks. Soft Comput 18:607–615CrossRefGoogle Scholar
  19. Gumus AT, Guneri AF, Keles S (2009) Supply chain network design using an integrated neuro-fuzzy and MILP approach: a comparative design study. Expert Syst Appl 36:12570–12577CrossRefGoogle Scholar
  20. Gumus AT, Guneri AF (2009) A multi-echelon inventory management framework for stochastic and fuzzy supply chains. Expert Syst Appl 36:5565–5575CrossRefGoogle Scholar
  21. Gunasekaran A, Kobu B (2007) Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. Int J Prod Res 45:2819–2840CrossRefMATHGoogle Scholar
  22. Gunasekaran A, Ngai EWT (2004) Information systems in supply chain integration and management. Eur J Oper Res 159:269–295MathSciNetCrossRefMATHGoogle Scholar
  23. Harding JA, Popplewell K (2006) Knowledge reuse and sharing through data mining manufacturing data. In: 2006 IIE annual conference and exhibitionGoogle Scholar
  24. He Z, Zhu P, Park S (2012) A robust desirability function method for multi-response surface optimization considering model uncertainty. Eur J Oper Res 221:241–247MathSciNetCrossRefMATHGoogle Scholar
  25. Holimchayachotikul P et al (2014) Value creation through collaborative supply chain: holistic performance enhancement road map. Prod Plan Control 25:912–922CrossRefGoogle Scholar
  26. Huang X, Ho D, Ren J, Capretz LF (2006) A soft computing framework for software effort estimation. Soft Comput 10:170–177CrossRefGoogle Scholar
  27. Jang JSR (1993) ANFIS: adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–684CrossRefGoogle Scholar
  28. Jilani TA, Burney SMA (2007) New method of learning and knowledge management in type-I fuzzy neural networks. Adv Soft Comput 42:730–740CrossRefGoogle Scholar
  29. Krzysztof S (2014) Neuro-fuzzy system with weighted attributes. Soft Comput 18:285–297CrossRefGoogle Scholar
  30. Leksakul K et al (2015) Forecast of off-season longan supply using fuzzy support vector regression and fuzzy artificial neural network. Comput Electron Agric 118:259–269CrossRefGoogle Scholar
  31. Liao SH et al (2014) Training neural networks via simplified hybrid algorithm mixing Nelder-Mead and particle swarm optimization methods. Soft Comput 19:679–689Google Scholar
  32. Linh TH, Osowski S, Stodolski M (2003) On-line heart beat recognition using hermite polynomials and neuro-fuzzy network. IEEE Trans Instrum Meas 52:1224–1231CrossRefGoogle Scholar
  33. Liu ZQ et al (2009) Self-spawning neuro-fuzzy system for rule extraction. Soft Comput 13:1013–1025CrossRefMATHGoogle Scholar
  34. Lopez-Cruz IL, Hernandez-Larragoiti L (2010) Neuro-fuzzy models for air temperature and humidity of arched and venlo type greenhouses in central Maxico. Modelos neuro-difusos para temperatura y humedad del aire en invernaderos tipo cenital y capilla en el centro de maxico. Agrociencia 44:791–805Google Scholar
  35. Marinakis Y, Marinaki M (2013) Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem. Soft Comput 17:1159–1173CrossRefGoogle Scholar
  36. Mariscal G, Marban O, Fernandez C (2010) A survey of data mining and knowledge discovery process models and methodologies. Knowl Eng Rev 25:137–166CrossRefGoogle Scholar
  37. Marx-Gómez J, Rautenstrauch C, Nurnberger A, Kruse R (2002) Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing. Knowl Based Syst 15:119–128CrossRefGoogle Scholar
  38. Mehrabad MS et al (2011) Targeting performance measures based on performance prediction. Int J Product Perform Manag 61:46–68CrossRefGoogle Scholar
  39. Mohandes M et al (2011) Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy 88:4024–4032CrossRefGoogle Scholar
  40. Paladini EP (2009) A fuzzy approach to compare human performance in industrial plants and service-providing companies. WSEAS Trans Bus Econ 6:557–569Google Scholar
  41. Pessin G et al (2013) Swarm intelligence and the quest to solve a garbage and recycling collection problem. Soft Comput 17:2311–2325CrossRefGoogle Scholar
  42. Shen KY, Tzeng GH (2015) A decision rule-based soft computing model for supporting financial performance improvement of the banking industry. Soft Comput 19:859–874CrossRefGoogle Scholar
  43. Sheu JB (2008) A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management. Eur J Oper Res 189:971–986CrossRefMATHGoogle Scholar
  44. Sun C et al (2014) A two-layer surrogate-assisted particle swarm optimization algorithm. Soft Comput 19:1461–1475CrossRefGoogle Scholar
  45. Svalina I et al (2013) An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: the case of close price indices. Expert Syst Appl 40:6055–6063CrossRefGoogle Scholar
  46. Tirian GO, Pinca CB, Cristea D, Topor M (2010) Applications of fuzzy logic in continuous casting. WSEAS Trans Syst Control 5:133–142Google Scholar
  47. Unahabhokha C et al (2007) Predictive performance measurement system: a fuzzy expert system approach. Benchmarking 14:77–91CrossRefGoogle Scholar
  48. Vukadinovic K, Teodorovic D, Pavkovic G (1999) An application of neurofuzzy modeling: the vehicle assignment problem. Eur J Oper Res 114:474–488CrossRefMATHGoogle Scholar
  49. Wang Y, Cai ZX (2012) A dynamic hybrid framework for constrained evolutionary optimization. IEEE Trans Syst Man Cybern Part B Cybern 42(1):203–217CrossRefGoogle Scholar
  50. Wong BK, Lai VS (2010) A survey of the application of fuzzy set theory in production and operations management: 1998–2009. Int J Prod Econ 129:157–168CrossRefGoogle Scholar
  51. Yao X, Xu G, Cui Y, Fan S, Wei J (2009) Application of the swarm intelligence in the organization of agricultural products logistics. In: Proceedings of 2009 4th international conference on computer science and education, ICCSE 2009, pp 77–80Google Scholar
  52. Zheng YJ, Ling HF (2013) Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Comput 17:1301–1314CrossRefGoogle Scholar

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

Personalised recommendations