RESEARCH PAPER
Artificial intelligence-based hybrid forecasting models for manufacturing systems
1
Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Centre for Advanced Manufacturing Technologies, ul. Łukasiewicza 5, 50-371 Wrocław, Poland
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):263-277
HIGHLIGHTS
- In the paper 4 new hybrid forecasting artificial intelligence-based models are proposed.
- A problem of defining explanatory variables when access to data is limited is addressed.
- Study fills in literature gap of hybrid forecasting application in manufacturing systems.
- The presented case studies cover production planning, maintenance and quality control.
- Algorithm for forecasting accuracy assessment and optimal method selection is presented.
KEYWORDS
artificial neural networksupport vector machineextreme learning machinehybrid forecastingproduction planningmaintenancequality control
ABSTRACT
The paper addresses the problem of forecasting in manufacturing systems. The main aim of
the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model
and Hybrid extreme learning machine model. The study focuses on solving the problem
of limited access to independent variables. Empirical verification of the proposed models
is built upon real data from the three manufacturing system areas – production planning,
maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting
accuracy assessment and optimal method selection for industrial companies is introduced. It
can serve not only as an efficient and costless tool for advanced manufacturing companies
willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0
implementation.
REFERENCES (90)
1.
Acquah D-G H. The effect of outliers on the performance of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in selection of an asymmetric price relationship. Russian Journal of Agricultural and Socio-Economic Sciences 2017; 65(5): 32-37, https://doi.org/10.18551/rjoas....
2.
Al-Garni A, Abdelrahman W, Abdallah A. ANN-based failure modeling of classes of aircraft engine components using radial basis functions. Ekspolatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(2): 311-317, https://doi.org/10.17531/ein.2....
3.
Aly HHH. A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid. Electric Power Systems Research 2020; 182: 106191, https://doi.org/10.1016/j.epsr....
4.
Ambrożkiewicz B, Syta A, Meier N et al. Radial internal clearance analysis in ball bearings. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(1): 42-54, https://doi.org/10.17531/ein.2....
5.
Amin-Naseri MR, Tabar BR. Neural network approach to lumpy demand forecasting for spare parts in process industries. 2008 International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia, IEEE: 2008: 1378-1382, https://doi.org/10.1109/ICCCE.....
6.
Areekul P, Senjyu T, Toyama H, Yona A. Notice of Violation of IEEE Publication Principles - A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market. IEEE Transactions on Power Systems 2010; 25(1): 524-530, https://doi.org/10.1109/TPWRS.....
7.
Bacchetti A, Saccani N. Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega 2012; 40(6): 722-737, https://doi.org/10.1016/j.omeg....
8.
Benis A, Notea A, Barkan R. Risk and Disaster Management: From Planning and Expertise to Smart, Intelligent, and Adaptive Systems. Studies in Health Technology and Informatics 2018: 286-290.
9.
Bi J, Yuan H, Zhang L, Zhang J. SGW-SCN: An integrated machine learning approach for workload forecasting in geo-distributed cloud data centers. Information Sciences 2019; 481: 57-68, https://doi.org/10.1016/j.ins.....
10.
Bounou O, El Barkany A, El Biyaali A. Parametric Approaches for Spare Parts Demand. International Journal of Supply Chain Management 2018; 7(4): 432-439.
11.
Bożejko W, Burduk A, Pempera J, Wodecki M. Optimization of production process for resource utilization. Archives of Civil and Mechanical Engineering 2019; 19(4): 1251-1258, https://doi.org/10.1016/j.acme....
12.
Burduk A, Jagodziński M. Assessment of Production System Stability with the Use of the FMEA Analysis and Simulation Models. In Jackowski K, Burduk R, Walkowiak K et al. (eds): Intelligent Data Engineering and Automated Learning - IDEAL 2015, Cham, Springer International Publishing: 2015; 9375: 216-223, https://doi.org/10.1007/978-3-....
13.
Burduk A, Musiał K, Kochańska J et al. Tabu search and genetic algorithm for production process scheduling problem. Logforum 2019; 15(2): 181-189, https://doi.org/10.17270/J.LOG....
14.
Caggiano A. Manufacturing System. In The International Academy for Production Engineering, Laperrière L, Reinhart G (eds): CIRP Encyclopedia of Production Engineering, Berlin, Heidelberg, Springer Berlin Heidelberg: 2014: 830-836, https://doi.org/10.1007/978-3-....
15.
Chen T-Y, Lin W-T, Sheu C. A Dynamic Failure Rate Forecasting Model for Service Parts Inventory. Sustainability 2018; 10(7): 2408, https://doi.org/10.3390/su1007....
16.
Chlebus E, Helman J, Olejarczyk M, Rosienkiewicz M. A new approach on implementing TPM in a mine - A case study. Archives of Civil and Mechanical Engineering 2015; 15(4): 873-884, https://doi.org/10.1016/j.acme....
17.
Coleman C, Damodaran S, Chandramouli M, Deuel E. Making maintenance smarter. Predictive maintenance and the digital supply network. Deloitte University Press 2017.
18.
Croston JD. Forecasting and Stock Control for Intermittent Demands. Journal of the Operational Research Society 1972; 23(3): 289-303, https://doi.org/10.1057/jors.1....
19.
De Gooijer JG, Hyndman RJ. 25 years of time series forecasting. International Journal of Forecasting 2006; 22(3): 443-473, https://doi.org/10.1016/j.ijfo....
20.
De Livera AM, Hyndman RJ, Snyder RD. Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association 2011; 106(496): 1513-1527, https://doi.org/10.1198/jasa.2....
21.
Dudek-Dyduch E, Tadeusiewicz R, Horzyk A. Neural network adaptation process effectiveness dependent of constant training data availability. Neurocomputing 2009; 72(13): 3138-3149, https://doi.org/10.1016/j.neuc....
22.
Frank A G, Dalenogare L S, Ayala NF. Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics 2019; 210: 15-26, https://doi.org/10.1016/j.ijpe....
23.
Ghobbar AA, Friend CH. Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model. Computers & Operations Research 2003; 30(14): 2097-2114, https://doi.org/10.1016/S0305-....
24.
Gopalakrishnan P, Banerji AK. Maintenance and spare parts management. 8. printing. New Delhi, PHI Learning - Private Limited: 2011.
25.
Górnicka D, Kochańska J, Burduk A. Production Resources Utilization Improvement with the Use of Simulation Modelling. In Świątek J, Borzemski L, Wilimowska Z (eds): Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology - ISAT 2019, Cham, Springer International Publishing: 2020: 41-50, https://doi.org/10.1007/978-3-....
26.
Gutierrez RS, Solis AO, Mukhopadhyay S. Lumpy demand forecasting using neural networks. International Journal of Production Economics 2008; 111(2): 409-420, https://doi.org/10.1016/j.ijpe....
27.
Hadi Hoseinie S, Ataei M, Khalokakaie R et al. Reliability analysis of drum shearer machine at mechanized longwall mines. Journal of Quality in Maintenance Engineering 2012; 18(1): 98-119, https://doi.org/10.1108/135525....
28.
Hajirahimi Z, Khashei M. Hybrid structures in time series modeling and forecasting: A review. Engineering Applications of Artificial Intelligence 2019; 86: 83-106, https://doi.org/10.1016/j.enga....
29.
Hall O P. Artificial Intelligence Techniques Enhance Business Forecasts. Computer-based analysis increases accuracy. The Graziadio Business Review 2002.
30.
Hua ZS, Zhang B, Yang J, Tan DS. A new approach of forecasting intermittent demand for spare parts inventories in the process industries. Journal of the Operational Research Society 2007; 58(1): 52-61, https://doi.org/10.1057/palgra....
31.
Hua Z, Zhang B. A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Applied Mathematics and Computation 2006; 181(2): 1035-1048, https://doi.org/10.1016/j.amc.....
32.
Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: Theory and applications. Neurocomputing 2006; 70(1-3): 489-501, https://doi.org/10.1016/j.neuc....
33.
Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. last accessed on 26.01.2021. 2018.
34.
Jaganathan S, Prakash PKS. A combination-based forecasting method for the M4-competition. International Journal of Forecasting 2020; 36(1): 98-104, https://doi.org/10.1016/j.ijfo....
35.
Kang R, Wang J, Cheng J et al. Intelligent forecasting of automatic train protection system failure rate in China high-speed railway. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 567-576, https://doi.org/10.17531/ein.2....
36.
Khashei M, Bijari M, Hejazi SR. Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting. Soft Computing 2012; 16(6): 1091-1105, https://doi.org/10.1007/s00500....
37.
Klindokmai S, Neech P, Wu Y et al. Evaluation of forecasting models for air cargo. The International Journal of Logistics Management 2014; 25(3): 635-655, https://doi.org/10.1108/IJLM-0....
38.
Kourentzes N, Petropoulos F. Forecasting with multivariate temporal aggregation: The case of promotional modelling. International Journal of Production Economics 2016; 181: 145-153, https://doi.org/10.1016/j.ijpe....
39.
Kourentzes N, Trapero JR, Barrow DK. Optimising forecasting models for inventory planning. International Journal of Production Economics 2019: 107597, https://doi.org/10.1016/j.ijpe....
40.
Kowalski A, Rosienkiewicz M. ANN-Based Hybrid Algorithm Supporting Composition Control of Casting Slip in Manufacture of Ceramic Insulators. In Graña M, López-Guede JM, Etxaniz O et al. (eds): International Joint Conference SOCO'16-CISIS'16-ICEUTE'16, Cham, Springer International Publishing: 2017; 527: 357-365, https://doi.org/10.1007/978-3-....
41.
Kozłowski E, Mazurkiewicz D, Żabiński T et al. Assessment model of cutting tool condition for real-time supervision system. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(4): 679-685, https://doi.org/10.17531/ein.2....
42.
Kozłowski E, Mazurkiewicz D, Żabiński T et al. Machining sensor data management for operation-level predictive model. Expert Systems with Applications 2020; 159: 113600, https://doi.org/10.1016/j.eswa....
43.
Kozłowski T, Wodecki J, Zimroz R et al. A Diagnostics of Conveyor Belt Splices. Applied Sciences 2020; 10(18): 6259, https://doi.org/10.3390/app101....
44.
Li R, Jiang P, Yang H, Li C. A novel hybrid forecasting scheme for electricity demand time series. Sustainable Cities and Society 2020; 55: 102036, https://doi.org/10.1016/j.scs.....
45.
Li Y, Wang K. Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 22(1): 63-72, https://doi.org/10.17531/ein.2....
46.
Liang T-F. Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains. Information Sciences 2011; 181(4): 842-854, https://doi.org/10.1016/j.ins.....
47.
Lucas Silva A, Ribeiro R, Teixeira M. Modeling and control of flexible context-dependent manufacturing systems. Information Sciences 2017; 421: 1-14, https://doi.org/10.1016/j.ins.....
48.
Makridakis S, Spiliotis E, Assimakopoulos V. The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting 2020; 36(1): 54-74, https://doi.org/10.1016/j.ijfo....
49.
Manafzadeh Dizbin N, Tan B. Optimal control of production-inventory systems with correlated demand inter-arrival and processing times. International Journal of Production Economics 2020; 228: 107692, https://doi.org/10.1016/j.ijpe....
50.
Manzke L, Keller B, Buscher U. An Artificial Bee Colony Algorithm to Solve the Single Row Layout Problem with Clearances. In Wilimowska Z, Borzemski L, Świątek J (eds): Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology - ISAT 2017, Cham, Springer International Publishing: 2018; 657: 285-294, https://doi.org/10.1007/978-3-....
51.
Mulay A, Ben BS, Ismail S, Kocanda A. Prediction of average surface roughness and formability in single point incremental forming using artificial neural network. Archives of Civil and Mechanical Engineering 2019; 19(4): 1135-1149, https://doi.org/10.1016/j.acme....
52.
Omar H, Hoang VH, Liu D-R. A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles. Computational Intelligence and Neuroscience 2016; 2016: 1-9, https://doi.org/10.1155/2016/9....
53.
Ömer Faruk D. A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence 2010; 23(4): 586-594, https://doi.org/10.1016/j.enga....
54.
Ou D, Tang M, Xue R, Yao H. Hybrid fault diagnosis of railway switches based on the segmentation of monitoring curves. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(4): 514-522, https://doi.org/10.17531/ein.2....
55.
Pereira DF, Oliveira JF, Carravilla MA. Tactical sales and operations planning: A holistic framework and a literature review of decisionmaking models. International Journal of Production Economics 2020; 228: 107695, https://doi.org/10.1016/j.ijpe....
56.
Pérez-Chacón R, Asencio-Cortés G, Martínez-Álvarez F, Troncoso A. Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand. Information Sciences 2020; 540: 160-174, https://doi.org/10.1016/j.ins.....
57.
Petropoulos F, Kourentzes N, Nikolopoulos K. Another look at estimators for intermittent demand. International Journal of Production Economics 2016; 181: 154-161, https://doi.org/10.1016/j.ijpe....
58.
Pham HT, Tran VT, Yang B-S. A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting. Expert Systems with Applications 2010; 37(4): 3310-3317, https://doi.org/10.1016/j.eswa....
59.
Praekhaow P. Determination of Trading Points using the Moving Average Methods. Bangkok, Thailand, 2010; GMSTEC: 6.
60.
Rathod S, Mishra GC, Singh KN. Hybrid Time Series Models for Forecasting Banana Production in Karnataka State, India. Journal of the Indian Society of Agricultural Statistics 2017: 9.
61.
Rego JR do, Mesquita MA de. Spare parts inventory control: a literature review. Production 2011; 21(4): 645-666.
62.
Rojek I, Kowal M, Stoic A. Predictive compensation of thermal deformations of ball screws in CNC machines using neural networks. Tehnicki vjesnik - Technical Gazette 2017, https://doi.org/10.17559/TV-20....
63.
Romański L, Bieniek J, Komarnicki P et al. Estimation of operational parameters of the counter-rotating wind turbine with artificial neural networks. Archives of Civil and Mechanical Engineering 2017; 17(4): 1019-1028, https://doi.org/10.1016/j.acme....
64.
Rosienkiewicz M. Accuracy Assessment of Artificial Intelligence-Based Hybrid Models for Spare Parts Demand Forecasting in Mining Industry. In Wilimowska Z, Borzemski L, Świątek J (eds): Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology - ISAT 2019, Cham, Springer International Publishing: 2020; 1052: 176-187, https://doi.org/10.1007/978-3-....
65.
Rosienkiewicz M, Chlebus E, Detyna J. A hybrid spares demand forecasting method dedicated to mining industry. Applied Mathematical Modelling 2017; 49: 87-107, https://doi.org/10.1016/j.apm.....
66.
Rosienkiewicz M, Kowalski A, Helman J, Zbieć M. Development of Lean Hybrid Furniture Production Control System based on Glenday Sieve, Artificial Neural Networks and Simulation Modeling. Drvna industrija 2018; 69(2): 163-173, https://doi.org/10.5552/drind.....
67.
Ruiz-Aguilar JJ, Turias IJ, Jiménez-Come MJ. Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting. Transportation Research Part E: Logistics and Transportation Review 2014; 67: 1-13, https://doi.org/10.1016/j.tre.....
68.
Schwarz G. Estimating the Dimension of a Model. The Annals of Statistics 1978; 6(2): 461-464, https://doi.org/10.1214/aos/11....
69.
Segovia Ramirez I, Mohammadi-Ivatloo B, García Márquez FP. Alarms management by supervisory control and data acquisition system for wind turbines. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2021; 23(1): 110-116, https://doi.org/10.17531/ein.2....
70.
Sekala A, Gwiazda A, Kost G, Banas W. Modelling of a production system using the multi-agent network approach. IOP Conference Series: Materials Science and Engineering 2018; 400: 052009, https://doi.org/10.1088/1757-8....
71.
Seliger G. Maintenance. In The International Academy for Production Engineering, Laperrière L, Reinhart G (eds): CIRP Encyclopedia of Production Engineering, Berlin, Heidelberg, Springer Berlin Heidelberg: 2014: 818-821, https://doi.org/10.1007/978-3-....
72.
Sikder S, Mukherjee I, Panja SC. A synergistic Mahalanobis-Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach. Journal of Manufacturing Systems 2020; 57: 323-337, https://doi.org/10.1016/j.jmsy....
73.
Sinisterra WQ, Cavalcante CAV. An integrated model of production scheduling and inspection planning for resumable jobs. International Journal of Production Economics 2020; 227: 107668, https://doi.org/10.1016/j.ijpe....
74.
Smyl S. A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting 2020; 36(1): 75-85, https://doi.org/10.1016/j.ijfo....
75.
Sobaszek Ł, Gola A, Świć A. Time-based machine failure prediction in multi-machine manufacturing systems. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 22(1): 52-62, https://doi.org/10.17531/ein.2....
76.
Suomala P, Sievanen M, Paranko J. The effects of customization on spare part business: A case study in the metal industry. International Journal of Production Economics 2002; 79(1): 57-66, https://doi.org/10.1016/S0925-....
77.
Valis D, Forbelská M, Vintr Z. Forecasting study of mains reliability based on sparse field data and perspective state space models. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22(2): 179-191, https://doi.org/10.17531/ein.2....
78.
Van der Auweraer S, Boute R. Forecasting spare part demand using service maintenance information. International Journal of Production Economics 2019; 213: 138-149, https://doi.org/10.1016/j.ijpe....
79.
Van Horenbeek A, Buré J, Cattrysse D et al. Joint maintenance and inventory optimization systems: A review. International Journal of Production Economics 2013; 143(2): 499-508, https://doi.org/10.1016/j.ijpe....
80.
Wagner SM, Jönke R, Eisingerich AB. A Strategic Framework for Spare Parts Logistics. California Management Review 2012; 54(4): 69-92, https://doi.org/10.1525/cmr.20....
81.
Wan C, Xu Z, Wang Y et al. A Hybrid Approach for Probabilistic Forecasting of Electricity Price. IEEE Transactions on Smart Grid 2014; 5(1): 463-470, https://doi.org/10.1109/TSG.20....
82.
Więcek D, Burduk A, Kuric I. The use of ANN in improving efficiency and ensuring the stability of the copper ore mining process. Acta Montanistica Slovaca 2019; 24(1): 14.
83.
Yang L, Li B. The Combination Forecasting Model of Grain Production Based on Stepwise Regression Method and RBF Neural Network. Advance Journal of Food Science and Technology 2015; 7(11): 891-895, https://doi.org/10.19026/ajfst....
84.
Yin S, Liu L, Hou J. A multivariate statistical combination forecasting method for product quality evaluation. Information Sciences 2016; 355-356: 229-236, https://doi.org/10.1016/j.ins.....
85.
Yu L, Liang S, Chen R, Lai KK. Predicting monthly biofuel production using a hybrid ensemble forecasting methodology. International Journal of Forecasting 2019. doi:10.1016/j.ijforecast.2019.08.014, https://doi.org/10.1016/j.ijfo....
86.
Zhang X, Wang J, Gao Y. A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM. Energy Economics 2019; 81: 899-913, https://doi.org/10.1016/j.enec....
87.
Zhou J, Qiu Y, Zhu S et al. Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Engineering Applications of Artificial Intelligence 2021; 97: 104015, https://doi.org/10.1016/j.enga....
CITATIONS (3):
1.
Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting
conditions
Guoxiao Zheng, Weifang Sun, Hao Zhang, Yuqing Zhou, Chen Gao
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Guoxiao Zheng, Weifang Sun, Hao Zhang, Yuqing Zhou, Chen Gao
Eksploatacja i Niezawodnosc - Maintenance and Reliability
2.
The Theory of Exploitation as a Support for Management Accounting in an Enterprise
Przemysław Drożyner, Stanisław Młynarski
Sustainability
Przemysław Drożyner, Stanisław Młynarski
Sustainability
3.
IDENTIFICATION OF SALES SERIES WITH TREND AND SEASONALITY USING SELECTED METHODS
Anna Borucka, Jolanta Wierzbicka
International Journal of New Economics and Social Sciences
Anna Borucka, Jolanta Wierzbicka
International Journal of New Economics and Social Sciences
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
