IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v231y2021ics092552732030205x.html
   My bibliography  Save this article

Forecasting of customer demands for production planning by local k-nearest neighbor models

Author

Listed:
  • Kück, Mirko
  • Freitag, Michael

Abstract

Demand forecasting is of major importance for manufacturing companies since it provides a basis for production planning. However, demand forecasting can be a difficult task because customer demands often fluctuate due to several influences. Methods of nonlinear dynamics have shown promising results in numerous applications but they have mostly been neglected in the context of demand forecasting. This paper evaluates the forecasting performance of local k-nearest neighbor models, which base on the theory of dynamical systems, in a comprehensive empirical study utilizing a large dataset of industrial time series of the M3-Competition. After a broad literature review, the theoretical background is described. Subsequently, different parameter configurations and model selection strategies are compared. A locally constant mean and a locally constant median are compared to locally linear regression models with four different regularization methods and different parameter configurations. In this comparison, the locally constant mean and the locally linear ridge regression with high regularization parameters provide the best trade-offs between forecast accuracy and computation times. Finally, these models achieve a high performance regarding low forecast errors, short computation times as well as high service levels in an inventory simulation compared to established benchmark methods. In particular, they obtain the best results among all applied methods regarding short time series. Moreover, they achieve the lowest errors considering the original accuracy criterion of the M3-Competition. Hence, local k-nearest neighbor models can be regarded as a valid alternative for demand forecasting in an industrial context, accomplishing high forecast accuracy with short computation times.

Suggested Citation

  • Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
  • Handle: RePEc:eee:proeco:v:231:y:2021:i:c:s092552732030205x
    DOI: 10.1016/j.ijpe.2020.107837
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S092552732030205X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2020.107837?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Crone, Sven F. & Hibon, Michèle & Nikolopoulos, Konstantinos, 2011. "Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 635-660.
    2. Lisi, Francesco & Schiavo, Rosa A., 1999. "A comparison between neural networks and chaotic models for exchange rate prediction," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 87-102, March.
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    4. Meade, Nigel, 2002. "A comparison of the accuracy of short term foreign exchange forecasting methods," International Journal of Forecasting, Elsevier, vol. 18(1), pages 67-83.
    5. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.
    6. Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon & Andrada-Felix, Julian, 1999. "Exchange-rate forecasts with simultaneous nearest-neighbour methods: evidence from the EMS," International Journal of Forecasting, Elsevier, vol. 15(4), pages 383-392, October.
    7. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    8. Stanislav Chankov & Marc-Thorsten Hütt & Julia Bendul, 2016. "Synchronization in manufacturing systems: quantification and relation to logistics performance," International Journal of Production Research, Taylor & Francis Journals, vol. 54(20), pages 6033-6051, October.
    9. Wallström, Peter & Segerstedt, Anders, 2010. "Evaluation of forecasting error measurements and techniques for intermittent demand," International Journal of Production Economics, Elsevier, vol. 128(2), pages 625-636, December.
    10. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    11. Syntetos, A. A. & Boylan, J. E., 2001. "On the bias of intermittent demand estimates," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 457-466, May.
    12. Florian Klug, 2016. "Analysing bullwhip and backlash effects in supply chains with phase space trajectories," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3906-3926, July.
    13. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    14. Kantz, Holger & Holstein, Detlef & Ragwitz, Mario & K. Vitanov, Nikolay, 2004. "Markov chain model for turbulent wind speed data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(1), pages 315-321.
    15. van Wingerden, E. & Basten, R.J.I. & Dekker, R. & Rustenburg, W.D., 2014. "More grip on inventory control through improved forecasting: A comparative study at three companies," International Journal of Production Economics, Elsevier, vol. 157(C), pages 220-237.
    16. Matthew P. Manary & Sean P. Willems & Alison F. Shihata, 2009. "Correcting Heterogeneous and Biased Forecast Error at Intel for Supply Chain Optimization," Interfaces, INFORMS, vol. 39(5), pages 415-427, October.
    17. Jaditz, Ted & Sayers, Chera L, 1998. "Out-of-Sample Forecast Performance as a Test for Nonlinearity in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 110-117, January.
    18. Xun Wang & Fotios Petropoulos, 2016. "To select or to combine? The inventory performance of model and expert forecasts," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5271-5282, September.
    19. Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P., 2011. "MLP ensembles improve long term prediction accuracy over single networks," International Journal of Forecasting, Elsevier, vol. 27(3), pages 661-671.
    20. Mulhern, Francis J. & Caprara, Robert J., 1994. "A nearest neighbor model for forecasting market response," International Journal of Forecasting, Elsevier, vol. 10(2), pages 191-207, September.
    21. Dunxian She & Xiaohua Yang, 2010. "A New Adaptive Local Linear Prediction Method and Its Application in Hydrological Time Series," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-15, June.
    22. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    23. Scholz-Reiter, Bernd & Tervo, Jan Topi & Freitag, Michael, 2006. "Phase-synchronisation in continuous flow models of production networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 32-38.
    24. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    25. Mizrach, B, 1992. "Multivariate Nearest-Neighbor Forecasts of EMS Exchange Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 151-163, Suppl. De.
    26. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
    27. Lee, Yun Shin, 2014. "A semi-parametric approach for estimating critical fractiles under autocorrelated demand," European Journal of Operational Research, Elsevier, vol. 234(1), pages 163-173.
    28. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    29. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.
    30. Fritzsche, R. & Gupta, J.N.D. & Lasch, R., 2014. "Optimal prognostic distance to minimize total maintenance cost: The case of the airline industry," International Journal of Production Economics, Elsevier, vol. 151(C), pages 76-88.
    31. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    32. Bontempi, Gianluca & Ben Taieb, Souhaib, 2011. "Conditionally dependent strategies for multiple-step-ahead prediction in local learning," International Journal of Forecasting, Elsevier, vol. 27(3), pages 689-699.
    33. Minner, Stefan, 2003. "Multiple-supplier inventory models in supply chain management: A review," International Journal of Production Economics, Elsevier, vol. 81(1), pages 265-279, January.
    34. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    35. Wichard, Jörg D., 2011. "Forecasting the NN5 time series with hybrid models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 700-707, July.
    36. S. Yakowitz, 1987. "Nearest‐Neighbour Methods For Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(2), pages 235-247, March.
    37. Luis A. Aguirre & Christophe Letellier, 2009. "Modeling Nonlinear Dynamics and Chaos: A Review," Mathematical Problems in Engineering, Hindawi, vol. 2009, pages 1-35, June.
    38. Adeodato, Paulo J.L. & Arnaud, Adrian L. & Vasconcelos, Germano C. & Cunha, Rodrigo C.L.V. & Monteiro, Domingos S.M.P., 2011. "MLP ensembles improve long term prediction accuracy over single networks," International Journal of Forecasting, Elsevier, vol. 27(3), pages 661-671, July.
    39. Prak, Dennis & Teunter, Ruud & Syntetos, Aris, 2017. "On the calculation of safety stocks when demand is forecasted," European Journal of Operational Research, Elsevier, vol. 256(2), pages 454-461.
    40. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    41. Aydin Cecen, A. & Erkal, Cahit, 1996. "Distinguishing between stochastic and deterministic behavior in foreign exchange rate returns: Further evidence," Economics Letters, Elsevier, vol. 51(3), pages 323-329, June.
    42. Soofi, Abdol S. & Cao, Liangyue, 1999. "Nonlinear deterministic forecasting of daily Peseta-Dollar exchange rate," Economics Letters, Elsevier, vol. 62(2), pages 175-180, February.
    43. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.
    44. Kusters, Ulrich & McCullough, B.D. & Bell, Michael, 2006. "Forecasting software: Past, present and future," International Journal of Forecasting, Elsevier, vol. 22(3), pages 599-615.
    45. Hwarng, H. Brian & Xie, Na, 2008. "Understanding supply chain dynamics: A chaos perspective," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1163-1178, February.
    46. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    47. Wichard, Jörg D., 2011. "Forecasting the NN5 time series with hybrid models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 700-707.
    48. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
    49. Petropoulos, Fotios & Fildes, Robert & Goodwin, Paul, 2016. "Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 842-852.
    50. Lisi, Francesco & Medio, Alfredo, 1997. "Is a random walk the best exchange rate predictor?," International Journal of Forecasting, Elsevier, vol. 13(2), pages 255-267, June.
    51. Cao, Liangyue & Soofi, Abdol S., 1999. "Nonlinear deterministic forecasting of daily dollar exchange rates," International Journal of Forecasting, Elsevier, vol. 15(4), pages 421-430, October.
    52. Jursa, René & Rohrig, Kurt, 2008. "Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 694-709.
    53. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    54. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.
    55. Marcos Alvarez-Diaz, 2008. "Exchange rates forecasting: local or global methods?," Applied Economics, Taylor & Francis Journals, vol. 40(15), pages 1969-1984.
    56. Cecen, A. Aydin & Erkal, Cahit, 1996. "Distinguishing between stochastic and deterministic behavior in high frequency foreign exchange rate returns: Can non-linear dynamics help forecasting?," International Journal of Forecasting, Elsevier, vol. 12(4), pages 465-473, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dolgui, Alexandre & Hashemi-Petroodi, S. Ehsan & Kovalev, Sergey & Kovalyov, Mikhail Y., 2021. "Profitability of a multi-model manufacturing line versus multiple dedicated lines," International Journal of Production Economics, Elsevier, vol. 236(C).
    2. You-Shyang Chen & Chien-Ku Lin & Chih-Min Lo & Su-Fen Chen & Qi-Jun Liao, 2021. "Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry," Mathematics, MDPI, vol. 9(20), pages 1-26, October.
    3. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    3. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    4. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    5. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    6. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.
    7. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    8. Nikolopoulos, Konstantinos I. & Babai, M. Zied & Bozos, Konstantinos, 2016. "Forecasting supply chain sporadic demand with nearest neighbor approaches," International Journal of Production Economics, Elsevier, vol. 177(C), pages 139-148.
    9. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    10. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    11. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    12. Cao, Liangyue & Soofi, Abdol S., 1999. "Nonlinear deterministic forecasting of daily dollar exchange rates," International Journal of Forecasting, Elsevier, vol. 15(4), pages 421-430, October.
    13. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    14. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    15. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    16. Olmedo,E. & Velasco, F. & Valderas, J.M., 2007. "Caracterización no lineal y predicción no paramétrica en el IBEX35/Nonlinear Characterization and Predictions of IBEX 35," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 25, pages 815-842, Diciembre.
    17. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    18. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    19. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    20. Lolli, F. & Gamberini, R. & Regattieri, A. & Balugani, E. & Gatos, T. & Gucci, S., 2017. "Single-hidden layer neural networks for forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 116-128.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:231:y:2021:i:c:s092552732030205x. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.elsevier.com/locate/ijpe .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.