Bir yem işletmesi için satış tahmin yöntemlerinin karşılaştırılması: Bir vaka çalışması

Yıl 2018, Cilt: 24 Sayı: 4, 705 - 712, 17.08.2018

Leyla Demir Selahattin Akkaş

Öz

Son
yıllarda küresel ısınmadan dolayı doğal kaynakların etkin ve verimli kullanımı
dünyamız için daha da önemli bir hale gelmiştir. Azalan doğal kaynaklar tarım
ve gıda zincirlerini daha etkin yönetim stratejileri benimsemeye zorlamaktadır.
Başarılı bir yönetimin ilk şartı doğru ve güvenilir tahminlere dayalı planlar
yapmaktır. Bu çalışmada tarım ve gıda zincirlerinin ilk halkası olan yem
endüstrisinde yer alan bir işletme için gerçek verilere dayalı olarak
oluşturulan tahmin modelleri karşılaştırılmıştır. Geleneksel istatistiksel
zaman serisi yöntemleri popüler ve hesaplamalı olarak etkin iki yapay zekâ
tekniği olan yapay sinir ağları ve destek vektör regresyonu yöntemleri ile
karşılaştırılmıştır. Tahminlerin doğruluğu ortalama mutlak hata (MAE), ortalama
mutlak yüzde hata (MAPE) ve ortalama hata kare (MSE) gibi üç farklı hata ölçütü
kullanılarak hesaplanmıştır. Sonuçlar destek vektör regresyonu yönteminin zaman
serisi ve yapay sinir ağları yöntemlerine göre daha iyi sonuçlar ürettiğini
göstermiştir.

Anahtar Kelimeler

Satış tahmini, Zaman serisi yöntemleri, Yapay sinir ağları, Destek vektör regresyonu, Tarım ve gıda zincirleri, Yem endüstrisi

A comparison of sales forecasting methods for a feed company: A case study

Öz

Due to
global warming in recent years, using natural resources in an effective way has
become more and more important to our world. Decreasing natural resources are
pushing agriculture and food chains to adopt more efficient management
strategies. The first condition for a successful management is to make plans
based on accurate and reliable forecasts. In this study, using real-world data,
forecasting models are compared for the products of a feed company which is the
first chain of agriculture and food chain systems. The traditional statistical
time series methods are compared to two popular and effective computational
intelligence techniques, i.e. artificial neural networks and support vector
regression. The accuracy of the forecasts is calculated by three different
error measures, i.e., the mean absolute error (MAE), the mean absolute
percentage error (MAPE), and the mean squared error (MSE). The results show
that support vector machines produces significantly better results comparing to
both time series methods and artificial neural networks.

Anahtar Kelimeler

Sales forecasting, Time series methods, Artificial neural networks, Support vector regression, Agriculture and food chains, Feed industry

Kaynakça

• McCarthy TM, Davis DF, Golicic SL, Mentzer JT. “The evolution of sales forecasting management: a 20-year longitudinal study of forecasting practices”. Journal of Forecasting, 25(5), 303-324, 2006.
• Syntetos AA, Babai Z, Boylan JE, Kolassa S, Nikolopoulos, K. “Supply chain forecasting: theory, practice, their gap and the future”. European Journal of Operational Research, 252(1), 1-26, 2016.
• Caniato F, Kalchschmidt M, Ronchi S, Verganti R, Zotteri G. “Clustering customers to forecast demand”. Production Planning and Control, 16(1), 32-43, 2005.
• Kalchschmidt M, Verganti R, Zotteri G. “Forecasting demand from heterogeneous customers”. International Journal of Operations & Production Management, 26(6), 619-638, 2006.
• Widiarta H, Viswanathan S, Piplani R. “Forecasting item-level demands: an analytical evaluation of top–down versus bottom–up forecasting in a production- planning framework”. IMA Journal of Management Mathematics, 19(2), 207-218, 2008.
• Sbrana G, Silvestrini, S. “Forecasting aggregate demand: analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework”. International Journal of Production Economics, 146(1), 185-198, 2013.
• Widiarta H, Viswanathan S, Piplani R. “Forecasting aggregate demand: an analytical evaluation of top–down versus bottom–up forecasting in a production-planning framework”. International Journal of Production Economics, 118(1), 87-94, 2009.International Journal of Production Economics, 118(1), 87-94, 2009.
• Kerkkänen A, Korpela J, Huiskonen J. “Demand forecasting errors in industrial context: measurement and impacts”. International Journal of Production Economics, 118(1), 43-48, 2009.
• Martins VLM, Werner L. “Forecast combination in industrial series: a comparison between individual forecasts and its combinations with and without correlated errors”. Experts Systems with Applications, 39(13), 11479-11486, 2012.
• Acar Y, Everette SGJ. “Forecasting method selection in a global supply chain”. International Journal of Forecasting, 28(4), 842-848, 2012.
• Ansuj AP, Camargo ME, Radharamanan R, Petry DG. “Sales forecasting using time series and neural networks”. Computers & Industrial Engineering, 31(1-2), 421-424, 1996.
• Alon I, Qi M, Sadowski, RJ. “Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods”. Journal of Retailing and Consumer Services, 8(3), 147-156, 2001.
• Thomassey S, Happiette M. “A neural clustering and classification system for sales forecasting of new apparel items”. Applied Soft Computing, 7(4), 1177-1187, 2007.
• Kuo RJ, Xue KC. “A decision support system for sales forecasting through fuzzy neural networks with asymmetric fuzzy weights”. Decision Support Systems, 24(2), 105-126, 1998a.
• Kuo RJ, Xue KC. “An intelligent sales forecasting system through integration of artificial neural network and fuzzy neural network”. Computers in Industry, 37(1), 1-15, 1998b.
• Kuo RJ, Wu P, Wang CP. “An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination”. Neural Networks, 15(7), 909-925, 2002.
• Efendigil T, Önüt S, Kahraman C. “A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis”. Expert Systems with Applications, 36(3), 6697-6707, 2009.
• Wong WK, Xia M, Chu WC. “Adaptive neural network model for time-series forecasting”. European Journal of Operational Research, 207(2), 807-8016, 2010.
• Lu C-J, Lee T-S, Lian C-M. “Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks”. Decision Support Systems, 54(1), 584-596, 2012.
• Štěpnička M, Cortez P, Donate JP, Štěpničková L. “Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations”. Expert Systems with Applications, 40(6), 1981-1992, 2013.
• Tkáč M, Verner R. “Artificial neural networks in business: Two decades of research”. Applied Soft Computing, 38, 788-804, 2016.
• Garćia FT, Villalba LJG, Portela J. “Intelligent system for time series classification using support vector machines applied to supply chain”. Expert System with Applications, 39(12), 10590-10599, 2012.
• Du XF, Leung SCH, Zhang JL, Lai KK. “Demand forecasting of perishable farm products using support vector machine”. International Journal of Systems Science, 44(3), 556-567, 2013.
• Wang F-K, Du T. “Implementing support vector regression with differential evolution to forecast motherboard shipments”. Expert System with Applications, 41(8), 3850-3855, 2014.
• Vahdani B, Razavi F, Mousavi SM. “A high performing meta-heuristic for training support vector regression in performance forecasting of supply chain”. Neural Computing and Applications, 27(8), 2441-2451, 2016.
• Reboiro-Jato M, Glez-Dopazo J, Glez D, Laza R, Gálvez JF, Pavón R, Glez-Peňa D, Fdez-Riverola F. “Using inductive learning to assess compound feed production in cooperative poultry farms”. Expert Systems with Applications, 38(11), 14169-14177, 2011.
• Makridakis S, Wheelwright SC, Hyndman JR. Forecasting: Methods and Applications. 3rd ed. New York, USA, John Wiley&Sons, 1998.
• Montgomery DC, Jennings CL, Kulahci M. Introduction to Time Series Analysis and Forecasting. 2nd ed. New Jersey, USA, John Wiley & Sons, 2016.
• Nahmias S. Production&Operation Analysis. 5th ed. New York, USA, McGraw-Hill, 2005.
• Holt CC. Forecasting seasonal and trends by exponentially weighted moving averages. Pittsburgh, USA, Carnegie Institute of Technology, Graduate School of Industrial Administration, 1957.
• McCulloch WS, Pitts W. “A logical calculus of ideas immanent in nervous activity”. Bulletin of Mathematical Biophysics, 5(4), 115-133, 1943.
• Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed., New Jersey, USA, Pearson Prentice Hall, 1999.
• Vapnik VN. The Nature of Statistical Learning Theory. New York, USA, Springer-Verlag, 1995.
• Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V. Support vector regression machines. Editors: Mozer, MC, Jordan, MI, Petsche, T. Advances in Neural Information Processing Systems 9, 155–161, Cambridge, MA, USA, MIT Press, 1997.
• Huang C-F. “A hybrid stock selection model using genetic algorithms and support vector regression”, Applied Soft Computing, 12(2), 807–818, 2012.