Penerapan Metode Exponentially Weighted Quantile Regression Untuk Peramalan Penjualan Mobil

Kontrol terhadap persediaan sangat dibutuhkan untuk semua jenis produk termasuk produk otomotifseperti mobil, hal ini dilakukan agar dapat memastikan produk yang tersedia dipasar berada dalam level safetystock. Cara yang dapat digunakan untuk memperkirakan jumlah unit yang tersedia di pasar adalah denganmelakukan peramalan.Pada Penelitian ini akan dilakukan peramalan dengan menggunakan metodeexponentially weighted quantile regression (EWQR) yang dikemukakan oleh James W. Taylor pada tahun 2006.Metode ini melakukan pendekatan dengan menggunakan exponential smoothing dari cumulative distributionfunction (cdf). Peramalan dilakukan dengan melihat data penjualan barang pada periode sebelumnya. Hasildari uji coba menunjukkan bahwa peramalan dengan menggunakan metode EWQR memiliki tingkat keakuratanyang tinggi yaitu dengan nilai mean absolute percentage error (MAPE) sebesar 0.0055%, padahal menurutoktafri (2001) hasil peramalan dengan MAPE kurang dari 25% dapat dikatakan baik. Hasil dari metode EWQRini juga lebih baik jika dibandingkan dengan metode Holt's yang memiliki nilai MAPE 13.3615% dan simpleexponential smoothing yang memiliki nilai 1.2915

Retail forecasting: research and practice

This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice

Machine learning for inventory management: forecasting demand quantiles of perishable products with a neural network

Accurate demand forecasting is a crucial component in building an efficient supply chain. Forecasting is a major determinant of inventory cost. Several methods and models for forecasting have been studied extensively over the last decades. In recent years, there has been a growing interest in the capabilities of Machine Learning algorithms in forecasting, and specifically in Neural Network models. Despite the expanding research on forecasting with Neural Networks, there have been only few studies focusing on the specific ramifications for forecasting demand of perishable products at the Stock Keeping Unit (SKU) level. Forecasting SKU-level demand for perishable products is a challenging task: time series for demand are volatile, skewed, subject to external factors, and frequently consist of only a few observations. Furthermore, SKU-level demand forecasts are typically used for inventory management, which imposes additional requirements on the forecasting procedure. This study examines how to design Neural Networks that address the specific ramifications of inventory management for several thousand SKUs. This work identifies central issues in the field and compiles successful approaches to overcome them. Next, a Neural Network architecture is suggested that takes these special requirements into account, building on insights from the literature. Namely, it learns from multiple hundred time series, incorporates external data into the prediction, and provides quantile forecasts of cumulative demand. In a large-scale experiment, the model forecasted the demand for several hundred SKUs in the fresh product segment of a German wholesale company. These forecasts were subsequently used for simulating the inventory development at the company for three months under close-to-real-life conditions. This study shows that Neural Networks are a promising approach to deal with large-scale forecasting problems for perishable products. The main finding of this study is that within the experimental setting, the base form of the suggested model for accurate daily demand forecasting yielded superior results to an array of competing baselines. In terms of inventory performance, the results are mixed, but present exciting directions for further research