Machine and Deep Learning Applications for Inventory Replenishment Optimization

openInventory replenishment is the process of obtaining the items, components, and raw materials required to make and sell products. It guarantees that items and resources are acquired and delivered in an efficient and timely manner. Poorly managed inventory replenishment can have a severe influence on customers and the overall health of a business, which may result in lost revenue, reduced profits and damaged reputation. Implementing the correct inventory replenishment helps manufacturers and sellers in avoiding major issues such as stock-outs, delayed deliveries and overstocking. Accuracy of forecasting is therefore crucial to retailers' profitability. Fashion businesses need precise and accurate sales forecasting tools to prevent stock-outs and maintain a high inventory fill rate. This thesis navigates the complex landscape of fashion retail forecasting, addressing the challenges posed by intermittent time series data and stock management. Advanced forecasting models have been implemented to account for the intermittent nature of fashion product demand, resulting in predictions more accurate and reliable.The study extends also to stock replenishment strategies, emphasizing the importance of the reorder point, the Cycle Service Level and the safety stock. Lastly, it culminates in the development of a replenishment algorithm aimed at reducing stock-outs, which is a modified version of the Periodic Review Policy: Order-Up-To-Level, now tailored to the sporadic nature of intermittent demand.Inventory replenishment is the process of obtaining the items, components, and raw materials required to make and sell products. It guarantees that items and resources are acquired and delivered in an efficient and timely manner. Poorly managed inventory replenishment can have a severe influence on customers and the overall health of a business, which may result in lost revenue, reduced profits and damaged reputation. Implementing the correct inventory replenishment helps manufacturers and sellers in avoiding major issues such as stock-outs, delayed deliveries and overstocking. Accuracy of forecasting is therefore crucial to retailers' profitability. Fashion businesses need precise and accurate sales forecasting tools to prevent stock-outs and maintain a high inventory fill rate. This thesis navigates the complex landscape of fashion retail forecasting, addressing the challenges posed by intermittent time series data and stock management. Advanced forecasting models have been implemented to account for the intermittent nature of fashion product demand, resulting in predictions more accurate and reliable.The study extends also to stock replenishment strategies, emphasizing the importance of the reorder point, the Cycle Service Level and the safety stock. Lastly, it culminates in the development of a replenishment algorithm aimed at reducing stock-outs, which is a modified version of the Periodic Review Policy: Order-Up-To-Level, now tailored to the sporadic nature of intermittent demand

Modeling digital camera monitoring count data with intermittent zeros for short-term prediction

Digital camera monitoring has revolutionised survey designs in many fields, as an important source of information. The extended sampling coverage offered by this monitoring scheme makes it preferable compared to other traditional methods of survey. However, data obtained from digital camera monitoring are often highly variable, and characterized by sparse periods of zero counts, interspersed with missing observations due to outages. In practice, missing data of relatively shorter duration are mostly observed and are often imputed using interpolation techniques, ignoring long-term trends leading to inherent estimation biases. In this study, we investigated time series forecasting methods that adequately handle intermittency and produced plausible estimates for imputation and forecasting purposes. The study utilised a yearlong digital camera monitoring data set of hourly counts of powerboat launches at three boat ramps in Western Australia. Several time series forecasting methods were evaluated and the accuracies of their point estimates of forecasts for various lead times in hours of up to one week were assessed using cross-validation techniques. Intermittent demand forecasting techniques, including Croston\u27s method and Syntetos-Boylan Approximation (SBA) models, and count data forecasting methods including autoregressive conditional Poisson (ACP) models, integer-valued moving average (INMA) models, and integer-valued autoregressive (INAR) models were evaluated. ACP and INAR models performed better than intermittent demand forecasting techniques for short forecast horizons and provided some evidence of their sufficiency in predicting the dynamics in recreational boating activities. This result established that, in as much as intermittency may be a key feature for a given dataset, it should not override the systemic characteristics of data in the application of forecasting techniques. Our results provide plausible estimates for short-term missing data and forecasts for monitoring events, with applications in supporting proper tracking of usage of facilities, guiding resource allocations and providing insightful perspectives for management decisions

Hierarchical forecasting for predicting spare parts demand in the South Korean Navy

In the South Korean Navy the demand for many spare parts is infrequent and the volume of items required is irregular. This pattern, known as non-normal demand, makes forecasting difficult. This research uses data obtained from the South Korean Navy to compare the performance of forecasting methods that use hierarchical and direct forecasting strategies for predicting the demand for spare parts. Among various forecasting methods tested, a simple combination of exponential smoothing models, which uses a hierarchical forecasting strategy, was found to minimise forecasting errors and inventory costs. This simple combination forecasting method was generated by a simple combination between an exponential smoothing model with quarterly aggregated data adjusted for linear trend at group level and an exponential smoothing model with monthly aggregated unadjusted data at item level. Logistic regression classification model for predicting the relative performance of alternative forecasting methods (Le. a direct forecasting method vs. a hierarchical forecasting method) by multivariate demand features of spare parts was developed. Logistic regression classification model is generalisable, because it is based on relationships between the relative performance of alternative forecasting methods and demand features. This classification model reduced inventory costs, compared to the results of only using the simple combination forecasting method. This classification model is likely to be a promising model to guide the selection of a forecasting method between alternative forecasting methods for predicting spare parts demand in the South Korean Navy, so that it could maximise the operational availability of weapon systems.EThOS - Electronic Theses Online ServiceKorean NavyGBUnited Kingdo

Artificial Intelligence Methods in Spare Parts Demand Forecasting

The paper discusses the problem of forecasting lumpy demand which is typical for spare parts. Several prediction methods are presented in the article – traditional techniques based on time series and advanced methods that use Artificial Intelligence tools. The research conducted in the paper focuses on comparison of eight forecasting methods, including classical, hybrid and based on artificial neural networks. The aim of the paper is to assess the efficiency of lumpy demand forecasting methods that apply AI tools. The assessment is conducted by a comparison with traditional methods and it is based on Root Mean Square Errors (RMSE) and relative forecast errors (ex post) values. The article presents also a new approach to the lumpy demand forecasting issue – a method which combines regression modelling, information criteria and artificial neural networks

Parametric Approaches for Spare Parts Demand

Forecasting of the spare parts needs is an important operational question. The problem in the needs forecast is that the demand is intermittent. In stock management, there are several forecasting tools based on demand history such as: linear regression, basic and modified Croston methods, simple and weighted moving average, exponential smoothing, and finally the bootstrap method. In this paper, we will treat the last method through three sections: literature review, procedure of the method and finally an application of the method which will be compared to simple exponential smoothing and hybrid method

Biasedness of forecasts errors for intermittent demand data

Purpose: Intermittent demand is defined as infrequent or sporadic. Many forecasting errors are inappropriate for intermittent data. In some periods, there could be no demand, so division by zero must be avoided. Usually, forecasts are computed for many products; therefore, errors should be scale-independent (or relative). Many ex-post forecast errors, such as MASE (Mean Absolute Scaled Error) or MAE (Mean Absolute Error), indicate as best very low forecasts, sometimes even zero forecasts. Therefore, many researchers think that measures taking into account stock and consumer service levels should be used instead of conventional forecasts. It might suggest that typical forecast errors are useless for intermittent data. In this article, the contradictory hypothesis is verified. It is stated that only unbiased forecast errors should be used if the conclusions are to be correct. Design/Methodology/Approach: Definition of unbiased forecast error is proposed and verified for popular forecast errors, such as ME (Mean Error), MSE (Mean Square Error), MAE, or MASE. The theoretical properties of these errors are considered concerning their biasedness. Forecasts are made based on Croston’s and TSB methods, but also average and median were used as forecasting methods to emphasize conclusions. Findings: In the empirical example, forecast errors are computed for intermittent demand times series to verify theoretical conclusions. The general conclusion is that only unbiased forecast errors provide proper indications according to forecast accuracy. This finding is true in general, not only for intermittent demand. Practical Implications: Presented considerations might be useful for enterprises dealing with intermittent demand forecasting such as distribution centers, warehouse centers, and so on. Originality/value: To the author’s knowledge, forecast error bias was not analyzed before in the literature. A new forecast error is proposed, which was named RMSSE (Root Mean Square Scaled Error).peer-reviewe

FORECASTING DAN ANALISIS PERENCANAAN KAPASITAS PRODUKSI DENGAN BILL OF LABOR APPROACH PADA PROYEK ENGINE CT7 PT. XYZ

PT. XYZ merupakan perusahaan mandiri sebagai anak perusahaan dari PT. Dirgantara Indonesia (PT. DI), yang bergerak dalam bidang jasa perawatan (maintenance) mesin turbin yang biasa digunakan pada pesawat terbang maupun mesin turbin yang digunakan untuk industri. Berdasarkan pengalaman dan data historis diketahui bahwa PT. XYZ menerima setiap pesanan yang diminta namun tidak mempertimbangkan sumber daya kapasitas produksi yang tersedia. Karena itu perlu dilakukan peramalan yang akan menghasilkan MPS dan menjadi dasar untuk melakukan perhitungan kapasitas agar lantai produksi dapat siap berproduksi ketika pesanan yang berfluktuatif datang. Dari hasil peramalan bahwa metode peramalan yang memiliki tingkat kesalahan yang terkecil adalah Simulasi Monte Carlo dengan 91% dari semua part number hasil peramalan sedangkan untuk Croston’s Method dan Metode Syntetos – Boylan Approximation memiliki 9% dari semua part number hasil peramalan. Sehingga part number yang menggunakan Croston’s Method adalah 4108T01G01, 5043T07G02, 5034T83P12, 4053T44G01 dan 6055T82P01. Perencanaan kapasitas produksi dengan menggunakan metode RCCP teknik BOLA telah dilakukan yang mengahasilkan bahwa setiap mesin memiliki kelebihan kapasitas, sehingga semua demand dapat terpenuhi. Dengan demikian perencanaan kapasitas produksi dengan metode RCCP teknik BOLA menghasilkan beberapa alternatif solusi perencanaan kapasitas produksi yang optimal yaitu dengan melakukan Preventive Maintenance, dan melakukan produksi part-part yang akan digunakan untuk mempermudah proses repair. Kata Kunci: Peramalan, Croston’s Method, Syntetos – Boylan Approximati, Simulasi Monte Carlo, Perencanaan Kapasitas, RCCP

Demand categorization, forecasting, and inventory control for intermittent demand items

[EN] It is commonly assumed that intermittent demand appears randomly, with many periods without demand; but that when it does appear, it tends to be higher than unit size. Basic and well-known forecasting techniques and stock policies perform very poorly with intermittent demand, making new approaches necessary. To select the appropriate inventory management policy, it is important to understand the demand pattern for the items, especially when demand is intermittent. The use of a forecasting method designed for an intermittent demand pattern, such as Crostons method, is required instead of a simpler and more common approach such as exponential smoothing. The starting point is to establish taxonomic rules to select efficiently the most appropriate forecasting and stock control policy to cope with thousands of items found in real environments. This paper contributes to the state of the art in: (i) categorisation of the demand pattern; (ii) methods to forecast intermittent demand; and (iii) stock control methods for items with intermittent demand patterns. The paper first presents a structured literature review to introduce managers to the theoretical research about how to deal with intermittent demand items in both forecasting and stock control methods, and then it points out some research gaps for future development for the three topics.This research was part of the project GEMA, supported by the Ministerio de Educación y Ciencia (Ref. DPI 2007-65441).Babiloni Griñón, ME.; Cardós, M.; Albarracín Guillem, JM.; Palmer Gato, ME. (2010). Demand categorization, forecasting, and inventory control for intermittent demand items. South African Journal of Industrial Engineering. 21(2):115-130. https://doi.org/10.7166/21-2-54S11513021