Syntetos Boylan Approximation Research Articles

Forecasting and Planning for Material Control in the Medical Device Industry

PT. XYZ is a company operating in the electromedical sector that has supported many domestic medical devices in recent years. The demand for electromedical equipment seen from sales data is very volatile and difficult to predict, apart from that 70% of the requests received come from government institutions. The problem is compounded by the absence of a forecasting policy so that production activity planning is not optimal. Product inventory that is not accompanied by careful calculations will result in overstock and stockout which can increase total costs and reduce service levels. In carrying out its business PT. XYZ implements an existing inventory management policy which is carried out by ordering product components in large quantities but there is no clear reorder point. So this research will observe the product components CMS-600 PLUS, ECG-1200G, TENSIONE, ECG-300G, SON-C, SON-B, SON-PRO, USG PROMAX, PROMIST-3, MED-01, ECG- 100G, and ULTRAMIST then proposed the Croston and Syntetos-Boylan Approximation (SBA) forecasting method to estimate possible demand in the future and then continuous review inventory management policies (s, S) and (s, Q) to determine the maximum reorder point value. stock and order quantity. After that, a Monte Carlo simulation was carried out to see and compare existing policies with improvement policies based on total cost and service level variables. Next, a sensitivity analysis was carried out to determine the robustness of the simulation model to changes in uncertainty. The results obtained are recommendations for a continuous review (s, Q) policy with total cost savings of IDR 22,206,322,981 and an increase in service level of 1.36% to 99.98% compared to the existing policy.

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's 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.

Forecasting intermittent demand for inventory management by retailers: A new approach

The forecasting of intermittent demand is a complex task owing to demand fluctuations and interval uncertainty. Intermittent demand is essentially random demand with a high percentage of zero values. In the retail industry, there are many products which face intermittent demand and this poses a problem of inventory management. This study proposes a Markov-combined method (MCM) for forecasting intermittent demand, which takes into account the inventory status and historical sales of products. We divide the prediction process into two stages. In the first stage, the transition probabilities of the four basic states of demand and inventory are calculated. In the second stage, the corresponding and appropriate prediction method is selected according to the predicted state. Further, using two large datasets from the two biggest e-commerce companies in China, we verify our results and show that the MCM forecasts more accurately than the Single Exponential Smoothing (SES), Syntetos-Boylan Approximation (SBA), and Croston (CR) methods. The MCM can be as an alternative method for forecasting intermittent demand because it is easy to compute and typically more accurate than the classical forecasting methods.

Forecasting of intermittent demands under the risk of inventory obsolescence

AbstractCroston and the other related methods, such as Syntetos‐Boylan approximation (SBA), are the most popular methods recommended in the literature for intermittent demand forecasting. However, these conventional methods are not considered suitable in inventory obsolescence as they do not update their forecast in the periods of zero demand. Therefore, in order to add to the methods suitable for the inventory obsolescence issue, we propose a new method that imparts flexibility to the SBA method. The proposed method updates forecast each period by the use of distinct smoothing constants for interdemand intervals. The method is further examined extensively on a simulated dataset considering gradual and abrupt obsolescence and an empirical dataset from the automotive sector. The study is not limited to assessing the forecasting accuracy but also focuses upon the inventory performance of the considered methods. The study results indicate the effectiveness of the proposed method, particularly under increased risk of obsolescence.

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 produce 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’s 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.

AIRCRAFT SPARE PARTS INVENTORY MANAGEMENT ANALYSIS ON AIRFRAME PRODUCT USING CONTINUOUS REVIEW METHODS

The research aims to find the factors that cause high inventory value, increase the value of forecasting precision, service level and cost efficiency with fishbone diagrams and proposed methods. The research sample is 9 spare parts included in classification A in the ABC analysis and maintenance list 2018. Forecasting methods use Moving Average, Single Exponential Smoothing and Syntetos-Boylan Approximation as well as Mean Square Error calculation, deterministic inventory calculation and Continuous Review Method. The results of this study are an increase in logistics costs by $ 808.71 in the inventory management proposal. An increase in service level from 95% to 99% and the error value in the calculation of the proposal becomes smaller using the proposed method. This study also found that the factor causing the high inventory value was due to inaccurate planning methods so that other comparative methods were needed that could increase the precision of demand forecasting.

On the empirical performance of some new neural network methods for forecasting intermittent demand

Abstract In this paper, new neural network (NN) methods are proposed to forecast intermittent demand and we empirically study their performance as compared to parametric and non-parametric forecasting methods proposed in the literature. The empirical investigation uses demand data for 5,135 spare parts for the fleet of aircrafts of an airline company. Three parametric benchmark methods are examined: single exponential smoothing (SES), Croston’s method and Syntetos–Boylan approximation, along with two bootstrapping methods: Willemain’s method and Zhou and Viswanathan’s method. The benchmark NN method considered in this paper is that proposed by Gutierrez et al. (2008) The paper shows the outperformance of SES and the NN methods for (a) their forecast accuracy and (b) their inventory efficiency (trade-off between holding volumes and backordering volumes) when compared to the other methods. Moreover, among the NN methods, a new proposed method is shown to be better than that proposed by Gutierrez et al. in terms of forecast accuracy and inventory efficiency.

KESİKLİ TALEPLERİN TAHMİNLENMESİNDE ATA METOT VE CROSTON TEMELLİ METOTLARIN KARŞILAŞTIRILMASI

Intermittent demand forecasting is crucial for firms and commercial activities. Recently, many researchers have focused on forecasting methods for intermittent demand and proposed various forecasting techniques. The most prominent methods among these proposed techniques are the Croston method, which is based on exponential smoothing, and its two popular variations: SBA (Syntetos-Boylan Approximation), SBJ (Shale-Boylan-Johnston Approximation). Croston method is widely used in forecasting of intermittent demand and inventory (stock) control. Since these demands usually include zero values, using the ground breaking method developed by Croston in this data becomes inevitable. Nevertheless, there are some shortcomings to this method such as producing biased forecasts and for this reason its variations have been proposed. ATA method is a recently developed forecasting method which is an alternative to exponential smoothing. In this paper we propose a modification of ATA method that can be used for forecasting of intermittent demand. We will compare the results of the proposed approach to those of Croston and other forecasting methods used for intermittent demand forecasting.

Intermittent demand forecasting in the Enterprise: Empirical verification

AbstractForecasting methods are often valued by means of simulation studies. For intermittent demand items there are often very few non–zero observations, so it is hard to check any assumptions, because statistical information is often too weak to determine, for example, distribution of a variable. Therefore, it seems important to verify the forecasting methods on the basis of real data. The main aim of the article is an empirical verification of several forecasting methods applicable in case of intermittent demand. Some items are sold only in specific subperiods (in given month in each year, for example), but most forecasting methods (such as Croston's method) give non–zero forecasts for all periods. For example, summer work clothes should have non–zero forecasts only for summer months and many methods will usually provide non–zero forecasts for all months under consideration. This was the motivation for proposing and testing a new forecasting technique which can be applicable to seasonal items. In the article six methods were applied to construct separate forecasting systems: Croston's, SBA (Syntetos–Boylan Approximation), TSB (Teunter, Syntetos, Babai), MA (Moving Average), SES (Simple Exponential Smoothing) and SESAP (Simple Exponential Smoothing for Analogous subPeriods). The latter method (SESAP) is an author's proposal dedicated for companies facing the problem of seasonal items. By analogous subperiods the same subperiods in each year are understood, for example, the same months in each year. A data set from the real company was used to apply all the above forecasting procedures. That data set contained monthly time series for about nine thousand products. The forecasts accuracy was tested by means of both parametric and non–parametric measures. The scaled mean and the scaled root mean squared error were used to check biasedness and efficiency. Also, the mean absolute scaled error and the shares of best forecasts were estimated. The general conclusion is that in the analyzed company a forecasting system should be based on two forecasting methods: TSB and SESAP, but the latter method should be applied only to seasonal items (products sold only in specific subperiods). It also turned out that Croston's and SBA methods work worse than much simpler methods, such as SES or MA. The presented analysis might be helpful for enterprises facing the problem of forecasting intermittent items (and seasonal intermittent items as well).

An Integrated Approach to Forecasting Intermittent Demand for Electric Power Materials

Forecasting intermittent demand for electric power materials is crucial for electric power companies to ensure the economical supply of electricity in terms of stock and cost management. This paper proposes an integrated approach to forecasting intermittent demand for electric power materials. The approach decomposes the demand time series into two parts: a binary time series representing demand occurrences and a series representing non-zero demands. From the perspective of the origin of demands, we forecast demand occurrences by both intrinsic and extrinsic factors. The probability of demand occurrence due to intrinsic factors is estimated by using the data of ontology failure, while for extrinsic factors is estimated by using the historical demand data and time series data of explanatory variables. The weighted sum of these two probabilities is compared with a critical value to predict demand occurrence. We apply the multivariate nonlinear regression method to forecast non-zero demands, which are combined with the predicted demand occurrences to form the final forecasting demand series. Two performance measures are developed to assess the forecasting methods. By making use of data set of 50 types of electric power materials from the State Grid Shanghai Electric Power Company in China, we show that our approach provides more accuracy forecast than Markov bootstrapping method, Syntetos–Boylan Approximation method and integrated forecasting method method.

Analisis Pengendalian Persediaan Spare Part Mesin Produksi di PT. Prima Sejati Sejahtera dengan Metode Continuous Review

<em>PT. Prima Sejati Sejahtera as one of the subsidiaries of PT. Pan Brothers Tbk. which is engaged in garment production. The company's mechanical department in managing spare part inventory is still using intuitive method, where the number of spare part order for certain periods based on spare part demands data onto the previous period. The company’s mechanical department often stock out of spare parts. Spare part’s inventory management becomes a complex issue because of the need for fast response to handle the downtime of machines, and the risk of obsolescence of spare parts. So in this research will discuss about spare part inventory control which is started with spare parts grouping by using ABC analysis method to determine the appropriate inventory control method for each group. There are 23 spare parts which included in group A. The forecast of spare part’s demands to use Croston, Syntetos-Boylan Approximation (SBA) and Single Exponential Smoothing (SES). Comparison of each forecasting method will be determined by the value of forecasting errors (MAD). It is known that there are 12 spare parts with Croston method in the best forecasting method, 6 spare parts in Syntetos-Boylan Approximation (SBA) method and 5 spare parts with Single Exponential Smoothing (SES) method. Based on the best forecasting result, it will be calculated the value of safety stock (SS), reorder point (ROP) and the optimal number of ordering (Q) using Continuous Review method for each spare part.</em>

Previsión de demanda intermitente con métodos de series de tiempo y redes neuronales artificiales: Estudio de caso

Este artículo tiene como objetivo estudiar la previsión de la demanda intermitente de un tipo específico de pieza de reposición en una industria brasilera de sistemas de refrigeración que comercializa sus productos en el mercado latinoamericano. La demanda es caracterizada en términos de intermitencia y variabilidad. Los resultados son calculados usando métodos clásicos de previsión intermitente fuera de la muestra: Croston, Aproximación Syntetos-Boylan (SBA), Corrección Shale-Boylan-Johnston (SBJ), Algoritmo de Previsión de Agregación Múltiple (MAPA) y modelos basados en Redes Neuronales Artificiales (RNA). El Error Cuadrático Medio (RMSE) y Desvío Medio Absoluto (MAE) son utilizados para efectos de comparación y selección del modelo de previsión. El análisis comparativo de los resultados muestra que las previsiones basadas en modelos RNA simple de tres capas y entrenadas con el algoritmo Resilient Backpropagation presentan mejor desempeño. Los cálculos fueron realizados con el software R, RStudio, bibliotecas “forecast”, “tsintermittent” y “neuralnet”.

Demand forecasting and inventory control: A simulation study on automotive spare parts

This paper presents results of a large-scale simulation study on spare parts demand forecasting and inventory control to select best policies within each SKU category. Simulations were conducted over 10,032 SKUs of an automaker that operates in Brazil, considering six years of demand data. Literature review drove the selection of different models simulated. The study included three alternatives to record demand data (individual orders data, weekly and monthly time buckets), three demand forecasting models (SMA – Simple Moving Average, SBA – Syntetos–Boylan Approximation and Bootstrapping) and six models for demand distribution during lead-time (Normal, Gamma, NBD-Negative Binomial Distribution, compound Poisson-Normal, compound Poisson-Gamma and Bootstrapping) resulting in 17 “combined” policies. These policies were applied under (s, nQ) inventory control (reorder point, multiples of fixed order quantity), considering two alternative frequencies for model parameters revision (monthly and semi-annually) and four Target-Fill-Rates (TFR=80%, 90%, 95% and 99%), totalizing 136 simulation runs over each SKU. Parameter values (s, Q) were calculated towards TFR using methods from literature. Performance of each combined policy was measured by total costs and RFR – Realized-Fill-Rate. Major contributions of the research are the policy recommendations within each SKU category, a new Bootstrapping procedure and the highlight of Single Demand Approach (SDA) as a promising area for future theoretical and empirical studies. Results shall be used as guideline for practitioners under similar operations.

More grip on inventory control through improved forecasting: A comparative study at three companies

Inventory control for parts with infrequent demands is difficult since forecasting their demand is problematic. Traditional forecasting methods, such as moving average and single exponential smoothing, are known not to suffice since they do not cope well with periods with zero demands. Croston type methods and bootstrapping methods are more promising. We propose a new bootstrapping method, which we term empirical plus. The added value of this method lies in the fact that it explicitly takes into account that besides the demand, also the supply lead time is stochastic. We compare its performance with a number of methods from all three above-mentioned categories. Opposite to what is done in most comparative studies, we do not focus on performance metrics that are related directly to the forecasting results (e.g., mean squared error), but we focus on the resulting inventory control policy (achieved fill rate and holding costs). We use in our study large data sets from three companies, which we make publicly available. We find that our empirical plus method outperforms the other methods when the average inter-demand interval is large and the squared coefficient of variation of the demand size is small. This class of parts often consists of the expensive parts, for which forecasting is both difficult, because of the infrequent demands, and important, because of the price. The Syntetos Boylan approximation performs best on the other classes of parts. These findings may be used in practice to use the right forecasting method for each type of part, thus achieving more cost-effective spare parts inventory control.

An evaluation of forecasting methods for aircraft non-routine maintenance material demand

Aircraft maintenance can be divided into routine and non-routine activities. Material demand associated with non-routine maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-routine maintenance material demand. Non-routine material consumption is linked to scheduled maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-routine maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-routine maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate maintenance C-check.

Forecasting-based SKU classification

Different spare parts are associated with different underlying demand patterns, which in turn require different forecasting methods. Consequently, there is a need to categorise stock keeping units (SKUs) and apply the most appropriate methods in each category. For intermittent demands, Croston's method (CRO) is currently regarded as the standard method used in industry to forecast the relevant inventory requirements; this is despite the bias associated with Croston's estimates. A bias adjusted modification to CRO (Syntetos–Boylan Approximation, SBA) has been shown in a number of empirical studies to perform very well and be associated with a very ‘robust’ behaviour. In a 2005 article, entitled ‘On the categorisation of demand patterns’ published by the Journal of the Operational Research Society, Syntetos et al. (2005) suggested a categorisation scheme, which establishes regions of superior forecasting performance between CRO and SBA. The results led to the development of an approximate rule that is expressed in terms of fixed cut-off values for the following two classification criteria: the squared coefficient of variation of the demand sizes and the average inter-demand interval. Kostenko and Hyndman (2006) revisited this issue and suggested an alternative scheme to distinguish between CRO and SBA in order to improve overall forecasting accuracy. Claims were made in terms of the superiority of the proposed approach to the original solution but this issue has never been assessed empirically. This constitutes the main objective of our work. In this paper the above discussed classification solutions are compared by means of experimentation on more than 10,000 SKUs from three different industries. The results enable insights to be gained into the comparative benefits of these approaches. The trade-offs between forecast accuracy and other implementation related considerations are also addressed.

Intermittent demand: Linking forecasting to inventory obsolescence

The standard method to forecast intermittent demand is that by Croston. This method is available in ERP-type solutions such as SAP and specialised forecasting software packages (e.g. Forecast Pro), and often applied in practice. It uses exponential smoothing to separately update the estimated demand size and demand interval whenever a positive demand occurs, and their ratio provides the forecast of demand per period. The Croston method has two important disadvantages. First and foremost, not updating after (many) periods with zero demand renders the method unsuitable for dealing with obsolescence issues. Second, the method is positively biased and this is true for all points in time (i.e. considering the forecasts made at an arbitrary time period) and issue points only (i.e. considering the forecasts following a positive demand occurrence only). The second issue has been addressed in the literature by the proposal of an estimator (Syntetos–Boylan Approximation, SBA) that is approximately unbiased. In this paper, we propose a new method that overcomes both these shortcomings while not adding complexity. Different from the Croston method, the new method is unbiased (for all points in time) and it updates the demand probability instead of the demand interval, doing so in every period. The comparative merits of the new estimator are assessed by means of an extensive simulation experiment. The results indicate its superior performance and enable insights to be gained into the linkage between demand forecasting and obsolescence.

Lumpy demand forecasting using neural networks

The current study applies neural network (NN) modeling in forecasting lumpy demand. It is, to the best of our knowledge, the first such study. Our study compares the performance of NN forecasts to those using three traditional time-series methods (single exponential smoothing, Croston's method, and the Syntetos–Boylan approximation). We find NN models to generally perform better than the traditional methods, using three different performance measures. We also independently validate earlier findings that the Syntetos–Boylan approximation performs better than the Croston's and single exponential smoothing methods in lumpy demand forecasting.