Stochastic demand forecast and inventory management of a seasonal product a supply chain system

Estimation of seasonal demand prior to an active demand season is essential in supply chain management. The business cycle of the seasonal demand is divided into two stages: stage-1, the slow-demand period, and stage-2, the peak-demand period. The focus here is to determine an appropriate demand forecast for the peak-demand period. In the first set of forecasting model, a standard gamma and an inverse gamma prior distribution are used to forecast demand. The parameters of the prior model are estimated and updated based on current observation using Bayesian technique. The forecasts are derived for both complete and incomplete datasets. The second set of forecast is derived by ARIMA method using Box-Jenkins approaches. A Bayesian ARIMA is proposed to forecast demand from incomplete dataset. A partial dataset of a seasonal product, collected from the US census bureau, is used in the models. Missing values in the dataset often arise in various situations. The models are extended to forecast demand from an incomplete dataset by the assumption that the original dataset contains missing values. The forecast by a multiplicative exponential smoothing model is used to compare all the forecast. The performances are tested by several error measures such as relative errors, mean absolute deviation, and tracking signals. A newsvendor inventory model with emergency procurement options and a periodic review model are studied to determine the procurement quantity and inventory costs. The inventory cost of each demand forecast relative to the cost of actual demand is used as the basis to choose an appropriate forecast for the dataset. This study improves the quality of demand forecasts and determines the best forecast. The result reveals that forecasting models using Bayesian ARIMA model and Bayesian probability models perform better. The flexibility in the Bayesian approaches allows wider variability in the model parameters helps to improve demand forecasts. These models are particularly useful when past demand information is incomplete or limited to few periods. Furthermore, it was found that improvements in demand forecasting can provide better cost reductions than relying on inventory models

Forecasting coin demand.

Shortages of coins in 1999 and 2000 motivated the authors to develop models for forecasting coin demand. A variety of models were developed, tested, and used in realtime forecasting. This paper describes the models that were developed and examines the forecast errors from the models both in quasi-ex-ante forecasting exercises and in realtime use. Tests for forecast efficiency are run on each model. Real-time forecasts are examined. The authors conclude with suggestions for further refinements of the models.Coinage

Nonparametric modeling and forecasting electricity demand: an empirical study

This paper uses half-hourly electricity demand data in South Australia as an empirical study of nonparametric modeling and forecasting methods for prediction from half-hour ahead to one year ahead. A notable feature of the univariate time series of electricity demand is the presence of both intraweek and intraday seasonalities. An intraday seasonal cycle is apparent from the similarity of the demand from one day to the next, and an intraweek seasonal cycle is evident from comparing the demand on the corresponding day of adjacent weeks. There is a strong appeal in using forecasting methods that are able to capture both seasonalities. In this paper, the forecasting methods slice a seasonal univariate time series into a time series of curves. The forecasting methods reduce the dimensionality by applying functional principal component analysis to the observed data, and then utilize an univariate time series forecasting method and functional principal component regression techniques. When data points in the most recent curve are sequentially observed, updating methods can improve the point and interval forecast accuracy. We also revisit a nonparametric approach to construct prediction intervals of updated forecasts, and evaluate the interval forecast accuracy.Functional principal component analysis; functional time series; multivariate time series, ordinary least squares, penalized least squares; ridge regression; seasonal time series

Modelling and forecasting short-term electricity load: a two step methodology

The goal of this paper is to develop a forecasting model of the hourly electricity load demand in the area covered by an utility company located in the southeast of Brazil. A di®erent model is constructed for each hour of day, thus there are 24 di®erent models. Each model is based on a decomposition of the daily series of each hour in two components. The ¯rst component is purely deterministic and is related to trends, seasonality, and special days e®ect. The second one is stochastic and follows a linear autoregressive model. The multi-step forecasting performance of the proposed methodology is compared with a benchmark model and the results indicate that our proposal is a useful tool for electricity load forecasting.

Determinants of power spreads in electricity futures markets: A multinational analysis. ESRI WP580, December 2017

The growth in variable renewable energy (vRES) and the need for flexibility in power systems go hand in hand. We study how vRES and other factors, namely the price of substitute fuels, power price volatility, structural breaks, and seasonality impact the hedgeable power spreads (profit margins) of the main dispatchable flexibility providers in the current power systems - gas and coal power plants. We particularly focus on power spreads that are hedgeable in futures markets in three European electricity markets (Germany, UK, Nordic) over the time period 2009-2016. We find that market participants who use power spreads need to pay attention to the fundamental supply and demand changes in the underlying markets (electricity, CO2, and coal/gas). Specifically, we show that the total vRES capacity installed during 2009-2016 is associated with a drop of 3-22% in hedgeable profit margins of coal and especially gas power generators. While this shows that the expansion of vRES has a significant negative effect on the hedgeable profitability of dispatchable, flexible power generators, it also suggests that the overall decline in power spreads is further driven by the price dynamics in the CO2 and fuel markets during the sample period. We also find significant persistence (and asymmetric effects) in the power spreads volatility using a univariate TGARCH model