CREATING ORDER FORECASTS: POINT‐OF‐SALE OR ORDER HISTORY?

Point‐of‐sale (POS) data, shared by retailers, is often touted as the solution to suppliers' ongoing challenge of accurate order forecasting. However, we find neither empirical evidence of increased order forecast accuracy from the literature, nor consistent use of POS data in suppliers' order forecasting processes. Using a sample containing weekly POS and order data for 10 ready‐to‐eat (RTE) cereal stock‐keeping‐units (SKU's), 7 yogurt SKU's, and 7 canned soup SKU's from 18 retailer distribution centers (DC's) throughout the U.S, our research compares historical POS and order data as order forecasting inputs and finds that POS data does not always outperform order data in terms of order forecast accuracy. While we did find that POS data is a better forecast input in a majority of the forecasts and that on average POS data produces a lower order forecast error, we find that there remain a large number of forecasts where order data is a better predictor than is POS data. Hence, we operationalize this comparison in terms of the frequency and magnitude of order forecast improvement based on POS data. We then hypothesize affecting factors and empirically test these relationships.     

Forecasting the World Economy in the Short Term

(1433) Audrone Jakaitiene and Stephane Dees Forecasting the world economy is a difficult task given the complex interrelationships within and across countries. This paper proposes a number of approaches to forecast short‐term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of forecast accuracy and, second, at checking whether methods forecasting directly aggregate variables (direct approaches) outperform methods based on the aggregation of country‐specific forecasts (bottom‐up approaches). Overall, all methods perform better than a simple benchmark for short horizons (up to 3 months ahead). Among the forecasting approaches used, factor models appear to perform the best. Moreover, direct approaches outperform bottom‐up ones for real variables, but not for prices. Finally, when country‐specific forecasts are adjusted to match direct forecasts at the aggregate levels (top‐down approaches), the forecast accuracy is neither improved nor deteriorated (i.e. top‐down and bottom‐up approaches are broadly equivalent in terms of country‐specific forecast accuracy).     

Forecasting With Temporally Aggregated Demand Signals in a Retail Supply Chain

Suppliers of consumer packaged goods are facing an increasingly challenging situation as they work to fulfill orders from their retail partners’ distribution facilities. Traditionally these suppliers have generated forecasts of a given retailer's orders using records of that retailer's past orders. However, it is becoming increasingly common for retail firms to collect and share large volumes of point‐of‐sale (POS) data, thus presenting an alternative data signal for suppliers to use in generating forecasts. A question then arises as to which data produce the most accurate forecasts. Compounding this question is the fact that forecasters often temporally aggregate data for consolidation or to produce forecasts in larger time buckets. Extant literature prescribes two countervailing statistical effects, information loss and variance reduction, that could play significant roles in determining the impact of temporal aggregation on forecast accuracy. Utilizing a large set of paired order and POS data, this study examines these relationships.     

Finding the True Voice of the Customer in CPG Supply Chains: Shopper‐Centric Supply Chain Management

Demand and supply integration is the subject of increasing scholarly attention. The theoretical emphasis on combining market and supply chain data as the basis for strategic and operational decision making is particularly relevant in the context of Consumer Packaged Goods (CPG) supply chains, and offers the basis for advancing our understanding and knowledge in this field. Point‐of‐sale (POS) data are commonly used as the demand signal in CPG supply chains. Using empirical data, this research demonstrates that POS data can be influenced by nondemand factors. We present a number of issues raised by this finding.     

Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variations and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method's first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.     

THE IMPACT OF POINT‐OF‐SALE DATA INACCURACY AND INVENTORY RECORD DATA ERRORS

The presence or absence of error in point‐of‐sale (POS) data and inventory system records directly affects retailer performance. This study identifies various error sources in retail supply chains and studies the influence of inventory and POS (demand) errors in a simulated retail outlet according to fill rate and average inventory. Other things being equal, we find that inventory record error reduces fill rate more than demand error. This study adds further evidence to other studies that suggest the costs caused by errors in POS systems may be overstated.     

我国入境旅游预测:基于ARFIMA模型的研究

旅游需求预测是旅游规划、开发与管理的基础和前提。将分整自回归移动平均模型(ARFI-MA)应用在旅游需求预测中,采用我国月度入境旅游人数建立ARFIMA模型,并依据RMSE,MAE和MAPE三个标准,将ARFIMA与AR IMA,SAR IMA模型的预测精度进行比较。结果表明ARFIMA模型的精度最高,在旅游需求预测中有较强的实用性。     

Bridging the intention–behavior gap among organic grocery customers: The crucial role of point‐of‐sale information

Although the interest in organic groceries has increased, actual buying behavior falls short for reasons that are mostly unknown to researchers and practitioners. This paper addresses this so‐called intention–behavior gap by investigating the impact of point‐of‐sale (POS) information on the perception of purchase barriers and behavior. While behavior and interest differ for various product categories, the organic groceries most frequently bought worldwide are differentiated on the basis of product category involvement in a pilot study. A laboratory experiment and a field experiment containing actual purchase behavior and market data revealed the possibility of enhancing organic purchases within low‐ and high‐involvement categories, while exposed to POS information. In low‐involvement product categories, POS information should reveal new product category‐specific organic features. In high‐involvement product categories, the perceived addition of value for money is crucial for purchasing organic groceries. While the effect of POS information on perceived trust and knowledge is higher for health conscious or green consumers in low‐involvement product categories, it is the converse in high‐involvement product categories.     

The Information Content of Model‐Free Implied Volatility

This study examines the information content of model‐free implied volatility (MFIV) estimates with respect to the options and futures markets in Hong Kong. In this study, the volatility forecasting performance of MFIV is compared, using different prediction horizons, to IV estimates based on Black's futures option pricing model (BIV) and time‐series forecasts based on historical volatility (TS‐HV). The results show that the BIV prediction is unbiased for different horizon forecasts. MFIV outperforms TS‐HV forecasts and, most importantly, BIV subsumes the information content of both MFIV and TS‐HV forecasts. The results are largely maintained for next‐day forecasts but the forecasting quality of the two IV measures declines as expiration day approaches. The information contents of MFIV and TS‐HV forecasts are complementary. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 32:792‐806, 2012     

Do investors learn about analyst accuracy? A study of the oil futures market

We study the impact of analyst forecasts on prices to determine whether investors learn about analyst accuracy. The straight‐forward relationship between supply and price, the economic importance of the market, the predictable timing of forecast error realizations, and the high frequency of the data make the crude oil market an interesting and advantageous setting. We find that prices rise (fall) when analysts forecast a decrease (increase) in supplies. During the 15 minutes following supply announcements, prices rise (fall) when forecasts have been too high (low). Importantly, both relationships are stronger for more accurate analysts, implying that investors learn about analyst accuracy. © 2009 Wiley Peridocals, Inc. Jrl Fut Mark 29:414–429, 2009     

Do futures prices help forecast the spot price?

This study proposes a futures‐based unobserved components model for commodity spot prices. Prices quoted at the same time incorporate the same information, but are affected differently, resulting in the different shapes of futures curves. This model utilizes information from part of the futures curve to improve forecasting accuracy of the spot price. Applying this model to oil market data, I find that the model forecasts outperform the literature benchmark (the no‐change forecast) and futures prices forecasts in multiple dimensions, with smaller average error variation over the sample period and higher chance of smaller absolute error in each period.     

EXPLORING THE POTENTIAL EFFECTS OF FORECASTER MOTIVATIONAL ORIENTATION AND GENDER ON JUDGMENTAL ADJUSTMENTS OF STATISTICAL FORECASTS

The growing adoption of demand collaboration initiatives such as Collaborative Planning, Forecasting, and Replenishment (CPFR) has made judgmental adjustments of forecasts, an already widespread forecasting practice, an increasingly routine part of many logistics managers' responsibilities. This article investigates how logistics managers might improve forecast accuracy by judgmentally adjusting statistical forecasts and potential factors that may influence the effectiveness of such adjustments. In particular, our goal is to expand current knowledge in this area by focusing on individual differences, specifically motivation and gender, which have been thus far neglected in the extant literature. Our findings indicate that motivation has a significant effect on accuracy improvement and this relationship is moderated by gender. Managerial implications of these findings and future research opportunities are also presented.     

Density forecast comparisons for stock prices,obtained from high‐frequency returns and daily option prices

This paper presents the first comparison of the accuracy of density forecasts for stock prices. Six sets of forecasts are evaluated for DJIA stocks, across four forecast horizons. Two forecasts are risk‐neutral densities implied by the Black–Scholes and Heston models. The third set are historical lognormal densities with dispersion determined by forecasts of realized variances obtained from 5‐min returns. Three further sets are defined by transforming risk‐neutral and historical densities into real‐world densities. The most accurate method applies the risk transformation to the Black–Scholes densities. This method outperforms all others for 87% of the comparisons made using the likelihood criterion.     

REPLACEMENT PARTS MANAGEMENT: THE VALUE OF INFORMATION

Exponential smoothing (ES) and weighted moving average (WMA) are the predominant methods used to predict future demand for replacement parts. They require simple calculations and make use of information readily available. By gathering more information and doing additional calculations, more accurate forecasts can be developed. However, the cost of collecting the additional data could exceed the inventory cost savings from the better demand forecast. This paper presents a straightforward method for determining when the benefits of a more complex forecasting method outweigh the total costs required to use the method.     

Forecasting volatility

The forecasting ability of the most popular volatility forecasting models is examined and an alternative model developed. Existing models are compared in terms of four attributes: (1) the relative weighting of recent versus older observations, (2) the estimation criterion, (3) the trade‐off in terms of out‐of‐sample forecasting error between simple and complex models, and (4) the emphasis placed on large shocks. As in previous studies, we find that financial markets have longer memories than reflected in GARCH(1,1) model estimates, but find this has little impact on outofsample forecasting ability. While more complex models which allow a more flexible weighting pattern than the exponential model forecast better on an in‐sample basis, due to the additional estimation error introduced by additional parameters, they forecast poorly out‐of‐sample. With the exception of GARCH models, we find that models based on absolute return deviations generally forecast volatility better than otherwise equivalent models based on squared return deviations. Among the most popular time series models, we find that GARCH(1,1) generally yields better forecasts than the historical standard deviation and exponentially weighted moving average models, though between GARCH and EGARCH there is no clear favorite. However, in terms of forecast accuracy, all are dominated by a new, simple, nonlinear least squares model, based on historical absolute return deviations, that we develop and test here. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:465–490, 2005     

Forecasting performance of extreme‐value volatility estimators

This study evaluates the forecasting performance of extreme‐value volatility estimators for the equity‐based Nifty Index using two‐scale realized volatility. This benchmark mitigates the effect of microstructure noise in the realized volatility. Extreme‐value estimates with relatively simple forecasting methods provide substantially better short‐term and long‐term forecasts, compared to historical volatility. The higher efficiency of extreme‐value estimators is primarily responsible for this improvement. The extent of possible improvement in forecasts is likely to be economically significant for applications like options pricing. By including extremevalue estimators, the forecasting performance of generalized autoregressive conditional heteroscedasticity (GARCH) can also be improved. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27: 1085–1105, 2007     

Density forecasts of crude‐oil prices using option‐implied and ARCH‐type models

The predictive accuracy of competing crude‐oil price forecast densities is investigated for the 1994–2006 period. Moving beyond standard ARCH type models that rely exclusively on past returns, we examine the benefits of utilizing the forward‐looking information that is embedded in the prices of derivative contracts. Risk‐neutral densities, obtained from panels of crude‐oil option prices, are adjusted to reflect real‐world risks using either a parametric or a non‐parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well as for regions and intervals that are of special interest for the economic agent. We find that non‐parametric adjustments of risk‐neutral density forecasts perform significantly better than their parametric counterparts. Goodness‐of‐fit tests and out‐of‐sample likelihood comparisons favor forecast densities obtained by option prices and non‐parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:727–754, 2011     

Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility

Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time‐series models based on historical daily returns. In this study both the measurement and the forecasting of financial volatility is improved using high‐frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with and even outperform implied volatility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1005–1028, 2004     

A Comparison of Consensus and BVAR Macroeconomic Forecasts

This paper provides a one-month-ahead, macroeconomic, Bayesian Vector Autoregressive (BVAR) forecasting approach that offers several advantages over conventional short-term forecasting procedures. In particular, it produces more accurate forecasts than the Bloomberg consensus forecasts, on average, for 20 major macroeconomic variables. In addition to a quantitative comparison of BVAR and Bloomberg consensus forecast, the paper focuses on five important areas of macroeconomic forecasting: the role of short-term macroeconomic forecasting, the importance of a robust forecasting approach, the importance of timing of data releases, forecast evaluation criteria, and the importance of changing model specifications as conditions warrant.     

The use of injury surveillance databases to identify emerging injury hazards

Among the many valuable uses of injury surveillance is the potential to alert health authorities and societies in general to emerging injury trends, facilitating earlier development of prevention measures. Other than road safety, to date, few attempts to forecast injury data have been made, although forecasts have been made of other public health issues. This may in part be due to the complex pattern of variance displayed by injury data. The profile of many injury types displays seasonality and diurnal variance, as well as stochastic variance. The authors undertook development of a simple model to forecast injury into the near term. In recognition of the large numbers of possible predictions, the variable nature of injury profiles and the diversity of dependent variables, it became apparent that manual forecasting was impractical. Therefore, it was decided to evaluate a commercially available forecasting software package for prediction accuracy against actual data for a set of predictions. Injury data for a 4-year period (1996 to 1999) were extracted from the Victorian Emergency Minimum Dataset and were used to develop forecasts for the year 2000, for which data was also held. The forecasts for 2000 were compared to the actual data for 2000 by independent t-tests, and the standard errors of the predictions were modelled by stepwise hierarchical multiple regression using the independent variables of the standard deviation, seasonality, mean monthly frequency and slope of the base data (R = 0.93, R(2) = 0.86, F(3, 27) = 55.2, p < 0.0001). Significant contributions to the model included the SD (beta = 1.60, p < 0.001), mean monthly frequency (beta = -0.72, p < 0.002), and the seasonality of the data (beta = 0.16, p < 0.02). It was concluded that injury data could be reliably forecast and that commercial software was adequate for the task. Variance in the data was found to be the most important determinant of prediction accuracy. Importantly, automated forecasting may provide a vehicle for identifying emerging trends.