Use and misuse of information in supply chain forecasting of promotion effects

Demand forecasting is critical to sales and operations planning (S&OP), but the effects of sales promotions can be difficult to forecast. Typically, a baseline statistical forecast is judgmentally adjusted on receipt of information from different departments. However, much of this information either has no predictive value or its value is unknown. Research into base rate discounting has suggested that such information may distract forecasters from the average uplift and reduce accuracy. This has been investigated in situations in which forecasters were able to adjust the statistical forecasts for promotions via a forecasting support system (FSS). In two ecologically valid experiments, forecasters were provided with the mean level of promotion uplift, a baseline statistical forecast, and quantitative and qualitative information. However, the forecasters were distracted from the base rate and misinterpreted the information available to them. These findings have important implications for the design of organizational S&OP processes, and for the implementation of FSSs.     

Using weather ensemble predictions in electricity demand forecasting

Weather forecasts are an important input to many electricity demand forecasting models. This study investigates the use of weather ensemble predictions in electricity demand forecasting for lead times from 1 to 10 days ahead. A weather ensemble prediction consists of 51 scenarios for a weather variable. We use these scenarios to produce 51 scenarios for the weather-related component of electricity demand. The results show that the average of the demand scenarios is a more accurate demand forecast than that produced using traditional weather forecasts. We use the distribution of the demand scenarios to estimate the demand forecast uncertainty. This compares favourably with estimates produced using univariate volatility forecasting methods.     

Short interval forecasting of emergency phone call (911) work loads

There has been an increasing emphasis over the last 5 to 10 years to improve productivity in the Service Sector of the U.S. economy. Much of the improvement obtained by these managers has come about through better scheduling of the work force in these organizations. Effective scheduling of this personnel requires good estimates of demand, which may exhibit substantial variations between days for certain times of the year. The Indianapolis Police Department (IPD) Communications area is one such organization that exhibits varying workloads and is interested in improving staff scheduling of dispatch operators.This article explores the use of six different forecasting techniques for predicting daily emergency call workloads for the IPD's communications area. Historical call volume data are used to estimate the model parameters. A hold-out sample of five months compares forecasts and actual daily call levels. The forecast system utilizes a rolling horizon approach, where daily forecasts are made for the coming month from the end of the prior month. The forecast origin is then advanced to the end of the month, where the current month's actual call data are added to the historical database, new parameters are estimated, and then the next month's daily estimates are generated. Error measures of residual standard deviation, mean absolute percent error, and bias are used to measure performance. Statistical analyses are conducted to evaluate if significant differences in performance are present among the six models.The research presented in this article indicates that there are clearly significant differences in performance for the six models analyzed. These models were tailored to the specific structure and this work suggests that the short interval forecasting problems faced by many service organizations has several structural differences compared to the typical manufacturing firm in a made-to-stock environment. The results also suggests two other points. First, simple modeling approaches can perform well in complex environments that are present in many service organizations. Second, special tailoring of the forecasting model is necessary for many service firms. Historical data patterns for these organizations tend to be more complex than just trend and seasonal elements, which are normally tracked in smoothing models. These are important conclusions for both managers of operating systems and staff analysts supporting these operating systems. The design of an appropriate forecasting system to support effective staff planning must consider the nature, scope, and complexity of these environments.     

Economic, organizational, and political influences on biases in forecasting state sales tax receipts

This paper investigates factors influencing fixed bias in forecasting state sales taxes revenues. By extending an existing model used to explain forecast accuracy to include a series of complex interactions related to the potential political and policy use of revenue forecasts, the paper extends our understanding of the forecasting process in government. Exploratory empirical analysis based on survey data is used to provide evidence that bias in forecasting results, at least in part, from political and policy manipulation. There is also evidence that institutional reforms associated with ‘good management’ practices affect forecast bias, but in complex ways depending upon the extent to which political competition exists within the state.     

Automatic feature identification and graphical support in rule-based forecasting: a comparison

We examined automatic feature identification and graphical support in rule-based expert systems for forecasting. The rule-based expert forecasting system (RBEFS) includes predefined rules to automatically identify features of a time series and selects the extrapolation method to be used. The system can also integrate managerial judgment using a graphical interface that allows a user to view alternate extrapolation methods two at a time. The use of the RBEFS led to a significant improvement in accuracy compared to equal-weight combinations of forecasts. Further improvement were achieved with the user interface. For 6-year ahead ex ante forecasts, the rule-based expert forecasting system has a median absolute percentage error (MdAPE) 15% less than that of equally weighted combined forecasts and a 33% improvement over the random walk. The user adjusted forecasts had a MdAPE 20% less than that of the expert system. The results of the system are also compared to those of an earlier rule-based expert system which required human judgments about some features of the time series data. The results of the comparison of the two rule-based expert systems showed no significant differences between them.     

High‐involvement work practices and analysts' forecasts of corporate earnings

Research has shown that high‐involvement work practices are positively related to corporate financial performance. However, it is unknown if investors are able to use information on high‐involvement practices to predict the performance of specific companies. In this study, we examine earnings forecasts for a sample of Fortune 1000 firms and find professional stock analysts consistently underestimated the earnings of firms that made greater use of high‐involvement practices during the 1990s. Based on data collected from newspaper articles and annual reports, we argue that these lower estimates resulted from a lack of information on innovative HR practices. Recommendations to managers for disseminating information on and leveraging highinvolvement HR practices are discussed. © 2006 Wiley Periodicals, Inc.     

Forecasting from time series subject to sporadic perturbations: Effectiveness of different types of forecasting support

How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.     

High performance work systems and organizational performance: an empirical study of Taiwan's semiconductor design firms

This paper presents a study examining the relationship between high performance work systems (HPWS) and organizational performance in Taiwan's semiconductor design industry. The hypotheses are tested by matching and analysing data collected from field interviews with 21 HR managers and surveys of 21 senior operations managers and 1,129 employees. The results of the statistical analysis demonstrate that the effective use of employee empowerment practices is positively related to organizational performance.     

Individual versus group spot price forecasting in the international petroleum market: A case study

This paper utilizes the conventional statistical tests associated with the rational expectations hypothesis so as to compare the relative accuracy of individual versus group forecasting within the organization. In order to maintain comparability between forecasting regimens the study employs like information sets for the two prediction methods. Using the rational expectations tests as criteria, the statistical results show group forecasts inferior to individually produced predictions These findings imply that group-produced forecasting accuracy may be hampered by the psychological interaction associated with consensus behavior. Conversely, we find forecasting accuracy improves when predictions are elicited from individuals in an isolated laboratory-like setting.     

Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting

When forecasting time series in a hierarchical configuration, it is necessary to ensure that the forecasts reconcile at all levels. The 2017 Global Energy Forecasting Competition (GEFCom2017) focused on addressing this topic. Quantile forecasts for eight zones and two aggregated zones in New England were required for every hour of a future month. This paper presents a new methodology for forecasting quantiles in a hierarchy which outperforms a commonly-used benchmark model. A simulation-based approach was used to generate demand forecasts. Adjustments were made to each of the demand simulations to ensure that all zonal forecasts reconciled appropriately, and a weighted reconciliation approach was implemented to ensure that the bottom-level zonal forecasts summed correctly to the aggregated zonal forecasts. We show that reconciling in this manner improves the forecast accuracy. A discussion of the results and modelling performances is presented, and brief reviews of hierarchical time series forecasting and gradient boosting are also included.     

Seasonal exponential smoothing with damped trends : An application for production planning

This paper reviews a spreadsheet-based forecasting approach which a process industry manufacturer developed and implemented to link annual corporate forecasts with its manufacturing/distribution operations. First, we consider how this forecasting system supports overall production planning and why it must be compatible with corporate forecasts. We then review the results of substantial testing of variations on the Winters three-parameter exponential smoothing model on 28 actual product family time series. In particular, we evaluate whether the use of damping parameters improves forecast accuracy. The paper concludes that a Winters four-parameter model (i.e. the standard Winters three-parameter model augmented by a fourth parameter to damp the trend) provides the most accurate forecasts of the models evaluated. Our application confirms the fact that there are situations where the use of damped trend parameters in short-run exponential smoothing based forecasting models is beneficial.     

Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information

How did DSGE model forecasts perform before, during and after the financial crisis, and what type of off-model information can improve the forecast accuracy? We tackle these questions by assessing the real-time forecast performance of a large DSGE model relative to statistical and judgmental benchmarks over the period from 2000 to 2013. The forecasting performances of all methods deteriorate substantially following the financial crisis. That is particularly evident for the DSGE model’s GDP forecasts, but augmenting the model with a measure of survey expectations made its GDP forecasts more accurate, which supports the idea that timely off-model information is particularly useful in times of financial distress.     

Forecasting spare part demand with installed base information: A review

The classical spare part demand forecasting literature studies methods for forecasting intermittent demand. However, the majority of these methods do not consider the underlying demand-generating factors. The demand for spare parts originates from the replacement of parts in the installed base of machines, either preventively or upon breakdown of the part. This information from service operations, which we refer to as installed base information, can be used to forecast the future demand for spare parts. This paper reviews the literature on the use of such installed base information for spare part demand forecasting in order to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) the value of using installed base information to improve forecasting; and (4) the limits of the existing methods. This serves as motivation for future research.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

Analysts' utilization of historical earnings information

This study examines whether security analysts (in)efficiently utilize the information contained in past series of annual and quarterly earnings in producing earnings forecasts. To do so, it investigates whether equal-weighted combinations of security analysts' forecasts with forecasts from statistical models based on historical earnings are superior, both in terms of being a better surrogate for the market's expectations of earnings and of accuracy, to forecasts from either one of these two sources. The empirical findings indicate that, although analysts' forecasts are superior to forecasts from statistical models, performance can be improved—both in terms of accuracy and also of being a better surrogate for market earnings expectations—by combining analysts' forecasts with forecasts from statistical models based on past quarterly earnings. Improvements in proxying for market earnings expectations were obtained even when analysts' forecasts made in June of the forecast year were used in the combinations. An implication of these findings is that investors can improve their investment decisions by using an average of the mean analysts' forecasts and the forecast produced by a time-series model of quarterly earnings in their investment decisions.     

Long-range U.S. Manpower forecasts in retrospect: How accurate were we?

This paper compares and evaluates the accuracy of long-range occupational manpower forecasts made for 1980 in the early 1970s by the U.S. Bureau of Labor Statistics and by the author. The different assumptions and forecasting methodologies utilized are discussed, and the occupational forecasts are then compared to the actual 1980 employment data. The relative accuracy of the different sets of forecasts is assessed according to several different criteria, and the larger question of the usefulness of either set of forecasts is addressed. It is found that neither set of forecasts was clearly superior, that the accuracy of both sets of forecasts was generally poor, and that the projections for individual occupations were often so wide of the mark as to be of questionable usefulness for manpower planning and vocational guidance. The implications of these findings for manpower forecasting are discussed.The author is grateful to several referees for helpful comments on an earlier draft of this paper, but retains sole responsibility for the opinions expressed here and for any errors.     

Influence of differentiated roles on group forecasting accuracy

While behavioral research on forecasting has mostly examined the individual forecaster, organizationally-based forecasting processes typically tend to rely on groups with members from different functional areas for arriving at ‘consensus’ forecasts. The forecasting performance could also vary depending on the particular group structuring utilized in reaching a final prediction. The current study compares the forecasting performance of modified consensus groups with that of staticized groups using formal role-playing. It is found that, when undistorted model forecasts are given, group forecasts (whether they are arrived at through averaging or by a detailed discussion of the forecasts) contribute positively to the forecasting accuracy. However, providing distorted initial forecasts affects the final accuracy with varying degrees of improvement over the initial forecasts. The results show a strong tendency to favor optimistic forecasts for both the staticized and modified consensus group forecasts. Overall, the role modifications are found to be successful in eliciting a differential adjustment behavior, effectively mimicking the disparities between different organizational roles. Current research suggests that group discussions may be an efficient method of displaying and resolving differential motivational contingencies, potentially leading to group forecasts that perform quite well.     

Managerial evaluation of exogenous forecast sources

This paper concerns the managerial evaluation of forecast vendors—individuals or firms offering for sale future forecasts of random variables relevant to managerial decision making. Assuming the forecasts are exogenous in the sense they are generated by a methodology unknown or unproven to management, the paper uses a logistic regression model to present a statistical test for informativeness that allows for an interpretation of the vendor's abilities. The advantage of the approach is that it requires as input only knowledge of the unconditional probability distribution of the variable being forecast and a relatively small historical track record of the vendor's forecasting performance. No benchmark forecast is necessary and few assumptions are required about the statistical process that generates the forecasts. As an illustrative empirical application, the paper presents an evaluation of the informativeness of the published long-range price forecasts by a veteran analyst of the Iowa hog market.     

Approximate Bayesian forecasting

Approximate Bayesian Computation (ABC) has become increasingly prominent as a method for conducting parameter inference in a range of challenging statistical problems, most notably those characterized by an intractable likelihood function. In this paper, we focus on the use of ABC not as a tool for parametric inference, but as a means of generating probabilistic forecasts; or for conducting what we refer to as ‘approximate Bayesian forecasting’. The four key issues explored are: (i) the link between the theoretical behavior of the ABC posterior and that of the ABC-based predictive; (ii) the use of proper scoring rules to measure the (potential) loss of forecast accuracy when using an approximate rather than an exact predictive; (iii) the performance of approximate Bayesian forecasting in state space models; and (iv) the use of forecasting criteria to inform the selection of ABC summaries in empirical settings. The primary finding of the paper is that ABC can provide a computationally efficient means of generating probabilistic forecasts that are nearly identical to those produced by the exact predictive, and in a fraction of the time required to produce predictions via an exact method.