The Impact of Special Days in Call Arrivals Forecasting:A Neural Network Approach to Modelling Special Days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, [264, 3, (2016)] DOI: 10.1016/j.ejor.2016.07.015© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0

Analysis of judgmental adjustments in the presence of promotions

Sales forecasting is increasingly complex due to many factors, such as product life cycles that have become shorter, more competitive markets and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers add information to the forecast, like future promotions, potentially improving accuracy. Despite the importance of judgment and promotions, the literature devoted to study their relationship on forecasting performance is scarce. We analyze managerial adjustments accuracy under periods of promotions, based on weekly data from a manufacturing company. Intervention analysis is used to establish whether judgmental adjustments can be replaced by multivariate statistical models when responding to promotional information. We show that judgmental adjustments can enhance baseline forecasts during promotions, but not systematically. Transfer function models based on past promotions information achieved lower overall forecasting errors. Finally, a hybrid model illustrates the fact that human experts still added value to the transfer function models

Quantile forecast optimal combination to enhance safety stock estimation

[EN] The safety stock calculation requires a measure of the forecast error uncertainty. Such errors are usually assumed to be Gaussian lid (independently and identically distributed). However, deviations from lid lead to a deterioration in the performance of the supply chain. Recent research has shown that, contrary to theoretical approaches, empirical techniques that do not rely on the aforementioned assumptions can enhance the calculation of safety stocks. In particular, GARCH models cope with time-varying heterocedastic forecast error, and kernel density estimation does not need to rely on a determined distribution. However, if the forecast errors are time-varying heterocedastic and do not follow a determined distribution, the previous approaches are inadequate. We overcome this by proposing an optimal combination of the empirical methods that minimizes the asymmetric piecewise linear loss function, also known as the tick loss. The results show that combining quantile forecasts yields safety stocks with a lower cost. The methodology is illustrated with simulations and real data experiments for different lead times. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.This work was supported by the European Regional Development Fund and the Spanish Government (MINECO/FEDER, UE) under the project with reference DPI2015-64133-R. The authors would like to acknowledge the useful comments and references of three anonymous referees that led to a considerably improved version of the article.Trapero, JR.; Cardós, M.; Kourentzes, N. (2019). Quantile forecast optimal combination to enhance safety stock estimation. International Journal of Forecasting. 35(1):239-250. https://doi.org/10.1016/j.ijforecast.2018.05.009S23925035

Cross-temporal coherent forecasts for Australian tourism

Key to ensuring a successful tourism sector is timely policy making and detailed planning. National policy formulation and strategic planning requires long-term forecasts at an aggregate level, while regional operational decisions require short-term forecasts, relevant to local tourism operators. For aligned decisions at all levels, supporting forecasts must be `coherent', that is they should add up appropriately, across relevant demarcations (e.g., geographical divisions or market segments) and also across time. We propose an approach for generating coherent forecasts across both cross-sections and planning horizons for Australia. This results in significant improvements in forecast accuracy with substantial decision making benefits. Coherent forecasts help break intra- and inter-organisational information and planning silos, in a data driven fashion, blending information from different sources

Forecasts combinations for intermittent demand

Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, the effects of combining, which is a standard practice for regular demand, have not been investigated. This paper empirically explores the efficiency of forecast combinations in the intermittent demand context. We examine both method and temporal combinations of forecasts. The first are based on combinations of different methods on the same time series, while the latter use combinations of forecasts produced on different views of the time series, based on temporal aggregation. Temporal combinations of single or multiple methods are investigated, leading to a new time series classification, which leads to model selection and combination. Results suggest that appropriate combinations lead to improved forecasting performance over single methods, as well as simplifying the forecasting process by limiting the need for manual selection of methods or hyper-parameters of good performing benchmarks. This has direct implications for intermittent demand forecasting in practice

Commentary:two sides of the same coin

Issue 41 of Foresight featured a short commentary by Sujit Singh on the gaps between academia and business. Powered by our focus to produce and disseminate research that is directly applicable to practice, in this commentary we present our views on some of the very useful and interesting points raised by Sujit and conclude with our vision for enhanced communication between the two worlds

Empirical safety stock estimation based on Kernel and GARCH models

Supply chain risk management is drawing the attention of practitioners and academics. A source of risk is demand uncertainty. To deal with it demand forecasting and safety stocks are employed. Most of the work has focused on point demand forecasting, assuming that forecast errors follow the typical normal i.i.d. assumption. The variability of the forecast errors is used to compute the safety stock, in order to reduce the risk of stockouts with a reasonable inventory investment. Nevertheless, real products' demand is very hard to forecast and that means that at minimum the normally i.i.d. assumption should be questioned. This work analyses the effects of possible deviations from these assumptions and it proposes empirical methods based on Kernel density estimators (non-parametric) and GARCH models (parametric) in order to compute the safety stock. The results show that Kernel density estimator is recommended when the forecast errors are fat tailed and GARCH models are well-suited when forecast errors present autocorrelation. Additionally, GARCH models present important improvements for lead time forecast errors, as shown in terms of customer service level, inventory investment and backorders volume. Simulations and real demand data from a manufacturer are used to illustrate our methodology

Simplifying Fitness Games for Users with Learning Disabilities

Motivating people with learning disabilities (LD) to carry out physical exercises is a difficult task. Simplified fitness games can address this problem. Yet we do not know much about the design characteristics of the fitness games for this particular user group. Based on Rouse’s process model, this paper explores the design characteristics in three development phases: ‘conceptual outline’, ‘implementation’ and ‘outcome’. A mixed-method approach has been adopted. First, interviews and observations were conducted. Based on the qualitative findings and a literature review, a questionnaire was generated addressing the important design characteristics in each phases. The questionnaire surveyed 235 people from both game and healthcare industries to assess their agreement to the design characteristics. By identifying critical design characteristics in each phase, our paper provides guidance for an inclusive and nuanced approach to designing games for the users with LD. It identifies concepts in fitness games that intrinsically motivate physical activities.

Complex Exponential Smoothing for Seasonal Time Series

The general seasonal Complex Exponential Smoothing (CES) model is presented in this paper. CES is based on conventional exponential smoothing and a theory of complex variables. The proposed seasonal CES can capture known forms of seasonality, as well as new ones that are neither strictly additive nor multiplicative. In contrast to exponential smoothing, CES can capture both stationary and non-stationary processes, giving it greater modelling flexibility. In order to choose between the seasonal and non-seasonal CES a model selection procedure is discussed in the paper. An empirical evaluation of the performance of the model, against ETS and ARIMA, on real data is carried out. The findings suggest that CES simplifies model selection, and as a result the forecasting process, while performing better than the benchmarks in terms of forecasting accuracy