Högskolan i Skövde

In operations of road freight transport, demand forecasts are mostly used for downstream optimization, following a predict then optimize setting. While the literature provides numerous optimization models that rely on accurate forecasts to support the planning of road freight transport, dedicated work on forecasting is limited, hindering a holistic treatment of the problem. Moreover, there is a disconnect to advances in the broader forecasting literature, limiting the adoption of modeling innovations and methodological advances. These can harm the quality and validity of forecasts designed to support road freight transport. We link the relevant forecasting publications to different prominent optimization problems for road freight transportation, highlighting disconnects between forecasting and optimization models in the area. By contrasting these with the current discourse in the forecasting literature, we identify relevant modeling and methodological improvements, in model building, the choice of loss function, and evaluation. These are important to better link forecasts with optimization models for road freight transport. Furthermore, we propose a unified hierarchical framework for freight demand to support aligned decisions across planning levels, which is an important consideration for practice. Our review helps structure and steer academic research and provides opportunities to improve existing forecasting models; a closer integration with optimization; and ultimately improve decisions.

Inventory management relies on accurate demand forecasts. Typically, these are univariate forecasts extrapolating patterns from past demand. The disaggregate nature of demand at the Stock Keeping Unit (SKU) level makes the incorporation of external information challenging. Nonetheless, such leading information can be critical to identifying disruptions and changes in the demand dynamics. To address the inventory planning needs of a global manufacturer we propose a methodology that identifies predictively useful leading indicators at an aggregate demand level, and translates that information to SKU-demand by leveraging on the hierarchical structure of the problem. Therefore, the proposed methodology provides probabilistic forecasts enriched by leading indicator information at SKU-level, as inputs for inventory management. The methodology automatically adjusts the choice of indicators for different required lead times, with some being more informative about the short-term demand dynamics and others for the long-term. We demonstrate the benefits both in the case of backorders and lost-sales, for a variety of lead times. We further benchmark the solution against solely using leading indicators or hierarchical forecasts, demonstrating that the benefits appear primarily by the proposed blending of the modelling approaches. The outcome is demonstratively better forecasts and inventory management for the case company. Additionally, management gains insights into the main drivers of their short and long-term demand, and the ability to adjust inventory replenishment accordingly. The ability to account for diverse macro and market information in operations is paramount for firms with a global reach that face different market conditions across countries. Additionally, the transparency of which leading indicators are influencing forecasts of different lead times is conducive to increased forecast trustworthiness. 

The Bullwhip Effect, the magnification of demand variability throughout the supply chain, poses a challenge to firms. Inaccurate forecasts increase it, with forecast errors translating into higher inventory costs at a local level and impacting other members of the supply chain, as their decisions are based on mis-estimated incoming orders. The conventional measure for the Bullwhip Effect does not reflect how forecast uncertainty evolves in the supply chain. A new metric is proposed that overcomes many of the limitations of the Bullwhip Ratio: the Ratio of Forecast Uncertainty. It benchmarks the upstream forecast errors to the downstream's. An inventory simulation is deployed to study the properties and usefulness of this measure. It connects to inventory costs at the upstream level and holds more explanatory power than the standard Bullwhip Ratio and the complementary Net Stock Amplification. Managers can use it to better understand the upstream impact of the forecasting process.

In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they provide one-on-one online computer-based learning support affording student modelling, adaptive pedagogical response, and performance tracking. This study provides a detailed description of the design and development of the first Forecasting Intelligent Tutoring System, aptly coined FITS, designed to assist students in developing an understanding of time series forecasting using classical time series decomposition. The system’s impact on learning is assessed through a pilot evaluation study, and its usefulness in understanding how students learn is illustrated through the exploration and statistical analysis of a small sample of student models. Practical reflections on the system’s development are also provided to better understand how such systems can facilitate and improve forecasting performance through training. 

Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography. 

This chapter elucidates liquidity forecasting within the context of technical assistance. The audience for this chapter is central bank staff with a strong quantitative background. Liquidity forecasting entails a process of estimating the near-term path of a bank’s reserves using a centralized framework. Short-term liquidity forecasts are used to calibrate the volume of central bank monetary operations to align liquidity with the announced stance of monetary policy, whether expressed as an interest rate or as a quantity. The best practice would be for the central bank to receive accurate information for counterparties that have accounts in its books, including its monetary counterparties (banks) or non-monetary counterparties, such as the government. However, the central bank may not have direct access to some counterparties (e.g., the public which demands banknotes) or the information could include significant errors. This chapter presents the statistical methods that have been used in technical assistance to forecast liquidity factors and the demand for liquidity. It also proposes solutions to select the best models, measure forecast accuracy, and reconcile forecasts. Some liquidity factors are relatively easy to forecast due to regular patterns (currency in circulation) while others require more sophisticated models, such as the government account. There is a tradeoff between the cost of implementing complex models and the accuracy gains.

Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise parameters through a prediction error loss function. Estimation methods based on multiple steps ahead forecast errors have been shown to lead to more robust and less biased estimates of parameters. However, a plausible explanation of why this is the case is lacking. In this paper, we provide this explanation, showing that the main benefit of these estimators is in a shrinkage effect, happening in univariate models naturally. However, this can introduce a series of limitations, due to overly aggressive shrinkage. We discuss the predictive likelihoods related to the multistep estimators and demonstrate what their usage implies to time series models. To overcome the limitations of the existing multiple steps estimators, we propose the Geometric Trace Mean Squared Error, demonstrating its advantages. We conduct a simulation experiment showing how the estimators behave with different sample sizes and forecast horizons. Finally, we carry out an empirical evaluation on real data, demonstrating the performance and advantages of the estimators. Given that the underlying process to be modelled is often unknown, we conclude that the shrinkage achieved by the GTMSE is a competitive alternative to conventional ones.

Retailers depend on accurate forecasts of product sales at the Store × SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives. 

In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model-build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.