The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Application of Shallow Neural Networks to Retail Intermittent Demand Time Series

Accurate sales predictions are essential for businesses in the fast-moving consumer goods (FMCG) industry. However, their demand forecasts are often unreliable, leading to imprecisions that affect downstream decisions. This dissertation proposes using an artificial neural network to improve intermittent demand forecasting in the retail sector. The research investigates the validity of using unprocessed historical information, eluding hand-crafted features, to learn patterns in intermittent demand data. The experiment tests a selection of shallow neural network architectures that can expedite the time-to-market in comparison to conventional demand forecasting methods. The results demonstrate that organisations that still rely on manual and direct forecasting methods could improve their predicting accuracy and establish a high-performing baseline for future development. The solution also offers an end-to-end systematic forecasting landscape enabling a lift-and-shift and easy transition from design to deployment. A practical implementation should bring about stable and reliable forecasts, resulting in cost savings, improved customer service, and increased profitability. Lastly, the research findings contribute to the broader academic field of forecasting and ML with a seminal proposal that provides insights and opportunities for future research

Utility-Based Data Mining Workshop Chairs:

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Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Oil Price Forecast Evaluation with Flexible Loss Functions

The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings suggest that, irrespective of the shape of the loss function, the class of financial models is to be preferred to time series models. Both financial and time series models are better than mixed and structural models. Results of the Diebold and Mariano test are not conclusive, for the loss differential seems to be statistically insignificant in the large majority of cases. Although the random walk model is not statistically outperformed by any of the alternative models, the empirical findings seem to suggest that theoretically well-grounded financial models are valid instruments for producing accurate forecasts of the WTI spot price.Oil Price, WTI Spot and Futures Prices, Forecasting, Econometric Models

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation

In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN-Flux, achieves AUROC and AUPR scores exceeding 0.95 for each classification. In addition, ANN-Flux reduces the remaining useful life RMSE by 38% for the same test split of the dataset compared to past work, with significantly less computational cost.Comment: Preprint with 10 pages, 5 figures. Submitted to International Journal of Prognostics and Health Management (IJPHM

Using artificial neural networks for prediction of logistics costs of engineering enterprises

В статті розглядається можливість застосування штучних нейронних мереж для прогнозування логістичних витрат на основі інформації про витрати попередніх періодів. Запропоновано використання тришарової мережі з навчанням за методом зворотного розповсюдження помилки. Визначено оптимальну конфігурацію такої нейронної мережі для використання з щомісячною інформацією щодо логістичних витрат машинобудівних підприємств.The article deals with investigation of possibility of using artificial neural networks to predict the logistics costs. The forecasting based on the information of previous periods is considered. Author proposes to use a three-layer feedforward neural network with learning by backpropagation algorithm. An optimal configuration of the neural network for use on monthly logistics cost information is defined. Article emphasizes the urgency of the approach usage at the variety of machine-building enterprises