Tactical sales forecasting using a very large set of macroeconomic indicators

Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8\% accuracy gains over the current forecasting process

Tactical sales forecasting using a very large set of macroeconomic indicators

Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8\% accuracy gains over the current forecasting process

Inventory routing problem with non-stationary stochastic demands

In this paper we solve Stochastic Periodic Inventory Routing Problem (SPIRP) when the accuracy of expected demand is changing among the periods. The variability of demands increases from period to period. This variability follows a certain rate of uncertainty. The uncertainty rate shows the change in accuracy level of demands during the planning horizon. To deal with the growing uncertainty, we apply a safety stock based SPIRP model with different levels of safety stock. To satisfy the service level in the whole planning horizon, the level of safety stock needs to be adjusted according to the demand's variability. In addition, the behavior of the solution model in long term planning horizons for retailers with different demand accuracy is taken into account. We develop the SPIRP model for one retailer with an average level of demand, and standard deviation for each period. The objective is to find an optimum level of safety stock to be allocated to the retailer, in order to achieve the expected level of service, and minimize the costs. We propose a model to deal with the uncertainty in demands, and evaluate the performance of the model based on the defined indicators and experimentally designed cases

Modelamiento y predicción del efecto COVID-19 en el sistema laboral ecuatoriano

COVID-19 has caused massive disruption at different levels.  Scholars around the world have produced a significant number of studies to understand, reduce and predict the effects of this pandemic. Prediction models are crucial at this time of uncertainty, and labor indicators are key macroeconomic variables to plan the recovery of the effects of COVID-19. This study aims to model and predict the trend of the Ecuadorian labor system. The statistical analysis model applied for this study was the X-13ARIMA to predict the behavior of four indicators of the Ecuadorian labor system and establish their natural tendency by isolating the COVID-19 variable and determining the quantitative impact of the pandemic. The results show that the labor system was greatly affected by the COVID-19 outbreak; however, a deterioration was already observed in full employment and expanded underemployment. The study concludes that the pandemic altered the seasonality of the labor indicators.El COVID-19 ha causado una interrupción masiva en diferentes niveles. Académicos de todo el mundo han producido una cantidad significativa de estudios para comprender, reducir y predecir los efectos de esta pandemia. Los modelos de predicción son cruciales en este momento de incertidumbre y los indicadores laborales son variables macroeconómicas clave para planificar la recuperación de los efectos del COVID-19. Este estudio tiene como objetivo modelar y predecir la tendencia del sistema laboral ecuatoriano. El modelo de análisis estadístico aplicado para este estudio fue el X-13ARIMA para predecir el comportamiento de cuatro indicadores del sistema laboral ecuatoriano y establecer su tendencia natural aislando la variable COVID-19 y determinar el impacto cuantitativo de la pandemia. Los resultados muestran que el sistema laboral se vio muy afectado por el brote de COVID-19; sin embargo, ya se observaba un deterioro en el pleno empleo y el subempleo ampliado. El estudio concluye que la pandemia alteró la estacionalidad de los indicadores laborales

Modeling and forecasting the effect of COVID-19 in the Ecuadorian labor system

COVID-19 has caused massive disruption at different levels.  Scholars around the world have produced a significant number of studies to understand, reduce and predict the effects of this pandemic. Prediction models are crucial at this time of uncertainty, and labor indicators are key macroeconomic variables to plan the recovery of the effects of COVID-19. This study aims to model and predict the trend of the Ecuadorian labor system. The statistical analysis model applied for this study was the X-13ARIMA to predict the behavior of four indicators of the Ecuadorian labor system and establish their natural tendency by isolating the COVID-19 variable and determining the quantitative impact of the pandemic. The results show that the labor system was greatly affected by the COVID-19 outbreak; however, a deterioration was already observed in full employment and expanded underemployment. The study concludes that the pandemic altered the seasonality of the labor indicators

The impact of macroeconomic leading indicators for tactical sales forecasting on SKU inventory management

An accurate sales forecasting has indispensable effects on the supply chain management as this input is essential in the decision making process. Macroeconomic leading indicators can provide early indications of global changing economic dynamics. By including this external information, the global tactical sales forecasting can be improved. This paper wants to quantify the impact on inventory level, where decisions are typically taken on an individual product base. For this, the high-level forecast needs to be disaggregated to the product level. Techniques that make use of the hierarchical structure present can benefit from pooling individual forecasts on different hierarchical levels. We propose an empirical technique to reconcile the forecast distributions of different aggregation levels in a hierarchical structure. We focus on the first and second moment of the forecasting distribution, the mean and variance. We evaluate our proposed method on inventory and service level via inventory simulations

Evaluation of bottom-up and top-down strategies for aggregated forecasts: state space models and arima applications

Abstract. In this research, we consider monthly series from the M4 competition to study the relative performance of top-down and bottom-up strategies by means of implementing forecast automation of state space and ARIMA models. For the bottomup strategy, the forecast for each series is developed individually and then these are combined to produce a cumulative forecast of the aggregated series. For the top-down strategy, the series or components values are first combined and then a single forecast is determined for the aggregated series. Based on our implementation, state space models showed a higher forecast performance when a top-down strategy is applied. ARIMA models had a higher forecast performance for the bottom-up strategy. For state space models the top-down strategy reduced the overall error significantly. ARIMA models showed to be more accurate when forecasts are first determined individually. As part of the development we also proposed an approach to improve the forecasting procedure of aggregation strategies

Modern Centaurs: How Humans and AI Systems Interact in Sales Forecasting

Recent achievements of artificial intelligence (AI) have caused organizations to increasingly bring AI capabilities into their core business processes. Such AI-supported business processes often result in human+AI centaurs, which consist of an AI system, which performs most of the execution, and humans, who monitor this execution and occasionally provide additional inputs and overrides. Using sales data from Walmart, we conduct an online study to investigate if human supervision can improve upon state-of-the-art AI forecasts. Furthermore, we analyze the perceptions and behavioral intentions of the human participants over time. We find that human interventions consistently lead to less accurate forecasts and that participants initially underestimate the AI system’s accuracy and overestimate their own potential to improve upon the AI forecasts. However, perceptions quickly shift over the course of the study, causing the participants to perceive the AI system increasingly favorably, which also leads to behavioral changes and better overall system performance

Forecasting stock market out-of-sample with regularised regression training techniques

Forecasting stock market out-of-sample is a major concern to researchers in finance and emerging markets. This research focuses mainly on the application of regularised Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regularised RT models involving model complexity were employed. The regularised RT models which include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the Regularised RT models demonstrate significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. Overall, the Ridge gives the best statistical performance evaluation results while the LASSO appeared to be most economical meaningful. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk

Pronósticos de costos por obligaciones de garantías a corto plazo

Cada mes la organización de finanzas de HP Inc. debe proveer un estimado de los costos mensuales de operación para atender obligaciones de garantía a corto plazo, con un máximo de 6 meses hacia el futuro. Actualmente, el proceso es intensivo en tiempo y en labor, dejando mucho que desear a la precisión. El enfoque de este trabajo es mejorar la precisión, usando datos de HP Inc., por medio de métodos de aprendizaje de máquina. Se evalúan modelos como series de tiempo, máquinas de vectores de soporte, y redes neuronales. Al final se determina cual modelo se ajusta mejor con respecto a ciertas métricas y se discuten brevemente los resultados para 1 de las 14,725 series de tiempo en el alcance del proyecto. La automatización del proceso para entrenar, evaluar y producción de pronósticos es también parte del reporte y del trabajo de implementación ya que es una actividad que se debe realizar mensualmente con los nuevos datos. La división organizacional requiere una división geográfica, por línea de costo, y por línea product