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

Francisco Toledo et l’Institut des arts graphiques de Oaxaca

Graveur, peintre, sculpteur et céramiste, Francisco Toledo est encore un véritable animateur de la vie artistique et culturelle au Mexique. Par les projets qu’il impulse, finance et porte, il place toute son énergie au service de l’art et de sa diffusion

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

Incorporating macroeconomic leading indicators in tactical capacity planning

Tactical capacity planning relies on future estimates of demand for the mid- to long-term. On these forecast horizons there is increased uncertainty that the analysts face. To this purpose, we incorporate macroeconomic variables into microeconomic demand forecasting. Forecast accuracy metrics, which are typically used to assess improvements in predictions, are proxies of the real decision associated costs. However, measuring the direct impact on decisions is preferable. In this paper, we examine the capacity planning decision at plant level of a manufacturer. Through an inventory simulation setup, we evaluate the gains of incorporating external macroeconomic information in the forecasts, directly, in terms of achieving target service levels and inventory performance. Furthermore, we provide an approach to indicate capacity alerts, which can serve as input for global capacity pooling decisions. Our work has two main contributions. First, we demonstrate the added value of leading indicator information in forecasting models, when evaluated directly on capacity planning. Second, we provide additional evidence that traditional metrics of forecast accuracy exhibit weak connection with the real decision costs, in particular for capacity planning. We propose a more realistic assessment of the forecast quality by evaluating both the first and second moment of the forecast distribution. We discuss implications for practice, in particular given the typical over-reliance on forecast accuracy metrics for choosing the appropriate forecasting model