یک چارچوب مبنی بر شاخص اقتصاد کلان راهبردی
Abstract
1- Introduction
2- Literature review
3- Proposed framework
4- Data and forecasting results
5- Conclusions
Acknowledgements
References
Abstract
Tactical sales forecasting is fundamental to production, transportation and personnel decisions at all levels of a supply chain. Traditional forecasting methods extrapolate historical sales information to predict future sales. As a result, these methods are not capable of anticipating macroeconomic changes in the business environment that often have a significant impact on the demand. To account for these macroeconomic changes, companies adjust either their statistical forecast manually or rely on an expert forecast. However, both approaches are notoriously biased and expensive. This paper investigates the use of leading macroeconomic indicators in the tactical sales forecasting process. A forecasting framework is established that automatically selects the relevant variables and predicts future sales. Next, the seasonal component is predicted by the seasonal naive method and the long-term trend using a LASSO regression method with macroeconomic indicators, while keeping the size of the indicator’s set as small as possible. Finally, the accuracy of the proposed framework is evaluated by quantifying the impact of each individual component. The carried out analysis has shown that the proposed framework achieves a reduction of 54.5% in mean absolute percentage error when compared to the naive forecasting method. Moreover, compared to the best performing conventional methods, a reduction of 25.6% is achieved in the tactical time window over three different real-life case studies from different geographical areas.
Introduction
Forecasting is one of the key aspects of operations management (Oliva & Watson, 2009). Sales forecasting plays a major role in the allocation of corporate resources (Stein, 1997), marketing (Crittenden, Gardiner, & Stam, 1993), and impacts decisions on production, transportation and personnel at all kinds of horizons in the supply chain (Hyndman & Athanasopoulos, 2014). Historically, forecasting research attempted to find the best model for the used data set (De Gooijer & Hyndman, 2006). With the rapid expansion of the internet, a lot of external data has become available. IBM estimates that in 2020 43 trillion GB of data will be created, which is 300 times the volume produced in 2008. This growth in data availability is causing a shift from finding the best model to finding the right data (causal method forecasting).
Traditional statistical forecasting methods only extrapolate historical trends and seasonal influences to predict future sales. As a consequence, these methods are not capable of anticipating macroeconomic changes in the business environment, which often significantly impact the demand. To account for these future changes, companies either adjust their statistical forecast manually or rely on expert forecasts. However, both approaches are notoriously biased, as humans are generally bad in making these adjustments, and are time consuming.