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
We propose a forecasting method to improve accuracy for tactical sales predictions at a major supplier to the tire industry. This level of forecasting serves as direct input for the demand planning, steering the global supply chain and is typically up to a year ahead. The case company has a product portfolio that is strongly sensitive to external events. Univariate statistical methods, which are common in practice, are unable to anticipate and forecast changes in the market, while human expert forecasts are known to be biased and inconsistent. The proposed method is able to automatically identify key leading indicators that drive sales from a massive set of macroeconomic indicators, across different regions and markets and produce accurate forecasts. Our method is able to handle the additional complexity of the short and long term dynamics from the product sales and the external indicators. We find that accuracy is improved by 16.1% over current practice with proportional benefits for the supply chain. Furthermore, our method provides transparency to the market dynamics, allowing managers to better understand the events and economic variables that affect the sales of their products.
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 indi
Blended learning is the thoughtful integration of classroom face-to-face learning with online learning experiences. The design of blended learning environments impacts student learning behaviour and in turn learning outcomes. Although some attributes for successful online learning are being reported, further research on what makes online learning effective is needed. This study investigated the effect of mastery learning (ML) versus self-directed learning (SDL) as instructional strategies for the online part of a blended course. ML is characterized by program controlled, linear learning paths. SDL is characterized by student controlled, non-linear learning paths. Participants for this study were 159 freshmen undergraduate nursing students. Two online learning environments were created with identical content but different instructional strategy (ML vs. SDL). Participants were assigned to a learning environment based on their campus and completed the Motivated Strategies for Learning Questionnaire (MSLQ) to measure self-reported self-regulatory learning at the beginning of the semester. All online activity was tracked from participants over the course of eight weeks prior to an online exam. Results showed that students from the ML-condition showed significantly more online actions compared to SDL students (M = 483 vs. 283 F(1,154) = 18.6, p < .001). Also this group completed more quizzes (M = 15 vs. 7 F(1,155) = 65.0 p < .001). No difference was found between conditions for exam results. No differences were found based on students self-reported self-regulation. These findings show that while the quantity of learning activity was influenced through the design of the learning environment the quality was not.
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