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This paper proposes a novel forecasting method that combines the deep learning method -long shortterm memory (LSTM) networks and random forest (RF). The proposed method can model complex relationships of both temporal and regression type which gives it an edge in accuracy over other forecasting methods. We evaluated the new method on a real-world multivariate dataset from a multichannel retailer. We benchmark the forecasting performance of the new proposition against neural networks, multiple regression, ARIMAX, LSTM networks, and RF. We employed forecasting performance metrics to measure bias, accuracy, and variance, and the empirical evidence suggests that the new proposition is (statistically) significantly better. Furthermore, our method ranks the explanatory variables in terms of their relative importance. The empirical evaluations are replicated for longer forecasting horizons, and online and offline channels and the same conclusions hold; thus, advocating for the robustness of our forecasting proposition as well as the suitability in multi-channel retail demand forecasting.

This research focuses on the impact of 'Industry 4.0' and 'Digital Transformation' on information sharing and decision making across the supply chain (SC). Following a qualitative approach, the findings are threefold: First, it is shown that the possibility of an entire SC integration based on new technologies is still at distance. Current burdens are the missing willingness to exchange far-reaching information even with long-term partners and the missing technological interface standards in order to enable a trouble-free communication alongside the SC. Second, the impact of Industry 4.0 and the Digital Transformation on decision making is greatly connected to information sharing. An increasing amount of decisions is prepared, recommended or even fully automated by information systems. However, usually, the human being still has the last word. Third, companies' preparations for these impacts differ greatly. Whereas some companies rely on classical phase-based strategies and long-term visions, others do not have a long-term plan at all.

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