APRIL - 20218 logisticstechoutlook.comIn My OPINIONLogistics is becoming an increasingly complex phenomenon, and it will likely continue to increase in complexity as the impacts of climate change, pandemics, and high customer demands become more challenging. Companies will need to identify areas of their business that they can optimize and focus their efforts on improving those areas through advanced analytics and technologies. Analytics can help provide insights on all aspects and phases of one's business. Obviously one of the first applications of analytics in logistics pertains to demand planning, which is the focus of this article.The Role of Demand Planning in LogisticsDemand planning is a process that requires demand signals (data) to predict future demand. The financial outcomes of any logistics business will be driven by the organization's ability to develop a robust and resilient demand planning process. In mature supply chains, demand planning is supported by statistical, machine learning, or AI models. Models can be simple (e.g., based on a moving average) or they can be complex (e.g., recurrent neural network algorithm). The type of model selected will depend on the amount and type of data available as well as the nature of the sales patterns (e.g., presence of seasonality, trends, noise components). Machine learning and AI models are particularly useful when a substantial amount of data exists in terms of history (number of years available), granularity (WRIN, store, and day level), and features that can serve as demand signals. At a minimum, one would need historical sales at the level of analysis desired. However, additional demand signals such as external local events, promotions or By Jeanette Shutay, Sr. Director, Analytics & Data Science Strategy, HAVILOGISTICS SUPPORTED BY ADVANCED ANALYTICS AND TECHNOLOGIESJeanette Shutay
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