APRIL - 2021 9logisticstechoutlook.commarketing tactics, school calendars, and weather events can provide substantial explanatory and predictive value. In fact, we found that external local events explained more than half of the forecast error for a given store that had erratic foot traffic patterns. However, it is important to note that obtaining and managing much of this data can be difficult, and it can be costly to purchase data from vendors. One key consideration in selecting a model for forecasting demand is the need for model explainability and interpretability. With black box models, it can be difficult or impossible to explain what is happening under the hood. Similarly, if the model output needs to be interpretable, then the data scientist is limited to models that provide cause and effect insights (a decrease in price of 20 percent results in a 30 percent increase in sales). Another consideration is the technology environment and infrastructure used to support and scale analytics. There are many options available such as Microsoft Azure, AWS, Google Cloud, etc. Regardless of the platform selected, importance should be placed on the tech ecosystem's capability for scaling analytics. Considering Trade-offs Associated with Advanced Analytics and TechnologiesWhile analytics are a requirement for increasing organizational capability and growth, there are trade-offs to consider. For example, building and sustaining an analytics or data science team can be difficult and costly. In addition, the technology costs associated with platforms, data acquisition, data storage and compute need to be considered. In other words, there is no free lunch. Understanding the ROI associated with implementing data analytics is a critical component of the business case and understanding the concept of diminishing returns will help guide decisions around the amount of effort to spend on improving a demand forecast.Regardless of the position taken (build, buy, do nothing), organizations that do not leverage advanced analytics and technologies will inevitably fall behind, lose market share, and struggle to survive. Improving the organization's demand planning process is likely a good place to start in terms of improving organizational effectiveness and financial outcomes. Therefore, projects aimed at identifying demand signals and testing out new models and technologies to support demand planning should be part of an organization's strategic vision and plan. One key consideration in selecting a model for forecasting demand is the need for model explainability and interpretability
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