In the current era of advanced Data Lakes and AI, businesses have unprecedented access to vast amounts of sales and product data. However, the process of predicting demand is more intricate than it appears. A common misconception is equating Demand Forecasting with Sales Forecasting. In this article, we will explore the complexities of Demand Forecasting and underscore its significance in shaping pivotal business choices.
Demand Forecasting ≠ Sales Forecasting
To gain a better understanding of forecasting, it is crucial to discern between two concepts: “demand” and “sales.” Sales forecasting involves predicting the number of products sold. In contrast, demand forecasting takes into account ideal conditions such as product availability and shopper accessibility to estimate what could have been sold. It is essential to recognise that these two concepts are distinct and should not be used interchangeably.
Understand, Forecast, Optimise
A reliable demand forecast model is a powerful tool for optimising business decisions, including inventory management and staffing. To construct an effective optimisation engine, businesses must first understand the complex relationship between sales and demand within their operations. This involves taking into account factors such as sell-out rates, wastage, and substitutability.
These fundamental components serve as the foundation, allowing for the development of accurate forecasting models that enable optimisation strategies.
Making it Happen
Building a demand forecasting solution may seem daunting, but the process becomes more manageable when approached step by step. Leveraging the tools available in the Data Lakehouse, businesses can begin exploring data and gradually build insightful models. The key lies in starting simple and progressively adding complexity to the models.
Each step of the process yields benefits, and with the proper guidance, businesses can navigate the intricacies of demand forecasting. The combination of tools, data, and methodologies empowers organisations to unlock the full potential of their Data + AI platform.
Business Use Case
Demand forecasting is a crucial process for businesses that rely on fresh products to attract customers. Convenience stores that manage the “bakers list” are a prime example of such companies. These stores face the challenge of balancing inventory levels to avoid selling out and minimise wastage while ensuring that they have adequate supplies of fresh products on hand.
To address this challenge, convenience stores are turning to advanced technology solutions such as machine learning and cloud computing. Python and SQL languages are the languages of choice for building models on their Data Lakehouse. Although these technologies offer significant benefits, the vast volume of data processed can make the task complex. Fortunately, the scalability offered by cloud computing makes it possible to handle this volume of data efficiently.
In the modern landscape of Data + AI platforms, understanding the nuances of Demand Forecasting is pivotal for businesses seeking to optimise their operations. By distinguishing between sales and demand, building comprehensive forecasting models, and leveraging available tools, organisations can navigate the complexities and make informed decisions that drive success in today’s dynamic markets.
About Arreoblue
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