Artificial intelligence and the underlying machine learning (ML) methods are increasingly finding their way into our working world. One of these areas is sales planning, where machine learning is used to leverage a variety of different input parameters such as prices, promotions, or the weather, to forecast sales, and therefore directly affects the production of products and goods. To satisfy the goal of environmental sustainability as well as address short shelf life, the food industry represents an interesting application field for the use of ML for optimizing sales planning. Within this paper, we will examine the design, and especially the application, of ML methods in the food industry and show the current challenges that exist in the use of such concepts and technologies from the end-user’s point of view. Our study of a smaller bakery company shows that there are enormous challenges in setting up the appropriate infrastructure and processes for the implementation of ML, that the output quality of ML processes does not always match the perceived result quality, and that trust in the functioning of the algorithms is the most important criterion for using ML processes in practice.

人工智能和底层机器学习 (ML) 方法越来越多地进入我们的工作世界。其中一个领域是销售计划，机器学习用于利用各种不同的输入参数（例如价格、促销或天气）来预测销售，从而直接影响产品和商品的生产。为了满足环境可持续性的目标并解决保质期短的问题，食品行业代表了一个有趣的应用领域，可以使用 ML 来优化销售计划。在本文中，我们将研究 ML 方法在食品行业中的设计，尤其是应用，并从最终用户的角度展示当前在使用此类概念和技术时存在的挑战。