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Making the Newsvendor Smart – Order Quantity Optimization with ANNs for a Bakery Chain

2020 | Americas Conference on Information Systems | Citations: 0
Authors: Seubert, Franz; Stein, Nikolai; Taigel, Fabian; Winkelmann, Axel

Abstract: Accurate demand forecasting is particularly crucial for products with short she ... Expand

Abstract: Accurate demand forecasting is particularly crucial for products with short shelf life like bakery products. Over-and underestimation of customer demand affects not only profit margins of bakeries but is also responsible for 600,000 metric tons of food waste every year in Germany. To solve this problem, we develop an IT artifact based on artificial neural networks, which is automating the manual order process and capable of reducing costs as well as food waste. To test and evaluate our artifact, we cooperated with an SME bakery chain from Germany. The bakery chain runs 40 points of sale (POS) in southern Germany. After algorithm based reconstructing and cleaning of the censored sales data, we compare two different data-driven newsvendor approaches for this inventory problem. We show that both models are able to significantly improve the forecast quality (cost savings up to 30%) compared to human planners. Collapse

Semantic filters: H2O.ai

Topics: decision making point of sale system customer satisfaction application programming interface

Methods: design artifact isolation forest computational algorithm artificial neural network design science
APPLYING DATA SCIENCE FOR SHOP-FLOOR PERFORMANCE PREDICTION

2017 | European Conference On Information Systems | Citations: 0
Authors: Stein, Nikolai; Flath, Christoph

Abstract: Against the backdrop of ubiquitous computing, companies from various industries ... Expand

Abstract: Against the backdrop of ubiquitous computing, companies from various industries are building up everincreasing amounts of business process data. Seeking to salvage these hidden “data treasures,” the need for analytical information systems is ever-growing to guide corporate decision-making. However, information systems research is still very much focused on static, explanatory modeling provided by business intelligence suites instead of embracing the opportunities offered by predictive analytics. Describing insights from a real-world manufacturing scenario, we seek to enhance the understanding of predictive modeling. In particular, we highlight that simply dumping data into “smart” algorithms is not a silver bullet. Rather, successful analytics projects require constant reﬁnement and consolidation. To this end, we provide guidelines and best practices for modeling, feature engineering and interpretation leveraging tools from business information systems as well as machine learning. Collapse

Semantic filters: H2O.ai

Topics: process mining big data business process management enterprise information system IS failure

Methods: machine learning computational algorithm gradient boosting feature engineering descriptive statistic

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Making the Newsvendor Smart – Order Quantity Optimization with ANNs for a Bakery Chain

APPLYING DATA SCIENCE FOR SHOP-FLOOR PERFORMANCE PREDICTION