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

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

The Big Data Newsvendor: Practical Insights from Machine Learning Analysis

A 2/6/2014 revision to this paper is available at http://hdl.handle.net/1721.1/85658.We present a version of the newsvendor problem where one has n observations of p features as well as past demand. We consider both \big data" (p=n = O(1)) as well as small data (p=n = o(1)). For small data, we provide a linear programming machine learning algorithm that yields an asymptotically optimal order quantity. We also derive a generalization bound based on algorithmic stability, which is an upper bound on the expected out-of-sample cost. For big data, we propose a regularized version of the algorithm to address the curse of dimensionality. A generalization bound is derived for this case as well, bounding the out-of-sample cost with a quantity that depends on n and the amount of regularization. We apply the algorithm to analyze the newsvendor cost of nurse sta_ng using data from the emergency room of a large teaching hospital and show that (i) incorporating appropriate features can reduce the out-of-sample cost by up to 23% relative to the featureless Sample Average Approximation approach, and (ii) regularization can automate feature-selection while controlling the out-of-sample cost. By an appropriate choice of the newsvendor underage and overage costs, our results also apply to quantile regression

Estimating the demand parameters for single period problem, Markov-modulated Poisson demand, large lot size, and unobserved lost sales

We consider a single-period single-item problem when the demand is a Markov-modulated Poisson process with hidden states, unknown intensities and continuous batch size distribution. The number of customers and lot size are assumed to be large enough. The estimators of demand mean and standard deviation for unobservable lost sales in the steady state are considered. The procedures are based on two censored samples: observed selling durations and the demands over the period. Numerical results are given

On the learning benefits of resource flexibility

Resource flexibility, arguably among the most celebrated operational concepts, is known to provide firms facing demand uncertainty with such benefits as risk pooling, revenue-maximization optionality, and operational hedging. In this paper, we uncover a heretofore unknown benefit: we establish that resource flexibility facilitates learning the demand when the latter is censored, which could, in turn, enable firms to make better-informed future operational decisions, thereby increasing profitability. Further, we quantify these learning benefits of flexibility and find that they could be of the same order of magnitude as the extensively studied risk-pooling benefits of flexibility. This suggests that flexibility’s learning benefits could be a first-order consideration and that extant theories, which view flexibility only as the ability to act ex post, could be underestimating its true value when learning the demand is desirable, for example, when it enables managers to make better ex ante capacity, assortment, or pricing decisions in future periods

The Big Data Newsvendor: Practical Insights from Machine Learning

This is a revision of previously published DSpace entry: http://hdl.handle.net/1721.1/81412.We investigate the newsvendor problem when one has n observations of p features related to the demand as well as past demands. Both small data (p=n = o(1)) and big data (p=n = O(1)) are considered. For both cases, we propose a machine learning algorithm to solve the problem and derive a tight generalization bound on the expected out-of-sample cost. The algorithms can be extended intuitively to other situations, such as having censored demand data, ordering for multiple, similar items and having a new item with limited data. We show analytically that our custom-designed, feature-based approach can be better than other data-driven approaches such as Sample Average Approximation (SAA) and separated estimation and optimization (SEO). Our method can also naturally incorporate the operational statistics method. We then apply the algorithms to nurse staffing in a hospital emergency room and show that (i) they can reduce the median out-of-sample cost by up to 46% and 16% compared to SAA and SEO respectively, with statistical significance at 0.01, and (ii) this is achieved either by carefully selecting a small number of features and applying the small data algorithm, or by using a large number of features and using the big data algorithm, which automates feature-selection