Sandra Transchel is Associate Professor for Supply Chain and Operations Management. From 2008 to 2011 Transchel was Assistant Professor for Supply Chain Management at the Pennsylvania State University, USA. In 2011 she was Visiting Assistant Professor at Tuck School of Business at Dartmouth, USA. In 2008 Dr. Transchel received her PhD from the University of Mannheim, Germany and graduated in March 2004 with a Diploma degree in Business Mathematics from the Otto-von-Guericke University in Magdeburg, Germany.
Transchel’s research interests are in the areas of supply chain management, inventory control, revenue management, and production scheduling. Her current research focuses on retail operations and supply chain management with the special interest in the integration of supply and demand management. Her research conducts theoretical research in inventories to study the relationship between replenishment policies, inventory levels, price strategies, perishability, and customers’ substitution behavior. She also studies optimal price and capacity management in Airline Alliances. Dr. Transchel’s research has appeared in numerous academic journals including Operations Research, European Journal of Operational Research, International Journal of Production Research, International Journal of Production Economics, and Business Research.
Ullrich, Kristoph and Sandra Transchel (2017): Demand-Supply Mismatches and Stock Market Performance: A Retailing Perspective, Production and Operations Management, 26 (8): 1444-1462.
Abstract: We provide empirical evidence that the volatility of inventory productivity relative to the volatility of demand is a predictor of future stock returns in a sample of publicly listed U.S. retailers over the period 1985–2013. This key performance indicator, entitled demand–supply mismatch (DSM), captures the fact that low variation in inventory productivity relative to variation in demand is indicative of the superior synchronization of demand- and supply-side operations. Applying the Fama and French (1993) three-factor model augmented with a momentum factor (Carhart 1997), we find that zero-cost portfolios formed by buying the two lowest and selling the two highest quintiles of DSM stocks yield abnormal stock returns of up to 1.13%. These strong market anomalies related to DSM are observed over the entire sample period and persist after controlling for alternative inventory productivity measures and firm characteristics that are known to predict future stock returns. Further, we reveal that DSM is indicative of lower future earnings and lower sales growth and provide evidence that the observed market inefficiency results from investors’ failure to incorporate all of the information that inventory contains into the pricing of stocks.
Dong, Chuanwen, Sandra Transchel and Kai Hoberg (2018): An Inventory Control Model for Modal Split Transport: A Tailored Base-Surge approach, European Journal of Operational Research, 264 (1): 89-105.
Abstract: Firms are increasingly interested in transport policies that enable a shift in cargo volumes from road (truck) transport to less expensive, more sustainable, but slower and less flexible transport modes like railway or inland waterway transport. The lack of flexibility in terms of shipment quantity and delivery frequency may cause unnecessary inventories and lost sales, which may outweigh the savings in transportation costs. To guide the strategic volume allocation, we examine a modal split transport (MST) policy of two modes that integrates inventory controls.We develop a single-product–single-corridor stochastic MST model with two transport modes considering a hybrid push–pull inventory control policy. The objective is to minimize the long-run expected total costs of transport, inventory holding, and backlogging. The MST model is a generalization of the classical tailored base-surge (TBS) policy known from the dual sourcing literature with non-identical delivery frequencies of the two transport modes. We analytically solve approximate problems and provide closed-form solutions of the modal split. The solution provides an easy-to-implement solution tool for practitioners. The results provide structural insights regarding the tradeoff between transport cost savings and holding cost spending and reveal a high utilization of the slow mode. A numerical performance study shows that our approximation is reasonably accurate, with an error of less than 3% compared to the optimal results. The results also indicate that as much as 85% of the expected volume should be split into the slow mode.
Transchel, Sandra, Saurabh Bansal and Mrinmay Deb (2016): Managing production of high-tech products with high production quality variability, International Journal of Production Research, 54 (6): 1689-1707.
Abstract: We consider production systems in technology industries where output quality of a single production run has a large variance. Firms operating such systems classify products into different quality bins and sell units in one bin at the same tagged quality level and the same price. Consumers have heterogeneous quality preferences and choose that quality that maximises their net utility. We examine firms’ assortment, production and pricing problem. We present a three-stage solution procedure that optimises the production quantity, quality specification and number of bins. In that regard, we show that for a manufacturing technology with known quality distribution and known distribution of customers’ quality preference, the optimal assortment and production quantity are set such that on average, the demand of each bin is exactly fulfilled. We examine the impact of an improved manufacturing technology, variation in consumer preferences and changing price premium on the optimal assortment, lot size, market share, yield loss and the overall profitability. We further show that when the quality distribution of the manufacturing process is unknown, downward substitution leads to product offering of higher quality and higher prices. Finally, we discuss practical considerations for pricing, technology and optimal product offerings, and explain the proliferation of bins witnessed in the last decade in the processor industry.
Bansal, Saurabh and Sandra Transchel (2014): Managing Supply Risk for Vertically Differentiated Co-Products, Production and Operations Management, 23 (9): 1577-1598.
Abstract: The manufacturing complexity of many high-tech products results in a substantial variation in the quality of the units produced. After manufacturing, the units are classified into vertically differentiated products. These products are typically obtained in uncontrollable fractions, leading to mismatches between their demand and supply. We focus on product stockouts due to the supply–demand mismatches. Existing literature suggests that when faced with product stockouts, firms should satisfy all unmet demand of a low-end product by downgrading excess units of a high-end product (downward substitution). However, this policy may be suboptimal if it is likely that low-end customers will substitute with a higher quality product and pay the higher price (upward substitution). In this study, we investigate whether and how much downward substitution firms should perform. We also investigate whether and how much low-end inventory firms should withhold to strategically divert some low-end demand to the high-end product. We first establish the existence of regions of co-production technology and willingness of customers to substitute upward where firms adopt different substitution/withholding strategies. Then, we develop a managerial framework to determine the optimal selling strategy during the life cycle of technology products as profit margins shrink, manufacturing technology improves, and more capacity becomes available. Consistent trends exist for exogenous and endogenous prices.
Minner, Stefan and Sandra Transchel (2010): Periodic review inventory-control for perishable products under service-level constraints, OR spectrum, 32 (4): 979-996.
Abstract: Food retail inventory management faces major challenges by uncertain demand, perishability, and high customer service level requirements. In this paper, we present a method to determine dynamic order quantities for perishable products with limited shelf-life, positive lead time, FIFO or LIFO issuing policy, and multiple service level constraints. In a numerical study, we illustrate the superiority of the proposed method over commonly suggested order-up-to-policies. We show that a constant-order policy might provide good results under stationary demand, short shelf-life, and LIFO inventory depletion.
Minner, Stefan and Sandra Transchel (2017): Order variability in perishable product supply chains, European Journal of Operational Research, 260 (1): 93-107.
Abstract: Abstract Empirical research has shown that the degree of order variability in supply chains is significantly influenced by product- and industry-specific factors. This paper analyzes the impact of perishability on order variability and the bullwhip effect in supply chains. We decompose the ordering process of a retailer into a sales and an outdating process and quantify their short- and long-term variability and correlation. We find differences to non-perishable product supply chains driven by the impact of the inventory depletion policy, stock-out management, and retailers service level requirement. These three factors significantly affect the retailer’s order variability and thus the decision making process and the profitability of the upstream supply stage. For the majority of instances, the perishable nature of a product results in the ordering process having a lower variability than the demand process. Only when inventory depletion is dominated by last-in-first-out in high service level environments, variability amplification can be observed. We propose a dynamic ordering policy for the upstream supply stage, taking into account negative correlation of retailer orders between periods. This dynamic policy may lead to substantial performance improvements. In a sensitivity analysis, we investigate the impact of shelf life, lead time and demand correlation.
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Associate Professor of Logistics and Supply Chain Management at Kühne Logistics University, Hamburg, Germany
Dean of Programs at Kühne Logistics University, Hamburg, Germany
|07/2011 - 12/2011|
Visiting Assistant Professor of Business Administration at Tuck School of Business at Dartmouth, Hanover, NH, USA
|11/2008 - 06/2011|
Assistant Professor of Supply Chain Management at the Department of Supply Chain and Information Systems, Smeal College of Business at The Pennsylvania State University, State College, PA, USA
|10/2006 - 02/2007|
Visiting Researcher at the Tuck School of Business, Department of Operations Management & Management Science (Prof. David F. Pyke) at Tuck School of Business at Dartmouth, Hanover, NH, USA
Doctoral Degree (Dr. rer. pol. equivalent to Ph.D.) in Business Administration at the University of Mannheim; Doctoral Thesis: Integrated supply and demand management in operations
Diploma in Business Mathematics at the Otto-von-Guericke University, Magdeburg; Diploma Thesis: “On the performance of linear replenishment policies of a production-inventory problem under random demand and yield”