Prof. Dr. Sandra Transchel

Publications

Copy reference link   DOI: 10.1016/j.ejor.2017.05.010

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.

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Copy reference link   DOI: 10.1016/j.ejor.2017.03.075

Abstract: We examine a stochastic inventory and pricing problem for a firm that sells two vertically differentiated products. The demands for the two products are determined by total (random) market size and the customers’ net utility from buying the two products, which is determined by the products’ quality attributes, the individual quality valuation (unknown to the firm), and the selling prices. In case the preferred product is out of stock, customers may be willing to buy a substitute instead, if their net utility is non-negative. Therefore, we analyze an inventory and pricing model, considering price-based and stockout-based substitution.We show that the demand function is not continuous in price. By decomposing the profit function into different price regimes, we are able to derive closed-form expressions for the stockout-based substitution rates (upward and downward substitution) and the optimal inventory levels under exogenous pricing. Under endogenous pricing, we find that the profit function is not necessarily unimodal. However, we show that a unique solution exists for the integrated price and inventory problem under price-based substitution only. Numerical results reveal that not considering stockout-based substitution (i) leads to lower profit margins for high-quality products and (ii) may cause severe supply-demand mismatches throughout the entire assortment. Finally, we show the performance of two approximated pricing policies.

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Copy reference link   DOI: 10.1111/poms.12687

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.

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Copy reference link   DOI: 10.1016/j.ijpe.2017.01.006

Abstract: In disassemble-to-order problems, where a specific amount of several components must be obtained from the disassembly of several types of returned products, random disassembly yields create a formidable challenge for appropriate planning. In this context, it is typically assumed that yields from disassembly are either stochastically proportional or follow a binomial process. In the case of yield process misspecification, it has been shown (see Inderfurth et al. (2015)) that assuming binomial yields usually results in a lower penalty than assuming stochastically proportional yields. While there have been heuristics developed for the disassemble-to-order problem with stochastically proportional yields, a suitable, powerful heuristic for binomial yields is needed in order to facilitate solving problems with complex real-world product structures. We present a heuristic approach that is based on a decomposition procedure for the underlying non-linear stochastic optimization problem and that can be applied to problems of arbitrary size. A comprehensive numerical performance study using both randomly generated instances as well as a full factorial experimental design and, additionally, the data of a practical case example reveals that this heuristic delivers close-to-optimal results.

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Copy reference link   DOI: 10.1016/j.ejor.2016.12.016

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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Copy reference link   DOI: 10.1016/j.ejor.2015.08.003

Abstract: Abstract Practical experience and scientific research show that there is scope for improving the performance of inventory control systems by delaying a replenishment order that is otherwise triggered by generalised and all too often inappropriate assumptions. This paper presents the first analysis of the most commonly used continuous (s, S) policies with delayed ordering for inventory systems with compound demand. We analyse policies with a constant delay for all orders as well as more flexible policies where the delay depends on the order size. For both classes of policies and general demand processes, we derive optimality conditions for the corresponding delays. In a numerical study with Erlang distributed customer inter-arrival times, we compare the cost performance of the optimal policies with no delay, a constant delay and flexible delays. Sensitivity results provide insights into when the benefit of delaying orders is most pronounced, and when applying flexible delays is essential.

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Copy reference link   DOI: 10.1080/00207543.2015.1053579

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.

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Copy reference link   DOI: 10.1111/poms.12173

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.

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Copy reference link   DOI: 10.1016/j.bushor.2013.11.002

Abstract: Better-aligned operational and strategic plans and a better balance of supply and demand bring tangible benefits to firms. However, functional departments in firms often operate without vertical and horizontal alignment. The outcomes are delays and amplification of the information flow, suboptimal corporate plans, uncoordinated reactions within the business, insufficient operational flexibility, and discrepancies in supply and demand. Sales and operations planning (S&OP) can circumvent these negative consequences and align the organization. Our multi-method research develops a holistic S&OP maturity model that firms can use for the assessment of their internal S&OP processes and shows the pathway to an integrated S&OP approach for the achievement of a better-aligned organization. We present a case study of a medium-sized, Swiss-based pharmaceutical company that has recently implemented S&OP to highlight why companies implement S&OP, the prerequisites and roadblocks encountered during implementation, and the benefits envisioned and achieved. Finally, we reveal the great relevance of the topic by means of a questionnaire survey which shows that organizations’ current S&OP performance is underdeveloped and that many improvements are indispensable to enjoy all benefits associated with the alignment process.

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Copy reference link   DOI: 10.1016/j.ijpe.2010.06.018

Abstract: We study a problem of dynamic quantity competition in continuous time with two competing retailers facing different replenishment cost structures. Retailer 1 faces fixed ordering costs and variable procurement costs and all inventory kept in stock is subject to holding costs. Retailer 2 only faces variable procurement costs. Both retailers are allowed to change their sales quantities dynamically over time. Following the structure of the economic order quantity (EOQ) model, retailer 1 places replenishment orders in batches and retailer 2 follows a just-in-time (JIT) policy. The objective of both retailers is to maximize their individual average profit anticipating the competitor's replenishment and output decisions. The problem is solved by a two-stage hierarchical optimization approach using backwards induction. The second-stage model is a differential game in output quantities between the two retailers for a given cycle length. At the first stage, the replenishment policy is determined. We prove the existence of a unique optimal solution and derive an open-loop Nash equilibrium. We show that both retailers follow contrary output strategies over the order cycle. The EOQ retailer, driven by inventory holding costs, decreases his market share whereas the output of the JIT retailer increases. Moreover, depending on the cost structure, the EOQ retailer might partially be a monopolist. At the first stage, the EOQ retailer determines the cycle length, anticipating the optimal output trajectories at the second stage.

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Copy reference link   DOI: 10.1080/00207543.2010.532910

Abstract: Tailored for a complex application in the process industry, this article examines a multi-product production planning and scheduling problem with sequence-dependent setup cost and times. The manufacturing process is characterised by a two-stage structure where the sequencing problem occurs on the first level and contribution margin, holding cost, penalty cost are accounted on the second level. We present a hybrid mixed-binary optimisation model based on the general lot-sizing and scheduling problem [Fleischmann, B. and Meyr, H. 1997. The general lotsizing and scheduling problem. OR Spectrum, 19 (1), 11–21], which combines discrete and continuous-time elements within a standard inventory and lot-size (I&L) formulation. Since the I&L formulation does not provide sharp linear programming-relaxation bounds, we present two alternative reformulations based on a transportation problem. In a numerical study inspired by real industry data, we show that on average, both reformulations yield significant improvements in computation time and integrality gap.

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Copy reference link   DOI: 10.1007/s00291-010-0196-1

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.

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Copy reference link   DOI: 10.1016/j.ejor.2008.10.011

Abstract: This paper analyzes the impact of dynamic pricing on the single product economic order decision of a monopolist retailer. Items are procured from an external supplier according to the economic order quantity (EOQ) model and are sold to customers on a single market without competition following the simple monopolist pricing problem. Coordinated decision making of optimal pricing and ordering is influenced by operating costs – including ordering and inventory holding costs – and the demand rate obtained from a price response function. The retailer is allowed to vary the selling price, either in a fixed number of discrete points in time or continuously. While constant and continuous pricing have received much attention in the literature, problems with a limited number of price changes are rather rare. This paper illustrates the benefit of dynamically changing prices to achieve operational efficiency in the EOQ model, that is to trigger high demand rates when inventories are high. We provide structural properties of the optimal time instants when the price should be changed. Taking into account costs for changes in price, it provides numerical guidance on number, timing, and size of price changes during an order cycle. Numerical examples show that the benefits of dynamic pricing in an EOQ framework can be achieved with only a few price changes and that products being unprofitable under static pricing may become profitable under dynamic pricing.

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Copy reference link   DOI: 10.1016/j.ijpe.2008.08.046

Abstract: In this paper, we consider the problem at the interface of marketing and operations to find the optimal lot-size and selling price for multiple products that share a warehouse with limited storage capacity. We analyze the impact of coordinated decision making on the selling price and the replenishment policy compared to a decentralized decision. Furthermore, we compare constant pricing where the selling price remains constant over the entire planning horizon and dynamic pricing where the firm is allowed to adjust the selling price continuously. The objective is to maximize the average profit by choosing the optimal pricing strategy, the optimal lot-sizes, and the optimal staggering of the order releases. This paper provides both analytical and numerical results on the impact of a joint optimization on the pricing and replenishment decisions and the potential benefits compared to the decentralized approach. We develop mathematical models for the different decision frameworks, provide algorithms to determine the optimal policy parameters, and show that the peak storage requirement is equal at each replenishment. Furthermore, we show in a numerical example that achieving operational efficiency through dynamic pricing in the warehouse scheduling problem is even more beneficial than in the economic order quantity framework.

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Copy reference link   DOI: 10.1016/j.ijpe.2008.08.018

Abstract: Random yield is still prevailing in several industries despite quality improvement efforts. In this case, the supply chain partners jointly must find the best way to cope with yield uncertainty. We focus on the inventory-related costs that can be influenced by adjusting the ordering, setup, and delivery policy to the random yield. The yield model of having a random proportion of defective items is assumed with known mean and variance. Two alternative scenarios are examined: when the buyer or when the supplier makes 100% inspection. We provide analytic tools and approximations to optimize the decisions. Our main contribution is to help in the cooperation and negotiation process by showing under which circumstances have the yield characteristics important effects and when are they negligible. We show that not the average yield but the yield uncertainty plays the critical role mainly in providing an appropriate service level but also in finding the optimal shipment and setup policy.

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Copy reference link   DOI: 10.1007/BF03342706

Abstract: We consider an economic order quantity model where the supplier offers an all-units quantity discount and a price sensitive customer demand. We compare a decentralized decision framework where selling price and replenishment policy are determined independently to simultaneous decision making. Constant and dynamic pricing are distinguished. We derive structural properties and developalgorithms that determine the optimal pricing and replenishment policy and show how quantity discounts not only influence the purchasing strategy but also the pricing policy. A sensitivity analysis indicates the impact of the fixed-holding cost ratio, the discount policy, and the customers' price sensitivity on the optimal decisions.

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Copy reference link   DOI: 10.1287/opre.1070.0420

Abstract: Bollapragada and Morton (1999) present several well-performing heuristics for solving the periodic inventory problem with random yield and demand. Their approach is essentially based on a transformation of the single-period problem into a standard newsvendor problem with deterministic yield and random demand which, however, is supply dependent. In our note, we show that their evaluation of the respective optimality condition is not correct. This explains the steady deterioration of their myopic heuristics for parameter constellations that correspond to increasing service levels. Some computational investigations reveal that the performance of the heuristics can become quite poor if service levels are high and exceed those values for which results are reported in the original study. Nonetheless, up to now these heuristics are still the best ones available for solving the joint random yield problem.

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