All Publications
Single-use packaging is one of the major contributors to the growing issue of plastic waste, pollution, and resource depletion. A promising solution is a switch to reusable packaging. However, although the market for reusable packaging is experiencing rapid growth, the large-scale adoption of reuse systems is still challenging. In this chapter, we review the development of reusable packaging systems in the B2C business. We specifically focus on three waste hotspots of consumer-facing packaging: (1) the fast-moving consumer goods (FMCG) sector, (2) e-commerce, and (3) takeaway food services. We first present a general framework characterizing the fundamental structure of reuse systems, independent of the application. For each application area, we then highlight different challenges to be mastered for large-scale adoption and, thus, effective reduction of single-use packaging. Moreover, we provide some directions for future research in the area of reusable packaging systems
We develop an inventory control policy for perishable products considering both random demand and random lead time. We consider a B2C retail environment where excess demand is lost. The policy dynamically determines the optimal replenishment quantity under a service level constraint in every period, allowing for order-crossing, a widely disregarded characteristic in the literature. Regarding perishability, we compare the two most extreme issuing policies, first-expired-first-out (FEFO) and last-expired-first-out (LEFO), and evaluate our policy to existing inventory policies for perishables that typically ignore lead time uncertainty.
We obtain several interesting findings. First, we show that ignoring lead time uncertainty and planning based on the expected lead time significantly undershoots the target service level. Even planning with the maximum lead time, under LEFO, the achieved service level would still fall considerably below the target, which the lost-sales structure can explain. On the other hand, under FEFO, the achieved service level would overshoot the target service level, which leads to unnecessary waste. Second, a more reliable lead time can significantly reduce waste, especially under LEFO. Third, our model allows us to distinguish between past, present, and future lead time uncertainty and thus to consider partial lead time information. We show the value of lead time information on outstanding orders. Fourth, we evaluate the impact of a fast but unreliable delivery option and a slow but reliable delivery option on the retailer’s average waste and ordering process. We find that the optimal choice depends on the demand characteristics.
In 2020, the world started a fight against a pandemic that has severely disrupted commercial and humanitarian supply chains. Humanitarian organizations (HOs), like the World Food Programme (WFP), adjusted their programs in order to manage this pandemic. One such program is cash and voucher assistance (CVA), which is used to bolster beneficiaries' freedom of choice regarding their consumption. In this vein, WFP supports local retailers to provide CVA to beneficiaries who do not have access to a functioning market. However, the operations of these stores can suffer from a very high transmission risk of COVID-19 unless preventive measures are put in place to reduce it. This paper discusses strategies that retailers and HOs can enact to maximize their service and dignity levels while minimizing transmission risk under a CVA program during a pandemic. We argue that HOs providing CVA programs can improve their assistance during a pandemic by implementing strategies that impact the retailing operations of their retailers.
Food is an important resource in disaster management, and food stock levels hold significance for disaster mitigation research and practice. The presence or absence of food stocks is a vulnerability indicator of a region. A large part of overall food stock, before a disaster strikes, is held by private companies (retailers, wholesalers and food producers). However, there is little-to-no information on the food stock levels of commercial companies, and no approach exists to derive such information. We develop an approximation model based on essential inventory management principles and available data sources to estimate aggregated food stock levels in supply networks. The model is applied in a case example that features dairy product stock levels in the German state of Saxonia. The resulting overall stock levels are normalised, and their usability is showcased in a simple vulnerability analysis. Disaster managers are provided with a model that can be used estimate otherwise unavailable data and facilitates investigations into the regional resilience of an area. The limitations of our study are based on the aggregated nature of the supply network structure and data usage (i.e. in the model, we do not consider any seasonality or trend effects).
There is an increasing interest in academic research studying supply planning and inventory control of perishable products, i.e., of products that have a finite shelf life and/or face physical quality decay. The majority of papers in this field consider various stochastic lifetime and demand processes; however, they usually consider a constant order lead-time. So far, very little research integrates lead-time uncertainty into supply planning and inventory control models for perishable products. In this paper, we develop and examine a dynamic inventory control policy for a perishable product with a finite shelf life considering an uncertain replenishment lead-time and a service level constraint that is targeted every period. This dynamic inventory control policy determines the optimal replenishment quantity based on the actual composition of the inventory level into different age categories (remaining shelf lives), the demand forecast during the uncertain lead-time, and the inventory issuing policy, i.e., the order in which the different age-categories are issued from inventory. We consider a B2B environment in which inventory can be issued in a first-in-first-out (FIFO) order and further consider a non-stationary demand process over time. Besides providing structural properties for the optimal replenishment quantity under lead-time uncertainty based on analytical results, we further show the impact of not considering lead-time uncertainty in the decision-making process on achieved service levels and waste rates using a simulation-based optimization technique.
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.
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.
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.
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.
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.
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.
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.
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.
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.
In disassemble-to-order (DTO) systems randomness of recoverable parts gained from used products creates a major challenge for appropriate planning. Typically, it is assumed that yields from disassembly are either stochastically proportional (SP) or follow a binomial (BI) process. In the case of yield misspecification, it can be shown that the BI yield assumption usually results in a lower penalty than the SP yield assumption. For BI yield, however, a suitable, powerful heuristic is needed in order to facilitate DTO problems olving for complex realworld product structures. We present a heuristic approach that is based on a ecomposition procedure for the underlying non-linear stochastic optimization problem and that can be applied to problems of arbitrary size. A numerical performance study reveals that this heuristic yields close - to - optimal results.
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.
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.
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.
