SELECTED PAPER ABSTRACTS

Teaching/Communication/Extension/Profession,

Minimizing food waste in grocery store operations: literature review and research agenda

Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailer’s perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field

Essays on Macroeconomics and Business Cycles

This dissertation consists of three essays on macroeconomics and business cycles. In the first chapter, written with Nicolas Crouzet, we ask whether news shocks, which change agents' expectations about future fundamentals, are an important source of business-cycle fluctuations. The existing literature has provided a wide range of answers, finding that news shocks can account for 10 percent to 60 percent of the volatility of output. We show that looking at the dynamics of inventories, so far neglected in this literature, cleanly isolates the role of news shocks in driving business cycles. In particular, inventory dynamics provide an upper bound on the explanatory power of news shocks. We show, for a broad class of theoretical models, that finished-good inventories must fall when there is an increase in consumption and investment induced by news shocks. When good news about future fundamentals lowers expected future marginal costs, firms delay current production and satisfy the increase in demand by selling from existing inventories. This result is robust across the nature of the news and the presence of different types of adjustment costs. We therefore propose a novel empirical identification strategy for news shocks: negative comovement between inventories and components of private spending. Estimating a structural VAR with sign restrictions on inventories, consumption and investment, our identified shock explains at most 20 percent of output variations. Intuitively, since inventories are procyclical in the data, shocks that generate negative comovement between inventories and sales cannot account for the bulk of business-cycle fluctuations. The second chapter looks into the dynamics of durables over the business cycle. Although transactions of used durables are large and cyclical, their interaction with purchases of new durables has been neglected in the study of business cycles. I fill in this gap by introducing a new model of durables replacement and second-hand markets. The model generates a discretionary replacement demand function, it nests a standard business-cycle model of durables, and it verifies the Coase conjecture. The model delivers three conclusions: markups are smaller for goods that are more durable and more frequently replaced; markups are countercyclical for durables, resolving the comovement puzzle of Barsky, House, and Kimball (2007); and procyclical replacement demand amplifies durable consumption. In the third chapter, written with Ricardo Reis, we study the macroeconomic implications of government transfers. Between 2007 and 2009, government expenditures increased rapidly across the OECD countries. While economic research on the impact of government purchases has flourished, in the data, about three quarters of the increase in expenditures in the United States (and more in other countries) was in government transfers. We document this fact, and show that the increase in U.S. spending on retirement, disability, and medical care has been as high as the increase in government purchases. We argue that future research should focus on the positive impact of transfers. Towards this, we present a model in which there is no representative agent and Ricardian equivalence does not hold because of uncertainty, imperfect credit markets, and nominal rigidities. Targeted lump-sum transfers are expansionary both because of a neoclassical wealth effect and because of a Keynesian aggregate demand effect

Inventory and Service Optimization for Self-serve Kiosks

In the retail industry, labor costs constitute a big chunk of total operating costs and retailers are advancing towards process automation to minimize their operating costs and to provide reliable services to their customers. One such example of technological advancement is self-service kiosks that are becoming an integral part of our life, whether it be for cashing a cheque, self-checkout at retail stores, airports, hospitals, or checkout-free stores. Although self-serve kiosks are cost-effective due to low setup and operating costs, the technology is relatively new and poses new research questions that have not been studied before. This thesis explores and addresses strategic and operational challenges associated with self-serve kiosk technology. The first part of the thesis is based on a collaboration with MedAvail Technologies Inc., a Canada-based healthcare technology company, developing self-serve pharmacy kiosk technology to dispense over-the-counter and prescription drugs. MedAvail faces several challenges related to assortment and stocking decisions of medications in the kiosk due to its limited capacity and the thousands of drugs being ordered in various quantities. We address these challenges by analyzing pharmaceutical sales data and developing a data-driven stochastic optimization approach to determine optimized kiosk storage capacity and service levels and recommend assortment and stocking decisions under supplier-driven product substitution. A column-generation based heuristic approach is also proposed to solve the models efficiently. The second part of the thesis extends the self-serve kiosk inventory planning problem to a robust optimization (RO) framework under fill rate maximization objective. We propose a data-driven approach to generate polyhedral uncertainty sets from hierarchical clustering and the resulting RO model is solved using column-and-constraint generation and conservative approximation solution methodologies. The proposed robust framework is tested on actual pharmacy sales data and randomly generated instances with 1600 products. The robust solutions outperform stochastic solutions with an increase in out-of-sample fill rate of 5.8%, on average, and of up to 17%. Finally, the third part of the thesis deals with an application of pharmacy kiosks to improve healthcare access in rural regions. We present a mathematical function to model customer healthcare accessibility as the expected travel distance when multiple pharmacy location (store and kiosks) choices are available to customers. Customer choice behavior is modelled using a multinomial logit (MNL) model where customer utility for a pharmacy location depends on travel distance which is not exactly known but rather depends on kiosk fill rate. We model the problem as a newsvendor problem with fill-rate dependent demand to decide on kiosk stock level (or capacity) to minimize the weighted sum of expected travel distance and total cost. Sensitivity analysis over modelling parameters is carried out to derive insights and to determine problem settings under which pharmacy kiosks improve customer accessibility

Constructive solution methodologies to the capacitated newsvendor problem and surrogate extension

The newsvendor problem is a single-period stochastic model used to determine the order quantity of perishable product that maximizes/minimizes the profit/cost of the vendor under uncertain demand. The goal is to fmd an initial order quantity that can offset the impact of backlog or shortage caused by mismatch between the procurement amount and uncertain demand. If there are multiple products and substitution between them is feasible, overstocking and understocking can be further reduced and hence, the vendor\u27s overall profit is improved compared to the standard problem. When there are one or more resource constraints, such as budget, volume or weight, it becomes a constrained newsvendor problem. In the past few decades, many researchers have proposed solution methods to solve the newsvendor problem. The literature is first reviewed where the performance of each of existing model is examined and its contribution is reported. To add to these works, it is complemented through developing constructive solution methods and extending the existing published works by introducing the product substitution models which so far has not received sufficient attention despite its importance to supply chain management decisions. To illustrate this dissertation provides an easy-to-use approach that utilizes the known network flow problem or knapsack problem. Then, a polynomial in fashion algorithm is developed to solve it. Extensive numerical experiments are conducted to compare the performance of the proposed method and some existing ones. Results show that the proposed approach though approximates, yet, it simplifies the solution steps without sacrificing accuracy. Further, this dissertation addresses the important arena of product substitute models. These models deal with two perishable products, a primary product and a surrogate one. The primary product yields higher profit than the surrogate. If the demand of the primary exceeds the available quantity and there is excess amount of the surrogate, this excess quantity can be utilized to fulfill the shortage. The objective is to find the optimal lot sizes of both products, that minimize the total cost (alternatively, maximize the profit). Simulation is utilized to validate the developed model. Since the analytical solutions are difficult to obtain, Mathematical software is employed to find the optimal results. Numerical experiments are also conducted to analyze the behavior of the optimal results versus the governing parameters. The results show the contribution of surrogate approach to the overall performance of the policy. From a practical perspective, this dissertation introduces the applications of the proposed models and methods in different industries such as inventory management, grocery retailing, fashion sector and hotel reservation

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Decision Making in Supply Chains with Waste Considerations

As global population and income levels have increased, so has the waste generated as a byproduct of our production and consumption processes. Approximately two billion tons of municipal solid waste are generated globally every year – that is, more than half a kilogram per person each day. This waste, which is generated at various stages of the supply chain, has negative environmental effects and often represents an inefficient use or allocation of limited resources. With the growing concern about waste, many governments are implementing regulations to reduce waste. Waste is a often consequence of the inventory decisions of different players in a supply chain. As such, these regulations aim to reduce waste by influencing inventory decisions. However, determining the inventory decisions of players in a supply chain is not trivial. Modern supply chains often consist of numerous players, who may each differ in their objectives and in the factors they consider when making decisions such as how much product to buy and when. While each player makes unilateral inventory decisions, these decisions may also affect the decisions of other players. This complexity makes it difficult to predict how a policy will affect profit and waste outcomes for individual players and the supply chain as a whole. This dissertation studies the inventory decisions of players in a supply chain when faced with policy interventions to reduce waste. In particular, the focus is on food supply chains, where food waste and packaging waste are the largest waste components. Chapter 2 studies a two-period inventory game between a seller (e.g., a wholesaler) and a buyer (e.g., a retailer) in a supply chain for a perishable food product with uncertain demand from a downstream market. The buyer can differ in whether he considers factors affecting future periods or the seller’s supply availability in his period purchase decisions – that is, in his degree of strategic behavior. The focus is on understanding how the buyer’s degree of strategic behavior affects inventory outcomes. Chapter 3 builds on this understanding by investigating waste outcomes and how policies that penalize waste affect individual and supply chain profits and waste. Chapter 4 studies the setting of a restaurant that uses reusable containers instead of single-use ones to serve its delivery and take-away orders. With policy-makers discouraging the use of single-use containers through surcharges or bans, reusable containers have emerged as an alternative. Managing inventories of reusable containers is challenging for a restaurant as both demand and returns of containers are uncertain and the restaurant faces various customers types. This chapter investigates how the proportion of each customer type affects the restaurant’s inventory decisions and costs