Pricing Perishables: Robust Price Assurance

As perishable products are worthless at end-of-life, for a given supply prices are often dynamically adjusted to ensure inventory is exhausted at end-of-life. When consumers expect such price reductions, they may strategically time their purchases. These two conditions pose a complex problem for pricing. Given inventory, cost of production is sunk. Thus, the dynamic path for prices must be set to maximize revenues with an eye on inventory take-down as well as to discourage strategic behavior. This problem is further challenged when prices and the extent of consumer strategic behavior are uncertain. This paper presents an approach for pricing a set of perishable products that are highly substitutable, yet differentiated to target a set of consumer segments. We propose and analyze a price assurance scheme as a solution to the strategic behavior of consumers and price uncertainty. We present and evaluate our price assurance approach by comparing two price assurance schemes: i) ex-post price assurance, and ii) ex-ante price assurance to risk neutral dynamic pricing without regard for consumer strategic behavior. These approaches have not to our knowledge been previously considered in our setting of perishables, uncertain consumer strategic behavior, and price uncertainty. Our numerical experiments show that our robust optimization model prevents loss when a firm encounters the worst-case demand and outperforms a risk neutral pricing model. Comparison across our different pricing schemes provides conditions under which particular schemes may dominate others

A Perishable Inventory System with Postponed Demands and Multiple Server Vacations

A mathematical modelling of a continuous review stochastic inventory system with a single server is carried out in this work. We assume that demand time points form a Poisson process. The life time of each item is assumed to have exponential distribution. We assume (s,S) ordering policy to replenish stock with random lead time. The server goes for a vacation of an exponentially distributed duration at the time of stock depletion and may take subsequent vacation depending on the stock position. The customer who arrives during the stock-out period or during the server vacation is offered a choice of joining a pool which is of finite capacity or leaving the system. The demands in the pool are selected one by one by the server only when the inventory level is above s, with interval time between any two successive selections distributed as exponential with parameter depending on the number of customers in the pool. The joint probability distribution of the inventory level and the number of customers in the pool is obtained in the steady-state case. Various system performance measures in the steady state are derived, and the long-run total expected cost rate is calculated

Pricing Perishables

 A key feature of food products is their perishability. Within the short marketing window that characterizes most food and ag products, demand is typically highly stochastic and difficult to predict. This combination of features poses substantial challenges to retailers when pricing products and has implications for performance that ripples through vertical food chains. For many food products, processing to forms that can be preserved and held in inventory has traditionally been used as a means of coping with these conditions, despite its high costs and ancillary risks introduced such as change in product attributes and deterioration. This paper presents an alternative ERM strategy that focuses on dynamic pricing to control the rate of sale for perishable products. The paper considers a retailer that has market power to price and supplies perishable products to a market with substitute products and demand originating from heterogeneous consumers. Perishability implies a finite horizon for the marketing of the products over which demand across market segments of consumers is both dynamic and stochastic. Faced with uncertainty, we suppose the firm has limited information about the stochastic properties of demand and must choose a pricing strategy that projects over the market horizon. This price trajectory represents a key control mechanism to cope with uncertainty of both the perishability of the product and of demand

An index for dynamic product promotion and the knapsack problem for perishable items

This paper introduces the knapsack problem for perishable items (KPPI), which concerns the optimal dynamic allocation of a limited promotion space to a collection of perishable items. Such a problem is motivated by applications in a variety of industries, where products have an associated lifetime after which they cannot be sold. The paper builds on recent developments on restless bandit indexation and gives an optimal marginal productivity index policy for the dynamic (single) product promotion problem with closed-form indices that yield estructural insights. The performance of the proposed policy for KPPI is investigated in a computational study.Dynamic promotion, Perishable items, Index policies, Knapsack problem, Festless bandits, Finite horizon, Marginal productivity index

A Multi-Server Retrial Queueing Inventory System With Asynchronous Multiple Vacations

This article deals with asynchronous server vacation and customer retrial facility in a multi-server queueing-inventory system. The Poisson process governs the arrival of a customer. The system is comprised of c identical servers, a finite-size waiting area, and a storage area containing S items. The service time is distributed exponentially. If each server finds that there are an insufficient number of customers and items in the system after the busy period, they start a vacation. Once the servers vacation is over and it recognizes there is no chance of getting busy, it goes into an idle state if the number of customers or items is not sufficient, otherwise, it will take another vacation. Furthermore, each server's vacation period occurs independently of the other servers. The system accepts a (s, Q) control policy for inventory replenishment. For the steady state analysis, the Marcel F Neuts and B Madhu Rao matrix geometric approximation approach is used owing to the structure of an infinitesimal generator matrix. The necessary stability condition and R matrix are to be computed and presented. After calculating the sufficient system performance measures, an expected total cost of the system is to be constructed and numerically incorporated with the parameters. Additionally, numerical analyses will be conducted to examine the waiting time of customers in the queue and in orbit, as well as the expected rate of customer loss.Comment: 43 pages, 12 figures, 5 table

Coordinated delivery in urban retail

In the Coordinated Delivery Problem (CDP), we study the passive and proactive coordination strategies that coordinate the delivery among urban retail stores. We formulate the CDP as mixed integer programs and develop a matheuristic, the effectiveness of which is evaluated via newly generated instances. Our numerical study shows that, when the stores prefer placing orders based on their own inventory policies, the proactive coordination strategy is able to achieve similar logistics and services performances to Vendor Managed Inventory (VMI), while respecting the store order decisions as under Retailer Managed Inventory (RMI), and thus offers an excellent combination of VMI and RMI

APPLICATIONS OF REVENUE MANAGEMENT IN HEALTHCARE

Most profit oriented organizations are constantly striving to improve their revenues while keeping costs under control, in a continuous effort to meet customers‟ demand. After its proven success in the airline industry, the revenue management approach is implemented today in many industries and organizations that face the challenge of satisfying customers‟ uncertain demand with a relatively fixed amount of resources (Talluri and Van Ryzin 2004). Revenue management has the potential to complement existing scheduling and pricing policies, and help organizations reach important improvements in profitability through a better management of capacity and demand. The work presented in this thesis investigates the use of revenue management techniques in the service sector, when demand for service arrives from several competing customer classes and the amount of resource required to provide service for each customer is stochastic. We look into efficiently allocating a limited resource (i.e., time) among requests for service when facing variable resource usage per request, by deciding on the amount of resource to be protected for each customer and surgery class. The capacity allocation policies we develop lead to maximizing the organization‟s expected revenue over the planning horizon, while making no assumption about the order of customers‟ arrival. After the development of the theory in Chapter 3, we show how the mathematical model works by implementing it in the healthcare industry, more specifically in the operating room area, towards protecting time for elective procedures and patient classes. By doing this, we develop advance patient scheduling and capacity allocation policies and apply them to scheduling situations faced by operating rooms to determine optimal time allocations for various types of surgical procedures. The main contribution is the development of the methodology to handle random resource utilization in the context of revenue management, with focus in healthcare. We also develop a heuristics which could be used for larger size problems. We show how the optimal and heuristic-based solutions apply to real-life situations. Both the model and the heuristic find applications in healthcare where demand for service arrives randomly over time from various customer segments, and requires uncertain resource usage per request

Integrated Production and Distribution Problem with Single Perishable Product

This dissertation investigated the extension of the Integrated Production and Distribution Scheduling Problem (IPDSPP) using a variety of perishable products, applying the JIP principle and make-to-order strategy to integrate the production and distribution schedules. A single perishable product with a constant lifetime after production was used in the model discussed here. The objective of the problem was to find the solution that results in minimal system total transportation costs while satisfying customer demand within a fixed time horizon. In the solution, the fleet size, vehicle route and distribution schedule, plant production batch size and schedule were determined simultaneously. This research employed non-identical vehicles to fulfill the distribution; each allowed multiple trips within the time horizon. Both the single plant and multiple plant scenarios were analyzed in this research. For each scenario, the complexity analysis, mixed integer programming model, heuristic algorithms and comprehensive empirical study are provided

Exploring risk pooling in hospitals to reduce demand and lead time uncertainty

Nearly every eighth German hospital faces an elevated risk of bankruptcy. An inappropriate use of inventory management practices is among the causes. Hospitals suffer from demand and lead time uncertainty, and the current COVID-19 pandemic worsened the plight. The popular business logistics concept of risk pooling has been shown to reduce these uncertainties in industry and trade, but has been neglected as a variability reduction method in healthcare operations research and practice. Based on a survey with 223 German hospitals, this study explores how ten risk pooling methods can be adapted and applied in the healthcare context to reduce economic losses while maintaining a given service level. The results suggest that in general risk pooling may improve the economic situation of hospitals and, in particular, inventory pooling, transshipments, and product substitution for medications and consumer goods are the most effective methods in the healthcare context, while form postponement may be unsuitable for hospitals due to the required efforts, delay in treatments, and liability issues. The application of risk pooling in healthcare requires willingness to exchange information and to cooperate, adequate IT infrastructure, compatibility, adherence to healthcare laws and regulations, and securing the immediate treatment of emergencies. Compared to manufacturing and trading companies, hospitals seem to currently neglect the variability reducing effect of risk pooling