Effective sourcing strategies for perishable product supply chains

Purpose – The purpose of this paper is to assess whether an existing sourcing strategy can effectively supply products of appropriate quality with acceptable levels of product waste if applied to an international perishable product supply chain. The authors also analyse whether the effectiveness of this sourcing strategy can be improved by including costs for expected shelf life losses while generating order policies. Design/methodology/approach – The performance of sourcing strategies is examined in a prototype international strawberry supply chain. Appropriate order policies were determined using parameters both with and without costs for expected shelf life losses. Shelf life losses during transport and storage were predicted using microbiological growth models. The performance of the resulting policies was assessed using a hybrid discrete event chain simulation model that includes continuous quality decay. Findings – The study's findings reveal that the order policies obtained with standard cost parameters result in poor product quality and large amounts of product waste. Also, including costs for expected shelf life losses in sourcing strategies significantly reduces product waste and improves product quality, although transportation costs rise. Practical implications – The study shows that in perishable product supply chain design a trade-off should be made between transportation costs, shortage costs, inventory costs, product waste, and expected shelf life losses. Originality/value – By presenting a generically applicable methodology for perishable product supply chain design, the authors contribute to research and practice efforts to reduce food waste. Furthermore, product quality information is included in supply chain network design, a research area that is still in its infancy

The Value of RFID Technology Enabled Information to Manage Perishables

We address the value of RFID technology enabled information to manage perishables in the context of a supplier that sells a random lifetime product subject to stochastic demand and lost sales. The product's lifetime is largely determined by the time and temperature history in the supply chain. We compare two information cases to a Base case in which the product's time and temperature history is unknown and therefore its shelf life is uncertain. In the first information case, the time and temperature history is known and therefore the remaining shelf life is also known at the time of receipt. The second information case builds on the first case such that the supplier now has visibility up the supply chain to know the remaining shelf life of inventory available for replenishment. We formulate these three different cases as Markov decision processes, introduce well performing heuristics of more practical relevance, and evaluate the value of information through an extensive simulation using representative, real world supply chain parameters.simulation;value of information;RFID;perishable inventory

Inventory control for a non-stationary demand perishable product: comparing policies and solution methods

This paper summarizes our findings with respect to order policies for an inventory control problem for a perishable product with a maximum fixed shelf life in a periodic review system, where chance constraints play a role. A Stochastic Programming (SP) problem is presented which models a practical production planning problem over a finite horizon. Perishability, non-stationary demand, fixed ordering cost and a service level (chance) constraint make this problem complex. Inventory control handles this type of models with so-called order policies. We compare three different policies: a) production timing is fixed in advance combined with an order up-to level, b) production timing is fixed in advance and the production quantity takes the age distribution into account and c) the decision of the order quantity depends on the age-distribution of the items in stock. Several theoretical properties for the optimal solutions of the policies are presented. In this paper, four different solution approaches from earlier studies are used to derive parameter values for the order policies. For policy a), we use MILP approximations and alternatively the so-called Smoothed Monte Carlo method with sampled demand to optimize values. For policy b), we outline a sample based approach to determine the order quantities. The flexible policy c) is derived by SDP. All policies are compared on feasibility regarding the α-service level, computation time and ease of implementation to support management in the choice for an order policy.National project TIN2015-66680-C2-2-R, in part financed by the European Regional Development Fund (ERDF)

Supply chain management of blood products: a literature review.

This paper presents a review of the literature on inventory and supply chain management of blood products. First, we identify different perspectives on approaches to classifying the existing material. Each perspective is presented as a table in which the classification is displayed. The classification choices are exemplified through the citation of key references or by expounding the features of the perspective. The main contribution of this review is to facilitate the tracing of published work in relevant fields of interest, as well as identifying trends and indicating which areas should be subject to future research.OR in health services; Supply chain management; Inventory; Blood products; Literature review;

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.Manufacturing;Revenue Management;Software;Advanced Planning Systems;Demand Fulfillment

Local Water Storage Control for the Developing World

Most cities in India do not have water distribution networks that provide water throughout the entire day. As a result, it is common for homes and apartment buildings to utilize water storage systems that are filled during a small window of time in the day when the water distribution network is active. However, these water storage systems do not have disinfection capabilities, and so long durations of storage (i.e., as few as four days) of the same water leads to substantial increases in the amount of bacteria and viruses in that water. This paper considers the stochastic control problem of deciding how much water to store each day in the system, as well as deciding when to completely empty the water system, in order to tradeoff: the financial costs of the water, the health costs implicit in long durations of storing the same water, the potential for a shortfall in the quantity of stored versus demanded water, and water wastage from emptying the system. To solve this problem, we develop a new Binary Dynamic Search (BiDS) algorithm that is able to use binary search in one dimension to compute the value function of stochastic optimal control problems with controlled resets to a single state and with constraints on the maximum time span in between resets of the system