This study deals with "open code dating," the movement by grocery manufacturers and distributors toward dating perishable food packages in such a manner that consumers can readily determine product freshness or length of time on store shelves. The study explores the desirability and feasibility of open code dating, placing greatest importance upon the response of the consumer to the concept. It was found that consumers were aware of open code dating and generally strongly desired its universal adoption. Shoppers were also confused by open dating and failed to understand freshness dates properly. The strongest desire for open dating was found in shoppers at the upper end of the socio-economic scale. Grocery retailers expressed satisfaction with open coding, believing it an aid in stock rotation and customer satisfaction. Possible disadvantages, such as increased throwaway costs and large conversion costs, were not perceived as being significant. The businessmen favored widespread adoption of open code dating. On the basis of data from interviews with shoppers, it is concluded that consumers desire adoption of open code dating and do use this service. It is also concluded that adoption of open code dating would be an economically sound decision which would constitute a desirable marketing strategy.

<p>Many types of services are provided using some equipment or machines, e.g. transportation systems using vehicles. Designs of these systems require capacity decisions, e.g., the number of vehicles. In my dissertation, I use many-server and conventional heavy-traffic limit theory to derive asymptotically optimal capacity decisions, giving the desired level of delay and availability performance with minimum investment. The results provide near-optimal solutions and insights to otherwise analytically intractable problems.</p> <p>The dissertation will comprise two essays. In the first essay, &ldquoAsymptotic Analysis of Delay-based Performance Metrics and Optimal Capacity Decisions for the Machine Repair Problem with Spares,&rdquo I study the M/M/R machine repair problem with spares. This system can be represented by a closed queuing network. Applications include fleet vehicles' repair and backup capacity, warranty services' staffing and spare items investment decisions. For these types of systems, customer satisfaction is essential; thus, the delays until replacements of broken units are even more important than delays until the repair initiations of the units. Moreover, the service contract may include conditions on not only the fill rate but also the probability of acceptable delay (delay being less than a specified threshold value).</p> <p>I address these concerns by expressing delays in terms of the broken-machines process; scaling this process by the number of required operating machines (or the number of customers in the system); and using many-server limit theorems (limits taken as the number of customers goes to infinity) to obtain the limiting expected delay and probability of acceptable delay for both delay until replacement and repair initiation. These results lead to an approximate optimization problem to decide on the repair and backup-capacity investment giving the minimum expected cost rate, subject to a constraint on the acceptable delay probability. Using the characteristics of the scaled broken-machines process, we obtain insights about the relationship between quality of service and capacity decisions. Inspired by the call-center literature, we categorize capacity level choice as efficiency-driven, quality-driven, or quality- and efficiency-driven. Hence, our study extends the conventional call center staffing problem to joint staffing and backup provisioning. Moreover, to our knowledge, the machine-repair problem literature has focused mainly on mean and fill rate measures of performance for steady-state cost analysis. This approach provides complex, nonlinear expressions not possible to solve analytically. The contribution of this essay to the machine-repair literature is the construction of delay-distribution approximations and a near-optimal analytical solution. Among the interesting results, we find that for capacity levels leading to very high utilization of both spares and repair capacity, the limiting distribution of delay until replacement depends on one type of resource only, the repair capacity investment.</p> <p>In the second essay, &ldquoDiffusion Approximations and Near-Optimal Design of a Make-to-Stock Queue with Perishable Goods and Impatient Customers,&rdquo I study a make-to-stock system with perishable inventory and impatient customers as a two-sided queue with abandonment from both sides. This model describes many consumer goods, where not only spoilage but also theft and damage can occur. We will refer to positive jobs as individual products on the shelf and negative jobs as backlogged customers. In this sense, an arriving negative job provides the service to a waiting positive job, and vice versa. Jobs that must wait in queue before potential matching are subject to abandonment. Under certain assumptions on the magnitude of the abandonment rates and the scaled difference between the two arrival rates (products and customers), we suggest approximations to the system dynamics such as average inventory, backorders, and fill rate via conventional heavy traffic limit theory.</p> <p>We find that the approximate limiting queue length distribution is a normalized weighted average of two truncated normal distributions and then extend our results to analyze make-to-stock queues with/without perishability and limiting inventory space by inducing thresholds on the production (positive) side of the queue. Finally, we develop conjectures for the queue-length distribution for a non-Markovian system with general arrival streams. We take production rate as the decision variable and suggest near-optimal solutions.</p> / Dissertation

The traditional air cargo supply chain is composed by the shippers, the freight forwarders and the airlines. The freight forwarders secure capacity with airlines in order to accommodate shippers' demand. They bid for capacity six to twelve months before the actual departure date of the aircraft, and confirm the needed capacity a few days before departure. We address the freight forwarders' problem of confirming needed capacity based on balancing the costs of ordering too much capacity versus ordering too little. We use a Markov decision process to model the problem. We show the value function is convex in the state variables for lead times of one and two periods. We present the structure of the optimal policy and show it is stationary. In addition we present solutions to the case with subcontracting options and order due dates. We also address the airlines' revenue management problem with respect to its cargo capacity available for free sale (after honoring committed capacity to freight forwarders), in particular the problems of (1) accepting/rejecting incoming bookings based on bid prices, and of (2) estimating the show-up rate (ratio of bookings handed in at departure over bookings on hand) with impact on overbooking. To address the lumpiness of demand, we split the cargo into two categories: small cargo, composed of mail and small packages, and large cargo, composed of the bulk of commercial cargo. The small cargo is approximated with the passenger arrival, and we propose a new algorithm to solve the traditional probabilistic nonlinear problem from the passenger side. The large cargo is solved using a dynamic program, which is decomposed at the leg level using a fare-prorating scheme. The solution from our new approach is shown via simulation to be superior to two approaches currently used: the first come first serve, and the deterministic linear program. The show-up rate is estimated using wavelets and we show that a discrete show-up rate is more suitable than the traditional Normal estimator used in practice. The new estimator results in considerable more potential revenue.

This dissertation focuses on topics related to the value of real-time information and/or to supply uncertainties due to uncertain lead-times and yields in supply chains. The first two of these topics address issues associated with freight transportation, while the remaining two topics are concerned with inventory replenishment. We first assess the value of dynamic tour determination for the traveling salesman problem (TSP). Given a network with traffic dynamics that can be modeled as a Markov chain, we present a policy determination procedure that optimally builds a tour dynamically. We then explore the potential for expected total travel cost reduction due to dynamic tour determination, relative to two a priori tour determination procedures. Second, we consider the situation where the decision to continue or abort transporting perishable freight from an origin to a destination can be made at intermediate locations, based on real-time freight status monitoring. We model the problem as a partially observed Markov decision process (POMDP) and develop an efficient procedure for determining an optimal policy. We determine structural characteristics of an optimal policy and upper and lower bounds on the optimal reward function. Third, we analyze a periodic review inventory control problem with lost sales and random yields and present conditions that guarantee the existence of an optimal policy having a so-called staircase structure. We make use of this structure to accelerate both value iteration and policy evaluation. Lastly, we examine a model of inventory replenishment where both lead time and supply qualities are uncertain. We model this problem as an MDP and show that the weighted sum of inventory in transit and inventory at the destination is a sufficient statistic, assuming that random shrinkage can occur from the origin to the supply system or destination, shrinkage is deterministic within the supply system and from the supply system to the destination, and no shrinkage occurs once goods reach the destination.

No description available.

This study was aimed at exploring data analysis techniques for generating accurate estimates of the loss in quality of fresh fruits, vegetables and cut flowers in chilled supply chains based on data from advanced sensors. It was motivated by the recent interest in the application of advanced sensors, by emerging concepts in quality controlled logistics, and by the desire to minimise quality losses during transport and storage of the produce. Cut roses were used in this work although the findings will also be applicable to other produce. The literature has reported that whilst temperature was considered to be the most critical post-harvest factor, others such as growing conditions could also be important in the senescence of cut roses. Kinetic modelling was the most commonly used modelling approach for shelf life predictions of foods and perishable produce, but not for estimating vase life (VL) of cut flowers, and so this was explored in this work along with multiple linear regression (MLR) and partial least squares (PLS). As the senescence of cut roses is not fully understood, kinetic modelling could not be implemented directly. Consequently, a novel technique, called Kinetic Linear System (KLS), was developed based on kinetic modelling principles. Simulation studies of shelf life predictions for tomatoes, mushrooms, seasoned soybean sprouts, cooked shrimps and other seafood products showed that the KLS models could effectively replace the kinetic ones. With respect to VL predictions KLS, PLS and MLR were investigated for data analysis from an in-house experiment with cut roses from Cookes Rose Farm (Jersey). The analysis concluded that when the initial and final VLs were available for model calibration, effective estimates of the post-harvest loss in VL of cut roses could be obtained using the post-harvest temperature. Otherwise, when the initial VLs were not available, such effective estimates could not be obtained. Moreover, pre-harvest conditions were shown to correlate with the VL loss but the correlation was too weak to produce or improve an effective estimate of the loss. The results showed that KLS performance was the best while PLS one could be acceptable; but MLR performance was not adequate. In another experiment, boxes of cut roses were transported from a Kenyan farm to a UK distribution centre. Using KLS and PLS techniques, the analysis showed that the growing temperature could be used to obtain effective estimates of the VLs at the farm, at the distribution centre and also the in-transit loss. Further, using post-harvest temperature would lead to a smaller error for the VL at the distribution centre and the VL loss. Nevertheless, the estimates of the VL loss may not be useful practically due to the excessive relative prediction error. Overall, although PLS had a slightly smaller prediction error, KLS worked effectively in many cases where PLS failed, it could handle constraints while PLS could not.In conclusion, KLS and PLS can be used to generate effective estimates of the post-harvest VL loss of cut roses based on post-harvest temperature stresses recorded by advanced sensors. However, the estimates may not be useful practically due to significant relative errors. Alternatively, pre-harvest temperature could be used although it may lead to slightly higher errors. Although PLS had slightly smaller errors KLS was more robust and flexible. Further work is recommended in the objective evaluations of product quality, alternative non-linear techniques and dynamic decision support system.

abstract: The use of Red Blood Cells (RBCs) is a pillar of modern health care. Annually, the lives of hundreds of thousands of patients are saved through ready access to safe, fresh, blood-type compatible RBCs. Worldwide, hospitals have the common goal to better utilize available blood units by maximizing patients served and reducing blood wastage. Managing blood is challenging because blood is perishable, its supply is stochastic and its demand pattern is highly uncertain. Additionally, RBCs are typed and patient compatibility is required. This research focuses on improving blood inventory management at the hospital level. It explores the importance of hospital characteristics, such as demand rate and blood-type distribution in supply and demand, for improving RBC inventory management. Available inventory models make simplifying assumptions; they tend to be general and do not utilize available data that could improve blood delivery. This dissertation develops useful and realistic models that incorporate data characterizing the hospital inventory position, distribution of blood types of donors and the population being served. The dissertation contributions can be grouped into three areas. First, simulations are used to characterize the benefits of demand forecasting. In addition to forecast accuracy, it shows that characteristics such as forecast horizon, the age of replenishment units, and the percentage of demand that is forecastable influence the benefits resulting from demand variability reduction. Second, it develops Markov decision models for improved allocation policies under emergency conditions, where only the units on the shelf are available for dispensing. In this situation the RBC perishability has no impact due to the short timeline for decision making. Improved location-specific policies are demonstrated via simulation models for two emergency event types: mass casualty events and pandemic influenza. Third, improved allocation policies under normal conditions are found using Markov decision models that incorporate temporal dynamics. In this case, hospitals receive replenishment and units age and outdate. The models are solved using Approximate Dynamic Programming with model-free approximate policy iteration, using machine learning algorithms to approximate value or policy functions. These are the first stock- and age-dependent allocation policies that engage substitution between blood type groups to improve inventory performance. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

El manejo de productos perecibles en la cadena de frío es un desafío para las empresas que comercializan este tipo de productos. Esto debido a que su corta vida útil requiere de estimaciones de demanda más precisas para evitar el sobre stock y que en el manejo del inventario se considere la conservación del producto como principal prioridad. El presente trabajo, analiza cómo caso de estudio una empresa comercializadora de carne en Perú, que importa productos cárnicos y que se enfrenta a altos niveles de devoluciones de sus productos en condición enfriada debido al deterioro del producto. Se propone la aplicación de tres herramientas: Mejora en el método de Picking, Pronósticos para la gestión de la demanda y un sistema Kanban que permitan tener un despacho adecuado del producto, disminuir el error de pronóstico actual e implementar un sistema ágil que reduzca los tiempos del producto fuera del almacén refrigerado. / The management of perishable products in the cold chain is a challenge for companies that market this type of product. This is due to the fact that its short useful life requires a more precise supply of demand to avoid overstock and that in inventory management, product conservation is considered as the main priority. This paper analyzes a case study of a meat trading company in Peru, which imports meat products and which faces high levels of returns of its products in cooled condition due to product deterioration. The application of three tools is proposed: Improvement in the Picking method, Forecasts for demand management and a Kanban system to execute, have an adequate dispatch of the product, reduce the current forecast error and implement an agile system that reduces times of the product outside the cold store. / Trabajo de investigación

Title: Perishable Inventory Management Solutions and Challenges of Kosovo FFRs. Avoiding Product Expiration at Retails ShelvesPurpose: In this thesis perishable inventory management solutions and challenges at Kosovo FFRs have been studied and identified. Hence, after identifying PIM solutions and challenges the research suggests ideas that will contribute to avoid the expiration of perishable products if selling them takes more time than their actual shelf life. This contributes to minimizing food waste in food supply chains and fresh food retailers. Methodology: Thesis consist of qualitative methods where multiple case studies in cooperation with Kosovo FFRs have been performed. Data collection methods included semi structured interviews, site visits and some financial data accessed from annual and government reports. Theory: Theoretical chapter has been developed from preexisting theory on perishable inventory management. Five phases of fresh food retailing inventory management have been developed and used as the basis for practical research. Moreover, part two of the theoretical chapter talks about the perishable inventory management challenges and is the basis for the second research question. Findings: The findings have shown that Kosovo FFRs use a mixture of PIM solutions with a focus on shelf life and replenishment solutions. The study also revealed that Kosovo FFRs are outdated regarding to product identification and software solutions, however, manage to perform somehow satisfactorily. Consequently, because of the lack of contemporary identification technologies Kosovo FFRs PIM challenges where found to be related to data accuracy and real time data access.

The aim of the study was to explore the fundamental operational risks, mitigation strategies and the key performance indicators (KPIs) in last-mile delivery (LMD) of perishable goods for African and Asian retailers. Particularly, the research was guided by the following research questions.  RQ1: What operational risks are related to LMD of perishable goods for African and Asian retailers? RQ2: How do African and Asian retailers mitigate these operational risks?  RQ3: What KPIs can be used to ensure the efficiency of LMD of perishable goods?