En la presente investigación se sustenta una propuesta de implementación de un modelo de gestión de inventarios, aplicado a una empresa dedicada a la distribución eléctrica en una de las más importantes ciudades del sur del Perú. Los principales objetivos son: Reducir los costos originados debido al sobre stock de inventario, reducir el riesgo de roturas de stock con la consecuente imposición de penalizaciones por parte del ente regulador y adicionalmente establecer un procedimiento para la disposición final de los materiales obsoletos y deteriorados que resultan de la operación de la empresa. / This research supports a proposal for the implementation of an inventory management model, applied to a company dedicated to electricity distribution in one of the most important cities in southern Peru. The main objectives are: To reduce the costs originated due to the stock of inventory, reduce the risk of stock breakage with the consequent imposition of penalties by the regulatory entity and additionally establish a procedure for the final disposal of obsolete and deteriorated materials that they result from the operation of the company. / Tesis

A survey of 25 industrial manufacturing organizations in the U. S. indicated that 70% of respondents experienced dissatisfaction with their outsourcing programs due to unfulfilled expectations, which caused negative continuance intentions. The purpose of this descriptive case study was to explore the experiences of customers who currently use 3PMRO outsourcing programs to determine what factors affect satisfaction levels in the Southern United States. The conceptual framework for this study was the expectancy disconfirmation paradigm, which connects consumer satisfaction level to the fulfillment of consumer expectations. Data were collected from interviews of 22 procurement professionals of maintenance, repair, and operating supplies; observations of 3PMRO supplier performance meetings; and the analysis of performance scorecard documents. Data were analyzed using pattern matching followed by thematic analysis. Three themes were identified through the data analysis that affected consumer satisfaction: inventory management services, utilization of outsourced labor resources, and total cost value of the 3PMRO program. According to results, satisfaction of 3PMRO consumers are based on the proper utilization of a 3PMRO program for the intended limitations of the organization, reduced MRO supply costs, improved inventory management strategies, and improved competitive advantage from the realignment of resources to focus on core competencies. Implications for positive social change include increased awareness of cradle-to-grave inventory management to prevent improper disposal of non-biodegradable materials into our environment.

This bachelor thesis is about a stochastic inventory theory and how changes in different parameters affect the cost system. The inventory is based on a stochastic version of an economic quantity order (EOQ) model with planned shortages. For the deterministic EOQ-model with planned shortages there is a convenient formula for optimal order quantity $Q$ minimizing the cost per time unit. For the stochastic version an ($R$,$Q$)-policy is applied where $R$ is a reorder point such that if the inventory level is below $R$ and order is sent and the ordered products arrive after a lead time $L$. Since a formula for the stochastic inventory is not known, optimal choice of $Q$ is numerically obtained by simulations and compared with the optimal $Q$ for the deterministic EOQ with planned shortages. The demand is for simplicity described by a Poisson process. Since having a stochastic inventory model the basic mathematical EOQ formula is inadequate and is replaced with an approximate EOQ formula with planned shortages. By the simulations the accuracy of the EOQ model with planned shortages approximation is investigated and optimal values for some of the  parameters are obtained.

In this dissertation, we study inventory and revenue management problems for perishable products with customer choice considerations. This dissertation is composed of six chapters. In Chapter 1, we provide an overview and the motivation of problems. Subsequently, in Chapter 2, we propose a joint inventory and pricing problem for a perishable product with two freshness levels. After a stochastic time, a fresh item turns into a non-fresh item, which will expire after another random duration. Under an (r, Q) ordering policy and a markdown pricing strategy for non-fresh items, we formulate a model that maximizes the long-run average profit rate. We then reduce the model to a mixed-integer bilinear program (MIBLP), which can be solved efficiently by state-of-the-art commercial solvers. We also investigate the value of using a markdown strategy by establishing bounds on it under limiting regimes of some parameters such as large market demand. Further, we consider an Economic Order Quantity (EOQ)-type heuristic and bound the optimality gap asymptotically. Our results reveal that although the clearance strategy is always beneficial for the retailer, it may hurt customers who are willing to buy fresh products. In Chapter 3, we extend this model to the dynamic setting with multiple freshness levels of perishable products. Due to the complexity of the problem, we study the structural properties of value function and characterize the structure of the optimal policies by using the concept of anti-multimodularity. The structural analysis enables us to devise three novel and efficient heuristic policies. We further extend the model by considering donation policy and replenishment system. Our results imply that freshness-dependent pricing and dynamic pricing are two substitute strategies, while freshness-dependent pricing and donation strategy are two complement strategies for matching supply with demand. Also, high variability in product quality under dynamic pricing benefits the firm, but it may result in significant losses with a static pricing strategy. In Chapter 4, we study a joint inventory-pricing model for perishable items with fixed shelf lives to examine the effectiveness of different markdown policies, including single-stage, multiple-stage, and dynamic markdown policies both theoretically and numerically. We show that the value of multiple-stage markdown policies over single-stage ones asymptotically vanishes as the shelf life, market demand, or customers’ maximum willingness-to-pay increase. In chapter 5, with a focus on blood products, we optimize blood supply chain structure along with the operations optimization. Specifically, we study collection, production, replenishment, issuing, inventory, wastage, and substitution decisions under three different blood supply chain channel structures, i.e., the decentralized, centralized, and coordinated. We propose a bi-level optimization program to model the decentralized system and use the Karush–Kuhn–Tucker (KKT) optimality conditions to solve that. Although centralized systems result in a higher performance than decentralized systems, it is challenging to implement them. Thus, we design a novel coordination mechanism to motivate hospitals to operate in a centralized system. We also extend the model to the case with demand uncertainty and compare different issuing and replenishment policies. Analysis of a realistic case-study indicates that integration can significantly improve the performance of the system. Finally, Chapter 6 concludes this dissertation and proposes future research directions. / Dissertation / Doctor of Philosophy (PhD)

No description available.

In this thesis, we study three periodic-review, finite-horizon inventory systems in the presence of supply and demand uncertainties. In the first part of the thesis, we study a multi-period single-station problem in which supply uncertainty is modeled by partial supply. Formulating the problem under a robust optimization (RO) framework, we show that solving the robust counterpart is equivalent to solving a nominal problem with a modified deterministic demand sequence. In particular, in the stationary case the optimal robust policy follows the quasi-(s, S) form and the corresponding s and S levels are theoretically computable. In the second part of the thesis, we extend the RO framework to a multi-period multi-echelon problem. We show that for a tree structure network, decomposition applies so that the optimal single-station robust policy remains valid for each echelon in the tree. Furthermore, if there are no setup costs in the network, then the problem can be decomposed into several uncapacitated single-station problems with new cost parameters subject to the deterministic demands. In the last part of the thesis, we consider a periodic-review Assemble-To-Order (ATO) system with multiple components and multiple products, where the inventory replenishment for each component follows an independent base-stock policy and product demands are satisfied according to a First-Come-First-Served (FCFS) rule. We jointly consider the inventory replenishment and component allocation problems in the ATO system under stochastic component replenishment lead times and stochastic product demands. The problems are formulated under the stochastic programming (SP) framework, which are difficult to solve exactly due to a large number of scenarios. We use the sample average approximation (SAA) algorithms to find near-optimal solutions, which accuracy is verified by the numerical experiment results. / Thesis / Doctor of Philosophy (PhD)

Yes / Despite the surge of literature on customer engagement (CE) in social media, few studies shed light on how to leverage CE to improve firms’ operational efficiency. This research proposes a fresh framework using social media data to improve demand forecasting accuracy, resulting in a cost-efficient inventory control strategy. Drawing upon the resource mobilization perspective in particular, this research quantifies the construct of CE from the view of input–output efficiency evaluation using the Data Envelopment Analysis (DEA) model, and then leverages CE to forecast consumer online demand and reconfigure inventory management strategy. Using a 71-week data set from a social commerce start-up in China, this research shows that this new framework dramatically increases demand forecasting accuracy and reduces operational costs in inventory management. This study contributes to the literature by demonstrating the value of social media data in improving operational efficiency, particularly regarding inventory management. / National Natural Science Foundation of China (Project No. 71671152)

yes / Simulation is undoubtedly a very useful tool for modelling a system specifically in the presence of stochastic elements and complex interactions between the system entities. In this paper, a simulation model to support decision making in ship scheduling for Maritime Inventory Routing Problem (MIRP) with particular application to cement distribution is presented. The system under study is a combined discrete and continuous system, where a heterogeneous fleet of ships with various sizes and types of contracts transport bulk cement products from production facility (Central Supply, CS) of a cement company to its packing plants (Distribution Centres, DCs). The simulation model in this study has been designed and developed thoroughly to emulate the complexity of the real system of the MIRP. The simulation model has demonstrated the capability to provide support for decision making in ship scheduling of the heterogeneous shipping fleet in the following forms: (a) real time states of inventory levels at CS and DCs and (b) ships’ routing. In addition, one of the main strength of this simulation model is its flexibility. It can be easily expanded or adjusted to different size of system entities for example number of CSs, DCs, berths, vessels, and products. / Support for this research is provided by the Directorate of Higher Education, Ministry of National Education, Republic of Indonesia

This thesis addresses an inventory management problem of what, when and how many products should be ordered from the supplier applying order line fees. Order line fee is a fixed fee which the company pays to the supplier per every ordered product not depending on the ordered quantity. Even though there are various inventory management methods and variety of research done in the field, there was no research found related to inventory management when supplier order line fees are applied. The described problem is real and currently exists at the company ASWO Baltic. The problem is solved by using experimental research method and CRISP-DM process. The historical company’s data of customer and supplier orders is used for the project. Data is analyzed and prepared for model creation by using feature engineering, data transformation and data normalization methods. Min/Max inventory management method is used as a base for model creation. The improvement proposed by the thesis is to use machine learning algorithms to predict Min and Max stock levels. Support Vector Regression, k-nearest neighbors, Random Forest, Artificial Neural Network, ARIMA, and Prophet machine learning algorithms are tested both for Min and Max level prediction. It was found out that the best results for Min stock level prediction were achieved by k-nearest neighbors algorithm with the average sMAPE measure of 7.0079%. The best predictions for Max stock level were done by Random Forest algorithm with the average sMAPE of 15.0303%. After the hyperparameter optimization sMAPE was improved to 6.8730% and 14.6813% accordingly. The simulation was run to evaluate if the proposed algorithm outperforms the current system. It showed that for the items which have more than 200 orders the algorithm decreased the number of supplier orders by 35,83% and the number of backorders by 49,29% while keeping almost the same inventory turnover. If the same results are achieved with the all products, it is expected that the company would save around 60K euros per annum on supplier order line fees and the lower number of backorders would increase sales by 24%. / Detta examensarbete tar upp ett lagerhanteringsproblem om vad, när och hur många produkter som ska beställas från leverantören som tillämpar orderradsavgifter. Orderradsavgift är en fast avgift som företaget betalar till leverantören för varje beställd produkt, inte beroende på beställd kvantitet. Även om det finns olika lagerhanteringsmetoder och olika undersökningar som gjorts inom området, hittades ingen forskning relaterad till lagerhantering när orderradsavgifter tillämpas. Det beskrivna problemet existerar idag på företaget ASWO Baltic. Problemet löstes genom att använda experimentell forskningsmetod och CRISPDM- process. Företagets historiska data om kund- och leverantörsbeställningar har används för projektet. Data har analyseras och förbereds och modellerats med hjälp av funktionsteknik, datatransformation och datanormaliseringsmetoder. Min/Max lagerhanteringsmetoden används som bas för att skapa en modell. Förbättringen som föreslås i avhandlingen är att använda maskininlärningsalgoritmer för att förutsäga Min och Max lagernivåer. Stöd för vektorregression, k-närmaste grannar, Random Forest, Artificiellt neuralt nätverk, ARIMA och Prophet maskininlärningsalgoritmer testas både för förutsägelse av min- och maxnivå. Det visade sig att de bästa resultaten för förutsägelse av Min lagernivå uppnåddes med algoritmen "k-nearest neighbors" med det genomsnittliga sMAPE-måttet på 7,0079%. De bästa förutsägelserna för Max lagernivå gjordes av Random Forest-algoritmen med den genomsnittliga sMAPE på 15,0303%. Efter hyperparameteroptimeringen förbättrades sMAPE till 6,8730 % och 14,6813 % i enlighet därmed. Simuleringen kördes för att utvärdera om den föreslagna algoritmen överträffar det nuvarande systemet. Den visade att för de artiklar som har mer än 200 beställningar minskade algoritmen antalet leverantörsbeställningar med 35,83 % och antalet restorder med 49,29 % samtidigt som det bibehöll nästan samma lageromsättning. Om samma resultat uppnås med alla produkter, förväntas företaget spara cirka 60 000 euro per år på leverantörsorderavgifter och det lägre antalet restorder skulle öka försäljningen med 24 %.

This thesis deals with optimization of inventory of spare parts and maintenance materials in NET4GAS, s.r.o. The aim of the thesis is to sort the items stored in the company and to propose specific supply methodology for the year 2013. The thesis is divided into two parts. The first, theoretical part includes theoretical knowledge of inventory management together with the methods used in the managing process, it also introduces specific inventory management of spare parts and maintenance materials. The second, practical part describes NET4GAS, s.r.o., its current system of inventory management of spare parts and maintenance materials, it identifies the local current problems in inventory management, it proposes selection of appropriate methods of inventory optimization and it demonstrates their application to real data. For the classification of stored items is selected ABC method. To draw up the supply plan is primarily used bootstrap method (also called bootstrapping), which makes estimates of future consumption of spare parts and maintenance materials. The final section summarizes all the recommendations for improving the current inventory management.