Sensitivity Analysis on Inventory Classification Methods for Oilfield Equipment Industry

2011 ◽  
Author(s):  
Juan Hincapie ◽  
Jim Lee ◽  
William Emblom
Keyword(s):  
Sensitivity Analysis ◽  
Decision Support ◽  
Inventory Management ◽  
Real World ◽  
Control Problems ◽  
Classification Methods ◽  
Real World Data ◽  
Equipment Industry ◽  
Oilfield Equipment

This paper presents a case study for inventory management for an oilfield equipment company. The management encounters the problem of deciding which parts to manufacture in-house and which ones to subcontract. A decision support system (DSS) is developed which ranks component parts by integrating multi-criteria classification methods considering both quantitative (e.g., cost and demand) and qualitative (e.g., importance) factors. The focus on this research is to perform a sensitivity analysis on weight assignment for each criterion. This information is important in applications of inventory management since industries may not be able to manufacture all the necessary parts on time. Real world data from an oilfield equipment industry are used where inventory control problems have arisen because the company does not have the capacity to manufacture all the required parts to satisfy customer orders.

Imbalanced Data Classification Based on Clustering

2013 ◽  
Vol 443 ◽  
pp. 741-745
Author(s):  
Hu Li ◽  
Peng Zou ◽  
Wei Hong Han ◽  
Rong Ze Xia
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Data Classification ◽  
Comprehensive Analysis ◽  
Classification Method ◽  
Classification Methods ◽  
Real World Data ◽  
Minority Class ◽  
Imbalanced Data Classification ◽  
Traditional Classification

Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.

scholarly journals Development of an Innovative Real-World Evidence Registry for the Herpes Simplex Virus: Case Study

10.2196/16933 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e16933 ◽  
Author(s):  
Michelle Helena van Velthoven ◽  
Ching Lam ◽  
Caroline de Cock ◽  
Terese Stenfors ◽  
Hassan Chaudhury ◽  
...  
Keyword(s):  
Herpes Simplex Virus ◽  
Herpes Simplex ◽  
Real World ◽  
Physical Symptoms ◽  
Use Cases ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence ◽  
Simplex Virus

Background Infection with the herpes simplex virus (HSV) is common but not well understood. Furthermore, there remains a social stigma surrounding HSV that can have psychosocial implications for those infected. Despite many patients infected with HSV experiencing mild-to-severe physical symptoms, only one subeffective treatment is available. A registry collecting real-world data reported by individuals potentially infected with HSV could help patients to better understand and manage their condition. Objective This study aimed to report on the development of a registry to collect real-world data reported by people who might be infected with HSV. Methods A case study design was selected as it provides a systematic and in-depth approach to investigating the planning phase of the registry. The case study followed seven stages: plan, design, prepare, collect, analyze, create, and share. We carried out semistructured interviews with experts, which were thematically analyzed and used to build use cases for the proposed registry. These use cases will be used to generate detailed models of how a real-world evidence registry might be perceived and used by different users. Results The following key themes were identified in the interviews: (1) stigma and anonymity, (2) selection bias, (3) understanding treatment and outcome gaps, (4) lifestyle factors, (5) individualized versus population-level data, and (6) severe complications of HSV. We developed use cases for different types of users of the registry, including individuals with HSV, members of the public, researchers, and clinicians. Conclusions This case study revealed key considerations and insights for the development of an appropriate registry to collect real-world data reported by people who might be infected with HSV. Further development and testing of the registry with different users is required. The registry must also be evaluated for the feasibility and effectiveness of collecting data to support symptom management. This registry has the potential to contribute to the development of vaccines and treatments and provide insights into the impact of HSV on other conditions.

Decision Support Tools for Managing Unpredictability in the Supply Chain

2000 ◽  
Author(s):  
Carol Vesier
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Inventory Management ◽  
Production Scheduling ◽  
Capital Investment ◽  
Investment Strategy ◽  
Decision Support Tools ◽  
Business Outcomes ◽  
Support Tools

Abstract Effectively managing unpredictability requires decision support tools that can predict the financial and business outcomes of various supply chain strategies. This paper will discuss the role of these decision support tools and their characteristics as well as review a case study. In the case study, decision support tools facilitated development of strategies that increased after tax profit by $140 Million. These strategies included: • Reliability improvement strategy: Identifying the reliability improvements that offered the biggest profitability impact. • Supply chain strategy: Defining inventory management and production scheduling rules that ensured order shipment within two days. • Capital investment strategy: Defining when new capacity should come on line as well as the minimum capital investment.

Inventory Management, a Decision Support Framework to Improve Operational Performance

2012 ◽  
pp. 581-600
Author(s):  
Jan van den Berg ◽  
Guido van Heck ◽  
Mohsen Davarynejad ◽  
Ron van Duin
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Inventory Management ◽  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Performance Improvements ◽  
Decision Support Framework ◽  
Comparable Performance ◽  
Potential Benefits

Enterprise Resource Planning systems have been introduced to support the efficient and effective execution of business processes. In practice, this may not fully succeed. This also holds in particular for inventory management (IM), which forms a part of supply chain management. Within this research, by analyzing the IM business process theoretically, eleven potential benefits are indicated. Next, by using a Business Intelligence approach, key performance indicators (KPIs) are selected to measure the performance of IM sub-processes. Integration of these approaches yields an IM performance decision support framework that can be used to obtain a generic, coherent picture of the fundamental IM processes in an organization. In addition, by tracking and analyzing KPI measurements, adequate decisions can be prepared towards the improvement of the operational IM performance. The proposed framework is validated using experts’ opinions and a comparative case study. The experts’ comments yielded a list of top-10 KPIs, based on the measurements of which a set of quick wins can be determined. The case study results show that some of the identified potential benefits are also observed in practice. Future research may reveal that comparable performance improvements are possible in other IM environments (and even in other supply chain domains) based on similar decision support frameworks.

Security of Railway Infrastructures

2012 ◽  
pp. 355-379
Author(s):  
A. Di Febbraro ◽  
F. Papa ◽  
N. Sacco
Keyword(s):  
Risk Analysis ◽  
Real World ◽  
Analysis Tool ◽  
Security Risk ◽  
Input Output ◽  
Real World Data ◽  
Security Problem ◽  
Output Characteristics ◽  
General Architecture

The chapter is organized as follows: In section 1, the basic definitions of the security risk analysis and the characteristics of the railway security problem are introduced, and a bibliography review is reported. Then, in section 2, the general architecture for designing a security risk analysis tool is presented, focusing on the relevant specifications, and on the input/output characteristics. Therefore, in section 3, with the aim of pointing out the characteristics of the presented architecture, an explicative case study is defined based on real world data coming from Italian railways. Finally, some conclusions and remarks are discussed in chapter 4.

scholarly journals Developing Markov Models From Real-World Data: A Case Study of Heart Failure Modeling Using Administrative Data

2020 ◽  
Vol 23 (6) ◽  
pp. 743-750
Author(s):  
Praveen Thokala ◽  
Peter Dodd ◽  
Hassan Baalbaki ◽  
Alan Brennan ◽  
Simon Dixon ◽  
...  
Keyword(s):  
Heart Failure ◽  
Administrative Data ◽  
Real World ◽  
Markov Models ◽  
Real World Data ◽  
World Data ◽  
Failure Modeling

Optimizing Bundling Policy of Single-Period Products

2014 ◽  
Vol 5 (4) ◽  
pp. 1-25
Author(s):  
Gregory Gurevich ◽  
Yuval Cohen ◽  
Baruch Keren
Keyword(s):  
Sensitivity Analysis ◽  
Real World ◽  
The Other ◽  
Order Quantity ◽  
End User ◽  
Demand Distribution ◽  
Common Strategy ◽  
User Demand ◽  
Coordination Tool

Combining different product types into standard discount bundles is a common strategy used by producers and wholesalers to increase overall sales profitability. While markets consist of many producers and retailers, a deal is typically made between a single producer and a single retailer. This paper deals with a producer who sells items separately, and considers setting and selling standard discount bundles. The purchased wholesale bundles are unpacked by the retailer and the items are sold to the end-users one by one. Thus, the end-user demand distribution is unchanged, but the retailer's order quantity grows with the magnitude of the discount. The paper explores the effect of bundle price and content on the profits of both the producer/wholesaler and the retailer, and derives a general objective function composed of a linear combination of these profits. Moreover, the paper establishes the conditions for bundling profitability and presents a way to optimize the profit of each party (producer, or retailer) without reducing the other party's profit. A real-world case study and sensitivity analysis demonstrate the solution's applicability. The results indicate that bundling can be a coordination tool for increasing expected profit for both the producer and the retailer.

scholarly journals Empty Container Management at Ports Considering Pollution, Repair Options, and Street-Turns

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Norberto Sáinz Bernat ◽  
Frederik Schulte ◽  
Stefan Voß ◽  
Jürgen Böse
Keyword(s):  
Inventory Management ◽  
Real World ◽  
Ecological Impact ◽  
Ecological Problem ◽  
Container Transportation ◽  
Periodic Review ◽  
Real World Data ◽  
Empty Container ◽  
Container Management ◽  
Empty Containers

International trade imbalances make the management of empty containers within shipping networks an important economic and ecological problem. While import-dominated ports accumulate large amounts of empty containers, export-dominated ports need them as transport resources, requiring a repositioning transportation of empty containers on the sea and land side. Acknowledging the importance of the problem, plenty of respective literature has appeared. Since periodic review inventory management systems allow to model the inherent stochasticity of empty container transportation, they have emerged as a major solution approach in the domain. Nevertheless, existing approaches often omit crucial economic and ecological real world conditions determining the success of empty container management. Pollution, repair options, and street-turns are important aspects in this context. In this work, we present new stochastic review policies incorporating a realistic allocation scheme for empty container emissions, realistic maintenance, and repair options as well as street-turns. We analyze the optimality of the proposed polices and evaluate them in a simulation model with metaheuristic parameter search based on extensive real-world data from a major global shipping company operating in Latin America. Results provide insights for academics and practitioners about the economic and ecological impact of the distinct empty container management polices within a shipping network.

scholarly journals A Novel Prediction Process of the Remaining Useful Life of Electric Vehicle Battery Using Real-World Data

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2174
Author(s):  
Xu Wang ◽  
Jian Li ◽  
Ben-Chang Shia ◽  
Yi-Wei Kao ◽  
Chieh-Wen Ho ◽  
...  
Keyword(s):  
Inventory Management ◽  
Real World ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Real World Data ◽  
World Data ◽  
The Real ◽  
Battery Capacity ◽  
Useful Life ◽  
Online Process

In modern society, environmental sustainability is always a top priority, and thus electric vehicles (EVs) equipped with lithium-ion batteries are becoming more and more popular. As a key component of EVs, the remaining useful life of battery directly affects the demand of the EV supply chain. Accurate prediction of the remaining useful life (RUL) benefits not only EV users but also the battery inventory management. There are many existing methods to predict RUL based on state of health (SOH), but few of them are suitable for real-world data. There are several difficulties: (1) battery capacity is not easy to obtain in the real world; (2) most of these methods use the individual data for each battery, and the computing processes are difficult to perform in the cloud; (3) there is a lack of approaches for real-time SOH estimating and RUL predicting. This paper adopts several statistical methods to perform the prediction and compars the results of different models on experimental data (NASA dataset). Then, real-world data were implemented for an online process of RUL prediction. The main finding of this research is that the required CPU time was short enough to meet the daily usage after the real-world data was implemented for an online process of RUL prediction. The feasibility and precision of the prediction model can help to support the frequency control in power systems.

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