Joint Installed System Test Facilties Operational Management and Capabilities

2004 ◽  
Author(s):  
Charles Steadman ◽  
David Elkins ◽  
David Schoch ◽  
Thinh Nguyen
Keyword(s):  
Operational Management

Effective outsourcing

2020 ◽  
Author(s):  
Nadezhda Rodinova ◽  
Vladimir Ostrouhov ◽  
Vladimir Bereznyakovsky ◽  
Irina Petrova
Keyword(s):  
Management System ◽  
Business Processes ◽  
Management Decisions ◽  
Economic Mechanism ◽  
Use Of Resources ◽  
Operational Management

The tutorial is aimed at the problems of using outsourcing as a factor of the reorganization of business processes of an enterprise to achieve efficient use of resources and competitiveness of the enterprise. The article reveals the organizational and economic mechanism for making management decisions on the transfer of individual business processes of enterprises to outsourcing, which contributes to their operational management in the management system.

Un-Supervised approach to backorder prediction using deep autoencoder

2019 ◽  
Vol 12 ◽  
Author(s):  
Gunjan Saraogi ◽  
Deepa Gupta ◽  
Lavanya Sharma ◽  
Ajay Rana
Keyword(s):  
Anomaly Detection ◽  
Predictive Model ◽  
Inventory Management ◽  
Supervised Classification ◽  
Operating Characteristic ◽  
Imbalanced Data ◽  
Bullwhip Effect ◽  
Detection Methods ◽  
Good Representation ◽  
Operational Management

Background: Backorders are an accepted abnormality affecting accumulation alternation and logistics, sales, chump service, and manufacturing, which generally leads to low sales and low chump satisfaction. A predictive archetypal can analyse which articles are best acceptable to acquaintance backorders giving the alignment advice and time to adjust, thereby demography accomplishes to aerate their profit. Objective: To address the issue of predicting backorders, this paper has proposed an un-supervised approach to backorder prediction using Deep Autoencoder. Method: In this paper, artificial intelligence paradigms are researched in order to introduce a predictive model for the present unbalanced data issues, where the number of products going on backorder is rare. Result: Un-supervised anomaly detection using deep auto encoders has shown better Area under the Receiver Operating Characteristic and precision-recall curves than supervised classification techniques employed with resampling techniques for imbalanced data problems. Conclusion: We demonstrated that Un-supervised anomaly detection methods specifically deep auto-encoders can be used to learn a good representation of the data. The method can be used as predictive model for inventory management and help to reduce bullwhip effect, raise customer satisfaction as well as improve operational management in the organization. This technology is expected to create the sentient supply chain of the future – able to feel, perceive and react to situations at an extraordinarily granular level

Settings, Experimentation and Evaluation of the Simulation Models

2013 ◽  
Vol 309 ◽  
pp. 366-371 ◽  
Author(s):  
František Manlig ◽  
Radek Havlik ◽  
Alena Gottwaldova
Keyword(s):  
Simulation Model ◽  
New Technology ◽  
Simulation Models ◽  
Manufacturing Processes ◽  
Customer Requirements ◽  
Evaluation Functions ◽  
Operational Management ◽  
Minimum Number ◽  
User Friendly ◽  
Setting Parameters

This paper deals with research in computer simulation of manufacturing processes. The paper summarizes the procedures associated with developing the model, experimenting with and evaluating the model results. The key area is of experimentation with the simulation model and evaluation using indicators or multi-criteria functions. With regards to the experiment the crucial variables are the simulation model. The key ideas are to set the number of variables, depending on what a given simulation will be. For example, when introducing new technology into production, modify the type of warehouse, saving workers, thus economizing. The simulation models for the operational management uses simplified models, if possible, a minimum number of variables to obtain the result in shortest possible time. These models are more user friendly and the course will be conducted mostly in the background. An example of a criteria function is the number of parts produced or production time. Multi-criteria function has given us the opportunity to make better quality decisions. It is based on the composition of several parameters, including their weight to one end point. The type of evaluation functions, whether it is an indicator or criteria function is selected and based on customer requirements. In most cases it is recommended to use the multi-dimensional function. It gives us a more comprehensive view of the results from the model and facilitates decision-making. The result of this paper is a display of setting parameters for the experimentation on a sample model. Furthermore, the comparisons of results with a multi-criteria objective function and one-criterion indicator.

scholarly journals Insights and Perspectives in the Clinical and Operational Management of Cancer-Related Anemia

2010 ◽  
Vol 8 (Suppl_7) ◽  
pp. S-38-S-55 ◽  
Author(s):  
Jennifer M. Hinkel ◽  
Edward C. Li ◽  
Stephen L. Sherman
Keyword(s):  
Quality Of Life ◽  
Radiation Therapy ◽  
Surgical Interventions ◽  
Patients With Cancer ◽  
Cancer Related Fatigue ◽  
Operational Management ◽  
Life Issues ◽  
Economic Perspectives ◽  
Cancer Related Anemia

Management of anemia in patients with cancer presents challenges from clinical, operational, and economic perspectives. Clinically, anemia in these patients may result from treatment (chemotherapy, radiation therapy, or surgical interventions) or from the malignancy itself. Anemia not only contributes to cancer-related fatigue and other quality of life issues, but also affects prognosis. From the operational perspective, a patient with cancer who is also anemic may consume more laboratory, pharmacy, and clinical resources than other patients with cancer.

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