Dealing with ex‐Soviet republics, American companies desperately need
reliable information about the current status of production and
operations management in the former Soviet Union. Describes some aspects
of managing operations in Russia and other ex‐Soviet republics. The
emphasis is made on forecasting, product design, facilities layout and
automation, work measurement, inventory management, and quality control.
Based on this analysis, presents some recommendations for improving
operations management in the former Soviet Union.
The United States Coast Guard (USCG), a part of the U.S. Department of Homeland Security, is the nation's leading agency in maritime security, safety, and stewardship. One of the primary USCG resources is a fleet of boats (maritime vessels less than 65 feet in length) of various types that must be allocated to USCG stations nationwide. This paper describes the academic-industry collaboration between the authors and the USCG, which resulted in the development of an integer linear programming model that optimally matches supplies of various types of boats to station demands. The paper also introduces a model for the optimal sharing of scarce boat resources. In addition, we generalize our model, using value-atrisk and robust optimization ideas, to manage the risk of boat shortages. The paper reports on the USCG implementation process and discusses internal resistance issues and eventual adoption. We describe USCG modifications to the model recommendations due to practicalities not captured by our model. Finally, we present the significant improvements to USCG quantitative performance metrics that resulted from our model's recommendations. These include a considerable reduction of excess capacity and boat shortages at the stations, a decrease in the overall fleet size with a simultaneous increase in boat utilization, and overall reduction of the fleet operating cost. We also discuss in depth how our model effected these improvements.
This chapter presents the main principles, features, applications, and future development of enterprise resource planning (ERP) systems. In more than 30 years of evolution, ERP systems have undertaken a significant transformation from function‐specific applications to fully integrated process‐driven systems that operate over the Internet. ERP is identified as an extended enterprise management system that integrates traditional ERP applications (manufacturing, distribution, financials, human resources, marketing, and sales) with supply chain management (SCM), customer relationship management (CRM), and B2B e‐commerce applications. The chapter describes the main ERP principles—integration and automation—as well as ERP features—process‐driven systems, single database, company‐wide information, scalability, etc. It also focuses on the ERP groups of applications emphasizing the core applications, application enhancements, and e‐commerce solutions. The chapter details ERP benefits and drawbacks. It discusses ERP solutions for various industries and small companies and presents ERP hosting utilizing applications service providers (ASP). Finally, the chapter describes the ERP selection and implementation process, concentrating on selection criteria and implementation measurements.
Describes the results of a survey in manufacturing, distribution and service organizations concerning total quality management (TQM). These results proved that an effective TQM programme should contain consistent training of all employees; significant improvement in communication between departments; and development of the standards to measure and control the cost of quality. Based on the survey results, identifies quantitative relationships between quality improvement characteristics and different internal factors. Statistical regression models developed revealed leading predictors of a successful TQM programme.
This research was motivated by the need to identify the most effective Data Envelopment Analysis (DEA) model and associated data analytics software for measuring, comparing, and optimizing building energy efficiency. By analyzing literature sources, the authors identified several gaps in the existing DEA approaches that were resolved in this research. In particular, the authors introduced energy efficiency indices like energy consumption per square foot and per occupant as a part of DEA models’ outputs. They also utilized inverse and min-max normalized output variables to resolve the issue of undesirable outputs in the DEA models. The evaluation of these models was done by utilizing various data analytics software including Python, R, Matlab, and Excel. The authors identified that the CCR DEA model with inverse output variables provided the most reliable energy efficiency scores, and the Python’s PyDEA package produces the most consistent efficiency scores while running the CCR model.
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