Outsourcing for Financial Success? an Exploratory Study
Bibliographic record
Abstract
CURRENT STATE OF OUTSOURCING Global competition dictates that manufacturing firms deliver quality goods to customers on demand and at lower costs. One of the ways to be competitive is through innovation in products, processes, and services. To increase productivity and lower costs, companies are using leading manufacturing approaches (such as continuous improvement program, just-in-time inventory system) and progressive human resource (HR) practices. Another way to remain competitive is through the business processes. Outsourcing is defined as purchasing ongoing services from an outside company that a company currently provides, or most organizations normally provide, for themselves (Linder, 2004). These activities may range from manufactured parts to services, such as payroll, human resources, accounting, etc. Outsourcing is not limited to domestic suppliers, but it also includes foreign suppliers (off-shoring). In this paper the term outsourcing is used to encompass both domestic as well as off-shoring activities and is consistent with the framework of activities provided by the GAO study (2004). Improvements in global telecommunications technology, infrastructure growth in developing countries, and decreasing data transmission costs have accelerated the pace of activities (The GAO Study, 2004). As a result, U.S. companies outsource not only manufacturing jobs but also high-paying professional jobs in the service sector in the areas of high-technology, office support, computers, business management, and architecture (Mangan, 2004). Some 3.3 million U.S. jobs, accounting for $136 billion in wages, will be outsourced overseas or off-shored by the year 2015 (Mangan, 2004). Outsourcing allows firms to offer products or services to their customers faster, cheaper, and better. Improved productivity, achieved through outsourcing, is assumed to contribute to the financial strength of a firm and make it globally competitive. Outsourcing has been used by many organizations to meet short-term objectives like downsizing and reducing costs. For example, Delta Airlines' activities resulted in $25 million savings in 2003 (Weidenbaum, 2005). Others have taken a long-term approach by non-essential work to free up resources and time to focus on areas of core competencies and competitive advantage (Chamberland,2003). In a recent survey of procurement executives (jointly done by CAPS Research and A.T. Kearney Inc.) more than 80 percent of the respondents indicated that cost reduction and need to focus on core business were the main drivers to (Monczka, Markham, Carter, Blascovich, and Slaight, 2005). The underlying assumption being that will make a firm financially strong. One industry that has been actively involved in activities is the automotive parts manufacturing. The industry uses different production technologies and manufactures a variety of products ranging from plastic molded parts for automobiles to components for airplanes; thus supporting different industries. The automotive parts manufacturing industry is heavily integrated between the U.S. and Canada. Today, every vehicle assembled in North America contains nearly $1,250 worth of parts manufactured in Canada. There is a high concentration of these firms in the state of Michigan and the Province of Ontario, Canada. The industry customers (the automakers) are keenly aware of their suppliers' potential in reducing costs. Foreign automotive parts manufacturers, particularly the ones in China, India, and Mexico are gaining competitive advantage over their North American counterparts by producing better quality products at lower prices. Hence, the automakers are demanding from North American suppliers prices that are in line with foreign suppliers' quotes. This has put a tremendous financial stress on the industry. Compared to others, the automotive industry provides well paying jobs to the U. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".