"Managing in the Global Economy and Outsourcing Offshore" Please respond to the following:
- From the scenario for Katrina's Candies, assuming the absence of quantitative data, determine the qualitative forecasting techniques that could be used within this scenario. Now, assume you have acquired some time series data that would enable you to make forecasts. Ascertain the quantitative technique that will provide you with the most accurate forecast.
Absence policies should cover, methods of measuring absence, setting targets for the level of absence, deciding on the level of short-term, absence that would trigger action, possibly using the Bradford Factor, the circumstances in which disciplinary action might be taken; what employees must do if they are unable, to attend work, sick-pay arrangements, provisions for the reduction and control of absence such as return-to-work interviews; other steps that can be taken to reduce , absence, such as flexible working patterns
Methods for forecasting the future and developing prediction models, and offers tips for using traditional and adaptable techniques for forecasting. The value of questioning, analyzing data trends, and general statistical analysis, as well as big data, the reliability of quantitative and qualitative sources, and personal and organizational biases. Prioritization, government transparency and accountability, and problem solving with technological advances
Data potholes can also include stale data, such as in social and economic data (which always has a short shelf life). But ongoing and continuous data updates can be expensive, and using smaller data snapshots is a common compromise. Then there is data lag -- a delay in response between a cause and effect that can range from minutes to years (e.g., birth defects triggered by a genetic disease or toxic exposure from decades before), thus complicating the accuracy of any causal analysis.
Finally, there is the challenge of data translation, where cross analysis between domains (e.g., between social and economic data) is complicated by disconnects across language, concepts, or assumptions.
Moving to model-management techniques, we find more of an emphasis on the setting and application of analytical tools. This is because many of the more subtle and unexpected elements of error in foresight involve psychological and cultural factors. These factors affect reliability in both quantitative and qualitative sources and include:
* The cost of detailed primary research (which can lead to shortcuts).
* Homogenization of distinct multiple data sources (for similar cost reasons).
* Lack of clear confidence intervals (how clean the data is).
* Mistaking correlation for causation (a common error).
* Confusing desirability and familiarity with probability (even more common).
* Forecaster's or technophile's bias, which involves a preference for change when there is none and for pattern over randomness (a professional risk).
* Political research sponsorship (influencing public opinion, attention, or awareness).
* Over-immersion in the local Zeitgeist -- social values or perceptions can shift over time (while researcher attitudes may not).
The ability to capture, process, and report data is expanding dynamically, but these are largely quantitative capabilities. Discovering the meaning and gaining insight from data trends continue to challenge us. We need to cross the bridge between quantitative and qualitative analysis (aka, creativity) in order to integrate multiple insights and come to a holistic and useful set of conclusions. One of the most intriguing and yet elusive subjects of inquiry in this arena is what are called weak signals, and we will be pursuing that quarry with enthusiasm.
First, let us consider basic methodology. To start, we take a look at the functional aspects of the extremely large data sets that have become so commonplace in global modeling. The size of these sets is driven by the exponential growth of the Internet, global communications, and surveillance technology.
Some of the models built to interpret this data flood are highly opaque, and a major concern is how many operators of large data models are actually struggling with these rather opaque analytical tools or are inattentive to the risks. One of the most useful risk-reduction tactics is a more complete understanding of the fundamentals of model construction and management. I have relied below on the excellent work of Adam Gordon.
- When deciding whether or not to outsource offshore, list the key factors aside from maximizing profits that managers should consider. Determine the key factors that you believe to be the most influential.
Off shoring knowledge and innovation activities enables many small and medium enterprises (SMEs) to successfully compete in a global economy. This offshore is largely driven by skills shortages and rising costs at home. However, while economic, political, and regulatory environments have traditionally been the main considerations when offshore, understanding culture and the cross-cultural discontinuities associated with offshore have received less attention.
The impact of offshore, begins with literature related to the growth of SMEs who offshore their knowledge-based activities. The methodology then uses interviews and focus groups to identify cross-cultural discontinuities at a case firm and links them to Hofstede's cultural dimensions. Results show five key cross-cultural discontinuities affecting work performance and discuss the implications for small businesses that offshore their knowledge related activities.
McGuigan, James R. (2014). Managerial Economics: Applications, Strategies and Tactics, 13th Edition