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There are several tools that assist in everyday situations and planning procedures such as daily production, cost control, quality control, inventory control, maintenance, and staffing schedules. Some of the tools used in these functions are "reports, spreadsheets, charts, and graphs," (Legenza Live Chat 8, 2011). These tools provide historical data and can assist in making current and future decisions based on that data. For example, a manager could use past data regarding labor usage to more accurately predict future labor needs. Analytical tools can also be utilized to improve production. For example, if reports indicate a deficiency in certain areas of production, a manager would be better able to pinpoint these areas and make the necessary changes. This would also be useful in most other areas of operations management such as staffing, maintenance, etc. Certain reports are especially useful in maintaining and improving inventory levels. This could have a huge impact on inventory costs and the cost of materials. I believe that analytical reports are an important factor and a useful tool for every division of an organization. Of course some departments depend on them more so than others. The accounting and finance departments depend almost complete on these types of tools. The majority of their work revolves around facts, figures, and numbers. Operations managers can also use these tools to improve the overall function of their department, but there is also a "hands on" element in their work that most other departments do not have. When using analytical tools such as graphs, charts, and spreadsheets there is always the possibility that some of the data was entered incorrectly. Even though a computer and software created the analysis a human had to put the numbers into the system. These mistakes can cost an organization thousands if not millions of dollars and unnecessary waste in human resources and materials. Most departments have a system of checks and balances in place to try to catch these mistakes before they are actually used to make any final decisions, but there is still always the possibility of errors. This isn't always the case due to the fact that there are machines and equipment that are directly linked to computers and send various types of data automatically, but generally most information is input by people. Although analytical tools and various types of software are extremely useful to an organization, it cannot replace the knowledge, leadership and decision making abilities that come with experience. A spreadsheet or graph can tell a manager how many people he/she should schedule for a particular shift based on historical data, but it cannot lead or motivate the employees. There is no substitute for human intuition. Each person in an organization has a different personality and needs to be treated according to their personality type. Various types of computer software can make suggestions as how to handle certain situations, but it takes the experience of a manager to know how to handle the different aspects and unique characteristics of each problem that may arise. I have worked in a few different types of industries and experienced many types of situation or problems, but I have never been motivated, inspired, or encouraged by a computer. Technology has advanced over time and has been able to create computer systems that can actually "learn" or think to a certain extent. "According to an article in The New York Times entitled, "Computers Learn to Listen and Some Talk Back", scientists at Microsoft have created a computer that has the ability to understand speech, recognize pediatric conditions and reason according to simple rules and to make an initial diagnosis of a childhood ailment and its seriousness. It is a very plain looking avatar and has somewhat jerky motions and speech patterns, but otherwise has the mannerisms of a human being. This computer is used as a receptionist in a pediatric physician's office. Its creator, Eric Horvitz, intended to show how people and computers may communicate in the near future," (Lohr & Markoff, 2010). According to another article in The New York Times entitled "Aiming to Learn as We Do, a Machine Teaches Itself", technology is expanding and growing at a very rapid pace. Scientists are actually building computers that can think and emulate human understanding. "The Never-Ending Language Learning System, or NELL, has made an dramatic showing so far. NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts, 390,000 to date, with an estimated efficiency rate of 87 percent. In addition, NELL learns facts that show relationships between components of two categories. For example, Peyton Manning is a football player (category). The Indianapolis Colts is a football team (category). By scanning text patterns, NELL can deduce with a high probability that Peyton Manning plays for the Indianapolis Colts even if it has never read that Peyton Manning plays for the Colts. "Plays for" is a relation, and there are 280 types of relations. The number of categories and relations has more than doubled since earlier this year, and will steadily increase. NELL is only one project in a growing field of research and investment aimed at enabling computers to better understand the meaning of language. Many of these efforts tap the Web as a rich trove of text to assemble structured ontologies, formal descriptions of concepts and relationships, to help computers mimic human understanding," (Lohr, 2010). As for the question, "Do computers think or learn from their mistakes?" I guess in a very basic sense the answer is yes, to an extent. Can they manipulate or change their environment? That's kind of a scary subject that I hate to even think about. I guess in a very basic sense, they can change their environment. Certain computers can control the temperature, lighting and level of sound in their environment, but other than something as simple as that, I wouldn't think so. But then again, I'm majoring in business management, what do I know? It seems to me that mankind is trying very hard to make itself obsolete. References Lohr, S. (2010, October 4). Aiming to Learn as We Do, a Machine Teaches Itself. The New York Times. http://www.nytimes.com/2010/10/05/science/05compute.html Lohr, S. & Markoff, J., (2010, June 24). Computer Learn to Listen, and Some Talk Back. TheNew York Times. Retrieved from http://www.nytimes.com/2010/06/25/science/25voice.html |
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