All about Business and management

November 23, 2008

Microsoft Yahoo merger good for tech sector?

Filed under: My Opinion — Jagdish Hiray @ 7:39 pm
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            There is lot of buzz around Microsoft and Yahoo merger. Microsoft has clearly denied possibility of taking over Yahoo Inc. Any one who is using Microsoft products and watching Microsoft’s business will agree that Yahoo will loose its characteristics if Microsoft takes its business over.


            There is couple of things to consider about Microsoft. First of all Microsoft is still struggling with its online search engine Microsoft seriously lacks expertise in search area.  Second point to consider, possibility of success merger and keeping Yahoo in business after merger. Looking at past, Microsoft failed to keep hotmail brand name in emai business and could not give head to head competition with gmail. Chances that Microsoft-Yahoo merger a success is close to impossible. Most software companies like Microsoft lack proven management skills to make such acquisition success. Next, Microsoft- Yahoo merger, in fact will benefit Goggle as possibility of retaining Yahoo employees is going to be big challenge. Which in turn will affect Microsoft negatively.


            Therefore, there are serious practical reasons why Microsoft denying Yahoo acquit ion. It is not only matter of existence of Yahoo but also posses serious concerns to Microsoft existence.










November 22, 2008

City of Toledo and $700 billion bailout plan

Filed under: My Opinion — Jagdish Hiray @ 9:02 am
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This is story about the city of Toledo in state of Ohio. In around 1980s, city of Toledo started offering incentives and concessions to attract new businesses as well as to existing business to stay around area. These incentives were ranging from funding these businesses to exceptional tax breaks. In couple of years city has attracted many known industries such as General Motors and Chrysler. When state of Ohio authorized an Enterprise Zone program to create industrial base, city of Toledo was ahead to take advantage. The main goal was to create more new jobs and prosper.

However, in next couple of decades, study found out that new jobs were not created as expected, new jobs were moved to different locations, jobs were outsourced and companies failed to keep their promises. Companies took advantage of tax breaks but failed to create new employment. The government spent over $280 million dollars to bring and keep new Chrysler plant in Toledo; in reality it caused more job losses than creating new jobs.

Study found out that there was no monitoring mechanism setup to monitor how these companies were using incentives and whether they are being appropriately utilized. Companies were not accounted for their promises. Tax breaks were gone to hundreds of companies that closed or reduced their facilities. There was no accounting for how public money was spent.


Today we have similar situation. In today’s economical crisis, we as people of this democratic country represent the city of Toledo. We have public bail out money $700 billion dollars approved by congress on behave of tax payers. Auto makers and various other well know companies of this so called ‘Capitalist’ system are coming forward to ask for help to keep their businesses running and hence to keep jobs for thousands of workers. Toledo (here we people) has second and probably last chance. If we want to help these industries to keep our jobs and keeps economy flowing, we have to get answer for some basics questions.  Are we going to have monitoring mechanism this time? Who is going to monitor this mechanism? Who is going to review their business plan (may be industry experts)? What if they do not use people’s money as promised, what will be penalty? Who is going to take personally responsibility? …


Toledo has money to fund but still fighting for survival … dark side of ‘Capitalism’.



November 3, 2008

Leadership and fellow-ship

Filed under: Business management — Jagdish Hiray @ 9:42 pm
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            All-important social accomplishment requires complex group effort and, therefore, leadership and follower ship. Leader-follower relationship is two way, leader as well as followers have great capacity to influence the relationship. Just as a leader is accountable for the actions and performance of followers, so followers are accountable for their leaders. Followers support leaders when necessary and help them correct their actions, just as leaders must support followers and help them to correct their actions. This is partnership and both sides must be proactive. Organizations are successful or not partly on the basis of how well their leaders lead, but also in great part on the basis of how well their followers follow. Courageous followers help leaders stay on track and manage their decision-making processes in the right direction. Responsible and effective followers have a critical role in maintaining the desired partnering dynamics. In his book (The Courageous Follower, 2003) Ira Chaleff points out that the old paradigm of the leader/follower is based on power. The leader has traditionally had the power to reward and promote, this has led to a relationship in which the follower avoids jeopardizing their chances of obtaining these rewards. Hence, the follower tends to do what the leader wants and, just as important, not offend or create a negative impression of them. A relationship based on this kind of power does not serve the organization, it shuts down the open flow of communication and candor a leader needs to order to optimize their effectiveness. Chaleff sees a very different kind of relationship between leader and follower.  He suggests a relationship where the leader and follower have equal power but different roles that orbit around support and fulfillment of the organizations’ purpose.   When both the leader and follower are focused on the common purpose a new relationship between them arises. This new relationship is candid, respectful, supportive and challenging.  It is a relationship that honors open communication, honesty and trust from both parties. According to Chaleff, there are three things we need to understand in order to fully assume responsibility as followers: understand out power, appreciate the value of the leader and work towards minimizing the pitfalls of power.

When we think about leadership, we tend to focus almost entirely on the leader. Yet without followers, there is no leader. Leadership is participatory: leaders and followers exist in a mutually beneficial relationship where each adds to the effectiveness of the other. Key to this process is listening, because leadership is as much about listening as it is about talking, or perhaps more so. From the beginning, a leader must be informed by the followers’ values, beliefs, and aspirations, the followers’ identity. The commitment gap people frequently experience, the difference between what the leader desires and what the followers actually do, can often be traced back to not aligning the elements of leaders’ and followers’ identities—who they think they are—to find common ground on which to function and grow. It is the quality of the relationship of leaders and followers, all the way up and down the organization chart, that makes or breaks organizations.

Leadership is one of the most widely talked about subject and is most elusive and puzzling. Leadership is a complex phenomenon involving the leader, the followers, and the situation. In general there are individuals who exhibits leadership qualities and there are people who do not.  People who are effective in the leader role have the vision to set goals and strategies, the interpersonal skills to achieve consensus, the verbal capacity to communicate enthusiasm to large and diverse groups of individuals, the organization talent to coordinate disparate efforts. Some people posses inbuilt personality traits like self-determinant, honest, strong desire to achieve goal, devotion and sacrifice. However, there are exceptions, some theorist believes each individual has built-in qualities to make difference and hence influence people around them. Their leadership qualities can be seen by their actions, reactions on situations they manage and support from their followers. However, some people do exhibits their leadership qualities under some circumstances. For example, Mahatma Gandhi was an ordinary person, he faced same realities youth of his age faced at that time, but he evolved as a charismatic leader who stood against British rule to give independence to his country. His thinking, initiatives, selfishness, care about people and honest actions made him a leader.

There are personalities, which are of ‘leader type’ (effective leaders), and there is not ‘leader type’ (poor leaders). Effective leaders are good communicators, especially in providing vision and purposes that are consistent with follower goals, values, dreams and myths. Effective leaders are socio-centric, physically strong, humanistic, approachable, visible, patient, decisive, and open-minded. They maintain high standards of dignity and integrity. Good leaders create a sense of trustworthiness as perceived by their followers.  They do this by being consistent, honest, and dependable. They are good role models, coaches, mentors and teachers. Effective leaders establish a strong participative management culture. They are technically competent but possess important interpersonal skills such as assertion, empathy and negotiation ability. Good leaders show “value focused leadership.” They have a set of purposes and ethics that guide their behavior and decision-making. Control theory suggests that effective leadership is goal-directed with synergy created by the alignment of group members on these goals and priorities. “Value focused leadership” requires that leaders help create “value” for both workers and customers.

Non-effective leaders fail to give clear direction, mission and purpose to the followers or organization. They fail to create cohesion and commitment by neglecting to give support and encouragement to followers. They neglect to energize followers and obtain their dedication and loyalty by providing consistent reward and recognition.  They fail to listen to followers and empower them to take a full, participative role in all-important decisions. When followers do offer suggestions these suggestions are ignored.  Poor leaders tend to tolerate incompetence, a fact that de-motivates followers who are trying hard to get work done.  Poor leaders fail to develop and support a “culture of quality.” Poorly led organizations have a scarcity of clear, consistent goals and when they do have goals they do not have benchmarks or outcomes measures with which to evaluate them. In poorly led organizations there is a paucity of effective communication and true consultation in the organization. Leaders usually find someone to take the blame for a negative event. They spend a great deal of time protecting themselves and their positions and neglect the overall welfare of the organization. The poorly led organization shows shoddy ethics. In the poorly led organization there is a great resistance to change, innovation, and new ways to integrate the various parts of the organization.






August 7, 2008

Financial intermediaries

Filed under: Business management — Jagdish Hiray @ 9:56 pm
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With advances in computer technology, one can transfer money instantly, anywhere in the word, you can trade your funds across major stock exchanges online, you can use your credit card across the globe and so on. Lending and borrowing of money is made simple by financial institutions called financial intermediaries. Financial intermediaries such commercial banks, credit unions and brokerage funds carryout these transactions on your behalf. A financial intermediary is a financial institution that borrows from savers and lend to individuals or firms that need resources for investment. The investments made by financial intermediaries can be in loan and/or securities. Basic role of financial intermediaries is transforming financial assets that are less desirable for a large part of the public into other financial asset, which is preferred more by the public. This transformation involves at least four economical functions: providing maturity intermediation, risk reduction via diversifications, reducing the costs of contracting and information processing and providing a payment mechanism.


Without financial intermediation we must not have seen revolution in financial services in past couple of decades. Financial intermediation is responsible for creation of institutional investors in financial market. Modern world would not have been so modern without financial intermediaries. Financial intermediation has won savers confidence by protecting their asset while providing efficient services to help manage their asset. On contrary, with pool of household savings from savers, they emerged as one large lender who can lend money to businesses and various other borrowers. Financial intermediaries are vital part of our economic system and they help to maintain constant flow of money in economy.


If there were no intermediaries, individual savers would have to directly purchase the securities of borrowers. There would have been incompatibility of the maturity needs of lenders and borrowers since most savers want to lend funds at short maturity, while borrowers want to borrow at longer maturities. It would have been difficult to match small amounts of individual savings to the larger loan amounts desired by borrowers. This would have cause borrowing more difficult and more tedious. Financial intermediaries perform important function of maturity intermediation to make investment from savers and money borrowing for borrowers seamless. Maturity intermediation involves a financial intermediary issuing liabilities against it that have maturity different from the assets it acquires with the fund raised. An example is a commercial bank that issues certificate of deposit and invests in assets with a longer maturity than those liabilities. Maturity intermediation offers more choice concerning maturity for their investments to investors and reduces cost of long term borrowing for borrowers. Financial intermediaries issue their own debt claims to the saver in forms more attractive to savers, and in turn, lend to borrowers on terms satisfactory to the borrowers.


Financial intermediaries bears risk on behalf of investors by investigating their savings across various sectors of business. They transform risk-by-risk spreading and risk pooling; they can spread risk across a range of institution. In turn institutions can pool risk by spreading investment across firms and various projects. Diversification allows a financial intermediary to allocate assets and bear risk more efficiently. Financial intermediaries do risk screening, risk monitoring and risk evaluation; it is more efficient for institution to screen investment opportunity on behalf of individuals than for all individuals to screen the risk. It helps individual saver to save time and money and offers low risk investment opportunity. One of the common example of this function is; a dollar deposited in a checking or savings account, it is not redeemed at less than a dollar but in turn one get paid interest on it over period of time. Therefore without financial intermediaries it would really have been difficult for individual investor to screen prospect borrower or investment opportunity, which would have discouraged individual savers from lending money and would have affected economical developments.


Financial intermediaries provide convenient and safe way to store finds and creates standardized forms of securities. It also facilitates easy exchange of funds. Due to high volume it is able to bear transaction and information search cost on behave of savers. Therefore, individual saver enjoys financial services that enable them to deposit and withdraw funds without negotiation whereas borrower avoids having to deal with individual investors. Since it has information available for both lenders and borrowers, it minimizes information cost for analyzing their data. Without financial intermediaries lenders and borrowers would have to pay higher transactional and information costs.


            Modern world would not have been so efficient, aggresive and progressive without financial intermediation.


August 1, 2008

Functions of investment banker

Filed under: Business management — Jagdish Hiray @ 7:04 pm
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When corporation sells new securities to raise funds, the offering is called a primary issue. The agent responsible for finding buyers for these securities is called the investment banker. The investment banker purchase primary issue from corporation and arranges immediate resell of these securities to the investors. Merrill Lynch & Co., Goldman Sachs are some examples of well-known investment banking firms. Broadly investment bankers (investment banking firms) perform three functions: Investigation, Analysis and Research (Origination), Underwriting (Public Cash offerings) and Distribution. Most of time a single investor banker performs all functions, however some investment bankers are specialized in certain functional areas only.


Investigation, Analysis and Research (Origination):            Origination includes the subsidiary operations of discovery, investigation, and negotiation. Discovery is the finding of a prospective issue of securities; investigation is the testing of the investment credit of the prospective security issuer, and the intrinsic soundness of the issue; negotiation is the determination of the amount, the price, and the terms of the proposed issue. Investigation usually involves an analysis of the financial history of the corporation by accountants, investigation of legal factors, a survey of its physical property by engineers, and in-depth review of operations. The purpose of investigation and analysis is to determine whether a proposed issue has sufficient merit to be offered to investment community. In other words, function of investment banking is careful analysis of the soundness and reliability of the corporation whose securities are seeking the investment market. The task of investigation and analyzing the numerous factors, which govern the value of investment securities, varies considerably with the different types of issuing bodies.


Underwriting (Public Cash offerings): When a corporation wishes to issue new securities and sell them to the public, it makes an arrangement with an investment banker whereby the investment banker agrees to purchase the entire issue at a set price, known as underwriting. Underwriting also refers to the guarantee by the investment banker that the issuer will receive a certain minimum amount of cash for their new securities. The investment banker buys a new security issue, pays the issuer, and markets the securities. The underwriter’s compensation is the difference between the price at which the securities sold to the public, and the price paid to the company for the securities. Underwriting can be done either through negotiations between underwriter and the issuing company (called negotiated underwriting) or by competitive bidding. A negotiated underwriting is a negotiated agreed arrangement between the issuing firm and its investment banker. Most large corporations work with investment bankers with whom they have long-term relationship. In competitive bidding, the firm awards offering to investment banker that bid the highest price.


In certain cases, for large or risky issues a number of investment bankers get together as a group, they are referred to as syndicate. A syndicate is a temporary association of investment bankers brought together for the purpose of selling new securities. One investment banker is selected to manage the syndicate called the originating house, which does underwriting of the major amount of the issue. There are two types of underwriting syndicates, divided and undivided. In a divided syndicate, each member group has liability of selling a portion of offerings assigned to them. However, in undivided syndicate, each member group is liable for unsold securities up to the amount of its percentage participation irrespective of the number of securities that group has sold.


Distribution: Another function of investment banker it to market the security issues. The investment banker acts as a specialist to distribute securities efficiently for the corporation. It can be very expensive and ineffective for a corporation to sell an issue by establishing marking and selling organization by its own. Investment banker has established marketing and sales network to distribute securities. For a reputed invest banker, with its past history of selecting good companies and pricing securities builds a broad client base over time, and further increases the efficiency with which securities can be sold.


            Invest banker offers security to both corporation issuing securities and investors buying securities. For corporations investment banker offers definite price guaranty on a certain date for securities to offer. The corporation runs no risk of the uncertainties of the market and do not have to spend on resources with which it is not equipped with.

To the investor, the responsible investment banker offers protection against unsafe securities. The offering of a few unsound issues can caused serious loss to its reputation, and hence loss of business. Therefore, investment banker play very important role in issuing new security offerings.


June 29, 2008

Oil crisis and we!!

Filed under: My Opinion — Jagdish Hiray @ 12:43 am
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                     In last few months oil price at pump suddenly gone up and we do not see any signs of coming it down. It made life essentials expensive forcing people to make choice between food and commute to work. Every smallest news affecting oil supply was considered to hike crude oil prices but major drop in oil consumption in past 17 years by people, increase in price of oil in major oil consuming countries to cause decrease in oil consumption, and decision to increase production of oil by major oil producers, did not caused a penny of decrease in the price of crude oil. Is it not mysterious? No body has explanation nor wants to explain people why it is so? Regulators once again failed to avoid ‘oil boom’ created by so called ‘capitalism’. Once again common has to pay price for it.


This reminds me people on ‘AXIOM’ space cruise from WALL-E movie. People’s life depicted on “AXIOM” space cruise is very much synonymous with our lives today; our lives are no more independent. This movie not only gives strange glimpse about our future but uncovers truth of our current lives.


How? Take couple of seconds to think, compare our lives with those on space cruise shown in the movie. Our lives are totally dependent on oil driven transportation, we are used to large luxurious cars sold by motor companies as per their business profitability model, and we are hypnotized by easily available loans and paralyzed by accumulating debt. For generations we are traditionalized by junk food chain. We are trapped in the web woven by corporate world under name of ‘capitalism’.


Our social position is not at all different than people on space cruise shown in the movie. Those people lost their existence, lost their thinking and could not think of anything else other than their own comfort. Are our lives different than those people from space cruise?






March 30, 2008

Measures of Central tendency and Variability

Filed under: Business management — Jagdish Hiray @ 9:10 pm
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            Central tendency is a statistical measure that identifies a single score as representative of an entire distribution of scores. The goal of central tendency is to find the single score that is most typical or most representative of the entire distribution. Unfortunately, there is no single, standard procedure for determining central tendency. The problem is that there is no single measure that will always produce a central, representative value in every situation. There are three main measures of central tendency: the arithmetical mean, the median and the mode. 

            The mean of a set of scores (abbreviated M) is the most common and useful measure of central tendency.  The mean is the sum of the scores divided by the total number of scores. The mean is commonly known as the arithmetic average. The mean can only be used for variables at the interval or ratio levels of measurement. The mean of [2 6 2 10] is (2 + 6 + 2 + 10)/4 = 20/4 = 5. One can think of the mean as the balance point of a distribution (the center of gravity). It balances the distances of observations to the mean. Another measure of central tendency is the median, which is defined as the middle value when the numbers are arranged in increasing or decreasing order. The median is the score that divides the distribution of scores exactly in half. The median is also the 50th percentile. The median can be used for variables at the ordinal, interval or ratio levels of measurement. If for example, daily expenses are $50, $100, $150, $350, $350 the middle value is $150, and therefore, $150 is the median. For odd number of count the median is middle value. If there is an even number of items in a set, the median is the average of the two middle values. For example, if we had four values—$50, $100, $150, $350—the median would be the average of the two middle values, $100 and $150; thus, 125 is the median in that case. The median may sometimes be a better indicator of central tendency than the mean, especially when there are extreme values. Another indicator of central tendency is the mode, or the value that occurs most often in a set of numbers. In other words, the mode is the score or category of scores in a frequency distribution that has the greatest frequency. In the set of expenses mentioned above, the mode would be $350 because it appears twice and the other values appear only once. The mode can be used for variables at any level of measurement (nominal, ordinal, interval or ratio). Sometimes a distribution has more than one mode. Such a distribution is called multimodal. A distribution with two modes is called bimodal. Note that the modes do not have to have the same frequencies. The tallest peak is called the major mode; other peaks are called minor modes. Some distributions do not have modes. A rectangular distribution has no mode. Some distributions have many peaks and valleys.

              Variability provides a quantitative measure of the degree to which scores in a distribution are spread out. The greater the difference between scores, the more spread out the distribution is.  The more tightly the scores group together, the less variability there is in the distribution. Variability is the essence of statistics. The most frequently used methods of measurement of this variance are: range, deviation and variance, interquartile range and standard deviation. The range is simply the difference between the highest score and the lowest score in a distribution plus one. This statistic can be calculated for measurements that are on an interval scale or above. In dataset with 10 numbers {99,45,23,67,45,91,82,78,62,51}, the highest number is 99 and the lowest number is 23, so 99−23=76; the range is 76. The interquartile range (IQR) is a range that contains the middle 50% of the scores in a distribution. It is computed as follows: IQR=75th percentile−25th percentile. A related measure of variability is called the semi-interquartile range. The semi-interquartile range is defined simply as the interquartile range divided by 2. Variance can be defined as a measure of how close the scores in the distribution are to the middle of the distribution. Using the mean as the measure of the middle of the distribution, the variance is defined as the average squared difference of the scores from the mean. When the scores are spread out or heterogeneous, the measure of variability should be large. When the scores are homogeneous the variability should be smaller. Another measure of variability is the standard deviation. The standard deviation is simply the square root of the variance. The standard deviation is an especially useful measure of variability when the distribution is normal or approximately normal (see Probability) because the proportion of the distribution within a given number of standard deviations from the mean can be calculated. Therefore standard deviation is the average distance from the mean. So the mean is the representative value, and the standard deviation is the representative distance of any one point in the distribution from the mean.

              While the measures of central tendency convey information about the commonalties of measured properties, the measures of variability quantify the degree to which they differ. If not all values of data are the same, they differ and variability exists. The measures of central tendency should be complemented by measures of variability for the same reason.

March 16, 2008

The Research process: an independent and dependent variables

Filed under: Business management — Jagdish Hiray @ 9:26 pm
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        Research is the foundation of any science, including both hard sciences such as physics, chemistry and the social sciences such as psychology, management and education. The steps and process involved in the research can vary depending on the type of research being done and the hypothesis being tested. Research methods such as Naturalistic observation and surveys are often less structured, where as experimental methods are more structured. Depending upon what is observed or experienced, new theories are developed. There are aspects of a theory or aspects of a study that can change or vary as part of interaction within the theory, defined as variables. Variables are anything that can change of effect the results of a study. In an experimental method, the experiment is conducted by changing the value of one variable and measuring the changes in another variable while holding or assuming surroundings constant. There is no limit to the number of variables that can be measured, although the more variables, the more complex the study and the more complex the statistical analysis.

        Every experiment has at least two types of variables: an independent and dependent. An independent variable is the variable that the researchers systematically manipulate in the experiment. An independent variable is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. This might be a variable that you control, like a treatment, or a variable not under your control, like an exposure. It also might represent a demographic factor like age or gender. While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification. In a research to demonstrate the increasing alcohol consumption during pregnancy actually causes a reduction in birth weight, researchers randomly assigned 50 pregnant rat either to an experimental group or to a control group. The 25 rats in the experimental group had bottles filled in with mixture of pure water and alcohol, 25 rats in the control group had bottles with water. Other than this, researchers treated all rats as much alike as possible. Aim was to have the two groups differ systematically along only one variable: alcohol versus pure water. This is independent variable; idea was to manipulate this variable independently of other factors such as diet that might affect pregnancy. In this example, birth weight is the dependent variable that is it represents the outcome that we measure, an outcome that is dependent on the manipulation of the independent variable. With experiments, then, researchers systematically manipulate the independent variable to determine if it causes a difference in the dependent variable. There are three ways to manipulate independent variables: presence or absence technique, amount technique and type technique. In presence or absence technique, the independent variable can be manipulated can be manipulated by presenting a condition or treatment to one group of individuals and withholding the condition or treatment from another group of individuals. In amount technique, the independent variable can be manipulated by varying the amount of a condition or variable such as the amount of a drug which is given to children within a learning disorder. In type technique, the independent variable is to vary the type of the condition or treatment administered.  

        In an experiment, a dependent variable is the factor which is observed and measured to determine the effect of the independent variable, that is, that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. The dependent variable is the participant’s response. The dependent variable is the outcome of experiment. In an experiment, it may be what was caused of what changed as a result of the study, in a comparison of groups; it is what they differ on. In research study on rats, described in previous paragraph, the experimental group is exposed to alcohol, and the control group is not. If we observe that the rat pups in the groups differ reliably in birth weight, and then we can conclude alcohol exposure caused this difference. In this example birth weight is a dependent variable.




        In an experiment, the independent variable is the variable that is varied or manipulated by the researcher, and the dependent variable is the response that is measured. An independent variable is the presumed cause, whereas the dependent variable is the presumed effect. The independent variable is the antecedent, whereas the dependent variable is the consequent. In experiments, the independent variable is the variable that is controlled and manipulated by the experimenter; whereas the dependent variable is not manipulated, instead the dependent variable is observed or measured for variation as a presumed result of the variation in the dependent variable. Dependent variables can be influence by controlled variables.

February 21, 2008

Time-series methods of forecasting

Filed under: Business management — Jagdish Hiray @ 10:19 pm
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         Forecasting is a method or a technique for estimating future aspects of a business or the operation. It is a method for translating past data or experience into estimates of the future. It is a tool, which helps management in its attempts to cope with the uncertainty of the future. Forecasts are important for short-term and long-term decisions. Businesses may use forecast in several areas: technological forecast, economic forecast, demand forecast. There two broad categories of forecasting techniques: quantitative methods (objective approach) and qualitative methods (subjective approach). Quantitative forecasting methods are based on analysis of historical data and assume that past patterns in data can be used to forecast future data points. Qualitative forecasting techniques employ the judgment of experts in specified field to generate forecasts. They are based on educated guesses or opinions of experts in that area. There are two types of quantitative methods: Times-series method and explanatory methods.

         Time-series methods make forecasts based solely on historical patterns in the data. Time-series methods use time as independent variable to produce demand. In a time series, measurements are taken at successive points or over successive periods. The measurements may be taken every hour, day, week, month, or year, or at any other regular (or irregular) interval. A first step in using time-series approach is to gather historical data. The historical data is representative of the conditions expected in the future. Time-series models are adequate forecasting tools if demand has shown a consistent pattern in the past that is expected to recur in the future. For example, new homebuilders in US may see variation in sales from month to month. But analysis of past years of data may reveal that sales of new homes are increased gradually over period of time. In this case trend is increase in new home sales. Time series models are characterized of four components: trend component, cyclical component, seasonal component, and irregular component. Trend is important characteristics of time series models. Although times series may display trend, there might be data points lying above or below trend line. Any recurring sequence of points above and below the trend line that last for more than a year is considered to constitute the cyclical component of the time series—that is, these observations in the time series deviate from the trend due to fluctuations. The real Gross Domestics Product (GDP) provides good examples of a time series tat displays cyclical behavior. The component of the time series that captures the variability in the data due to seasonal fluctuations is called the seasonal component. The seasonal component is similar to the cyclical component in that they both refer to some regular fluctuations in a time series. Seasonal components capture the regular pattern of variability in the time series within one-year periods. Seasonal commodities are best examples for seasonal components. Random variations in times series is represented by the irregular component. The irregular component of the time series cannot be predicted in advance. The random variations in the time series are caused by short-term, unanticipated and nonrecurring factors that affect the time series.

            Smoothing methods (stable series) are appropriate when a time series displays no significant effects of trend, cyclical, or seasonal components. In such a case, the goal is to smooth out the irregular component of the time series by using an averaging process. The moving averages method is the most widely used smoothing technique. In this method, the forecast is the average of the last “x” number of observations, where “x” is some suitable number. Suppose a forecaster wants to generate three-period moving averages. In the three-period example, the moving averages method would use the average of the most recent three observations of data in the time series as the forecast for the next period. This forecasted value for the next period, in conjunction with the last two observations of the historical time series, would yield an average that can be used as the forecast for the second period in the future. The calculation of a three-period moving average is illustrated in following table. Based on the three-period moving averages, the forecast may predict that 2.55 million new homes are most likely to be sold in the US in year 2008.

Year Actual sale(in million) Forecast(in million) Calculation
2003 4    
2004 3    
2005 2    
2006 1.5 3 (4+3+2)/3
2007 1 2.67 (3+2+3)/3
2008   2.55 (2+3+2.67)/3

Example: Three-period moving averages

In calculating moving averages to generate forecasts, the forecaster may experiment with different-length moving averages. The forecaster will choose the length that yields the highest accuracy for the forecasts generated. Weighted moving averages method is a variant of moving average approach. In the moving averages method, each observation of data receives the same weight. In the weighted moving averages method, different weights are assigned to the observations on data that are used in calculating the moving averages. Suppose, once again, that a forecaster wants to generate three-period moving averages. Under the weighted moving averages method, the three data points would receive different weights before the average is calculated. Generally, the most recent observation receives the maximum weight, with the weight assigned decreasing for older data values.

Year Actual sale(in million) Forecast(in million) Calculation
2005 2 (.2)    
2006 1.5 (.3)    
2007 1 (.4)    
2008   .42 (2*.2+1.5*.3+1*.4)/3

Example: Weighted three-period moving averages method

A more complex form of weighted moving average is exponential smoothing. I this method the weight fall off exponentially as the data ages. Exponential smoothing takes the previous period’s forecast and adjusts it by a predetermined smoothing constant, ά (called alpha; the value for alpha is less than one) multiplied by the difference in the previous forecast and the demand that actually occurred during the previously forecasted period (called forecast error). Exponential smoothing is mathematically represented as follows: New forecast = previous forecast + alpha (actual demand − previous forecast) Or can be formulated as  F = F + ά(D − F)

         Other time-series forecasting methods are, forecasting using trend projection, forecasting using trend and seasonal components and causal method of forecasting. Trend projection method used the underlying long-term trend of time series of data to forecast its future values. Trend and seasonal components method uses seasonal component of a time series in addition to the trend component. Causal methods use the cause-and-effect relationship between the variable whose future values are being forecasted and other related variables or factors. The widely known causal method is called regression analysis, a statistical technique used to develop a mathematical model showing how a set of variables is related. This mathematical relationship can be used to generate forecasts. There are more complex time-series techniques as well, such as ARIMA and Box-Jenkins models. These are heavier duty statistical routines that can cope with data with trends and the seasonality in them.

         Time series models are used in Finance to forecast stock’s performance or interest rate forecast, used in forecasting weather. Time-series methods are probably the simplest methods to deploy and can be quite accurate, particularly over the short term. Various computer software programs are available to find solution using time-series methods.  

February 15, 2008

Waiting lines and Queuing system

Filed under: Business management — Jagdish Hiray @ 7:09 pm
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             Waiting in lines is a part of our everyday life. Waiting in lines may be due to overcrowded, overfilling or due to congestion. Any time there is more customer demand for a service than can be provided, a waiting line forms. We wait in lines at the movie theater, at the bank for a teller, at a grocery store. Wait time is depends on the number of people waiting before you, the number of servers serving line, and the amount of service time for each individual customer. Customers can be either humans or an object such as customer orders to be process, a machine waiting for repair. Mathematical analytical method of analyzing the relationship between congestion and delay caused by it can be modeled using Queuing analysis. Queuing theory provides tools needed for analysis of systems of congestion. Mathematically, systems of congestion appear in many diverse and complicated ways and can vary in extent and complexity.

              A waiting line system or queuing system is defined by two important elements: the population source of its customers and the process or service system. The customer population can be considered as finite or infinite. The customer population is finite when the number of customers affects potential new customers for the service system already in the system. When the number of customers waiting in line does not significantly affect the rate at which the population generates new customers, the customer population is considered infinite. Customer behavior can change and depends on waiting line characteristics. In addition to waiting, a customer can choose other alternative. When customer enters the waiting line but leaves before being serviced, process is called Reneging. When customer changes one line to another to reduce wait time, process is called Jockeying. Balking occurs when customer do not enter waiting line but decides to come back latter.   Another element of queuing system is service system. The number of waiting lines, the number of servers, the arrangements of the servers, the arrival and service patterns, and the service priority rules characterize the service system. Queue system can have channels or multiple waiting lines. Examples of single waiting line are bank counter, airline counters, restaurants, amusement parks. In these examples multiple servers might serve customers. In the single line multiple servers has better performance in terms of waiting times and eliminates jockeying behavior than the system with a single line for each server. System serving capacity is a function of the number of service facilities and server proficiency. In queuing system, the terms server and channel are used interchangeably. Queuing systems are either single server or multiple servers. Single server examples include gas station food mart with single checkout counter, a theater with a single person selling tickets and controlling admission into the show. Multiple server examples include gas stations with multiple gas pumps, grocery stores with multiple cashiers, multiple tellers in a bank. Services require a single activity or services of activities called phases.  In a single-phase system, the service is completed all at once, such as a bank transaction or grocery store checkout counter. In a multiphase system, the service is completed in a series of phases, such as at fast-food restaurant with ordering, pay, and pick-up windows. Queuing system is characterized by rate at which customers arrive and served by service system. Arrival rate specifies the average number of customers per time period. The service rate specifies the average number customers that can be serviced during a time period. The service rate governs capacity of the service system. It is the fluctuation in arrival and service patterns that causes wait in queuing system. Waiting line models that assume that customers arrive according to a Poisson probability distribution, and service times are described by an exponential distribution. The Poisson distribution specifies the probability that a certain number of customers will arrive in a given time period. The exponential distribution describes the service times as the probability that a particular service time will be less than or equal to a given amount of time. A waiting line priority rule determines which customer is served next. A frequently used priority rule is first-come, first-served. Other rules include best customers first, high-test profit customer first, emergencies first, and so on. Although each priority rule has merit, it is important to use the priority rule that best supports the overall organization strategy. The priority rule used affects the performance of the waiting line system.

             Basic single server model assumes customers are arriving at Poisson arrival rate with exponential service times, and first come, first serviced queue discipline, and infinite queue length, and infinite calling population. By adding additional resources to single server system either service rate can be increased or arrival rate at each server can be decreased with additional cost overhead.  In Single server single-phase system, customer is served once completed. Common examples of single server single-phase are a teller counter in bank, a cashier counter in super market, automated ticketing machine at train station. In single server queuing system wait time or performance of system depends on efficiency of serving person or service machine. Single server single-phase queuing system is most commonly automated system found in our regular life. For example many superstores have replaced manual billing counters with automated machines. Single server multiple-phase system incorporates division of work into phases to keep waiting line moving as completion of whole complete operation might increase wait in a line. Common examples of these systems are automatic or manual car wash, drive through restaurants.

             Waiting line models are important to a business because they directly affect customer service perception and the costs of providing service. If system average utilization is low, that suggests the waiting line design is inefficient. Poor system design can result in over staffing. Long waits suggest a lack of concern by the organization or can be view as a perception of poor service quality. Queuing analysis has changed the way businesses use to run and has increased efficiency and profitability of businesses.

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