All about Business and management

May 23, 2008

Mutual funds and their investment objectives

Filed under: Business management — Jagdish Hiray @ 5:48 am
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Mutual funds control a significant portion of US financial assets. Mutual funds offer investment diversification, professional management and convenience to investors. Every mutual fund has an investment objective, which it describes in its prospectus. The fund’s name often reflects its investment objective; for example, a fund that seeks a balance of growth and income might call itself the Growth and Income Fund. Most fund objectives are designed to provide a particular type of return. As a result, the fund objective has a major impact on the types of securities that dominate the fund’s portfolio. The Investment Company Institute classifies mutual funds into various categories according to their objectives.

There are six broader categories of Mutual funds according to their investment objectives: Common Stock Funds (also known as Equity funds, Stock funds), Special purpose funds, Income Funds, Bond Funds, Balanced Funds and Money Market Funds.

Common Stock Funds: In this type of Mutual funds, funds are invested almost entirely in common stock of companies, although their objectives vary considerably. Some of the different types of stock fund are: growth funds, aggressive growth funds, growth and income funds, Income-equity funds and Option income funds.  

Growth funds are seeking capital appreciation by selecting companies that should grow more rapidly than the general economy. The primary objective of these funds is capital appreciation rather than current dividend income. Growth funds hold the common stocks of more established, large growth-type companies. Aggressive growth funds invest in small or more speculative growth companies for maximum capital appreciation. The primary investment objective of these funds is capital appreciation, however, the investment policies tend to be more aggressive and riskier that for growth funds. These funds may hold common stocks in startup companies, new industries, and regular growth-type stocks. Growth and income funds seek long-term capital appreciation with income. Funds are invested in common stocks of well-established companies that are expected to show reasonable growth of principal. Their risk level is moderate. Index funds, a popular type in recent years, buy representative stocks to simply match the market indices. Income-equity funds (Dividend Yield funds) tend to invest in common stocks of companies with stable and good dividend returns. The emphasis is on secure and reasonable dividend yields and not on capital appreciation. Investment in this type of funds carries relatively low risk. Option-income funds invest in common stocks of companies to seek maximum current return by writing call options on the stock they hold. Option income funds sell option contracts against the stocks they buy. Large Cap funds invest in stocks of large companies, such as General Motors, and General Electrics which have strong business background, large market and lower business risk. Small Cap funds invests in stocks of smaller companies. Smaller firms have comparatively greater business risk than larger firms, but they have greater potential for profit.

Sector funds/Special purpose funds: Funds in this category have objective or limit their investment to a specialized industry or sector such as energy-related firms or companies that produce precious metals. These fund permits investors to concentrate on a specific investment segment. These funds can also use futures and options and short selling to meet more aggressive objectives.

Income Funds: Income funds are portfolios consisting of bonds and common stocks as well as preferred stocks. Income fund managers try to obtain satisfactory interest and dividend income for the shareholders.

Bond Funds: Bond funds seek high income and preservation of capital by investing primarily in bonds and selecting the proper mix between short term, intermediate-term, and long-term maturities. A bond fund may restrict its investments to certain categories of bonds, such as corporate, municipal, or foreign bonds. In recent years, tax-free municipal bonds funds have been popular.

Balanced Funds: Unlike most of mutual funds that make investment exclusively in one asset class, balanced funds invests a portion of its assets into each of major asset classes: cash and cash equivalents, government securities, corporate bonds, and corporate stocks. Main object behind is that if one asset class were to fall in value, another would rise to compensate, thus giving investor a balanced rate of return.

Money Market Funds:  Money market funds are a special form of mutual funds. Main investment objective of these funds is to provide more safety of principal or investment. The investor can own a portfolio of high yielding CDs, T-bills, and other similar securities of short-term nature, with a small amount to invest. Their investment portfolio covers investment in short-term government securities, commercial papers, and certificates of deposit. Each share has a net asset value of $1, however, yield fluctuates daily. 

 

 

 

 

 

April 24, 2008

Open-ended, multiple-choice, and Likert-scales items in surveys

Filed under: Business management — Jagdish Hiray @ 7:26 pm
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          Identifying and writing survey items or questions are important aspect of any survey process. Survey items are the building blocks of the survey. The way the survey questions perform, the adequacy with which they obtain the desired information, has a greater influence on the results of the survey than any other single part of the process. Therefore, great care should be taken while building survey items. Survey questions fall into two general categories: closed-ended or multiple-choice and open-ended

 

Open-ended questions let the respondent verbalize the answer. An open-ended question does not provide the participant with a choice of answers. Open-ended questions are also referred to as free-response or free-answer questions. Instead, participants are free to answer the question in any manner they choose. Closed-ended, or multiple choice, questions ask the respondent to choose an answer from list of alternatives. While open-ended questions encourage respondents to answer in their own terms, they can lead to repetition, the gathering of irrelevant information, and misunderstandings about the intent of the question. The results obtained from open-ended questions are also more difficult to analyze. Multiple-choice questions limit respondent’s input into the wording of answers but ensure that the interviewer or anyone else is not influencing the answer by randomly encouraging elaboration or making suggestions for answers. Multiple-choice questions are easier for respondents to answer. They are also easier to analyze and tabular than open-ended questions. Unstructured questions are open-ended questions that respondents answer where as questions are more structured in close-ended questionnaires. Compare to open-ended questions in survey, multiple-choice questions reduces changes of interviewer’s bias opinion on a particular subject. In multiple-choice survey, questions are well administered and structural towards goal of survey compare to open-ended survey questions. Some researchers prefer multiple-choice questions to gather quantitative data. It is difficult to develop effective multiple-choice questions compare to open-ended questions. In general, open-ended questions are useful in exploratory research and as opening questions. However, multiple-choice questions or Likert scales are advantages in large surveys. Open-ended survey questions allow respondents to answer in their own words and allow the researcher to explore ideas that would not otherwise be aired and are useful where additional insights are sought. In contrast, multiple-choice questions require the respondent to choose from among a given set of responses. Multiple-choice questions with ordered choices require the respondent to examine each possible response independent of the other choices. It is difficult to develop effective multiple-choice questionaries.

 

The Likert scale is one of the most widely used itemized scales. The end-points of a Likert scale are typically strongly disagree and strongly agree. The respondents are asked to indicate their degree of agreements by checking one of five response categories: strongly disagree, disagree, neither agree nor disagree, agree, and strongly agree. The Likert scale has several advantages. It is easy for the researcher to construct and administer this scale, and it is easy for the respondent to understand. Therefore, it is suitable for mail, telephone, personal, or electronic surveys. The Likert scale is widely used in marketing surveys. The major disadvantage of the Likert scale is that it takes longer to complete than other itemized rating scales.

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.

February 8, 2008

Management science modeling techniques

Filed under: Business management — Jagdish Hiray @ 9:17 pm
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             Management science is the science for managing and involves decision making. It utilizes what is controllable, and tries to predict what is uncontrollable in order to archive a specific objective. Science is a continuous search; it is a continuing generation of theories, models, concepts, and categories. Management science uses analytical methods to solve problems in areas such as production and operations, inventory management, and scheduling. Typical management science approach is to build a model for the problem being studied, such a model is often a mathematical model. Practical problems are often unstructured and lack clarification in definition of problem which makes mathematical modeling a challenge. Therefore modeling of a problem is important phase in problem solving technique. Once model is built, algorithms are used to solve problem. Various techniques are devised to model problem and solve it for possible solutions.

              Linear programming is one of the widely used modeling techniques. Linear programming problems consist of an objective function (also know as cost function) which has to be minimized or maximized subject to a certain number of constraints. The objective function consists of a certain number of variables. The constraints are linear inequalities of the variables used in the objective function. This technique is closely related to linear algebra and uses inequalities in the problem statement rather than equalities. A linear programming problem can fall in three categories: infeasible, unbounded and an optimal solution. In an infeasible problem values of decision variables do not satisfy constraint condition. A problem is unbounded if the constraints do not sufficiently restrain the objective function so that for any given feasible solution, another feasible solution can be found that makes further improvement to the objective function. In an optimal solution, the objective function has a unique maximum or minimum value. Linear programming problems can be solved using graphical analysis method. Sensitive analysis is extension to solution found in linear programming to find out effect of parameter changes on the optimal solution. The parameters are called coefficients and can be quantity or value used in objective function. In linear programming results are rounded to get reasonable output, however rounded solution might not be feasible and many not give an optimal solution. Therefore, Integer programming model is used with fractional values. Linear programming is also use to solve transportation, transshipment, and assigning problems. Linear programming is widely used in production planning and scheduling. It is very well used in airline industry for aircraft and crew scheduling.

             Probabilistic techniques are another class of modeling approach for problem solving. It is based on application of statistics for probability of uncontrollable events as well as risk assessment of decision. In this technique risk means uncertainty for which the probability of distribution is know. Therefore risk assessment involves study of the outcomes of decisions along with their probabilities. Probability assessment tries to fill gap between what is know and what need to be know for an optimal solution. Therefore, probabilistic models are used to prevent events happening due to adverse uncertainty. Decision analysis and queuing systems are example of probabilistic techniques. The modeling technique use to solve physical problems such as transportation or flow of commodities is Network modeling. Network problems are an abstract representation of processes and activities for a give problem and illustrated by using network branches and nodes. This technique uses most cost effective way to transport the goods, to determine maximum/minimum possible flow from source to destination and to find shortest critical path in large projects. 

            The management science modeling process helps businesses to improve their operations through the use of scientific methods and the development of specialized techniques. It is the process of re searching for an optimal solution to the existing problem. Management science modeling process provides systematic, analytical and general approaches to the problem solving for decision-making, regardless of the nature of the system, product, or service. Management science modeling process is the application of scientific methods to complex organizational problems. Models are aimed at assisting the decision-maker in decision-making process. Management science modeling process is one of the innovative decision making tool of the twentieth century. 

December 1, 2007

Housing market crisis

Filed under: My Opinion — Jagdish Hiray @ 1:23 pm
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            Housing market crisis was very well expected, however everybody tried to took advantage of this boom. I got introduced to this market first when I was looking for my first house to buy. What I had seen, experienced was unbelievable as most of us would had. This article is based on my true observations and real facts I came across in this line of business.  

            Many of loan agents and realtors were totally unaware and lacking basic knowledge of rules and regulations of California’s Department of Real Estate. Many of them never passed license exam from state. Some of them were even not eligible to work legally in this line of business. Brokers were hiring people without proper eligibility and state license requirements. There was solid chain of loan agent, broker, and account executive from lenders and underwriters. These people can do anything to get loan done or deal get done for monetary benefits. They used to charge high fees and loan program for their benefits. Fees in turn used to get added into homebuyer’s principal amount of loan. Everything was well set to extract money from homeowners’ loan amount. Realtor’s created hypothetical conditions to increase bidding on houses during boom to get maximum highest bid possible to get higher commission. 

            These people sold loan program, which benefited them not homeowners, to get more commission from these deals. Many lenders came with 1% interest rate program to lower monthly payments, which was very popular because loan agents were getting more rebate on those programs from lenders. There was no limit to the grid. Many young people got attracted towards this business because of this easy money. There were thousands of people appearing for real estate license exams every weekend. Poor homebuyer was confused and was surrounded by these greedy people, was helpless only to fulfill dream of life: owning own home.  

            Due to hypothetical increase in home prices, they were constantly pressured by real estate advertisers to refinance their loan amount to refinance and to spend particularly during holiday seasons and for remodeling. That caused increase in loan amount and in turn more burden on homeowners. But it was benefited to real estate community; they were becoming richer and richer day-by-day, spending money on their expensive vacation and expensive cars at expense of common man’s loan amount.           

            Surprisingly, not a single regulatory organization took steps to research the cause for this boom and took proactive preventive steps to control these behaviors. Once more, common man in this country suffered under the name of ‘Capitalism’. This is very similar boom as we have seen in 2000, so-called regulations and rules were implemented after boom busted, however not sure how many of them responsible for that really got punished.  

            Real estate boom and its effect are worst than ever. Even decrease in short-term rate is not going to help. Increase in oil prices had made this situation worst, and made common man’s life more challenging than ever. 

            This time common man has to be very careful. Save as much money as you can to face future financial challenges, time ahead is very promising and one should not expect any honest and positive efforts from so called ‘Capitalist’ economy, to think about common man.

 

November 6, 2007

The Shipbuilder by Jack Myrick

Filed under: Book review — Jagdish Hiray @ 6:35 am
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                  This is the one of the best book on leadership. This book is based on five ancient principles of leadership and author has explained those principles using an example of a ship builder. Book starts with a troubled ship builder in a town who follows, implements and practice five principles of leadership taught by his master. Ship builder was best in the town but he was not able to meet his goal and commitments. By practicing those five principles ship builder realizes, his job, as a leader is not to build ships but to build men. With proper leadership, he can increase productivity and employee retention rate. He learned to appreciate employee’s hard work; he learned to recognize potential in people, learned importance of authority. By practicing five principles, ship builder realizes how his team members worked hard to deliver ship on time.

              The five principles described in this book are very simple and can be easily implemented in life. Just knowing these principles will not help but you must practice and apply them to your life. You must read this book to understand these five principles of leadership.  Author has explained these principles very effectively and has inspired readers to practice those everyday. Simple in language, very thoughtfully written book, it keeps readers attached to it till end.

              Must read book for everyone who wants to live life with goals and achievements.

Jagdish Hiray 

September 29, 2007

Segmentation in Marketing

Filed under: Marketing management — Jagdish Hiray @ 12:06 pm
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This article explains basic concept of marketing segmentation and different type of segmentation commonly used by companies in today’s market. Article explains each type of segmentation with example to give more understanding of each area.

              Success in marketing in the world today is dependent on a strategy of market segmentation. Market segmentation is the way in which the marketing system maximizes the alternatives available to consumers of products and services. It does so by recognizing that the total market is made up of smaller submarkets called segments of consumers who have needs that are homogenous in many geographic, demographic, economic, cultural and psychological ways. A market segmentation strategy attempts to find such a market and penetrate it to the greatest extent possible by customizing products, services and, the marketing efforts to fit its needs. For example car manufacturers such as Toyota and Ford manufacture cars for different segments of population, giving consumers what they want. Market segmentation is the basis for developing targeted and effective marketing plans.            

             Market segmentation can be categorized into four levels: segment marketing, niche marketing, local marketing and individual marketing.In segment marketing, segments are identified into homogenous groups of customers, each of them reacting differently to promotion, communication, pricing and other variables of the marketing mix. There are a huge number of variables that could be used for market segmentation in theory. They comprise easy to determine demographic factors as well as variables on user behavior or customer preferences. Market segments should be formed in that way that differences between buyers within each segment are as small as possible. Thus, every segment can be addressed with an individually targeted marketing mix.            

             Niche marketing is targeting a product or service to a small portion of a market that is not being readily served by the mainstream product or service marketers. Almost every business-fast food chain, convenience stores was begun to fill perceived voids in the marketing place. Market niches can be geographic areas, a specific industry, ethic or age groups or any other particular group of people. Some of niche markets and products are, organic vegetables: consumers wanting vegetables grown without pesticides, SUV: for drivers desiring a vehicle with room, power and strength, pasteurized goat milk: consumers allergic to cow milk. Niche marketing serves a portion of a unique market or portion of a common market not already served. 

             Another form of segmentation is local marketing – reaching individual communities with specialized messages. The latest trend is to bring marketing down to the neighborhood level and make it personal to the customer. Some marketing experts, advocates targeting your marketing efforts to specific neighborhoods, “making sure your message is delivered only to people most likely to be your customers — those within 10 miles or 10 minutes of your door.” It is all about thinking small and keeping your marketing local. Local marketing offers several advantages; it allows businesses to tap the potential of greatest profit opportunity within their trading area – the customer base that is right in their back yard. Businesses, schools, churches, community events and even fellow retailers can become promotional allies in building cost-effective programs to capture consumer dollars right within reach. Local marketing is face-time marketing. The local marketing approach eschews institutional “exposure” advertising. 

             Today customer is very well aware about what it needs to buy and more focus on its liking and preferences. Therefore companies are more focused towards individual marketing or customization. Individual marketing is traditional form of marketing, offering products and services on individual basis. Many online companies are offering customers a choice based option interactive system that allows online user to design their own products from pool of options available with suppliers. Customizing its products, services, and messages on a one to one basis, customizes a company when it is able to respond to individual customers. However it is not possible for every company to customize its products such as automobile manufacturing companies.

             A segment-orientated marketing approach generally offers a range of advantages for both, businesses and customers. It is possible to satisfy a variety of customer needs with a limited product range by using different forms, bundles, incentives and promotional activities. It is often difficult to increase prices for the whole market. Nevertheless, it is possible to develop premium segments in which customers accept a higher price level. Such segments could be distinguished from the mass market by features like additional services, exclusive points of sale, product variations and the like. By segmenting markets, organizations can create their own ‘niche products’ and thus attract additional customer groups. Organizations that serve different segments along a customer’s life cycle can guide their customers from stage to stage by always offering them a special solution for their particular needs.  

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