This document discusses various tools and techniques for demand forecasting that can help entrepreneurs with production planning. It describes several statistical methods like the Delphi technique, nominal group technique, opinion polls, moving average, trend analysis, and time series analysis that can be used to estimate demand. It also discusses concepts like seasonality, trends, cycles, and Box-Jenkins models that can aid in demand forecasting. The document provides links to download additional resources on statistics, reasoning, English language improvement, mathematics, and general knowledge.
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
FIRE ADMIN UNIT 1 .orct121320#ffffff#fa951a#FFFFFF#e7b3513VERSON.docxAKHIL969626
FIRE ADMIN UNIT 1 .orct121320#ffffff#fa951a#FFFFFF#e7b3513VERSON2.2MAYOR/CITY COUNSELxNO#66b66cCITY MANAGER1zNO#CD6A80FIRE CHIEF2zNO#504DCDOPERATIONS ASSISTANT CHIEF3zNO#FF8C00ADMINISTRATIVE ASSISTANT CHIEF3zNO#8E388ECHIEF OF PREVENTION5zNO#00ae00CHIEF OF TRAINING5zNO#ff6e01CONFIDENTIAL AMINISTRSTIVE ASSISTANT3x8#935c24ADMINISTRATIVE ASSISTANT4x9#388E8EADMINISTRATIVE ASSISTANT5y10#5483a2BATTALION CHIEF (1 PER SHIFT4zNO#B0171FDISTRICT CHIEF (3 PER SHIFT)11zNO#912CEECAPTAIN (18 PER SHIFT)12zNO#0000EELIEUTANENT (18 PER SHIFT)13zNO#00868BDRIVER/OPERATOR (18 PER SHIFT)14zNO#698B22FIREFIGHTER-1 (18 PER SHIFT)15zNO#FFA500RESCUE SPECIALIST II (10 PER SHIFT)12zNO#7171C6RESCUE SPECIALIST I (10 PER SHIFT)17zNO#418cf0SENIOR FIRE INVESTIGATOR6zNO#00BFFFSENIOR FIRE SAFETY EDUCATOR6zNO#4682B4SENIOR FIRE INSPECTOR6zNO#FF8C00FIRE INVESTIGATOR II19zNO#0000EEFIRE INVESTIGATOR I22zNO#6E7B8BFIRE SAFETY EDUCATOR II20zNO#FF6103FIRE SAFETY EDUCATOR I24zNO#FFE4E1FIRE INSPECTOR II21zNO#808000FIRE INSPECTOR I (2)26zNO#9BCD9BSENIOR TRAINING OFFICER7zNO#87CEFATRAINING OFFICER II (2)28zNO#D02090TRAINING OFFICER I (3)29zNO#308014MAINTENANCE SUPERVISOR/MASTER MECHANIC5zNO#9ACD32ADMINISTRATIVE ASSISTANT31y32#418cf0MAINTENANCE TECHNICIAN II31zNO#CD6A80MAINTENANCE TECHNICIAN (2)33zNO#504DCDzNO#FF8C00yNO#8E388ExNO#00ae00zNO#ff6e01xNO#935c24yNO#388E8ExNO#5483a2zNO#B0171FxNO#912CEExNO#00ae00yNO#00868ByNO#698B22xNO#FFA500yNO#7171C6zNO#418cf0xNO#00BFFFyNO#4682B4xNO#FF8C00yNO#0000EExNO#6E7B8BxNO#FF6103zNO#FFE4E1xNO#808000yNO#9BCD9ByNO#87CEFAxNO#D02090xNO#308014yNO#9ACD32zNO#418cf0yNO#CD6A80xNO#504DCDyNO#FF8C00xNO#8E388ExNO#00ae00yNO#ff6e01zNO#935c24xNO#388E8EyNO#5483a2xNO#B0171FxNO#912CEEyNO#00ae00yNO#00868BxNO#698B22zNO#FFA500zNO#7171C6yNO#6E7B8BxNO#00BFFFyNO#FFE4E1zNO#FF8C00yNO#0000EEyNO#6E7B8BxNO#FF6103yNO#FFE4E1zNO#808000yNO#9BCD9BxNO#87CEFAyNO#D02090xNO#308014xNO#9ACD32yNO#418cf0xNO#CD6A80zNO#504DCDzNO#FF8C00yNO#8E388ExNO#00ae00yNO#ff6e01zNO#935c24yNO#388E8EyNO#5483a2xNO#B0171FyNO#912CEEzNO#00ae1eyNO#00868BxNO#698B22yNO#FFA500xNO#7171C6
Business Decision Making Project Part 2
Jared Linscombe
QNT/275
Dr. Davisson
September 12, 2016
Descriptive Statistics
Descriptive statistics are statistics that describe or summarize features of collected data. Descriptive statistics simply present quantitative information in a manner that can be easily managed. The large amount of data is reduced into a simple summary and therefore the whole process of describing the data is less laborious.
For example, finding the mean helps to summarize a lot of individual information into a way that is quickly understood. The samples are likely to produce different independent variables that affect the sales of Elite Technologies Limited. For this reason, we opt to use bivariate analysis in the describing the statistics. Bivariate analysis of the descriptive statistics that is derived from the data will help in drawing relationships between different variables.
For a more accurate representa ...
Interventions required to meet business objectives - from Forecasting Methods,
Forecast Accuracy / Error Reduction,
Integrate – Sales Forecast / Production to undertaking a CPFR
FIRE ADMIN UNIT 1 .orct121320#ffffff#fa951a#FFFFFF#e7b3513VERSON.docxAKHIL969626
FIRE ADMIN UNIT 1 .orct121320#ffffff#fa951a#FFFFFF#e7b3513VERSON2.2MAYOR/CITY COUNSELxNO#66b66cCITY MANAGER1zNO#CD6A80FIRE CHIEF2zNO#504DCDOPERATIONS ASSISTANT CHIEF3zNO#FF8C00ADMINISTRATIVE ASSISTANT CHIEF3zNO#8E388ECHIEF OF PREVENTION5zNO#00ae00CHIEF OF TRAINING5zNO#ff6e01CONFIDENTIAL AMINISTRSTIVE ASSISTANT3x8#935c24ADMINISTRATIVE ASSISTANT4x9#388E8EADMINISTRATIVE ASSISTANT5y10#5483a2BATTALION CHIEF (1 PER SHIFT4zNO#B0171FDISTRICT CHIEF (3 PER SHIFT)11zNO#912CEECAPTAIN (18 PER SHIFT)12zNO#0000EELIEUTANENT (18 PER SHIFT)13zNO#00868BDRIVER/OPERATOR (18 PER SHIFT)14zNO#698B22FIREFIGHTER-1 (18 PER SHIFT)15zNO#FFA500RESCUE SPECIALIST II (10 PER SHIFT)12zNO#7171C6RESCUE SPECIALIST I (10 PER SHIFT)17zNO#418cf0SENIOR FIRE INVESTIGATOR6zNO#00BFFFSENIOR FIRE SAFETY EDUCATOR6zNO#4682B4SENIOR FIRE INSPECTOR6zNO#FF8C00FIRE INVESTIGATOR II19zNO#0000EEFIRE INVESTIGATOR I22zNO#6E7B8BFIRE SAFETY EDUCATOR II20zNO#FF6103FIRE SAFETY EDUCATOR I24zNO#FFE4E1FIRE INSPECTOR II21zNO#808000FIRE INSPECTOR I (2)26zNO#9BCD9BSENIOR TRAINING OFFICER7zNO#87CEFATRAINING OFFICER II (2)28zNO#D02090TRAINING OFFICER I (3)29zNO#308014MAINTENANCE SUPERVISOR/MASTER MECHANIC5zNO#9ACD32ADMINISTRATIVE ASSISTANT31y32#418cf0MAINTENANCE TECHNICIAN II31zNO#CD6A80MAINTENANCE TECHNICIAN (2)33zNO#504DCDzNO#FF8C00yNO#8E388ExNO#00ae00zNO#ff6e01xNO#935c24yNO#388E8ExNO#5483a2zNO#B0171FxNO#912CEExNO#00ae00yNO#00868ByNO#698B22xNO#FFA500yNO#7171C6zNO#418cf0xNO#00BFFFyNO#4682B4xNO#FF8C00yNO#0000EExNO#6E7B8BxNO#FF6103zNO#FFE4E1xNO#808000yNO#9BCD9ByNO#87CEFAxNO#D02090xNO#308014yNO#9ACD32zNO#418cf0yNO#CD6A80xNO#504DCDyNO#FF8C00xNO#8E388ExNO#00ae00yNO#ff6e01zNO#935c24xNO#388E8EyNO#5483a2xNO#B0171FxNO#912CEEyNO#00ae00yNO#00868BxNO#698B22zNO#FFA500zNO#7171C6yNO#6E7B8BxNO#00BFFFyNO#FFE4E1zNO#FF8C00yNO#0000EEyNO#6E7B8BxNO#FF6103yNO#FFE4E1zNO#808000yNO#9BCD9BxNO#87CEFAyNO#D02090xNO#308014xNO#9ACD32yNO#418cf0xNO#CD6A80zNO#504DCDzNO#FF8C00yNO#8E388ExNO#00ae00yNO#ff6e01zNO#935c24yNO#388E8EyNO#5483a2xNO#B0171FyNO#912CEEzNO#00ae1eyNO#00868BxNO#698B22yNO#FFA500xNO#7171C6
Business Decision Making Project Part 2
Jared Linscombe
QNT/275
Dr. Davisson
September 12, 2016
Descriptive Statistics
Descriptive statistics are statistics that describe or summarize features of collected data. Descriptive statistics simply present quantitative information in a manner that can be easily managed. The large amount of data is reduced into a simple summary and therefore the whole process of describing the data is less laborious.
For example, finding the mean helps to summarize a lot of individual information into a way that is quickly understood. The samples are likely to produce different independent variables that affect the sales of Elite Technologies Limited. For this reason, we opt to use bivariate analysis in the describing the statistics. Bivariate analysis of the descriptive statistics that is derived from the data will help in drawing relationships between different variables.
For a more accurate representa ...
ForecastingDiscuss the different types of forecasts to include tim.pdfamolmahale23
Forecasting
Discuss the different types of forecasts to include time-series, causal, and qualitative models.
When might a researcher or project manager utilize exponential smoothing?
What benefit does a Delphi technique provide when working with qualitative-based decision
making?
Solution
Forecasting is basically the process of estimating or predicting the future trend, based on the
trend and information of the past and the present.Forecasting is a calculated assumption of how
the trend is going to be in a future date based on what we saw in the past and what we are
observing in the present scenario.
Time series methods:
These methods use historical data to assume future trends.
There are various time series methods such as,
1)Simple Moving Average Method: it is commonly used in technical analysis of financial data
such as stock prices,trading volumes or returns.Among the most popular technical indicators,
moving averages are used to gauge the direction of the current trend.It is calculated by averaging
a number of past data points. Once determined, the resulting average is then plotted onto a chart
in order to allow traders to look at smoothed data rather than focusing on the day-to-day price
fluctuations that are inherent in all financial markets.
As new values become available, the oldest data points must be dropped from the set and new
data points must come in to replace them. Thus, the data set is constantly \"moving\" to account
for new data as it becomes available. This method of calculation ensures that only the current
information is being accounted for.
for example, to calculate a basic 10-day moving average you would add up the closing prices
from the past 10 days and then divide the result by 10. The average thus obtained is plotted on a
chart. As the time progresses, we replace the first variable with the latest variable available ie.
latest closing price of 11th day, therefore getting a new avaerage. We plot this one too in the
chart. The chart thus formed gives a trend which is used for forecasting future movements.
2)Exponentially smoothed moving average:
Over the years, technicians have found two problems with the simple moving average. The first
problem lies in the time frame of the moving average (MA). Most technical analysts believe that
price action, the opening or closing stock price, is not enough on which to depend for properly
predicting buy or sell signals of the MA\'s crossover action. To solve this problem, analysts now
assign more weight to the most recent price data by using the exponentially smoothed moving
average (EMA).It is a type of infinite impulse response filter that applies weighting factors
which decrease exponentially. The weighting for each older datum decreases exponentially,
never reaching zero.
The exponentially smoothed moving average addresses both of the problems associated with the
simple moving average. First, the exponentially smoothed average assigns a greater weight to the
more recent data..
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Demand forecasting – tools and techniques for entrepreneurs
1. DEMAND FORECASTING – TOOLS AND TECHNIQUES FOR ENTREPRENEURS by : DR. T.K. JAIN AFTERSCHO ☺ OL centre for social entrepreneurship sivakamu veterinary hospital road bikaner 334001 rajasthan, india FOR – PGPSE / CSE PARTICIPANTS [email_address] mobile : 91+9414430763
2. My words..... My purpose here is to give a few questions on fundamentals of statistics and business mathematics. I welcome your suggestions. I also request you to help me in spreading social entrepreneurship across the globe – for which I need support of you people – not of any VIP. With your help, I can spread the ideas – for which we stand....
3. Can we really forecast demand ? Demand is dependent on many factors like market trend, competition, business environment We cant predict exact demand. But using statistical tools, we can estimate demand, which can help us in production planning.
4. What are the methods for demand forecasting ? Delphi Nominal group technique opinion poll Moving average method Trend analysis Time series analysis
5. What is DELPHI ? Here we invite different experts and take their opinion and they finally try to find an average of their ideas. We again intimate the experts about the average opinion, and give them an opportunity to revise their forecast. Delphi is useful when you want to have an overall subjective assessment about complex business environment. All the experts are distant and they dont know each other, therefore each one tries to give the best possible estimate
6. What is Nominal Group Technique? It is similar to Delphi, but here we ask experts to sit together and explain their perspective to others so that others can also give their opinion. People frame their estimates individually, but thereafter they give justification for their opinion.
7. What is opinion poll method ? Here we collect information about the issue from people using opinion poll. We may take interview, we may collect data using questionnaire, or we may organise meeting / conference to find opinion of people. Variants of opinion poll are : focus group discussion – which is used in marketing to know about consumer opinions
8. Intuition method This is traditional method intuition = personal opinion on the basis of one's experience intuition is not just a hunch. It is based on experience, and past understanding. We can take our decision on the basis of intuition, if we have considerable experience in that sector
9. What is moving average method ? Here we take moving average of either 3 days or 5 days or 7 days and try to forecast using this moving average. We may also use smoothing to remove exceptional fluctuations Moving average is a case where data can be used to forecast on the basis of past trend
10. Example of moving average : Period data 3 year moving average 2001 300 2002 400 400 2003 500 434 2004 400 500 2005 600 600 2006 800 700 2007 700
11. What is smoothing ? Here we use the past data to smooth the data. Here we use the past data to predict the future
12. Time series or stockastic models Stockastic means where we are using time as an independent variable and predict demand using this variable.
13. What is a trend ? There are 4 components of trend : 1. secular trend 2. cyclical fluctuation 3. seasonal fluctuation 4. random / irregular variations
14. What is secular trend It is overall trend that continues for a long period of time. It is having long time perspective
15. What is cyclical fluctuation? It represents fluctuations due to economic cycles like boom, recession etc. These fluctuations last for a few years
16. What is seasonal variation ? It is due to seasonal components like summer, winter, monsoon, or some other such factors which have impact for a few months
17. What is random variation? These are beyong prediction they happen by chance
18. Causal / econometrics models These models use cause -effect relationship to predict demand. In these models, we try to estimate demand using an econometric models – here we try
19. What are the types of trends and cycles ? 1. linear trend = there is constant rate of change (it is a straight line) 2. parabolic trend = a varying rate of change 3. exponential / logarithmic trend = a constant % rateof change 4 S shape : slow initial growth, then fast growth and then again slow growth – showing S shape
20. What is autocorrelation ? Correlation of a variable with itself (with a time lag) is called auto (self) – correlation this can be used to identify the impact of seasonality. If Auto-correlation is zero, it denotes that data are random. If data has seasonality, there will be autocorrelation – which can be identified. We have to remove the component of autocorrelation to identify the trend line
21. Box Jenkins Model This model was developed in 1976. It can help you in demand forecasting. It uses two types of methods : 1 AR (auto regressive model ) 2. moving average method
22. contd... Data in a series are of two types : 1. stationary (fixed around mean) 2. non-stationary (data are not fixed around mean). Box Jenkins model convert non-stationary trend into stationary trend, thus prediction is possible.
31. Download links for material in english http://www.authorstream.com/presentation/tkjainbkn-146799-english-error-spotting-sentence-im-law-cat-gmat-mba-management-business-research-cfp-cfa-frm-cpa-ca-cs-icwa-india-rajasthan-improvement-entertainment-ppt-powerpoint/ http://www.docstoc.com/docs/3921499/ENGLISH-%E2%80%93-ERROR-SPOTTING-AND-SENTENCE-IMPROVEMENT http://www.slideshare.net/tkjainbkn/english-error-spotting-and-sentence-improvement-presentation http://www.scribd.com/doc/19641980/Error-Spotting http://www.scribd.com/doc/11629005/English-Error-Spotting-and-Sentence-Improvement http://www.scribd.com/doc/14660441/English-Afterschoool-23-May http://www.scribd.com/doc/6583519/English-Afterschoool-21-May http://www.scribd.com/doc/6583520/English-Afterschoool-21-May-2
32. Download links for material on English http://www.scribd.com/doc/6583315/English-Improvement-Afterschoool http://www.scribd.com/doc/6583518/English-20-May-Afterschoool http://www.scribd.com/doc/28531795/Mock-Paper-Cat-Rmat-Mat-Sbi-Bank-Po-Aptitude-Tests
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