Business Forecasting Time Series
business forecasting time series
Examples Of Key Performance Indicators In Business
A Key Performance Indicator, which is also known as a KPI, is a measurement of performance of a certain function within a business. These are quantitative measurements plotted against a time range and is generally represented in graphs or scorecards.
However, some of the KPIs can also be based on geographic locations, percentage of a whole, etc. Still, when looking at performance over time, a time-series chart is the best way to show it. KPIs are derived data based indicators that we are using regularly in our businesses to keep track of the various key departments. Read further for a few examples.
KPIs in the area of marketing are really very frequently circulated data. We see it advertised by the companies all the time. One of the most common KPI here is the customer base and customer base growth rate. The customer base is usually used to refer to the total number of unique customers that an organization has and hence the customer base growth rate is the rate at which the customer base is growing. Depending on how successful (or not) the business is, the growth rate can be positive or negative. The customer base is represented in actual numbers, and the growth rate is usually in percentage.
In the area of manufacturing, the usual KPIs are rate of production, volume of production, performance, quality etc. While rates are usually calculated per unit time (units produced per second, units manufactured per day), performance and quality are usually expressed in percentage. The Overall Equipment Effectiveness or OEE is often used as the standard set of metrics to gauge the performance of a manufacturing unit.
In the sales department, the usual KPIs that require monitoring are sales volumes, percentage of refunds, growth, etc. So KPIs for sales are the indicators that show the data representing these factors. For instance, sales volume can be represented in numbers per day or volume per day, depending on the SKU. Out of this number, the ratio that was returned would the percentage of refunds.
There are also KPIs for personal performance of staff. If they are salespeople, these would include the monthly revenue that they single handedly generate. Their growth rate, fluctuation etc can be easily graphed. For websites and global companies, geographical data is also very important to locate the locations of the visitors and customers.
KPIs are mainly used to monitor of performance, changes and to identify patterns and repetitions and deviations from forecast. These are then interpreted to find underlying factors, trends, etc. For example, if more companies are buying from the organizationin one area, then the organization needs to make sure of – a) holding the good results area, b) identifying what worked successfully and c) implementing the best practices in other regions if possible. Another application of key performance indicators is problem solving. For example, if a typical day is always depressed for orders, then other factors need to analyzed, for instance customer profiles of those who are ordering and those who are not, etc. KPIs thus are essential in business analysis, business planning, problem solving and trend spotting.
About the Author
Mark Flaherty
InetSoft Technology
KPI examples
Statistics Help?
True or False
1. Correlation analysis is the study of the relationship between variables.
2. When looking at a trendline the dependent variable is the one being predicted.
3. Trendlines take the form of:
a.)Y= aX^2 + 2bX + c
b.)Y = X^2 + b
c.)Y = aX + b.
d.)None of the above
4. The correlation coefficient must be between _____ and _____.
True or False
5. Management can use a time series forecast to predict the future.
6. A trendline is unable to make predictions about future events.
7. In business a cyclical variation usually take place quarterly.
8. When the correlation is positive the trendline is increasing with the value of X.
9. When the correlation is zero the trendline is decreasing with the value of X.
1) true
2) true
3) c
4) -1 and 1
5) false
6) true
7) false
true
9) false
The correlation coefficient, r, is a measure of the linear relationship between two variables. If the data is non-linear then the correlation coefficient is meaningless.
r takes on values between -1 and 1. negative values indicate the relationship between the variables is indirect, i.e., on a scatter plot the data tends to have a negative slope. Positive values for r indicate the data tends to have a positive slope. if r = 0 we say the variables are uncorrelated.
the closer the absolute value of r is to 1, the stronger the linear association between the two variables.
there are many different formulas for calculating the value of r. if we let xbar and ybar be the means of two data sets. sx and sy are the standard deviations in the data sets and n = total sample size then:
r = 1/(n – 1) * Σ( ((xi – xbar)/sx) * ((yi – ybar)/sy)) with the sum going from i = 1 to n
r = Covariance(X,Y) / [(√(Var(X))√(Var(Y))]
r = Σ(xi – xbar)(yi – ybar) / [ √(Σ(xi - xbar)²) √(Σ(yi - ybar)²)]
the second equation shows that the correlation coefficient the ratio between the measure of spread between the variables and the product of the spread within each variable.
r is unit less.
r is not affected by multiplying each data set by a constant, and a constant to each data set or interchanging x and y.
r is subject to outliers.
r² is called the coefficient of determination. It is a measure of the proportion of variance in y explained by regression.
Also note that correlation is not causation. Here is an example: the shoe size of grade school students and the student’s vocabulary are highly correlated. In other words, the larger the shoe size, the larger the vocabulary the student has. Now it is easy to see that shoe size and vocabulary have nothing to do with each other, but they are highly correlated. The reason is that there is a confounding factor, age. the older the grade school student the larger the shoe size and the larger the vocabulary.
you cannot compare models by comparing the r values. This is a long discussion, a full day lecture in the prob/stat courses I’ve instructed. Model comparison is a topic usually saved for high level under grad courses or graduate level courses.
good sites with info about correlation are:
http://mathworld.wolfram.com/CorrelationCoefficient.html
http://mathworld.wolfram.com/LeastSquaresFitting.html
|
|
Forecasting, Time Series, and Regression (with CD-ROM) (Forecasting, Time Series, & Regression) $50.00 Awarded Outstanding Academic Book by CHOICE magazine in its first edition, FORECASTING, TIME SERIES, AND REGRESSION: AN APPLIED APPROACH now appears in a fourth edition that illustrates the vital importance of forecasting and the various statistical techniques that can be used to produce them. With an emphasis on applications, this book provides both the conceptual development and practical motiva… |
|
|
Essentials of Business Statistics $112.64 The new edition of Essentials of Business Statistics delivers clear and understandable explanations of core business statistics concepts, making it ideal for a one-term course in business statistics. The author team—Bowerman/O’Connell/Murphree/Orris—emphasize the importance of interpreting statistical results to make effective decisions to improve business processes. The text offers real ap… |
|
|
Practical Time Series Forecasting: A Hands-On Guide [2nd Edition] (Practical Analytics) $4.95 Practical Time Series Forecasting is a hands-on introduction to quantitative forecasting of time series. Quantitative forecasting is an important component of decision making in a wide range of areas and across many business functions including economic forecasting, workload projections, sales forecasts, and transportation demand. Forecasting is also widely used also outside of business, such as i… |
|
|
Time Series Models for Business and Economic Forecasting $250.09 This book is in Used condition |
|
|
Introduction to Time Series and Forecasting $113.7 No Synopsis Available |
|
|
Sas for Forecasting Time Series $64.93 This book is in New – Excellent condition |
|
|
Forecasting Economic Time Series $120.9 No Synopsis Available |
|
|
Time-Series Forecasting $116.95 No Synopsis Available |
|
|
Time Series Forecasting $48.7 No Synopsis Available |
|
|
Computational Intelligence In Time Series Forecasting $248.93 This book is in Acceptable condition |
|
|
Applied Time Series Modelling and Forecasting $127.49 This book is in Used condition |
|
|
An Introduction to Time Series Analysis and Forecasting $98.8 This book is in New – Excellent condition |
|
|
Time Series Analysis and Forecasting by Example $90 No Synopsis Available |
|
|
Forecasting and Time Series : An Applied Approach $145.6 No Synopsis Available |
|
|
Introduction to Time Series Analysis and Forecasting $142.35 No Synopsis Available |
|
|
Applied Time Series: Analysis and Forecasting $87.7 No Synopsis Available |
|
|
Introduction to Time Series Analysis and Forecasting Solutions Set $153.84 No Synopsis Available |
|
|
Forecasting, Time Series, and Regression (with CD-ROM) $207.01 No Synopsis Available |
|
|
Introduction to Time Series and Forecasting, by Brockwell, 2nd Edition $37.65 This book is in New – Excellent condition |
|
|
Forecasting, Time Series, and Regression, by Bowerman, 4th Edition $122.7 This book is in Used condition |
