Session 3: Data: Overview, Analysis, and Presentation Demand Forecasting and Planning in Crisis...

Session 3: Data: Overview, Analysis, and Presentation

Demand Forecasting and Planning in Crisis30-31 July, Shanghai

Joseph Ogrodowczyk, Ph.D.

Session 3 Joseph Ogrodowczyk, Ph.D.

Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 2

Data: Overview, Analysis, and Presentation Session agenda

Data as a tool for forecasting Determining the “right” quantity of data Getting good forecasts from bad data Guidelines for addressing poor data Presenting data: tables and graphs Correcting for missing data

Activity: Become familiar with sample data, transform data into pivot table form, and make some simple graphs



Data: Overview, Analysis, and Presentation

Data as a tool for forecasting Forecasts are only as good as the information and knowledge

used to generate them Forecasters have easy access to review and analyze data

because of advances in computers More data are not always good for forecasting Need to know how to study the data and understand ways to

analyze that data



Data as a tool for forecasting Questions for data sets

How much data are available? What type of model will be used?

How reliable are the data? What is the source of the data? Has the definition of the data changed?

Are any data missing? Can the missing data points be estimated?





Are the data aggregated or disaggregated? What is the underlying organizational hierarchy of the data? What methods will be used to aggregate or disaggregate the

forecasts? Is that method used consistently throughout the company?

What is the product life cycle phase of the data? Is there a structural change in the data?

Did a product group experience a new product line launch? Was there a promotion? Did market conditions change because of an acquisition? Did market conditions change because of an economic or financial crisis?





Are there outliers in the data? Can these be corrected or should they be included?

Do the time buckets have different working days? Example: If data are monthly, do all months have the same number

of weeks? Are there seasonal variations in the data?

Are there business cycles in the data? What type of trend do the data contain?

Can assumptions be made about the data trend based on the forecast time horizon?




Determining the right quantity of data Product life cycle

Mature products have more stable demand New products have increasing demand Aging products have declining demand

Depending on the specific product type, each stage will have varying data lengths Need to match the length of the data set with the life cycle If possible, don’t mix data between life cycles




Determining the right quantity of data Model type

Different models require different quantities of data Single Exponential smoothing models require less data than Triple

Exponential smoothing models because they don’t need to account for seasonality

Regression models’ requirements depend on the number of independent variables being used to explain demand variation

Models with seasonal components require at least two season cycles

Forecast horizon Short term forecasts require a smaller data set than long term

forecasts and are influenced by recent historical information Long term forecasts need to include trends, seasonality, and

business cycles




Getting good forecasts from bad data Causes of poor data quality

Data collection Wrong data collected (e.g. shipments instead of backlog) Varying parameters (e.g. prices, advertising, weather) are not

collected or formatted in a usable form Gaps or errors in data collection Change in collection methods leading to essentially two different data

series Data storage

Lack of historical data Not enough detail – aggregation too high




Getting good forecasts from bad data Causes of poor data quality

Operations Inconsistent product quality causing changes in demand Process changes driving data collection changes Sudden changes in external factors (e.g. strike, weather disruptions,

trade disputes, economic/financial crises) Marketplace

Changes in marketing can disrupt demand Changes in competitive landscape (more or fewer rival firms)

Finance and accounting Financial requirements drive spikes and valleys in demand behavior




Guidelines for addressing poor quality data The purpose of forecasting data is to predict the future

Modifying data may be necessary to create a viable forecasting data set Create a separate data set Change the level of aggregation or time buckets Calculate missing values or modify outliers Add additional variables to account for the effects of internal factors

(e.g. promotions) or external factors (e.g. business cycles, weather changes, and economic/financial crises)




Guidelines for addressing poor quality data The purpose of forecasting data is to predict the future

Corporate data organization may be not suitable for forecasting Fiscal periods may not correspond with actual periods Understand the periodicity of the data which may not correspond to

the calendar periodicity Days between holidays, moon cycles, customer purchasing habits

May also regroup customers and products




Guidelines for addressing poor quality data Understand the data relevant to the forecasts

Statistically test for relevant variables among company tradition Data collection analysis may suggest additional variables

Be clear on the business question Make sure the forecasts address the real problem

Is the forecast too detailed? Is the time horizon long enough?




Presenting data: Tables and graphs Example: Assume we know that we have enough good data

to be able to produce the necessary forecasts What is our next step?

Always visually inspect the data

The following example uses Microsoft Excel. For the purposes of simple models, Excel is acceptable. For more statistically robust models, I recommend using a forecasting software, and will suggest several packages in Session 7.




Presenting data: Tables and graphs Example: Monthly wood sales Begin with data in table format

Date WoodFeb-2009 63.87Jan-2009 64.64Dec-2008 67.37Nov-2008 75.57Oct-2008 81.42Sep-2008 85.92Aug-2008 89.81Jul-2008 88.65Jun-2008 92.97May-2008 90.38Apr-2008 91.60Mar-2008 90.12Feb-2008 88.55Jan-2008 88.23Dec-2007 88.89Nov-2007 91.82Oct-2007 99.53Sep-2007 100.57Aug-2007 103.03Jul-2007 101.42Jun-2007 107.00

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec2000 102.3 105.7 108.0 109.1 106.7 109.2 101.6 105.0 105.4 103.7 97.7 90.22001 90.1 92.2 96.2 97.0 98.5 103.5 95.9 102.3 102.7 100.1 95.3 91.72002 94.1 96.4 100.2 102.1 101.2 106.4 99.3 104.3 103.1 103.8 97.0 92.02003 94.4 97.5 98.1 99.9 99.5 104.4 100.2 104.3 104.4 106.4 103.6 96.42004 99.0 102.5 103.3 105.6 105.5 108.1 104.8 108.2 105.2 109.6 103.0 97.72005 104.2 105.2 105.7 109.0 107.9 112.4 108.2 111.1 114.3 121.2 116.1 109.92006 111.9 113.2 114.8 114.5 113.9 116.4 111.7 112.7 109.8 105.0 98.8 97.12007 95.6 97.8 101.3 101.2 102.0 107.0 101.4 103.0 100.6 99.5 91.8 88.92008 88.2 88.5 90.1 91.6 90.4 93.0 88.6 89.8 85.9 81.4 75.6 67.42009 64.6 63.9




Presenting data: Tables and graphs Changing the table format (creating pivot tables)





Layout button





Option button





Copy and paste-special (values) of the pivot table




Presenting data: Tables and graphs What is the best way to display the data? It depends on understanding the forecast question (including the

needed time horizon) How much historical information is needed? Line graph with data points for the single table format




0

20

40

60

80

100

120

140

Jan-72Jan-73Jan-74Jan-75Jan-76Jan-77Jan-78Jan-79Jan-80Jan-81Jan-82Jan-83Jan-84Jan-85Jan-86Jan-87Jan-88Jan-89Jan-90Jan-91Jan-92Jan-93Jan-94Jan-95Jan-96Jan-97Jan-98Jan-99Jan-00Jan-01Jan-02Jan-03Jan-04Jan-05Jan-06Jan-07Jan-08Jan-09

Woods sales

Notice that the time horizon and sales quantities have changed Alternative ways to display the data set that depends on the forecast objective

60

70

80

90

100

110

120

130Wood sales




Presenting data: Tables and graphs

60

70

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90

100

110

120

130

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2000

2001

20022003

2004

2005

2006

20072008

2009




Correcting for missing data What happens if we are missing some entries? Should the missing values be equal to zero or to some other

number? Some software packages will ignore missing values while other

will assume a missing value is zero. Some modeling software programs will fail to produce a forecast and will show an error.




Correcting for missing data Using only 2004-2008 of the prior data example

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec2004 99.0 103.3 105.6 105.5 108.1 104.8 108.2 105.2 109.6 103.0 97.72005 104.2 105.2 105.7 107.9 112.4 108.2 111.1 114.3 121.2 116.1 109.92006 111.9 113.2 114.8 114.5 113.9 111.7 112.7 109.8 105.0 98.8 97.12007 95.6 97.8 101.3 101.2 102.0 107.0 101.4 103.0 100.6 91.8 88.92008 88.2 88.5 90.1 91.6 90.4 93.0 88.6 85.9 81.4 75.6 67.4




Correcting for missing data Two suggested methods

1. Bookends Calculate an average based upon the preceding and following entries

(months) For 2004, February is missing. January sales were 99 and March

sales were 103.3. (99+103.3)/2 = 101.15 This would be the estimate for February sales





1. Bookends The table below shows the calculated averages of the bookend

approach with the actual values

Missing month

Preceeding sales

Following sales

Average sales

Actual sales

2004 February 99 103.3 101.15 105.72005 April 105.7 107.9 106.80 109.02006 June 113.9 111.7 112.80 116.42007 October 100.6 91.8 96.20 99.52008 August 88.6 85.9 87.25 89.8





2. Time bucket average Suppose that certain months contain a seasonal component (January

and Chinese New Year) In this case, the preceding and following months may not be a good

estimation for demand If enough data are available, a historical average per month can be

calculated





2. Time bucket average Calculate an average using the corresponding time buckets

Other February data, first week of month data, third quarter data February 2004 has a missing value. Use February data from 2005-

2008 (the remaining years in the data set)

Year Sales2005 105.22006 113.22007 97.82008 88.5

Average 101.2





2. Time bucket average

Year Sales Year Sales Year Sales Year Sales2004 105.6 2004 108.1 2004 108.2 2004 109.62006 114.5 2005 112.4 2005 111.1 2005 121.22007 101.2 2007 107.0 2006 112.7 2006 105.02008 91.6 2008 93.0 2007 103.0 2008 81.4

Average 103.2 Average 105.1 Average 108.7 Average 104.3

April 2005 June 2006 August 2008 October 2007




References Bonnell, Ellen. 2007. How to get good forecasts

from bad data. Foresight. Summer: 36-40. Jain, Chaman L. and Jack Malehorn. 2005.

Practical Guide to Business Forecasting (2nd Ed.). Flushing, New York: Graceway Publishing Inc.

Data: Overview, Analysis and Presentation

Session 3: Data: Overview, Analysis, and Presentation Demand Forecasting and Planning in Crisis...

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