1. Master Planning of Resources
Session 2
Forecasting Demand

2. What is a Forecast?
Forecast – An estimate of future demand. A forecast can be determined by mathematical means using historical data, it can be created subjectively by using estimates from informal sources, or it can represent a combination of both techniques.
Forecast Error – The difference between actual demand and forecast demand, stated as an absolute value or as a percentage.
Forecast Management – The process of making, checking, correcting, and using forecasts. It also includes determination of the forecast horizon.
A forecast is meaningful only in relation to planning and decision-making in business application. Thus, an important aspect of forecasting system is knowing and planning how it will be used in business planning, budgeting, and the operations aspects of master scheduling and inventory planning. Forecast does not have to be 100 percent accurate, but the level of accuracy must be presented for improvement.

3. Why Forecast? To plan for the future by reducing uncertainty
To facilitate a company in taking control of operations. Without forecast, it would be a chaos.
To anticipate and manage change
To increase communication and integration of planning teams
To anticipate inventory and capacity demands and manage lead times
To project costs of operations into budgeting processes
To improve competitiveness and productivity through decreased costs and improved delivery and responsiveness to customer needs

4. Areas Impacted by the Forecast
Investment decisions
Capital equipment decisions
Inventory planning
Capacity planning
Operations budgets
Lead-time management

5. Forecast System Design Issues
Determine information that needs to be forecasted
Assign responsibility for the forecast
Set up forecast system parameters
Select forecasting models and techniques
Collect data
Test models
Record actual demand
Report accuracy
Determine root cause of variance
Review forecasting system for improved performance

6. General Forecasting Techniques
Qualitative Techniques—based on intuitive or judgmental evaluation
Quantitative Techniques—based on computational projection of a numeric relationship
Qualitative techniques are based on expert or informed opinion regarding future product demands. In many instances, this information is intuitive and based on subjective judgment. Qualitative techniques include gathering information from customer focus groups, groups of experts, think tanks, research groups, etc.
Quantitative techniques rely on mathematical formulas to analyze historical demand patterns and predict future demand. Some of the most popular include moving averages, seasonal indexes, and exponential smoothing.

7. Qualitative Techniques
Expert opinion
Market research
Focus groups
Historical analogy
Delphi method
Panel consensus
Expert opinion: Experts give their views on current trends and likely future developments that may have an impact on the general economy or a specific industry or market.
Market research is generally used to support product development and promotions. It can provide insights into the likely sales of a product, but it should be used with other techniques. Customer surveys must be constructed carefully to ensure that the questions elicit the kind of responses that are useful. It is also important to establish a sufficient sample size of target customers to ensure the reliability of survey results.
Focus groups consist of panels of customers who are asked to provide their opinions about a product or service.
Historical analogy is a method that compares the sales of a new product or service with the sales of a previous similar product or service. The assumption is that the sales patterns associated with the previous product or service can be transferred to the new product or service.
Delphi method is based on the knowledge and judgment of a small group of experts. Responses are collected from all the respondents and then compiled into a prediction of the future, according to the experts.
Panel consensus: Here a group of people provides opinions about the future, and a facilitator brings the group to a consensus. The idea is that the group as a whole would make better decisions than would each member individually.
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8. Quantitative Techniques
Moving average
Exponential smoothing
Regression analysis
Adaptive smoothing
Graphical methods
Econometric modeling
Life-cycle modeling
Moving Average is an arithmetical average of a certain number (n) of most recent observations. The value of n (the number of periods to use for the average) reflects responsiveness versus stability in the same way that the choice of smoothing constant does in exponential smoothing. Moving averages do not work well with seasonal demand.
Exponential Smoothing is a mathematical equivalent to a weighted moving average in which a constant is developed which determines how much the forecast will vary from the actual data received.
Regression Analysis is a statistical trend analysis tool which determines the best mathematical expression to be used to describe actual data.
Adaptive Smoothing is a form of exponential smoothing in which the smoothing constant is automatically adjusted as a function of forecast error measurement.
Graphical methods: The use of visual information to predict demand patterns typically involves plotting information in a graphical form.
Econometric modeling: The use of a set of equations intended to be used simultaneously to capture the way in which dependent and independent variables are interrelated.
Life-cycle modeling: A quantitative forecasting technique based on applying past patterns of demand data (covering introduction, growth, maturity, saturation, and decline) of similar products to a new product family.

9. General Forecasting Data Methods
Intrinsic forecasting methods are based on historical patterns of the data itself from company data
Extrinsic forecasting methods are based on external patterns from information outside the company such as published data and data available from the Internet
Qualitative and quantitative forecasts may be generated based on intrinsic or extrinsic information.

10. Internal (Intrinsic) Factors
Product life-cycle management
Planned price changes
Changes in the sales force
Resource constraints
Marketing and sales promotion
Advertising
Product life-cycle management: (the introductory, growth, maturity, and decline phases)
Planned price changes: Forecasts for future sales can often be influenced by planned price changes.
Changes in the sales forces can influence the sales of a product or service. In many companies, there is a direct correlation between the size of the sales force and the level of sales revenue.
Resource constraints can have a large impact on predictions of future sales. If a company has historically faced resource constraints, then the sales history reflects only what was capable of being delivered, not the true level of actual demand from the customers.
Marketing and sales promotion is a primary source of demand variation. The purpose of the special promotion is to try to gain market share from the competition and become a more dominant supplier to the market. Often, however, the promotion creates artificial demand that is transitory at best.
Advertising is another driver of sales revenue.
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11. External (Extrinsic) Factors
Competition
New customers
Plans of major customers
Government policies
Regulatory concerns
Economic conditions
Environmental issues
Weather conditions
Global trends
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12. Leading Indicators Indicator (Causal Factor) Influences volume of
Housing starts
Birth rate
Health trends
Desire for
Healthier lifestyle
Influences volume of
Building materials
Home furnishings
Baby products
Medical supplies
Nutritional products
Fitness products
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13. Demand A need for a particular product or component
Independent demand is demand for an item that is unrelated to the demand for
other items. Independent demand items are saleable products or services that are
added to the master schedule.
Dependent demand can be calculated directly from the demand for other products.
It is related to the bill of material structure.
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14. Sources of Demand Demand can come from many sources: Consumers
Customers
Referrers
Dealers
Distributors
Interplant
Service parts
Consumers are the ultimate users of products or services such as patients using drugs.
Customers are people who will receive an invoice and pay for a product.
Interplant are purchases made from affiliated business units of the same company.
Service parts are items subject to independent demand for service reasons.

15. Demand Characteristics
Internal Factors
Product promotion
Product substitution
External Factors
Random fluctuation
Seasonality
Trend
Economic cycle
Changing customer preferences and demands
Internal Factors
Product promotion is the use of marketing ideas to stimulate sales of a product or service. Examples of product promotions are special packaging, special pricing, and special location of consumer goods in a retail store to increase sales for the product.
Product substitution is the replacement of one product with an alternative product.
External Factors
Random fluctuation is caused by unknown or random factors.
Seasonality is a repetitive pattern of demand from year to year (or other repeating time interval), with some periods having considerably higher demand than others. E.g. children’s toys, winter or summer clothes, and sunblock.
Trend is the general upward or downward movement of demand over time. Trend analysis can be used to determine if a product or service is increasing or decreasing in popularity. Trends can also be used to determine the potential impact of changing customer preferences and demands.
Economic cycle is a repetitive pattern of demand based on movements in the general economy. Phases of economic cycle include inflation, deflation, boom, recession, and depression. Economic cycle tends to be longer than one year.
Changing customer preferences can refer to the need for customers to increase or decrease existing orders. These customer preferences and demands are typically outside, or external to, company efforts.

16. Seasonality

17. Seasonality Calculation
Measures seasonal variation of demand
Relates the average demand in a particular period to the average demand for all periods

18. Calculation of Seasonal Index
Sales of Ice Cream

19. Seasonality Exercise

20. Economic Cycle
Economic cycle do not follow the same pattern of annual or seasonal sales. Economic cycles are dependent on global economic conditions and these depend on conditions of the global business environment. Economic conditions will determine the nature of economic cycles, periods of increasing prices known as inflationary periods, and periods of decreasing prices known as deflationary periods. Consecutive periods of zero or negative growth in the gross domestic product (GDP) are known as recessionary periods, and periods of extreme recession are known as depressionary periods. Economic cycles will determine the readiness of the consumers to buy products or services and will particularly affect the sales of major consumer purchases such as houses and cars.

21. Pyramid Forecasting Roll Up Forecast Force Down Adjustment Total
business
volume
(dollars)
Roll Up Forecast
Product family volume
(units/dollars)
Force Down Adjustment
It is important to note that the higher the item in the hierarchy, the more accurate the forecast. Forecasting at the top of the pyramid is not sufficient to provide the planning necessary for operations. Forecasting at the lowest level in the hierarchy, by customer by location, does provide the detail, but unfortunately, this level also contains the highest level of forecast error. This is because forecasts are more accurate for larger groups than smaller groups. The most effective level to forecast is at the product family or product subfamily level.
Product/item volume
(units)

22. Pyramid Forecasting

23. Pyramid Forecasting

24. Technique—Pyramid Forecasting Example
ROLL-UP
 Product-level forecast
X1 units—8,200
price—$20.61
 Family-level forecast
 Family-adjusted forecast
FORCE-DOWN
X2 units—4,845
price—$10.00
—units—13,045
Family avg price—$16.67
—units—15,000
15,000
13,045 X1
× 8,200 = 9,429 units
15,000
13,045 X2
× 4,845 = 5,571 units

25. Pyramid Forecasting Using Revenue
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26. Pyramid Forecasting Exercise
Historical Demand
Product A
Region
Region
Selling Price $4.50
Management has determined that next year’s demand will be $10,000 total.
CALCULATE the projected demand in units for products A and B in each region.
Product B
Region
Region
Selling Price $8.50
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27. Pyramid Forecasting Exercise—Solution
Based upon historical demand
A = = 450 × $4.50 = $2,025
B = = 750 × $8.50 = $6,375
Total = $8,400
$10,000
$8,400
= 1.19 (19% increase)
A: Region 1 = 1.19 × 150 = 178.5
Region 2 = 1.19 × 300 = 357.0
B: Region 1 = 1.19 × 300 = 357.0
Region 2 = 1.19 × 450 = 535.5
= × $4.50 = $2,409.75
= × $8.50 = $7,586.25
$9,996.00
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28. Moving Average Forecasting
Advantages
A simple technique that is easy to calculate
It can be used to filter out random variation
Longer periods provide more smoothing
Limitations
If a trend exists, it is hard to detect
Moving averages lag trends
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29. Moving Average Exercise
Actual sales
Next month’s forecast
variation
Jan
100
Feb
500
Mar
1000
Apr
1500
May
2800
June
5100
Jul
6200
Aug
5700
Sep
3200
Oct
1200
Nov
Dec

31. Exponential Smoothing
New Forecast = ∝x Actual Demand + (1 - ∝) x Old Forecast
New Forecast = Old Forecast + ∝ x (Actual Demand – Old Forecast)
Provides a routine method of updating item forecasts
Alpha is a weighting factor applied to the demand element
Works well for items with fairly constant demand
Is satisfactory for short-range forecasts
Lags trends
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32. Smoothing Factor Referred to as Alpha (a)
Determines the weight of historical data on projection
Sets responsiveness to changes in demand
Range 0  a  1
a = 2
n + 1
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33. Smoothing Factor (cont.)
Determines how many periods of actual demand will influence forecast
1.00 = 1 period
0.50 = 3 periods
0.29 = 6 periods
0.15 = 12 periods
0.10 = 19 periods
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34. Comparison of Exponential Smoothing Alpha Factors
0.1 Low weighting -most smoothing
0.9 High weighting - close to actual
Actual sales
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35. Exponential Smoothing Examples
New forecast = Old forecast + smoothing factor (a)
 (actual demand - old forecast)
Example: old forecast = 160, actual = 200, a = 0.1
new forecast = (0.1  ( ))
= (0.1  40) = 164
Example: old forecast = 160, actual = 200, a = 0.8
new forecast = (0.8  ( ))
= (0.8  40) = 192
Adapted from: Manufacturing for Survival, B.R. Williams, Addison Wesley, 1996
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36. New Product Introduction
Every new product/service is a calculated risk.
Every new product/service has the potential to be the next
Blockbuster
Lifesaver
Money loser
Disaster
Liability nightmare.
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37. Product Life Cycle Introduction Growth Maturity Decline
Product Life Cycle Stages
Volume
Time
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38. Focus Forecasting—Assumptions/Methods
The most recent past is the best indicator of the future
One forecasting model is better than the others
Methods
All forecasting models for all items forecasted will be compared against recent sales history
The model that achieves the closest fit will be used to forecast this item this time
Next time, a different model may be selected
Focus Forecasting is a technique invented by Bernard Smith. Focus forecasting is a computer-based simulation technique that compares the forecasts generated using any one of 14 simple strategies. The computer selects the best strategy to forecast such an item at this moment in time. Whichever strategy gives the best fit to the actual results becomes the strategy selected to forecast this item at this point in time. The analytical simulation can result in different strategies being applied to an item every period.
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39. Data Issues for Forecasting
Availability of data
Consistency of data
Amount of history required
Forecast frequency
Frequency of model reevaluation
Cost and time issues
Recording true demand
Order date vs. ship date
Product units vs. financial units
Level of aggregation
Customer partnering
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40. Planning Horizon and Time Periods
Forecast Length
Short Mid Long
Planning
Horizon
Weeks Months Quarters
Time Periods (week numbers)
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41. Data Preparation and Collection
Record sales data in same periods as forecast data (daily, weekly, or monthly)
Monitor demand, not sales and/or shipments
Record the circumstances of exceptional demand
Record demand separately for unique customer groupings and market sectors
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42. Dealing with Outliers 55 50 2-42
Outlier – A data point that differs significantly from other data for a similar phenomenon. For example, if the average sales for some product were 10 units per month, and one month had sales of 500 units, this sales point might be considered an outlier.
In reality, the outlier may be caused by events that have a probability of recurrence. It is always easier to remove the problem outlier than to adjust a model that may need to be changed. For this reason, it is important to record the circumstances associated with a particular outlier.
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43. Decomposition of Data Purify the data Adjust the data
Take out the baseline and components
Identify demand components
Trend
Seasonality
Nonannual cycle
Random error
Measure the random error
Project the series
Recompose
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44. Session 2 Review You should now be able to
Explain why forecasting is important
Identify and describe general methods of forecasting
Identify factors influencing demand
Describe considerations in using data for forecasts
Outline the process of data decomposition
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