1. Forecasting & Demand Planning
Chapter 8
Forecasting & Demand Planning

2. Objectives
After reading the chapter and reviewing the materials presented the students will be able to:
Explain the impact of forecasting on supply chain management
Describe the forecasting process
Identify key forecasting models
Explain how to measure forecast accuracy

3. What is Forecasting?
Forecasting is the process of predicting future events.
Forecasting is one of the most important business activities because it drives all other business decisions.
Decisions such as which markets to pursue, which products to produce, how much inventory to carry, and how many people to hire are all based on forecasts.
The consequences can be costly in terms of lost sales or excess inventory that cannot be sold.

4. Planning
Planning is the process of selecting actions in response to the forecast.
Planning involves the following decisions:
1. Scheduling existing resources: This includes the production process, transportation, labor, facilities, and capital.
2. Determining future resource needs: This depends on forecasts of emerging market opportunities, new technology, new products, and competition.
3. Acquiring new resources: Plans must be made well in advance, and procedures to acquire new resources and capabilities put in place well ahead of time.

5. Impact on the Organization
Marketing forecasts size of markets, new competition, future trends, emerging markets, and changes in consumer preference.
Financing, in turn, uses forecasting to assess financial performance, capital investment needs, and set budgets.
Operations makes decisions regarding production and inventory levels, conducts capacity planning and scheduling.
Sourcing uses forecasts to make purchasing decisions and select suppliers.

6. Impact on Supply Chain Management
The forecast of demand is critical to the entire supply chain, as it affects the plans made by each company in the chain.
When there is collaboration between suppliers and manufacturers in generating the forecast, all entities are responding to the same level of demand.
Independent forecasting by members of the supply chain gives rise to the bullwhip effect (volatility in orders as they propagate through the supply chain).

7. Principles of Forecasting
1. Forecasts are rarely perfect: There are too many factors in the business environment that cannot be predicted with certainty.
2. Forecasts are more accurate for groups than for individual items: Higher degree of accuracy can be obtained when forecasting for a group than for individual items , their individual high and low items cancel each other out.
3. Forecasting are more accurate for shorter than longer time horizons: Data does not change much in the short run.

8. Steps in the Forecasting Process
1. Decide what to forecast: Remember forecasts are made in order to help plan for the future. We have to decide what forecasts are actually needed to guide the plan.
2. Analyze appropriate data: It is important to identify which patterns are present. The most common data patterns are:
a. Level or horizontal. It is common for commodity products in the mature stage of the life cycle, such as table salt or toothpaste.
b. Trend. Increasing or decreasing pattern over time.
c. Seasonality. Any pattern that regularly repeats itself, like ice cream sales in summer or snow shovels in winter.
d. Cycles. Do not have a predictable or repeating length or magnitude. They are most difficult to predict.
3. Select the forecasting model: Once data patterns have been identified, the next step is to select the appropriate forecasting model (more later).
4. Generate the forecast: Using the data.
5. Monitor forecast accuracy: This information should be used to improve the forecasting process. Remember forecasting is an ongoing process.

9. Factors in Method Selection
1. Amount and type of available data: Different forecasting methods require different types and quantities of data.
2. Degree of accuracy required: The costs of the forecasting method need to justify the importance of the forecast.
3. Length of the forecast horizon: Some forecasting methods are better suited for short term forecasts whereas others are better suited for long term.
4. Patterns in the data: It is critical to select a forecasting model that is appropriate for the identified patterns in the data.

10. Types of Forecasting Methods
1. Qualitative forecasting methods: are methods based on subjective opinions and judgment of individuals such as managers, sales staff, or customers.
2. Quantitative forecasting methods: are generally more accurate than qualitative methods. They require data in quantifiable form.

11. Qualitative Forecasting Methods
Executive opinion: a group of managers, executives, or sales staff meet and collectively develop a forecast. It is often used to forecast sales, market trends, make strategic forecasts, or forecast new products.
Market research: uses surveys and interviews to determine customer likes, dislikes, and preferences, and to identify new product ideas.
The Delphi method: Questionnaires are sent to experts, the findings summarized, and the process repeated, until consensus is reached. It is excellent to forecast long range demand, technological change, and scientific advances.

12. Quantitative Forecasting Methods
Time series models: generate forecasts from an analysis of time series of the data (data over time taken at regular intervals). Example is student enrollment per semester over the past five years.
Causal models: Variable being forecast is related to other variables in the environment. For example university enrollment may be related to unemployment rates, recession levels, or salary levels. The forecasting process involves identifying these relationships, and using them to generate theforecast.

13. Time Series Forecasting Models
1. The Mean: The forecast is made by simply taking the average of all data.
Ft+1 = ( ∑ Dt )/n = (Dt + Dt-1 +…+ Dt –n )/n
where Ft+1 = forecast of demand for next period, t+1
Dt = demand for current period, t
n = number of data points
This method is reasonable to forecast stable and mature products.
2. Moving Averages: generates a forecast by averaging a specified number, n, of the most recent data rather than the entire data set.
3. Exponential Smoothing: The equation for the forecast is:
Ft+1 = α Dt + ( 1 - α ) Ft
Where Ft+1 = forecast of demand for next period, t+1
Dt = actual value for current period, t
Ft = forecast of demand for current period t
α = smoothing coefficient (between 0 and 1)
The critical aspect of exponential smoothing is selecting the correct value of alpha (α ).

14. Time Series Forecasting Models
4. Trend Adjusted Exponential Smoothing:
FITt+1 = Ft+1 + Tt+1
Where FITt+1 = forecast including trend for next period t+1
Ft+1 = trend factor for next period = β(Ft+1 - Ft ) + ( 1 – β) Tt
Tt = trend factor for the current period, t
β = smoothing constant for the trend adjustment factor
Fist generate an unadjusted forecast Ft+1. Then generate trend Tt+1 . Add Ft+1 and Tt+1
5. Seasonality Adjustment: Seasonality is any regularly repeating pattern. 1. Compute the average demand for each season. Total annual demand divided by the number of seasons. For quarterly data the number of seasons would be four. 2. Compute a seasonal index for each season. A seasonal index is obtained by dividing the actual demand for each season by the average demand for each year. 3. Adjust the average forecast for next year by the seasonal index. Generate a forecast for next year using any of the methods discussed earlier and calculate average demand per season. Use seasonal indexes to generate seasonally adjusted forecasts. See example in text.

15. Causal Models 1. Linear Regression:
It is a forecasting model that assumes a linear or straight line relationship between two variables.
The variable being forecast, called the dependent variable, is linearly related to another variable, called the independent variable.
For example, if we assume that a person’s weight is related and height are linearly related, we can use the model to forecast weight based on a person’s height.
2. Multiple Regression:
It extends linear regression by looking at a relationship between the independent variable and multiple dependent variables.
For example, the dependent variable might be university student enrollment per semester and the independent variables might be unemployment rate and per capita income.

16. Measuring Forecast Accuracy
Measuring forecast accuracy tells us how our forecasting methods are performing and enables us to improve performance over time.
The first step in measuring forecast accuracy is to measure the forecast error.
Forecast error is the difference between actual demand and the forecast for a given period.
Two of the most commonly used error measures are the mean absolute deviation (MAD) and the mean square error (MSE).
MAD is the average of the sum of the absolute errors:
MAD = ∑ |Actual – Forecast| / n
MSE is the average of the squared error:
MSE = ∑ (Actual – Forecast )2 / n
Both error measures provide different information and a good forecaster learns to rely on multiple measures.

17. Collaborative Forecasting & Demand Planning
1. Collaborative Planning, Forecasting & Replenishment (CPFR):
CPFR is a collaborative process of developing joint forecasts and plans with supply chain partners. Trading partners jointly set forecasts, plan production, replenish inventories, and evaluate their success in the marketplace.
VICS (Voluntary Inter-industry Commerce Standards) association offers a 5 step process of adoption: 1. Create joint objectives. 2. Develop a business plan (forecasting needs, production schedules, key performance metrics). 3. Create a joint forecast . 4. Agree on replenishment strategies. 5. Agree on a technology partner to bring CPFR to fruition.
2. Sales and Operations Planning (S&OP):
S&OP is an organizational process intended to match supply and demand through functional collaboration. S&OP requires teamwork among sales, distribution, and logistics, operations, finance, and product development. This enables firms to provide better customer service, lower inventory, reduced customer lead times, and stabilize production schedules.
It consists of a five step process: 1. Generate quantitative sales forecasts. 2. marketing adjusts the forecast based n introduction of new products or elimination of old products. 3. Operations checks forecasts against existing capability. And resources such as inventory, production capacity, scheduling, and labor for meeting demand. 4. Marketing, operations, and finance jointly review forecast and resource issues. Attempts are made to solve capacity issues and balance supply and demand. The forecast is converted into dollars to see if it meets the financial plan of the organization. 5. Executives meet to finalize forecast and capacity decision. Executives meet and reach agreement to convert it into the operating plan for the organization.

18. Summary Forecasting is the process of predicting future events.
Forecasting is one of the most important business activities because it drives all other business decisions. Decisions such as which markets to pursue, which products to produce, how much inventory to carry, and how many people to hire are all based on forecasts.
Planning is the process of selecting actions in response to the forecast. Planning involves the following decisions: 1. Scheduling existing resources. 2. Determining future resource needs. 3. Acquiring new resources.
Marketing forecasts size of markets, new competition, future trends, emerging markets, and changes in consumer preference.
Financing, in turn, uses forecasting to assess financial performance, capital investment needs, and set budgets.
Operations makes decisions regarding production and inventory levels, conducts capacity planning and scheduling.
Sourcing uses forecasts to make purchasing decisions and select suppliers.
The forecast of demand is critical to the entire supply chain, as it affects the plans made by each company in the chain.
The most common data patterns are: a. Level or horizontal. It is common for commodity products in the mature stage of the life cycle, such as table salt or toothpaste. b. Trend. Increasing or decreasing pattern over time. c. Seasonality. Any pattern that regularly repeats itself, like ice cream sales in summer or snow shovels in winter. d. Cycles. Do not have a predictable or repeating length or magnitude. They are most difficult to predict.
Qualitative forecasting methods: are methods based on subjective opinions and judgment of individuals such as managers, sales staff, or customers.
Quantitative forecasting methods: are generally more accurate than qualitative methods. They require data in quantifiable form.
Linear Regression: It is a forecasting model that assumes a linear or straight line relationship between two variables.
Multiple Regression: It extends linear regression by looking at a relationship between the independent variable and multiple dependent variables.
Measuring forecast accuracy tells us how our forecasting methods are performing and enables us to improve performance over time.

19. Home Work 1. Explain why forecasting is important?
2. What is planning? What are the 3 steps in planning?
3. Explain the most common data patterns?
4. Compare qualitative and quantitative forecasting methods.