Chapter 2. Forecasting

Basics of Supply Chain Management : BSCM

Main Issues :

· Definition and Uses of Forecasting.

· Demand Management : Roles and Activities.

· Characteristics of Demand : Trend, Seasonality, Economic Cycle, and Random Error.

· Independent vs. Dependent Demand.

· Principles of Forecasting and Date Collection.

· Various Forecasting Techniques : Moving Average, Exponential Smoothing, and Seasonal Index

Analysis.

· Tracking Forecasting : Mean Absolute Deviation(MAD) and Tracking Signal(TS)

1. Introduction.

□ Definitions.

- 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.

- Forecasting.

· The business function that attempts to predict sales and use of products so they

can be purchased or manufactured in appropriate quantities in advance.

- Others.

· Forecast horizon - The period of time into the future for which a forecast is

Ch.2 Forecasting

prepared.

· Forecast interval - The time unit for which forecasts are prepared, such as

week, month, or quarter. (Syn : Forecast period)

· Forecast error - The difference between actual demand and forecast demand,

stated as an absolute value or as a percentage.

· Forecast accuracy - A measure of forecast usefulness, often defined as the

average difference between the forecast value to the actual

value.

· Forecast management - The process of making, checking, correcting and using

forecasts. It also include determination of the forecast

horizon.

□ Why Forecast ?

- To develop plans to satisfy future demand.

- Demand delivery in reasonable time for customer.

- To anticipate future demand for products or services.

- To plan to provide the capacity and resources to meet the demand.

- Firms that make standard products.

: Need to have available to shorten the delivery time.

- Firms that make to order.

: Cannot begin making a product before a customer places an order but must have

; the resources of labor and equipment available to meet demand.

□ Factors Influencing Demand.

- General business and economic conditions.

- Competitive factors.

- Market trends such as changing demand.

- The firm's own plans for advertising, promotion, pricing, and product changes.

□ Use of Demand Forecasting.

- Strategic business plan

· Concerned with overall markets and direction of the economy over the next two

to ten years.

· Concerns for production - plant expansion, capital equipment purchase, and

anything requiring a long lead time to purchase.

- Production Planning (or S&OP)

· Concerned with manufacturing activity for the next one to three years.

· Forecasting for product groups - budgets, labor planning, long lead time,

procurement item, overall inventory levels.

- Master Scheduling

· Concerned with production activity from the present to a few months ahead.

· Forecasting for individual items - a master production schedule, individual item

inventory levels, raw materials, component parts, and labor planning.

2. Demand Management

□ Definition.

- The function of recognizing and managing all of the demands for products to support

the marketplace.

- It involves doing what is required to help make the demand happen and prioritizing

demand when supply is lacking.

- Proper demand management facilitates the planning and use of resources for profitable

business results.

- Activities.

· Forecasting.

· Order entry, and order promising.

· Determining branch warehouse requirement, interplant orders, and service parts

requirement.

Figure 1. Demand Management

□ Order Service

- Order Service.

· The function that encompasses receiving, entering, and promising orders from

customers, distribution centers, and inter-plant operations.

· It is also typically responsible for responding to customer inquires and interacting

with the master scheduler on availability of products.

- Order Entry.

· The process of accepting and translating what a customer wants into terms used

by the manufacturer or distributor.

· In Make-to-Stock.

: Creating shipping documents for finished goods.

· In Make-to-Order.

: It might be a more complicated series of activities, including design efforts.

- Order Promising.

· The process of making a delivery commitment, i.e., answering the question,

when can you ship ?

· For MTO products, this usually involves a check of uncommitted material and

availability of capacity, often as represented by the master schedule

available-to-promise.

3. Characteristics of Demand

□ Characteristics of Demand.

Figure 2. Pattern of Demand.

- Demand vs. Sales.

· The difference between "demand" and "sales" are obvious. The difference is that

sales implies what is actually sold whereas demand shows the need for the

item.

· Sometimes demand cannot be satisfied, and sales will be less than demand.

- Demand patterns.

1. Trend

· Demand is increasing in a steady pattern of demand from year to year.

· The trend can be level, having no change from period to period, or it can rise or fall.

2. Seasonality

· The demand pattern shows each year's demand fluctuating depending on

the time of year.

· Reasons of the fluctuation : The weather, holiday seasons, particular events.

· Seasonality is usually thought of as occurring on a yearly basis, but it can

also occur on a weekly or even daily basis.

3. Nonannual (Economic) cycle

· A repetitive pattern of demand based on movements in the general  economy.

· Phases of economic cycles include inflation, deflation, boom, recession, and

depression.

· Economic cycles tend to be longer than one year.

4. Random error

· Many factors affect demand during specific periods and occur on a random basis.

· The pattern of variation can usually be measured, and this will be

discussed in the section on tracking the forecast.

□ Independent vs. Dependent Demand.

- Independent Demand.

· The demand for an item that is unrelated to the demand for other items.

· Examples.

1. Demand for finished goods.

2. Parts required for destructive testing.

3. Service parts requirements.

- Dependent Demand.

· Demand that is directly related to or derived from the bill of material structure

for other items or end products.

· Such demands are therefore calculated and need not and should not be forecast.

· A given inventory item may have both dependent and independent demand at

any given time.

· For example, a part may simultaneously be the component of an assembly and

sold as a service part.

Figure 3. Independent Demand Inventory.

Figure 4. Dependent Demand Inventory.

4. Principles of Forecasting and Data Collection.

□ Principles of Forecasting.

1. Forecasts are almost always wrong.

: Even a forecast that looks correct on the average may vary considerably from

: actual data in any given period.

: Errors are inevitable and must be expected.

2. Every forecast should include an estimate of error.

: Every forecast should include an estimate of error often expressed as a

: percentage (plus and minus) of the forecast or as a range between maximum

: and minimum values.

3. Forecasts are more accurate for large groups of items than for individual items in a group.

: The behavior of individual items in a group is random even when the group has

: very stable characteristics.

4. Forecasts are more accurate for nearer periods of time.

: The near future holds less uncertainty than the far future.

: Forecasting what will happen in the immediate future is easier than forecasting for

: the distant future.

□ Collection and Preparation of Data.

- Forecasts are usually based on historical data. Thus, a forecast can only be as good

as the data on which it is based.

- Four principles.

1. Demand quantities is not always equal to shipment quantities.

· If the purpose is to forecast demand on production, data based on

demand, not shipments, are needed.

2. Record data in the same terms needed for the forecast.

· The forecast period, in weeks, months, or quarters, should be the same as

the schedule period.

· The item forecast should be the same as those controlled by manufacturing.

3. Record circumstances relating to the data.

· Events will influence demand, and they should be recorded.

· For example, sales promotions will initially increased demand, but after a

promotion is over, demand will often be reduced.

4. Record the demand separately for different customer groups.

· Different customer groups often have different buying patterns, which should

be considered in forecasting demand.

5. Forecasting Techniques.

□ Qualitative Techniques.

- Projections based on judgment, intuition, and informed opinions.

- Used to forecast general business trends and the potential demand for large families

of products over an extended period time.

- Some Kinds of Qualitative Methods

1. Management Estimate (Visionary)

2. Market Research

3. Structured Group

4. Historical Analogy

- Types of Problems addressed

· New products, process, or markets

· Rates of development of processes, markets, or technologies

· Projecting outcome of major societal or technological changes

· Adjustments of quantitative forecasts

□ Quantitative Techniques.

- Based on historical data usually available in the company.

- Assume future will repeat past.

- Quantitative Techniques.

1. Trend Analysis : Double exponential smoothing, Regression, and Triple smoothing.

2. Seasonal Adjustment

· Seasonal models take into account the variation of demand from season to season.

3. Decomposition.

· A method of forecasting where time series data are separated into up to

three components - Trend, Seasonal, and Cyclical.

□ Extrinsic Techniques.

- A forecast based on a correlated 'Leading Indicator'.

- Extrinsic forecasts tend to be more useful for large aggregations, such as total

company sales, than for individual product sales.

- Examples :

1. Sales of bricks are proportional to housing starts.

2. Sales of automobile tires are proportional to gasoline consumptions.

- Economic indicators : construction contract awards, automobile production, farm

income, steel production, and gross national income.

□ Intrinsic Techniques.

- A forecast based on internal factors, such as an average of past sales.

- Several rules.

· Demand this period will be the same as last month.

· Demand this period will be the same as demand the same periods last year.

□ Moving Average.

- An arithmetic average of a certain number [eqn: n] of the most recent observations.

- As each new observation is added, the oldest observation is dropped.

- The value of [eqn: 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.

- Example 1.

Question) Demand over the past three months has been 120, 135, and 114 units.

Using a three-moving average, calculate the forecast for the fourth month.

Solution)

· Forecast for month 4 =	120 + 135 + 114	=	369	= 123
	3		3

Actual demand for the fourth month turned out to be 129. Calculate the forecast

for the fifth month.

· Forecast for month 5 =	135 + 114 + 129	=	369	= 126
	3		3

- Some characteristics.

· Moving average are best used for forecasting products with stable demand

where there is little trend or seasonality.

· It are also useful to filter out random fluctuation.

· One drawback to using moving averages is the need to retain several periods of

history for each item to be forecast.

□ Exponential Smoothing.

- A type of weighted moving average forecasting techniques in which past observations

are geometrically discounted according to their age.

- The heaviest weight is assigned to the most recent data.

- The smoothing is termed exponential because data points are weighted in accordance

with an exponential function of their age.

- The techniques makes use of a smoothing constant to apply the difference between

the most recent forecast and the critical sales data.

· New forecast = (□)(latest demand) + (1-□)(previous forecast)

Ft      = (□) At-1           + (1-□) Ft-1

- Example 2.

(Question) The old forecast for May was 220, and the actual demand for May was

190. If alpha(□) is 0.15, calculate the forecast for June. If June demand

turns out to be 218, calculate the forecast for July.

(Solution)

· June forecast = (0.15) (190) + (1 - 0.15)(220) = 215.5

· July forecast = (0.15) (218) + (1 - 0.15)(215.5) = 215.9

6. Seasonality.

□ Seasonal Index

- This index is an estimate of how much the demand during the season will be above

or below the average demand for the product.

- The form for the seasonality index :

· Seasonal Index =	Period average demand	=	Period average demand
	Average demand for all periods		Deseasonalized demand

- Example : Seasonal Series

Year	Quarter
	1	2	3	4	Total
1	122	108	81	90	401
2	130	100	73	96	399
3	132	98	71	99	400
Average	128	102	75	95	400

· Average quarterly demand = 100 units.

· Calculating seasonal index.

1. Seasonal Index for Quarter 1 = 128/100 = 1.28

2. Seasonal Index for Quarter 2 = 102/100 = 1.02

3. Seasonal Index for Quarter 3 =  75/100 = 0.75

4. Seasonal Index for Quarter 4 =  95/100 = 0.95

□ Seasonal Forecasts

- Seasonal demand :

· Seasonal demand = (Seasonal Index) (Deseasonalized Demand)

- Example : Seasonal Forecasts

· The company with the following seasonal index forecasts an annual next year

of 420 units.

· Calculate the forecast for quarterly sales.

1. Seasonal index

Quarter 1 : 1.28               Quarter 2 : 1.02

Quarter 3 : 0.75               Quarter 4 : 0.95

2. Forecast average quarterly demand  = [eqn: 420 over 4 ~= 105] units

3. Expected quarter demand

Expected first-quarter demand    = 1.28 × 105 = 134.4  units

Expected second-quarter demand = 1.02 × 105 = 107.1  units

Expected third-quarter demand   =  0.75 × 105 =  78.75 units

Expected fourth-quarter demand  =  0.95 × 105 =  99.75 units

- Example : Deseasonalized Demand

· A company uses exponential smoothing to forecast demand for its products. For

April, the deseasonalized forecast was 1000, and the actual seasonal demand

was 1250 units.

The seasonal index for April is 1.2 and for May is 0.7. If □ is 0.1, calculate :

1. The deseasonalized May forecast

Deseasonalized actual demand for April = 1250/1.2 = 1042

2. The deseasonalized May forecast

□(latest actual) + (1 - □)(previous forecast) = 0.1(1042) + 0.9(1000) = 1004

3. Seasonalized May forecast

(seasonal index)(deseasonalized forecast) = 0.7(1004) =703

7. Tracking the Forecast.

□ Concepts in tracking the forecast.

- To determine accuracy, we must measure actual demand and compare it to what we forecast.

- Tracking the forecast will allow us to plan around the error and to improve our

forecasts in the future.

- Tracking forecast is the process of comparing actual demand with the forecast.

□ It we can measure the amount of random variation, we can

- Make a judgement about the reasonable of the error.

- Make contingency plans and set safety stock.

□ Forecast Error : Bias vs. MAD

- Bias.

· A consistent deviation from the mean in one direction (high or low)

· A normal property of a good forecast is that it is not biased.

- Mean Absolute Deviation (MAD)

· The average of the absolute values of the deviations of observed values from

some expected value.

· MAD can be calculated based on observations and the arithmetic mean of those                    observations.

Period	Forecast	Actual	Error (Actual - Forecast)	Absolute Error	Error Squared
1	1000	1200	200	200	40000
2	1000	1000	0	0	0
3	1000	800	-200	200	40000
4	1000	900	-100	100	10000
5	1000	1400	400	400	160000
6	1000	1200	200	200	40000
7	1000	1100	100	100	10000
8	1000	700	-300	300	90000
9	1000	1000	0	0	0
10	1000	900	-100	100	10000
Total	10000	12000	200	1600	4000000

· Cumulative Sum of Error = [eqn: sum (A_t - F_t )] = 200.

· Bias = Cumulative Sum of Error / Number of periods = 200/10 = 20.

· MAD = Sum of Absolute Error / Number of periods = 1600/10 = 160.

· Standard Deviation (SD) = 1.25 × MAD

□ Uses of MAD

- MAD and Normal distribution.

· The mean absolute deviation measures the difference (error) between actual and

forecast.

· A graph of the number of times(frequency) actual demand is of a particular

value produces a bell-shaped curve called a normal distribution.

Figure 5. MAD and Normal distribution.

□ Tracking Signals

- The ratio of the cumulative algebraic sum of the deviation between the forecasts and

the actual values to the mean absolute deviation.

· Tracking Signal =	Cumulative Sum of Error
	MAD

- Used to signal when the validity of the forecasting model might be in doubt.

- Tracking Signal vs. MAD

Period	Forecast	Actual	MAD	Cumulative Sum of Error	Tracking Signal
1	1000	800	200	- 200	- 1
2	1000	800	200	- 400	- 2
3	1000	1200	200	- 200	- 1
4	1000	1200	200	0	0
5	1000	1200	200	200	1
6	1000	1200	200	400	2
7	1000	1200	200	600	3

■ Performance Check

1. Which of the following is a characteristic of demand ?

A. Mean absolute variation.        B. Seasonal variation.

C. Average variation.              D. Standard deviation.

2. Product A is made from components B and C. Component B is made from parts D and E. Which items should be forecast ?

A. Only A.                       B. A, B, and C.

C. D and E.                     D. B, C, D, and E.

3. Which of the following is the best statement about the general principles of forecasting ?

A. Forecasting are more accurate for individual items than for groups of items.

B. Forecasting are more accurate for distant periods of time.

C. Every forecast should include an estimate of error.

D. Forecasts are usually accurate.

4. What important assumptions is made about statistical (quantitative) forecasting methods ?

A. The past is a valid indicator of the future.

B. Demand trend is seldom linear.

C. Seasonal variations are small.

D. Random variations are small.

5. Which forecasting technique takes the average demand for some past number of period ?

A. Trend time average.              B. Moving average.

C. Demand smoothing.              D. Qualitative analysis.

6. Why is important to track the forecast ?

A. To compare the actual sales with the forecast.

B. To improve our forecasting methods.

C. To utilize actual sales data.

D. To satisfy marketing's need to know.

7. Which of the following statements is most accurate ?

A. Demand fluctuations that depend on the time of the year, week, or day are called trend.

B. The seasonal index is an estimate of how much the demand during the season will be

above or below the average demand.

C. Seasonality always occurs in summer, winter, spring, and fall.

D. Random variation is constant from period to period.

8. Which of the following causes forecast error ?

A. Random variation from the average demand.

B. Errors in tracking forecast.

C. Difference in lead times.

D. Difference between sales and demand.

9. Which of the following statements is most accurate ?

A. Independent demand items should be forecast.

B. A forecast for sales next year will not be as accurate as a forecast for a year from now.

C. Forecasts for families of products should be built up from individual product forecasts.

D. Forecasts are almost always accurate.

10. Which of the following statement is most accurate ?

A. If we wish to forecast demand, past sales must be used for the forecast.

B. Forecasts made in dollars for total sales should be used by manufacturing.

C. Forecasts should be made for individual items in a group.

D. The circumstances relating to demand data should be recorded.

Solution :

1  2  3  4  5  6  7  8  9  10

B A C A B B B A A D