© 2007 Pearson Education Forecasting Chapter 13. © 2007 Pearson Education How Forecasting fits the...

© 2007 Pearson Education

Forecasting

Chapter 13Chapter 13


How Forecasting fits the Operations Management

Philosophy

Operations As a Competitive Weapon

Operations StrategyProject Management Process Strategy

Process AnalysisProcess Performance and Quality

Constraint ManagementProcess LayoutLean Systems

Supply Chain StrategyLocation

Inventory ManagementForecasting

Sales and Operations PlanningResource Planning

Scheduling


Forecasting at Unilever

Customer demand planning (CDP), which is critical to managing value chains, begins with accurate forecasts.

Unilever has a state-of-the-art CDP system that blends historical shipment data with promotional data and current order data.

Statistical forecasts are adjusted with planned promotion predictions.

Forecasts are frequently reviewed and adjusted with point of sale data.

This has enabled Unilever to reduce its inventory and improved its customer service.


Demand Patterns

Time Series: The repeated observations of demand for a service or product in their order of occurrence.

There are five basic patterns of most time series.

a. Horizontal. The fluctuation of data around a constant mean.

b. Trend. The systematic increase or decrease in the mean of the series over time.

c. Seasonal. A repeatable pattern of increases or decreases in demand, depending on the time of day, week, month, or season.

d. Cyclical. The less predictable gradual increases or decreases over longer periods of time (years or decades).

e. Random. The unforecastable variation in demand.


Demand Patterns

Horizontal Trend

Seasonal Cyclical


Designing the Forecast System

Deciding what to forecast Level of aggregation.

Units of measure.

Choosing the type of forecasting method: Qualitative methods

Judgment

Quantitative methods Causal

Time-series


Deciding What To Forecast

Few companies err by more than 5 percent when forecasting total demand for all their services or products. Errors in forecasts for individual items may be much higher.

Level of Aggregation: The act of clustering several similar services or products so that companies can obtain more accurate forecasts.

Units of measurement: Forecasts of sales revenue are not helpful because prices fluctuate. Forecast the number of units of demand then translate

into sales revenue estimates Stock-keeping unit (SKU): An individual item or product

that has an identifying code and is held in inventory somewhere along the value chain.


Choosing the Type ofForecasting Technique

Judgment methods: A type of qualitative method that translates the opinions of managers, expert opinions, consumer surveys, and sales force estimates into quantitative estimates.

Causal methods: A type of quantitative method that uses historical data on independent variables, such as promotional campaigns, economic conditions, and competitors’ actions, to predict demand.

Time-series analysis: A statistical approach that relies heavily on historical demand data to project the future size of demand and recognizes trends and seasonal patterns.

Collaborative planning, forecasting, and replenishment (CPFR): A nine-step process for value-chain management that allows a manufacturer and its customers to collaborate on making the forecast by using the Internet.

© 2007 Pearson Education© 2007 Pearson Education

Demand Forecast Applications

• Causal• Judgment

• Causal• Judgment

• Time series• Causal• Judgment

ForecastingTechnique

• Facility location• Capacity planning• Process management

• Staff planning• Production planning

• Master production scheduling

• Purchasing• Distribution

• Inventory management

• Final assembly scheduling

• Workforce scheduling

• Master production scheduling

Decision

Area

• Total sales• Total sales• Groups or families

of products or services

• Individual products or services

Forecast Quality

Long Term(more than 2 years)

Medium Term(3 months– 2 years)

Short Term(0–3 months)

Application

Time Horizon


Judgment Methods

Sales force estimates: The forecasts that are compiled from estimates of future demands made periodically by members of a company’s sales force.

Executive opinion: A forecasting method in which the opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast. Executive opinion can also be used for technological

forecasting to keep abreast of the latest advances in technology.

Market research: A systematic approach to determine external consumer interest in a service or product by creating and testing hypotheses through data-gathering surveys.

Delphi method: A process of gaining consensus from a group of experts while maintaining their anonymity.


Guidelines for Using Judgment Forecasts

Judgment forecasting is clearly needed when no quantitative data are available to use quantitative forecasting approaches.

Guidelines for the use of judgment to adjust quantitative forecasts to improve forecast quality are as follows:1. Adjust quantitative forecasts when they tend to be

inaccurate and the decision maker has important contextual knowledge.

2. Make adjustments to quantitative forecasts to compensate for specific events, such as advertising campaigns, the actions of competitors, or international developments.


Time Series Methods

Naive forecast: A time-series method whereby the forecast for the next period equals the demand for the current period, or Forecast = Dt

Simple moving average method: A time-series method used to estimate the average of a demand time series by averaging the demand for the n most recent time periods. It removes the effects of random fluctuation and is most

useful when demand has no pronounced trend or seasonal influences.

…


Forecasting Error

For any forecasting method, it is important to measure the accuracy of its forecasts.

Forecast error is the difference found by subtracting the forecast from actual demand for a given period.

Et = Dt - Ft whereEt = forecast error for period t

Dt = actual demand for period t

Ft = forecast for period t


Moving Average Method Example 13.2

a. Compute a three-week moving average forecast for the arrival of medical clinic patients in week 4. The numbers of arrivals for the past 3 weeks were:

PatientPatientWeekWeek ArrivalsArrivals

11 40040022 38038033 411411

b. If the actual number of patient arrivals in week 4 is 415, what is the forecast error for week 4? c. What is the forecast for week 5?

© 2007 Pearson EducationWeekWeek

450 450 —

430 430 —

410 410 —

390 390 —

370 370 —

| | | | | |00 55 1010 1515 2020 2525 3030

Pat

ien

t ar

riva

lsP

atie

nt

arri

vals

Actual patientActual patientarrivalsarrivals

Example 13.2Solution

The moving average method may involve the use of as many periods of past demand as desired. The stability of the demand series generally determines how many periods to include.


Week Arrivals Average1 4002 3803 411 3974 415 4025 ?

Example 13.2 Solution continued

Forecast for week 5 is the average of the arrivals for weeks 2,3 and 4

Forecast error for week 4 is 18. It is the difference between the actual arrivals (415) for week 4 and the average of 397 that was used as a forecast for week 4. (415 – 397 = 18)

Forecast for week 4 is the average of the arrivals for weeks 1,2 and 3

FF44 = = 411 + 380 + 400411 + 380 + 40033

a.

c. b.


Comparison of 3- and 6-Week MA Forecasts

Week

Pat

ien

t A

rriv

als

Actual patient arrivals

3-week moving average forecast

6-week moving average forecast


Application 13.1

We will use the following customer-arrival data in this moving average application:


Application 13.1a Moving Average Method

F5 D4 D3 D2

3790 810 740

3780

780 customer arrivals

F6 D5 D4 D3

3805 790 810

3801.667



Weighted Moving Averages

Weighted moving average method: A time-series method in which each historical demand in the average can have its own weight; the sum of the weights equals 1.0.

Ft+1 = W1Dt + W2Dt-1 + …+ WnDt-n+1


Application 13.1b Weighted Moving Average

F5 W1D4 W2D3 W3D2 0.50 790 0.30 810 0.20 740 786


F6 W1D5 W2D4 W3D3 0.50 805 0.30 790 0.20 810 801.5



Exponential Smoothing

Ft+1 = (Demand this period) + (1 – )(Forecast calculated last period)

= Dt + (1–)Ft

Or an equivalent equation: Ft+1 = Ft + (Dt – Ft )

Where alpha (is a smoothing parameter with a value between 0 and 1.0is a smoothing parameter with a value between 0 and 1.0Exponential smoothing is the most frequently used formal forecasting method because of its simplicity and the small amount of data needed to support it.

Exponential smoothing method: A sophisticated weighted moving average method that calculates the average of a time series by giving recent demands more weight than earlier demands.


Reconsider the medical clinic patient arrival data. It is now the end of week 3. a. Using = 0.10, calculate the exponential smoothing forecast for week 4.

Ft+1 = Dt + (1-)Ft

F4 = 0.10(411) + 0.90(390) = 392.1

b. What is the forecast error for week 4 if the actual demand turned out to be 415?

E4 = 415 - 392 = 23

c. What is the forecast for week 5?F5 = 0.10(415) + 0.90(392.1) = 394.4

Exponential SmoothingExample 13.3

Week Arrivals1 4002 3803 4114 4155 ?


Application 13.1c Exponential Smoothing

Ft1 Ft Dt Ft 783 0.20 790 783 784.4


Ft1 Ft Dt Ft 784.4 0.20 805 784.4 788.52



Using Multiple Techniques

Research during the last two decades suggests that combining forecasts from multiple sources often produces more accurate forecasts.

Combination forecasts: Forecasts that are produced by averaging independent forecasts based on different methods or different data or both.

Focus forecasting: A method of forecasting that selects the best forecast from a group of forecasts generated by individual techniques.

The forecasts are compared to actual demand, and the method that produces the forecast with the least error is used to make the forecast for the next period. The method used for each item may change from period to period.


Forecasting as a Process

The forecast process itself, typically done on a monthly basis, consists of structured steps. They often are facilitated by someone who might be called a demand manager, forecast analyst, or demand/supply planner.


Some Principles for the Forecasting Process•Better processes yield better forecasts.•Demand forecasting is being done in virtually every company.

The challenge is to do it better than the competition.•Better forecasts result in better customer service and lower

costs, as well as better relationships with suppliers and customers.

•The forecast can and must make sense based on the big picture, economic outlook, market share, and so on.

•The best way to improve forecast accuracy is to focus on reducing forecast error.

•Bias is the worst kind of forecast error; strive for zero bias.•Whenever possible, forecast at higher, aggregate levels.

Forecast in detail only where necessary.•Far more can be gained by people collaborating and

communicating well than by using the most advanced forecasting technique or model.

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