Forecasting

Learning objectives

Forecasting is essential

Quantitative forecasting models

Forecasting error

Qualitative forecasting

Collaborative forecasting

Forecasting in OSCM

• Vital for every business decision
• For accounting, finance forecasts are the basis for budgetary planning and cost control
• For marketing-new product decision, compensating sales personnel, marketing communications
• Production & operations- supplier selection, process selection, capacity planning, lay-out, purchasing, production scheduling, inventory
• Approach to forecast is determined by the purpose of forecast

Approaches to forecast

• Strategic forecast (Ex: Shawpno Location Planning)

How to meet the demand

For decisions related to process design, capacity planning, sourcing, location and distribution planning

Medium to long-term

Estimate aggregate demand

• Tactical forecast (Ex: Shawpno’s Milk Availability given lead-time expectation)

Ensure product/service availability given the lead-time expectations of the customers

Weekly or monthly demand forecast

Quantitative forecasting models

• Time Series Analysis
• Data relating to past demand can be used to predict future demand.
• Past data may include several components, such as trend, seasonal, or cyclical influences�
• Causal Relationship Analysis (e.g. GDP & Coffee Consumption Demand)
• Certain economic, social, demographic or other factors influence demand
• Regression analysis is done�
• Simulation
• Based on large data set estimate the distribution of the variables that influence demand
• Estimate future demand using simulated data sets

Components of Demand

Six components:

1. Average demand for the period,

2. A trend,

3. Seasonal element,

4. Cyclical elements (political, economical factors, war, sociological issues may exert cyclicality in demand),

5. Random variation (chance events), and

6. Autocorrelation (persistence of occurrence)

Common trends

Selecting appropriate forecasting method

Which forecasting model a firm should choose depends on:

Time horizon to forecast

Data availability

Accuracy required

Size of forecasting budget

Availability of qualified personnel

Short term (less than three month) for tactical decision e.g., setting safety stock level, estimating peak load, respond to random variations etc.

Medium term ( 3 month to 2 years) for planning a strategy for meeting demand over the next six months to a year and a half; useful for capturing seasonal effects.

Long term (more than 2 years) detect general trends and are especially useful in identifying major turning points.

Simple and weighted moving average

Choice of time period depends on the purpose of the forecast.

Monthly data for budgeting

Weekly data for production scheduling or inventory planning

​

Shorter periods in the average allow the ups and downs to persist in the forecasted data; whereas longer periods in the average smooth out the ups and downs (random variations) in data set.

​

Choosing weights in case of weighted moving average- general rule is to assign more weight to the most recent data

Exponential smoothing

• Moving average’s drawback is over reliance on historical data
• But in most contexts, the importance of data diminishes as the past becomes more distant
• Only three pieces of data are needed: the most recent forecast, the actual demand that occurred for that forecast period, and a smoothing constant alpha (α).
• The value of the smoothing constant is determined both by the nature of the product and by the manager’s sense of what constitutes a good response rate. It is the reaction rate to forecast error of the immediate past period. High value is suggested in case of volatile demand.

​

Exponential smoothing with trend

• Exponential forecasts always lag behind the trends in data set

​

Choosing the Appropriate Value for Alpha and Delta 

Typically, fairly small values are used for alpha and delta in the range of 0.1 to 0.3.

The values depend on how much random variation there is in demand and

How steady the trend factor is.

Regression

• Linear regression is useful for long-term forecasting of major occurrences and aggregate planning.
• The data should be plotted first to see if they appear linear or if at least parts of the data are linear.
• Y_t= a + bt

​

Linear Regression

Decomposition of a time series

Computing trend and seasonal index

Forecasting causal relationship

Using independent variables other than time to predict future demand

Forecast error

• Sources of error
• Random (unexplained)
• Biases (in the data set, trend and/or seasonality identification, unexpected change in trend/seasonality)
• Measurement error
• Error in identifying the causal variables
• Measurement of error
• MAD
• MAPE
• Tracking Signal

​

Measurements of forecast errors

Qualitative Techniques in forecasting

Panel Consensus

Panel forecasts are developed through open meetings with a free exchange of ideas from all levels of management and individuals. The difficulty with this open style is that lower-level employees are intimidated by higher levels of management.

Historical Analogy

In trying to forecast demand for a new product, an ideal situation would be where an existing product or generic product could be used as a model.

The Delphi Method

As we mentioned under panel consensus, a statement or opinion of a higher-level person will likely be weighted more than that of a lower-level person. To prevent this problem, the Delphi method conceals the identity of the individuals participating in the study. The step-by-step procedure for the Delphi method is:

Choose the experts to participate. There should be a variety of knowledgeable people in different areas.

Through a questionnaire (or e-mail), obtain forecasts from all participants.

Summarize the results, and redistribute them to the participants along with appropriate new questions.

Summarize again, refining forecasts and conditions, and again develop new questions.

Repeat step 4 if necessary. Distribute the final results to all participants.

Collaborative forecasting

• Collaborative Planning, Forecasting, and Replenishment (CPFR) is a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners.
• CPFR is being used as a means of integrating all members of an n-tier supply chain, including manufacturers, distributors, and retailers.
• The ideal point of collaboration utilizing CPFR is the retail-level demand forecast, which is successively used to synchronize forecasts, production, and replenishment plans upstream through the supply chain.

Exercises

Sunrise Baking Company markets doughnuts through a chain of food stores. It has been experiencing overproduction and underproduction because of forecasting errors. The following data are its demand in dozens of doughnuts for the past four weeks. Doughnuts are made for the following day; for example, Sunday’s doughnut production is for Monday’s sales, Monday’s production is for Tuesday’s sales, and so forth. The bakery is closed Saturday, so Friday’s production must satisfy demand for both Saturday and Sunday. Make a forecast for this week on the following basis:

• Daily, using a simple four-week moving average.
• Daily, using a weighted moving average with weights of 0.40, 0.30, 0.20, and 0.10 (most recent to oldest week).
• Sunrise is also planning its purchases of ingredients for bread production. If bread demand had been forecast for last week at 22,000 loaves and only 21,000 loaves were actually demanded, what would Sunrise’s forecast be for this week using exponential smoothing with α = 0.10?
• Suppose, with the forecast made in part (c), this week’s demand actually turns out to be 22,500. What would the new forecast be for the next week?

​

​

​

Exercises

Given the following information, make a forecast for May using exponential smoothing with trend. Make a second forecast using linear regression.

​

​

For exponential smoothing with trend, assume that the previous forecast (for April) including trend (FIT) was 800 units, and the previous trend component (T) was 50 units. Also, alpha(α) = 0.3 and delta(δ) = 0.1.

For linear regression, use the January through April demand data to fit the regression line. Use the Excel regression functions SLOPE and INTERCEPT to calculate these values.

Exercises

Here are the quarterly data for the past two years. From these data, prepare a forecast for the upcoming year using a linear regression with seasonal indexes.

​