Chapter 5 - Discussion Questions - Answers
1.
Why is forecasting important?
Answer:
If companies knew exactly what customers wanted and had the ability to instantaneously produce the goods and services wanted by the marketplace, there
would be no need for forecasting. Unfortunately, knowledge of customer demand is uncertain and companies must plan ahead to ensure they have the products and productive capabilities available whenever customers want them.
Forecasting enables businesses to manage uncertainty by projecting within a
range what will be the anticipated demands on the company. In the absence of
a forecast companies resort to reactionary methods of operations whereby the
latest rush order becomes the highest priority, and all attempts to provide schedules and plans are futile.
A forecast is also useful in creating, anticipating, and managing change
within an organization. The forecast is a good rallying point for an organization
and helps to increase communication and awareness of the likely impact of
sales on operations. A forecast can also be the vehicle used to anticipate inventory levels and capacity demands, and to manage lead time. A forecast can be
used to project costs into the budgeting process and to improve competitiveness
and productivity through decreased costs and improved performance. This improved performance will lead to improved delivery to the customer and greater
responsiveness to customer needs.
2.
Describe the general characteristics of forecasting.
Answer:
Companies and supply chain managers should be aware of the following
characteristics of forecasts:
 Forecasts are always wrong. Because all forecasts are subject to random
variation, the probability of any forecast corresponding to the actual demand is doubtful. The measure of a competent forecast, therefore, can be
said to be the one exhibiting the least forecast error for time.
 Long-term forecasts are usually less accurate than short-term forecasts.
As a forecast extends further out into the future, the probability of error
grows as the level of variation increases.
 Aggregate forecasts are usually more accurate than disaggregate forecasts. The forecast for an entire product family will be more accurate
than the forecast for an individual item. The reason is that instances of
high variation for individual items are dampened by being combined in a
group. The greater the degree of aggregation, the more accurate the forecast.
 Forecasts are most useful when accompanied by a method for measuring
forecast error. As variation occurs in the historical data through time, an
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effective forecasting model will provide planners with alerts to out-ofbounds situations so that effective and timely adjustments to the forecast
can be made.
3.
What are the three basic forecasting categories?
Answer:
The three basic forecasting categories are judgmental, time series (intrinsic),
and causal (extrinsic). These models can be described as follows:
 Judgmental models use qualitative data such as expert opinion, intuition,
and subjective evaluation to arrive at a forecast. Such forecasts are most
often made as individual judgments or by committee agreements or decisions. Models in this category are best used for forecasting marketing,
new product development, and promotional strategies.
 Times-series models employ time series analysis and projection to search
for historical patterns in the demand data that can be extrapolated into the
future. This category uses statistical models to calculate the forecast or
using observed demand outcomes in the past and then developing a forecast by applying those relationships to the future. Models in this category
require a great deal of historical data that are easily accessible and consistent.
 Causal models are the most sophisticated type of forecasting tool. This
category attempts to express mathematically the relationships between
the forecast objective and factors, such as technological, political, economic, and socioeconomic forces, combined with times series models
based on a large database of historical patterns and relationships.
4. What are the four levels of forecasting?
Answer:
Forecasting normally occurs on four levels. The critical factor separating each
level is the length of the forecast. The highest level can be described as longrange forecasting. This level has a time horizon of at least one year and is used
in the development of top management planning. Among the decisions associated with this level of forecasting are developing corporate goals and objectives, performing corporate capital budgeting; planning for facilities, equipment, and other fixed assets; and structuring channel networks.
The next level, medium-range forecasting, has a time horizon of six months
to one year and is normally applied to operations planning and control activities. Forecasting decisions on this level impact sales planning, planning warehouse and transportation capacities, business area capital planning, and contracting for public warehousing and transportation services.
Short-range forecasting is the third level of forecast planning. Business functions, required to project operations execution activities one to three months
out into the future, use this level of forecasting. Forecasting decisions on this
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level will impact such activities as manufacturing scheduling, supplier scheduling, inventory procurement plans, transportation planning, material handling equipment utilization, and detailed operating budgets.
Finally, immediate-range forecasting is used for daily performance of ongoing active-ties. Examples include transportation scheduling, receiving, stock
put-away, shop floor and value-added processing scheduling, order filling, and
accounts receivables and payables flow through.
Each forecast level has its own unique characteristics relative to the most appropriate forecasting method and data required. It would be improper, for example, to employ a forecast method used to calculate product demand on a
weekly basis for the purpose of determining yearly aggregate sales income.
5.
What are the five characteristics of demand important for forecasting?
Answer:
As demand occurs through time it tends to form discernable patterns. In forecasting terms, this pattern is called a “times series.” There are basically five
types of demand patterns described as follows:
 Random. Random patterns of demand are the most difficult to forecast
because they, in fact, exhibit no historical pattern. Random demand often
experiences wide and unpredictable swings and can be said to exhibit dynamic demand. While it is possible to calculate an average demand, the
possibility of large positive and negative demand around this mean make
forecasting extremely difficult.
 Trend. Trends are consistent upward or downward patterns observable in
the occurrence of a series of demand values. A trend exists when the demand pattern exhibits either an upward or downward slope for approximately seven or more periods.
 Horizontal. Horizontal patterns exhibit relative stability and consistency
with minor upward or downward changes in actual demand over time and
can be said to exhibit stable demand. Such patterns are characteristic of
products with stable sales patterns or aggregate product family-level forecasts. Products with horizontal patterns of demand are the easiest to forecast.
 Seasonal. Seasonal is described as a consistent pattern of increasing and
decreasing demand that occurs periodically within a limited time frame
year after year. This recurrence of demand may be the result of the
weather, holiday seasons, or particular events that take place seasonally.
Examples would be demand for snow shovels during the winter months
and beach equipment during the summer months. It is important to note
that seasonal demand can occur over a long-term window of time, such
as a year, or over a week, or even daily. A restaurant’s demand varies
regularly with the hour of the day, and supermarket sales vary with the
day of the week.
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Cyclical patterns are used by forecasters to track long-range trends in the
overall economy that could have an impact on a firm's strategies. Cyclical
influences often last for one to five years and then recur. Forecasting cycles is difficult because the demand does not reoccur at constant intervals
of time and its duration is not uniform.
6.
Explain the differences between qualitative and quantitative forecasting models.
Answer:
When generating and managing forecasts, planners will choose between two
primary techniques: qualitative and quantitative. While some forecasting techniques are largely qualitative in nature, most are quantitative and rely on objective data.
Qualitative techniques are based on expert or informed opinion regarding
future product or service 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, and others.
Quantitative techniques are based on the premise that past demand is a good
indicator of future demand. Quantitative techniques rely on mathematical formulas to analyze historical demand patterns and predict future demand. There
are many types of quantitative models. Some of the most popular include moving averages, seasonal indexes, and exponential smoothing. Each technique has
its advantages and disadvantages, so the forecaster must know the formulas and
also understand the appropriateness of a given technique.
In many companies, a mixture of historical information analyzed by quantitative models, combined with qualitative inputs from groups of experts, is useful in establishing a more accurate forecast. Qualitative and quantitative forecasts may be generated based on intrinsic or extrinsic information.
7.
Describe some qualitative forecasting models.
Answer:
There are several qualitative forecasting models as described as follows:
 Independent judgment. The most common technique used is individual
judgment based on intuition. In this method, an industry expert with personal knowledge and past experience produces a forecast based purely on
subjective judgment. Advantages of this technique are its relative low
cost and speed of formulation. Drawbacks focus around the fact that although the method may render some good forecasts, results are extremely
variable. Forecasters who follow a formal forecasting process will always
out-perform forecasts based on subjective opinions.
 Executive/management judgment. There are several variations of this
model. In the panel consensus model, experts review historical data and
forecast possible demand for a product, product group, or service using
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techniques such as pyramid forecasting. Another model is the Delphi
method. In this approach, a panel of experts, who do not physically meet,
is interrogated by a sequence of questionnaires concerning a new product, event, or process. Responses to the questionnaires are passed to all
the panel members for evaluation and rating as to the likelihood of occurrence and are then used to produce the next set of questionnaires. The object is to narrow down a field of opinions that can be used as forecast
data. This technique is quick and Easy, requires minimal preparation of
statistical data, and pools the collective experience and judgment of experts from across the supply network.
Market research. This technique attempts to forecast future demand
trends and activity levels by surveying a customer market segment whose
past behavior could indicate future buying patterns. The drawback of using this technique is that market surveys are expensive and time-consuming to execute and monitor. In addition, they rely on the ac-curacy of the
randomness of the sample and the conclusions drawn from the analyses.
Sales force estimates. This technique consists of the projection of future
sales compiled by the firm's sales force based on individual salesperson
estimates, management expertise, or surveys of supply chain demand.
The advantage of using this technique is that it employs the specialized
knowledge of those closest to the marketplace.
Historical analogy. This technique is used to develop a demand forecast
for a new product or process. The model seeks to establish the demand
patterns for like-products and then extrapolates a comparative forecast
from these data. The advantage of this model is that it is comparatively
easy to development and provides a reasonable estimate for products in
the early life-cycle stage.
Explain the function of the alpha factor ( ) used in exponential smoothing.
Answer:
The key to exponential smoothing is the smoothing constant expressed as .
The purpose of the constant is to give relative weights to the actual values of
the last past period and the historical forecasted values. If greater weight is
given to the most recent actual values, then a high smoothing constant is chosen, and vice versa. Calculation of the smoothing constant is normally based
on the number of periods the forecaster would use if a moving average were
employed. For example, the equation for calculating an  equivalent to a 6period moving average would be:
2
=
= .286
(6 + 1)
Using this formula, the following chart can be produced:
For alpha of .1: n = 2/.1 - 1 = 19 period average
For alpha of .3: n = 2/.3 - 1 = 5.67 period average
For alpha of .6: n = 2/.6 - 1 = 2.33 period average
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For alpha of .9: n = 2/.9 – 1 = 1.22 period average
When selecting a smoothing constant, use a low for a very random historical
time series of demand values and a high for a very smooth historical time
series.
9.
Explain quantitative causal techniques.
Answer:
Quantitative intrinsic techniques focus on analyzing time series data, decomposing demand patterns into trend, seasonal and cyclical, and patterns, and determining the mathematical relationships within the data and demand patterns
and extending them into the future.
The other category of quantitative forecasting consists of causal techniques.
These techniques are often referred to as extrinsic models. They are calculated
by applying data that are attained from outside an historical times series. The
idea behind the method is to leverage other occurrences in the marketplace up
and above historical data to attempt to predict more precisely the course of future demand.
Another critical difference between intrinsic and extrinsic models relates to
the size of the planning horizon and data sample. Quantitative models are primarily short-range to medium-range techniques. Causal methods focus on
long-range forecasts that use qualitative and quantitative macro measurements
such as political, demographical, new technology, and other forces to predict
the future. In addition, they are best employed when making projections of aggregate demand such as the total sales demand of a company, sales of a product
group, or sales in a specific geographical region. Examples of possible causal
forecasts would be predicting when a new product or process will be available,
what the impact of new inventions or discoveries will have on the marketplace,
or what effects or changes might emerge as a result of technology developments.
10. Explain why forecasts fail?
Answer:
Forecasts will fail without proper oversight, performance measurement, people
involvement, and method to address out-of-bounds situations. Detailed reasons
are as follows:
 Management involvement. Perhaps the foremost reason why forecasts fail
is because of a lack of participation by management both in the development and in the execution of the forecasting process. To be successful,
forecasting should be viewed as a team effort and not be entrusted solely
to an expert or “black box” approach.
 Over-sophistication and cost. Forecasting systems that are too difficult to
understand are doomed to failure. In addition, the more complex the forecasting model, the more costly it is to run and maintain. Most organizations rely on simple, yet effective, heuristics to develop a forecast.
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Compatibility. Forecasts fail when there is a lack of compatibility between the forecasting system and the capabilities of the using organization. When the organization does not understand the techniques or trust
the produced output, there is a strong likelihood that it will not follow the
forecast.
Data accuracy. Although it is obvious that the data used by a forecasting
technique must be accurate, errors do arise in the data collection process
resulting from:
1. Errors due to the size of the sample data and incorrect application
in statistical calculations.
2. Errors in the collection, data entry, and forecast calculation.
3. Information overlooked, deliberately falsified, or withheld.
4. Errors from omission or double-counting data elements.
5. Poorly classified or defined data elements.
6. Time periods and the data collected misaligned.
Unnecessary items. Often forecasts are developed for items that should
not be forecasted. One example is dependent demand item usage. Also,
forecasts should not be established for final assembled products that are
the result of features and options.
Lack of management control. Review and maintenance are critical to
forecast effectiveness. By its very nature, every forecast developed is
likely to be wrong. Forecasters must be diligent in monitoring the forecast to ascertain the degree of error, when the forecast should be altered,
and what parameters should be used to guide forecast adjustment.
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