Running head: Business Forecasting
An Annotated Bibliography
MSC – 337-A
Dr. Cynthia Knott
May 1, 2018
The field of forecasting includes supply forecasting, such as for agricultural commodities or the
oil industry, and extends to economic forecasting, such as GDP forecasts produced by the Federal
Reserve Bank. The six articles cited in this annotated bibliography are published in The Journal of
Business Forecasting (JBF), a publication of the Institute of Business Forecasting and Planning.
Established in 1982, JBF is a peer-reviewed academic journal edited by Dr. Chaman Jain, Professor
of Economics at St. John’s University. Articles in the journal are written for corporate demand
planners and S&OP professionals. The field of business demand forecasting is dynamic, and
practitioners need to stay abreast of current trends in the field. In his letter to readers in the
current issue of the journal (Winter 2017-2018), Dr. Jain comments on the expanding role of ecommerce, the advent of automated forecasting software, and the growing need for demand
planners to adopt the increasing availability of sophisticated analytical tools.
Forecasting methods are covered in chapter four of Heizer and Render’s (2014) textbook,
Operations Management: Sustainability and Supply Chain Management (104-136). The articles
annotated here build on that material and seek to create general familiarity with the field of
business forecasting. Using an evaluative annotation approach, each article has been given an
extended exposition of its content. The accompanying PowerPoint presentation, rather than
introducing articles alphabetically by author (mea culpa to APA), approaches the bibliography in a
thematic sequence. Beginning with an overview of forecasting models, the topics cover the use of
external data and economic indicators, matching forecasting methods to product segments, the
impact accurate forecasts have on inventory and financial metrics, and talent management as an
important part of the forecasting process. The annotated bibliography that follows is alphabetical
by author’s last name.
Chase, Charles W. (Winter 2015-2016). The Importance of Product Segmentation. The Journal of
Business Forecasting; 34, 4, 36-40.
Charles Chase is an expert in sales forecasting, market response modeling and supply chain
management; he has worked for companies such as Johnson & Johnson, Coca-Cola, and Heineken,
among others (p. 36). Companies are understanding that not all their products are forecastable,
says Chase, and “are asking themselves what is forecastable and what is not, and how can they
segment their products to get the most accuracy across their product portfolio” (p. 37).
Forecastability is impacted by consumer demand and data constraints. Demand planners must
segment their brands and products and apply the correct forecasting method to obtain the best
performance across their product lines.
Many practitioners realize poor forecast performance simply because they use one
methodology to forecast all of their products (p. 37). The author states that in many cases the
forecast accuracy is less than 70%, whereas desired results would be in the 85% to 95% range (p.
37). Product groups have different data patterns, depending on their life cycles and marketing and
sales support. When demand planners “cleanse” a product’s historical demand data, they also lose
data on sales promotion responses and seasonality, which can result in their ERP (Enterprise
Resource Planning) system picking a moving average model instead of a seasonal exponential
model (p. 37). The right model can improve forecasts by 10% across the firm’s product portfolio.
Chase recommends decomposing each data set by product or brand to find the trend, seasonality,
cyclical variance, and unexplained error (p. 37). This kind of time series analysis can uncover true
The value that Chase brings to demand planning and forecasting is his concept of
segmenting products and brands into four quadrants and then overlaying the best forecasting
method over each quadrant to achieve better forecast results. The four general product quadrants
are: 1) low value, low forecastability, 2) low value, high forecastability, 3) high value, low
forecastability and 4) high value, high forecastability (p.37). He then provides more specific product
characteristics for each quadrant: 1) slow moving, 2) new products, 3) fast moving, and 4) steady
state (p.37). The table below, reproduced from the article, shows these four quadrants and product
portfolio management principles:
Product Line Extensions
Short Life Cycle Products
High Priority Products
Low Priority Products
Minor Sales Promotions
Slow Moving Products
Forecastability ---------------> High
The appropriate forecasting methodologies can also be segmented into four quadrants and
matched with the segmented product characteristic quadrants shown above. For example, slow
moving, low forecastability products can only be approximately forecasted using some trend and
seasonal fluctuation analysis and moving averaging, whereas high value, high forecastability
products show strong trend and season fluctuations and are responsive to sales promotions and
advertising (p. 38). This group of products can be further analyzed using causal models (dynamic
regression, multiple linear regression) to reduce unexplained variance. Chase’s breakout of
statistical methods into four corresponding quadrants (not shown here) can then be overlaid on top
of the product characteristics segmentation table, above. In brief, new product demand can be
forecasted using judgmental methods, clustering and data mining. Slow moving products can
benefit from moving averages, sales force composites and intermittent demand models. Causal
methods work well for the fast-moving products quadrant, and time series methods such as
exponential smoothing work well for the steady-state products quadrant (p. 39).
Chase underscores the point that more sophisticated statistical methods are needed to
improve on time series methods which are useful for segmenting product group demand history
into basic trend, seasonality, cyclical and unexplained variance (p. 40). Heizer and Render (2014)
explain time series methods in chapter four of Operations Management (pp. 108-125). Chase goes
the additional step in this article by mapping the most applicable statistical and forecasting
methods against the segmented product characteristics framework to help demand planners
achieve better forecasts.
Chase, Charles W. (Spring 2016) Forecast Accuracy Has No Impact on Inventory! Really? The
Journal of Business Forecasting, 35, 1, 26-29.
Charles Chase is an expert in sales forecasting, market response modeling, and supply chain
management. Currently with the SAS Institute, Inc., he has worked for companies such as Johnson &
Johnson, Coca-Cola, and Heineken (p. 26). Chase’s main argument here is that companies have
sorted themselves out into two supply chain camps: demand or supply. In his opinion, firms have
swung back and forth between these two camps; in recent years, companies have abandoned
supply chain strategies because the use of buffer inventory is not as cost efficient as it was (p. 26).
He states, “although poor forecasting has been identified as the root cause, companies continue to
use traditional statistical methods like moving averaging and non-seasonal exponential smoothing
models, which are only accurate one period into the future” (p. 26).
There is a lot of room for forecasting accuracy. Based on his work with 100 companies over
10 years, “the average forecast accuracy is between 50-65 percent at the aggregate level, and
between 35-45 percent at the lower mix levels” (p. 27). A classic cause of poor demand forecasts is
the use of moving average and non-seasonal exponential smoothing models that are only accurate a
few periods into the future. Upper and lower forecast ranges tend to be cone shaped beyond that
point, whereas more advanced statistical methods like ARIMA and dynamic regression are more
accurate farther out (p. 28). The use of better models translates into lower safety stock. Often
businesses’ ERP (Enterprise Resource Planning) software only supports the simpler methods. The
author says that demand forecasting and planning has received “little attention and investment in
people, analytics, and technology over the past decade” (p. 28).
Companies have shown 10-30 percent improvements in forecast accuracy by using holistic
modeling based on predictive analytics. Businesses often “cleanse” their products’ demand history
by breaking it down into two streams: 1) baseline, and 2) promoted. The promoted stream is
actually a combination of seasonality and promoted volume, which can be spiky. The baseline
stream tends to be a moving average. Demand planners often try to put these two data streams
back together with “the result, 1+1 now equals 5” (p. 29). Advanced analytics for supply planning
together with better forecasts creates a synergy effect of an additional 15-30 percent reduction in
finished goods inventory (p. 29). The result is reduced inventory costs, increased revenues and
profit, and more available working capital (p. 29).
Companies that are moving toward becoming more demand-driven are doing better with
demand forecasts and reducing inventory safety stock. New forecasting models, sometimes
referred to as “Consumption Based Modeling”, link downstream data to upstream data using a
process called “Multi-Tier Causal Analysis” (MTCA) (p. 29). Heizer and Render (2014) clarify
“downstream” as distributors and retailers, and “upstream” as suppliers (p.447). Chase references
his own Spring 2015 article in The Journal of Business Forecasting for more information about the
correlation between upstream and downstream data. By not relying entirely on either demand or
supply forecasts businesses can be better at solving their supply chain challenges. A holistic view of
supply and demand can lead to more success in building a responsive supply chain. Heizer and
Render (2014) cover forecasting methods in chapter four of Operations Management and explain
supply chain logistics and inventory models in chapters 11 and 12, respectively.
Homareau, Jack. (Fall 2015) Forecasting Sales Volumes with Economic Indicators. The Journal of
Business Forecasting, 34, 3, 32-34.
The author brings many years of macroeconomic modeling and forecasting experience to
this article, which outlines a methodology for forecasting sales volume using economic indicators.
Specifically, he showed how to create a baseline product forecast for US automobile sales using U.S.
housing starts data as the economic driver. The Housing Starts data series correlates well with
automobile sales volume, and along with product promotions and marketing plans can improve
sales forecasts. He also evaluated the forecasting model’s performance. The sales data used in this
example are quarterly U.S. auto sales from 1999 through 2012. Actual sales for 2013 were used to
evaluate the model’s predictions for 2013 sales. The methodology used was a simple regression
model, with housing starts as the explanatory variable and auto sales as the response variable (pp.
31-32). For 2013, actual U.S. housing starts (rather than projected U.S. housing starts) were used
to forecast auto sales over the same period. Data from the U.S. Census Bureau was converted from
monthly to quarterly totals.
Homareau’s next step was to calculate the quarterly percentage change in both data series
on a year-over-year (YoY) basis. Then he developed the regression model, where x is the YoY
quarterly percentage change in housing starts, and y is the YoY quarterly percentage change in U.S.
auto sales (pp. 33-34). The resulting coefficient of correlation is 0.74, and the regression line
equation is Y=1.1 + 0.5 x. The third step was to prepare 2013 forecasts. The forecasted auto sales
growth rate for 2013 Q1 was calculated at 18.1%, based on U.S housing starts growth of 33.9% in
Q1 of 2013. Automobile sales growth rates for Q1- Q4 of 2013 are calculated below (p. 34).
Housing Starts Growth Rate
Vehicle Sales Growth Rates (YoY: quarterly)
1.1 + (.05 x 33.9)= 18.1
1.1 + (.05 x 17.0) = 9.6
1.1 + (.05 x 13.0) = 7.6
1.1 + (.05 x 12.9) = 7.6
This growth rate is multiplied against 2012 Q1 actual sales to obtain projected 2013 Q1 sales of
4,179.9. The following table shows these calculations and 2013 sales projections (p. 34).
Motor Vehicles Sales
Projected Quarterly U.S.
Motor Vehicles Sales (000)
3,539.3 x 1.181 = 4,179.9
3,883.1 x 1.096 = 4,255.9
3,699.5 x 1.076 = 3,980.7
3,665.8 x 1.076 = 3,944.4
The final step in the process was to compare forecasted sales with actual sales, and compute
the MAPE (Mean Absolute Percentage Error). For 2013 Q1, actual sales were 3,754.6 and projected
sales were 4,179.9. The forecast error here is 425.3, with a MAPE of 10.2%. The following table
shows these results as well as the calculated overall 2013 MAPE of 2.9% (p. 34). The author
states this error rate is fairly good by industry standards, but that an additional economic variable
and some manual adjustments by a person with “domain knowledge” (auto industry sales data
background), would have improved the projected sales for 2013 and reduced forecast error.
Heizer and Render (2014) cover regression analysis and MAPE in chapter four of Operations
Management (pp. 106-132). It was helpful to see Homareau use quarterly percentage change in the
data series in his regression rather than actual auto unit sales regressed on the actual number of
housing starts. The use of percentage growth rate will be a helpful data transformation tool in other
cases where the explanatory and dependent variables may be measured on different scales. I
entered all of the author’s data into Excel and ran the regression. I can confirm the coefficient of
correlation is 0.74; however, the regression model is more accurately stated as Y = 1.1 + 0.455 x
(the author rounds it up to Y= 1.1 + 0.5 x). The more accurate coefficient will result in a smaller
forecast error. See the Excel “Summary Output” below for more results. This article will be quite
helpful for students who are learning how to forecast business data using a simple regression
model that includes U.S. economic data as the explanatory variable (independent variable).
Adjusted R Square
Housing YoY % Change
Jain, Chaman. (Winter 2006/2007) Benchmarking Forecasting Models. The Journal of Business
Forecasting, 25, 4, 14-17.
The purpose of this article is to help business forecasters determine how to apply the right
model to their data sets, taking into consideration that data sets have patterns. The author
describes three types of forecasting models, identifies fundamentals of modeling, discusses the
types of models used in business, and concludes with model selection. The three types of models
are: 1) Time Series, 2) Cause-and Effect, and 3) Judgmental (p. 14). Time Series models are based on
the assumption that the future will resemble the past. They work best for short-term forecasting
due to data stability in the short run. They include: 1) Simple- and Moving Averages, 2) Trend, 3)
Exponential Smoothing, 4) Decomposition, and 5) Autoregressive Integrated Moving Average
(ARIMA) (p. 14). Cause-and- Effect Models involve independent and dependent variables, where
the average relationship between the two can be projected into the future. Jain provides examples:
1) Regression, 2) Econometric, and 3) Neural Network. Judgmental Models can be used when there
is no historical data, when the market is volatile, and/or when a very long-term forecast is
contemplated (p. 14).
Jain offers nine fundamentals of models: 1) Datasets form patterns and the model should
attempt to capture the pattern as best as possible. The actual model will include pattern plus error.
2) 100% accuracy is not necessary. Minimization of error is the goal. 3) More data are not
necessarily better. In the consumer products industry, most companies use data of 30 months or
less. 4) Sophisticated models are not always better. Start with a simple model and move to more
complex models. 5) There is no magic model. Change in the real world means models need to adapt
to the datasets being analyzed. 6) Models age with time. Over time, data is usually dynamic and
models will not always work as they used to. 7) Each model has its own data requirements. Some
models require 35 or more observations to prepare a forecast. 8) Statistical forecasts are nothing
more than baseline forecasts. The art and science of forecasting may require a judgmental overlay.
9) Forecasts should not be prepared in isolation. It is important to consult stakeholders such as
marketing or finance people (pp. 15-16).
Based on a 2006 survey, Time Series models are used by 72% of industries, Cause-and
Effect by 17% and Judgmental by 11% (p. 16). Jain reports that within Time Series model usage,
Averages and Simple Trend are used 60% of the time, Exponential Smoothing 30%, and other
methods are used 10%. Cause-and-Effect models are being used more often in business forecasting,
with Regression used the most. Judgmental models that are used most are Survey (50%), followed
by Analog at 27% (p. 17). Jain provides bar charts that show all the model usage survey results.
Software systems often have an internal “expert system” that select the best model for the
application. An example of a criterion might be minimizing Mean Absolute Percentage Error
(MAPE). Forecasting software systems often are based on time series models and some provide an
expert system in regression. Forecasters can use suggested software tools or select their own model
preference. Jain recommends starting by choosing an acceptable error rate and then using a simple
model before progressing to complex models (p. 17). Monitoring the model’s performance is
important, as is using “ex post forecasts” to determine if the model is good.
Heizer and Render (2014) present this mater ...
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