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Operation management: forecasting techniques
Forecasting Technique
College of Business Administration
BUSI 3321- Operation Management
201
Dr. Dalia Younis
Spring 2012
Done by:
Elham Al-Bokhari
200801550
(list of figures, literature review, case study 123, appendix A, and references)
Malika Al-Sharif
200801556
(executive summary, introduction, recommendation, and conclusion)
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Operation management: forecasting techniques
Table of context
List of figures and tables……………………………………………………………3
Executive summary…………………………………………………………………4
Introduction……………………………………………………………………………6
Literature review…………………………………………………………………7
Case study 1…………………………………………………………………………13
Case study 2…………………………………………………………………………22
Case study 3…………………………………………………………………………..24
Recommendation…………………………………………………………………27
Conclusion………………………………………………………………………………29
Appendix A………………………………………………………………………….30
References ……………………………………………………………………………….33
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Operation management: forecasting techniques
List of figures and tables
Figure 1: the needs for marketing forecasts
figure 2: methodology tree of forecasting
Figure 3: average forecasting error
Figure4: Theil’s inequality coefficient
Figure5: average forecasting error
Figure6: MAE in the 18 month
Figure 7: Mean Absolute Error
Figure 8: MAE in the 18 month
Figure 9: Average Forecasting Error
Figure 10: MAE in the 18 month
Figure 11: Mean Absolute Error
Figure 12: MAE in the 18 month
Figure 13: Mean Absolute Error
Figure 14: Theil’s Inequality Coefficient
Figure 15: forecast types
Figure 16: demand forecasting benefits
figure 17: demand forecasting benefits examples
Figure 18: EPSDT costs FY 1994 to 2006
Figure 19: EPSDT costs FY 1994 to 2006
Table 1: advantages and problems of qualitative methods
Table2: out of sample forecast
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Operation management: forecasting techniques
Executive summary:
We learned in the operation management course that operation management is the part of
the business organization that response of producing the good and services to the customer
and we can define it in too many ways like operation management is the process or system
that organizations used to create and provide services. Also, operation management deals
with the design of the product and the utilization, development and acquisition that firm need
to deliver the goods and there is a strategic issue on the operation management including the
size and location of the manufacturing plant. Moreover, operation management is worldwide
subject that have many techniques and method such as forecasting that is so necessary for any
new business because it’s a statement of the future value of the variable of interest and it the
process that companies and organizations used to analyze relevant data and graphs to
determine how best to proceed in business decisions for the future by predicting what the
future will look like, and it have many techniques in determine the demand and the resource
availability.
Forecasting is very important in making informed decision even though it’s not
always perfect and it more accurate for group of items than for individual. Furthermore,
forecasting technique is broadly considered as a method or a technique for estimating many
future aspect for business and operation because forecasting have numerous technique that
can be used to accomplish forecasting goal future forecast on the past data.
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Operation management: forecasting techniques
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Operation management: forecasting techniques
Introduction:
In this research we will talk about the method and technique of forecasting that
used by organization related to forecast their product. As we know forecasting is a
planning tool help the management in attempt to cope with uncertainty of the future
and this valuable tool can be used to evaluate the future of the business, the future of
the product, and the future of the particular market sector based on what the company
part of. The future of forecasting start with assumptions, knowledge and judgment of
management experience, and accurate forecast is significant for numerous reasons
because it prevented the losses by talking all the relevant information and make a
proper decision. To be more specific forecasting technique enable entrepreneurs,
organizations and stockholders that have small business to demonstrate a high quality
and accurate forecast that can give them an idea on whether the development of the
product would have chance to be a successful or not and it prevent them from wasting
time, money, manufacturing, and market.
Moreover, because risk and uncertainty are central to forecasting technique it’s
generally considered good practice to indicate the uncertainty of forecasting technique
because of its fail in forecast the actual result the organization will have a negative
effect for the company and their stock price or financial positions. Forecasting have
many accurate method such as qualitative, time series ,simple moving average,
weighted moving average, exponential smoothing, adjusting trends, multiplicative
seasonal method, casual method, adjusting for trend,, multiplication seasonal method ,
casual method, focus forecasting and back to index. Thus, this is a figure that can
show how forecasting help in taking decision.
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Operation management: forecasting techniques
Literature review:
Sometimes managers need to forecast the demand directly, and forecast the action and the
decision for suppliers, competitors, the government, and themselves. Especially when there
are some issues may be involved. And these actions may help to forecast market share. The
needs for marketing forecasts are shown in figure 1.
Source: JSA-KCG, 2005
Forecasts can be used in some organization for planning, inventory, financial, budgeting, and
for productivity.in this case forecasting need to be accurate and effective to accomplish the
organization gaols. Organization need to have a planning first then match it with the
forecasting process (Oliva, Watson, 2007).
There are relationships between the forecasting methods and it will be shown in the
figure 2: methodology tree of forecasting
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Operation management: forecasting techniques
Source: JSA-KCG, 2005
Unaided judgement is often used when experts are unbiased, experts possess privileged
information, large changes are unlikely to happen, and relationships are well understood by
experts, do not hold (Green, Armstrong, 2005)
Qualitative forecasting can be used to express forecasts for new products with no historical
data; and to adjust mid or long-term forecasts for organization planning; to adjust productline forecasts; and to adjust patterns made by quantitative techniques (Mentzer, 2004).
When a forecaster practices an endogenous quantitative forecasting method, there will be
some assumption that no systematic changes from previous periods had occurred (Mentzer,
2004). The qualitative forecasting underlines predicting the future, rather than explaining the
past (Makridakis & Wheelwright, 1989).
The qualitative techniques can have some advantages and problems, this table will illustrate
the advantages and the problems of qualitative techniques
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Operation management: forecasting techniques
Table 1:
advantages
problems
Qualitative forecasting techniques have the
The ability to forecast accurately can be
ability to predict changes in sales patterns.
reduced when forecasters only consider
readily available and/or recently perceived
information
Qualitative forecasting techniques allow
The ability to forecast accurately can be
decision makers to incorporate rich
reduced by the forecasters’ inability to
data sources consisting of their intuition,
process large amounts of complex
experience, and expert judgment.
information.
Accurate forecasts can be difficult to produce
when forecasters are overconfident in their
ability to forecast accurately
The ability to accurately forecast may be
significantly reduced by political factors
within organizations, as well as political
factors between organizations.
The ability to forecast accurately may be
reduced because of the forecasters’ tendency
to infer relationships or patterns in data
when there are no patterns.
The ability to forecast accurately can be
affected by anchoring; that is, forecasters
may be influenced by initial forecasts (e.g.,
those generated by quantitative methods)
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Operation management: forecasting techniques
when making qualitative forecasts.
Future ability to forecast accurately may be
reduced when a forecaster tries to justify,
rather than understand, a forecast that proves
to be inaccurate.
Qualitative forecasting techniques
encourage inconsistencies in judgment
due to moods and/or emotions, as well as
the repetitive decision making inherent in
generating multiple individual product
forecasts.
Qualitative forecasting techniques are
expensive and time intensive.
Source for the table: Hogarth and Makridakis (1981)
Expert evaluations use the experience and the knowledge of people, like executives,
managers, marketing people, external experts, and sales people, who know the products very
well, and help in generating sales forecasts. The techniques in expert evaluation usually
involve merging inputs from multiple sources. The advantage of the assistances from more
than one person, it can balance the biases introduced into a forecast when the forecast is
created by one person ( Mentzer, 2005).
Quantitative forecasting depends on numerical data and mathematics to predict future
conditions. It relies on two methods which they are causal and timer series approach.
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Operation management: forecasting techniques
A time series approach can occur in a lot of applications such as economics, medicine, and
finances. The time series approach can detect inflation and economic condition and the
change of the economic over period of time (Chiquiar, Noriega, and Francia, 2007).
Accuracy and forecasting horizon should allow elaboration and execution of plans in order to
make the best advantage of the forecast (Makridakis et al.,1998; Mentzer and Bienstock,
1998).
For example, a quick response planning approach need an input and uncertainty of the
forecasts in order to accomplish the production, therefore, forecasting process like the
planning approach should provide virtual measure of uncertainty (Fisher et al., 1994; Fisher
and Raman, 1996).
However, forecasting is not precise science. In an administrative situation, the forecasting
process needs information from multiple sources, and a multiplicity of formats, not always
agreeable to integration and manipulation (Armstrong, 2001b; Fildes and Hastings, 1994;
Lawrence et al., 1986; Makridakis et al., 1998). Some case studies in the field of electronic
and financial underline some information deficiency in making organization forecasts,
because of lack of communication. The variety of data formats and sources can create major
challenges for forecasting, like not all information are accurate and have a judgmental calls
(Armstrong, 2001a; Sanders and Manrodt, 1994; Sanders and Ritzman, 2001).
The judgmental conditions to make and evaluate forecasts can have an effect in individual
and functional restrictions and biases that can possibly compromise the quality of the
forecasts (Oliva, Watson, 2007).
Therefore, the forecasting process needs to clearly be able to manage the biases that may
affect the result of the process (Oliva, Watson, 2007).
The significance of forecasting for operations management cannot be exaggerated. Within the
firm forecasts sharing and generation are used by managers to lead the circulation of
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Operation management: forecasting techniques
resources (Antle and Eppen, 1985; Stein, 1997), and to offer targets for organizational efforts
(Hamel and Prahalad, 1989; Keating et al., 1999), and to integrate the operations
management purpose with the marketing, sales, and product development (Crittenden et al.,
1993; Griffin and Hauser, 1992).
Errors in forecasting sometimes it can cross the organizational border and interpret it into
misallocation of resources that can have a major influence on shareholders’ return on
investment (Copeland et al.,1994), and it may affect customers’ view of service quality
(Oliva, 2001; Oliva and Sterman, 2001).
In supply chain, forecast is a predominant practice for proactively supporting capacity
and managing supply (Cachon and Lariviere, 2001; Terwiesch et al., 2005).
.
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Operation management: forecasting techniques
Case study 1:
Microeconomic forecast
For over than 16 years the forecasting showed and provides a successful quality of evaluated
material. Also it helped assessing the users and makes them realize how they can know the
future of the macroeconomic over a period of time whether it is a long run or short run. It also
shows the economic change from in the Czech Republic. The researchers divided the periods
to two periods from (1995 to 2002) and from (2003 to 2010) so they can know how the
forecasting can develop over time.
Most of the macroeconomic forecasts are nature and made up from assumption, but there are
some other factors that cannot be predictable like change in the political environment in the
same country or around the world, natural disaster, and change in the financial market. Some
of these factors can be difficult to analyse it, or sometimes it can be impossible to forecast it.
So the analyses decided to eliminate those unpredictable factors
The forecast analysis team they used some methods like: average forecasting error, mean
absolute error, Theil’s inequality coefficient, and naive forecast.
Real GDP growth:
After they divided the periods into two periods, the forecasting team analysis forecasted the
years from (1995 to 2002) and found out that the real GDP of the Czech Republic were
overvalued, so from that time the real GDP growth it was actually lower. And in the short run
horizon the GDP growth rate was somewhat undervalued. And they found that there were
some inaccurate estimates in the GDP growth which it showed high mean error in the short
run horizon. And this inaccuracy happened in 1998 and 2009. Also there were development
in the external environment at the same time of the recession in 2009, which it made a
connection between the external environment development and the inaccuracy estimates.
It is difficult to predict the development in the future, so the analysis team made a
comparison of forecast of other organization at the same period, so they used a different
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Operation management: forecasting techniques
method which it was Theil’s coefficient and it showed the same information when using the
average forecasting error.
Figure 3: average forecasting error
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Figure 4: Theil’s inequality coefficient
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Nominal GDP growth
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Operation management: forecasting techniques
The analysis found out the nominal GDP growth was somewhat overvalued in the long run,
while the average forecasting error was lower and nearly zero of the 9 months horizon. The
mean absolute error was lower by 35% average in the second period. Also there were
increases in the quality of forecast. In the 18 month horizon it showed decreasing in the
absolute error. In the time of recession there were some high values in the years 1997 to
2009. And in 1999 this time were in the period of disinflation. So the forecasting of 2010 was
accurate.
Figure 5: average forecasting error
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Figure 6: MAE in the 18 month
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
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Operation management: forecasting techniques
GDP Deflator Growth
In the two periods that were divided the GDP deflator was overvalued, but the average error
was not high, not more than 1.5 in both periods. There was major decrease in the absolute
error in 2003 to 2010 compare to the first period this had a significant decrease in error.
1999 was the period of disinflation, so the error in that year falls down.
Theil’s coefficient for the whole two periods did not reach 0.85 in the horizon.
While the average values were to some extent higher in the second period.
Figure 7: Mean Absolute Error
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
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Operation management: forecasting techniques
Figure 8: MAE in the 18 month
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Real Household Consumption Growth
The growth of household consumption in the first period was overestimated, and the forecast
in the second period was slightly balanced, and the average forecasting error was not over 0.5
in short run horizon. The mean absolute error was lower than the real GDP growth and the
value of it reached 2.5 in medium horizon but it decreased to less than 1 in the short horizon.
In the period of recession the errors fall down in the years 1997 and 1998, and the declining
of household was predicted but at the same time it topped all expectations. Also in 2009 the
period of recession the error falls down and the prediction for 2010 was not very accurate.
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Operation management: forecasting techniques
Figure 9: Average Forecasting Error
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Figure 10: MAE in the 18 month
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
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Operation management: forecasting techniques
Average Inflation Rate
The forecasts to some
extent overvalued the average inflation rate. In a horizon of 30 months, the average forecasti
ng error was not over 1 in both periods. But in the second period the absolute error was lower
and decreased over the 18 month horizon. The average inflation rate fell in 1999 and the
absolute error was not over 1 during the 18 month horizon in both periods. Also the Theil’s
inequality coefficient did not exceed 0.75 in both periods.
Figure 11: Mean Absolute Error
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Figure 12: MAE in the 18 month
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Operation management: forecasting techniques
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Current Account to GDP Ratio
In compare between the GDP ratio and the current account, the forecasts overvalued the GDP
ratio in both periods. The mean absolute error was between 1 and 2 and it was lower in the
second period. In the 18 month horizon of the absolute error there was some decreasing.
Theil’s coefficient was lower in the first period of forecasting, but in the second period it
raise from 9-18 month series. And this was because of some changes in the revision system
occurred in the same period which it caused the increasing.
Figure 13: Mean Absolute Error
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
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Operation management: forecasting techniques
Figure 14: Theil’s Inequality Coefficient
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
In the conclusion of this case study the evaluation of the historic data of the Ministry of
Finance forecasting showed the quality of improving over the time passed.
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Operation management: forecasting techniques
Case study 2:
Gartner demand forecasting and planning
Many organizations struggle with forecasting, with the ability to improve customer
satisfaction, inventory planning and management, productivity, quality, and enhance trading.
Demand forecasting should be the priority for every organization.
Gartner found out in their survey and case study that demand forecasting can enhance and
increase the ability for retailers and organizations for 20% of on-shelf availability, 3%
increase in revenue and 1 to 3% increase in profit margin, 5% improve in productivity, and
10% reduce of obsolete inventory.
Gartner’s interviewed some retailers and technology providers and found out some common
type of forecasting have been used commonly. And they found out that most organization
treat each forecasting type separately and do not view it as one big comprehensive view
point.
Figure 15: forecast types
Significant benefits from improving demand forecasting
The research Gartner’s made found out that there are different segments and area that had
primary and secondary benefits
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Operation management: forecasting techniques
Figure 16: demand forecasting benefits
And they spoke to 15 organizations to help quantify the impact of using demand forecasting
and here are the results they found out in figure 17: demand forecasting benefits examples
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Operation management: forecasting techniques
Case study 3:
Health care
Hospitals and medical centres need to manage effectively the costs, resources, workforces,
and the inventory of the whole departments and institutions.
This case talks about the California Institute for Mental Health, Early and Periodic Screaming
Diagnosis and Treatment (EPSDT)
Researcher within the children mental health department in California institute set some goals
to achieve, first gaol was to have a forecast of their annual total costs, have less error, use
their own expertise rather than using an external expertise, know the estimate total workforce
needed, flexibility with the outcome of the forecasting.
They used a forecast method to weight the most recent years so they used auto regression
with linear trend:
Cost2008 = (Wt2007 x Cost2007) + (Wt2006 x Cost2006) + (Wt2005 x Cost2005) +
(Wt2004 x Cost2004) + (Wt x TimeTrend) + Error
The accuracy showed 2 to 4 % of error which if it was 4% it will cost them 4 million dollar.
So they need to reduce the error percentage.
Then the researchers tried to apply the policy analysis model and used cost driver to produce
forecast, and understand how can the cost drivers actions will affect the total cost of the
department and institution, so the EPSDT costs included: service category, policy changes,
unit costs, service caseload, services and clients, diagnosis, and demographics
Also they forecasted the data by using time series approach (ARIMA) model, and after using
it they compare it to other forecasting methods, and found out time is critical to understand
the process they needed and the observation was per time point and dynamic. The data they
used in the forecasting was a historical data and they found out the historic data can lead to
accurate forecasting.
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Operation management: forecasting techniques
Figure 18: EPSDT costs FY 1994 to 2006
Source: California Institute for Mental Health (CiMH)
Figure 19: EPSDT costs FY 1994 to 2006
Source: California Institute for Mental Health (CiMH)
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Operation management: forecasting techniques
From this (ARIMA) model they reduced the error to 0.6% and they found out that using
ARIMA model forecast perform better in short run and require some practices
Table2: out of sample forecast
Month
July 04
August 04
September 04
October 04
November 04
December 04
January 05
February 05
March 05
April 05
May 05
June 05
FY 04-05
Actual EPSDT $
64,842,182
62,517,247
66,706,588
68,120,110
66,729,985
61,132,935
70,400,621
69,936,858
82,518,211
79,338,306
76,384,412
73,914,413
842,541,868
ARIMA forecast $
70,763,598
60,494,032
69,445,670
75,372,668
59,826,262
62,837,041
69,884,559
69,009,538
83,859,663
74,966,753
71,389,066
69,522,102
837,370,952
Source: California Institute for Mental Health (CiMH)
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Operation management: forecasting techniques
Recommendation:
As researches has shown that there is a little difference between the accuracy of
forecasting performance and knowledge and also, there is a similarities such as that
studies shows that forecasting not accurate. Forecasting is a historical data that appear
to be rather technique or planning to the future to manage the aspect of the
organization or business so we recommend to small businesses to use forecasting
technique in measuring their future economy, sale and make a good judgment because
it’s not easy to convert a feeling about what will happen in the future so forecasting
technique or method can help estimate many future aspects for business operation. In
addition we recommend forecasting technique to organization because forecasting is
able to identify the factors of the future sales of the products by reviewing the
historical data over time can provide for them a good understanding of the previous
sales.
We recommend for organizations and for the cases we used in our research to use
the qualitative method or technique of forecasting where as qualitative forecasting
consist of time serious forecasting method that based on analysis historical data that
can make the assumption that past pattern in data can used to the future and moving
average appear that forecast is based on arithmetic average of a given number from
the past data. Also, exponential smoothing that have 2 type single and double and it
allows inclusion of trends. In addition, to have the best model of forecasting we
recommend for them the box-Jenkins method which is a method that autocorrelation
used to identify underlying time serious and find the best model. Moreover, there is a
two type of technique that can help organization measure the error of forecasting such
as the bias and the accuracy. Bias is the forecast that biased on forecasting errors more
in one direction and is tend to be under forecast or over forecast, and Accuracy
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Operation management: forecasting techniques
forecast where accuracy refers to the distance of the forecasts from actual demand
ignore the direction of the error. The last recommendation for organizations is the
focus forecasting which refers to an approach to forecasting that develop forecast by
various technique and then pick a forecast that was produced by the best technique of
them and focus forecasting is always for the first six month of the year.
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Operation management: forecasting techniques
Conclusion:
In conclusion, forecasting technique is process used by companies and organization to
analyze data and graph to help them determine the best future decision, and the elements of
doing business and various factors. Also, using these historical data over time can help
forecaster develop the understanding of making decision for future sales of the product. In
this research we talked about forecasting technique and how it can benefit the organization
and how it is related to the successful operation management. Also, we used some cases
related to the forecasting technique and analyze them such as the macro case for real GDP
growth that evaluate the historical value of the ministry of finance macroeconomic forecasts
that show the quality that improve by the time. In the end of the research we recommended
some of the forecasting technique that would help the organization and the market place.
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Operation management: forecasting techniques
Appendix A
Tables used in the case study 1: macroeconomics forecast of Czech Republic
Table 1: forecast of real GDP growth
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Table 2: forecast of nominal GDP growth
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
30
Operation management: forecasting techniques
Table 3: forecast of GDP deflator growth
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Table 4: forecast if real household consumption growth
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
31
Operation management: forecasting techniques
Table 5: forecast of average inflation rate
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
Table 6: forecasts of current account and GDP ratio
Source: Ministry of Finance of the Czech Republic, European Commission, OECD, IMF, Mo
F estimates
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Operation management: forecasting techniques
References
Antle, R., G.D. Eppen, 1985. Capital rationing and organizational slack in capitalbudgeting. Management Sci. 31 (2), 163-174.
Armstrong, J.S. (ed.), 2001a. Principles of Forecasting. Kluwer Academic Publishers,
Boston.
Armstrong, J., & Green, K. (2005, September ). Demand forecasting: Evidence-based
methods.
Armstrong, J.S., 2001b. Combining forecasts. In: J.S. Armstrong (Ed), Principles of
Forecasting. Kluwer Academic Publisher, Boston, pp. 417-439.
Cachon, G.P., M.A. Lariviere, 2001. Contracting to assure supply: How to share
demand forecasts in a supply chain. Management Sci. 47 (5), 629-646.
Copeland, T., T. Koller, J. Murrin, 1994. Valuation: Measuring and Managing the
Value of Companies, 2nd ed. Wiley, New York.
Crittenden, V.L., L.R. Gardiner, A. Stam, 1993. Reducing conflict between marketing
and manufacturing. Ind. Market. Manag. 22 (4), 299-309.
Fisher, M.L., A. Raman, 1996. Reducing the cost of demand uncertainty through
accurate response to early sales. Oper. Res. 44 (1), 87-99.
Fildes, R., R. Hastings, 1994. The organization and improvement of market
forecasting. J. Oper. Res. Soc. 45 (1), 1-16.
33
Operation management: forecasting techniques
Green, K. C. (2005), Game theory, simulated interaction, and unaided judgment for
forecasting decisions in conflicts: Further evidence. International Journal of
Forecasting, 21, 463-472.
Green, Kesten C. & J. S. Armstrong (2005a), “Value of expertise for forecasting
decisions in conflicts" Department of Marketing Working Paper, The Wharton
School.
Green, Kesten C. & J. S. Armstrong (2005b), “Structured analogies for forecasting,”
Department of Marketing Working Paper, The Wharton School.
Griffin, A., J.R. Hauser, 1992. Patterns of communication among marketing,
engineering and manufacturing: A comparison between two new product teams.
Management Sci. 38 (3), 360373.
Griswold, M., & Streneckert, K. (2011, March 18). Business case for demand
planning.
Hamel, G., C.K. Prahalad, 1989. Strategic intent. Harvard Bus. Rev. 67 (3), 63-78.
Keating, E.K., R. Oliva, N. Repenning, S.F. Rockart, J.D. Sterman, 1999.
Overcoming the improvement paradox. Eur. Mgmt. J. 17 (2), 120-134.
Lawrence, M.J., R.H. Edmundson, M.J. O'Connor, 1986. The accuracy of combining
judgmental and statistical forecasts. Management Sci. 32 (12), 1521-1532.
Lee, L. (2007, September ). Early and periodic screening diagnosis and treatment
(epsdt).
Makridakis, S., S.C. Wheelwright, R.J. Hyndman, 1998. Forecasting: Methods and
Applications, 3rd ed. Wiley, New York.
34
Operation management: forecasting techniques
Mentzer, J. (2004, February 11). Qualitative sales forecasting.
Mentzer, J.T., C.C. Bienstock, 1998. Sales Forecasting Management. Sage, Thousand
Oaks, CA.
Oliva, R., N. Watson. 2004. What drives supply chain behavior? Harvard Bus. Sch.,
June 7, 2004. Available from: http://hbswk.hbs.edu/item.jhtml?id=4170&t=bizhistory.
Oliva, R., & Watson, N. (2007). Managing functional biases in organizational
forecasts: A case study of consensus forecasting in supply chain planning.
Sanders, N.R., K.B. Manrodt, 1994. Forecasting practices in U.S. corporations:
Survey results. Interfaces 24, 91-100.
Sanders, N.R., L.P. Ritzman, 2001. Judgmental adjustment of statistical forecasts. In:
J.S. Armstrong (Ed), Principles of Forecasting. Kluwer Academic Publishers, Boston,
pp. 405-416.
Stein, J.C., 1997. Internal capital markets and the competition for corporate resources.
Journal of Finance 52 (1), 111-133.
Terwiesch, C., Z.J. Ren, T.H. Ho, M.A. Cohen, 2005. An empirical analysis of
forecast sharing in the semiconductor equipment supply chain. Management Sci. 51
(2), 208-220.
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