Quantitative and Qualitative Forecasting Techniques

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This assignment delves into the world of forecasting techniques, examining both quantitative and qualitative approaches. It presents a numerical example illustrating how quantitative methods are applied, followed by a detailed exploration of qualitative techniques such as market research, panel consensus, historical analogies, and the Delphi method. The text emphasizes how these qualitative methods contribute to sales trend projections and decision-making in business settings.

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Quantitative and Qualitative Forecasting 1
QUANTITATIVE AND QUALITATIVE FORECASTING
Name
Course Number
Date
Faculty Name

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Quantitative and Qualitative Forecasting 2
Quantitative and Qualitative Forecasting
Example 13.2: Comparing Simple and Weighted Moving Average, Exponential Smoothing and
Linear Regression Analysis
Table 1: Insurance company claim form process control
Sample Number Inspected
Number of
forms
Completed
Incorrectly
Fraction
Defective
Simple
Moving
Average
Weighted
Moving
Average
Exponential
Smoothing
1 300 10 0.0333 #N/A #N/A
2 300 8 0.0267 0.0300 0.0333 0.0333
3 300 9 0.0300 0.0283 0.0283 0.0330
4 300 13 0.0433 0.0367 0.0296 0.0329
5 300 7 0.0233 0.0333 0.0399 0.0334
6 300 7 0.0233 0.0233 0.0275 0.0329
7 300 6 0.0200 0.0217 0.0244 0.0324
8 300 11 0.0367 0.0283 0.0211 0.0318
9 300 12 0.0400 0.0383 0.0328 0.0320
10 300 8 0.0267 0.0333 0.0382 0.0324
1 2 3 4 5 6 7 8 9 10
-
0.0050
0.0100
0.0150
0.0200
0.0250
0.0300
0.0350
0.0400
0.0450
0.0500
0.0550
0.0600
0.0650
Fraction Defective Linear (Fraction Defective)
Lower Limit Upper Limit
Simple Moving Average Weighted Moving Average
Exponential Smoothing
Figure 1: Claim form control chart
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Quantitative and Qualitative Forecasting 3
Figure 1 above shows various line graphs for claim form control chart, representing
differing seasonality effects. The control chart displays the lower and upper limits, linear
regression best fit line, exponential smoothing, simple & weighted moving averages and the
original scatter plot for the time against the defection fraction. Comparing the five plotted lines,
the seasonality effects decreases significantly from the original line through simple moving
average, weighted moving average, exponential smoothing and finally the linear plot which does
not have any form of defects and seasonal effects (Brockwell and Davis, 2016). Based on the
stated order, the power of time series analysis decreases significantly between these line charts,
which the original plot depicting the best scenario and the linear regression the worst scenario to
measure seasonality. However, as the plot is smoothened, an analyst is able to summarize and
clearly understand the trend, hence acquiring powerful administrative information (Al-Omari and
Al-Nasser, 2011). According to the linear regression line, there is a general reduction in
defective proportions from the first to the tenth sample. Also, based on the exponential
smoothing, the trend decreases significantly with minimal seasonal effects (Ahangar and
Chimka, 2015).
Quality Management – Toyota
Washer thickness
1. The probability of having washer above 2.4mm if the data is normally distributed.
Count above 2.4mm = 0
The count of washers above the thickness of 2.4mm in the sample means that there will be no
washer above the threshold (Lawrence, Klimberg and Lawrence, 2009).
2. The proportion within 1.4 – 2.4mm range.
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Quantitative and Qualitative Forecasting 4
Count of washer between 1.4 – 2.4mm range = 0
Therefore, 100% of the washers produced will be within the tolerable range of thickness if the
lower and upper bounds would be 1.4mm and 2.4mm respectively.
3. The Cpk value will be zero(0).
4. The Cpk if the process is centred for the same standard deviation
Cpk = min { USLmean
3 S . D , meanLSL
3 S . D (Ott, Schilling and Neubauer, 2005)
Where, mean = 1.9625, USL = 2.4mm, LSL=1.4mm
Cpk=min(0.1458, 0.1875)
Cpk will be 0.1458.
5. Percentage out of tolerance
% out of tolerance=(0.1458+0.1875)100
¿ 33.33 %
6. X-bar and range control charts
X-bar chart
Sample LCL UCL Mean x-bar
1 0.0030 3.9030 1.9530 1.89
2 0.0030 3.9030 1.9530 1.91
3 0.0030 3.9030 1.9530
1.98181
8
4 0.0030 3.9030 1.9530 2.03
UCLr = D4R-bar

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Quantitative and Qualitative Forecasting 5
LCLr = D3R-bar; where D3 = 0.223, D4 = 1.777
Ranges
R1 0.5
R2 0.6
R3 0.7
R3 0.8
R-bar 0.65
7. Charts
X-bar chart
1 1.5 2 2.5 3 3.5 4
0.0000
0.5000
1.0000
1.5000
2.0000
2.5000
3.0000
3.5000
4.0000
4.5000
LCL UCL x-bar Mean
Figure 2: X-bar chart
Range chart
UCLr = 1.777*0.65 = 1.15505
LCLr = 0.223*0.65 =0.14495
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Quantitative and Qualitative Forecasting 6
R-bar charts
1 2 3 4
0
0.2
0.4
0.6
0.8
1
1.2
1.4
LCL Range UCL R-bar
Figure 3: R-bar chart
The process appears to be in control because the ranges and averages are within the tolerable
standard deviation (Al-Omari and Al-Nasser, 2011).
8. Setting the standard deviation to only 0.1mm would mean that the fraction of defective
would increase because the process significance highly increased. It would be better to
have smaller standard deviation because it will ensure that the process is more accurate,
hence producing quality washers.
Example 18.4: Computing trend and seasonal factor
Table 2: Example 18.4 calculations
Year Quarter Actual Amount Trend from Equation Ratio of Actual/Trend Seasonal Factor
2011
I 300 228.332 1.314
II 200 280.594 0.713
III 220 332.856 0.661 1.251
IV 530 385.118 1.376 0.785
2012
I 520 437.38 1.189 0.700
II 420 489.642 0.858 1.277
III 400 541.904 0.738
IV 700 594.166 1.178
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Quantitative and Qualitative Forecasting 7
I II III IV I II III IV
2011 2012
0
100
200
300
400
500
600
700
800
f(x) = 52.2619047619048 x + 176.071428571429
Quarter
Amount
Figure 4: Line chart for example 18.4
Qualitative forecasting techniques
There are various qualitative forecasting techniques that are used by most of the investors
to determine the future sales. These techniques require money and human resources among other
resources to be able to define the predefined objectives. Market research is one of the main
techniques that entails thorough analysis and understanding of the target customers, groups and
areas. Some of the concerns include distributions of sales among other competitors and
production tendencies. The qualities of products developed by other producers also assist a
company in determining the quality range. In most occasions, businesses depend on panel
consensus to make decisions on the best methods on investments and business conducts.
Occasionally, the board of governors make decisions pertaining production processes
specifically on the quality and adherence to the best dimensions. It has turned out to be a
productive way of production governing because the councils consist of people from different
disciplines who contribute anonymously towards the success of the process. The decisions are
based on the customers’ preferences and predicted changes in the markets. On the same note,

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Quantitative and Qualitative Forecasting 8
historical analogies are also referenced to avoid issues that might affect the production hence
affecting the general performance of the business. To acquire sufficient information from the
area of interest, Delphi method is used to occasionally or repeatedly seek similar information
from the same people or population for clarity, hence reducing bias. These forms of qualitative
information are used to project and forecast the possible sales trend (DuBrin, 2009).
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Quantitative and Qualitative Forecasting 9
References
Ahangar, N. and Chimka, J. (2015). Attribute Control Charts with Optimal Limits. Quality and
Reliability Engineering International, 32(4), pp.1381-1391.
Al-Omari, A. and Al-Nasser, A. (2011). Statistical Quality Control Limits for the Sample Mean
Chart Using Robust Extreme Ranked Set Sampling. Economic Quality Control, 26(1).
Brockwell, P. and Davis, R. (2016). Introduction to Time Series and Forecasting. Cham:
Springer International Publishing.
DuBrin, A. (2009). Essentials of management. Mason, OH: South Western College.
Lawrence, K., Klimberg, R. and Lawrence, S. (2009). Fundamentals of forecasting using Excel.
New York, N.Y.: Industrial Press.
Ott, E., Schilling, E. and Neubauer, D. (2005). Process quality control. Milwaukee: ASQ Quality
Press.
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