1. SUPPLY CHAIN PLANNING
DEMAND ANALYSIS PROJECT
G. Lauson, M.S., P.E.
January 31, 2017
Purpose
This paper presents the findings of an analysis of four (4) pharmaceutical products. Four (4)
forecasting methods were applied and three (3) error measurements were applied to those
forecasting methods to determine which method most accurately estimated next-future-month
demand for each product. Monthly data was selected at the request of the manufacturing
division, as this was determined by them as optimum for their production schedule planning.
Note that all forecasting and error methods / descriptions were taken from a Coursera / Rutgers
University online course titled “Supply Chain Planning” which the author completed in January
2017. The course is part of a five-course offering within Coursera’s “Supply Chain Management
Specialization.”
Forecasting Methods
The following forecasting methods were evaluated for accuracy with respect to each of the four
pharmaceutical products. A brief explanation of the rationale behind each method is provided,
along with how the method is computed.
1. Naïve Method. The simplest of the four, this method uses the prior-period actual demand
as the forecast for the next-period demand. The computation is:
Ft = Dt-1
2. Cumulative Mean Method. This method averages all demand from prior periods – that
average becomes the forecast. Based on information provided in the above-referenced
Coursera course, this is a very stable forecast that averages out all noise; however, that
stability may be contrary to current demand conditions. The assumption behind this method
is that “all prior data is equally useful.” The computation is:
Ft = Σ i = 1 to t–1 [Di] / (t – 1)
3. Simple Moving Average (SMA) Method. An adaptable forecasting method that can be
made reactive or stable. Companies with stable demand tend to like this indicator. The
computation is:
Ft = Σ i = (t-N+2) to t–1 [Di] / N
4. Exponential Smoothing (or Exponential Moving Average (EMA)) Method. A weighted

2. average of all past demand; most of the weight is on the latest observed period and the
remainder of the weights decline exponentially. α changes the weights and varies how much
weight (in the weighted average) is applied to each period. The computation is:
Ft = α * [Dt-1] + (1 – α) * Ft-1
Error Estimation Methods
The following methods were used to estimate the error associated with each of the above
forecasting methods.
1. Mean Error. This is the simplest forecast accuracy measure, and is more a measure of
bias than accuracy. This error measurement averages the difference between true demand
and associated forecast for the forecasted time periods. The computation is:
ME = Σ i = 1 to N [Di - Fi] / N
2. Mean Absolute Percentage Error. The next-simplest forecast accuracy measure
measures accuracy (as opposed to bias). Averages the absolute value of the difference
between true demand and associated forecast (divided by true demand) for the forecasted
time periods. The computation is:
MAPE = Σ i = 1 to N ABS[(Di - Fi) / Di] / N
3. Mean Squared Error. Probably the most important error measurement; it averages the
squared differences between true demand and associated forecast for the forecasted time
periods. Squaring gives more weight to large errors (which are the ones we want to avoid;
small errors can be tolerated). Large errors surprise us and make life and planning much
more difficult. The computation is:
MSE = Σ i = 1 to N (Di - Fi)2
/ N
Application of VBA/Excel
Visual BASIC for Applications (VBA) is a programming language that is attached to all Microsoft
applications, including Excel. VBA programming was used in this project to optimize the Simple
Moving Average span (i.e., N, the number of demand values used to compute the Moving
Average), as well as to optimize the weight parameter, α, that is the controlling part of
Exponential Smoothing. The VBA program applied trial span and alpha values and evaluated
each value according to the following selection criterion:
Minimize [1 * ABS(ME) + 2 * MAPE + 3 * MSE]
The above criterion applies judgment-based weights (i.e., the "1," "2," and "3" in the above
expression) that emphasize the relative importance of the Mean Error (ME), the Mean Absolute
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3. Percent Error (MAPE), and the Mean Squared Error (MSE) in determining the Simple Moving
Average and Exponential Moving Average ability to forecast accurately. Span and alpha values
were then subjected to visual inspection for each of the four products (via graphed data). The
spans and alphas were visually acceptable from the following perspectives:
1. Minimized bias.
2. Minimized error.
3. Trend capture.
4. Responsiveness to non-random fluctuation.
Forecasting Decisions
The optimum forecasting method was decided on the basis of the smallest Mean Squared Error
(MSE) value. The standard deviation (s) was computed from the MSE by taking the square root
of the MSE; s was then used to determine the forecasting confidence interval. The confidence
interval computations are:
LCL = Fi – si * 1.64 (where 1.64 is the 90-percent confidence normal-distribution z-score)
UCL = Fi + si * 1.64
The 90-percent confidence z-score was selected on the basis of a visually-suitable width
confidence interval (i.e., a judgment was applied in selecting the 90-percent interval).
The following table provides the summary monthly data on which the best forecasting methods
for each product are based. Again, the criterion of relevance used to determine the best
forecasting method is the smallest Mean Squared Error value.
MONTHLY DATA ACCURACY ASSESSMENT
Product A Product B Product C Product D
Method E MAPE MSE E MAPE MSE E MAPE MSE E MAPE MSE
NM -2,040 16.8% 90,837,443 -49 23.4% 2,887,528 9 12.7% 1337 -41 20.0% 70,472,694
CMF -9,720 30.6% 206,087,159 94 15.5% 1,418,775 71 32.8% 8635 4,402 29.0% 138,201,620
MA -4,252 17.4% 68,944,942 -163 15.6% 968,698 13 11.2% 1146 -607 21.2% 66,255,361
EMA -2,888 16.4% 75,744,998 201 15.3% 1,296,510 13 11.2% 1118 -46 18.8% 64,604,584
Best Method NM EMA MA NM EMA MA NM MA EMA NM EMA EMA
The following table provides the next-future month forecasts for each product. The Mean is the
forecast, and the Lower-Control and Upper-Control Limits provide the 90-percent probable
extreme-value range for the next-future month demand. Note that Sales and Operations
Planning (S&OP) can be brought to bear on critical products, with an informed team selecting
the “best” forecast from within the 90-percent confidence interval; i.e., from within the estimated
future demand range: [LCL, UCL].
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4. MSE-BASED MONTHLY DATA FORECAST
Product
A
Product
B
Product
C
Product
D
Mean 28,908 4,936 296 20,187
UCL 42,526 6,550 351 33,369
LCL 15,291 3,322 241 7,005
Sy/x 8,303 984 33 8,038
n 24 24 26 26
z90% 1.64 1.64 1.64 1.64
Finally, the following monthly graphs provide visual aids of the selected forecasting methods and
of the applied confidence interval statistics. True demand values are represented via blue line;
forecast values are represented via red line. Note that each chart’s present-time (zero) value is
at its right side; past-time (negative) values are to the left.
PRODUCT A (3m SMA) w/ 90% Confidence Interval
10,094
20,094
30,094
40,094
50,094
60,094
70,094
80,094
90,094
100,094
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Months (0 is Present Time)
Demand
PRODUCT A MA_A LCL UCL
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5. PRODUCT B (3mSMA) w/ 90% Confidence Interval
3,094
4,094
5,094
6,094
7,094
8,094
9,094
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Months (0 is Present Time)
Demand
PRODUCT B MA_B LCL UCL
PRODUCT C (EMA a = 0.672) w/ 90% Confidence Interval
0
50
100
150
200
250
300
350
400
450
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Months (0 is Present Time)
Demand
PRODUCT C EMA_C LCL UCL
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6. PRODUCT D (EMA a = 0.639) w/ 90% Confidence Interval
50
10,050
20,050
30,050
40,050
50,050
60,050
70,050
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Months (0 is Present Time)
Demand
PRODUCT D EMA_D LCL UCL
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7. PRODUCT D (EMA a = 0.639) w/ 90% Confidence Interval
50
10,050
20,050
30,050
40,050
50,050
60,050
70,050
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
Months (0 is Present Time)
Demand
PRODUCT D EMA_D LCL UCL
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