Chapter 4 5 Inventory

1. 2
3 Jan 2023
ASSIGNMENT
- Wednesday
5 Jan 2024
MIDTERM
–
Friday
15 Jan 2024
FINAL EXAM –
Monday

2. CAHPTER 4
3

3. TRENDADJUSTED
EXPONENTIAL
SMOOTHING
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Second order exponential
smoothing estimates an equation
with an intercept and a slope
If there is trend in the demand, it is
important to be able to estimate it
for inventory management
purposes, whether it be an upward
or a downward trend.

4. 5
The intercept is
referred to as the
level of demand
The slope is
referred to as the
trend component
of demand.

5. TRENDADJUSTED
EXPONENTIAL
SMOOTHING
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To forecast two periods
into the future you take
the level and add two
times the trend
component.
To forecast n periods into
the future, you take the
level and add n times the
trend component.

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11. SEASONALLY
ADJUSTED
FORECASTS
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Seasonality exists when demand
increases significantly at particular
intervals of time each year.
Seasonality can be a complex
phenomenon because it is sometimes
caused by dates, such as Christmas;
other times it is the result of causal
variables, such as increased
temperatures; and sometimes both
dates and causal variables

12.  The demand for candy increases at
Halloween because candy is part of the
celebration of the holiday.
 The demand for bottled water increases
when the temperature increases because
people consume more water when the
temperature goes up.
 However, people also consume more bottled
water on Independence Day because people
buy it to be available for Fourth of July parties,
but even more is consumed if it is particularly
hot outside
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13.  If you forecast the seasonality
of bottled water sales strictly
with dates (e.g., summer), and
then it turns out to be an
unusually cool summer, you will
probably over forecast the
demand for bottled water.
 So, for bottled water, you
probably want time and
temperature in the equation.
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14.  Forecasting seasonality with time
requires a time series forecasting
method that directly addresses
seasonality.
 Forecasting seasonality with a causal
variable such as temperature requires a
method such as regression, which we
discuss later in this chapter.
 One simple way of forecasting quarterly
demand with seasonality is with seasonal
factors applied to annual forecasts.
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15. F
O
R EXAMPLE
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6
For example, you could
forecast annual demand and
not have to worry about
seasonality because
seasonality occurs within the
year.
So, with this approach you
look back over several
years and see what
percentage of the demand
occurs in each quarter.

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17. 3
1
Forecasting
Trend and
Seasonality

18. CAUSAL
MODELS
Regression is a complex topic, and we
just skim the surface and discuss it
from an applied perspective.
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There are other types of time series
forecasting models, but we now go on
to talk some about causal models, and
in particular, regression models.
Building effective regression models
requires more skill than building time
series smoothing models.
We use regression later in the book for
other purposes, so it is worthwhile to
learn it here for multiple reasons.

19. To build a regression model you must define your
dependent and independent variables.
Since we are forecasting sales, the dependent
variable is sales.
Regression finds a line that fits the data by
minimizing the sum of the squared errors.
Errors are forecast errors
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20. As we discussed earlier, the forecast error for
period i is defined as FEi = ai − fi, where ai is the
actual realized sales for period i and fi is the
forecast for period i.
In regression, they are not referred to as forecast
errors, but are referred to as residuals, because
most of the time regression is not used for
forecasting but for testing hypotheses.
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23. Many software packages, including
Microsoft Excel, can be used to
estimate regression equations.

24. 3
1
Using
Multiple
Regression
in Excel for
Predictive
Analysis

25. ADDITIVE A
N
D
MULTIPLICATIVE
MODELS
When we are testing hypotheses, we
need to be more careful in using the
regression model than when we are
just forecasting.
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Sometimes you need to make
forecasts that are based on price,
promotional spending, advertising,
the price of substitutes, and other
variables.
In these cases, you might not only be
interested in forecasting but also
interested in testing hypotheses, such
as increased promotional spending
leads to higher sales.

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28. CHAPTER 5
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7

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Many inventory processes are difficult or impossible to model
mathematically and statistically without the use of a computer.
Discrete event simulation is an easy way to model these inventory
processes.
Discrete event simulation is an academic discipline per se with a lot
of depth and breadth, and many different software packages can be
used to implement discrete event simulation.

30. DISCRETE EVENT
SIMULATION OF
INVENTORY
PROCESSES
Finally, such a discrete event simulation
allows you to estimate the performance of
the inventory replenishment process if
parameters are changed, if execution is
improved,or even if the process is changed.
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Discrete event simulation not only allows for
the modeling of complex inventory
replenishment processes, but it also allows
you to incorporate uncertainty into the
demand, lead time, and other areas where
uncertainty exists.
You can also explicitly model process
execution errors.

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32. 3
1
Using
Excel to
Simulate
Discrete
Events

33.  When working with discrete event simulation experts, being
able to create a prototype makes it more likely that you will be
able to more fully communicate the process you want to have
modeled, the types of decisions you want to make,and the
performance metrics and how they should be calculated.
 Many times there is a gap between what a manager wants
modeled and what he eventually receives from an expert in
discrete event simulation.What you learn in this chapter
should help you close that gap.
 A side benefit is that you will become much more aware of
the intricacies of the inventory process you are attempting to
model.
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34. Before you begin the discrete event simulation modeling
process, make sure you clearly understand why you are
modeling the process.
 Who are the customers of the output of the modeling and
analysis?
 What will occur if you succeed and have a clear model with
decisive output?
 How will the results be presented? How much understanding do
the customers have of inventory processes, inventory theory, and
discrete event simulation?
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35. Within discrete event
simulations of inventory
processes you must
clearly understand how
the process actually
works to model it.
So, the first step is
documentation of the
inventory process
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45. REFERENCES
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