Chapter 3 (1) Inventory.pdf Chapter 3 (1) Inventory.pdf

Chapter 3 (1) Inventory.pdf

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6 June 2023
ASSIGNMENT -
TUESDAY
8 June 2023
MIDTERM –
THURSDAY
16 June 2023
FINAL EXAM –
FRIDAY

2. CLASS
TEXTBOOK
2
Week 1: Inventory Control &
Material Management

3. CHAPTER 3 | JUNE 2023
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In inventory control, we have uncertainty in demand, lead time, and
sometimes the review interval itself.
When we place an order, if demand spikes, we might stockout
before the inventory arrives.
On the other hand, demand might be steady while lead time takes
longer than expected, possibly resulting in a stockout.

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The fact is, there is uncertainty in both demand and
lead time and that has a significant impact on the
overall performance of an inventory control system.
In addition to uncertainty in demand and
lead time, there is uncertainty in execution
of tasks involved in the inventory process.

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7. ▪Many variables affect the actual sales of
a given SKU: weather, number of
shoppers in a store, stockouts of
substitute products, advertisements,
promotions, changing demographics,
traffic congestion, social media, price,
placement, assortment depth and
breadth, parking lot expansions, road
construction, news reports, and many
others.
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8. ▪Similarly, there are a plethora of drivers
of lead time, including distance, order
receiving processes, order picking
processes, availability of product, order
staging processes, carrier reliability,
mode of transportation, and many
others.
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9. ▪So when you combine all
these into the demand
during the lead time, you
have many sources of
uncertainty.
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10. ▪Consider a retail
distribution center
ordering a
particular SKU of
laundry detergent
from a supplier.
Table 3-1 shows
the demand
during lead time
for 60 orders:
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11. ▪The columns are DDLT or demand during
lead time.
▪After the distribution center places an
order with this specific supplier, it keeps
track of how many units are ordered from
the stores and adds them together until the
order is received and available for use.
▪So, the uncertaintyin the demand during
lead time in Table 3-1 represents a
combination of demand uncertainty and
lead time uncertainty.
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12. ▪In Figure 3-1, the
horizontal axis is the order
number, and the vertical
axis is demand during lead
time.
▪There is no pattern in the
demand during lead time
so the variability is due to
randomness.
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14. ▪Histograms is used to chart a
distribution of values or results
from a set of observations.
▪Related to a bell curve or skewed
curve to describe continuous
data or series of data
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https://www.referenceforbusiness.com/encyclopedia/Gov-Inc/Histograms.html

15. ▪Figure 3-2 is a histogramof demand
during lead time, where the horizontal axis
is the bin number and the vertical axis is
the frequency.
▪For example, there were 2 orders where
demand during lead time was 30 units or
less, 10 orders where demand during lead
time was greater than 30 but less than or
equal to 35, and so on.
▪This could be converted into an empirical
probability distribution that could be used
to represent the demand during lead time.
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16. ▪For each bin, the frequency of
observation is divided by the
total number of observations.
▪For example, for the bin
representing 30 or less, there
were 2 observations so 2/60 =
0.03. You could even use a bin
for each actual level of DDLT
observed.
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17. ▪Comparing Figures 3-3 and 3-2 shows
that Figure 3-2 looks more like a normal
distribution.
▪We only have 60 observations, so it
naturally doesn’t look very close to a
normal distribution, but it certainly looks
more like one than Figure 3-3.
▪Table 3-3 shows this histogram as an
empirical probability distribution.
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18. ▪Table 3-3 is more granular and represents
what actually happened.
▪However, the fact that we had one
observation of 55 units, no observation of 56
units, and two observations of 57 units means
that if we were to use this distribution to
represent demand during lead time, there
would be no chance of 56 units.
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19. ▪The problem with the normal distribution is
that it can have negative values, which don’t
make sense for demand during lead time.
▪So, you probably should not use the normal
approximation if the probability of negative
values is greater than 0.01.
▪To check this in Excel, use the function
=NORMDIST(0,49,12,1) which returns a value
of 0.00002, much less than 1 percent.
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20. ▪The “0” in the argument means less than
zero, “49” is the mean, “12” is the standard
deviation, and “1” is a cumulative distribution.
▪This can be read as the following: The
probability that a normal distribution with a
mean of 49 and a standard deviation of 12 will
have a value less than 0 is 0.00002 or 2
chances in 100,000.
▪If it is greater than 0.01, then the gamma
distribution is an alternative.
▪We discuss this distribution later in the
chapter.
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As a supply chain manager you want to get to the point where you can
draw such graphs to illustrate your ideas or to ask questions.
When people are vague about their descriptions of a replenishment
process, a process can seem really innovative and compelling.
However, some of these processes, after careful and rigorous thought,
are found to have fatal flaws

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With inventory models there are
two things to consider:
•Continuous review versus periodic review,
and
•Continuous levels of inventory versus
discrete levels of inventory.

23. ▪the inventory level is
continuously monitored,
and as soon as a reorder
point (ROP) is reached,
an order can be placed.
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24. orders can only
be placed at
certain points in
time.
IN A PERIODIC
REVIEW SYSTEM
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25. ▪Inventory control
systems can assume
continuous levels of
inventory as in gallons of
fuel or discrete levels of
inventory such as cases
of candy bars.
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27. REFERENCES
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