Chapter 3 (1) Inventory.pptx Chapter 3 (1) Inventory.pptx

Chapter 3 (1) Inventory.pptx

1. 2 3 Jan 2023 ASSIGNMENT - Wednesday 5 Jan 2024 MIDTERM – Friday 1 5 Jan 202 4 FINAL EXAM – Monday

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

3. CHAPTER 3 3

4. 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. 4

5. 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. 5

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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. 7

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. 8

9. So when you combine all these into the demand during the lead time, you have many sources of uncertainty. 9

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: 10

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 uncertainty in the demand during lead time in Table 3- 1 represents a combination of demand uncertainty and lead time uncertainty. 11

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. 12

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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 https:/ /w w w.referenceforbusiness.com/encyclopedia/Gov-Inc/Histograms.html 14

15. Figure 3- 2 is a histogram of 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. 15

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. 16

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. 17

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. 18

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 19 =NORMDIST(0,49,12,1) which returns a value of 0.00002, much less than 1 percent.

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 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. 20

21. 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 21

22. With inventory models there are two things to consider: 22 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. 23

24. orders can only be placed at certain points in time. IN A PERIODIC REVIEW SYSTEM 24

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. 25

26. Inside Amazon's secret robot factory code name 'Bos 33'

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28. REFERENCES 27