Operations Management
Inventory Management
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The course on Inventory Management is part of the Operations Management Training Program which includes a number of eight sections also presented as individual courses for your convenience.
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Taught by
Sorin Dumitrascu
Reddit Posts and Comments
0 posts • 4 mentions • top 3 shown below
2 points • grandzooby
Everything else aside, I agree with /u/Foofski's thought that you should consider forward-looking average sales for your denominator. This should give you better results... unless your demand is essentially unchanging.
Also note that this is more of an Inventory Optimization problem than it is a statistical one, so you might want to look into an "Operations Management" approach to this. For example: https://www.udemy.com/course/inventory-management-h/
There are some key things you want to think about when determining your inventory levels:
- what is the typical/mean level of demand
- how variable is that demand
- what is the lead-time to replenish your supply
- is there a minimum number when you order your re-supply
- what's the cost of placing and shipping an order to your warehouse
- what's the cost of holding inventory
- what happens to the inventory you don't sell
- do you have back-orders with insufficient inventory? (or is the sale lost)
- if you don't have back-orders, what's the cost of a lost sale
This might be helpful to you as well: https://en.wikipedia.org/wiki/Economic_order_quantity
With the probabilities for your demand formulation, you can actually use those to do a probability-based analysis where you see what the outcomes of various inventory choices are in the face of each type of demand and see which gets you the preferred outcomes. You can use different strategies based on your risk tolerance and other assumptions... such as maxi-min, mini-max, mini-max-regret, etc. See this for some ideas: http://home.ubalt.edu/ntsbarsh/ECON/DecisionAnalysis.pptx
> It's much harder to argue against stats.
I've not found that to be the case. Most managers will ignore stats that aren't compelling and match up with their experience. If you're going to use stats to make your case where they're normally used, it will be on you to make a compelling case that you're right.
I also agree with /u/varaaki ... your variables do seem a bit muddled. One thing that can really help here is to include units with the variables and then do dimensional analysis to make sure your units check out (https://phys.libretexts.org/Bookshelves/University_Physics/Book%3A_University_Physics_(OpenStax)/Map%3A_University_Physics_I_-Mechanics%2C_Sound%2C_Oscillations%2C_and_Waves(OpenStax)/01%3A_Units_and_Measurement/1.05%3A_Dimensional_Analysis).
For example, both inventory, demand, and supply are typically in units. MOI (Month of Inventory) will be in months. So u isn't just 15, it's 15 units/month. Then carry your units through all your calculations.
It also might be useful to construct something my company calls an SRI (Sales, Receipts, Inventory) to see how things work out. It might look like this, assuming you have orders coming in every month but only fill up your inventory once, at the beginning. (only for 6 months...)
Month: | 1 | 2 | 3 | 4 | 5 | 6 :--|--:|---:|--:|--:|--:|--: Starting Inventory|345|322|299|276|253|230 Sales (forecast)|23|23|23|23|23|23 Receipts|0|0|0|0|0|0 Ending Inventory|322|299|276|253|230|207 Trailing MOI|-|-|12|11|10|9 Leading MOI|14|13|12|11|-|-
Then you can load in different demand values and see how it impacts your MOI values.
Lastly, I wonder why you'd have a target or expectation of holding 23 months of inventory (assuming "total quantity", as you've used it here, is the amount of inventory you're holding).
1 points • ViralMedia007
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