Optimizing Inventory (Online App)

Optimizing Inventory (Online App)

Unified Supply Model is the most effective method for the optimization of the supply chain, under the uncertain demand. In previous articles, I’ve tried to explain how it works, in different situations. Because the core mechanism is strong and flexible, we can apply it to any type of the chain, with different aspects.

However, these are just about the theory and fundamentals. Through feedbacks from the audience, I’ve developed an online application of the model. The aim is to present the model using numerical methods and let the users try their own data and compare the results with actuals. Here’s the link of it; USM-Online App.

I placed five different versions and tried to make it as simple as possible. With this article, I will explain how to use the first one; Simple Model.

Simple model requires the uncertain demand parameters, CSL and regular order quantity as input values. In the screen shot below, you see how it looks. You just need to enter some numbers into the text boxes. For each of them, when you hover on “?” icon, you see an explanation. And also, if you make a mistake during entrance, a warning appears in the "Comment" column. 

Let’s make an example. Suppose a retailer selling chocolates. We consider the period as days. The daily average demand is 120 units. It is the average of the past 60 days. And the standard deviation is 50 units. The retailer assumes Customer Service Level (CSL) as 95%. Finally, the regular order quantity to the supplier is 200 units. So, under these circumstances, what should be our Reorder Point and Safety Stock? Also, what is our expectation for over and understock?

To solve it, let’s enter these figures to the online model. Then, click “Find Reorder Point” button.

The online application responds with some figures. Let’s check one by one;

1)   Reorder Quantity: When the sum of the physical stock and open orders are equal to reorder quantity, we need to place a new order. So, for this example, when our stock and open orders are equal to 145, we need to order 200 units.

2)   Safety Stock: It is considered as the security against the variation on the demand. It is the difference of the reorder quantity and average demand. For this case, our safety stock is 25 units.

3)   Expected Overstock: We expect some surplus in the inventory, at the end of each period. The model provides how it looks like. For this case, we expect 118 units. It’s something like the sum of the safety stock and half of the order quantity.

4)   Expected Understock: A supply chain model should allow back-order or lost sales. As we assume a CSL lower than 100%, we should know some portion of the demand is not covered. Here, USM is quite compatible with that claim . For this case, we expect 1,3 units / period as not covered amount of demand.

Basically, our retailer needs to check the physical stock regularly. When it’s lower than 145 pieces, a new order should be placed. It arrives one period later. Till that time, 145 piece is enough to cover periodic average demand, 120 units. And have a safety of 25 units, to cover some variations. This safety is limited by the CSL, 95%.

You can play with these figures, and check some different scenarios. Or, you may put the numbers of your real life situation and see how the model can meet the real life expectations easily.

This simple model is just to give a perspective on how the Unified Supply Model works. The details of the mechanism is in the article Inventory Optimization.

Utkan Ekinci

Founder at Arkhon Optima

6 å¹´

Thanks Safaa. Yes, it's implemented in several companies. They use it for material supply and production planning, mainly. And results are really good. Lower inventory, less backorders and costs. For retailers, as you said, an integrated barcode system is enough to trigger the order system.? I will share the results of the implementations.

Dr Safaa Sindi

Assistant Professor of Operations and Supply Chain, Coventry University

6 å¹´

Very good article, and extremely interesting. Has this model been tested with a company? perhaps some clarification as to how can retailers check their stock regularly? would this be via integrated bar code and scanning system? due to time and labour limitations, SMEs usually perform random cycle counts every quarter before a full stock check twice a year. It would be interesting to see the results from testing this model.?

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