EMS-OEM-ODM businesses: 20 days of inventory saved by re-scheduling back-orders

First, a disclaimer: my sincere apologies for this (potentially) misleading title. If you are in a low-mix high-volume industry such as computers, communications or consumer electronics, then odds are that you experience no issues and would discover next-to-no inventory saved by re-scheduling back-orders. However, if your business is high-mix low-volume such as industrial, military, aeronautical, medical or even some instances of automotive, then this post is for you.

In either case, I encourage you to read on.

Inventory saved by re-scheduling

The more I’m working with businesses in the electronic manufacturing industry, the more interested I become in sales and operations planning (S&OP) processes. I’m increasingly convinced that the most efficient way to reduce inventory is to properly filter client demand, but taking supply constraints into account in the highly volatile electronic components market is a big challenge. Especially in a high-mix, low-volume industry, where planners are the ‘under-recognized’ heroes.

That said, there is a pattern which I see systematically infecting high-mix, low-volume factories. Back-orders – orders you haven’t shipped because you’re late – are simply not rescheduled.

As a result, those factories have 20–30 days of late orders and no time in the supply chain schedule in which to catch up. It therefore becomes a 20–30 day inventory handicap. No more, no less.

In this post, I’ll demonstrate a simple dashboard which, if this is an issue that you recognize, you should build at least once to see how much you can save through back-order re-scheduling.

As with my previous posts containing such simulations, on safety lead-time optimization and shortage-risk ranking, this entire simulation can be completed in Excel.

Excel simulation for re-scheduling back-orders

Step 1: Excel upload [optional]

If you have more than 100k+ rows of data, begin by downloading this Google Sheet in Excel format. It is a lot faster, however you will need to re-build the pivot table once downloaded – it will break in the format change from Sheets to Excel.

Back-order simulation for saving inevntory

Step 1: data extraction

To build the dashboard, what you will need from your MRP are all the quantities required and their respective dates, per article, plus their unit prices. To re-iterate, only required quantities i.e. negative MRP quantities. No stock, no orders, no purchase requisitions.

In columns F to H, we are splitting the requirements per rolling 30 day period.

data extraction file for back-order simulation

Step 2: number of working days per 30 day period

pivot table for back-order simulation in ExcelIf you have downloaded the file to Excel, as mentioned above chances are the pivot table is broken. You’ll need to re-build it: it is just the number of distinct need_date (working days) per period.

As you can see in this example, the factory seems to work just 5 days per week. And the plant seems to shut down for a week in period 4.

Step 3: result

Now you can enjoy — or cry — at the results:

results of back-order simulation in Excel

For this factory, the required value per working day is $88k in period 1, $51k in period 2, $44k in period 3 and $47k in period 4. This means that period 1 is 185% of the period 2–4 average.

What does all this mean? It means that back-orders are not being rescheduled properly.

Unless this factory is an exception to every rule and is capable of producing 185% of an average month all at once, then it is working with a $808k inventory handicap, or 85% of the average value of any given period.

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Conclusion

Of course this is only a monitoring of your back-orders, you still have the re-scheduling job to look forward to! But now you have a much clearer indication of the problem AND the value of the inventory handicap.

If you know of some good alternatives to looking at or solving this problem, or any tools to reschedule back-orders, then let us know by contacting us! We’d be interested to hear of alternatives and possibly write more about them in future.

My final warning: remember you should never link supply-chain performance to your objectives. Read this story as a must in order to understand the limits of such an indicator… lesson I’ve learned the hard way.

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Originally published on EBN

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