Given that my day job is centered around production schedules and materials requirements planning (MRP), I wanted to try to create a simple MRP model that utilized reference functions. The new function for me is the OFFSET function. After using it I can certainly recommend it, although if anyone knows a better way to accomplish the same thing in a more elegant fashion please leave a comment below and I’ll give it a shot in the next iteration. No matter how much I already know, I’m always trying to improve my Excel skills.

I am assuming you have an idea how MRP works in materials management or you wouldn’t have read this far, but if you need a tutorial Wikipedia is a decent place to start. In simplest terms, it is the recipe for building a product; it calculates how many components it takes to build a quantity of end-items, and when the components need to be ordered.

Download the Single-Level MRP Model (249)

Among my career experiences I was fortunate to work in marketing for a period of time when a new VP was hired to bring organizational leadership to the business development process. The first thing he did was hunker down in his office with several members of his staff, dissecting the business development status of the company. After a couple weeks he emerged from his office to give a presentation to the staff, explaining that the first thing he does when he takes on new responsibilities is to put the challenge into perspective.

As I mentioned, this is a mature program with significant challenges. Taking my cue from this former manager, I pulled my usual post-MRP-run reports: Backlog, Planned Orders, Open Orders, On-Hand Inventory. I normally lay the data out in Excel and do the math: how many units have to be built, how many units are on-hand, how many units are being built in the open orders, and how many units remain to be built. Simply put, the way production scheduling and MRP work is:

On-Hand Inventory + Open Orders + Planned Orders = Backlog

Anything else, and you have a problem.

So, I pulled the data expecting to see everything balance and my challenge would be a function of lead time and material availability. Except, the data wasn’t adding up correctly.

By the end of the first 14-hr day I’d found some major human errors that had contributed to a portion of the problem. Once corrected and MRP was re-run, the numbers still weren’t adding up. By the end of the second day I’d found an open order that wasn’t consuming material properly. Once I showed it to my manager, she diagnosed the specific problem in about 30 seconds. I won’t go into it here because it was human error, not system error (although it would have been nice if the system hadn’t allowed the error).

So, my “normal” process for getting my arms around program materials responsibilities has been short-circuited. Fixing the issues in the system are beyond my abilities, but I’m fortunate to have a manager with the technical skills to do so and the integrity to shoulder that responsibility rather than pawn it off on me. Even so, this will be the biggest challenge of my career thus far.

I have many weeks of long hours ahead while I try to make sense of the situation from a schedule, capacity, and availability perspective. But as I tell my team, when the system is used properly, time is your friend – eventually, material is ordered properly, arrives on time, and you recover and stabilize.

photo credit: David G. Romero

# Supplier may deliver their product late or not at all
# The warehouse may be on strike
# A number of items at the warehouse may be of poor quality and replacements are still on order
# A competitor may be sold out on a product, which is increasing the demand for your products
# Random demand (in reality, random events occur)
# Machinery Breakdown
# Unexpected increase in demand

There are many reasons and methods for calculating safety stock, and one of the more common reasons I’ve seen in the world of complex electronic assemblies is Test Yield. As electronic assemblies get more complex due to smaller component packages and denser placement on the board, as well as tougher requirements for the boards themselves, there will always be issues with yield. Couple this with complex testing requirements for certain high-reliability electronic assemblies, and yield issues begin to multiply. While zero defects is the goal we all strive for through continuous improvement programs, the real world has to take defects into account. Along with the actual defect comes accounting for the turnaround time to troubleshoot and repair boards, and finally, accounting for the loss of irreparable assemblies.

It might be easy to say “Well, I’ll just take whatever my first-pass fall-out is and release that many more in each work order”. But, releasing extra quantities in each work order isn’t safety stock; safety stock is the quantity of units required in your supply chain to account for VARIANCE and achieve your target service levels to the demands from your customer. While there are statistical formulae to determine order quantities and safety stock, if you understand your planning factors and your processes, you should be able to model your order quantities while achieving inventory and service goals.

Depending how the planning factors are set in your materials requirement planning (MRP) module, you won’t be releasing the same quantity in each work order. Most MRP systems will “see” those defective units unless they are immediately placed in an inventory status that “blocks” MRP. If your processes allow for defective units to sit in WIP through the next MRP cycle, your planned orders will be reduced accordingly.

For the example below, I assumed a first-pass yield of 97% and a scrap rate of 0.5%. Another factor that is important in the cycle below is that the test occurs on Day 5 of a 5-day cycle and it takes 2 days to troubleshoot and repair the units, which means the units show in WIP at the time of the next order release. See this example:

Manufacturing Lead Time (days)	5
Weekly Demand	100
Historical First-Pass Yield	97%
Historical Final Yield	99.50%
Historical Troubleshoot & Repair Time (days)	2
Day Test Occurs	5
Initial Order Qty + Safety Stock	103

	Wk 1	Wk 2	Wk 3	Wk 4	Wk 5	Wk 6	Wk 7	Wk 8	Wk 9	Wk 10
Demand	100	100	100	100	100	100	100	100	100	100
Order Release	103	100	101	100	101	100	101	100	101	100
Order Shipment	100	100	100	100	100	100	100	100	100	100
Fallout/Scrap		1		1		1		1		1
Balance in Troubleshoot WIP	3	2	3	2	3	2	3	2	3	2

As you can see, the quantity of units in WIP from one cycle to the next fluctuates between 2 and 3 units – 2%-3%.

There will be some in your organization (for instance, the production supervisor ) suggesting you should always release 103 units in each work order. Not only are they wrong, doing so will actually drive up your work-in-process (WIP) inventory – a cerain percentage of your first-pass defects will go through a troubleshoot-and-repair cycle, making them available for finished good shipments. If you continue to release “X” extra in each work order, WIP inventory builds according to this formula:

(Units released) + (Units Repaired) – (Shipments)

In the example above, watch what happens to your WIP inventory:

	Wk 1	Wk 2	Wk 3	Wk 4	Wk 5	Wk 6	Wk 7	Wk 8	Wk 9	Wk 10
Demand	100	100	100	100	100	100	100	100	100	100
Order Release	103	103	103	103	103	103	103	103	103	103
Order Shipment	100	100	100	100	100	100	100	100	100	100
Fallout/Scrap		1		1		1		1		1
Balance in Troubleshoot WIP	3	5	8	10	13	15	18	20	23	25

As you can see, your inventory will build to infinity unless some undefined variable consumes these units. Obviously, this is not a satisfactory solution in any organization due to the impact his has on cash flow.

These examples are simple and focus only on one variable – Yield. In a robust safety stock model one would account for other sources of supply and demand variance – forecast accuracy, supplier performance, additional quality yields, etc. But the examples do demonstrate how one can easily get to a safety stock determination through logical thinking, an understanding of their internal processes, and simple math.

I would love to see other examples of how to account for variance in the supply chain, so please leave me a comment if you care to continue the conversation.

photo credit: dan paluska