In the last week or so, a couple more articles popped up thanks to the magic elves in the Twitter Box.

From DemandCaster: Whatever happened to JIT?

…The dominant term was JIT. But, JIT and Lean were bandied about interchangeably. There was no different save for the marketing spin of one author or consulting firm over another. But that has changed. The change resulted because the manufacturing base moved, mostly, to Asia. This was contrary to the principle of reducing lead times so central to JIT. The lead time for goods from Asia dramatically increased to four, five, six, or more weeks. Just-in-Time simply did not fit.No matter what the lead times, one could be “lean” by taking the slack out of the lead times. But as the overall lead times increased so much that planning was once again based on forecasts.

Not to mention the additional inventory in the supply chain pipeline thanks to the longer lead times.

From @chrischip: Just in Time Just Isn’t

…But don’t pull your hair out over inventory. Your customers won’t wait for you to ramp up to fulfill their order, and your forecast won’t save you because it isn’t correct. The answer is a buffer. What I hear time and time again is that the cost of a buffer is well worth eating when you can promise delivery from stock. Without that assurance, you may well lose the order, and that’s a heavier price to pay in terms of dollars and reputation.

Buffers are consistent with, and I would argue REQUIRED, to lower inventory investment. Several weeks ago I revealed my personal view with regard to maximizing inventory turnover:

The secret: The fastest way to reduce  on-hand inventory balances is to ship it.

Many supply chain professionals are penny-wise and pound-foolish: They focus on reducing inventory investment by slowing the delivery of component inventory and insist on sticking to corporate strategies, goals, and metrics with regard to ordering and stocking policies. This is the domain of ivory tower academics and corporate theorists. I work in a Manufacturing Plant, where we Build Things Customers Pay For.

It is far better to grasp the universe as it really is than to persist in delusion, however satisfying and reassuring.
~ Carl Sagan

If the sales exist to consume the finished goods, the proper strategy is to ensure 100% order fulfillment. Unless your forecast is perfect (it’s not), your inventory accuracy is 100% (it’s not), your suppliers deliver exactly what you need exactly when you need it with no quality rejects (they don’t), your machines have 100% up-time (they don’t), and you have 100% yield (you don’t), then you have to be honest with yourself as a material planning professional and do the right thing: Buffer.

Better never than late.
~ George Bernard Shaw

That doesn’t mean you start writing blank checks, however. At the risk of sounding arrogant (too late, I know! ), this is the domain of Smart People. Data-oriented decision-making. Bounded risk. Iterative modeling. Yeah, I can spew the buzzwords with the best corporate policy wonk. If you don’t have robust simulation tools, buy them. If you can’t buy them, build them. They won’t be perfect starting out. Probably won’t be right, either. But the second iteration will be better than the first. You’ll do it faster, too. Same with the third, fourth, fifth, sixth… Models will never be perfect, but like a good spouse they get better with age.

When strategies, tactics, and actions are set, execute in the real world using proven, time-tested material management techniques.

How?

Use inventory stratification and manage by exception. Remember ABCs? Remember Exception Reports? Classical stuff. Material Planning 101. APICS Basics of Supply Chain Management.

I don’t worry about C-items. Period. I want tons of C-items. If I go line-down because a penny-part wasn’t in stock, that’s not the cost of doing business – that’s a DISASTER: Shipments are missed, Revenue goals aren’t met, and all of the on-hand inventory associated with the assemblies that now can’t be built will just sit on your books, festering. I watch B-items to the extent that I don’t buy excess but I try to buffer with time; instead of coming in just-in-time, I try to pick up extra days/weeks whenever I can. Finally, I focus completely on A-items. I have a number of homegrown tools I developed to track the supply/demand of A-items over time and any volatility is ruthlessly drilled to root cause and strategies developed to balance the supply with the demand.

Better three hours too soon than a minute too late.
~ William Shakespeare

Most importantly, I am disciplined about reviewing data versus plan and taking action with a sense of urgency. You can’t set up your plans and walk away expecting your great plan will be executed flawlessly. You have to constantly check, double-check, triple-check. Plan and re-plan. This is where Exception Reports are worth their weight in gold. In most companies where bills-of-material are complex and just plain LONG, it’s impossible to do a top-down or bottom-up material analysis on any kind of cycle that permits rapid decision-making. Exception reports separate the wheat from the chaff. By definition, the A-items consistent of ~80-90% of your inventory investment dollars. Planning and re-planning ~5-10% of your components offers an increased likelihood of successfully managing 80% of the dollars, rather than attempting to manage 100% of the component investment with no likelihood of success.

A good plan violently executed now is better than a perfect plan executed next week.
~ George S. Patton

Which is funny because oftentimes when management makes a push to improve metrics there are always a cadre of homegrown materials people armed with excuses why the textbook approach won’t work in the real world.

The secret: The fastest way to reduce inventory on-hand balances is to ship it.

With regard to production scheduling, it is critical to know capacity and lead time. Many companies simply schedule to maximize revenue or achieve the customer’s need date regardless of what it means for operational excellence. Knowing capacity means you can assess and manage risk when outside forces require production schedules in excess of capacity. Contingency plans can be made to increase capacity and buy material inside lead time. With regard to our schedule, we were several months late starting due to redesigns from our customer. This required our manufacturing and test engineers to get creative and solve some capacity issues.

As far as material availability, I managed my “A” items tightly – super-tight. But I didn’t worry about the “B” and “C” items beyond ensuring I had enough on hand to build the schedule. That’s the whole point of inventory stratification: focus where the dollars lie. After that, it’s all about execution from the team: purchasing managing the supplier deliveries, and production control getting the material to manufacturing with enough lead time to build and ship quality product. In a prior post I stated:

As a Master Scheduler, I control much of the front-end of the manufacturing process – any variation on my part bullwhips through the organization. Variation isn’t something that can be avoided, however, but as a professional I need to be diligent about controlling those factors under my control… I track demands over time, supply exceptions over time, excess/obsolete over time… well, you notice “over time” is the critical factor. After each MRP run (we run weekly), I export all of my data and review several critical factors: has my backlog changed, is my planned order report correct, has my excess/obsolete moved unexpectedly in either direction, and has the exception report changed positively or negatively. There are many other items I track, but I start with these and use them to uncover issues and troubleshoot them prior to someone else asking me that dreaded question, “What happened?

So, by focusing on demand stability and material availability over time in a structured and disciplined manner, I did my part as Master Scheduler to maximize inventory turnover. When a team maximizes the inventory going out the front door, good things happen.

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

In contrast to this is planning for a mature product or program. I am taking over a mature program with a family of very complex assemblies on Monday, and this weekend will be spent preparing. Except for when I’ll be watching the Florida-Georgia game. I do have my priorities, after all.

I plan to “live-blog” the process as much as I can without giving away any proprietary information. To set the groundwork, the program is very important to my company’s current and future business and is generally considered a cash-cow. It is not without challenges however; inside lead-time demands, material shortages, and the sub-optimal impacts these have on inventory, operations, and profitability.

Step one in this effort:

Bill of Material (BOM)

Since the products are fairly complex from a technical and process perspective, I need to understand the Bill of Material. I find the best way to do this is to review the backlog and analyze the BOM and construct product trees for each active end item. Yes, I’m sure this exists already, but a few hours spent building it myself is a smart investment that will pay off in a deeper understanding of the product structure.

When I build a product tree, I like to just use Excel and I include the item attributes like:

• Item Number
• Item Description
• Procurement Lead Time (longest component lead time)
• Manufacturing Lead Time
• Fixed Days Supply
• Lot Size

Besides helping me understand the product structure and planning factors, this exposes disconnects between the way a product is planned and the way it is built in real life. For instance, if a product has a Fixed Days attribute set to 10 days, but over time the manufacturing team has found it easier to build in 15 day lots/cycles, the materials organization will always be chasing those last 5 days of components.

I’m not saying that is the case with the program I’m working on, but it is an example of what can be uncovered by doing regular reviews of the BOM.

After I complete this effort I will post again about the next step I’ll take in my preparation. If you have questions, fire away in comments.

I’ve been involved early in the sales pursuit process several times, as well as had products/programs “thrown over the transom” as they say. I have a simple process I follow to manage the material planning function on those programs where I’ve been involved early. I follow the same process on those “over the transom” programs to place the challenge into perspective for management and the program team so recovery decisions can be made as appropriate. Here it is in a simplified flow chart:

If your company has a formal S&OP process, each of these steps is probably documented in excruciating detail and the ability to drive component orders on forecast is defined through bounded risk calculations. If your company does not have a formal process, as a material professional you can’t just sit on your hands and wait for someone to hand you the approval to buy material before you start developing your availability analysis. A professional reaches out to the rest of organization, uncovers these “hidden demands” and gets to work.

I am considering a series of posts that describes how I go through each of these steps; we’ll see if the response to this post warrants the effort.

photo credit: AMagill

# 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