Saturday, April 28, 2012
Very often lean initiatives, or six sigma projects or agile programs are started in organizations. But is it always clear what needs to be done? And more importantly: what needs to be achieved? I’m currently in a manufacturing plant outside of Shanghai, China and the directive for the production lines was set a year ago: “we need one-piece flow!” the plant manager determined. So one of the lines was used as a model and the six stations on the assembly line identified. Then six workers were trained; one for each station and a heijunka schedule was provided every day. When I was taken on a tour, this line was proudly presented, but the plant manager was not happy. There was WIP piling up in front of some stations and others were starving. “My workers just don’t do what I tell them to do. They assemble batches and don’t adhere to my ‘one-piece-at-a-time’ directive.”
Taking a closer look it was quickly apparent what the problem has been. The line was not balanced. Meaning that the cycle time on station 4 was much longer than the cycle time on station 3. That has the effect that WIP piles up in front of station 4 since the worker on station 3 can produce much more product in an hour than the worker on station 4 can handle. So Mr. 3 has two choices: either he idles for most of the time not doing anything or he produces to the WIP after his station. Even though he was told not to make parts to inventory, he felt even queasier with the option to be seen doing nothing. What I am trying to say here, is that all these initiatives only work if they are thought through top to bottom; left to right.
In the case described here, we should obviously balance the line. SAP ERP 6.0 has great graphical tools to do that. But you will have to use rate routings and the tools of repetitive manufacturing to do so. And who would think that this is a repetitive manufacturing process? It is, isn’t it? We are making product in a repetitive manner; even if there is a mix.
A balanced line simply means that the activities performed on each station take up the same amount of time; approximately. If that is the case you can release orders into the line according to a takt time. With that each worker on each takt can make exactly one piece and hand it off to the next. If variability is low, there will be flow and no wip piling up on any work station. Below is an example of line balancing performed with transaction LDD1
The takt time – the time period after which a new order is released into the line – is 26 minutes and 23 seconds. For product 9004192 we have a takt time violation on station 1A. Meaning that operation 12 or operation 14 should be executed on work station 1B. That way the time to execute material 9004192 on station 1A and on station 1B would be roughly the same and less than the takt time – namely about 25 minutes.
If you have any lean initiatives, whether it would be to achieve one piece flow, reduce waste (the operations that you see in above screen, can all be identified as either value-add or non-value-add activities) or shorten your cycle times, look in SAP repetitive manufacturing (most lean advocates think that an ERP system is counter to any lean principles – and I strongly disagree). The word Repetitive, however, triggers rejection in many people. Have a closer look; it’s worth it
Friday, April 27, 2012
Your materials should be categorized into product portfolios the material planners have responsibilities for. And the most common object to do this with in SAP is the MRP controller on the MRP1 field in the material master. If you do that, you can monitor your portfolio in MD06 and MD07, the collective MRP or stock / requirements lists.
There are many parameters you can use to perform an ABC / XYZ classification. I would suggest to use purchase value for ABZ and consumption value or volume for XYZ segmentation. The ABC analysis can be executed within standard SAP ERP; for the XYZ analysis you need Excel (export 12 months of consumption and determine average and standard deviation. Then divide one by the other and you will get a Coefficient of Variation) or you use an Add-On tool by SAP Consulting like the MRP Monitor. (The MRP Monitor allows you to perform an XYZ and ABC analysis and also allows you to update your material master records en masses after each analysis – drop me an email if you need more info on SAP Consultings Add-On tools for ERP and APO)
And you can also use a variety of standard reports in the LIS to perform inventory analysis, determine a purchasing effectiveness or classify your portfolio for a replenishment strategy segmentation. This segmentation will help you to group your purchased parts into classes and assign replenishment strategies, the way you want to trigger replenishment (which quantities and when) for your materials.
In an effort to determine the best strategy for every purchased part, you can use three buffers to reach two conflicting goals which you must balance to the best of your abilities, using the method of segmentation:
After you performed the analysis you have segmented your portfolio and can now assign your strategies. It is important to understand that a replenishment strategy is not just PD or V2, but rather a combination of MRP type, lot sizing strategy, safety stock procedure, forecast settings and more.
Here is an example of how your segmentation could look like. As your materials fall into a certain class, you can pick form the various strategies…
Why does the SAP supply chain need optimization? “Didn’t we cover all the decision making during the implementation?” you might ask. You and your team went through countless classes and knowledge transfer sessions where the SAP supply chain was laid out right in front of you. Then you blueprinted and developed the prototype where all decisions for replenishment, planning, how to schedule and when to execute to a forecast, sales order demand or a production program where made.
Then all the (functionality) gaps were covered by add-on programs, third party products and maybe even modifications. As time goes on, new business requirements appear. You introduce a new group of products, for example, and you do not have any historical information based on which you could predict sales or inventory requirements. So you decide to wait for the actual customer orders and trade the inventory buffer for a time buffer, where you tell the customer that they have to wait out the replenishment lead time before they can have the product. But temporary Make to Order was never blueprinted!
Or you realize that what the Sales people forecast, exceeds the capacity on the production line by far and your schedulers give up trying. The fact that SAP’s S&OP allows for a rough capacity check during the planning phase might not be known, so we come to the conclusion that SAP ERP cannot forecast and one either opts for APO or, to make things worse, an external planning and forecasting system.
Or you have a “lean” initiative going on and SAP seems to be counterproductive to anything you learn about “lean”. That is because your consultant did not know how to configure repetitive manufacturing and sequencing in ERP and even though you produce standard products over and over, you are using discrete production or process orders to manage production lot for lot for lot. So you come to the conclusion that SAP is too cumbersome and you opt for an external production scheduling and capacity planning package.
You work yourself out of the system time and time over until you are running a silo ZAP system (Z is usually used to name modifications or add-ons) without integration
Figure 1: working yourself out of the system
Let’s get back on track and pull the most out of what you already own, instead of spending money on enhancements, modifications or the purchase of a best-of-breed forecasting package that promises to perfectly forecast your sales with exponential linear regression to the mean (yeah right!).
And by the way… SAP has added functionality over the last 35 years, using thousands of very experienced architects in an organized fashion, based on experiences from thousands of customers in almost every industry and spending an insane amount of money in their R&D investment and budget. How is your consultant, who promises to fix that perceived gap, stacking up against that?
Monday, April 23, 2012
Sunday, April 22, 2012
In a previous post I talked about using science in supply chain optimization. The science I was talking about is mostly rooted in Factory Physics (based on the great work of Mark Spearman and Wallace Hopp). There are many more, but the three most basic principles in Factory Physics are:
1. Little's Law, which describes working capital performance (WIP = cycle time * throughput)
2. the VUT Equation, which relates capacity, variability and time buffers (CT(q) = V * U *t) and is vital to understand costing implications
3. Variance of Lead Time Demand, which drives inventory and performance (sigma2 = Lead Time sigma2D + Demand Sigma2LT)
These formulas don't need derivation or mathematical explanations, but they describe some basic concepts which we can relate to as we define where we are standing and, maybe even more important, where we want to go in our efforts to optimize our SAP supply chain.
This blog deals with the first of these principles and its visualization by way of 'flow benchmarking'. Let's have a look on how to collect the information necessary to resolve Little's Law. SAP value stream mapping is a nice way to not only document the information and material flows in your supply chain, but also to determine at least two of the three variables in Little's Law. And the nice thing about that formula is the fact that it calculates the third. In an SAP values stream, which I will deal with in a future blog, one can identify lead or cycle times, inventory values and throughput. Depending on how you look at it, throughput in your supply chain may be identified as your daily sales value, WIP can be the average inventory holding (in dollars or euros) and the cycle time is the time (in days) to get raw materials through your production facilities and distribution network until the finished product arrives at the customer. Add the range of cover in days at every inventory point to that number.
Here is a slice of the value stream as I usually put it together. Note that there is information about the cycle time, and average inventory which represents WIP (in the example the values are left blank). The throughput I pull from a Sales report.
value stream example1
Now you can use Little's Law to determine the third parameter (if you don't have it already), double check on all parameters measured or calculate the minimum WIP (or average inventory holding) necessary to achieve a desired throughput (this can be the forecast-ed Sales).
With these values we can now plot a flow benchmark. In a flow benchmark you can visualize a Best Case Performance and a Marginal Performance. Then you identify your current position and hopefully you find yourself above the Marginal Case Performance and, if not, you can take measures to get into the lean zone.
It’s a busy chart but stick with me and you’ll see it’s fundamentally important for understanding your value stream and accompanying opportunities for improvement. First, the axes:
A. The left axis is throughput (or revenue) and is associated with the red lines and icons.
B. The right axis is cycle time (responsiveness)
C. The x-axis is Work in Process (a component of working capital). Note of paramount
importance: Both throughput and cycle time are related to WIP. This is a law of nature,
like the law of gravity. It is one of those fundamental Factory Physics laws, Little’s Law, and is stated as WIP = (Cycle Time) x (Throughput). You can ignore it if you want to but you will be affected by it. We have seen blind devotion to WIP and cycle time reduction (increased responsiveness) lead also to huge decreases in throughput (revenue). Huge decreases in revenue typically lead to changes in management.
To simplify the explanation, let’s stick with just the throughput (red) portion of the Flow Benchmark for now.
The red solid line (— ) shows “best case” throughput performance. This is optimal performance under perfect conditions i.e. zero variability. Your value stream cannot ever perform any better than this and typically does not perform close to this line.
The black, curved line shows “marginal case” throughput performance. This represents a practical lower limit for throughput. In the marginal case, substantial variability has been assumed (i.e. the standard deviation of process time is equal to the average process time) and most managers would agree that as a practical target they should be able to control their process times (e.g. VDF time at a furnace, tableting time at a tablet press, machining time at a mill) to have less variability than in the marginal case. The region between the Best Case and the arginal Case is called the "lean zone".
The red 'double triangle' shows what sales (throughput) you have achieved using how much average inventory (WIP) in the system.
The straight black line represents the forecast or current customer demand.
Now that we know how to read the chart we can tell a few things:
- your performance at point 1 is slightly below the lean zone
- Given the current capability of the value stream, you can meet demand with reductions in variability alone - move Point 1 up vertically (increase throughput) without changing the amount of WIP or corresponding cycle time—though that is not necessarily the first approach you might want to take. Pure variability reductions are typically hard to implement. You could chose to improve performance merely by establishing WIP control, such as kanban or CONWIP, and reduce WIP from red point 1 to red point 2. Note that this also reduces cycle times, but also reduces throughput. The reduction in throughput is not that big of a problem in this case, however, imagine you would further reduce WIP from point 2. As you can see, the reduction in throughput would be unacceptable. That is because you need a certain amount of inventory to get your lines going and to achieve a certain throughput.
This relates the need for a scientifiv approach! A blind inventory reduction strategy only makes sense if you find yourself at point 1. At point 2 it would be fatal. At point two, we will have to take other measures to move up into the lean zone. That measure is a reduction in variability.
- Therefore, once you are at red point 2, you can increase throughput and go to red point 3 to meet customer demand either by decreasing pure variability effects (e.g. reduce flow variability) or by reducing variability and increasing available capacity (e.g. reducing setups).
As a manager looking at a local value stream (a production line) or an executive looking at a global value stream (a number of assembly and test operations in a supply chain feeding a distribution center), you can with one glance see how your resources are performing versus how they could be performing and in the same glance determine the type of improvement opportunities that are going to provide you with significant performance impact.
Now it is time to work with your SAP team and translate the optimization activities into the SAP system. We can now define Kanban, conWIP, a heijunka scheduling strategy or work with replenishment and planning strategies to get things moving into the right direction.
As we do this, I usually update my SAP value stream map and create a future map with all the required settings in customizing, the material master and transaction settings.
Friday, April 20, 2012
in a previous blog I discussed the existence of three major groups of supply chain strategies in SAP. Here we discuss replenishment strategies actuated by the MRP type and a combination of lot sizing, safety stock, laad time and availability checking rules.
Plan on Demand (PD), the most widely used replenishment strategy in the SAP universe, also requires the most manual labor. In no way would I ever say "don't use PD", but give me a break; you use it for 98 percent of your raw and packaging materials? Well, maybe you don't and then I'm particularly proud of you. since you are definitely the exception.
PD is deterministic and therefore, in its purest form, waits for demand before it springs into action. If there is demand and the MRP run gets executed, a supply proposal is generated to cover that demand. No magic, no automation, nothing. It's as simple as that. Before your kid doesn't ask for a bathroom you don't look for one, right? And as long as she gives you enough lead time you don't have a problem (ever took your three kids on a stroll through midtown Manhattan, though?)
No demand, no supply! Which works really well when the purchased part is expensive and therefore costly to store, its consumption is highly variable and unpredictable and the lead time to procure is short. But when your production lines starve because a component is missing you have a big problem. Try telling your customers that you can't deliver the Porsche because you plan the standard cigarette lighter on demand or your bakery starts making pretzels after you walk in to buy one (or your butchers starts raising pigs after you order a pork sausage)… then we are in real trouble!
Your PDs should be worth the constant attention they need. It is ok to carefully watch and monitor how much of Johnny Walker's Blue Label you hold behind the bar, but to tell a patron that you ran out of salt because you were waiting to buy a sack until they asked for it, is flat out ridiculous (take a quick check to see if any one of your highest consumable, standard parts is set to PD)
There are ways to make a PD work for situations described above. You can set a safety stock, create a parts forecast or work with lot sizing procedures. That way you cover up the disadvantages of a PD, with stochastic (consumption driven) methods which help you somewhat to automate. However if the part calls for such methods, why not employing a standard consumption based planning method altogether? That is why they are there and they work beautifully if combined with the right lot sizing, safety stock or availability checking rule.
Reorder level planning is a consumption based method because it requires a minimum inventory to be available at all times and does not wait until there is demand before replenishment is triggered. Imagine the way your metabolism works. Its inventory is energy and when that energy level drops you get hungry and a desire to fill it back up is triggered. You get some food and eat. Now, you don’t wait until you’re completely depleted of all energy; there is an acceptable level – or range – from where you trigger replenishment. When you trigger energy replenishment, you usually have some lead time to deal with until you get that food, eat it and metabolize it so it becomes energy. You instinctively know that you have to have enough energy left at the trigger point so that you don’t run out completely within the replenishment lead time. This is no different with the raw materials you need to keep your lines going.
This kind of replenishment, like all other ones too, only works well in certain situations. Since you can predict really well what your rate of loss of energy is over time, you intuitively know how to set your trigger point. If your energy loss rate would be completely unpredictable, the trigger point would have to be set very high, because you really don’t want to risk losing your life when you have a very sudden drop in energy.
Also, if you are very far away from food - let’s say on a marathon run where you can’t stop and sit down for lunch – you may eat some extra carbohydrates beforehand so that your energy level is very high and gets you through a long lead time. And last, but certainly not least, you want to think about your service level. What is the percentage of time that would be acceptable for you to wither away? (Now this metaphor does not work that well anymore).
These three variables determine where you set your reorder level. The more predictable the consumption, the lower the reorder level needs to be. The longer the lead time, the higher the reorder level needs to be. And the higher your expectation to never run out (e.g. a 99% service level), the higher the reorder level for safety. In the latter case the reorder level moves up exponentially. This kind of thinking will also help us to determine at what situation reorder level planning does not make sense anymore. Obviously, if you have unpredictable consumption in combination with a long lead time and high expectations to never run out, you should look for another strategy. Your reorder level, and therefore your inventory holding, is too high.
Oh… and don’t forget about the other dimensions: value and size. Salt, something that is cheap and does not take up much room, is assumed to be in inventory at all times (I wouldn’t go back to a restaurant that could not get me a salt shaker on the table, after I asked for it). Even if the use is unpredictable, or it takes a long time to get it, or I never want to run out. It still makes sense to bring it back in after it breaks through an even very high reorder level, since it is cheap to hold and easy to store.
Of course you could also plan salt on demand, but the point is, that if you do that you would have to watch your salt at all times and with the reorder level procedure you get automation; you don’t have to watch it; it’s out of the way and plans itself.
SAP ERP provides you with four standard reorder level procedures to choose from (technically there is a fifth and sixth for time-phased planning which we will cover later):
- VB, the most basic of them all, where you set your reorder level manually and MRP just simply creates a supply proposal when inventory breaks through that level
- V1, which also uses external requirements, like a sales order, within the replenishment lead time only, to calculate when the reorder level is broken
- VM, where the reorder level (and the safety stock) is calculated automatically by the material forecast
- And V2 which is a combination of V1, using external requirement s and VM, which calculates reorder level and safety stock using the material forecast
Be careful with the automated reorder level procedures. They use consumption patterns, lead time and service level to calculate reorder levels and if one of the parameters is off, your inventories might go through the roof. I always suggest to set the procedure to ‘manual’ and simulate a calculation procedure without saving it. If you do it that way, you can perform “what-ifs” and monitor what’s happening without risk.
Before we get to other consumption based replenishment strategies, I would like to explore another method, which is very often confused with a reorder level procedure and is not controlled by the MRP type on the MRP1 screen. However, it is a consumption based replenishment strategy nonetheless: Kanban!
In its original, simple sense, Kanban uses two bins with a certain quantity of parts in each, and when one is empty, replenishment is executed while the other bin – or its content – is used up. You just have to design the quantity available in each bin, so as to have enough in one bin to not run out while the other is filled back up.
So when do you use that kind of thing? Instead of a reorder level procedure? Because it’s the same thing? I don’t think so. Going back to our energy example, it becomes clear that there are situations where you cannot simply trade a reorder level procedure for Kanban. I don’t have a second bin of energy that I can switch to, while I fill the empty one up. On an airplane you usually have more than one tank and on my 1957 Money M20A, I was able to switch over to the right wing tank before the left wing tank emptied out, but that is simply not always possible (hmm… was my fuel supply really Kanban controlled?). When you fill Rum into bottles from a tank over the bottling line, you don’t want to switch back and forth between two tanks but rather start the replenishment process for the blending at some point when that one available tank gets to a level where the replenishment lead time fills it back up to where it needs to be, before you run out.
Kanban is great for parts needed on an assembly line. You put two bins of screws on there and the worker takes what she needs. When the bin is empty, she takes screws from the second bin and sends the empty one to the warehouse for replenishment.
Material forecast: I have not yet seen an SAP installation where the MRP type VV is used to its full potential. Here are my five cents:
First off: a VV can also be used for finished goods. It’s just that SAP never thought about configuring that option into the initial version, so they didn’t customize the standard software delivery that way. You will have to maintain some entries for VV in customizing transaction ???? before you can sell a VV product in a sales order. There are many situations that would call to set a finished product to VV. As an example, you can create a forecast in the material rather then in S&OP and then copy the VV forecast as a VSF into demand planning. This has the advantage that you have perfect, individual control over the product’s forecast and the added advantage that sales orders consume that forecast.
So what does the VV do? It is a consumption-based replenishment strategy, in that it maintains inventory in anticipation of actual demand. The inventory is replenished to a forecast which is based on the materials own consumption history. Hence ‘material forecast’. This is a good strategy when you have predictable demand but the lead time to replenish is long. Since you put ‘artificial’ demand out there by way of a forecast, MRP is able to generate all supply elements way ahead of time and all you have to do is to turn the requisition into an order at the date the system tells you to do so. But beware; it does not take demand spikes into consideration. Any changes in demand will flow into the consumption pattern and eventually be picked up by the forecast module. The system might increase or decrease the forecast or tell you that the current underlying model does not hold water anymore. So, like all the other strategies, you can only use a VV when it fits the bill. Don’t blame SAP when you use VV for a finished product and you complain that it does not pick up immediately on a demand spike. It simply won’t.
It’s like a squirrel planning for his family for the winter. Rocky has a forecast in his head and brings walnuts in to provide for the upcoming winter season. Should he become unusually hungry, he just eats up what he has and does not bring in more to cover that spike. There are no more nuts! So it is with your long lead time items that are predictable. If it takes 6 months to bring in peach skin micro fiber from China, you don’t want additional sales orders introduce nervousness into your procurement schedule… because it just won’t do any good anyway.
You can cover variability in demand; but in case of the VV you do this with safety stock. Either static, forecast adjusted, or dynamic with a range of coverage profile. Once the safety stock is depleted you run out and the service level degrades.
A VV provides a high degree of automation, but it needs to be monitored and SAP provides various options to do so. One of the parameters you can look at to see how good the forecast was, is the error total (FS). It looks at each period where there was a forecast and subtracts the forecast values from what was actually consumed. As the consumption most likely differs from the forecast, the question is: how much different? If the underlying model (constant, trend, season or seasonal trend) is correct then the error should sometimes exceed and sometimes fall short of what was forecasted and over the long run average out and approximate zero.
Another parameter calculated by the forecasting app is mean absolute deviation (MAD). This is a measure of variability and forecast quality. The MAD is calculated by adding up all absolute values of Error and dividing it by the sum of the actual consumption values (it’s done by way of an ex-post forecast which takes the new forecast and applies to past periods). This provides you with a measure on how much the actual consumption deviates from the forecast on average. The smaller the MAD, the better the forecast was; the smaller the average deviation, the better.
Now the system is able to calculate the tracking signal for you which is determined taking the error total divided by the MAD. If you think about it; when that coefficient is high, then you have an error total which is high above zero (therefore a bad model underlying your forecasting) and a low mean absolute deviation (meaning that consumption follows some pattern, just not the one you had selected). Or, in different terms, the error total should be close to zero and therefore if you get a high number out of the formula FS/MAD, you have such a high error total that you might have to change gears and select a different strategy altogether.
What is being compared to the tracking signal (TS = FS/MAD) in SAP is the tracking limit. It is maintained on the forecast screen and in standard is set to 4.0. If the tracking limit is exceeded by the tracking signal, you get an exception message in MP33 and you can even set the system so that a new model selection procedure is automatically initialized.
As you can see we have options here. I believe in learning as much as one can, in order to understand the standard options available in SAP. They are the ones that are thought through from beginning to end and tested in many companies. They simply work if applied right...
Note that you cannot just switch from a PD to a V1 or any other one of these options. As you do so, you will have to look for and pick the right lot sizing strategy, safety stock procedure and planning strategy. Only the right combination of all of these produces the right result.
When BMW design a new line of cars, the engineers put their heads together and figure out an optimal performance within limits that are known based on physical laws and restrictions. They do not just shoot from the hip and estimate that the gas consumption will maybe lie between 10 and 20 miles per gallon, or think in silos when it comes to choosing an engine, tires and the gear box. All departments work together to ensure that the most effective chassis design goes with the performance of the engine and provides housing for all control mechanisms.
Why don’t we do the same for our supply chain performance? How does a manager come up with the call to reduce raw materials inventory by 25 percent? How do we know what to do when the new company directive is ‘reduction of cycle times by elimination of waste’? How does the material planner know whether ordering more frequently will result in lower average inventory levels and better availability of the component? And how frequent? And how much?
In SAP we just go with that MRP type PD – the only one we know - a couple of lot size procedures and a static safety stock level to the best of our guesstimate.
Back to car design. When marketing determines what the customer wants, all departments start figuring out what can be done - in an integrated fashion. A chassis is designed, the body fashioned and power requirements are determined. Let’s say the body and chassis design result in a car that will have a mass of 1000 kg and marketing’s research calls for a sports car with an acceleration of 2.5 meters per second squared. The engine department knows that they cannot produce an engine with more than 200 Newton of force.
Instead of starting the procurement of parts and running the lines to start producing, any automobile producer would thoroughly calculate the expected results, considering all integrational and inter-relational aspects of the end product and its production. In many cases formulas are used. In our example we would calculate:
F = ma (Force equals Mass time Acceleration)
200 Nt ≠ (1000 kg) (2.5 m/s2) = 2,500 Nt
Looking at the result, it becomes clear that one has to go back to the drawing board and, getting all parties involved, working on a new design and specifications.
Is this any different in the design of our supply chain? In fact we need to create a vehicle which allows us to accelerate or decelerate the flow of goods between locations, go down a different route when the situation calls for it (change policy from deterministic replenishment to consumption based), and we need to switch gears when our sales turn out to be more predictable, when it wasn't two months ago (set an MTO product to MTS).
How would you like your driving, if all you know is how to use second gear? You can make it work, but there is a better way to do it with that $30,000 investment in a car. Then why do you only use strategy group 40? PD for MRP type? And a static safety stock?
Every company sets goals for their supply chain. A desired service level to the customer, rate of production, availability of raw material, cycle times, utilization of resources and min/max ranges of investment tied up in inventories. These indicators are frequently measured and there are many initiatives (lean, six sigma, agile and the like) underway to improve on them. However, the problem is that each KPI is put in a silo and when the targets are set individually then, holistically, they state conflicting goals.
The only way to achieve improvements as a whole is to relate the individual indicators to one another.
This is what Factory Physics strives to do with laws, principles and corollaries. As an example, the famed Little's Law states that Work in Process equals the product of Throughput and Cycle Times.
WIP = TH x CT
Insight from this law would tell us that if we want to reduce inventory in the supply chain (WIP), we have to reduce either cycle time or throughput (not a good idea) or both. We can turn the formula around to, maybe
TH = WIP/CT
which clearly demonstrates that throughput can be held constant for high WIP and long cycle times as well as low WIP and short cycle times. Imagine the possibility. When the output rate of a supply chain can be held the same with long lead times and a lot of capital tied up in stocks, as it is with a short time to deliver and minimal inventory, then the goal is clear: achieve the same sales with reduced cycle times and inventory!
Factory Physics (by Spearman and Hopp) tells us that the difference lies in variability. The stuff which is caused by unreliable suppliers, production lines going down and customers ordering as much as they want, whenever they want it. Variability is part of our daily lives. Last time I went through immigration at Newark airport, on my way back from an international business trip, I stood in the wrong line. When I was guided towards the passport checking booth there were 5 people in front of me and the lady coming behind me was placed in a line right next to me and had 9 people in front of her. Unfortunately to me, she was through customs about 10 minutes before I even got to the baggage line. The reason was variability; sometimes the officer takes fingerprints, sometimes she doesn’t.
Is there a way now to use this kind of information to our advantage? Absolutely. If you understand the relationship between cycle time, throughput and wip and you can predict what happens if one of these levers is changed, then we are making decisions and look for solutions based on a solid foundation. ..
…more in a future blog titled “Factory Physics and it’s possible application in SAP”