In my previous post, I discussed the importance of knowing “what we should do” versus only doing “what we can do”. But now I would like to delve into the next big question. How do we verify that we are doing the things we should do? How do we know if we are making actual progress in our Lean transformation efforts? How do we know if the things we are doing are actually turning into “Lean” improvements and are not just “busy work” generating cosmetic changes?
What can we measure that will tell us if we are doing the right things (versus just doing things right)?
The first criteria for this magic metric must be that it must be applicable to the entire system that will be impacted by any changes we make. I can’t stress this enough. If we are implementing changes to a single or a few processes in our system, we must be able to measure the impact of these changes on the entire system. Only measuring the impact of changes to the subsystems we are trying to improve can and, in too many cases, will result in unexpected consequences somewhere else.
I learned this rule early in my career. I can still remember the many occasions where Plant Management would ask all of the departments under their control to come up with cost reduction targets for each department. Each department would then dutifully identify projects in their department that would result in significant cost savings. Everyone would get excited and work hard to implement these world saving efforts. But on too many occasions, when the end of the year financials were published, each department’s project savings were there for everyone to see – but the Plant wide results were, all too often, less stellar than anticipated. Sometimes much less. It wasn’t until much later that I realized that what we had done, over and over, was just move costs around. Improvements in one area often caused deterioration in another area. The various departments were not independent of one another, but interdependent with each other.
So, what system wide metric is best for capturing our Lean progress? I’m sure those of you who have read other parts of my blog have already come to a conclusion as to my choice for this metric. Lead Time, right? After all, you can measure lead time for any process, but it’s also easy enough to measure lead time for the entire system. And it is the cornerstone of my guiding principle:
Thus the underlying principle behind TPS/Lean is the systemic creation of the shortest possible lead time for the continuous flow of materials and information in order to generate the highest quality and lowest cost.
While lead time should always be measured and is a cornerstone for identifying/quantifying improvements to the system, it has a couple of drawbacks to becoming our universal magic metric.
- Lead time is too relative.
- Lead time does not have a financial dimension.
Lead time is relative since there is no stated universal target for lead time. What lead time is too long? What lead time is too short? Can lead time be too short? The best we can say is that shorter is usually better than longer. When Boeing implemented a moving flow line around the turn of this century, lead times went from 22 days to 11 days. That’s great! But is an 11 day lead time good? I don’t know. Does the customer think it’s good? I don’t know. But I’m sure the customer would be happy if it was shorter. If I go to my bank and it takes 15 minutes to cash a check, do I think the lead time is good? No. What should the lead time be? I don’t know. But I want it to be shorter. That’s a relative judgement.
So, the best I can do with a lead time metric is to set a goal of a certain percentage reduction from the existing lead time. My goal is a relative measure. Can I calculate a financial impact for my reduced lead time? Not really. I know I should get a positive benefit with respect to cost, quality and delivery but I can only guess at a financial quantification of that result. Do you think Upper Management or the accountants will pay any attention? Not likely.
Value Stream Mapping is a tool we can use to better quantify lead time in absolute terms. If we map out all the process steps contained within a value stream system, and do it correctly, we will get a very good estimate of total lead time. But we also get something else. We can actually see the individual components that make up that total lead time. And we should also be able to measure or calculate the time consumed by each of these individual system components. The components are usually shown as individual process steps or groupings and any inventory waiting between steps, with shipment to the customer being the last step (remember that inventory consumes time – always and forever more).
OK, now what?
Well there is a way to quantify these various time buckets in a manner that sheds a more revealing light on whether or not our total lead time is “good” or “bad”. This analysis technique is called Process Cycle Efficiency (PCE) by Value Stream mappers. Most of you are familiar with this technique even if you don’t recognize the name. It simply involves looking at each time bucket and quantifying how much of that time can be considered “value-added time” (VAT) with the rest being “non-value-added time” (NVAT). Add these various time buckets across the entire value stream and you will get a total lead time (TLT):
VAT + NVAT = TLT
And the Process Cycle Efficiency (PCE) can be calculated as:
PCE = VAT / TLT
(A video tutorial that demonstrates the entire calculation process can be seen at this Gemba Academy blogsite)
So now we have a ratio that quantifies the value-added time content of the total lead time of our Value Stream System. Now we are getting somewhere! We can quantitatively compare the “value-add efficiency” of our current value stream with other value streams. We can quantitatively compare our future state value stream with the current state. We can quantitatively compare Boeing’s lead time with my bank’s lead time. We have moved one step closer to an absolute measure of lead time from a purely relative one.
What is a good value for PCE? What is a bad value for PCE? It depends on the type of processes we are dealing with. Simple processes tend to have higher values and complex processes tend to have lower values. Manufacturing, for instance, will usually fall into the more complex category. A non-Lean manufacturing operation will usually have PCE values of 5% or less. A world class operation will approach PCE values of 25%. My bank, since cashing checks is a relatively simple system, should have comparable PCE values twice that for manufacturing.
OK, now we are making progress.
We still lack a financial dimension to our metric. Will Upper Management and the accountants pay attention? Maybe – but probably not for very long. They will be thinking: “Why do they keep bringing up this Time thing? They need to use calculators, not clocks. What will it mean to our bottom line? That’s what we need to know”.
But there is a way to turn Time into Money. Dr. John Little of MIT provided us with a tool we can use way back in 1961. A most basic form of Little’s Law can be expressed as:
Throughput x Lead-Time = Work in Process
All terms are averages. As I discussed in an earlier post, we can also express Little’s Law in terms of a financial entity:
Production Cost ($/unit time) x Total Production Lead Time = Cumulative Cash Invested ($)
Production cost can be obtained directly from the P&L. Just note the timeframe for the P&L statement (monthly, quarterly, etc.) to determine the cash input rate, then convert to a daily rate. Lead Time is usually in days, but just be sure to match the time dimension with the P&L. Cash invested is the sunk cost tied up in our production system. If we use my river system analogy, Production Cost is the river input, Lead Time is the flow time to traverse the river from beginning to end. Cash invested is the total river volume.
Let’s stop for a moment and reflect on what we have done here. We have linked up the P&L (Production Cost) with the Balance Sheet (Cash Invested). And we have done that by using “Time”. We have turned the clock into a calculator. And by doing so, we directed the focus from the P&L onto the much-neglected balance sheet. More on that in a minute.
There is one important caveat when using this cash flow version of Little’s Law. As written, all of the production cost is loaded at the beginning of the Lead Time bucket. All of that cash input flows through the entire length of the river. Most manufacturing operations are not constructed that way. There will usually be distinct cost inputs at different points in your manufacturing flow (departments, cost centers, etc.). A separate cash calculation can be done for each of these input points. The various cash calculations can then be added together to get total Cumulative Cash invested. Just make sure your Lead Time represents the total lead time from cash injection to the final system output (e.g., when shipped to the customer). If you are working off a Value Stream Map, this should be easy to do. And most P&L’s have production cost information for departments, cost centers, etc. But just remember, once your lead times become very short (the goal), the need to perform separate calculations becomes much less important. And if your cost inputs are somewhat front-loaded, just do the one calculation to get a very good estimate.
Well, you’ve probably figured that out already. Let’s go back to the “PCE equation” and convert those “times” into “cash”. But instead of Process Cycle Efficiency, a pretty boring term, let’s call it Cash Investment Efficiency (CIE). That’s still pretty boring, but probably not to the top guys and those accountants.
CIE = (VAT x Production Cost) / (TLT x Production Cost) = % of Cash Investment that Adds Value
Will Upper Management and the accountants pay attention to this? If they aren’t Lean, they probably won’t want to. But they really have to. When more than 95% of your cash investment doesn’t add any value to the customer – you must think about it. And that Balance Sheet may not seem so ignorable anymore. Will they know what to do about it? I don’t know….
But we know!
OK, we’ve got a metric that borders on the absolute – and we definitely have a financial dimension embedded into it – have we found our magic metric?
We still do not have a metric that encompasses the entire system. If we visualize Ohno’s River System analogy, we are still falling short. The P&L covers the river inputs and output, the Balance Sheet, Lead Time and CIE do a very good job describing the river body itself, but we don’t yet have a singular metric that covers the whole river system (although the CIE comes close).
Oh yes, we do!
ROI – Return on Investment.
This is the DuPont Model of ROI. I covered the history and structure of this model in quite some detail in a previous post. I also contrasted the difference between the use of ROI versus the P&L in a Lean environment here. I urge everyone to read these posts if you have not already done so. The DuPont Model is not the ROI your accountant counterparts use to calculate whether or not to purchase a piece of equipment or a building expansion. This model was used to manage the entire business enterprise of DuPont and General Motors for almost a half century – along with most other corporations during that time period.
The best analysis of the use of ROI in a world class manufacturing environment that I have seen is in the 2013 book Demand Driven Performance by Debra and Chad Smith. To give you a flavor of why I think ROI is the magic metric for Lean Production, I will quote from an early part of the book:
“What happens when revenue is maximized and protected, inventory is minimized, and additional and/or unnecessary ancillary expenses are eliminated? Return on Investment (ROI) is high. ….The best, sustainable way to achieve that goal [high ROI] is to promote and protect flow. This is the very definition of an efficient manufacturing system. ….Once we realize the importance of flow, a few key principles emerge: Time is the ultimate constraint. Time is the most precious resource employed in the manufacturing process. …What we must always keep in mind is that the important time is the time it takes to move through the system.”
Here is a basic version of the above DuPont ROI model:
ROI = Earnings / Total Investment = (Sales – Cost of Sales) / (Working Capital + Permanent Investment)
Note that the numerator (Earnings = Sales – Cost of Sales) is the P&L. And this represents the real value added as defined by the customer. The various customers set the sales price by buying or not buying the offered product/service. It is up to the producer to maximize the value-added contribution by minimizing their cost. The denominator (Balance Sheet) represents the amount of cash contribution the producer must invest to generate the value-added contribution represented by the Earnings.
ROI is a value-added ratio that encompasses the entire business enterprise. ROI is a total system metric. The river inputs and outputs, as well as the river body are all included in this one metric.
Is this metric a relative or absolute measurement? While all metrics are relative when you are measuring your progress (or lack thereof), ROI has been in use for almost a hundred years and has a long documented history. You can find extensive detailed tabulations of ROI values online that list ROI by industry/business type and many other categories. And you can always lookup the ROI performance of your competitors. It’s usually included in their quarterly and/or annual reports. You can’t ask for more than that.
In order to better mesh with the other various metrics I have discussed in this post, I would like to make a few minor modifications to this equation. For our purposes “Cost of Sales” is equivalent to “Production Cost”. Since we are interested in the things we can control on a daily/monthly basis, I will remove the category “Permanent Investment” (buildings, equipment, etc.). “Working Capital” (inventory + accounts receivable + Cash), while a great term, is usually defined differently in today’s financial accounting world (versus management accounting – what we want), so I am going to rename this term as, no surprise, “cumulative cash invested”.
Here is the revised equation:
ROI = (Sales – Production Cost) / (Cumulative Cash Invested)
And if you think I’m going to forget the cash version of Little’s Law you would be mistaken. Here is the final version of my ROI:
ROI = (Sales – Production Cost) / (Production Cost x Total Lead time)
If you have read my last two posts (located here and here), you will know that significant reductions in Lead Time almost always result in significant reductions in Production Cost (as well as significant improvements in quality and delivery). And when all these factors improve, Sales should also improve.
Do the math! If we concentrate on lead time reduction, we should also see a corresponding reduction in Production Costs and hopefully an eventual increase in Sales. If we can cut “Cumulative Cash Invested”, the denominator, in half (a very achievable goal based on my experience), our ROI will double, even without an increase in Sales. And it will more than double if Production Costs come down, as they should, since Earnings will also increase. I think Upper Management and those accountants will definitely notice this one.
Concentrate on Lead Time (i.e., flow) at the gemba where everyone can see it. And use CIE to wake people up. But measure ROI to confirm that your Lean Production System is in fact improving.
That’s a big WIN-WIN for everybody!!
One last thought. As you proceed along your Lean/TPS journey and you read all the myriad of blog posts, articles, books, videos, etc. based on today’s versions of Lean, just keep this quote from Mark Twain in the back of your mind:
“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”