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Theory of Base6© – Successfully Implementing the Lean Supply Chain – Part II

In Theory of Base6

Lean Accounting vs Throughput Accounting

While hashing through the concepts of Lean Accounting and Throughput Accounting, I came across this presentation that seeks to outline the two concepts, compare and contrast them. The presentation is available for free on the web and was prepared by Peter Milroy of Constraints Management Systems Inc. So let’s dive into the presentation right away:
Peter summarizes Throughput Accounting the following way:

  • Measurement and decision-making tools that align analysis with bottom-line results
  • Simple, common-sense financial categories aligned with generating sales (throughput), improving cash flow (investment) and providing capacity (operating expense)
  • All measurements and decision-making approaches are based on ‘relevant cash flows’ – no allocations are used
  • The system constraint(s) provide the basis for our understanding of which cash flows are relevant at any time

He then goes on to outline a hypothetical case of TA measurements on a month to month basis:


So how does one make ongoing decisions on the basis of Throughput Accounting?
Since there are only three basic variable outlined above, it follows that changes can be made in three main categories namely delta(Througput), delta(Investment) and delta(Operating Expense). As opposed to traditional cost accounting, the decision are not made on the basis of unit costs. I had made an earlier post that talked about Time as a fourth variable in Throughput Accounting and the role that is played by time is in the calculation of profit rate (which is calculated as Throughput per unit/Time per unit).
The essential difference, according to Peter, between Lean Accounting and Throughput Accounting is captured in the slide below:

And further more,

Very interesting, to say the least.

Categorized as: Throughput Accounting_, Lean_, Theory of Constraints_
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What is throughput accounting?

If you’re even slightly familiar with “Lean thinking”, then you would have heard the phrase – “Lean is all about flow!”. There is a good deal of sense in capturing the essence of Lean in that pithy way. (And if you’re familiar with linear programming as well) You might also aver that “Lean is about eliminating waste” – the latter phraseology being the dual of the former i.e. maximizing flow does minimize waste. Observations like these are exciting because, Lean thinking and Optimization borrow insights from each other pointing towards a unity that very much doesn’t exist in the business planning and optimization of an enterprise but exists very much in the fundamental aspects of both disciplines.
In any case, this post is about throughput accounting. I took some time out to familiarize myself with the Theory of Constraints which is where I came across the term and description of Throughput Accounting. Pascal van Cauwenberghe of Thinking for a Change has posted an article at his site that summarizes the key aspects of Throughput Accounting.
Pascal outlines the three basic variables of throughput accounting:

  1. Throughput = fresh money coming in from sales.
  2. Operating Expense = money going out to keep the company going. Once spent, the money is gone (wages, energy, rent

Theory of Base6© – Successfully Implementing the Lean Supply Chain

A featured story (in three parts no less) at the Council of Supply Chain Management’s website describes the Theory of Base6 – Successfully implementing the lean supply chain. The authors of the article are Robert Martichenko and Dr. Thomas Goldsby. In the first part of the article, a list of persistent ideas in business are listed on account o fthe fact that these ideas have some value proposition. The list includes:

  1. Total Quality Management – Dr. W. Edwards Deming influence
  2. Six Sigma – Dr. W. Edwards Deming influence – Motorola and GE developed
  3. Lean Manufacturing – Dr. W. Edwards Deming influence -Taiichi Ohno, Eiji Toyoda, Shigeo Shingo, Toyota Motor Manufacturing developed
  4. Theory of Constraints – Eliyahu Goldratt
  5. ISO Certification – influenced by engineering groups from many countries
  6. Good to Great – Jim Collins
  7. Seven Habits of Highly Effective People – Steven Covey

Further, they distil the above business ideas around six common themes (hence, i suppose the term Base6) which are:

  1. Customer Focus
  2. Vision Deployment
  3. Process Management
  4. Teamwork
  5. Quality at the Root
  6. Continuous Improvement

Each theme is then expanded briefly in their article. I am waiting for the next part in the series to see how these themes are fleshed out in greater detail and how these themes are linked up towards developing and implementing a lean supply chain.

Categorized as: Reviews_, Lean_, Supply Chain Managment_, Process Management_
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The False God of the Almighty algorithm

Evolving Excellence has a great article (that gave me some pause when I read it) about the False God of the Almighty algorithm. The reason that I read the article at least twice and then all the following comments as well is because I do have a deep interest in algorithms as well as lean principles and the article made it seem as the twain shall never meet. Perhaps, they should and perhaps they shouldn’t but a larger point about invoking, adopting and implementing ERP systems was made which I find quite relevant. I am of the opinion that ERP systems are bound to face radical competition but I didn’t think about the source of that radical competition. The reasons for holding this opinion are as follows:
1. They’re too cumbersome to implement and consequently the failure rate is high for firms that go that route
2. The firm has to adapt its processes to what the ERP is mostly configured to do (No, you won’t hear that from any vendor. In fact, you’d hear exactly the opposite). This is quite akin to Ford’s famous quip a while ago, “You can have your car in any color as long as its black.”
But that is not what the article is really about. In a nutshell, the following quotes illustrate what the story is really about:

“These are two radically opposite worlds … One is the tech-savvy and IT-powered optimization world and the other is the pencil-and-paper problem-solving world. Which world should we live in?”

and,

“What do we know about SAP, and how well it integrates with lean principles (or lean implementations).”

The first thing to note is that optimization problems are notorious to handle all by themselves (but elegant in the formulations that have been drawn up) but when they’re applied to real world problems, the modeler first creates a system (an artificial world), then models the problem within the system and finally attempts to solve (hopefully) the problem to optimality (hopefully) in a brief flurry of command line code instructions. And voila, you have an optimal answer. However, the model, the answer and the system are first and foremost matters of interpretation. Why there is a lot of WIP sitting between two furious machines is not a matter of interpretation as much as it is a matter of fact. The other (secret and don’t you ever say that I said it) shady part of optimization applied to real world problems is that not all of what is called ‘optimal’ is truly optimal (or mathematically optimal) but no salesman will ever tell you that either out of sheer ignorance or cognizance that its the best that can be done. If I were to peer behind any ERP vendors so called optimization algorithms, I’d be sure to find (hopefully) a hybrid of optimization and heuristic algorithms embedded within that are spitting out answers. So that’s what really happens on the optimization side of things.
On the lean side of things (and as lean goes, I shall be very brief in my answer) – you work in a philosophical framework that is bent on eliminating waste in a real system.
So what happens when the optimization world of IT clashes against the problem solving world of lean (or at least that’s the way the clash is cast):

wasteful processes being proceduralized in algorithmic stone, monstrous amounts of extra inventory generated to accommodate the cascading “schedule risk” of individual operations, and of course implementation costs that can exceed $100 million buckaroos. And interestingly enough, several people chimed in with how they have gone back to using simple visual controls and Excel spreadsheets to schedule complex operations.

If one wants to clarify the clash, it would be instructive to consider some real evidence (as opposed to the neat tick-tock world of a an optimization/heuristic modeler) and that evidence is Toyota:

About the most complex type of factory is one that makes almost a thousand cars with several hundred permutations every day. And Toyota does it with no MRP-type shopfloor control. MRP is used to handle financials, inventory costing, and the like… but shop floor control is pure manual pull with a small number of e-kanban type applications.

This kind of evidence is particularly damning because in contrast to the system that the optimization resides in, the application of lean on a shop floor deals with reality – a tangible profit creating and wealth generating reality. You can’t explain that away. And,

Excellence through simplicity.” To me that quote from Lao Tzu has always epitomized one of the fundamental tenets of real lean. Don’t proceduralize complexity, and don’t make something more complex than it needs to be. Manufacturing really isn’t all that hard… you make something, preferably one of it, and you get it out of the operation as quick as possible. Once you remove the loads of WIP from the floor by focusing on the velocity of the single unit, you begin to realize how so much of that perceived complexity is due to not having an unwavering desire to get a product through the flow as quickly as possible.

The above discussion (or polemic, depending on how you see it) is why I go hmmmm… because ERP is asking for competition. So what am I going to do? That is the only question.

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The blind spot in cost calculation…

Early on in my career (i.e. just after my undergraduate degree), I interviewed with BCG. (The only thing I learnt from those heady days was not to be a pompous arrogant ass). Personal distraction aside, I worked through a case with one of the consultants about “saving” a business making tractors from almost certain ruin given such and such market conditions. Coming from a mechanical engineering background, I was not only completely sold on the importance of R&D, good product design, quality etc etc but also unwilling to brook any spending cuts in those areas. The “answer” (or the best supportable answer most likely) to the case was cutting R&D and product development costs for the tractor manufacturer and redeploying those funds to marketing and what not. That and a love for engineering that couldn’t be hidden meant a ride on the road less taken and this during the Asia Financial Crisis of 97-98 (though it quite felt like 97-2000). Today, I have come full circle and its taken me 8 whole years – I still love engineering, trust marketing to elucidate value (not create value), am skeptical of accounting variances and varied accountants (though I realize the value and the limits thereof of accounting practice) and might still fail the BCG case again – taken honestly. Though, I work in supply chain consulting and using a quantitative and optimization approach, I am always keenly observing the limits of what works and what is plain hooey even if it is based on incontestable numbers – that might seem rather piquant but even incontestable numbers come from contestable underlying philosophies. And its precisely this underlying philosophy that this article at Panta Rei: Gemba Keiei, Chapter 6: The blind spot in cost calculation is all about.
Let me jump right in:

Just in Time is the design of production activity to be closely synchronized with customer demand according to the three principles of Takt Time, One-Piece Flow, and Downstream Pull. One of the major goals of Just in Time is to prevent the mother of the 7 wastes – overproduction – by making only what the customer needs, when they need it, in the right amount.

And thus manufacturing became an art. There is the science of manufacturing, the logic of manufacturing and the art of manufacturing. The above statement encapsulates what can be termed the logic of manufacturing. But somewhere in elucidating the above logic, manufacturing turned from being a science, from numbers, yields, WIP and lead times into an art. The art is the whole thing subsuming logic and science together into a practical effort.

Taiichi Ohno begins the chapter by saying that there is a misconception in the minds of people who calculate cost. They believe costs can be lowered on the basis of volume produced without considering the actual customer demand.

Well, it depends on where you draw the system boundaries. It is not that either of the above logic described is incorrect but one has to look at the completeness of the description. Costs can be lowered on the basis of production volume = economies of scale, nothing wrong here. However, the system drawn up here doesn’t take any inputs from any agent outside the purview/control of the firm when it calculates its manufacturing cost. In this day and age, when a firm goes through its product design and development process, it is not averse to getting customer input at critical design junctures or in the whole design process – doing so mitigates serious risks and thus future costs down the line. Why should manufacturing be any different? The key customer input here – the true nature and magnitude of customer demand. Sometimes demand data is simply unavailable or too difficult to obtain from customers and if manufacturing proceeds without taking efforts to control the risks that such uncertainty introduces in their business model – then system failure is not far off. Adopting JIT as a manufacturing philosophy means creating a system where customer demand uncertainty is reduced by waiting sufficiently long enough until the manufacturer can “see the whites of his customers’ eyes” before acting.

Ohno says “Make only as much as the customer will buy. Don’t make things the customer won’t buy” but the cost accountants reply “What are you talking about? Of course it’s cheaper to make 20 than to make 10.” Ohno recognizes that in terms of simple math what the accountants say may be true but says the reality of costs is not so simple.

Its not the simplicity of the math as Ohno says but the simplicity of the system that the accountants have drawn up and by which they practice their craft. The real world is not just that simple and we ought to adapt ourselves to mitigating uncertainty and risk wherever possible.
However, Ohno makes a larger point next which harkens back to the incontestability of numbers that are available when it is the underlying philosophy that is actually the source of contest.

Here he introduces the famous three equations for cost. Mathematically they are the same. They are very different in terms of the point they bring across. The equations are:
1) Price – Cost = Profit
2) Profit = Price – Cost
3) Price = Cost + Profit

Mathematical axioms are not subject to change by definition and therefore there cannot be anything logically untrue in the above statements. However, the question to us who have to act always is – what variable/constant is known, what are fixed/changeable and what are the underying systems that deal with the three variables/constants above?

In the case of equation 1 the market is competitive and the price is set by the customer.
In the case of equation 2 you need to make a certain profit, let’s say $0.25 per unit. So here you have to increase the value and increase the price so that if your cost is $1 you can now sell it for $1.25.
In the case of equation 3 the math may be the same but the underlying thinking is different, says Ohno. If $0.25 is a fair profit and the cost per unit is $1, then you may set the selling price at $1.25. However if the customer can purchase the same thing elsewhere at $1 then you can not set the price this way.

Nothing mysterious here. All you’ve got to do is choose your mode of competition or adapt a hybrid mode of competition – you can deliver value while trying at the same time to drive costs (what’s the definition of cost agian?) out of your system.
Here is something to savor from Taiichi Ohno:

Costs do not exist to be calculated. Costs exist to be reduced.”

Now, should an accountant’s performance be based on cost reporting or cost reduction? You decide.

Drucker on Real Transformation

Bill Waddell at Lean Affiliates writes about Principles for Real Manufacturing Transformation:

Sixteen years ago, Peter Drucker’s article, “The Emerging Theory of Manufacturing”, appeared in the Harvard Business Review. That is probably about the right incubation period for the rest of us to catch up to his thinking. Drucker points to four principles that defines what’s needed for real transformation, and establishes the critical role of the chief executive. These principles are:

  1. Integrate the factory into the total value stream
  2. Instill a statistical quality focus across the entire company
  3. Implement a completely new accounting model
  4. Treat the entire business as a system

Remember that Drucker is writing this at about the time that Six Sigma had just appeared on the scene but his recommendation seems to be spot on. Bill goes on to elucidate the differences between looking lean and being lean and how the above principles, proposed by Drucker in his “The Emerging Theory of Manufacturing” are essential to a lean transformation.
1. Integrate the factor into the total value stream: A quote from Taiichi Ohno of Toyota captures the central thought here –

All we are doing is looking at the time line, from the moment the customer gives us an order to the point where we collect the cash. And we are reducing the time line by reducing the non-value adding wastes.

. Bill calls this idea – “from call to cash”, meaning that every activity that occurs in the intervening period and process space is evaluated on the basis of its integrity with respect to the value to customer that each activity confers. Such totality begs for C-level executive involvement, not in sense of micro management but in the authority conferred to such a process overhaul.
2. Instill a statistical quality focus across the entire company: Ever since the surpassing of Newtonian physics, scientists have been engaged in the description of the natural world through statistical techniques. However, the education system relies primarily on the promulgation of facts implying the completeness of the description which actually only statistically implied. Its nice to see that businesses that imbibe this statistical approach to their activities are achieving coherence with the kind of descriptive and prescriptive pattern inference also called science. Though, I think that the day is so far off into the future when accountants shall describe a firms financial activities in statistical terms – what a sea change that would be? The other key takeaway is the relentless focus on quality that such statistical processes describe.
3. Implement a completely new accounting model: Accounting is quite integral to how management decisions are made and how they’re represented to the world at large. However, if even half as much continual transformation and improvement were carried out in the accounting departments as are carried out in the manufacturing department, the manufacturing department might be four times better than it is today. Yes, the ratios of supposed benefit are imaginary. However, that management often makes decisions based on accounting gimmickry is and should be the object of scorn because accounting doesn’t report information in a way that lends itself to decision making. While accounting should be about informing business decisions, I suspect accounting is really about accounting. And that’s Drucker’s view as well though I suppose I am twice as cynical as he is prescient.
4. Treat the Entire Business as a System:The central point here is that the manufacturer within his ecosystem provides a solution over and above providing a product/widget/service. Therefore, a manufacturer is as much part of the solution as he is part of the problem that crops up because of a particular deficiency in the solution that he provides. The expectation of a customer is not how finely finished a product might be (if that were the sole contribution of the manufacturer) but that it meets or exceeds his expectations of product peformance and value.

In the end, abstractions such as performance and value are what the manufacturer or business is aiming at fulfilling. The problem with abstraction is that people abstract without particular attention to reason or logic. One part of the business should be about addressing those abstractions in a value laden way but another part of the business should be about clarifying the abstractions, informing and educating the customer as well.

About me

I am Chris Jacob Abraham and I live, work and blog from Newburgh, New York. I work for IBM as a Senior consultant in the Fab PowerOps group that works around the issue of detailed Fab (semiconductor fab) level scheduling on a continual basis. My erstwhile company ILOG was recently acquired by IBM and I've joined the Industry Solutions Group there.

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