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A Guide to Implementing the Theory of Constraints (TOC) |
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Tell Me
How You Will Measure Me It has been said; “Tell me how you will measure me,
and I will tell you how I will behave (1).”
The measurements in any organization are the No. 1 formal feedback
system in that organization. So let’s
start with measurements in order to better understand our current approach to
business, and to help us do that let’s also return to our simple model that
we first saw in the introduction. How would you be measured in such a system? If you recall it was likely that you were
in work up to your eyeballs. Is that
an issue? It certainly shows that your
position is important and necessary and that you have lots of work to get
through. If others have even larger
piles of work it might lead you to believe that they are not as efficient as you
are. Is relative efficiency an
issue? It would seem so.
Does this frustration ever look like becoming
despair? Well probably annually if
your company has performance reviews.
Maybe even more frequently if you are accountable for departmental
performance. Accountability for
departmental performance might be some derived efficiency report, it might be
some sort of derived profit or cost report. The
whole internal business performance measurement system is based upon local
optimization, either in the form of departmental utilization/efficiency
measures or as departmental cost/profit performance measures - or both! And we don’t need to limit ourselves to
departments within a business. It
could equally be businesses within a company, or companies within a
corporation. It
takes some conscious effort to realize that the formalization of local
efficiency measures through the activities of “scientific” management –
Taylorism – is only about 100 years old (2, 3). Scientific management is such a seductive
idea because it legitimizes the actions that we as individuals find so
effective in our local settings (family, friends etc) and applies it directly
to our work processes. We
assume that the total performance of the system is the sum of all the local
performances. In fact it is so common
that we probably don’t even give it much thought. This approach then is the reductionist/local optima
approach; departmental cost or efficiency is just a symptom or an output of
this method. Let’s look at local profit centers a little closer.
However there is condition that must be met in order
to sum the local profits as we have just done. The condition is that there is independence
between departments. Let’s examine
that condition.
We typically determine success in several ways. In absolute terms by net profit, in
relative terms by return on investment, or in survival terms by cash flow
(4). But how do we relate our on-going
operational decisions – our departmental decisions – to overall system
success? How do we bridge between our
operational decisions and system success?
Goldratt and Fox have coined this bridge the “cost” bridge (4). Let’s draw it.
In the Haystack
Syndrome, Goldratt presented a small thought experiment, known as the P
& Q problem (5). It is named after
the two products it produces – “P’s” and “Q’s.” This elegant little example, strictly
educational, seems to have taken on a life of it’s own as it turns up in
various examples to illustrate concepts as broad as total preventative
maintenance. Often the numbers and the
story have mutated somewhat in the process, but at the heart is the P & Q
problem. Because the example is educational, I have split it
out here into separate pages (you wouldn’t cheat but you might accidentally
see the answer before you see the question).
The P & Q question is here. Try to work through it
first. The first part of the answer is
here. Check
the answer after your first attempt. The P & Q is important. Please try to do it before you go any
further. What happen in the P & Q when you worked through
it based upon your experience? You
probably tried to optimize it following some fairly rational arguments, you
probably also got a less than desirable answer. What went wrong? Saving cost is synonymous with local
optimization. However, we have seen
from the example above that saving cost alone is not a sufficient bridge
between local actions and the bottom line measures of; net profit, return on
investment, and cash flow. In fact
neither is maximizing labor utilization nor any other of the local
optimizations. Maybe the P&Q was an exceptional example? However if you want to believe this, then
please read Debra Smith’s war stories in “Unbelievable decisions by companies
you would know if I could name them (6).”
In fact to do justice, please don’t stop at the end of the first
chapter of that book, read the whole book. What then if you were to observe the financial
manager of a large business or the owner/manager of a small business over a
period of time? Then, I think that you
will see that decisions based upon cost are indeed undertaken – up to a
point. That point is where the
person’s intuition takes over and the cost based decision is overturned or
moderated (moderated by common sense as it happens). The important point is that intuition
should at some point take over. The
danger is that non-operational people, or financial people who “believe” the
numbers but don’t have access to their composition, may make erroneous
decisions as a consequence. Cost-based
decisions often give rise to the wrong answers. Thus the bridge between local actions and the bottom
line results of net profit, return on investment, and cash flow must be based
on cost + intuition, and not on cost alone (4).
Well human endeavor sort of “grew” into this
mess. Think about it for a
moment. It’s not all that long ago
that there were no process-based businesses.
Certainly the industrial revolution – the steam powered one that is –
is only about 200 years old. And the
earlier phase of the industrial revolution – that waterwheel powered one –
extends that time frame back about another 100 years. Before the industrial revolution there were
no process industries only cottage industries. In a cottage industry – even where whole towns were
involved, there were many small parallel systems of few parts – fulling,
spinning, dyeing, and weaving wool spring to mind. In fact they are rather like the first profit/cost
system diagram that we drew with totally independent inputs and outputs in
each department. With the onset of the
industrial revolution there was a move from many small parallel systems to a
smaller number of larger serial systems of many parts. The first processes industries grew out of
linen milling and similar agricultural based processing. The rest, as they say, is history. Of course a large number of parallel systems in a
loose network is an extremely robust form of process (8). However, as we will learn in later pages,
there are also ways to create very robust serial systems as well. In all pre-industrial history local optimization
equaled global optimization – they were one and the same. However, as a process becomes more serial
in nature it is less likely that local optimization can equal global
optimization. We outgrew local
optimization in serial (industrial) processes, but we forgot to replace local
optimization with something else. Let’s
turn to natural systems for a moment for some guidance, “Living systems have
integrity. Their character depends on
the whole. The same is true for
organizations; to understand the most challenging managerial issues requires
seeing the whole system that generates issues (9).” Let’s
start again from scratch then – or if we want to be more proper – first
principles. It seems that we need to
know what the system is that we are dealing with, where does it start, and
where does it end. We need to know
what the system exists for, and we need to know how to measure progress
towards the reason for its existence. Scheinkopf expresses this as (10); (1)
Define the system and its purpose. (2)
Determine the
system’s fundamental measurements. Why do we have this particular order? Well, the organization in fact defines the
measurements rather than the other way around – the measurements define the
organization. Margaret Wheatley is more
articulate. She argues that in too
many organizations “… the measures
define what is meaningful rather than letting the greater meaning of the work
define the measures. As the focus
narrows, people disconnect from any larger purpose, and only do what is
required of them (11).” We can’t
afford to have people disconnect from the larger purpose, we are not going to
let that happen here. So, let’s expand this expression out a little more
to get the following; (1)
Define the system. (2)
Define the goal of the system. (3)
Define the necessary conditions. (4)
Define the fundamental measurements. This is going to be our basis, our rules of
engagement. Let’s look at each facet
in turn. Let’s return to our simple model of a system
again. It seems that we are defining
our systems as something like the beginning + middle + near-the-end +
end. We will call it “our system” for
short.
And let’s not leave out not-for-profit. How about a public health system as an
example?
“’The owners have the sole right to determine the
goal.’ If we are dealing with a
privately held company, no outsider can predict its goal. We must directly ask the owners (12).” For a company whose shares are traded in
the open market “a company’s goal is to make more money now as well as
in the future.” I underlined the word “open” because in some
instances publicly held companies are not traded in the open market. Consider Japan for instance. Many publicly listed companies in Japan
have tightly held multiple cross-shareholdings (and often considerable debt
finance). In instances like this we
might expect that these companies will behave more like a private company
would in other parts of the world. It
is not enough to assume that making money is the goal of these organizations. Returning to our definition of the goal in openly
traded companies; most often we find that the “more money” has been dropped
from the definition, and that is what we will adopt here. However, the word “more” indicates that the
goal is in fact open-ended. This
highlights that fact that in contrast to a “necessary condition” you can’t
have enough of the goal. Maybe in the
context of money therefore, the word “more” is redundant. You can test this, ask someone, anyone,
whether they wouldn’t like to make more money now and in the future or
whether they are contented with what they currently receive. Let’s write the goal for a public company traded on
the open stock exchange.
Once we
have defined the goal, we must define any necessary conditions. Necessary conditions are minimum levels of
other entities that must be present
in order to satisfy the goal. In this
respect necessary conditions can be viewed as having limits. Once a necessary condition is satisfied,
additional levels of input will not result in an increased attainment of the
goal. The two
most generic necessary conditions are (12); (1)
Provide employees
with a secure and satisfying workplace now and in the
future. (2)
Satisfy
customers now and in the future. Let’s
add these to our goal.
Is this
too pecuniary for you? We could
rearrange it a little.
But
what if we are a not-for-profit? Such
as a government health provider.
“Necessary conditions are important to identify, especially for
not-for-profit organizations.
Sufficient cash is usually a necessary condition. Expenses might constitute a necessary
condition if, for example, funding levels are fixed (15).” That is to say, some not-for-profit
organizations, charitable trusts for instance, might carry out some trading
activity that is used to raise sufficient cash to carry out their goal. Others that rely upon fixed funding must
watch their outgoings with utmost care. Let’s
rewrite this diagram for a not-for-profit.
We need
to determine the fundamental measures for our system and then ensure that our
performance measures are subordinated to these fundamental measures. “Not just any measurements, but
measurements that will enable us to judge the impact of a local decision on
the global goal (16).” “Measurements
are a direct result of the chosen goal.
There is no way that we can select a set of measurements before the
goal is defined.” The measurements
should enable us to judge whether a local decision has an impact of the
global goal (16). In a
commercial organization the fundamental measures are defined by the following
questions (16); (1)
How much money is generated by our company? (2)
How much money is captured by our company? (3)
How much money do we have to spend to operate it? Essentially
we are asking; what is the flow of money into the system, what is the flow of
money out of the system, and how much is kept in the system? Goldratt calls these 3 measures;
Throughput, Inventory, and Operating Expense.
These are often shortened to T, I, and OE and are defined as follows
(16); (1)
Throughput is the rate at which the system generates money through sales. (2)
Inventory is all the money that the system invests in purchasing things which it
intends to sell. (3)
Operating expense is all the money the system spends in order to turn inventory
into throughput. Throughput
can be considered to be revenue less totally variable costs (17). A totally variable cost is anything that
varies in a direct 1:1 relationship with sales; for instance transportation
charges, or commission charges, might be totally variable costs. If the company produces and sells another unit
of product it will incur this cost, and if it produces one unit less it will
not incur this cost (18). Direct labor
therefore is not included as a totally variable cost. We need to be clear; variable costs are variable
per unit sale. Throughput
is the financial value of an organizations output and must be measured in
monetary units. Output is the volume
of product or service produced by the organization and is measured in
physical units of some sort (19). The
next major measure, inventory, includes not only the traditional classes of;
raw material, work-in-process, and finished goods inventory, but all other
money invested in the organization such as in buildings, machinery, and other
capital items – in other words the total investment. More recently the term investment has been
used synonymously with inventory. Operating
expense is the on-going cost of running the business including both direct and
indirect labor. You might like to
consider these as the unavoidable costs of doing business. In an example that I like to use, I ask
people to imagine for instance what would happen if we were to close a ward
in a public service hospital to patients for one week. This has certainly happened in the name of
saving costs. How much cost do you
think is saved? In all honesty? Probably not very much at all. The unsaved portion is the unavoidable
operating expense. Of the
saved portion in this example – the avoidable cost; some may be directly
variable cost, and some may indeed be operating expense. Although changes in operating expenses are
not directly variable with volume this certainly doesn’t mean that they are
not variable over time (20). As
production increases or decreases over time, so too might the operating
expense. Although as we shall soon
see, we should strive to hold operating costs constant while increasing
throughput. It
seems that accountants are more familiar with the term “period costs” or
period expenses rather than operating expense (21). This more clearly accommodates changes over
time. Thus another way to consider
operating expense is that if an expense is incurred by unit of time – be
that; hourly, daily, weekly, monthly, or whatever – then it is an operating
expense. It isn’t considered to vary
in any direct relationship with the number of units processed. We can
now see that considering all labor as an operating expense is consistent with
this definition. “Labor is normally
purchased in units of time – by
compensating people for hours per week or month, or, in the case of salaried
employees, for a year (22).” Let’s
attach these new labels to the bar graph that we used in the previous section
on the bottom line.
Throughput =
Sales - Totally Variable Costs Net Profit =
Throughput - Operating Expense Using period expenses rather than operating expense
may make matter clearer (21). Net Profit =
Sales - Totally Variable Costs - Period Expenses Net profit is the bottom line measure that we use
the most. But we must also consider
return on investment (16, 17).
There
is a further useful measure, expressed as a ratio of these fundamental
operating measures (16, 17), it is a measure of productivity;
Let’s draw both of these situations.
We could also graph these two situations.
The accent is on process profit, not individual
product profit. We should apply the
productivity definition as a test not only at a tactical level (improvement)
but also at a strategic level (investment). We need to be careful when we define the fundamental
measurements. It is insufficient to add new and relevant measures. We must also remove old and irrelevant measures. Leaving old and irrelevant measures in
place is a common and disastrous mistake.
We might think what harm could leaving our old and familiar
measurements in place possible do? The
answer is that they can do a great deal of damage. Well a not-for-profit, or expressed more positively
a for-cause organization, doesn’t look much different.
Scheinkopf
notes that at not-for-profit organizations “there is a tendency to believe
that the measures are so intangible and that attainment of purpose is such a
subjective call, that such measures are simply not discussed. The focus ends up to be on measuring and
managing the things we call ‘tangible,’ such as money (23). We can easily see this in the New Zealand
public health system where district health boards are charged with making an
adequate return to the Government on its investment. One way to meet that is to defer
operations! In the
section on marshalling and replenishment in supply chain two easy to
implement and highly relevant non-financial measures are offered for
healthcare. We saw
earlier in this section that the cost bridge does not always lead to the best
decisions being made. You might have
experience this directly if you used your experience to answer the first part
of the P&Q problem. We were left
in the undesirable position of having to use cost + intuition if we wanted to
link local actions to bottom line results. Taking
a more systemic approach we have defined 3 measures, throughput, inventory
and operating expense and shown through a set of definitions that each of
these measures has an effect on the bottom line measures that we have proposed. If we look at the definitions above for a
profit based organization we can see the following; If
throughput increases, then net profit, return on investment and cash flow
will also increase. If we decrease
operating expense, then net profit, return on investment, and cash flow will
increase. If inventory decreases, then
return on investment and cash flow to increase, while net profit
decreases. Goldratt and Fox summarized
the situation in the following diagram (24).
Accrual
accounting tells us that as inventory increases net profit must also
increase, and yet nowadays most people understand that increasing inventory
in the long run is harmful. We will
examine the role of inventory as outlined by Goldratt and Fox in 1986 in the
section on drum-buffer-rope. It is
sufficient for now to know that in fact decreasing inventory increases
future throughput. Thus we can
reconcile our experience of the success of low inventory systems such as
just-in-time and complete our bridge (24).
In fact
maybe our thinking in drawing these diagrams is also subject to inertia. We should remove the departmental
boundaries that we have used to date.
Let’s see how it looks.
Well
yes, an “extreme form of variable costing” and one in which “financial
reports are consequently much simpler and easier to understand and can be
compiled more quickly and frequently than conventional financial reports
(25).” Hmm; quick to compile,
frequent, and easy to understand.
Sounds like a prescription for a real management decision support
methodology. Let’s discuss this more
fully in the section on accounting for change. How did
we get this far in discussing measurements without even mentioning
constraints. The cue was a paragraph
or so ago. If we want increase
throughput we had better know where the constraint is in the system and how
to maximize its capability. Or to put
it another way; we now know how to relate local actions to the bottom line,
but we still need to know how to evaluate the local actions themselves. “The
key to know what to do locally is the realization of the role the system
constraints are playing (26).” In fact
now would be a good time to return to the P&Q problem for the second part
of the answer. I strongly recommend
that you have a look at this here before continuing on. So long
as system throughput exceeds operating expense then we know we are making a
profit. At the product level however
it is essential to know at least the relative contribution of different
products. In order to do this we need
to know where the constraint is. We can
modify our departmentalized system model to reflect this reality. Let’s draw it with a constraint in the
department near-the-end.
Resist
all temptation to allocate the total operating expense to the constraint
operating time to derive an operating cost per unit time on the constraint. Many
people do this - I have done it, it seems so natural. It is not, however, a part of Theory of
Constraints. Some software vendors
sell this type of calculation as bottleneck accounting, it isn’t (27). Purge it from you mind. In fact, let’s replace our departmentalized
view once again with a more systemic approach. And for completion let’s add all of our
flows in and out of this system. This
is an important model, we will refer to it again.
Yes,
you are absolutely right. In fact, if
you approached an unknown process and you had sufficient data, and that data
was accurate – then linear programming and Theory of Constraints would both
arrive at the same results over the location of the constraint and the
throughput that it could generate
(31). But
tell me in all honesty – have you ever achieved a bottom line result that was
anything like the objective target in the linear program? Probably not, not without lots of padding
in the assumptions. Sure, the data
probably wasn’t complete, the picture changed after the model was run, the
numbers weren’t as accurate as you would have liked, but the real issue is
that linear programming still does not furnish the logistical scheduling and
control needed to obtain the calculated result. Drum-buffer-rope does. Put
another way, linear programming yields the “what” – the result, without ever
addressing the “how” – the process. It
addresses an ideal “end” without addressing the “means” from which it is
derived. The production solution for
Theory of Constraints, drum-buffer-rope, gives you a real chance of realizing
the objective function of a linear program.
It gives you an operational methodology that will allow you to attain
the objective function. If we
look at linear programming carefully then it becomes apparent that it is, in
fact, a detail complexity approach to a dynamic complexity problem. Without the detail you can not solve the
dynamics in this instance. As Johnson
and Kaplan explain, the whole of the operations research development, of
which linear programming is a major part, is an outgrowth of scientific
management from 50 years earlier (32).
Scientific management deals in detail complexity. Perhaps
a more fundamental point, however, is that linear programming is an
optimization process within the bounds of existing constraints. As we are going to learn soon, we want at
the very least to challenge the assumptions about the existing constraints,
not just to accept them as they are, and if at all possible to bust the
existing constraint in favor of the next constraint. That way we move the whole system forward
to a new level of achievement. However,
the undeniable power of linear programming is as a tool in drum-buffer-rope
analysis. Once a constraint has been
located and managed under drum-buffer-rope, linear programming allows you to
evaluate complex product mix considerations with ease. Even using rough and ready data will
provide ready indications for multiple “what ifs?” The point is that once you know what data
is important and what data is not and you can tailor the model accordingly.
As we
have drawn the diagrams and considered the situation so far, the constraint
has been internal to the system. What
happens when the constraint moves out into the market? Well, firstly, there will still be one
“weakest link” in the internal system even when the constraint is in the
market. As we shall see in the section
on the production application, drum-buffer-rope, the internal constraint
becomes a control point synchronized with the market demand. However, financial considerations may
change. Let’s examine this with the
forth and final part of the P&Q answer here. Once
the constraint is truly in the market place a number of new possibilities
exist for the manufacturing process.
Rather than use drum-buffer-rope, a more recent development called
simplified drum-buffer-rope can be used (33).
This is described briefly at the end of the section on
drum-buffer-rope. In addition the
process may be able to switch to frequency based refill as described in the
section on distribution. We have
successfully derived the global operating measurements of throughput,
inventory, and operating expense. We
now know how to leverage these through knowledge of the constraints to
maximize our bottom line impact. But
how do we ensure local alignment within subsections of our system? We can’t use throughput, inventory, and
operating expense for parts of the system because they are whole system
measures. “Local performance
measurements should not judge the end result, rather they should judge only
the impact the local area being measured has on the end result. Local performance measurements should judge
the quality of the execution of a plan, and this judgment must be totally
separate from judging the plan itself (34).” Let’s
use our own experience of public health waiting lists to examine the two key
local performance measures. What is
the plan in this case? Surely it is to
provide a timely and appropriate outcome.
Well the appropriateness of the outcome will be on a case-by-case
basis but we can investigate the timeliness of the matter. One
aspect of timeliness is how long we have to wait. To answer this we need to know what the
inventory is in a public health system.
How about the patients, they are certainly a major component – that
is, after all, why the system exists.
Let’s say for instance that a certain outpatients’ clinic for
referrals has 50 people on the waiting list at any one time and last year
these people waited on average for 12 weeks, this year we still have 50
people on the waiting list at any one time but they now wait on average for
16 weeks. If you are a politician you
will say the waiting list is exactly the same. However, we know that last year that there
was on average 12 weeks by 5 days per week by 50 people = 3000
patient-days-waiting. In comparison,
this year there are 4000 patient-days-waiting on the list. Is the performance better or worse? It’s worse of course. If we can stop patient-days-waiting from
increasing, or better still reduce it, then we must have improved the
system. How would such a local
performance measure look? Let’s add it
to our diagram.
What
happens then in a for-profit situation?
Well, we can attach the raw material cost to each item of inventory in
the system and multiply it by the days of waiting in a particular subsystem
of the process to obtain total inventory-dollar-days
waiting in that subsystem. Businesses
have all the components of this information already; it just needs a line of
code to produce the result. Another aspect of timeliness is that regardless of how long we must
wait, do we still receive attention at the end of the wait or are we
late. Let’s continue with our
analogy. We have 50 patients on our
waiting list and we assumed that last year our patients were expected to be
seen by a specialist within a recommended guideline of 12 weeks of
referral. Some, however, weren’t seen
within this time-frame. Let’s say that
3 patients were seen after 13 weeks and 2 were seen after 14 weeks. Again we might argue that just 1 in 10
patients were not seen within the recommended guidelines. However, a more realistic measure is that 3
were 1 week late and 2 were 2 weeks late.
This gives us 1 week by 5 days per week by 3 patients plus 2 weeks by
5 days per week by 2 patients = 35 patient/late days. Is this bad? Of course it is, it should be zero. Let’s add this measure to our diagram as
well.
In a
for-profit situation we can attach the throughput value (sales price –
totally variable costs) to each late product/procedure and multiply by the
number of days late to obtain throughput-dollar-days
late. Again businesses have all the
components of this information already; it just needs a line of code to
produce the result. In
addition to subsystems within an existing business we could equally use
either of these measures in individual businesses that are aligned with one
another in a supply chain. By using
inventory waiting and lateness expressed as unit/days we obtain a measurement
of system stability, we can quickly see where the system or a subsystem is
becoming misaligned and therefore take corrective action. Inventory-dollar-days waiting and
throughput-dollar-days late are measures that can be used within all of the
Theory of Constraints logistical solutions, and along with buffer management
they are a vital part of the feedback and control mechanism. We
could have used these measures in departmental example at the beginning of
this page – except that they weren’t treated a sub-systems; they were treated
as a group of stand alone systems.
Would using these local performance measures have made that system
easier to manage and more satisfying to work in? I think so, but to do so requires explicit
knowledge of the system, the goal, the fundamental measures, and the role of
the constraints in order to be effective. Let’s
use an airline as an analogy to make sure that we completely understand the
ramifications of throughput per unit of scarce resource. Let’s use for this example the current
discounted prices for an around the world ticket originating from New
Zealand. The prices are; NZD 2,200 for
economy class, NZD 7,100 for business class, and NZD 9,700 for first
class. These are our products. What is
the constraint? Well for an airline it
must be the seating space per flight as the ultimate constraint. We can’t add an extra carriage like a
train, neither can we shorten it.
Planes come in units of 1. And
usually quite big units of 1 at that.
In fact in this example we will use the main deck seating on a
747-400. If we
treat the flight as the constraint then how do we maximize our profit? We could sell only first class tickets –
and wait for a plane load of first class passengers – maybe once a
month. Clearly on price they are the
most valuable. Or because we could
most easily get a load of economy class passengers we might just forget about
the other two classes and become a no-frills airline. However, at the moment, we assume a
full-service airline, so let’s work with that reality. Let’s
work out the throughput. In order
to know the throughput we must subtract any variable costs. Fuel is obviously a variable cost but
probably commercially sensitive.
However, it is also uniform regardless of class so we can ignore it
for the moment. Let’s assume then that
an economy class passenger consumes, in value, about $200 worth of food. The business class passenger twice as much,
and the first class passenger three times as much. This allows us to calculate the throughput.
So a
first class passenger generates $9100 of throughput per seat, business class
$6700 per seat, and economy just $2000 per seat. First class passengers still look like the
best deal. However, let’s see how many
seats per row we have.
First
class has 4 seats per row, yielding $36,400 per row. Business class has 7 seats per row,
yielding $46,900 per row. Economy has
10 seats per row, yielding 20,000 per row. Hmm, suddenly
the business class passenger is looking like a decidedly better deal. Economy class certainly doesn’t generate as
much as first class. However to be
more accurate yet, we really need to know how many rows of each different
class we can get in. As you know, the
knee room in economy isn’t always generous; you can get more rows of economy
in than either of the other two classes.
Let’s look at the seat pitch then.
In fact
what we have done is converted throughput per row to throughput per row
inch. Of course you can’t replace just
an inch, but it does give us a good idea on the relative earning power of
each type of product (sorry – customer).
Now, the surprise is that a business class passenger is worth almost
twice as much as a first class passenger and 60% more than an economy class
passenger. And a first class passenger
is worth less than an economy class passenger; even though they paid 4 times
as much for the ticket! This
example holds when airline capacity is the constraint – when the constraint
is internal to the system. When the
constraint becomes external – when it moves to the market then a different
set of logic applies. We will examine
this in more detail on the accounting for change page. Of
course airlines are masters at load maximization within these classes,
opening and closing discounted fares according to complicated algorithms
based upon demand and date of departure.
However, look at the original ticket prices. If you were are sales person from outside
of the airline industry, then based upon ticket price, which type of seat
would you rather sell? Which type of
seat would your prefer that your marketing people supported? Now based upon revenue per row inch which
type of seat is better for the airline?
Which type of seat should the marketing people support? Well we
were just playing with numbers. We
should hope there is sufficient throughput to pay for all the operating
expenses (including the fuel).
However, this example does help to illustrate the absolute need to
determine the constraint. Because we
can only make relevant business decisions after we know where the constraint
is. In fact it doesn’t matter what we
do; whether it is machining one-off endmills for cutting the inside casing of
a steam turbine, or converting a saw log into timber, the principle is
exactly the same. Did you
feel at all uneasy at the prospect of the suggested 10% across-the-board
reduction in operating expense in the previous section on the bottom
line? Sure it looked good; it should
have resulted in a 13% increase in profit.
But would we have really achieved that? Was
that unease your intuition telling you that across-the-board cuts not only
cut the fat, they also cut production?
Let’s have a look at this. And
let’s assume that our 10% cut does in fact reduce our output by 10% as well
because it reduces the productivity of our scarce resource – our constraint –
in direct proportion. Of course it
could be worse, it could be better.
90% total – 36% operating expense – 27% raw
material = 27% profit. (27% new profit - 30% old profit) / 30% old
profit = 10% decrease Well
maybe this is a more rigorous way of saying that if we decrease operating expense
by 10% and we cause output to decrease by 10%, then we must decrease raw
material by 10% and profit also. There
remains another question, if you do cut operating expense by 10% and the
market picks up, are we going to be on the ball, or behind the game? Does
this sort of thing really happen? I
think you can answer that question yourself.
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