The Thinking Processes (TP) and uncertainty

Have a quick look at the small cause and effect branch.  Is the logic sound?

Can it be that in reality effects 1-3 are valid, but effect 4 is not?

We can come up with various explanations of insufficiency in the above logic.  For instance, if the clients are not free to make their own decisions, like in totalitarian countries, then could be that the regime prefers something else.  Another explanation might be that the brand name of Product P1 is much less known.

The generic point is: the vast majority of the practical cause and effect connections are not 100% valid.

In other words, most good logical branches are valid only statistically, because they might be impacted by uncertain additional effects that distort the main cause-and-effect.  Actually the uncertainty represents insufficiencies we are not aware of, or we know about them but we cannot confirm whether they exist or not in our reality.  For all practical purposes there is no difference between uncertainty and information we are not able to get.

This recognition has ramifications.  Suppose we have a series of logical arrows:

eff1 –> eff2 –> eff3 –> eff4 –> eff5

If every arrow is 90% valid (it is true in 90% of the cases) then the long arrow from eff1 to eff5 is only 60% valid.

The point is that while we should use cause-and-effect because it is much better than to ignore it, we can never be sure we know!  The real negative branch of using the TP to outline various potential impacts is that frustrated people could blame the TP and its logic and refrain from using it in the future.  This false logic says:  if ([I behave according to the TP branch] à [Sometimes I do not get the expected effect]) then [I stop using the TP].

The way to deal with this serious and damaging negative branch is to institute the role of uncertainty in our life and the idea that partial information is still better than no information – provided we take the limitations of being partial seriously.  We can never be sure that whatever we do will bring benefits.  However, when we use good logic then most-of-the-time we’ll get much better benefits than the total damage.

It’d be even better to consider the possibility of something going wrong in every step we do.  This would guide us to check the results and re-check the logic when the result is different than what we have expected.  It is always possible that there is a flaw in our logic and in such a case we better fix the flawed part and gain better logical understanding of the cause-and-effect.  When we do not see any flaw in our logic – there is still room for certain crazy insufficiency to mess our life and this is the price we pay for living with uncertainty.

The Mysterious Power of Synergy

Synergy means that a system can achieve more, sometimes much more, than the sum of its parts.  This extra power is not easily understood and thus it is difficult to manage.

It is straight-forward to see the value of synergy in sports.  You can build a basketball team by bringing together great players, each excels in one particular role, and let them play and hopefully win.  Does it ALWAYS work?

When it does, one might get the impression that the power of the team is far more than the accumulated level of each player.  This is when the mystery of synergy works.

When it does not work then there is no ‘team’ but just a group of excellent players, each playing according to his own interests.  We in TOC call it: local thinking rather than holistic.  I think it is quite natural that a person thinks and acts based on his own interests.  The only rational way to cause a person to think holistically is to make a convincing argument that synergy does work, in other words the success of the whole would contribute much more to the person than whatever he can achieve by himself.

Theoretically there is a way to create such a clear win-win structure that the interests of every player are exactly the same as the holistic ones.  I understand the theory, but I admit I have not been able to construct such a network of win-wins in reality.  Still, the intuitive recognition that synergy exists in a big way could help in aligning different parts into a holistic system.

Very large organizations use their natural synergy to gain much more value.  We can recognize some of the causes of such synergy, and by that reduce its ‘mysterious’ impact.  When some of the products/services of a giant company get excellent recognition the other services gain recognition as well.  The stability and security radiated by large organizations is a synergy asset and its cause is pretty clear.

However, many other causes for synergy are not all that clear, but this does not mean they do not exist.  A strange way of speech calls ‘chemistry’ the effect where two players play with great understanding of each other and thus generate synergy.  It is funny, on my side, to call a ‘scientific’ name something that is hard to map the cause and effect of.  Still, in reality we see how some product-mix have more impact than others.  One needs to look for the overall characteristics of a ‘package’ to understand the advantage of one supplier on another rather than go to the details of every product.  It is exactly as recognizing a forest rather than trees.

Project-portfolio is a managerial topic that calls for assessment of its synergy.  It means that when we consider a new project there is a need to assess the somewhat vague impact of adding this project to the portfolio and predict the total impact of the whole portfolio on the organization.

As such assessment is mainly intuitive we need to recognize it as ‘partial information’ or basically uncertain information.  We should NOT ignore the synergy impact just because we are unable to predict its exact impact.  While we recognize “never say I know” we should not take the position of “we don’t know”, because we do know something.  We are able to carefully assess the impact of synergy as a reasonable range and thus take the range as part of the decision making process.

Any good Strategy planning has to strive to gain synergy from all the initiatives that are integrated into one effective decisive-competitive-edge.  Synergy is a critical part in the creation of ever-flourishing organization and it requires a holistic view and good tolerance of using ‘partial information’ to guide our decisions.

The problematic relationships between the individual and the organization – Part 2

A common belief is: Many employees don’t want to make all the efforts they are required to make

The point is not so much whether the belief is true, but whether the belief is self-fulfilling, meaning employees try to avoid too much work and efforts because they realize they are not being trusted. When you are not trusted then the objective of feeling good with what you have done evaporates into thin air.

Suppose management succeeds in creating a culture of trust. Would the employees be willing to be loyal to the organization they work for? By ‘being loyal’ I mean do whatever it takes to achieve more of the organizational goal.

Employees come to work because they need the money. This starting point has several ramifications.

1. If the employees think they are getting less than what they deserve – then they become frustrated and hostile towards the organization, which is the opposite of loyalty.
2. As the money is important the employees choose to stay in the organization until something better pops up. This forces them to try hard to be considered “good”, or at the very least “OK”.

Other ramifications are caused by the mere fact that the employees spend large part of their life in their work place:

1. Most employees prefer to “do something” while they are at the work place, and so they usually work willingly according to what is expected from them, unless they have a reason not to.
2. Employees that have the passion to excel look for an appropriate chance.

An important observation:

It is easier for an employee to be loyal to the organization than to the organization to be loyal to the employee.

The organization always looks at the cost and compares it to the perceived value from the employee. However, as we have seen, that value is not easy to assess. It is even more difficult to assess the indirect damage of being disloyal to the employee, as many of the employees become disillusioned and even hostile in a hidden way.

From the above it seems that even when the management trust their employees and is loyal to them there is no guaranty that all the employees would be loyal to the organization. It is enough that a specific employee believes he is underpaid to cause him to betray that trust. And if this is the case then the organization should actively look for signals of low-motivation and disloyalty from the employees.

However, the need for making sure all employees are loyal does not necessarily mean the solution has to be the use of personal performance measurements.

What are the true needs of organizations in assessing their employees?

I can see two such needs:

1. Identify employees that generate damage. Some might simply lack the appropriate capabilities. Others might be ‘rotten apples’ – those who are disloyal. Such people might influence others to become disloyal.
2. Identify potential ‘stars’ – employees who can bring huge value in the future – if they are nurtured in the right way.

All the rest are good employees who bring value when management makes it possible. Is there a point of measuring performance in a more accurate way?

If the organization would maintain a culture of respect and loyalty then the employees will do their best to organization because this is their work – a substantial part of their life. What the organization has to do is to make sure there is a certain code of work and when there is a signal that the code is broken then, and only then, those employees should be chased out.

In some cases, the organization has no choice but to let people go because they cannot yield value anymore. The point is that when this happen management needs to recognize it as its own failure! Management then has to be aware that they need to re-build the trust and loyalty of the employees who are still in the organization to prevent the next disaster.

The problematic relationships between the individual and the organization – Part 1

“Tell me how you measure me and I’ll tell you how I’ll behave.”

This famous citation states an inherent conflict between the individual and the organization.  The individual, according to Goldratt, behaves according to the way he is measured, which is not necessarily to the good of the organization.

Why would an organization measure its employees???

Do you measure the performance of your spouse, children or close friends?  You have certain expectations, which are not always met, but do you look for ways to quantify your expectations?

What is the gain from performance measurements?

One real and important gain is to know whether you need to analyze much deeper how come the results deviate from your initial expectations.  In order to get that objective you need recorded expectations.  Now – expectations are never one clear number – are they?

As a father I expected from my children to achieve good grades from school, say B and above.  Getting a low grade did not call for punishment, but we tried to understand the reason and what can be done next time.  Other parents push their children much more.  Is the push valuable?  Certainly, treating the grades as performance measurements that invoke positive and negative reactions would improve the grades.  The more important question is: would they improve the life of the children?

Do performance measurements improve performance?

Goldratt has shown us how flawed measurements could reduce global performance.  There are three different causes for a performance measurement to radiate the wrong message:

1. They are the wrong measurements.
2. Dependency – the measurement depends not just on my performance, but also on other factors, like the performance of others.
3. Variation. My performance varies. Some of the causes for the variance can be explained by external factors (like headache) and some have no clear explanation.

How significant is the variation factor?  I like to watch sport on TV in order to spot the unexplained variation in the performance of the players.  It is most noticeable in Tennis how the number of “unforced errors” and the “first serve percentage” fluctuate within one game.  It demonstrates that the variation in our ability to do things is quite significant.

When the performance measurements ignore the impact of dependency and variation, the employee distrusts the measurement and is led to do whatever he can to manipulate them to his own sake.

See what we have got so far:  ignoring complexity (dependency), ignoring uncertainty (variation) both are direct consequences of lack of trust.

Trust, or the lack of it, is a critical factor in life as well as in business and most certainly in managing organizations. The fact that the organization does not trust its employees and thus uses performance measurements actually causes the employees to distrust their bosses and pushes them to plan carefully their behavior – against the interests of the organization.

It all starts with the relationships between the CEO and the owners (shareholders and possibly the board). Every CEO likes to make sure the organization achieves the results that please the owners and makes him/her a highly appreciated CEO.

An obvious difficulty for the CEO is to make sure all other employees do whatever they need to do to achieve the results. Thus, when possible, they impose performance measurements to push their subordinates to make more efforts.  Do they really make more efforts? Or do they just manipulate their available capacity according to the specific measurements and nothing else is important?

A simple cause-and-effect tree to showing the rush for using performance measurement on employees

Is there anything wrong with the above logic?

What do you think of entity 2.3 – is it really valid in reality?  Let’s discuss it further to lead us to the direction of a solution.

The balance between Statistics and Intuition

Joel-Henry Grossard has made an important comment I like to share with you and express my view. He wrote:

“However you can have two distributions which have the same average and the same standard deviation, but which are profoundly different when you look at the numbers. The missing factor is time: to know how the numbers are spread over time is critical to decide. Using Statistical Process Control can help.”

Do we know how the variables we look at in our practical reality behave with time?

Let’s consider a process in the shop-floor where we are able to record a lot of data and how they spread over time.  What we get is a time series of results, but that graph represents only one possible spread of the results and it is not a replicate of the real distribution function. Usually we don’t really know the full characteristics of the distribution function.  For instance, if an operator gets tired after one hour and if this tiredness can be expressed in the quality of the output then we should see a certain deviation. But, unless we suspect this could happen there is no big chance that such cause for deviations would be detected.

When the process is fully under our control we are able to confirm that the basic parameters of the process haven’t gone through a significant change. Even in such a convenient case my understanding is that even Prof. Deming did not apply the full power of Statistics, and just went for standard heuristics to establish good-enough quality control.

Once we step out of what is fully under our influence we know even less about the behavior of the surrounding uncertainty. We don’t even know whether all the recorded results belong to the same distribution function.

Suppose a new Harry Potter book suddenly appears. The last book in the series appeared in 2007. What statistical model could predict the number of copies to be sold in the first week?  We do have past results, and they are relevant to a certain degree, but due to the long intermission between the former series and the surprising new book the original distribution function has changed and we don’t exactly know what the change is.  We don’t even know whether the demand will be up or down relative to the last book.

Does it mean we don’t know anything?  Can it really be any number?  We know some of the parameters that impact the demand for the next book.  The reputation of the original series is still high. But many of the past readers are now older and it is not clear whether their interest is still high. Thus, some intuitive estimation can be made regarding the reasonable minimum demand.  We can also estimate how much more demand is reasonable, taken into account the demand for the last book, but what meaning do the previous results carry?  The one conclusion from them is that the last book was not a single incident and the whole series was a big success.  However, the detailed results and how they spread over time do not add much value to the prediction.

I put a special emphasis on the word “reasonable”.  First, we know that sometimes unreasonable things do happen.  But, assuming we do have intuition based on care and experience, most of the time what is happening is reasonable to us.  Our intuition is shaped by many small events and whether they are considered reasonable or unreasonable by us.

This intuition is a source of valuable, but partial, information to guide our decisions concerning common and expected uncertainty.

Partial information is what we usually have that could help us.  Not enough to prevent us from some damaging decisions but good enough to guide us so that overall we’d get much more benefits than damage.

But, when we knowingly ignore the partial information, we cause definite damage.  This is what most organizational policies do: force certainty on uncertain situation, like using one-number forecasts and turn them into sales objectives as prime performance measurements.

I claim that forcing certainty on uncertainty is due to fear from being unjustly criticized.  I like to deal with the cause and effects of the fear from the performance measurements on people and highlight the nasty sides of the relationships between the organization and its employees.  This is, hopefully, going to be my next post, subject to the inherent uncertainty.

The current TOC achievements in handling uncertainty

TOC has always been focused on the common and expected uncertainty.  It just did not generalize in full the global ramifications of its tools to handle uncertainty.  In this post I like to highlight the wider impact that stem from DBR, CCPM and Replenishment.

The critical TOC terminologies that are a key in handling common and expected uncertainty are:

1. Buffers
2. Buffer Management
3. Protective capacity
4. Thin and focused planning

Buffers:  The concept of inserting visible buffers as anintegral part of the plan is, for me, a landmark in managing uncertainty and by “managing” I mean also the behavioral side.  People use buffers all the time to protect themselves, but they have to hide the buffers. The main problem in using hidden buffers is that they are wasted by being always fully consumed because the organization does not recognize the need for buffers.

The visible use of buffers in the planning raises several issues that planners have to consider:

1. What to buffer? Should we spread buffers everywhere or concentrate on specific locations?
2. How should we size buffers?
3. What is the cost of maintaining such buffers? What are the benefits?

Struggling with the above questions force people to recognize the impact of uncertainty and to employ certain key insights from Probability Theory.

Buffer Management:  It is a unique concept of TOC, I don’t know of any similar idea to inquire the actual usage of buffers to guide decisions.  Buffer Management is relevant only to buffers that are frequently partially consumed.  Buffers that are either fully consumed or not at all, like alarm systems or insurance, cannot be managed by buffer management.

The value of buffer management is for two different fronts:

1. Dictating a priority system in the execution phase, striving to achieve all the planning true objectives.
2. Generating valuable feedback on the planning, thus improving the future planning, including the more appropriate size of the buffers.

Protective Capacity:  This is the most revealing concept, as it is in direct clash with the utopia of being able to match capacity to demand and the efficiency syndrome.  The important message is that due to both external and internal uncertainty lack of enough excess capacity hurts the delivery performance to the market.  Note that there is no formula for how much protective capacity is necessary.  Buffer management let us know when one or more resources come close to the protective capacity, but is unable to tell us whether we have too much protective capacity.

Thin and focused planning:  Is a TOC concept even though it was never verbalized as such.  From the five focusing steps we realize that the key planning rule is exploitation of the constraint.  Subordination is about adding buffers to the planning and mainly about execution – making sure the exploitation plan progresses smoothly.  Both DBR and Replenishment use very thin planning, leaving many decisions for the last-minute where the actual impact of uncertainty is known.   CCPM does not fully follow the thin planning direction and it leads to recent ideas, by James Holt and Sanjeev Gupta, of simplifying the CCPM planning.

The above achievements should encourage us all to develop more tools that will allow management to recognize and manage the uncertainty.  I think most managers are aware of the need, but simply are caught within the fear of being unjustly criticized.

A superior level of performing well in spite of significant uncertainty will be achieved ONLY when a decision making process is established that verbalizes the uncertain potential results and lead the decision makers to contemplate decisions that would achieve high gains most of the time, but also take into account that in some cases limited damage will occur.  The emphasis is on ‘limited damage’, meaning the organization is able to tolerate, and thus the potential results considered can be used in the future to demonstrate the validity of the decision at the time.

The problems with “Common and Expected Uncertainty”

Not all the decisions managers have to make are about risk,meaning decisions that might cause a serious loss, but might also cause a considerable gain.  Actually those risky decisions are very infrequent.  While I’m still claiming that the vast majority of the organizations force the managers to be ultra-conservative, the losses from those decisions are small relative to the huge loss from wrong policies dealing with “common and expected uncertainty.”

Take CCPM (TOC Project Management Solution) and ask yourself how come that planning a clear project buffer is such a dramatic new insight?  How come people insist that there is clear time duration for a task?

Eli Goldratt said that organization force certainty on uncertain situations.

The paradox is that by forcing certainty management increases the negative impact of uncertainty. We see projects that take too long and shop floors that process too much inventory.  Many organizations suffer from hazards because of lack of manpower, relatively cheap resource.

The common cause is the concern of every human manager of being blamed of creating “waste”.

The prime example I like pointing to is the use, actually misuse, of sales forecasts.  We know from Probability Theory, or Statistics, that the minimum description of an uncertain variable contains two numbers, usually the average and the standard deviation.   However, the vast majority of the forecast reports, used for various decisions, include only ONE number.

What is the value of one central measure for a forecast when nothing describes the spread around that measure?  If next month sales of Product134 are forecasted to be 10,000 what is the likelihood that the actual sales would be 4,776, 8,244, 13,004 or even 18,559?

Suppose that the magic number of 10,000 comes from assessment of salespeople, is it clear that it represents an estimated average (expected value in the mathematical language)?  Isn’t it possible that salespeople, who do not have any magic power to see the future, state a number they are comfortable with?  If they are measured by meeting sales objectives, that are set according to the forecast, then they would reduce their estimation. But, if they need Operations to provide availability they would inflate the forecast.

I think that there is no way to manage an organization without forecasting!

I also think that Dynamic-Buffer-Management is actually a forecast looking at the combination of sales and inventory and predicts whether the stock buffer is about right.

However, treating a forecast as one number is a gross mistake.  The reliance on one number allows top management to judge their sales and operations people, however that judgment is flawed and the sales and operations managers have to protect themselves from the ignorance of top management.

The overall impact of mishandling the common and expected uncertainty is HUGE.  Management don’t recognize the need for protective capacity and thus look for high “efficiency”, causing people to pretend being very busy, which means they constantly look for “something to do”, regardless whether it creates valueor not

However, protective capacity is truly required in order to maintain enough flexibility to deal with Murphy, as well as with temporary peaks of demand. TOC buffers help a lot to stabilize the flow and by that improve the overall performance, but they do not cover all the areas where people are using their own hidden buffers, causing huge damage:  The hiring process is basically flawed with ridiculous requirements of 100% technical fit instead of requiring learning capabilities; Budgeting processes are flawed carrying no appropriate reserves;  Even the need for maintaining presence in several different market segments is not fully recognized in many organizations.

Is it possible to learn how to deal with uncertainty, particularly the common and expected uncertainty?  The vast majority of the managers have been in a basic course on Statistics, but it does not lead them to handle uncertainty that: does not have clear probabilities, is definitely different than the Gaussian (Normal distribution function), and the samples of similar occurrences in the near past are very small.

The real obstacle for improving the policies, making them a better match to the inherent uncertainty, is getting rid of the utopia of “optimal decisions” replacing it by “good enough” and stop measuring people by numbers which are exposed to both uncertainty and dependencies.

Is that doable? For me that is what TOC is all about.