Mutual Decisions: Checking the Full Ramifications of Uncertainty

The previous post showed the connections between decisions concerning the market, the capacity profile and how to get good answers by checking certain options.

Concept Handwritten With Chalk On A Blackboard.

The example of last post had relatively little uncertainty around it. In most cases any action to enhance the demand is subject to high uncertainty. Other factors like the predicted capacity utilization are also impacted by uncertainty.

Let’s first remember the key question we need an answer to:

Considering the decision on the table would net profit (NP) go up or down?

This means we don’t have to know the exact impact on NP in order to make a sound decision! We need to find out the direction of the impact. We need also to validate that in the worst case the organization would be still viable – it should NOT kill the organization. The practical meaning is to establish a RED-LINE, defining the state the organization would not tolerate and thus no decision should bring the organization too close to that state!

Any prediction of the future is uncertain. However, the valuable intuition of the key executives and key professionals could carefully define the boundaries of what is highly unlikely. What should come out of these assessments is a range of likely results. My suggestion is to check the full ramifications of the two extreme values of the range, provided they are not exaggerated.

For example, suppose that the cost of materials went up by 20%, bringing the T-per-unit down from $20 to only $14 (70% of the original T). The financial people push to increase the selling price by 10% (from $50 to $55) to compensate for most of the increase in cost. The VP of Sales claims such a move would reduce the number of units sold by 20% at the very least, possibly by 50%. She also claims that if the price is not changed then sales would go up by 10-20% because some of the competitors are going to increase the price.

How would YOU handle the case, taking into account the intuition of Sales as there is no other reliable source of information?

My proposed suggestion is to create the reasonable pessimistic scenarios for both alternative decisions, and the optimistic scenarios for both.

Suppose the current T is NT.  Let’s call the new total resulting T: RT

The pessimistic scenario of no change in price would come to: RT = (0.7*NT)*1.1 = 0.77NT.

The pessimistic scenario of increasing the price by 10% (bringing the T to about .95NT) RT = (0.95*NT)*0.5 = 0.475NT

The optimistic scenario and no change in price is: RT = (0.7*NT)*1.2 = 0.84*NT

The optimistic scenario and increasing the price by 10% is: RT = (0.95*NT)*0.8 = 0.76*NT

What should the decision be?

If the pessimistic case is closer to reality then we should not change the price.

If the optimistic case is closer to reality then we should not change the price.

So, the decision is clear.

If we would have conflicting results then the managerial judgment has to take into account the possible loss versus the possible gain. When you consider the loss you need to ensure it does not penetrate into the RED-LINE for the organization. If this happens – choose the other decision.

Note, we did not check the ramifications on capacity of 10-20% more sales. It could change the above results if, and only if, a penetration into the protective capacity of even one resource happens. My assumption is that the current sales are achieved by available capacity, maybe with some overtime that is fully covered by the current T. It might not be the case for 10-20% more sales. So, this part has to be checked, but this post has to be short and simple enough and thus I assume no lack of capacity even for 20% more sales.

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Sales, Capacity and improved Bottom-line

Company G, a small manufacturer of textile, looks into its next quarter. Assuming no major decision is taken the company predicts its total T for the quarter to be $485,465. However, its total OE for the period is $485,000, leaving net profit before tax of only $465. Too close to breakeven, and that is just a forecast!

The sales force got instructions to come up with additional sales – offering lucrative deals to potential clients, as long as there were no concerns for cannibalization. If intuition says some negative impact on other sales might happen, it should be part of the proposed change and the T and load calculations should reflect it. It turned out that early negotiations seemed good. Several new options were brought before management.

The graph of the predicted load looked like that:

SSS0

Each row presents the relative load versus capacity of one critical resource. The Blue/Violet part shows the regular sales. The new special sales contribution to the load is represented by the pink part. The Dye, the current ‘weakest-link’, is now loaded to about 91% of its available capacity.

The financial state is depicted by this table:

SSS001

It seems the additional load uses only available capacity. Even the Dye resource does not penetrate the 95% line, which represents the limit for the protective capacity of the weakest link. There is no need for any delta(OE).

The meaning is that by pushing the sales force the company succeeded to increase its predicted bottom-line from $465 to $45,965.

This means that realizing the amount of truly excess capacity in Operations should lead to putting pressure on Sales to come up with more opportunities. Of course, we require that the new opportunities should generate higher overall T, taking into account that sometimes new sales cause reduced sales of other products.

Another requirement is to make sure that the capacity profile is valid for delivering all the sales according to the market requirements. That means having enough protective capacity on all resources.

Looking at the improved situation for Company G reveals that even with the additional sales the management of that company should continue to push their salespeople to bring more sales.

The weakest-link, the Dye Resource, has still 3-4% of load to reach its protective level. All the rest have much more. Sales of products that require more capacity of the other critical resources than from the Dye might be lucrative depending on their T.

The main concern is that the additional sales might create too much load.

When we reach this stage then two different actions should be contemplated:

  1. Using the capacity buffer to increase available capacity. The financial calculations should consider the delta(OE) making sure it is smaller than delta(T).
  2. Reducing some other sales that yield less T relative to the capacity of the overloaded resources.

Suppose that after checking the above additional sales and realizing there is still room for more, additional ideas were checked, but then the following problematic capacity profile emerged:

screen101

The management of G realized they cannot promise delivery to all their potential sales with this overload. So, something had to be done about it, giving up some sales and being ready to pay more for additional capacity, under the condition that eventually the profit would be significantly higher.

Going carefully through both types of actions. Reducing some sales, where the VP of Sales confirmed it did not disrupt any commitment, and also using some additional capacity, which caused delta(OE), the final capacity profile, using fast computerized calculations, looked valid:

screen301

And the financial table became:

S01

In other words, pushing Sales to come up with new opportunities, coupled with careful analysis of the capacity.  Then reducing some opportunities that required too much capacity of the overloaded resources. Then also using additional capacity to provide valid capacity profile, the bottom line went up from mere $465 to $130,065!!!

When Sales, Operations and Finance key executives sit together in a mutual-decision meeting to plan their activities for next month, or quarter or even a year, supported by fast computerized calculations, superior decisions with huge impact on the business are made.

Protective Capacity and Capacity Buffer

Linkages, differences and the strategic impact

OLYMPUS DIGITAL CAMERA
Shelf space and its protective capacity

It is impossible to match capacity to demand!

The above is a major TOC realization, which is the key for challenging all current cost-account methods.  In a previous post on “Non Linear Behavior of the Cost of Capacity” I have stated the three main causes for being unable to use (drawing value from) all available capacity.  The first cause is the need to maintain protective capacity to ensure adequate delivery in the face of dependencies and statistical fluctuations.

What dictates the right amount of protective capacity?

TOC does not have a formula to answer the question!  When the protective capacity of just one resource is penetrated then some time later the amount of red-orders would go up sharply. So, looking back to the recent load could identify the amount of excessive load that threatens the stability of the delivery performance.  The intuition of the production manager, being exposed to such cases, should be used to assess the practical limit on the load that will be used for operations planning.

What impacts the level of protective capacity?

Three critical factors:

  • The acceptable response time in the market.
  • The level of fluctuations of demand within the acceptable response time.
  • The size of time or stock buffers used.

The impact of maintaining the appropriate protective capacity on the global performance of the organization is very significant. It impacts the following issues:

  1. Establishing the service level that matches the commitment of the organization to its customers.  This match should be part of the organization global strategy.
  2. The vast majority of I and OE of the organization is spent on maintaining adequate capacity to support sales.  The protective capacity is crucial in deciding how much capacity to maintain.
  3. The short-term planning of Sales and Operations has to consider the limit imposed by the protective capacity.

While we cannot match capacity to demand, we like to draw the most of the available capacity to generate high throughput relative to I and OE levels.

My insight is that Operations has to recognize two different protective capacity levels:

  1. For the weakest-link resource!  Even when the weakest-link is an active CCR, being able to satisfy the market is still a must that forces leaving certain protective capacity on the CCR, providing flexibility to deal with Murphy  and urgent requests.
  2. For all the other resources, making sure they have more protective capacity to support the CCR and all the other urgent needs. In other words, properly subordinate to the exploitation planning.

Definition: Capacity buffer are all the fast means to increase capacity for a cost.

Overtime, extra shifts at night and/or during weekends, using temp manpower and outsourcing are included in the capacity buffer.  The main objective is to protect from peaks of demand.

Capacity buffers are critical for growth where instead of investing in much more available capacity the organization is ready to pay-per-usage of extra capacity. It should definitely be an integral part of the Strategy of the organization.

Capacity buffers can be used as protective capacity.  It allows 100% utilization of the resource and when additional capacity is required the capacity buffer is used.  It does impact T,I and OE calculations because any usage of a capacity-buffer causes additional truly-variable-cost (TVC) and by that reduce the Throughput-per-unit.  I think, by the way, that it is much more practical to treat those additional TVC as delta-OE, but that is another discussion for a different post.

Maintaining capacity buffers has to be part of the strategy planning and buffer-management should be used to monitor their consumption.  Any capacity buffer has a limit.  Thus, when too much of the capacity-buffer is used a red-signal should be raised.  As long as the organization does not exhaust a specific capacity buffer no red-orders are created by it, but once it is exhausted then it is question of time until red and black orders would appear and then it is too late to quickly fix the situation!

The concept of capacity buffers is new to TOC.  It impacts our understanding of what is an active capacity constraint (CCR), and it has a big impact on Throughput Accounting.

Any comments, questions or reservations?  Please, let’s talk about it.

A concise history of constraints

Magnifying glass focusing on the weakest link of an iron chain isolated on white background

A recent discussion on what is the appropriate TOC definition for ‘constraint’ leads me to state some historical facts that highlight the development of Goldatt’s approach to constraints.

Prior to the Theory of Constraints (TOC) the breakthrough idea was to distinguish between bottlenecks and non-bottlenecks.  The definition of a bottleneck was simple: “The load placed on the resource is more than what the resource is able to do.” Thus, a bottleneck is always a resource.

The term ‘constraint’ was defined “Anything that limits the system versus its goal”.  It was conceived to answer three significant limitations of the term ‘bottleneck’.

1. When all resources have enough capacity to process all the demand then there is no bottleneck. However the system is able to do more.  Thus, looking on the market demand as a ‘constraint’ is quite valuable.  It allowed managers to understand that there is no excuse not to ship everything on time.

2. Being a bottleneck does not ensure being a constraint. There might be another bottleneck with even more load.

3. We might have a true resource-capacity-constraint (CCR) that is not a bottleneck.  While on average there are idle times, in other times the queue behind the CCR is so long that some potential demand is lost.

It was realized from the start that the constraint limits the throughput (T) of the organization.  Goldratt even played with the idea of introducing the term of ‘inventory constraints’ referring to trouble-makers that force the organization to maintain more WIP.  He backed off this term to keep the simplicity.

The real power of the term ‘constraint’ came through the paradigm that an organization cannot have many constraints.  Dependencies coupled with statistical fluctuations do not allow interactive constraints in the chain. This realization led to the conclusion that the shop-floor can handle only one constraint without creating chaos.  In 1989 Goldratt wrote The Haystack Syndrome and presented a rather complicated algorithm to handle multiple constraints.  The whole development of the ideas was set around capacity constraints.  The chain analogy, where there has to be one, and only one, weakest link in the chain was widely used. Thus, the default for a constraint was lack of enough capacity of a resource.

Limited capabilities, like being unable to produce top quality products, were not considered constraints.  Limited capabilities are less exposed to statistical fluctuations.

The wide definition of the term constraint did cause problems.  People used to say that the constraint lies between the eyes of the CEO.  Flawed policies, especially policies concerning efficiency, were called ‘policy constraints’.  So, the idea was that the system is limited by a capacity constraint, and failing to exploit it is due to policy constraints.

The full set of TP (thinking processes) was developed in 1990. Effect-cause-effect trees and the cloud existed before (even before the 5fs) but not the other tools we know today.  The definition of the CRT raised the notion of the core problem – the conflict (cloud) that causes all the undesired-effects.  Resolving the conflict by challenging a basic assumption behind the conflict would push the organization to a new level of performance.

So, is the core-problem the real constraint?

It remained an open question for a while.  Core problems touched upon local versus holistic thinking, but also on behavioral patterns and opened the door for re-evaluating the value the organization brings to the market.  The core-problem could also challenge the paradigm why do we exploit a CCR rather than immediately elevate it.

Fact is: we did not ask ourselves these questions in the 80s.

Goldratt publicly regretted calling flawed policies “policy constraints” sometime in the 90s, explaining that policies should be eliminated and not exploited and subordinated to.

A major development in the TOC thinking came around 2003 with the idea of the Viable Vision.  Suddenly the way to improve an organization did not come through elevation of a capacity constraint and even not through challenging the conflict behind a policy-constraint.  With the term “decisive-competitive-edge” the TOC thinking has realized the need to challenge the value the organization offers to its customers.  The core idea was to answer a need of the customer in a way no other competitor can.

Explaining how come the VV did not care what is the constraint, Goldratt spoke about two different changes.  One is minus-minus – you identify something that is not right (minus) and you change it (minus of the minus) and a plus-plus change where you take a big step towards the “pot-of-gold”.  When such a step is taken one needs to carefully re-think all the conditions that would be sufficient to bring the organization to growth along the “red curve”.  Lack of capacity of a specific resource becomes a triviality that needs to be eliminated.  Many other potential constraints would be elevated long before they become constraints.

Food for thought?

Mutual decision-making process

Part 3 of a series on using T, I and OE for key decision making

Men shaking Hands Closing a Deal

In the last post I showed the need to have inputs based on intuition for making sound decisions.  Thus, for any structured decision making the involvement of people with the best relevant intuition is absolutely required.

This is not enough.  There is still a need to check the wider ramifications of the decision at-hand considering the various intuitive inputs.  This check has to be based on logic, serving both as an intuition-control mechanism and being able to look at the bigger picture.

There is a known managerial practice where the top manager calls his people to a meeting, lay down a decision to consider and asks every one of the participants to voice their view one at a time.  In the end the top manager states HIS opinion and this is the decision to be acted upon.

While that practice ensures everyone has an opportunity to present his/her view and intuition exposing the top guy to the inputs, it lacks a critical element: logical analysis of the full ramifications of every alternative!

Some of the frequent, but very basic, decisions every company has to make are about its product-mix and capacity.  Suppose the following decision is now considered:

Currently the company sells two different chocolate packages containing the same basic product. The idea is to sell a much larger package for a reduced price per one unit of product.

The intuition of the sales people is required for the following inputs:

  • What might be the pessimistic and optimistic sales of the new package?
  • By how much would the sales of the other two packages be impacted?
    • We can be reasonably certain the sales of the other packages will be reduced – but by how much?
  • Would other products, somehow similar to the above product, face reduced sales?

Given the above intuition and simple calculations the impact on the total T can be derived – both according to the pessimistic and optimistic estimations.

One more issue needs to be resolved:

Do we have enough capacity to sustain the possible increase in sales, especially according to the optimistic assessment?

It is enough that we’d lack capacity on just one resource to invalidate the above T and OE calculations.  We need also to understand that by “lack of capacity” we also consider the case that on average we do have enough capacity, but lack capacity at specific points in time causing delays to the market.  We call “protective capacity” the amount of excess-capacity that is absolutely necessary for keeping the delivery performance in “good-enough” state.  When the protective-capacity is penetrated there is damage.

How much protective capacity is required?

Eventually we need the intuition of the key people in Operations to assess the answer.  There is no TOC formula determining the right amount of protective capacity.

Calculations can easily depict the load on critical resources generated by the assessment of the demand.

If there is enough capacity then the calculated total T, with and without the new package, is all the support management truly needs.

If one or more of the resources lost their protective capacity then the management team has to consider quick ways to increase capacity, or find products where it is possible to reduce their sales (maybe by increasing the price).  Again we need the intuition of sales and operations to make sure the solution is doable.

What might happen with the decision making is that while the optimistic assessment brings very nice addition to the profit, the pessimistic scenario shows a loss. We expect that if making much higher profit is more likely than the having a relatively small loss then accepting the new idea is the right decision. However, one more point needs to be checked.  Small losses might accumulate to the point it endangers the organization.  The current state of cash-flow plus the intuition of the finance guy should be part of the mutual decision process.

Mutual Decision Making Process is a managerial must. Such a process has to use the intuition of key people as legitimate and necessary inputs.  Then data processing and logical analysis would lead the management team to make sound decisions.

Common sense – combining intuition and logic

We all know that common sense is not common at all, especially within organizations that have the ‘optimization’ culture.  Common sense tells us that reaching optimization is an illusion, which drives damaging behaviors and keeps us far away from even a good enough state.

What is the common sense way to assess the worthiness of a new idea?

The first common sense question is:  what information is required to assess the idea?

A little girl playing real chess in competition. Black and white photo. Concentrated kid. Power of concentration ** Note: Soft Focus at 100%, best at smaller sizes

Reminder, Goldratt defined information as “an answer to a question asked. In a way it means that there are some things we need to know.  So, when we have to make a decision there are several inputs we look for – and these are the necessary information items.

Example:

On behalf of your organization you look for a vendor for office supplies.  You talk with the representative of a large office supplies company and also with the enthusiastic owner of a new office supply business.  The large company rep. is a tired and not-too-bright fellow that just cite the normal sales-pitch text.  The owner of the new business is definitely brilliant, and your intuition tells you he is going to be successful.

What information/answers-to-questions you have to look for?

  1. From whom you’re going to get better overall deal?
  2. From whom you’re going to get better overall service, especially better response to any urgent request?

The first question gets a precise numerical answer.  Suppose the new business offers a cheaper price of 4% for the first 6 months.  After that the prices would be the same. Let’s also lay down the data that the total expense on office supplies in your organization comes to .94% of the turnover.

The second question has to use intuition, as any question about the future has no precise number and I doubt whether you find any valid statistical model for this specific question.

Your intuition, based roughly on your life experience plus some emotions and biases, tells you the new business is going to give much better service.  You don’t expect from a large business to do “favors”, but a new business with the wish for future growth is more open to respond to special requests.

Decisions are certainly impacted by emotions, and in this case the emotion and the intuition go hand-by-hand for making the new business.

So, is the decision obvious? 

Here comes the important role of applying logic as a critical control mechanism and a mean to look at the bigger picture.

What might be the damage from failing to serve at the required level and is it likely to happen?

Large suppliers might miss items here and there.  However, we do expect them to fix those in a day or two.  A new business might face more difficulties, especially when the new business tries to grow too fast, like exhausting their resources and possibly suffer from low cash-flow. They could also suffer from less expertise in the area.

Let’s now inquire the question: what is the size of the damage and to whom?

Most organizations do not suffer too much from incidental lack of office supplies.  However it creates a hassle and when there is a hassle there is a person who is responsible for the hassle. So, while the real impact on the performance of the organization is relatively low, the well being of the decision maker, the common-sense decision, is to take the safer alternative, which is the larger supplier!

The need for safety, in this case, is usually stronger than the very little impact on the cost. Well, this is my intuition even for organizations that are in the Cost World.

My general observation

Decisions involve emotions, intuition and logical analysis.  To my mind the emotions have negative impact on organizational decisions.  Intuition is critically necessary for the main information inputs.  The final decision has to look at the bigger picture, consider the ramifications of the inputs on other aspects of the bigger picture, and for that you need logical analysis.

Is it really an opportunity?

Part 2 of a series on using T, I and OE for key decision making

Opportunities present themselves in various ways.  Only seldom we see an opportunity which is so good that there is no point asking any more questions.  Most of what looks like a potential opportunity comes with the doubt embedded in: is it really an opportunity or a trap?

Opportunity Concept.

A typical managerial conflict happens when Sales proposes a promotion, offering several products for a certain price reduction.  Sales managers believe this would significantly enhance the sales of those products next month, and this belief is backed up by past experience.

A promotion creates huge pressure on the shop-floor, reduces the sales of other products and mainly reduces the sales for a certain period after the promotion is over.  Yet, sometimes the extra revenues (minus the variable costs) generated, especially selling to new clients and gain their future purchases, more than compensate for the damage.

  • How can we truly check the net financial impact of a promotion?
  • How can we check the financial impact of penetrating into another market segment?
  • How can we check the financial impact of launching a series of new products?
  • How can we check the financial impact of purchasing a new production line as an elevation of our current capacity constraint?

We are aware that cost-per-unit is not the right tool to support sound decisions. So, how should we make such decisions?

The most straight-forward way is to assess the financial impact of the decision-at-hand on the bottom-line without relying on some funny ‘per-unit’ fabricated measures.  It looks quite difficult objective due to the complication of the various expenses.  However, when we look on the decision as an optional addition to the current level of sales we can see two clear factors that simplify the situation:

  1. The change in the incoming flow of money: the revenues from the change in sales, both the additional sales and possible loss of other sales, minus the truly variable costs of those sales.  This is what we call Throughput (T).
  2. The change in the outgoing flow of money (all the other operating expenses called OE). Note, those additional expenses are all due to the required changes in the available capacity!  This insight was revealed in the previous posts about the behavior of the cost of capacity.

What we get is:   Delta(P) = Delta(T) minus Delta(OE)

Delta(P) is the change in net profit before tax.  For the decision-at-hand we like to know whether delta(P) is positive or negative.

What information we need in order to get a good estimation of the above equation?

One obvious problem is the impact of uncertainty, which includes everything we don’t know at the time of the decision.  We should come back to this issue in later posts.

From the general direction described so far the first step has to define the current state of the organization, as we like to evaluate the difference between the state with the additional decision and without.

There are two main categories of information describing the current state:

  1. The current sales. The items sold, their respective quantities, prices and truly-variable-costs (mainly cost of materials).   We can then calculate the generated throughput (T) per item and the resulting total T – the flow of incoming money.
  2. The available capacity and the load generated by the current sales.
    • In order to calculate the load we need to know how much capacity, for every resource, is required for every product sold!

Then we need the following categories of information for every new opportunity / deal / idea:

  1. The new sales / T to be generated by this idea, including longer term impact
  2. The impact of the new sales on the current sales – would some current sales be reduced?
  3. The updated load versus capacity – do one or more resources lack enough available capacity?
  4. When one or more resources lack capacity what special options are open?
    • Purchasing additional capacity for extra cost (how much?)
    • Reduce some sales, provided it can be practically done without tampering with other sales!

Critical questions for advancing ahead:

  • Is it possible to gather all the above information?
  • How long into the future we need to look in order to make a decision?
  • How can we handle many different opportunities for the same time frame?
  • How do we consider the impact of uncertainty?
  • What is the structured process to make a sound decision?
  • Is it too complex? If so, can we simplify it without distorting the decision process?