Impact of more than one Constraint - 5

Alan Barnard (at the 2006 TOC-ICO conference) asked the question whether the simple Throughput per Constraint Unit rule is valid with 2 (or more) overloaded resources. Alan used Eli Goldratt’s P-Q thought experiment for his discussion. His question is important because it is common to see businesses reduce ‘excess’ capacities to balance (or almost balance) capacities. The practice often results in two) or more concurrent constraints or ‘almost’ constraints. Since 2006 I have observed several factories that wonder why their output collapses below the theoretical capacity of their (almost) balanced lines.

I plan to show that that the Throughput per Constraint Unit rule continues to be valid using the same P-Q thought experiment. I also want to discuss this result in relation to the real World – how should companies manage resource capacities.

I would like readers to follow Eli Goldratt’s recommendation that they solve the problems – before I provide the solution and before my discussion of results. The learning experience will be greater and readers should be better able to discuss and critique my conclusions. If you are familiar with the thought experiment you can jump to the second part of this article.

The key part of the article comes at the end when the solution used in the P-Q thought experiment is discussed in relation to REALITY. The thought experiment should not lead managers to an easy solution. The tool is useful but requires thought and care.

 

Reality

 

As Eli Schragenheim[1] says, “The problem is not mathematical, the problem is the assumption reality is linear.” AND, a second problem is the assumption that “reality is deterministic”.

 

The real World is of course quite different from our thought experiment. In a real situation, customers will not allow their suppliers to dictate what they buy. Customers will not wait to buy Q because we want to make sure all the P demand has fulfilled (the original P-Q experiment). In reality we are unlikely to ever achieve the optimum. This is especially so when you consider that a business does not sell just 2 products, but more likely in the hundreds if not thousands of products. So forget about ever reaching the optimum.

 

BUT, is it still possible to move sales in the direction of better constraint utilisation?

 

In a real example, a business protects their engineering constraint by favouring the sale of standard products requiring no engineering. Their second favourite products are those that require only a small amount of engineering. Their annual sales have always been, and still are, limited by engineering. However, since their sales focus has changed the maximum possible annual sales have increased and, in turn, the business has become more profitable. Added sales are realised without the need for added to operating expenses. All that happened is sales of those items that do not need (much) of the constraint have increased significantly – without creating a new second (interactive) constraint.

 

Another example of using the Throughput per Constraint Unit as a tool to help decide what to sell is the Aramid fibre business (Kevlar®, Twaron®, Technora® are examples of brand names). A wide variety of aramids are produced with different structures and different fibre strengths (different decitex or weight per 10’000meters). Not only do many different varieties exist; aramids are also used in many different applications like sailcloth, ropes, filtration, tires, brakes, protective clothing (bullet proof vests) and more. Prices will vary from application to application and, of course, the price per unit of weight varies according to the decitex of the product. Since the various applications are so different, the sales and marketing organisation is divided into profit centres – each competing for supply. All this would be no problem until the factory is sold out and product must be allocated to customers.

 

A business can allocate fairly – give everyone the same relative amount less than they need; a difficult thing to do since clients catch on quickly and will order more to protect their business. The aramid supplier can also look at his product line and favour those products with the highest Throughput per Constraint Unit. (There is a fair chance that the most favourable or unfavourable products are not those with the highest or lowest margins.) He can also use the situation to target those customers that pay the lowest price in terms of Throughput per Constraint Unit and raise prices in those markets; higher prices tend to reduce demand or the client realises that, higher prices are justified.

 

The sales organisation needs to know the situation in relation to Throughput per Constraint Unit. They cannot switch away from clients and markets or raise prices as easily as I write this, but they can have a tool to help decide what they should do; where they should focus their sales and pricing efforts for greatest benefit.

 

Throughput per Constraint Unit is a tool to help decide what to do. Other considerations are part of the decision – including things like the importance of certain clients. When multiple profit centres are involved the tool can help resolve which of the profit centres should get preferential supply.

 

Eli Schragenheims work to develop a decision support system may well become the tool for business managers to use. (see footnote.)

 

Conclusions

 

1. The simple Throughput per constraint unit does not always work, so be very careful (think carefully) when you believe you should be using it.
2. The 5 focusing steps remain an excellent guide to manage a business. They do not absolve management from some critical thinking about how to approach initiatives to increase sales or to improvement operations.
3. Many companies, by trying to balance capacity for cost reasons, will often leave a lot of money on the table. Judging how much money is left on the table is not an easy task. It is easier to decide to have just one constraint and to decide where the constraint should be. The lost sales, Throughput and profit due to balanced capacity can far outweigh the additional cost for just one constraint or even the additional cost to move the constraint into the market.
4. Throughput and the impact of decisions on Throughput should take precedence over thinking about and taking actions to manage (reduce) inventories and/or cost. (A business is here to make money, not to save cost. If you want to save cost, do not start a business!) BTW – that Throughput should take precedence does not mean inventories and operating expense are unimportant.
5. To think about what to sell (what to favour when selling) is a discussion that involves most of a business. Finance and controlling should lead the discussion. Sales and production are key participants. The managing director should probably also participate – after all some pretty key decisions will be proposed and made during such a discussion. To do this, scenarios must be built based on experience, knowledge and intuition about the constraining elements and the direction and opportunities in market demand. Such scenarios can be built and compared (see Eli Schragenheim’s work with his DSTOC software).
6. So far the sales and operations planning process was not mentioned in the article; but this process, if it exists in the company, is a good place for such discussions. A good place as long as the appropriate participants are present, as S&OP design says they should be.

 

---

[1] Below are 3 article titles in relation to Eli Schragenheims thinking and his work in the area. They are found on his blog (https://elischragenheim.com/) that is well worth reading. Look for the following article titles: 1.) The Non-Linear Behavior of the Cost of Capacity; 2.) Is it really an opportunity? and 3.) The TOC Key Decisions Support (DSTOC).

After the portage around one of the waterfalls on the Manicougan River. ca. 1958 

Impact of more than one Constraint - 4

 

Alan Barnard (at the 2006 TOC-ICO conference) asked the question whether the simple Throughput per Constraint Unit rule is valid with 2 (or more) overloaded resources. Alan used Eli Goldratt’s P-Q thought experiment for his discussion. His question is important because it is common to see businesses reduce ‘excess’ capacities to balance (or almost balance) capacities. The practice often results in two) or more concurrent constraints or ‘almost’ constraints. Since 2006 I have observed several factories that wonder why their output collapses below the theoretical capacity of their (almost) balanced lines.

I plan to show that that the Throughput per Constraint Unit rule continues to be valid using the same P-Q thought experiment. I also want to discuss this result in relation to the real World – how should companies manage resource capacities.

I would like readers to follow Eli Goldratt’s recommendation that they solve the problems – before I provide the solution and before my discussion of results. The learning experience will be greater and readers should be better able to discuss and critique my conclusions. If you are familiar with the thought experiment you can jump to the second part of this article.

The key part of the article comes at the end when the solution used in the P-Q thought experiment is discussed in relation to REALITY. The thought experiment should not lead managers to an easy solution. The tool is useful but requires thought and care.

Interactive Constraints (like machines B and D in our example)

As we have just seen interactive constraints in my little PQ factory cut the maximum possible profit from 300€ per week to just 120€ per week (that is a 60% drop!). This is what interactive constraints can do to your business – they interfere with your capability to get the most from your most constraining resource.

Cost and efficiency pressures cause many business attempts to “balance” capacities – make all resources have about the same capability. This effectively introduces a second and potentially even more constraints into a production system. For most businesses, a collapse of capacity, is the surprising result – they cannot achieve even the original constraint’s capacity. Sales and profits are damaged. Not only do sales and profit suffer because current demand is not met; customers, because of poor delivery performance, leave for the competition, a much longer term loss and probably a much greater damage. 

To maximise profits and profitability most resources must have sufficient protective (or buffer) capacity so that capacity constraints cannot interact to damage the most constraining resource’s capability. Just one bottleneck is already one too many! Because, no matter what the supply chain does – when a demand spike occurs the company must either lose the added sales represented by the spike, or the company must promise delivery it cannot physically do. If a company promises what it cannot do, then the longer-term damage of lost clients will, sooner or later, happen.

To balance capacities is a ‘policy’ (or simply the way a company works). This ‘policy’ is a kind of (fake) constraint because the policy limits how much money the business can make. In such policy constraint cases; it is NOT this (fake) constraint the business must decide how to exploit (the 2^nd step of the 5 focusing steps). The first step must be: change the faulty policy, including the faulty assumption that caused the business to balance capacities in the first place. Change the hidden assumption that resources are independent (operate in isolation) and therefore do not impact other (production) resources. A further assumption, not evident in the P-Q experiment is that resources are not subject to variability. Variability enhances negative effects caused by interactive constraints – if one breaks down the other constraints can easily be starved of work.

Observe this from the point of view of Lean and ask yourself this question: Does it make sense to reduce capacity/capability to balance capacity? Damaging your customers (because you cannot reliably supply) is a huge waste. Think about Lean as focused on maximising Throughput (and profit) and not focused first on minimising cost (waste). Consider lost Throughput as part of the waste you want to eliminate. The obstacle is managers do not view lost Throughput as waste. It is impossible for managers to put a “single definitive” number on Throughput waste – it’s an uncertain number dependent not only on what the company does, but also on client demand. On the other hand, cost ‘saved’ by firing a resource is a number you can easily determine – cost per person is in the ‘system’. (Never forget the impact firing a resource has elsewhere in your factory (because it can create interactive constraints). Also, remember how employees may react to the firing of their colleagues and friends to ‘balance’ capacities. How well will the remaining employees be motivated to help improve the business in the future?)

Observe this also from the point of view of Agile. We know that demand is uncertain, and often very uncertain at the article level. Ask yourself the question: Does it make sense to operate so close to capacity that any small spike in demand must be left to the competition to fulfil? Alternatively, does it make sense that we make promises to customers we cannot keep? To be agile means to be able to capture all demand our uncertain World sends our way. To do that we must be able to respond and capacity or capability is part of the answer.

Here is a second question: Does it make sense to keep a certain amount of protective capacity (“free” capacity) to be able to respond quickly? Protective capacity makes a company more agile, protects current Throughput and gives the business a better chance to gain new (more) Throughput.

You want your business to be Lean, but never anorexic. Lean means enough reserves to respond quickly and correctly to the changes our environment throws at it? Lean means having the stamina to win against your competitors. Anorexia will not do it.

This was some thoughts about interactive constraints and protective capacity. You may want to check out Eli Schragenheim’s blog for more about the importance of capacity buffers.

Manicouagan River Canoe Trip (1958?)  we slept under the canoes on that island … to avoid the black flies. The river is in Québec, islands like the one above are under water due to the Hydroelectric dams built since then.

Impact of more than one Constraint -3

The P - Q thought experiment with 2 constraints

 

Alan Barnard (at the 2006 TOC-ICO conference) asked the question whether the simple Throughput per Constraint Unit rule is valid with 2 (or more) overloaded resources. Alan used Eli Goldratt’s P-Q thought experiment for his discussion. His question is important because it is common to see businesses reduce ‘excess’ capacities to balance (or almost balance) capacities. The practice often results in two) or more concurrent constraints or ‘almost’ constraints. Since 2006 I have observed several factories that wonder why their output collapses below the theoretical capacity of their (almost) balanced lines.

I plan to show that that the Throughput per Constraint Unit rule continues to be valid using the same P-Q thought experiment. I also want to discuss this result in relation to the real World – how should companies manage resource capacities.

I would like readers to follow Eli Goldratt’s recommendation that they solve the problems – before I provide the solution and before my discussion of results. The learning experience will be greater and readers should be better able to discuss and critique my conclusions. If you are familiar with the thought experiment you can jump to the second part of this article. 

The key part of the article comes at the end when the solution used in the P-Q thought experiment is discussed in relation to REALITY. The thought experiment should not lead managers to an easy solution. The tool is useful but requires thought and care.

Most of us ‘solve’ the problem without much thinking. It’s like a simple arithmetic problem from grade school. From groups we usually get quite number of ‘wrong’ answers – some from arithmetic mistakes, some from faulty thinking. Below you will find the answers from inadequate thinking and the explanation for the ‘right’ solution.

The only change in my little factory is the amount of time needed at D to produce 1 P. The time has increased from 15 to 25 minutes. Check it for yourself - both Band D machines have insufficient capacity to produce all the weekly demand.

 

Below is the table showing how many minutes of each resource would be required to produce all the P and Q demand.

 

Our simple rule to use Throughput per constraint unit is in difficulty – we have 2 constraints. But B is still the most constraining unit so lets try the rule. The table below shows the result.

The rule does not seem to work. But we do have the more important rule that tells us to decide how to exploit the constraint (here we choose B, the more constraining resource). But to consume all the valuable B minutes we must have at least some D capacity available. So, we plan to sacrifice some P sales to liberate D capacity and make Q sales possible and to consume the primary (B) constraint’s capacity. B has 960 minutes of capacity available - enough to produce 32 Qs. To produce 32 Qs, I need 160 minutes; I must sacrifice 7 Ps.  BUT Sacrificing 7 Ps gives me 15 additional B minutes. I cannot use these to produce at the B machine (I need at least 30 minutes at B for 1 more Q).

Let’s sacrifice 8Ps and produce 36 Qs to consume all of B’s capacity – will our profits improve? Our secondary constraint (D) will still have 20 minutes of unused capacity after producing the 36 Qs.

 

This looks good; but we still have 20 minutes of D capacity left with which we could produce up to 4 Qs. Lets produce just 1 more Q, which requires us to give up 2 Ps, but might increase profit.

Not quite as good. 1 additional Q delivers 60€ more Throughput while giving up 2 of P costs 90€ Throughput – net we lose 30€ with every additional Q we produce. Not a good way to go. So lets try the other way – produce 1 more P.

Also, not so good since we gain 45€ from 1 extra P but lose 60 from 1 less Q (net we lose 15€); AND we have 15 minutes of unexploited B left over. These 15 minutes would be enough for 1 added P. But by producing 1 P, we need 25 minutes of D capacity, which we no longer have. It looks like we have found the maximum profit possible, 120€/week.

By introducing a second constraint into the P-Q experiment the maximum possible is cut by more than 50%.

The rules to followed were

1. Find the constraint (the most constraining resource).
2. Decide how this resource is to be exploited.
3. Subordinate everything else to that decision.

If we follow these 3 rules we should always find our way to the best mix to maximises our profit. (Alan Barnard used linear programming to find the same result.

In the next post I will discuss the impact of interactive constraints - like B and D are.

 

 

Fall in the Canadian sub-arctic - near Baie Comeau Oct. 1955

Impact of more than one Constraint -2

Solving the P – Q Experiment

Alan Barnard (at the 2006 TOC-ICO conference) asked the question whether the simple Throughput per Constraint Unit rule is valid with 2 (or more) overloaded resources. Alan used Eli Goldratt’s P-Q thought experiment for his discussion. His question is important because it is common to see businesses reduce ‘excess’ capacities to balance (or almost balance) capacities. The practice often results in two) or more concurrent constraints or ‘almost’ constraints. Since 2006 I have observed several factories that wonder why their output collapses below the theoretical capacity of their (almost) balanced lines.

I plan to show that that the Throughput per Constraint Unit rule continues to be valid using the same P-Q thought experiment. I also want to discuss this result in relation to the real World – how should companies manage resource capacities.

I would like readers to follow Eli Goldratt’s recommendation that they solve the problems – before I provide the solution and before my discussion of results. The learning experience will be greater and readers should be better able to discuss and critique my conclusions. If you are familiar with the thought experiment you can jump to the second part of this article.

The key part of the article comes at the end when the solution used in the P-Q thought experiment is discussed in relation to REALITY. The thought experiment should not lead managers to an easy solution. The tool is useful but requires thought and care.

Most of us ‘solve’ the problem without much thinking. It’s like a simple arithmetic problem from grade school. From groups we usually get quite number of ‘wrong’ answers – some from arithmetic mistakes, some from faulty thinking. Below you will find the answers from inadequate thinking and the explanation for the ‘right’ solution.

Attempt 1:

Many do not check whether or not my factory has sufficient capacity to produce all the Ps and Qs. They do not identify the constraint. These people, barring arithmetic errors come up with the answer shown below – 1500€ profit per week. That would be nice, but why would I ask a consultant for help? The constraint makes it impossible to earn 1500€.

So, let’s find the “Find the constraint!”

The table below identifies it.

Clearly the B machine cannot produce all of the necessary components for both P and Q. We need to decide how many Ps and how many Qs we can produce (the optimal mix) to maximise profit. B is the constraint and, in this case, also a bottleneck.

Attempt 2

Those that found the bottleneck usually ask things like ‘Can we buy another machine?’ or ‘Can we use overtime?’ For the purpose of the experiment no additional machine and no overtime is possible. The job is to maximise profit within the given parameters. To expand capacity by either overtime or adding a machine would mean we jump questions 2 and 3 of the 5 focusing steps.

Step 2 of the 5 steps is to decide how to exploit the constraint (our bottleneck). Most people, from high school students to CEOs, will check at least some of the following to see which product is the more profitable and should be favoured by the constraint:

1. Which product has the higher price?  Q (100 vs. 90€ for P)
2. Which product has the higher contribution margin (Throughput[1])?  Q (60 vs. 45€ for P)
3. Which product requires the least amount of effort to produce it?  Q (50 vs. 60 minutes of effort per unit of P)

Based on these 3 checks it looks like Q is the better choice. Most people choose Q as the more profitable product. We should, therefore, produce all the Q (50) and fill our bottleneck’s capacity with P sales.

If we sell 50Q we consume 1500 minutes of B capacity (2, 15 minute operations multiplied by 50 units of Q). We have 900 minutes of B capacity left – enough to produce 60 P units. The table below shows our result.

Not so good! Despite using commonly practiced checks for product profitability we generate a loss. Is this the best we can do? Might there a better way to decide what we should produce?

We did not apply the second of the 5 focusing steps correctly. The 3 checks we made had nothing to do with deciding how to exploit the bottleneck machine B. Maybe we should ask: How long does it take B to create 1 € of Throughput? How many minutes of B do each of our 2 products P & Q to require produce 1€ of Throughput?

• Q Throughput is 60€ per unit. It takes 30 minutes to produce this Throughput. Producing Q our resource B delivers 2 Euros of Throughput every minute. It takes 30 sec. of B’s time to produce 1€.
• P Throughput is 45€ per unit. It takes 15 minutes to produce these Euros. Producing P our resource B delivers 3 Euros every minute; it takes only 20 sec. to produce 1€ with P.

Shouldn’t our decision be to produce the product that delivers the greatest number of €s per unit of time available at B (the constraint; the limiting factor for Throughput)? (Alternatively shouldn’t the decision be to produce that product with which B delivers 1€ in the least amount of time?) If yes, then our decision must be to produce 100 P and 30 Q (100 P consume 1500min of B capacity, leaving 900min for Q. Since one Q requires 30min of B capacity only 30 can be produced). The table below shows the result.

 

It looks like we have found a nice rule to determine what we should sell when a bottleneck exists. Throughput per constraint unit will tell us which product to favour – bearing in mind that our customers and our markets may not allow us to reach the theoretical maximum.

During Alan Barnard's presentation in 2006 he raised the question whether this nice simple rule (deciding based on Throughput per Constraint Unit) always works. He introduced a second constraint (at D) and asked how much we can produce now that two constraints are active at the same time. Is our simple rule still valid? If not, can we adapt the rule so that (sales) managers continue to have something simple on which to base their preferred product mix? The next post will show the P Q thought experiment modified for a second constraint at D.

---

Chateau Frontenac, Québec City,  May 1954, 

[1] Throughput = the rate at which we make money = sales less totally variable cost (usually just materials).

Impact of more than one Constraint -1

Alan Barnard (at the 2006 TOC-ICO conference) asked the question whether the simple Throughput per Constraint Unit rule is valid with 2 (or more) overloaded resources. Alan used Eli Goldratt’s P-Q thought experiment for his discussion. His question is important because it is common to see businesses reduce ‘excess’ capacities to balance (or almost balance) capacities. The practice often results in two) or more concurrent constraints or ‘almost’ constraints. Since 2006 I have observed several factories that wonder why their output collapses below the theoretical capacity of their (almost) balanced lines.

I plan to show that that the Throughput per Constraint Unit rule continues to be valid using the same P-Q thought experiment. I also want to discuss this result in relation to the real World – how should companies manage resource capacities.

I would like readers to follow Eli Goldratt’s recommendation that they solve the problems – before I provide the solution and before my discussion of results. The learning experience will be greater and readers should be better able to discuss and critique my conclusions. If you are familiar with the thought experiment you can jump to the second part of this article.

The key part of the article comes at the end when the solution used in the P-Q thought experiment is discussed in relation to REALITY. The thought experiment should not lead managers to an easy solution. The tool is useful but requires thought and care.

 

The original P-Q Experiment

 

Goldratt frequently used this experiment as part of his presentations. Before introducing the thought experiment he would usually present and discuss his 5 focusing steps listed here:

 

1. Identify the constraint of the system.
2. Decide how to exploit the constraint.
3. Subordinate everything else to the above decision.
4. If you cannot extract any more from the constraint, elevate (expand) it.
5. If during any of the previous steps the constraint is broken (has moved to another location) go back to step 1. BUT do not let your inertia become the systems constraint!

 

The P-Q Company

 

The company is mine. I have hired you to help me maximise my profits from the resources I have.

 

1. I have 4 machines (A, B, C and D). All 4 are required to produce my 2 products P and Q.
2. There are 40 hours of production time available per week (1 shift). (40 hours are 2400 minutes.)
3. Product P sells for 90€ per unit and Q for 100€ per unit.
4. Demand per week is 100 of P and 50 of Q – if my factory can make all 150 units.
5. Raw materials 1 and 2 plus a purchased part are required to produce P (1 of each raw material and purchased part).
6. Raw materials 2 and 3 (no purchased parts) are required to produce Q (1 of each).
7. All raw materials cost 20€ per unit. The purchased part costs 5€/unit.
8. Operating expenses (all costs other than materials and the purchased part) are 6000€ per week.
9. Raw materials and purchased parts are always immediately available.
10. All set-up times are ‘one-touch’ – set-ups take no time at all.
11. All resources are perfect – all work 8 hours per day without any breaks, even my people work 8 hours without fail.
12. Quality is perfect; I experience no losses due to defective parts or products.
13. The graphic below shows the routing (how materials flow through my factory from raw material to finished product). The routing shows the sequence of operations each product goes through and not the layout of the factory. Clearly raw material 2 is required for both P and Q as are the operations on the B and C machines in the middle path.
14. How much money can I make per week?

 

Work it out – the answer can be found using what has been written so far, or you might want to use linear programming. Either way, try to understand why you decided on one or the other option to produce and sell. When you are finished, carry on to the next page.

 

A solution to the problem is demonstrated starting on the next page or in my next post.

Winter in Baie Comeau - Feb. 1954!!!

On Clear days you can see Corporate HQ - 6

Why the 5 Focusing Steps are so Important

Most middle and senior managers do not understand or simply are not interested in how their business system works. They are content to focus on their local department and optimise that – rather than understanding the business as a whole to cause it to maximise results. Even top management (CEOs) often do not understand their business. They condone and even encourage their management teams to optimise their local departments – production, marketing sales, finance etc. Wherever local optimisation is the rule the business concerned will always harm the bottom line significantly. Local optimisation is a massive mistake!

The 5 Focusing Steps are guidelines that, properly used, will cause a management team to always reflect on their (local) decisions. Doe the action or decision taken locally help or damage the business as a whole? As we will see the 5 Focusing Steps are a guide, but they do not replace a deep understanding of the business system.

What follows is a description of the 5 focusing steps, how to apply them, why each step is important and a series of examples of common practice that violate the 5-Steps. This sixth post is a short discussion of the fifth (the step with an important warning that is often not heeded) of the 5 steps. This last step is actually simple the first step in the next cycle of improvement.

The 5 Focusing Steps

Step 5: If the constraint has been broken (has moved) go back to step 1. WARNING: Do not let you inertia become the systems constraint!

Clearly the 5 steps are a never-ending improvement cycle since there is no way to ever eliminate constraints. At best they will move to a new location.

The warning is extremely important! We humans develop a new paradigm very quickly as our business simulations show. We can explain the 5 steps in depth and emphasize the 5th step, run the simulation with manager participants and within 30 minutes they will forget the 5th step's warning. While this step makes eminent sense to them it seems managers cannot follow it correctly – it takes time practice and probably at least 2 people that constantly remind each other – of not just of step 5 but all of them.

Sometimes a business will tell me their constraint wanders (or dances) throughout the factory or business. This is simply a phenomenon of too big batches. The operation swallows too much at a time so that the constraint seems to move through the factory. To see the effect in animal life watch an Anaconda snake swallow a pig. The pig is swallowed whole and travels slowly through the snakes digestive system. The huge batch makes the snake lethargic and largely inactive … similar to a constipated factory. 

Factories with wandering constraints have too much WIP and very likely no constraint at all once they choke the release or freeze half their projects (see Production the TOC Way or Critical Chain - both by Eli Goldratt)

On Clear days you can see Corporate HQ - 3

Why the 5 Focusing Steps are so Important

Most middle and senior managers do not understand or simply are not interested in how their business system works. They are content to focus on their local department and optimise that – rather than understanding the business as a whole to cause it to maximise results. Even top management (CEOs) often do not understand their business. They condone and even encourage their management teams to optimise their local departments – production, marketing sales, finance etc. Wherever local optimisation is the rule the business concerned will always harm the bottom line significantly. Local optimisation is a massive mistake!

The 5 Focusing Steps are guidelines that properly used will cause a management team to always reflect on their (local) decisions. Is the action or decision taken locally help or damage the business as a whole? As we will see the 5 Focusing Steps are a guide, but they do not replace a deep understanding of the business system.

What follows is a description of the 5 focusing steps, how to apply them, why each step is important and a series of examples of common practice that violate the 5-Steps. This third post is a short discussion of the second of the 5 steps.

The 5 Focusing Steps

Step 2: Decide how to exploit the constraint.

This is easy to say, but it is not so easy to do. How should I use my constraint to maximise profits (assuming maximum profit is the business goal)? Which of my products have the highest margins? Which of my products consume constraint capacity most effectively? What are the implications of my decisions in the market? What are the implications for the future? Clearly this is not an easy decision for any to make. It is in fact the responsibility of top management to decide!
If management does not take the decision or does not take it correctly the business will suffer. The bottom line will reflect this suffering. Unfortunately business results will not point to the culprit for less than optimal (even poor) results. The culprit is almost always the way we manage our system, our constraint, or could it be anything else? If it is something else let me know at rgb@vistem.eu!
The constraint might be our market. The market may not be buying enough of our products to fill our factories resulting in a less than satisfactory bottom line. Our decision may then be to cause our markets to buy more and more from us (and this must not be done with lower prices since our competitors can easily copy these). Some sort of powerful competitive advantage is essential.
It is important to remember that a policy ‘constraint’ should simply be changed. Deciding how to exploit an inappropriate policy does not make any sense. An inappropriate policy was originally put in place for a good reason. That is why it should be changed but not necessarily be eliminated. Chang such policies, KPIs or just the way work gets done.

Where to Focus your Scarce Resources - 1

In the “Your Businesses Potential” article you were asked to evaluate your business against 5 criteria (Global vs. Local Optimisation; Aligned Key Performance indicators; Your Limiting Factor(s) (Constraints); Reliability and Effectiveness. Local optimisation (every department and function optimises its area of responsibility) is wasteful of scarce resources because most of the optimisation efforts bring nothing to the bottom line – since your limiting factor determines what your bottom line can be. Focus your scarce resources on your limiting factor to always realise the greatest positive bottom line impact!

So, where is your limiting factor or business constraint located? What part of your business is blocking your ability to increase profits and profitability?

Your Goal and Necessary Conditions

To determine where to focus scarce resources necessitates an understanding of your goal and necessary conditions. For our purposes we will assume your goal is something like: “We want to make money now and more in the future”. The necessary conditions we assume are: 1. We want to satisfy clients now and in the future; and 2. We want provide our employees with a satisfying and secure employment. Whether or not you agree with the goal and the necessary conditions matters not. Key is the goal.

Your limiting factor is that part of your business system that is blocking you from making more money (and that you believe will continue to block you in the future). Now is a good time to think about what might be blocking your bottom line performance. From the following list pick that part of your business you believe is blocking performance. Since your business is a system of interdependent entities (the functions and departments) only one of them can be the blocker!

• Sales & Marketing; the Market
• Production•Distribution
• Engineering, Research and Development, New Product Development
• Sourcing/Purchasing; Suppliers
• Human Resources; Employees
• Finance
• Legal
• Management; Senior Management
• Policies, Performance Indicators, Behaviours, Company Culture

What does your intuition tell you? Try to verbalise why you believe your pick is the blocker or limiting factor. Explain to yourself through cause and effect analysis why your choice must be correct. This blocker is the place for your scarce resources to focus and improve performance – is it not?

Your intuition might be faulty. Would that be dangerous for your business? It would not, since your efforts to improve the non-limiting factor would overload the real constraint even more than it already is. If this does happen, then switch your focus to your real constraining factor.

Might this kind of focus result in much greater bottom line improvement vs. spreading improvement resources across all departments?

What follows may help you to qualify your intuition will deciding on the location of your limiting factor.

Strategic Inventory Placement(Pharmaceutical, Agrichemicals…)

In many B-to-B industries suppliers produce the same basic product for several to many customers. The product may be the same, but often the packaging and (or) the labelling will be different. Labelling and packaging is often different from customer to customer, but also within a single customer packaging and labelling might well be different from country to country. Agrichemicals and generic pharmaceuticals are examples.
This variation in packaging causes inventory and availability problems that endanger suppliers’ business since clients can get the same or very similar product from another supplier. Switching does not solve the problem for a client it simply transfers it to another company. Performance is unlikely to improve in any sustainable way.
Suppliers need a solution that makes it possible to guarantee availability, with, at the same time, reducing the amount of stock in the supply chain. A robust effective solution would most probably also take some of the pressure off price.

Are Suppliers & Customers Partners, Competitors or Enemies?

Businesses in a supply chain are really only paid when the final customer, the consumer, has paid for the product – everything else is an advance payment. Every company along the supply chain is competing for that consumer’s dollar. In the B-to-B part of the supply chain a supplier and his customer negotiate price that will, to a large extent, determine profits and return on investment of the B-to-B pair. It is clearly a conflict situation since both the purchasing agent and the salesman concerned are charged with maximizing profit for their company … at least usually. Companies within a supply chain are certainly competitors.
Supply chain collaboration is a buzzword that sounds and real collaboration could be great. Whatever level of cooperation or collaboration is actually achieved, the fact remains that two parties within a chain compete. A level of mistrust is likely to remain.
In many supply chains customers throw orders to their suppliers over a wall– many times as a surprise (in terms of volume or timing). Orders can arrive at really inconvenient times. The supplier may suggest a way to solve the problem – for instance by sharing demand information. A supplier might offer a significant discount for much earlier commitments from clients. Can these proposals work?
Sharing demand information is often problematic because it seems to give away proprietary information that may get into competitors’ hands. On top of that, even if demand forecasts are shared, we all know that these tend to be quite inaccurate. Actual orders are often significantly different. Demand forecasts are inadequate and usually not so useful for suppliers.
A firm order placed well in advance sounds very attractive for a supplier (and for the client if he gets a nice discount). However reality will almost certainly catch up with the client. Close to delivery time he needs a different mix of products. He will request last minute changes to his orders. The supplier loses the discount and gains no stability benefit for his production unit.
Our problem is to find the robust, simple solution to get the best information about near term demand to the supplier’s production unit and to his suppliers. The criteria for such a solution must be something like:

1. Visible, transparent information about current demand for all stocked items (those not made to order).
2. Visibility must be such that all nodes in the supply chain have absolute clarity about the priorities to ensure correct replenishment of stocks in the supply chain.
3. The system must have a simple and dynamic way to adjust target stock levels to current demand as it changes over time.
4. A supply chain must carry stock at the most appropriate strategic locations with the following two targets: a. Minimize stock levels within the supply chain. b. Guarantee near 100% product availability.
5. A monitoring system to provide early warning of an arising capacity problem.
6. The key performance indicators that show the supplier and his customers what their respective performance levels are.

Make for Availability is a process and system that meets the above criteria. If a part of the business is Make to Order, then the two processes can be easily integrated into one mixed mode solution.
In addition to the basic criteria above, the process needs to be able to cope with:

1. Seasonality (Agrichemicals have very seasonal demand).
2. Promotions – especially in retail situations.
3. Sudden peaks in demand – for instance when the supplier gains a new large customer.
4. Sudden loss of a significant account.
5. The need to forecast longer-term demand remains – in order to support decisions about capacity changes.

In today’s business environment the technical challenges to support such a process can easily be overcome.

Selling the Concept

That companies compete for the profit of a supply chain makes the sale (of such a concept) a difficult one. In addition most purchasing personnel and their counterparts from the supplier’s sales organisation do not normally negotiate about inventory, information and availability. They are used to discuss price, product quality, product features and benefits. These people will need to master the core of Make for Availability and the corresponding business offer being made.
Before purchasers and salesmen can even talk about the solution the process must be absolutely clear for a sales or purchasing person to make a coherent offer. The solution may well be a paradigm shift for clients (or suppliers). The process may well indicate that a better location for stocks exists and should be used. There are considerable implications in the way the process must function; who will do what; where will inventories be stored etc. Not too difficult a process to understand, but a purchasing organisation must be in a position to sell the benefits in such a way that will almost certainly gain the clients’ cooperation.

Where should Inventory be located?
Do we need so much?