The Objective and the Challenge of Improving the Supply Chain – and the Personal Dilemma of the Key People

Improving the supply chain applies to two different scopes:

  1. Improving the performance of our own organization by improving the flow to our clients as well as the flow of supply from our suppliers.
  2. Improving the overall performance of a whole supply chain, across many different organizations, up to the end users of the end product/service.

My assumption is that the first, relatively local perspective of the supply chain, is the current common focus for considerable improvements.  It already contains some thinking and vision on how that improvement is going to impact the overall supply-chain, especially as it looks on both suppliers and clients of the organization.

The long-term vision for the whole supply chain, developed by Eli Goldratt, deserves a special post sometime soon. Let’s focus now on the challenge of one link in the supply chain to improve its business.  An organization usually has variety of products for variety of clients. It has also to maintain good relationships with various suppliers.  Part of the value generated by the organization depends on the perceived value of the products, their quality, and the level of usage by the clients. A major point is the size of the target market segment that truly likes the end products.  Another critical part of the value is the quality of the delivery to the clients.  This fully depends on the management of the flow.

What blocks the flow of products according to the true wish of the market?

One of the two key obstacles to fast flow is batching, meaning the grouping of many pieces and working on them together as they move from one work station to the next.  Naturally every batch serves many customers.  Batching is in the center of attack by Lean, once called Just-in-Time, in order to come closer as possible to the idea of one-piece flow.

Batching is caused by either long setups or by certain resources that work on a whole batch, like ovens or transportation vehicles. One of means of Lean to dramatically reduce the batching is by reducing the setup time. Another mean is using more resources with lower capacity, for instance, smaller trucks for more frequent deliveries.  The common concern is that these means would add cost.

A different key obstacle of the flow is lack of enough capacity, which causes long wait time. The first obstacle, batching, clearly impacts the second, the lack of capacity. When the batches are smaller capacity is spent on more setups, which seems like cost is wasted. TOC clearly shows this is not necessarily the case.  But, it is certainly possible that too many setups would turn the specific non-constraint resource into a bottleneck, causing huge delays.

TOC has the right tools to deal with the obstacles, and by that maintain good flow, without becoming too orthodox, through sensible management of capacity and considering the real impact on cost and on the demand.

The two obstacles seem to be a major problem because of their impact on the flow of products and services and through that on the organization goal. But, when we examine the goal there is even more critical obstacle that makes the life of all managers so difficult:

Having to deal with the considerable uncertainty of the demand

The connection with managing the flow is soon to be analyzed. Meanwhile let’s understand the way common management practices deal with the uncertainty.

The common way most businesses deal with uncertainty is using forecasts to predict the future. The problem with forecasts is that they are, at best, partial information.  In Probability Theory every stochastic behavior has to be expressed, at the very least, by two parameters.  The most common are the predicted ‘average’ and the standard deviation.  Forecasting methods use past results plus previous forecasts to generate the estimation of the average result and the ‘forecasting error’, which estimates the equivalent standard deviation for the coming forecast.  The big problem of using the forecasts is that when looking for the demand in far away weeks the estimation of the ‘forecasting error’ becomes messy. Actually the whole notion of the forecasting error is problematic because when the error is relatively big, like when you look for the weekly forecast of one SKU at one location three months from now, then the burden on the decision maker is quite significant.  Pretending the forecast determines accurately the demand seems like a good solution.

The personal fear of every decision maker of being proved wrong dominates the current practice.  The usual response is that the forecast was RIGHT but the execution wrong – this provides a way to blame somebody else.

In reality the demand at any specific geographical location fluctuates in a much bigger way than the total inventory.  Another complicating factor is that the level of uncertainty grows sharply with time.  The longer the horizon the weekly/monthly demand forecasts are subject to more uncertainty.  The weekly demand a year from now might even be irrelevant, due to new products and/or different economical situation.

The practical ramification for every supply chain is that the upstream nodes in the supply chain face much harder decisions on what to make and how much of each specific item. This is because the time between the relevant decisions of what to produce, and the actual sales of the end-products is relatively long, which means high level of uncertainty.

If reality would have been deterministic then the two obstacles of flow would not matter and optimized solution for capacity utilization, using the optimal batches could have been provided by good, but routine enough, software algorithm. This is, of course, not our reality.

One critical insight should be well understood:

Instead of improving the forecast, which might be either impossible or very minor, it is possible to improve the flow throughout the supply chain to quickly react and adjust to the actual demand!

Even with fast reaction to actual demand we have to make sure there is enough stock either on-hand or in the pipeline, to answer the immediate demand. It seems impossible to determine the exact stock, due to the volatile uncertainty, but we can come up with a good-enough estimation and adjust it based on the actual behavior.

Estimating the right amount of stock is a kind of FORECAST! But, we need to be clever in how we use the partial information to come up with a good-enough estimation of how much stock to hold in the system.  In TOC the underlining assumption is that the demand for tomorrow is roughly the same as today, unless we get a signal that this might not be the case.  So, we need to be clever in analyzing the signals that the current stock level might NOT be right.  There is no viable way to determine a precise number, and being slightly “mistaken” would not matter if the fast reactive flow to actual demand is working properly.

These are the key core insights of TOC for managing a supply chain. The process needs to be much more detailed, but this is certainly beyond this post.

Still a troubling question is:

Is “good enough” truly enough for an organization that values optimization?

There is a strange conflict in the minds of most of the key managers in the area of supply chains. On one hand they recognize the need to improvise because everything changes all the time.  Production managers are certainly used to improvisation. This requires having the appropriate infrastructure built in, like having enough stock and available capacity for sudden changes.  On the other hand, managers are aware that improvisation means extra cost for the maintaining flexibility and such an approach cannot be optimized, and even worse, it is considered to be far from the common best practices of today.  It is frightening to go against the common best practices, and every manager whose career depends on the judgment of others, like his boss or the board of directors, has a lot to fear from doing something different than what is accepted as “right”.

Here is the conflict cloud:

Conflict SCM

Actually, the TOC rules of dealing with uncertainty do not require frequent improvisations, but simply follow common-sense and make the decisions for relatively short periods of time, resulting from the fast flow that management is able to maintain.

What comes from handling uncertainty in such a way is being superior to most competitors in the eyes of the market, which could lead to very successful business.

The TOC approach challenges the need for the ‘C’ entity above to achieve the objective. So, the resolved conflict looks now like this:


Overcoming the natural fear to go against the common practices could be dealt with running a Proof-of-Concept. It has to be a good enough “proof”, and it has to limited, so even failing would not create too high perceived loss.  A former post on Proof-of-Concept can be found at:



Managing a mix of make-to-availability (MTA) and make-to-order (MTO)

A critical insight has emerged into TOC around 2,000:

There should be clear separation between customer orders, which specify quantities and due-dates, and work orders to stock!

This is NOT the common practice, which strives to merge the quantities required by customer orders with stock quantities based on forecast. TOC clearly refrained from merging customer orders with different due-dates into one work-order.  But, up to that time even the TOC methodology used to assign artificial due-dates to make-to-stock orders.  By realizing that no due-date should be assigned to stock orders TOC achieved a true separation between make-to-order and make-to-stock/availability.  This vastly simplified the production process by noticing two different types of flow with two different types of buffers: time and stock.

Standard products with good and relatively stable demand, certainly fast-runners, fit being produced to availability. Fully customized products naturally fit make-to-order.  In between the two groups there are products that could be treated either as MTO or as MTA.  There are several gray-area categories of products that can be treated this way or the other way. For instance, slow movers that the market still expects to be available or when the required delivery time in more than one day, but less than the current production lead-time.

In some cases a combination of make-to-order and make-to-stock is applied to the same item (SKU), like regular MTA, but dealing with few very large orders that could be bigger than the whole stock buffer is better handled as MTO. Another case, which is pretty common, deals with supplying big clients, like automobile producers, that give the supplier rolling forecasts for several weeks ahead, but then expecting to draw on the spot somewhat different quantities.  Here also a combination of MTO and MTA is the preferable direction of solution.

The situation where a company runs both MTO orders along their time-buffers and MTA orders, controlled by stock buffers, and both compete on the capacity of the same resources, should be very common. TOC is effective in maintaining the separation and by this every single order is exposed to the true priorities dictated by buffer management no matter what type of buffer it is.

Are there generic problems in managing a product mix that contains both MTA and MTO?

The MTO buffer is based on time. The order is released to the floor time-buffer prior to the due-date. So, the consumption of the buffer is linear – the buffer is consumed day by day in the same pace.  An important advance in the TOC methodology was to use the Planned-Load, the load on the weakest-link/constraint, to determine the “safe-date” for any incoming orders.  This provides a mechanism to flatten a temporary peak of demand by increasing the promised customer lead-time based on the incoming demand.  The safe-date mechanism smoothers the load and by that ensures stable performance.  During off-peak periods the company is able, depending on its strategy, to offer shorter response times.  This offering has to be carefully thought of as it might cause negative ramifications of customers expecting fast delivery at all times and, when relevant, refuse to pay more for truly faster response.

The MTA buffer is based on stock, so the buffer status depends on the actual sales. The immediate consequence is that the buffer status of an order can jump from Green to Red in one day.

On the other hand, an order in the Green might stay in Green for very long time when the sales of that item are very low. All in all we see more volatility in the buffer management sorted list of priorities due to MTA orders, while the MTO orders keep steady pace.

This difference in behavior is not relevant to the question: if we have two red-orders, one MTO and one MTA, which one is more urgent?

One insight has to be considered here:

Buffer Management is effective as long as there is a fair chance to deliver ALL red orders on time!

Both MTO and MTA radiate commitments made to the market. TOC uses its capability to stabilize the operational system in order to gain reliability in meeting all the commitments and make this a decisive-competitive-edge.  When violating at least one commitment given to the market the emerged conflict is which order should be delayed then a new question is raised:

Which order would create less damage when its specific commitment is violated?

The buffer management algorithm doesn’t consider the size of the orders, the throughput they generate and ignores the identity of the client. However, when it seems clear the company is (temporarily) unable to meet all its commitments then the truly critical information is: Who is the client?

The answer should generate more questions about how this particular client is going to react and how this might impact the other businesses the client has with the company.

So, when an MTO order competes with an MTA order which one would turn Black – then the deciding factor is the predicted damage and it could easily be either the MTO or the MTA order.

When the company manages a mix of MTO and MTA then there is relevancy to the question:

What is the ratio of capacity consumption of the weakest link between MTO and MTA orders?

The reason of having to ‘reserve capacity’ of, say, 40% to MTO and 60% to MTA, is to enable smoothing the load of the MTO part through the mechanism of the “safe-date”. In order to do that we assume that every day 40% of the available capacity, on average, would be dedicated to MTO.  Thus, we can consider the planned-load for the MTO orders, which means calculating how long it’d take the weakest-link to process all the current MTO orders.  That time is converted to a date, when only 40% of the daily capacity is considered.  The calculated date represents when the weakest link would be free to process a new MTO order just received. The safe-date for that order is the calculated planned-load date plus half of the time buffer for that order.

The reader can find more detailed description of the determining the safe-dates for MTO orders elsewhere. The very brief description is intended to explain that the average reserved capacity for MTO orders, in a mix environment, is required just for that mechanism.

The ratio of 40/60 might make the wrong impression that the weakest-link, the potential capacity constraint, is planned to utilize 100% of its available capacity. This is a major mistake.  The commitments of reliability in delivering MTO and maintaining excellent availability of MTA items at all times clearly requires a certain level of protective capacity.  When the mechanism of quoting safe-dates for MTO is working properly there is still a need for protective capacity to cover for unexpected downtime, need for rework and mainly inaccurate capacity data.

Maintaining excellent availability of the MTA items requires MORE protective capacity because of the impact of incidental peaks of load.

Dr. Goldratt required that the total capacity utilization of any resource in an MTA environment including a mixed environment would not be over 80% of the available capacity. Goldratt’s concern was not just the fluctuations in the total demand for MTA items, but also to have enough time to deal with an increase in the total demand.  Many simulations showed that when the demand is growing there is a point where suddenly the number of red-orders goes up sharply and then it is just a matter of time until many items become short.  Note, maintaining large buffers restrain the impact of lack of protective capacity just for short time.  The use of dynamic-buffer-management at that point in time makes the situation WORSE, because increasing the buffers increases the demand at that point of time, where too many red-orders compete for the limited capacity of the weakest-link, which turned to be a bottleneck.  The emergency policies at this stage should be reducing the buffers, while looking to quick means to add capacity as soon as possible.  We better not experiment too much the tolerance of the protective capacity, especially for MTA.

The idea behind setting the line of average utilization of the weakest-link to 80% is that when the total demand is up there is still an opportunity to increase the capacity before the reputation of the company as one that meets all its commitment will deteriorate.

Having to exploit the internal capacity constraint to only 80% of its theoretical capacity is problematic. When more potential demand exists the temptation to draw more of the constraint is considerable.  However, the risk of overexploiting the constraint and by that ruin the decisive-competitive-edge of reliability is also high.

The solution, offered by Goldratt, is to maintain a market with no, or very limited, commitment! For instance, when the constraint is idle it could produce to stock items that can be sold to another market segment without any commitment for future availability for that market segment.  These make-to-stock orders are definitely ‘The Least Priority’ orders – carrying less priority than green-orders.

This idea is based on an important insight that is good to end the article with:

Specific commitments that provide high value to the clients should be directed at specific market segments. Other segments could be offered less binding commitments

My views on the right focusing for the HighX case

This post continues the case and the comments from the previous post. Please, read the previous one first.

The fictional case raised several very interesting comments. I hereby present just my own analysis. I suggest the readers read all the exchange of comments, make up their own mind regarding the case, and then try to generalize the lessons to assist in more general cases of dilemmas what should be included in our focus.

It is customary of TOC consultants to require that the TOC project, most certainly the Strategy and Tactic (S&T) project, gets precedent on any other change project that is running at the same time. The justification is both the assumption that TOC project would generate much more impact on the organization’s future than any other project, and that the level of management attention to the TOC S&T project is significant because it is based on paradigm shift(s).

The effectiveness of the decisive-competitive-edge (DCE), which is based on reliability plus the fast-response option, depends on the existing pain in the market. It also depends on whether the full value of removing the pain is properly recognized today and if so whether HighX, aided by the TOC expert, can accomplish a change in the awareness of the clients in short time.  So, such a DCE could be very effective, but in other cases it is less effective.

There is another relevant variable that impacts the expectations from the proposed DCE: the level at which the other parameters that impact customer satisfaction are judged by the market. In the definition of the DCE as answering a need in a way no competitor can, there is also a necessary condition that all other characteristics of the company products and performance are on par with the competition.

So, it is necessary to re-check the seven projects that add new features to current products. Can those projects wait? The answer depends on the state of the competition regarding those features, assuming also that the features are truly needed.

Suppose that three or four of those projects are critical in order to preserve HighX position in those markets. Would we want to delay those until the launch of the new DCE?  A message that HighX radically improves its delivery and response, but, at the same time is slow to introduce very needed new features, which the competitors already offer, would be very problematical!

A critical question is:

Is there enough management attention in HighX to focus on both the TOC initiative as well as on three or four truly critical developments of new features?

Management attention is a very loose term.  Who are the managers that have to dedicate substantial amount of their attention capacity to the TOC initiative?

Are those managers also have to dedicate substantial attention to the development of the features?

The TOC S&T requires, at the very start, between four to eight days that are fully dedicated to understand the holistic concept, the related paradigm shifts, agreeing to the plan, developing the required actions, and attention, from every function and put it on the timeline. This initial part of the process creates a pressure on the attention and feeling of responsibility of all participants, but the limited duration should not seriously harm the current open projects.

One possible outcome of building the S&T and deriving the detailed plan is to freeze the three or four development projects that are not mandatory to keep the image of HighX intact. Still, three or four critical projects for maintaining the current image of HighX are still urgent.

In order to analyze the required load on management attention we should notice that after the consensus on applying the S&T the main attention burden will be first put on the shoulders of the production managers. They are faced with the new paradigm of choking the release and another of following the priorities of buffer management.

Are the same managers also need to pay a lot of attention to the projects developing new features?

They are definitely impacted by requirements for proto-type production and participating in some meetings to present the production obstacles. When the development is completed they need to introduce all changes into the regular production procedures.  This is relatively routine work, which should not put major pressure on the production managers.

First conclusion: Have HighX focused on the TOC S&T and, at the same time, on several of the key R&D development of new features, while the other R&D projects are frozen until the completion of such a project would let to unfreeze another project.

What about the two bigger projects, which look for two new market segments?

Here we see a much more serious load on the Marketing and Sales Management. The S&T process after completing the build-up of the TOC procedures, policies and measurements in the Production, would face the serious challenge of developing the new value offer to the market! The project will also deal with training the sales force to be able to sell such a value offer to the market.

How would the Marketing and Sales managers be able to go through such challenges, while also preparing two more big moves of opening new market segments? This is where the load on the relevant managers might be way too high.  It seems impossible to deal with two, certainly not three, such critical missions at the same time.

Another result of the introduction of the DCE is the possible dramatic increase of the load on the regular resource capacity in production. In the S&T after the part of ‘Capitalize’ comes the part of ‘Sustain’ – prepare for significant increase in demand in order to be able to keep the new delivery performance intact.  It is clear that succeeding to enter a new market segment might bring more demand. It seems unwise to have the two different efforts to substantially increase the demand to take place at the same time!

Conclusion: The two bigger projects should be frozen at least until the Capitalize part of the S&T process is well established. Then it could be considered to continue, while making sure that the higher level of demand would not harm the delivery performance, which will be, at that time, a key in the image of HighX.

A Difficult Choice of Focus: A fictional case for open public discussion

HighX is a job-shop manufacturing company, producing 12 different categories of products, each according to detailed specifications from the client. There is no practical way to make-to-stock/availability; it is a pure MTO environment.

Steve, the CEO, got interested in the Strategy and Tactic (S&T) process in order to achieve a decisive-competitive-edge over his competitors. The idea is to offer high-reliability and also fast-response delivery to clients and by that gain a decisive competitive edge.  Clients currently suffer from not-too-good delivery, which characterizes also the performance of the main competitors.

Some clients need, from time to time, much faster delivery. The current order lead-time is four weeks and if that is achieved in high reliability it would relieve much pain from the clients.  If it’d be possible also to offer fast response, say delivering a specific order in just one week, then 25% markup on the original price will be considered fair.  It is predicted, based on actual queries from clients, that 17% of the orders would be for one-week special delivery at the higher price.  TOC operational methodology can achieve the capability of reliable delivery including the faster delivery order.

However, during the initial sessions on the S&T Steve raised the issue of the current improvement initiatives that are already on progress. The R&D department is busy on adding new features to the most lucrative seven categories of products.  These features are demanded by the customers and it is reasonable that the competitors are working on them as well.

More, there are two even bigger initiatives, coupling R&D and business development, of entering two promising new market segments with new categories of products. The two market segments offer higher ratio of T/Selling-Price.  The two projects have been initiated nine months ago and are expected to be ready to launch between six to twelve months from now.

James, the key TOC expert leading the S&T session, said that all those development projects should be frozen until the basic TOC procedures, including the new TOC performance measurements, would be implemented.

Steve reacted that he cannot afford to freeze those projects as they are critical to maintain the position of HighX in this competitive market. James suggested waiting with the S&T for six months until three most important projects would complete and then start the implementation.  He also suggested that while the three most important projects are making progress, the other projects would be frozen until the three are completed and also until the operational part of the TOC methodology is successfully implemented.

What do you suggest Steve should do? How such a decision has to be made?

I call you to write your comments on the above.  It is time to discuss this matter.

Managing peaks as a strategic issue

Most organizations face few peak demand periods and much longer off-peak periods.  A peak is defined as a surge of demand that is above normal fluctuations.  The throughput generated during the peaks is, many times, very substantial relative to the total throughput generated in a year.   Thus, handling the peak has to be regarded as a key strategic issue, setting the relationships between the policies employed for the peak period and the different policies for off-peak periods.  The operational criticality of the peak is that it is reasonable to expect the internal weakest link to become an active constraint during the peak, and be non-constraint during the off-peaks.  This is often caused by the recognition that the level of available capacity maintained by the organization should broadly match the requirements during a peak.

If it is possible not only to prevent the loss of sales and the cost of holding too much inventory but also to strengthen the reputation of the organization then such a move, developing a TOC solution for peaks, has to be part of the overall strategy of the organization.

An important article entitled Peak Management, by Boaz Ronen, Alex Coman and Eli Schragenheim, was published back in 2001 in the International journal of Production Research. A post, publish in May 2016 in this blog, Facing Seasonality, the True Problematic Issue, offered some focused ideas on handling an anticipated peak that repeats itself every year.  The link appears at the end of this post.

Generally speaking we need to discuss three related time periods when we design the most effective way to handle a peak:

  1. The off-peak period before the peak itself, where the appropriate planning and preparations, like producing or purchasing the appropriate stock levels for the peak, are taking place. One generic insight for planning for the peak is to draw some of the typical peak demand to the off-peak periods around the peak. This could be achieved by pricing in a way that encourages customers to buy before or after the peak. Certainly airlines, hotels, and the whole vacation business are well tuned to these means. Others should adopt the generic insight to their specific environment.
  2. The policies, priorities and processes during the peak itself, using most effectively the limited degrees-of-freedom. This definitely includes the actual exploitation of the active constraint, and the subordination. For instance, a policy to offer only part of the product-mix and services during the peak, while offering the whole choice at off-peaks should be seriously considered.
  3. The after-the-peak period has to deal with some ramifications of the peak, which need to be considered in the planning. It is usually not possible to go to “normal” behavior immediately after the peak. The policies that fit the peak are different than during the off-peak. Human resources usually need to rest after the pressure during the peak, causing a true slow period after the peak that is lower than the average off-peak activity. Inventories that were not purchased during the peak have to be taken care of.  Many times such inventories are sold at significantly reduced price in order to reach the normal off-peak stable behavior. The overall success or failure to draw the most from the peak depends not only on what is sold during the peak, but also on what happens after the peak. For instance, the contribution from a promotion depends a lot on how deep is the drop in sales after the promotion.

First let’s distinguish between four different types of peaks defined by two parameters: very short peaks versus long peaks and anticipated peaks versus unanticipated. The post on seasonality is focused mainly on anticipated long peaks of demand. Let’s now deal with the other three types.

Short anticipated peaks

A “short” peak means that during the peak there is no valid way to replenish the stock during the peak. In services it could mean there is no way to add resources that have not been prepared before the peak for such a case.  So, basically what you have at the start of the peak is the maximum you can hope to use.

Consider for instance a major sport event, like the Super Bowl, which generates two different types of short peaks. One is sales of special items connected with the specific event, like hats and shirts with the logo of one of the teams.  There is no valid opportunity to replenish the initial inventory of hats during the peak.  The other type is a service like the police that have to be ready for any possible disruption.  It is not practical in such an event to be able to bring more security people in a hurry to handle a sudden problematic situation, unless they are held on alert very close to the stadium.

The ultimate dependency on early prediction of the requirements within the peak makes it the focus of the analysis.  Hence, the quality of the forecast and the decision rules based on that forecast are the core of the preparations.

As already have appeared elsewhere in this blog, forecasts of one number lead to wrong answers.  The only way to gain forecasting information that can be used for sensible decisions is to come up with two forecasts, one is based on reasonably pessimistic one and the other on an optimistic one.

The pessimistic forecast provides the vital information of what should definitely be prepared a-priori. The optimistic forecast should lead the decision by how much to add risk in the specific case. The possible damage of every quantitative decision, like preparing N items in stock, can be estimated by checking two scenarios. One assumes that the demand would be according to the pessimistic case leaving N minus pessimistic-quantity to be sold after the peak.  The other scenario is according to the optimistic forecast leaving Optimistic minus N of lost sales.  The rule is trying to find the best N that would, overall, yield the least potential damage or loss-of-sales.  Note that it is unrealistic to commit to perfect availability in short peaks.

The planning for the peak itself should include the exact placement of stock or resources at the start of the peak. In the case of selling hats at the Super Bowl event the different points of sale should get an initial amount, while the rest is stocked somewhere in the middle, making it possible to replenish, not from production, but from a central point, to the selling points.  Similarly, in the case of policemen controlling the security during the game, there is a need to have policemen located at several critical locations, while others wait at a central point to help handling situations that are too severe for the stationed policeman to handle.

The focus of managing the after-the-peak period is to return to normal as fast as possible. This might include logistics, dumping large inventories and carrying reduced activity for some time.

Shifting demand from the peak to the pre-peak or after-peak, when applicable, is an important part of planning for short peaks.  It exploits much better the constraint-at-the-peak and reduces the risk during the peak.

Short unanticipated peaks

Some organizations are aware of possible sudden emergence of a peak. For some, like the Fire Squad or the Emergency Room in a hospital, this is a critical part of their goal.  So, certain stock, and/or capacity, are prepared for such a case, even though the actual timing of the peak is not known.  Such organizations have a structured process for such a peak.  Using the first three steps of the five-focusing-steps should be the core of the “book” on how to handle a big, but short, unanticipated peak.

Organizations that are not prepared for such an occurrence have to improvise. Again, understanding the first three focusing steps is a major guidance in treating such a surprising peak.

Long unanticipated peaks

Unanticipated long peaks are pretty rare. When the demand starts to grow in an unexpected way, the challenge is to understand whether it is a trend or a peak, meaning the demand will go back to “normal behaviour”.  One case is when the organization launches a big surprising hit without being aware of the potential success. Diagnosing whether this surprising success would continue or not is critical.  If the organization wrongly assumes that the future success is guaranteed, meaning assuming it is a trend, and rushes to invest in doubling and tripling the capacity, the result could be catastrophic.  The other obvious mistake is sticking to the current capacity levels and letting other competitors take over the new market demand.  TOC has the tools to inquire the true causes behind the surprising surge of demand.

A basic TOC message when going into a surprising peak is to identify the emerging internal constraint and make quick changes to the procedures to match the new exploitation and subordination requirements. Of course, management needs good TOC knowledge to be able to adjust to the new situation in the best way.  The remaining big challenge is whether to elevate the constraint, probably also other resources, or wait and see and maybe lose part of the future market demand.

As already mentioned, dealing with long anticipated peak of demand, has been treated in the post entitled:  Facing Seasonality – the true problematic issue, see

We have significantly improved Operations – what we should do next?

The key TOC applications, so far, are focused on improving the Flow of value to the market. The five focusing steps, DBR, CCPM and replenishment are all target to achieve superior delivery performance to the clients.

The next obvious question is:

How do we capitalize on the operational improvement to achieve much better sales?

In this post I’m not going to focus on how you get your clients to see the full added value of the improved operations to them. It is definitely a non-trivial task.  But, before this task becomes critical there is a need to define:

What is the new offering to the market?

For instance, if the current standard for customer-lead-time is six-weeks, and now Operations can do it in three weeks, should the offer to the market be: “We deliver reliably in three weeks”? If so, should the price be the same?

The ability to truly improve the flow comes from challenging the efficiency syndrome and focusing on the system constraint through exploitation and subordination.

However, one critical factor that might block the flow is temporary, or fixed, lack of capacity. Just to be clear, lack of capacity does not necessarily mean a true bottleneck, where the load is higher than the available capacity.  It is enough that there is not enough protective capacity to cause temporary peaks of load creating delays in the flow.  The less the available protective capacity is the longer are the possible unanticipated delays.

This should not bother us if the demand is kept about the same as before. As long as the offering to the market does not change there is no reason for new clients to come.  As the operational improvements, based on TOC, have revealed considerable amount of excess capacity, the availability of enough protective capacity is guaranteed.

But, what happens when the organization comes with a new offering to the market?

In an ideal deterministic world the offering could contain both added-value, like shorter and guaranteed response time, coupled with higher prices, resulting in the same amount of demand, but the higher prices yield much better total throughput and no additional operating expenses.

The world, unfortunately, is not like that. When you offer more value for more money the result could be much lower demand, or much higher demand.  The situation is relatively easy to control if dynamic pricing, like the existing norm in the airline business, are accepted by the market.  Then simple trial-and-error would bring us safely to superior financial results.   However, dynamic pricing is far from the norm in most other business sectors, and I daresay that even in the airline business it is time to re-consider the full ramifications of dynamic pricing.

What about offering shorter and reliable delivery without, at least in the beginning, asking for higher price?  This could work well when the customers have a lot to gain from faster, yet reliable, delivery.

Problem is: it could be too good to the point of becoming a threat to the survival of the organization. When too much demand is coming in there is no way to deliver according to the new commitment.  This might be disastrous to the future of the organization.

How can we handle the situation where we have an excellent opportunity to gain more market, but we cannot deal with too high demand?

Generally speaking there are two directions of solution:

  1. Be very careful by being slow to introduce changes to the offering. This means the new offering should be only fraction of what can be offered today to the existing market. Then, continue to improve the offering by small changes.
  2. Prepare for the best reasonable scenario. This requires two additional capabilities:
    1. Have a control mechanism in place that checks the incoming demand, translates it quickly into capacity requirements and checks whether it still leaves enough protective capacity.
    2. Have fast means to increase capacity in relatively small amounts, even when the cost is much more expensive than the regular available capacity. In a previous post I called these means “capacity buffers”.

My general observation is that most organizations take the seemingly safer route, but they pay the price of very little impact of their improved offerings. The point is that the second direction can be as safe as the first, but the two necessary capabilities have to be in place.

The required control mechanism, focused on the stream of arriving demand, is based on the combination of two different mechanisms that complement each other: Buffer Management and the Planned-Load.

Buffer Management is based on the idea that if you have included buffers in your planning then you draw valuable information when you monitor the actual consumption of the buffers. The big advantage is that buffer management looks only on the state of the planned-buffer; it does not rely on any other data, which might not be accurate.

Readers who are not familiar with the concept of the Planned-Load are invited to read a previous post “The Critical Information behind the Planned-Load”.

A demonstration of the dilemma and the two ways to resolve it are offered in my TOCICO webinar “The TOC Challenge”. The live free webinar, for TOCICO members, would take place on Saturday, September 9th and a repeat on Sunday, September 10th, 2017. Starting with December 10th, 2017, the recording of the webinar will join the series of webinars on the TOCICO site that can be freely watched at any time by the TOCICO members.

Details can be found on:

Developing the most difficult managerial skill

There is one critical skill that every manager truly needs, but only few have it at the required level. Any manager who leads people to achieve certain objectives, or focuses on business development and other strategic issues, or tries to radiate the unique value of the products, needs to understand the reality as seen by the other people.

We all have that skill to a certain degree. Most of us live with a spouse, family and neighbors where a certain level of understanding the viewpoint of another person is necessary. The difference is that we know these people personally.  This is not the case with managers who need to speculate the response of people they have never met, like most of their clients and suppliers.  And how many executives understand the perspective of the union leaders, with whom they meet, but don’t really know well?

Politicians have to have this skill sharpened to the degree of understanding mass of people.  They watch the crowd through the media and, even more importantly, through chats and blogs and by that develop a deep understanding of the inner fears and desires of the individuals within the crowd.

The term “understanding” means being able to reasonably predict the response to the actions we take.  In order to be able to predict there is a need to construct the key relevant cause-and-effect relationships as viewed by the other person.

An ironic point is that “understanding the other” usually expresses noble feelings, but it is also a pragmatic need for making good managerial decisions.

The mission of understanding the cause-and-effect as seen by people we don’t know is always difficult. A big obstacle appears when we try to understand the behavior of people from different cultures or socio-economic state.  The differences make it hard to identify the key cause-and-effects behind the actions of the other person, because of different values that lead to quite different causes than ours.

An emotional obstacle is raised when the other person is viewed as an enemy or a rival, because understanding means being empathetic to the other person.  Problem is, these are the cases where understanding the other side is most required. Throughout history the successful army generals were able to analyze the perspective of the enemy, but in order to do so they had to restrain the emotion of shying away from such understanding.

The problem of most top managers is that they are not aware that the other might see a very different picture. Managers have to understand what their customers and users truly want, which is quite different than what the managers want.  The analysis of the situation from the competitor perspective is also frequently required.  Yet, such an analysis of the cause-and-effect from the customer, competitor or supplier perspective is not common.

Is it possible to develop the skill to get into the shoes of somebody else and construct the relevant cause-and-effect from that perspective?

There are four categories of people that the managers should be able to understand their perception of reality:

  1. The employees at every organizational level.
  2. Individuals who have an official role within another organization that is, or could be, in direct business relationships with our organization.
  3. Individual customers and users of the product/services of the organization.
  4. Individuals and organizations that have other interests in the organization. These include regulators, media and people who live in the neighborhood.

TOC includes two categories of generic tools that support a view of an organization from the outside leading to quick, yet valid, observations of the key cause-and-effect entities that impact the performance of that organization. It is easier to predict the behavior of an organization, actually the individual decision-makers within the organization, than predicting the behavior of an individual operating on his/her own.  But, part of what works with an organization is useful for understanding a specific person.  The difference is caused by having to synchronize between many individuals and by that forcing the organization to simplify the goal, values and procedures.  Organizations also try to be rational, which is not necessarily true for most individuals.

The first category of tools to understand other people, or an organization, is the group of insights, notably the five focusing steps, that simplify the seemingly complex and uncertain environment and provide us with the effective FOCUS on what truly counts. This is true also when we look on another organization.  Being aware of the inherent uncertainty is part of the insights that allows us to focus just on the right elements, whose impact is stronger than the uncertainty (noise).

For instance, when employees are required to subordinate to a scheme that its rationale is unclear, we can imagine the resistance. The use of any specific performance measurement impacts behavior in a way that is easy to predict, when one asks the question: how should the employees react to the measurement?

The TOC Thinking Processes (TP) is another, but highly related, category of tools enabling us to predict the response of an organization, a group of potential customers, or an individual to a certain act.

Let’s examine the overwhelming negative response of many individuals who have witness how a passenger has been dragged by force out of a flight. Fact is that this harsh response came as a surprise to United Airlines management.  Was it too complicated to consider the possibility that other passengers might record the incident and make the video viral?  Was it too complex to predict the social networks and media responses? How long should have taken the local managers to understand the huge developing threat?  Eventually top management had to humbly apologize, pay hefty compensation to the passenger and suffer severe damage in the company reputation.

Here is a simple cause-and-effect branch outlining the situation:

The key question is: How come the local management did not predict the response of the passengers? The simple answer:  they are not used to think of the possible response of other people, because it seems too complex.

The TP are usually focused on OUR own cause-and-effect with one clear exception; a conflict between two parties, which is depicted on one cloud, so both needs are recognized and verbalized. However, the same cause-and-effect tools can support a careful buildup of small cause-and-effect trees that focus on the possible ramifications of a change from the perspective of other people.  What we lack in intuition can be gained by logical reasoning and a-priori focused search for meaningful information.

Let’s consider a case where a manufacturing company contemplates to stop producing a specific product family. The arguments are relatively low T per unit and, at the same time, high capacity consumption from a loaded critical resource that currently prevents the sales expansion of other products.

Should the company consider how the distributors, the immediate clients, react to such a decision?

Assuming there are good reasons not to ask the opinion of the distributors before taking the step – is there a way to predict how the distributors will behave?

Goldratt complained that too many executives don’t have a clue on the business of their clients and suppliers. This is quite similar to the lack of interest to understand the other perspective of reality.

What can someone who never worked in distribution know about the perspective of a distributor?

Let’s put some simple facts about distributors:

  • They deal with very large number of SKUs.
  • Most of the clients of a distributor buy many different items.
  • Some of the SKUs offered by the distributor, but definitely not all SKUs, have replacements that are acceptable to most of their clients.
  • Logistics is a big player in running the business.

When you look on the very short, pretty obvious list, you can easily deduce when a supplier pulling out a family of products creates a big problem to the distributors forcing them to react.

From the distributor perspective there is a difference whether he is already carrying replacements of that product family that his clients accept as good enough. When the answer is positive then the distributor should not grossly suffer from the supplier’s act.

However, when no acceptable replacements are available then some clients might look for such replacements elsewhere and shift their purchasing to another distributor. Forcing the distributor to look for acceptable replacements from another supplier raises the risk that many more items would be supplied by the new supplier.

Another cause to be concerned with is the emotional reaction of the managers of the distribution company against such an action that causes them considerable damage.

The need to predict the behavior of business partners should be obvious to everyone. However it is not.  It is critical not just to predict negative response, but also what would generate great positive response.

The skill to understand the perspective of another organization can be vastly improved by TOC. It requires being aware that this is possible, beneficial and truly required.  It is part of recognizing the inherent simplicity.