Here’s a small abstract from the paper:

“The ability of ever more sophisticated algorithms to make a perceptible improvement in supply chain performance is nearing its limit. In today’s volatile demand environment, response management solutions present a largely untapped opportunity to maximize profit potential by optimizing day-to-day execution. “

The paper also features response management case studies on Jabil and Aviat Networks.

Here on the 21^st Century Supply Chain Blog, we’re no strangers to volatility. In fact, we’ve been talking about embracing volatility for a while! Here are a couple of posts:

VUCA, a useful acronym for today’s supply chain

Embrace Complexity

We recently held an introductory webcast on Supply Chain Response Management, and it provided a high-level overview of the philosophy and framework for response management as a supply chain competency.

This is a follow-up webcast that will dive deeper into what supply chain response management means to you and your business. The presentation will cover:

• How response management can be applied across the business, providing specific use case examples.
• The required technology capabilities, that when put together, enable a strategic response management competency.
• The differentiators between response management approaches and systems.

Register now for this free webcast.

If you missed the first webcast, make sure to check it out here: http://www.kinaxis.com/campaign/on-demand-supply-chain-response-management/

Kerry Zuber, one of my colleagues who is a Lean/Six Sigma black belt, has introduced me to two great terms that are crucial to understanding the value and key capabilities required of effective response management:

• Time to detect
  This is all about knowing sooner that some event has occurred that is creating risk or harm to your organization or supply chain.  This may be that a customer has changed their mind about an order, or a supplier has de-committed on a delivery date or quantity, or that a tsunami has occurred in Japan that has wiped out a large part of world-wide semiconductor manufacturing capacity. But not only knowing about the event sooner, but also knowing about the impact sooner (which lines will go down, which orders will be impacted), and, perhaps as importantly, knowing who is impacted sooner.  Without knowing all of these three things you cannot act.
• Time to correct
  Once you know what went wrong, you need to act quickly to find a solution through compromise across multiple functions, even multiple tiers, often with competing objectives.  The timeliness of resolution is a key measure of the quality of the solution. We still hear of companies that have not fully understood the impact of the Japanese tsunami on their operations and their ability to satisfy demand, let alone put into place a recovery plan. And yet it is often the day-to-day events, such as when a customer changes their mind on an order, where most margin or customer satisfaction is lost because either the response to the customer is too slow or actions are taking with little understanding of the financial and operational impact.

The significance of these two terms is that for the most part the physical supply chain is a constraint in that your ability to change it in the short term is very limited so every minute lost in detecting something ‘wrong’ and, once the event has been detected, every minute used to determine what to do to reduce or eliminate the risk or harm, is a minute taken away from being able to use the physical supply chain in some manner to reduce the risk.  Even in situation where there is next to nothing that can be done to eliminate the risk, if you know sooner you can at least tell your customers who are impacted about the impact sooner so that they have more time to react.

There are three core capabilities required that impact the time to detect and the time to correct:

• Being able to represent several versions of a multi-tier supply chain in a single data model so that there is a single version of the truth, and being able to represent several states – historical, present, and future – of the supply chain in order to determine what of significance has changed that is causing future harm to the organization, as well as several scenarios that represent alternative ways of solving the problems.
• Being able to determine who is impacted by the change and therefore who needs to be alerted, not only in your own function, but in other function within your own organization and people in your customers, contract manufacturers, or component suppliers.
• Being able to bring those people impacted by other the original threat or proposed actions together in order to collaborate on the resolution of a problem by testing several ways of resolving the issue and being able to reach compromise through sharing of the impact of possible courses of action on financial and operational metrics.

These concepts and capabilities can be applied to a broad range of business problems, not just the supply chain.  In the diagram on the left we can substitute other function’s analytics for the supply chain analytics and still retain the core capabilities described above to solve response management problems in a wide range of business activity.  Within manufacturing industries the supply chain is absolutely central to being able to get product to market.  But within manufacturing companies response management issues are not constrained to the supply chain.  Workforce management is an easy one to describe in this context because it is (or rather it should be) tightly coupled to the longer term revenue forecast. If a company anticipates their demand or sale revenue growing they need to ramp up recruitment because of the long lead time in recruiting and training people, but they also need to be very quick in shutting off the recruitment process if they see that their revenue is not growing as anticipated. In other words workforce management also requires the ability to plan for the future based upon anticipated demand, monitor actual performance against anticipated performance, and a quick response to correct the misalignments.

What drives the need for response management is the fundamental fact that we cannot predict the future with a great deal of accuracy and our success or failure is dependent on how effectively we respond to the present, how we manage in the “now”.  What varies across industries, across functions, and across business processes is how far into the future we need to predict what we think is going to happen, and how quickly we can affect change.  But what does not change is that the time it takes us to detect that something of significance has changed and decide on the best course of action determines both the time we have available to carry out the course of action and the feasibility of the course of action, which is of course a key measure of the quality of the decision.

So where do you think the next breakthrough will come from?  Better forecasting or better response management?

First let’s examine why companies create a revenue or sales forecast.  It is intuitively obvious that they do so to determine how much of their product they believe the market will purchase. But this is only the 10 percent of the iceberg that floats above the water.  The other 90 percent of the story  is that the forecast is used to drive investment plans in marketing, engineering, manufacturing capacity, inventory planning, and strategic sourcing.  In other words, if the forecast is inaccurate there are a whole bunch of consequences beyond just getting the revenue forecast incorrect.  Of course here I am referring to the longer term forecast used to drive the S&OP process or even the annual operating plan/budget and the principal forecast is investment.

The medium term forecast is used to drive how an existing infrastructure, particularly the supply chain infrastructure, can be used to meet market demand.  This is the period in which investments are not going to make a difference.  As one colleague told me, nine woman are not going to be able to produce a baby in one month, no matter how hard they try.  But it is important to realize that even within this period the significance of forecasting varies by industry, as is reflected in the diagram to the left from an Aberdeen report titled “Demand Management – Bridging External Market Inputs with Internal Statistical Forecasting” published in June 2011.  In this context it is important to understand how the difference between demand lead time and supply lead time varies across industries.  Airplane manufacturers typically have a 3-5 year lead time from a firm order to delivery date, with a manufacturing lead time of about six months.  A diaper manufacturer has between a one day to one week demand lead time, and about a three week supply lead time.  On the other hand, an airplane is highly customized to a specific customer’s order, whereas  a diaper is a diaper, even though there are variations.  The complexity in a plane is in how bought materials are coordinated to assembly a customer specific product.  The complexity in a diaper is in how it is distributed with hundreds, in some cases, thousands, of distribution locations through a multi-tier distribution network.

The combination of the difference between demand and supply lead time and degree of final product configurability determines the extent to which a company can use postponement as a strategy to mitigate the risks associated with forecasting incorrectly.  CPG companies, such as diaper manufacturers, typically have little opportunity to postpone at the manufacturing stage, and therefore will use a make-to-stock supply model. However, CPG companies can postpone distribution through their multi-level distribution network. On the other had an airplane manufacturer will seldom order components before they have a firm order, which is a “build-to-order “ postponement strategy.  Imagine the financial risk that an airplane manufacturer would be taking on if they built an airplane and then tried to find someone to buy it.  In reality a diaper manufacturer is taking on similar risk, but the risk is distributed over millions of diapers and many thousands of consumers, so their risk per item is much smaller.

And yet a semiconductor company takes on the same level of risk when they commit $3B-$5B to build a new factory over the next 24-36 months that an airplane manufacturer would be taking on if they used a “build-to-stock” supply chain model.  It is an equivalent risk when a company decides to invest in penetrating a new market and needs to invest in establishing a local legal entity, office rentals, marketing, and hiring and training local staff.  These are big commitments of funds that are based upon the anticipated behavior of the market, a forecast, and once in execution they take a lot unwind.

Terra Technology, one of the leading forecasting technology companies focusing on the CPG space, where statistical forecasting is very prevalent, published a study on forecast accuracy titled “2011Forecasting Performance Benchmark Study” in which they study best practice demand forecasting in leading CPG companies.  The reason that statistical forecasting is so prevalent in CPG is that demand is relatively stable – when compared with other industries – and products have fairly long life cycles so there is a lot of history to rely on.  And yet in the introduction the authors note that:

• Promotional volume jumped about 75 percent in 2010 as companies looked to drive sales by offering consumers additional value. Contrary to conventional wisdom, promotional periods are actually forecast as accurately as non-promotional periods for the same items. Perhaps this is due to the extra time demand planners spend on promotions. Not surprisingly, the bias is considerably higher during promotions.
• New products remain hard to forecast with weekly item/location error rates of 65 percent, compared to 46 percent for existing products
• Demand Sensing continues to provide a consistent step change in forecast accuracy for all scenarios, including promotions and new products. For the combined 2009-2010 period, Demand Sensing reduces average weekly error by 40 percent.
• Outdated mathematics and optimistic marketing departments continue to undermine the performance of Demand Planning. This highlights the opportunity for a structured approach to forecasting based on additional demand signals and new mathematics.
• MAPE is the correct measure for supply chain performance since it is the error that Product Supply contends with. However, insight from the report raises questions regarding MAPE as the proper metric to evaluate the performance of Demand Planning because it may not properly reflect the value add by planners. Using the dataset as a resource, Terra plans to evaluate a number of different metrics in the future editions of the study.

Before analyzing the numbers, let me reiterate that CPG, when compared with industrial equipment manufacturing for example, has stable demand and long product life cycles, which, in theory, means that CPG companies should be able to use statistical forecasting to predict demand fairly accurately, but, as the Terra Technology study shows, they can’t. When we consider the three month digital camera lifecycles and six months cell phone lifecycles, the relevance of the second bullet about the forecast accuracy of new products becomes very apparent.  New product launch is typically the time when the most margin is captured, an yet it is also the time when the forecast is most inaccurate, meaning that a lot of margin is not captured.  And therefore anything Terra describes in the report is significantly worse in industries with shorter product life cycles, which automatically leads to more volatile demand. In fact our anecdotal evidence from speaking to companies in consumer electronics is that their forecast accuracy is very seldom above 50 percent regardless of the life cycle stage of the product largely because the short life cycle means that the product is either being introduced or being phased out.

In the Aberdeen study referred to above, the author notes that Best in Class performance

• Average percent forecast accuracy at product family level (across a three-month time period) is 87.1 percent
• Average percent forecast accuracy at individual SKU item level (across a three-month time period) is 70.8 percent

In the Terra Technology study the author notes that

• During 2010, the average weekly error was 48 percent with a slight difference by top performers, who came in six points lower at 42 percent.
• Meanwhile, monthly error averaged 33 percent with a five point spread between top performers and the average.

With leading CPG companies struggling to get forecast error below 30-40 percent after years of trying, with product life cycles shrinking every year, and with market differentiation leading to product proliferation, what is the likelihood that forecast accuracy will improve dramatically over the next five years?  In my opinion very little.  So where do you think your next breakthrough in supply chain performance will come from?

• Learning to forecast and plan better?
• Learning to respond profitably to actual demand, or plan variance?

The third bullet from the Terra Technology report introduction states that “Demand Sensing continues to provide a consistent step change in forecast accuracy for all scenarios, including promotions and new products. For the combined 2009-2010 period, Demand Sensing reduces average weekly error by 40 percent.” hints at the importance of response management, but does not go far enough since it only indicates how to get a better understanding of demand in the short term. Response management is about satisfying that demand in the most profitable manner.  Demand sensing is about knowing sooner about demand shifts. Actually I find this standard definition of demand sensing a bit funny and it is a good illustration of how planning is seen from the wrong perspective. The term demand shift implies that the forecast is correct and somehow demand has shifted in time or location, which is of course completely wrong. The customer didn’t buy the ‘wrong’ stuff in the ‘wrong’ amount at the ‘wrong’ time. What actually happened is that we predicted incorrectly what they wanted, how much they wanted, and when they wanted it, even where they wanted it.  Nevertheless demand shift captures the concept that actual demand occurs at a time, quantity, price, and/or location that was not expected.  While demand sensing is really important, even perhaps the most important aspect of supply chain execution, it only describes part of the response management story. Response management is about knowing sooner about a broad range of supply chain disruptions and acting faster to provide a profitable response.

Stay tuned for part two of “The importance of Response Management” tomorrow!

The webinar takes place on Wednesday, August 3 from 1 p.m. to 1:45 p.m. EDT.

Here’s the abstract:

In a perfect world, you could plan against a forecast and sleep soundly at night, but the reality is that disruptions are constant and unpredictable. This webinar will provide an introduction to response management. Join us and learn how large organizations are developing a corporate competency in coping with unexpected, but ever-occurring events such as receiving customer orders inside of lead time, part shortages, or forecast changes.

Registration is free. Click here to sign up for the event.

A post by “the doctor” over on the Sourcing innovation blog, led me to this article in Industry Week. In it, they present a short case study about how a company survived the chaos and upheaval of Hurricane Katrina, only to fall victim to other risks:

• Rapid growth – Rapid growth sounds like a great situation to be in, and it can be. But it can also cause significant problems. Companies have growing pains just like people do and processes that worked before can become problematic with higher volumes. Suppliers might not be able to grow at the same pace you are and qualified people might not be available to meet hiring demand.
• Expanded and new facilities – New facilities are really systems wrapped in a building and like any system there can be problems getting things working.  As you bring new facilities on-line or as you renovate and expand existing facilities, you need to plan for potential start-up issues.
• Increased and changing product range – Things can be simple when we have one or two products but as the product offering grows, complexity grows with it. Care must be taken when adding new products not to kill demand for existing products, at least until supply has been whittled away.  Apple is a master at this – when a new product is coming, even before it is announced, supply starts to dry up for existing products so that as Apple transitions to the new product, they reduce the risk that they will be left with large quantities of the outgoing product.
• New, large (and more demanding) customers – New customers are wonderful. Large customers, even better! But… there can be a downside. Maybe others have noticed this little truism – the larger the customer, the more demanding the customer. They want what they want, when they want it. And because this customer now makes up a healthy portion of a company’s revenues, the incentive is there to make sure these customers are satisfied.  The risk is that if you have successfully ramped the company to meet this demand and the customer subsequently leaves, you are left with significant excess capacity and expense.
• Changes to the supplier base – Suppliers come and go.  As the supplier base changes, so does the performance characteristics of your supply base.  Quality, on-time delivery performance, lead times, costs, all impact profitability.
• Changes to IT systems – This can be a killer.  IT systems are often the brains of the operation.  If the brains aren’t working, not a lot of stuff can get done.  Companies are often lured into huge projects to overhaul and replace their systems.  If done right, the change can be quite beneficial.  If done incorrectly, the results can be disastrous, sometimes leading to litigation.

The Industry Week article has a number of suggestions for improving a company’s ability to assess and mitigate risks of all kinds. If you were to pick one that you absolutely must implement, it would be to build risk management into all aspects of your business.  Make risk assessment and mitigation a key part of your decision making process.  A good place to start would be to add risk assessment and mitigation to your monthly S&OP process. What better place to ensure that all teams are thinking in terms of what risks exist and how best to manage them?

Have you experienced some of these risks? How did you overcome them? Comment back and let us know.

This was a fascinating time for supply chain. High tech electronics led the way with increasingly shorter life cycles for products aimed at a customer base that was quickly becoming more educated and demanding. The early stages of Response Management as its own category were set (with Kinaxis coining the term and defining the category by the way). There emerged the first of two components of Response Management: “sense and respond.” For the most part this was all about anticipating demand because at this point historical analysis was of no use. Companies needed to get even closer to the customer, product life cycles became more difficult to manage, and it was the contract manufacturers who took the lead with emerging Response Management capabilities. From a functional standpoint, the requirements for a world class Response Management system were being defined:

• One place for all global supply chain data and information. More often than not this is coming from many different systems in different formats and different time zones
• “What-if” simulation anytime. For example what if demand dropped earlier than expected? What if this product was promoted? People could no longer wait for answers. They needed to know the risks and how best to respond or it could ultimately mean a loss of market share.
• One place for all supply chain participants. Demand and supply managers could no longer do things in silos. They needed to collaborate efficiently to effectively respond to change.

Even through all this, the term Response Management was still slow to catch on (though there were some industry analysts that understood and bought in See report: Response Management: Next Wave of Supply Chain Innovation?)

The second component of Response Management began to make headlines. The first component, “sense and respond,” I think was exciting. New product introductions, emerging markets, anticipating demand. Highly competitive aspects of supply chain and the companies that were the leaders started to be recognized, for example today you can go to The Gartner Supply Chain Top 25. This second component though does not yet have a catch phrase associated with it and is much different than “sense and respond.” Trying to think of something that is respectful of the human tragedies associated with this component of Response Management you might call it “respond responsible.” We are talking about the catastrophes nobody can “sense.” You don’t have to look too far for examples but since 9/11 they seem to be more frequent. I’ll just point you to my last blog, “Top 5: What Else Could Go Wrong?” for other examples. On top of the human element, supply chain professionals still need to source supply, allocate limited inventories and respond with confidence to their customers.

It has taken these events for many companies including the big ERP vendors to recognize that there is a need for something between planning and execution. Something when things don’t run like clockwork, when demand is unpredictable, or when you get hit with the unexpected. Response Management has arrived as an official category that companies will start, or for some continue to build strategies around.

The same keynote also described the foundation that today’s supply chains are built on. In particular, there were two analogies that stood out and challenged the audience to think about their own supply chains. The first analogy was having your supply chain foundation built on technology that resembles that of a “picket fence.” In this case, there are gaps where key information may “slip through the cracks” and it becomes difficult to do any meaningful supply chain analysis. If you use Excel this may sound familiar. Supply chain analysis is difficult because those that need to participate are looking at different data, missing data, and there are gaps in the analytics. If your supply chain analytics are built around Excel spreadsheets then you may be bound by the “picket fence.” Excel may be sufficient in an environment where there is little “VUCA,” but when managing today’s supply chain challenges, Excel is not a scalable solution.

The second analogy of interest used to describe the technology a supply chain foundation may be built on was that of “bricks and mortar.” In this case, the analytics required to respond to “VUCA” are just not flexible enough. Old processes that are cast in stone, long analytic run times, and user interfaces that are far from being user friendly are just a few reasons to describe this technology as “bricks and mortar.” If you are trying to get your supply chain analytics from your ERP system, which for the most part is the transaction system, this may sound familiar. In keeping with the analogy it would be like getting blood from a stone. This is the reason many people continue to turn to Excel for supply chain analytics but soon run into the “picket fence” problems.

This is where the “T” in VUCAT comes from, Technology that does not support today’s “VUCA”.

So what is one to do with “VUCAT”? Some analysts were talking about “best of breed.” This may mean abandoning your Excel spreadsheets or getting rid of ERP components that are either difficult to deploy or are not doing the job. In these cases “VUCAT” is actually a verb. For example, if somebody asks, “What are we going to do with all these Excel spreadsheets?” You might say, “We just have to VUCAT and replace them.”  Look for this new word in the next APICS dictionary

If you are going to replace the “picket fences” or “bricks and mortar,” what characteristics will allow you to manage “VUCA”? Here are three key requirements for today’s supply chain analytics;

• “Know sooner” by being able to continuously monitor plan versus actual and alert participants not only of the event but of the consequences.
• “Collaborate now” by automatically identifying not only the consequences of change but those individuals impacted by it and thtat can contribute to its resolution.
• “Respond immediately” by evaluating any number of resolution scenarios in seconds with anyone in any place and compare alternative actions against operational and financial metrics.

This means you can embrace “VUCA” and create a competitive advantage by being a “first mover.”

Did you hear the term “VUCA” used at the Gartner conference or anywhere else? What is your take? Is it a new more descriptive term for today’s supply chain or more of the same?

Although there may be no such thing as a perfect forecast, there are plenty of opportunities for improvement and managing demand in support of a more cost-effective supply response. Better collaboration between trading partners is the necessary next step toward waste elimination in the value chain.

Maximizing value from the supply chain won’t be reached without improved collaboration between trading partners.

In fact, Mike identifies the lack of collaboration between trading partners as the primary cause of demand volatility. (I agree that the lack of demand visibility increases demand volatility, but there are other industry dynamics that are increasing demand variability.)  Of course, visibility and collaboration are the key messages behind The Beer Game (or beer distribution game) which was originally invented in the 1960s by Jay Forrester at MIT as a result of his work on system dynamics to illustrate the bull-whip effect.  Absolutely critical to a smoother functioning supply chain is demand visibility across the entire supply chain.  Of course visibility isn’t a very mature form of collaboration, but it is at least a start and a necessary precursor to more mature forms of collaboration. And let’s be honest, after 50 years we still haven’t really got to effective demand visibility, although there have been improvements.  To get some estimate of how well your company is performing in this regard you can take the Gartner Demand-Driven Value Network (DDVN) maturity self-assessment.(Registration or Gartner membership required.)

But it isn’t just so-called Easy-West collaboration between customers and suppliers that is required.  I would be happy to start with the so-called North-South collaboration between strategic and financial plans, usually the focal area of the executive suite and Finance, and operational plans. Some analysts and commentators have called this integrated business planning, which also dates in concept from the 1960’s. I’d even settle for East-West collaboration between demand and supply functions within one organization, including Gartner’s stage 4 Sales & Operations Planning (S&OP) maturity. There is no inter-company process that is a better measure of collaboration than S&OP done well.  According to Jane Barrett of Gartner, most companies are stuck in stage 2 maturity.  This is largely due to the inertia and resistance to change so prevalent in many companies.

But how is it that 50 years on from Jay Forrester’s work we are still struggling with this issue? My opinion is expressed in a blog titled “Do you trust yourself to collaborate? The real barrier to collaboration is not technology, but trust”.  Let’s face it, technology can always be improved, especially the user experience. But the capabilities of the technology available for inter-company and intra-company collaboration exceeds the willingness of organizations to share information at the moment.  As Mike Burkett points out, huge gains can be achieved by collaboration, so why don’t companies try to capture these benefits?  My opinion is that the issue is the same that is preventing them moving from stage 2 to stage 3 S&OP process maturity, namely that they simply want to automate their existing manual processes and don’t realize that effective collaboration requires both new ways of working and supporting technology capabilities. 

Nothing captures this better than a Henry Ford quote that “If I had asked my customers what they wanted, they would have said faster horses.”  There is a strong synergy, even symbiosis, between technology and process.  In a great slide show titled “What if Peter Drucker taught Enterprise 2.0?”, Mark Fidelman states that

Neither technology nor people determines the other, but each shapes the other.

But let us recognize that the financial and operational risks of collaboration are tangible and can be high.  There is a good series on collaboration in the Harvard Business Review.  The one I like best dates from June 7, 2011 and is titled “Collaboration is risky.  Now get on with it.”  The author, Whitney Johnson, writes that

Why is teamwork so difficult?

Because collaboration is actually a pretty risky business. Perhaps, like me, you are generally of the mindset that two heads are better than one. But because your ideas frequently get co-opted, there’s a risk-reward imbalance that makes you reluctant to engage. Or maybe you’ve reached out to a potential collaborator only to have your lack of expertise exploited. So, rather than ever again experiencing the one-two punch of ignorance and vulnerability, you’d prefer to soldier on alone. In both instances, the fundamental barrier to collaboration is a lack of trust.

While writing about individuals within a group, clearly Whitney’s observations and opinions are very applicable to collaboration between functions and organizations.  Her prescription is to

1. Start with simple exchanges where the cost of betrayal is low.
2. Remember that our collaborators are competent.
3. Don’t take advantage of our collaborators’ deficiencies.
4. Give others their due, and expect yours in return.

Sound advice.  Yet so difficult to put into practice.  The comments section is well worth a read too, including links to some interesting materials.

Mike Burkett concludes his First Things Monday contribution with the observation that

By closing gaps between partner nodes in the supply chain, there’s an opportunity to address the unnecessary waste across the value chain.

These are the hard benefits of supply chain collaboration.  They are real and they are achievable.  Yes, the risks are real, but the rewards are equally real.

The need to reroute to Hong Kong is a great example of the difference between planning and response, and the costs that are embedded in a design or plan.  It turned out that for my original trip, given that I had three lay-overs of more than 24 hours, it made sense to buy a Star Alliance ‘Round the World’ ticket.  It was considerably cheaper than buying each leg from a different carrier, and considerably easier to book and manage given that there is a single web site and service center for all legs. In addition, the flexibility of the ticket is great though there are moderate change fees.  The catch is that the ticket is not refundable (in-built cost) and that you have to have at least three lay-overs of at least 24 hours (in-built inflexibility). With the Tokyo leg cancelled I still had to fly somewhere for at least 24 hours or forfeit the entire ticket. But I had to buy my ticket with sufficient lead time in order to get a good fare, meaning I had to buy it before the earthquake in Japan. However, had I bought a ticket at shorter notice to Shanghai and London only, it would have been quite a bit less if we include the costs associated with my stay in Hong Kong, not to mention the opportunity cost of me being away for longer.

What I find interesting in this description is to contrast it with a theme emphasized by several speakers at the Manufacturing Supply Chain Officers conference in Shanghai, particularly John Gattorna, but by others too (Incidentally we received a copy of John’s book titled “Dynamic Supply Chains – Delivering Value through People” as part of the event.)  The theme is that planning is good, responsiveness is expensive. The underlying assumption is that if we could only plan better we good run a very efficient (in other words low cost) supply chain. What we so often forget to evaluate is the in-built costs created by the plans we make. In my example above, I would have saved money by buying my ticket later. What cost me money was ‘planning’.  Being ‘responsive’ would have saved me money. But my management, all the way up to the CEO through the CFO would have shot me for buying the ticket within the 14 day period. My trip wasn’t any more expensive than if I had gone to Tokyo ($125 for a change fee), but I had to spend more than 24 hours in Hong Kong doing ‘nothing’.  Actually, I did some site seeing in the late afternoon and evening. Great for me; not necessarily the right thing for the company.

OK, I agree, on average it would be cheaper to buy the ticket 14 days in advance. But that is not my point. My point is that we measure the cost of responsiveness, but we don’t measure the cost of inflexibility. Had John Gattorna brought up the built-in cost of inflexibility and the need to balance between the cost of inflexibility and the cost of responsiveness, I would have no issue with his statements. But I would go further than this and say that there are ways of providing profitable response to changing circumstances, provided you haven’t built in too much inflexibility into your supply chain through ‘better’ planning.  I just don’t buy into the either/or assumption that is made about efficiency versus effectiveness in supply chain management.  We have to look for the AND.  To John Gattorna’s credit, he did bring up the issue of ‘brittleness’ of the supply chain, but only in the context of the last recession in which companies drew down inventories to unsustainably low levels.  I did not hear him state that ‘brittleness’ is a side effect of not only the recession, but also of planning and systems that only focus on efficiency. And that there are costs associated with this brittleness that we do not measure. Instead, I heard John state more than once that we need to plan better because responsiveness is expensive. Na-uh! It is only expensive if you approach supply chains from the perspective of efficient OR effective, rather than efficient AND effective.  To be fair to John, I have not finished his book yet, so I can only go by the statements he made during the conference.

I was perhaps even more startled by a presentation given by a company whom I will keep nameless other than to say that they are a mobile handset manufacturer. The central theme of the talk was don’t be different, because being different is expensive. Wow.  Needless to say, this company does not have a large share of the market.  But to be fair, the focus of the presentation was on logistics, and there I agree.  While there are opportunities to do Logistics ‘smartly’, it is a commodity and should really be outsourced to 3/4PL’s.  It was during the Q&A session that I was really surprised.  When asked (challenged?) about Apple’s success in this space, the speaker said that Apple doesn’t do anything different in there supply chain.  What?  Guess the speaker missed the part about Apple spending close to $4B earlier this year to buy up the majority of the flat panel displays used in mobile phones and tablets for the next umpteen years.  Obviously I am not privy to the actual contracts, but my interpretation of what I have read is that Apple hasn’t given the suppliers a long term delivery schedule by part, but rather they have put money on the table to ensure that they secure a huge portion of the available production capacity.  In other words, Apple made the decision that spending on flexibility is cheap.  This is smart.

So where do you think your next breakthrough in performance come from?  From learning to plan better?  Or from learning to respond profitably to real demand?